mbon-bio-idx.bib

@misc{AddMarineData,
  title = {Add Marine Data Layers to "3 {{Env Data}}" Tab: {{Bio-ORACLE}} Using Sdmpredictors {{R}} Package {$\cdot$} {{Issue}} \#113 {$\cdot$} {{wallaceEcoMod}}/Wallace},
  shorttitle = {Add Marine Data Layers to "3 {{Env Data}}" Tab},
  journal = {GitHub},
  urldate = {2023-06-22},
  abstract = {A great next step for relevance to the marine biodiversity community would be to add the Bio-ORACLE : Marine data layers for ecological modelling using R package sdmpredictors to this Env Data sect...},
  howpublished = {https://github.com/wallaceEcoMod/wallace/issues/113},
  langid = {english},
  file = {/Users/bbest/Zotero/storage/8ZN4L86C/113.html}
}

@article{antaoTemperaturerelatedBiodiversityChange2020,
  title = {Temperature-Related Biodiversity Change across Temperate Marine and Terrestrial Systems},
  author = {Ant{\~a}o, Laura H. and Bates, Amanda E. and Blowes, Shane A. and Waldock, Conor and Supp, Sarah R. and Magurran, Anne E. and Dornelas, Maria and Schipper, Aafke M.},
  year = {2020},
  month = jul,
  journal = {Nature Ecology \& Evolution},
  volume = {4},
  number = {7},
  pages = {927--933},
  publisher = {{Nature Publishing Group}},
  issn = {2397-334X},
  doi = {10.1038/s41559-020-1185-7},
  urldate = {2023-06-22},
  abstract = {Climate change is reshaping global biodiversity as species respond to changing temperatures. However, the net effects of climate-driven species redistribution on local assemblage diversity remain unknown. Here, we relate trends in species richness and abundance from 21,500\,terrestrial and marine assemblage time series across temperate regions (23.5\textendash 60.0\textdegree{} latitude) to changes in air or sea surface temperature. We find a strong coupling between biodiversity and temperature changes in the marine realm, where species richness mostly increases with warming. However, biodiversity responses are conditional on the baseline climate, such that in initially warmer locations richness increase is more pronounced while abundance declines with warming. In contrast, we do not detect systematic temperature-related richness or abundance trends on land, despite a greater magnitude of warming. As the world is committed to further warming, substantial challenges remain in maintaining local biodiversity amongst the non-uniform inflow and outflow of `climate migrants'. Temperature-driven community restructuring is especially evident in the ocean, whereas climatic debt may be accumulating on land.},
  copyright = {2020 The Author(s), under exclusive licence to Springer Nature Limited},
  langid = {english},
  keywords = {Biodiversity,Climate-change ecology,Macroecology},
  file = {/Users/bbest/Zotero/storage/NK44GIW2/Antão et al. - 2020 - Temperature-related biodiversity change across tem.pdf}
}

@article{araujoStandardsDistributionModels2019,
  title = {Standards for Distribution Models in Biodiversity Assessments},
  author = {Ara{\'u}jo, Miguel B. and Anderson, Robert P. and M{\'a}rcia Barbosa, A. and Beale, Colin M. and Dormann, Carsten F. and Early, Regan and Garcia, Raquel A. and Guisan, Antoine and Maiorano, Luigi and Naimi, Babak and O'Hara, Robert B. and Zimmermann, Niklaus E. and Rahbek, Carsten},
  year = {2019},
  month = jan,
  journal = {Science Advances},
  volume = {5},
  number = {1},
  pages = {eaat4858},
  publisher = {{American Association for the Advancement of Science}},
  doi = {10.1126/sciadv.aat4858},
  urldate = {2023-06-22},
  abstract = {Demand for models in biodiversity assessments is rising, but which models are adequate for the task? We propose a set of best-practice standards and detailed guidelines enabling scoring of studies based on species distribution models for use in biodiversity assessments. We reviewed and scored 400 modeling studies over the past 20 years using the proposed standards and guidelines. We detected low model adequacy overall, but with a marked tendency of improvement over time in model building and, to a lesser degree, in biological data and model evaluation. We argue that implementation of agreed-upon standards for models in biodiversity assessments would promote transparency and repeatability, eventually leading to higher quality of the models and the inferences used in assessments. We encourage broad community participation toward the expansion and ongoing development of the proposed standards and guidelines.},
  file = {/Users/bbest/Zotero/storage/H93BLD3K/Araújo et al. - 2019 - Standards for distribution models in biodiversity .pdf}
}

@article{assisBioORACLEV2Extending2018,
  title = {Bio-{{ORACLE}} v2.0: {{Extending}} Marine Data Layers for Bioclimatic Modelling},
  shorttitle = {Bio-{{ORACLE}} v2.0},
  author = {Assis, Jorge and Tyberghein, Lennert and Bosch, Samuel and Verbruggen, Heroen and Serr{\~a}o, Ester A. and De Clerck, Olivier},
  year = {2018},
  journal = {Global Ecology and Biogeography},
  volume = {27},
  number = {3},
  pages = {277--284},
  issn = {1466-8238},
  doi = {10.1111/geb.12693},
  urldate = {2023-06-22},
  abstract = {Motivation The availability of user-friendly, high-resolution global environmental datasets is crucial for bioclimatic modelling. For terrestrial environments, WorldClim has served this purpose since 2005, but equivalent marine data only became available in 2012, with pioneer initiatives like Bio-ORACLE providing data layers for several ecologically relevant variables. Currently, the available marine data packages have not yet been updated to the most recent Intergovernmental Panel on Climate Change (IPCC) predictions nor to present times, and are mostly restricted to the top surface layer of the oceans, precluding the modelling of a large fraction of the benthic diversity that inhabits deeper habitats. To address this gap, we present a significant update of Bio-ORACLE for new future climate scenarios, present-day conditions and benthic layers (near sea bottom). The reliability of data layers was assessed using a cross-validation framework against in situ quality-controlled data. This test showed a generally good agreement between our data layers and the global climatic patterns. We also provide a package of functions in the R software environment (sdmpredictors) to facilitate listing, extraction and management of data layers and allow easy integration with the available pipelines for bioclimatic modelling. Main types of variable contained Surface and benthic layers for water temperature, salinity, nutrients, chlorophyll, sea ice, current velocity, phytoplankton, primary productivity, iron and light at bottom. Spatial location and grain Global at 5 arcmin (c. 0.08\textdegree{} or 9.2 km at the equator). Time period and grain Present (2000\textendash 2014) and future (2040\textendash 2050 and 2090\textendash 2100) environmental conditions based on monthly averages. Major taxa and level of measurement Marine biodiversity associated with sea surface and epibenthic habitats. Software format ASCII and TIFF grid formats for geographical information systems and a package of functions developed for R software.},
  copyright = {\textcopyright{} 2017 John Wiley \& Sons Ltd},
  langid = {english},
  keywords = {Bio-ORACLE,bioclimatic modelling,environmental data,global,kriging,macroecology,marine,species distribution modelling},
  file = {/Users/bbest/Zotero/storage/C2PDQACX/Assis et al. - 2018 - Bio-ORACLE v2.0 Extending marine data layers for .pdf;/Users/bbest/Zotero/storage/VUD8G897/geb.html}
}

@article{benraislasramOpensourceFrameworkModel2020,
  title = {An Open-Source Framework to Model Present and Future Marine Species Distributions at Local Scale},
  author = {Ben Rais Lasram, Frida and Hattab, Tarek and Nogues, Quentin and Beaugrand, Gr{\'e}gory and Dauvin, Jean Claude and Halouani, Ghassen and Le Loc'h, Fran{\c c}ois and Niquil, Nathalie and Leroy, Boris},
  year = {2020},
  month = sep,
  journal = {Ecological Informatics},
  volume = {59},
  pages = {101130},
  issn = {1574-9541},
  doi = {10.1016/j.ecoinf.2020.101130},
  urldate = {2023-06-22},
  abstract = {Species Distribution Models (SDMs) are useful tools to project potential future species distributions under climate change scenarios. Despite the ability to run SDMs in recent and reliable tools, there are some misuses and proxies that are widely practiced and rarely addressed together, particularly when dealing with marine species. In this paper, we propose an open-source framework that includes (i) a procedure for homogenizing occurrence data to reduce the influence of sampling bias, (ii) a procedure for generating pseudo-absences, (iii) a hierarchical-filter approach, (iv) full incorporation of the third dimension by considering climatic variables at multiple depths and (v) building of maps that predict current and potential future ranges of marine species. This framework is available for non-modeller ecologists interested in investigating future species ranges with a user-friendly script. We investigated the robustness of the framework by applying it to marine species of the Eastern English Channel. Projections were built for the middle and the end of this century under RCP2.6 and RCP8.5 scenarios.},
  langid = {english},
  keywords = {Automated modelling framework,Bioclimatic envelope models,Future projections,Habitat models,Pseudo-absences,Vertical gradient},
  file = {/Users/bbest/Zotero/storage/ESRUA597/Ben Rais Lasram et al. - 2020 - An open-source framework to model present and futu.pdf;/Users/bbest/Zotero/storage/YNQTB6KJ/S1574954120300807.html}
}

@phdthesis{boschSdmpredictorsPackageSpecies2017,
  title = {Sdmpredictors: An {{R}} Package for Species Distribution Modelling Predictor Datasets},
  shorttitle = {Sdmpredictors},
  author = {Bosch, Samuel and Tyberghein, Lennert and De Clerck, Olivier},
  year = {2017},
  file = {/Users/bbest/Zotero/storage/YULZBTUG/Bosch et al. - 2017 - sdmpredictors an R package for species distributi.pdf}
}

@misc{boydOccAssessAssessBiases2023,
  title = {{{occAssess}}: Assess Biases, Uncertainties and Coverage in Species' Occurrence Data},
  author = {Boyd, Rob},
  year = {2023},
  month = may,
  urldate = {2023-06-26},
  abstract = {Assess biases, uncertainties and coverage in species' occurrence data.},
  copyright = {MIT}
}

@article{boydOperationalWorkflowProducing2023,
  title = {An Operational Workflow for Producing Periodic Estimates of Species Occupancy at National Scales},
  author = {Boyd, Robin J. and August, Thomas A. and Cooke, Robert and Logie, Mark and Mancini, Francesca and Powney, Gary D. and Roy, David B. and Turvey, Katharine and Isaac, Nick J. B.},
  year = {2023},
  journal = {Biological Reviews},
  volume = {n/a},
  number = {n/a},
  issn = {1469-185X},
  doi = {10.1111/brv.12961},
  urldate = {2023-06-26},
  abstract = {Policy makers require high-level summaries of biodiversity change. However, deriving such summaries from raw biodiversity data is a complex process involving several intermediary stages. In this paper, we describe an operational workflow for generating annual estimates of species occupancy at national scales from raw species occurrence data, which can be used to construct a range of policy-relevant biodiversity indicators. We describe the workflow in detail: from data acquisition, data assessment and data manipulation, through modelling, model evaluation, application and dissemination. At each stage, we draw on our experience developing and applying the workflow for almost a decade to outline the challenges that analysts might face. These challenges span many areas of ecology, taxonomy, data science, computing and statistics. In our case, the principal output of the workflow is annual estimates of occupancy, with measures of uncertainty, for over 5000 species in each of several defined `regions' (e.g. countries, protected areas, etc.) of the UK from 1970 to 2019. This data product corresponds closely to the notion of a species distribution Essential Biodiversity Variable (EBV). Throughout the paper, we highlight methodologies that might not be applicable outside of the UK and suggest alternatives. We also highlight areas where the workflow can be improved; in particular, methods are needed to mitigate and communicate the risk of bias arising from the lack of representativeness that is typical of biodiversity data. Finally, we revisit the `ideal' and `minimal' criteria for species distribution EBVs laid out in previous contributions and pose some outstanding questions that should be addressed as a matter of priority. Going forward, we hope that this paper acts as a template for research groups around the world seeking to develop similar data products.},
  copyright = {\textcopyright{} 2023 The Authors. Biological Reviews published by John Wiley \& Sons Ltd on behalf of Cambridge Philosophical Society.},
  langid = {english},
  keywords = {biodiversity,citizen science,essential biodiversity variable,occupancy model,species distributions},
  file = {/Users/bbest/Zotero/storage/JTQX9EBZ/Boyd et al. - 2023 - An operational workflow for producing periodic est.pdf;/Users/bbest/Zotero/storage/E8Q3ITF3/brv.html}
}

@article{callejasGEEToolkitWater2022,
  title = {A {{GEE}} Toolkit for Water Quality Monitoring from 2002 to 2022 in Support of {{SDG}} 14 and Coral Health in Marine Protected Areas in {{Belize}}},
  author = {Callejas, Ileana A. and Osborn, Katie and Lee, Christine and Mishra, Deepak R. and Auil Gomez, Nicole and Carrias, Abel and Cherrington, Emil A. and Griffin, Robert and Rosado, Andria and Rosado, Samir and Jay, Jennifer},
  year = {2022},
  journal = {Frontiers in Remote Sensing},
  volume = {3},
  issn = {2673-6187},
  urldate = {2023-05-10},
  abstract = {Coral reefs are highly diverse ecosystems that provide many goods and ecosystem services globally. Coral reef ecosystems are also threatened by environmental stressors from anthropogenic sources and shifting climates. The United Nations Sustainable Development Goal 14 (``Life Below Water'') addresses the need to conserve and sustainably use the ocean, seas, and marine ecosystems, including reef systems. Belize's coral reef system is the second largest in the world, providing sources of income to Belizeans through tourism and fisheries as well as coastline protection. In order to conserve their marine ecosystems, Belize has a network of Marine Protected Areas (MPAs) throughout their coastal waters. Using Aqua MODIS satellite imagery from 2002 to 2022, Google Earth Engine, and RStudio, we present a workflow to calculate stress days on MPAs and a coral vulnerability index based on sea surface temperature (SST) and Kd (490), a proxy of water clarity. The Corozal Bay, Swallow Caye, Port Honduras, and South Water Caye MPAs had the highest percentages of stress days and coral vulnerability stress index score based on these two parameters among the 24 MPAs analyzed. Additionally, SST in the warmest month of the year in Belize were seen to increase across all MPAs from 2002 to 2022 (p {$<$} 0.01). This GEE toolkit provides a straightforward and accessible tool to help governments monitor both water quality and risks to coral reefs in accordance with SDG 14.},
  file = {/Users/bbest/Zotero/storage/7MTTSBKY/Callejas et al. - 2022 - A GEE toolkit for water quality monitoring from 20.pdf}
}

@article{cervantesperaltaBIRDIEDataPipeline2023,
  title = {{{BIRDIE}}: {{A}} Data Pipeline to Inform Wetland and Waterbird Conservation at Multiple Scales},
  shorttitle = {{{BIRDIE}}},
  author = {Cervantes Peralta, Francisco and Altwegg, Res and Strobbe, Francis and Skowno, Andrew and Visser, Vernon and Brooks, Michael and Stojanov, Yvan and Harebottle, Doug and Job, Nancy},
  year = {2023},
  month = mar,
  journal = {Frontiers in Ecology and Evolution},
  volume = {11},
  pages = {1131120},
  doi = {10.3389/fevo.2023.1131120},
  abstract = {Introduction Efforts to collect ecological data have intensified over the last decade. This is especially true for freshwater habitats, which are among the most impacted by human activity and yet lagging behind in terms of data availability. Now, to support conservation programmes and management decisions, these data need to be analyzed and interpreted; a process that can be complex and time consuming. The South African Biodiversity Data Pipeline for Wetlands and Waterbirds (BIRDIE) aims to help fast and efficient information uptake, bridging the gap between raw ecological datasets and the information final users need. Methods BIRDIE is a full data pipeline that takes up raw data, and estimates indicators related to waterbird populations, while keeping track of their associated uncertainty. At present, we focus on the assessment of species abundance and distribution in South Africa using two citizen-science bird monitoring datasets, namely: the African Bird Atlas Project and the Coordinated Waterbird Counts. These data are analyzed with occupancy and state-space models, respectively. In addition, a suite of environmental layers help contextualize waterbird population indicators, and link these to the ecological condition of the supporting wetlands. Both data and estimated indicators are accessible to end users through an online portal and web services. Results and discussion We have designed a modular system that includes tasks, such as: data cleaning, statistical analysis, diagnostics, and computation of indicators. Envisioned users of BIRDIE include government officials, conservation managers, researchers and the general public, all of whom have been engaged throughout the project. Acknowledging that conservation programmes run at multiple spatial and temporal scales, we have developed a granular framework in which indicators are estimated at small scales, and then these are aggregated to compute similar indicators at broader scales. Thus, the online portal is designed to provide spatial and temporal visualization of the indicators using maps, time series and pre-compiled reports for species, sites and conservation programmes. In the future, we aim to expand the geographical coverage of the pipeline to other African countries, and develop more indicators specific to the ecological structure and function of wetlands.},
  file = {/Users/bbest/Zotero/storage/P4G9M9VR/Cervantes Peralta et al. - 2023 - BIRDIE A data pipeline to inform wetland and wate.pdf}
}

@article{clarkEfficientBayesianAnalysis2019,
  title = {Efficient {{Bayesian}} Analysis of Occupancy Models with Logit Link Functions},
  author = {Clark, Allan E. and Altwegg, Res},
  year = {2019},
  journal = {Ecology and Evolution},
  volume = {9},
  number = {2},
  pages = {756--768},
  issn = {2045-7758},
  doi = {10.1002/ece3.4850},
  urldate = {2023-06-26},
  abstract = {Occupancy models (Ecology, 2002; 83: 2248) were developed to infer the probability that a species under investigation occupies a site. Bayesian analysis of these models can be undertaken using statistical packages such as WinBUGS, OpenBUGS, JAGS, and more recently Stan, however, since these packages were not developed specifically to fit occupancy models, one often experiences long run times when undertaking an analysis. Bayesian spatial single-season occupancy models can also be fit using the R package stocc. The approach assumes that the detection and occupancy regression effects are modeled using probit link functions. The use of the logistic link function, however, is algebraically more tractable and allows one to easily interpret the coefficient effects of an estimated model by using odds ratios, which is not easily done for a probit link function for models that do not include spatial random effects. We develop a Gibbs sampler to obtain posterior samples from the posterior distribution of the parameters of various occupancy models (nonspatial and spatial) when logit link functions are used to model the regression effects of the detection and occupancy processes. We apply our methods to data extracted from the 2nd Southern African Bird Atlas Project to produce a species distribution map of the Cape weaver (Ploceus capensis) and helmeted guineafowl (Numida meleagris) for South Africa. We found that the Gibbs sampling algorithm developed produces posterior samples that are identical to those obtained when using JAGS and Stan and that in certain cases the posterior chains mix much faster than those obtained when using JAGS, stocc, and Stan. Our algorithms are implemented in the R package, Rcppocc. The software is freely available and stored on GitHub (https://github.com/AllanClark/Rcppocc).},
  copyright = {\textcopyright{} 2019 The Authors. Ecology and Evolution published by John Wiley \& Sons Ltd.},
  langid = {english},
  keywords = {Bayesian spatial occupancy model,imperfect detection,occupancy model,Rcppocc,restricted spatial regression},
  file = {/Users/bbest/Zotero/storage/LI8AWGX3/Clark and Altwegg - 2019 - Efficient Bayesian analysis of occupancy models wi.pdf;/Users/bbest/Zotero/storage/499FQNJ6/ece3.html}
}

@article{clarkGibbsSamplerMultispecies2023,
  title = {A {{Gibbs}} Sampler for Multi-Species Occupancy Models},
  author = {Clark, Allan Ernest and Altwegg, Res},
  year = {2023},
  month = jun,
  journal = {Environmental and Ecological Statistics},
  volume = {30},
  number = {2},
  pages = {189--204},
  issn = {1573-3009},
  doi = {10.1007/s10651-023-00558-7},
  urldate = {2023-06-26},
  abstract = {Multi-species occupancy (MSO) models use detection-nondetection data from species observed at different locations to estimate the probability that a particular species occupies a particular geographical region. The models are particularly useful for estimating the occupancy probabilities associated with rare species since they are seldom observed when undertaking field surveys. In this paper, we develop Gibbs sampling algorithms that can be used to fit various Bayesian MSO models to detection-nondetection data. Bayesian analysis of these models can be undertaken using statistical packages such as JAGS, Stan, and NIMBLE. However, since these packages were not developed specifically to fit occupancy models, one often experiences long run-times when undertaking analysis. However, we find that these packages that were not developed specifically to fit MSO models are less efficient than our special-purpose Gibbs sampling algorithms.},
  langid = {english},
  keywords = {Bayesian multi-species occupancy model,Imperfect detection,Occupancy model,Reversible-jump Markov chain Monte Carlo,Species richness},
  file = {/Users/bbest/Zotero/storage/9GURB9KY/Clark and Altwegg - 2023 - A Gibbs sampler for multi-species occupancy models.pdf}
}

@article{clarkGibbsSamplerMultispecies2023a,
  title = {A {{Gibbs}} Sampler for Multi-Species Occupancy Models},
  author = {Clark, Allan Ernest and Altwegg, Res},
  year = {2023},
  month = jun,
  journal = {Environmental and Ecological Statistics},
  volume = {30},
  number = {2},
  pages = {189--204},
  issn = {1573-3009},
  doi = {10.1007/s10651-023-00558-7},
  urldate = {2023-06-26},
  abstract = {Multi-species occupancy (MSO) models use detection-nondetection data from species observed at different locations to estimate the probability that a particular species occupies a particular geographical region. The models are particularly useful for estimating the occupancy probabilities associated with rare species since they are seldom observed when undertaking field surveys. In this paper, we develop Gibbs sampling algorithms that can be used to fit various Bayesian MSO models to detection-nondetection data. Bayesian analysis of these models can be undertaken using statistical packages such as JAGS, Stan, and NIMBLE. However, since these packages were not developed specifically to fit occupancy models, one often experiences long run-times when undertaking analysis. However, we find that these packages that were not developed specifically to fit MSO models are less efficient than our special-purpose Gibbs sampling algorithms.},
  langid = {english},
  keywords = {Bayesian multi-species occupancy model,Imperfect detection,Occupancy model,Reversible-jump Markov chain Monte Carlo,Species richness},
  file = {/Users/bbest/Zotero/storage/A4YRDTWV/Clark and Altwegg - 2023 - A Gibbs sampler for multi-species occupancy models.pdf}
}

@article{cregoImplementationSpeciesDistribution2022,
  title = {Implementation of Species Distribution Models in {{Google Earth Engine}}},
  author = {Crego, Ramiro D. and Stabach, Jared A. and Connette, Grant},
  year = {2022},
  journal = {Diversity and Distributions},
  volume = {28},
  number = {5},
  pages = {904--916},
  issn = {1472-4642},
  doi = {10.1111/ddi.13491},
  urldate = {2023-06-22},
  abstract = {Aim Google Earth Engine (GEE) is a free Web-based spatial analysis platform that requires only a web browser and an Internet connection to programmatically access and analyse data from its multi-petabyte catalog of regularly updated satellite imagery (e.g. MODIS, Landsat, Sentinel) and other geospatial datasets. The high computing capacity of GEE can make computationally demanding analyses more accessible to researchers and practitioners, especially those with limited access to advanced computational resources. Here, we present a workflow in GEE to fit species distribution models, offering direct access to a multi-petabyte catalog of raster products to obtain estimates of habitat suitability. Innovation We implemented a workflow for species distribution modelling in GEE that includes importing species occurrence data into the GEE platform, selecting and preparing predictor variables, and performing model fitting with spatial or temporal split-block cross-validation techniques. We present three case studies that demonstrate: (i) a baseline SDM workflow that produces informative model predictions, (ii) a workflow that accounts for temporal variability in predictor variables to study changes in habitat suitability over time and (iii) a complex and computationally demanding analysis incorporating thousands of satellite images for modelling habitat suitability at high spatial resolution. Main Conclusions Our SDM workflow allows users to benefit from the high speed and performance of GEE without the need for significant computing infrastructure. This workflow may be especially beneficial to researchers in countries where computing power is limited, as SDMs frequently require the download, storage and processing of large raster datasets. We also discuss key limitations of implementing SDMs in GEE, such as user memory limits and the lack of high-level functions. We include a step-by-step guide for the general model workflow and for each of the case studies presented to facilitate its implementation.},
  copyright = {\textcopyright{} 2022 The Authors. Diversity and Distributions published by John Wiley \& Sons Ltd.},
  langid = {english},
  keywords = {gradient boosting,habitat suitability models,JavaScript,niche models,occurrence data,random forest},
  file = {/Users/bbest/Zotero/storage/FNMJYNXG/Crego et al. - 2022 - Implementation of species distribution models in G.pdf;/Users/bbest/Zotero/storage/LWZERTP6/ddi.html}
}

@article{dicolaEcospatPackageSupport2017,
  title = {Ecospat: An {{R}} Package to Support Spatial Analyses and Modeling of Species Niches and Distributions},
  shorttitle = {Ecospat},
  author = {Di Cola, Valeria and Broennimann, Olivier and Petitpierre, Blaise and Breiner, Frank T. and D'Amen, Manuela and Randin, Christophe and Engler, Robin and Pottier, Julien and Pio, Dorothea and Dubuis, Anne and Pellissier, Loic and Mateo, Rub{\'e}n G. and Hordijk, Wim and Salamin, Nicolas and Guisan, Antoine},
  year = {2017},
  journal = {Ecography},
  volume = {40},
  number = {6},
  pages = {774--787},
  issn = {1600-0587},
  doi = {10.1111/ecog.02671},
  urldate = {2023-06-22},
  abstract = {The aim of the ecospat package is to make available novel tools and methods to support spatial analyses and modeling of species niches and distributions in a coherent workflow. The package is written in the R language (R Development Core Team) and contains several features, unique in their implementation, that are complementary to other existing R packages. Pre-modeling analyses include species niche quantifications and comparisons between distinct ranges or time periods, measures of phylogenetic diversity, and other data exploration functionalities (e.g. extrapolation detection, ExDet). Core modeling brings together the new approach of ensemble of small models (ESM) and various implementations of the spatially-explicit modeling of species assemblages (SESAM) framework. Post-modeling analyses include evaluation of species predictions based on presence-only data (Boyce index) and of community predictions, phylogenetic diversity and environmentally-constrained species co-occurrences analyses. The ecospat package also provides some functions to supplement the `biomod2' package (e.g. data preparation, permutation tests and cross-validation of model predictive power). With this novel package, we intend to stimulate the use of comprehensive approaches in spatial modelling of species and community distributions.},
  copyright = {\textcopyright{} 2016 The Authors},
  langid = {english},
  file = {/Users/bbest/Zotero/storage/L92XXDAQ/Di Cola et al. - 2017 - ecospat an R package to support spatial analyses .pdf;/Users/bbest/Zotero/storage/QI6RDGWX/ecog.html}
}

@misc{doserGuidelinesUseSpatiallyvarying2023,
  title = {Guidelines for the Use of Spatially-Varying Coefficients in Species Distribution Models},
  author = {Doser, Jeffrey W. and K{\'e}ry, Marc and Finley, Andrew O. and Saunders, Sarah P. and Weed, Aaron S. and Zipkin, Elise F.},
  year = {2023},
  month = jan,
  number = {arXiv:2301.05645},
  eprint = {2301.05645},
  primaryclass = {stat},
  publisher = {{arXiv}},
  doi = {10.48550/arXiv.2301.05645},
  urldate = {2023-06-26},
  abstract = {Species distribution models (SDMs) are increasingly applied across macroscales. However, assumptions of stationarity in species-environment relationships or population trends inherent to most SDM techniques are frequently violated at broad spatial scales. Bayesian spatially-varying coefficient (SVC) models can readily account for nonstationarity, yet their use is relatively scarce, due, in part, to a gap in understanding both the data requirements needed to fit SVC SDMs, as well as the inferential benefits of applying a more complex modeling framework. Using simulations, we present guidelines and recommendations for fitting single-season and multi-season SVC SDMs. We display the inferential benefits of SVC SDMs using an empirical case study assessing spatially-varying trends of 51 forest birds in the eastern US from 2000-2019. We provide user-friendly and computationally efficient software to fit SVC SDMs in the spOccupancy R package. While all datasets are unique, we recommend a minimum sample size of \$\{\textbackslash sim\}500\$ spatial locations when fitting single-season SVC SDMs, while for multi-season SVC SDMs, \$\{\textbackslash sim\}100\$ sites is adequate for even moderate amounts of temporal replication. Within our case study, we found 88\% (45 of 51) of species had strong support for spatially-varying occurrence trends. We suggest five guidelines: (1) only fit single-season SVC SDMs with more than \$\{\textbackslash sim\}500\$ sites; (2) consider using informative priors on spatial parameters to improve spatial process estimates; (3) use data from multiple seasons if available; (4) use model selection to compare SVC SDMs with simpler alternatives; and (5) develop simulations to assess the reliability of inferences. These guidelines provide a comprehensive foundation for using SVC SDMs to evaluate the presence and impact of nonstationary environmental factors that drive species distributions at macroscales.},
  archiveprefix = {arxiv},
  keywords = {Statistics - Applications},
  file = {/Users/bbest/Zotero/storage/2MUEPCSF/Doser et al. - 2023 - Guidelines for the use of spatially-varying coeffi.pdf;/Users/bbest/Zotero/storage/I6TMKICB/2301.html}
}

@misc{doserJointSpeciesDistribution2022,
  title = {Joint Species Distribution Models with Imperfect Detection for High-Dimensional Spatial Data},
  author = {Doser, Jeffrey W. and Finley, Andrew O. and Banerjee, Sudipto},
  year = {2022},
  month = dec,
  number = {arXiv:2204.02707},
  eprint = {2204.02707},
  primaryclass = {stat},
  publisher = {{arXiv}},
  doi = {10.48550/arXiv.2204.02707},
  urldate = {2023-06-26},
  abstract = {Determining spatial distributions of species and communities are key objectives of ecology and conservation. Joint species distribution models use multi-species detection-nondetection data to estimate species and community distributions. The analysis of such data is complicated by residual correlations between species, imperfect detection, and spatial autocorrelation. While methods exist to accommodate each of these complexities, there are few examples in the literature that address and explore all three complexities simultaneously. Here we developed a spatial factor multi-species occupancy model to explicitly account for species correlations, imperfect detection, and spatial autocorrelation. The proposed model uses a spatial factor dimension reduction approach and Nearest Neighbor Gaussian Processes to ensure computational efficiency for data sets with both a large number of species (e.g., {$>$} 100) and spatial locations (e.g., 100,000). We compare the proposed model performance to five candidate models, each addressing a subset of the three complexities. We implemented the proposed and competing models in the spOccupancy software, designed to facilitate application via an accessible, well-documented, and open-source R package. Using simulations, we found ignoring the three complexities when present leads to inferior model predictive performance. Using a case study on 98 bird species across the continental US, the spatial factor multi-species occupancy model had the highest predictive performance among the candidate models. Our proposed framework, together with its implementation in spOccupancy, serves as a user-friendly tool to understand spatial variation in species distributions and biodiversity metrics while addressing common complexities in multi-species detection-nondetection data.},
  archiveprefix = {arxiv},
  keywords = {Statistics - Applications},
  file = {/Users/bbest/Zotero/storage/9YINCPB8/Doser et al. - 2022 - Joint species distribution models with imperfect d.pdf;/Users/bbest/Zotero/storage/DUHXURCB/2204.html}
}

@article{doserMultiseasonOccupancyModels2022,
  title = {Multi-Season Occupancy Models for Assessing Species Trends and Spatio-Temporal Occurrence Patterns},
  author = {Doser, Jeffrey W and K{\'e}ry, Marc},
  year = {2022},
  langid = {english},
  file = {/Users/bbest/Zotero/storage/QZQ7PDX9/Doser and Kéry - Multi-season occupancy models for assessing specie.pdf}
}

@misc{doserSpOccupancy2023,
  title = {{{spOccupancy}}},
  author = {Doser, Jeff},
  year = {2023},
  month = jun,
  urldate = {2023-06-26},
  abstract = {Single-species, Multi-species, and Integrated Spatial Occupancy Models},
  copyright = {GPL-3.0}
}

@article{doserSpOccupancyPackageSinglespecies2022,
  title = {{{spOccupancy}}: {{An R}} Package for Single-Species, Multi-Species, and Integrated Spatial Occupancy Models},
  shorttitle = {{{spOccupancy}}},
  author = {Doser, Jeffrey W. and Finley, Andrew O. and K{\'e}ry, Marc and Zipkin, Elise F.},
  year = {2022},
  month = aug,
  journal = {Methods in Ecology and Evolution},
  volume = {13},
  number = {8},
  pages = {1670--1678},
  publisher = {{John Wiley \& Sons, Ltd}},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.13897},
  urldate = {2023-06-26},
  abstract = {Occupancy modelling is a common approach to assess species distribution patterns, while explicitly accounting for false absences in detection\textendash nondetection data. Numerous extensions of the basic si...},
  langid = {english},
  file = {/Users/bbest/Zotero/storage/KFI4PNU4/Doser et al. - 2022 - spOccupancy An R package for single-species, mult.pdf}
}

@article{drouillyMultispeciesOccupancyModelling2018,
  title = {Multi-Species Occupancy Modelling of Mammal and Ground Bird Communities in Rangeland in the {{Karoo}}: {{A}} Case for Dryland Systems Globally},
  shorttitle = {Multi-Species Occupancy Modelling of Mammal and Ground Bird Communities in Rangeland in the {{Karoo}}},
  author = {Drouilly, Marine and Clark, Allan and O'Riain, M. Justin},
  year = {2018},
  month = aug,
  journal = {Biological Conservation},
  volume = {224},
  pages = {16--25},
  issn = {0006-3207},
  doi = {10.1016/j.biocon.2018.05.013},
  urldate = {2023-06-26},
  abstract = {The transition from natural habitat to agricultural land use is widely regarded as one of the leading drivers of biodiversity loss. Despite this, most wildlife still lives outside protected areas on private agricultural land, particularly on rangeland used for livestock grazing. Understanding which species persist and which decline in agricultural landscapes is important for global biodiversity monitoring, management and conservation. In this study, we used hierarchical multi-species occupancy modelling to estimate terrestrial vertebrate (body mass\,{$>$}\,0.5\,kg) richness in the Karoo, a semi-arid region of South Africa. We evaluated species-specific responses to different anthropogenic and environmental variables in rangeland and a nearby protected area of similar size. We grouped mammal species according to trophic guild and body size and compared their occurrence between areas. In total we detected 42 species over 4035 6-day pooled trap nights across 322 sites. Community species richness was not significantly different between the two types of land use and decreased with increasing elevation in the protected area. Human disturbance did not affect individual species occupancy in either area. Carnivores, omnivores and medium-sized species occupancy probabilities were similar between the two areas but were higher for herbivores and large species in the protected area and for insectivores and small species in rangeland. Our results reveal that drylands in the South African Karoo region, including rangeland used for small-livestock farming, support a diverse community of terrestrial vertebrates. Private landowners are thus important custodians of key components of indigenous biodiversity outside of protected areas, especially in low-lying areas.},
  langid = {english},
  keywords = {Body size,Camera trap,Hierarchical Bayesian model,Land use,Species richness,Trophic guild},
  file = {/Users/bbest/Zotero/storage/D36S88PK/Drouilly et al. - 2018 - Multi-species occupancy modelling of mammal and gr.pdf;/Users/bbest/Zotero/storage/NWF2TJFP/S0006320718303975.html}
}

@misc{ecospatEcospatMiscSDM2023,
  title = {Ecospat - Misc {{SDM}} Methods \& Utilities ({{Guisan}} Lab)},
  author = {{ecospat}},
  year = {2023},
  month = apr,
  urldate = {2023-06-22},
  abstract = {Miscellaneous methods and utilities for spatial ecology analysis, written by current and former members and collaborators of the ecospat group of Prof. Antoine Guisan, Department of Ecology and Evolution (DEE) \& Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Switzerland.}
}

@article{ellis-sotoContinentalscaleKmHummingbird2021,
  title = {Continental-Scale 1 Km Hummingbird Diversity Derived from Fusing Point Records with Lateral and Elevational Expert Information},
  author = {{Ellis-Soto}, Diego and Merow, Cory and Amatulli, Giuseppe and Parra, Juan L. and Jetz, Walter},
  year = {2021},
  journal = {Ecography},
  volume = {44},
  number = {4},
  pages = {640--652},
  issn = {1600-0587},
  doi = {10.1111/ecog.05119},
  urldate = {2023-06-26},
  abstract = {Anthropogenic change is affecting mountain regions worldwide. Managing this change and advancing biodiversity information for research requires spatially detailed information on species distributions which often is incomplete. Here, we provide a model-based approach for the integration of expert-based elevational range information with expert range maps and point occurrences to address this need. These integrated models use expert knowledge on elevational and distributional ranges as offset in a Poisson point process species distribution model (SDM). We use this approach to model the distribution of 276 hummingbird Trochilidae species at 1 km resolution and validate model performance with extensive survey data (presence\textendash absence inventories). Models including expert elevation information consistently outperformed those lacking this information. Improvements were greatest when the number of available occurrences was small, highlighting the added value from expert elevation information, especially for data-poor species. Separate validation data indicated significant increases in true skill statistics, based on higher specificity and slightly improved sensitivity. SDMs that included expert range information out-performed presence-only models based only on occurrence data in 92.5\% of cases and had higher sensitivity, lower false positive rates and smaller predicted range sizes. Generally, the integrated models removed unsuitable areas from range estimates and decreased overestimates in geographic range size (false presences) inherent in expert maps and in models lacking elevation information. By stacking SDM output we provide a first, hemispheric map of predicted hummingbird species richness modelled at 1 km resolution and identify southwest Colombia as a richness hotspot. Our study highlights the value gained from integrating multiple data types in a single framework. The presented approach improved high-resolution range predictions for single species (reducing false presences) and aggregate biodiversity patterns (e.g. reducing species richness overestimates). The method is now being implemented and expanded in Map of Life.},
  copyright = {\textcopyright{} 2021 The Authors. Ecography published by John Wiley \& Sons Ltd on behalf of Nordic Society Oikos},
  langid = {english},
  keywords = {biodiversity,biogeography,data fusion,elevation gradients,range model,species distribution models,species richness,Trochilidae},
  file = {/Users/bbest/Zotero/storage/VU2M8UZV/Ellis-Soto et al. - 2021 - Continental-scale 1 km hummingbird diversity deriv.pdf;/Users/bbest/Zotero/storage/FUH9Z57P/ecog.html}
}

@article{fengChecklistMaximizingReproducibility2019,
  title = {A Checklist for Maximizing Reproducibility of Ecological Niche Models},
  author = {Feng, Xiao and Park, Daniel S. and Walker, Cassondra and Peterson, A. Townsend and Merow, Cory and Pape{\c s}, Monica},
  year = {2019},
  month = oct,
  journal = {Nature Ecology \& Evolution},
  volume = {3},
  number = {10},
  pages = {1382--1395},
  publisher = {{Nature Publishing Group}},
  issn = {2397-334X},
  doi = {10.1038/s41559-019-0972-5},
  urldate = {2023-06-22},
  abstract = {Reporting specific modelling methods and metadata is essential to the reproducibility of ecological studies, yet guidelines rarely exist regarding what information should be noted. Here, we address this issue for ecological niche modelling or species distribution modelling, a rapidly developing toolset in ecology used across many aspects of biodiversity science. Our quantitative review of the recent literature reveals a general lack of sufficient information to fully reproduce the work. Over two-thirds of the examined studies neglected to report the version or access date of the underlying data, and only half reported model parameters. To address this problem, we propose adopting a checklist to guide studies in reporting at least the minimum information necessary for ecological niche modelling reproducibility, offering a straightforward way to balance efficiency and accuracy. We encourage the ecological niche modelling community, as well as journal reviewers and editors, to utilize and further develop this framework to facilitate and improve the reproducibility of future work. The proposed checklist framework is generalizable to other areas of ecology, especially those utilizing biodiversity data, environmental data and statistical modelling, and could also be adopted by a broader array of disciplines.},
  copyright = {2019 The Author(s)},
  langid = {english},
  keywords = {Biogeography,Ecological modelling,Research data,Theoretical ecology},
  file = {/Users/bbest/Zotero/storage/55BVZTT7/Feng et al. - 2019 - A checklist for maximizing reproducibility of ecol.pdf}
}

@article{ferrierUsingGeneralizedDissimilarity2007,
  title = {Using Generalized Dissimilarity Modelling to Analyse and Predict Patterns of Beta Diversity in Regional Biodiversity Assessment},
  author = {Ferrier, Simon and Manion, Glenn and Elith, Jane and Richardson, Karen},
  year = {2007},
  journal = {Diversity and Distributions},
  volume = {13},
  number = {3},
  pages = {252--264},
  issn = {1472-4642},
  doi = {10.1111/j.1472-4642.2007.00341.x},
  urldate = {2023-06-22},
  abstract = {Generalized dissimilarity modelling (GDM) is a statistical technique for analysing and predicting spatial patterns of turnover in community composition (beta diversity) across large regions. The approach is an extension of matrix regression, designed specifically to accommodate two types of nonlinearity commonly encountered in large-scaled ecological data sets: (1) the curvilinear relationship between increasing ecological distance, and observed compositional dissimilarity, between sites; and (2) the variation in the rate of compositional turnover at different positions along environmental gradients. GDM can be further adapted to accommodate special types of biological and environmental data including, for example, information on phylogenetic relationships between species and information on barriers to dispersal between geographical locations. The approach can be applied to a wide range of assessment activities including visualization of spatial patterns in community composition, constrained environmental classification, distributional modelling of species or community types, survey gap analysis, conservation assessment, and climate-change impact assessment.},
  copyright = {\textcopyright{} 2007 The Authors. Journal compilation \textcopyright{} 2007 Blackwell Publishing Ltd},
  langid = {english},
  keywords = {Beta diversity,biodiversity,compositional turnover,conservation assessment,generalized dissimilarity modelling},
  file = {/Users/bbest/Zotero/storage/9AC6P7JI/Ferrier et al. - 2007 - Using generalized dissimilarity modelling to analy.pdf;/Users/bbest/Zotero/storage/IXM6QPJ7/j.1472-4642.2007.00341.html}
}

@article{fletcherjr.PracticalGuideCombining2019,
  title = {A Practical Guide for Combining Data to Model Species Distributions},
  author = {Fletcher Jr., Robert J. and Hefley, Trevor J. and Robertson, Ellen P. and Zuckerberg, Benjamin and McCleery, Robert A. and Dorazio, Robert M.},
  year = {2019},
  journal = {Ecology},
  volume = {100},
  number = {6},
  pages = {e02710},
  issn = {1939-9170},
  doi = {10.1002/ecy.2710},
  urldate = {2023-06-26},
  abstract = {Understanding and accurately modeling species distributions lies at the heart of many problems in ecology, evolution, and conservation. Multiple sources of data are increasingly available for modeling species distributions, such as data from citizen science programs, atlases, museums, and planned surveys. Yet reliably combining data sources can be challenging because data sources can vary considerably in their design, gradients covered, and potential sampling biases. We review, synthesize, and illustrate recent developments in combining multiple sources of data for species distribution modeling. We identify five ways in which multiple sources of data are typically combined for modeling species distributions. These approaches vary in their ability to accommodate sampling design, bias, and uncertainty when quantifying environmental relationships in species distribution models. Many of the challenges for combining data are solved through the prudent use of integrated species distribution models: models that simultaneously combine different data sources on species locations to quantify environmental relationships for explaining species distribution. We illustrate these approaches using planned survey data on 24 species of birds coupled with opportunistically collected eBird data in the southeastern United States. This example illustrates some of the benefits of data integration, such as increased precision in environmental relationships, greater predictive accuracy, and accounting for sample bias. Yet it also illustrates challenges of combining data sources with vastly different sampling methodologies and amounts of data. We provide one solution to this challenge through the use of weighted joint likelihoods. Weighted joint likelihoods provide a means to emphasize data sources based on different criteria (e.g., sample size), and we find that weighting improves predictions for all species considered. We conclude by providing practical guidance on combining multiple sources of data for modeling species distributions.},
  copyright = {\textcopyright{} 2019 by the Ecological Society of America},
  langid = {english},
  keywords = {citizen science,data fusion,ecological niche model,habitat suitability model,integrated model,spatial point process,Special Feature: Data Integration for Population Models,species distribution model},
  file = {/Users/bbest/Zotero/storage/E3RSV9DC/ecy.html}
}

@article{fletcherjr.PracticalGuideCombining2019a,
  title = {A Practical Guide for Combining Data to Model Species Distributions},
  author = {Fletcher Jr., Robert J. and Hefley, Trevor J. and Robertson, Ellen P. and Zuckerberg, Benjamin and McCleery, Robert A. and Dorazio, Robert M.},
  year = {2019},
  journal = {Ecology},
  volume = {100},
  number = {6},
  pages = {e02710},
  issn = {1939-9170},
  doi = {10.1002/ecy.2710},
  urldate = {2023-06-26},
  abstract = {Understanding and accurately modeling species distributions lies at the heart of many problems in ecology, evolution, and conservation. Multiple sources of data are increasingly available for modeling species distributions, such as data from citizen science programs, atlases, museums, and planned surveys. Yet reliably combining data sources can be challenging because data sources can vary considerably in their design, gradients covered, and potential sampling biases. We review, synthesize, and illustrate recent developments in combining multiple sources of data for species distribution modeling. We identify five ways in which multiple sources of data are typically combined for modeling species distributions. These approaches vary in their ability to accommodate sampling design, bias, and uncertainty when quantifying environmental relationships in species distribution models. Many of the challenges for combining data are solved through the prudent use of integrated species distribution models: models that simultaneously combine different data sources on species locations to quantify environmental relationships for explaining species distribution. We illustrate these approaches using planned survey data on 24 species of birds coupled with opportunistically collected eBird data in the southeastern United States. This example illustrates some of the benefits of data integration, such as increased precision in environmental relationships, greater predictive accuracy, and accounting for sample bias. Yet it also illustrates challenges of combining data sources with vastly different sampling methodologies and amounts of data. We provide one solution to this challenge through the use of weighted joint likelihoods. Weighted joint likelihoods provide a means to emphasize data sources based on different criteria (e.g., sample size), and we find that weighting improves predictions for all species considered. We conclude by providing practical guidance on combining multiple sources of data for modeling species distributions.},
  langid = {english},
  keywords = {citizen science,data fusion,ecological niche model,habitat suitability model,integrated model,spatial point process,Special Feature: Data Integration for Population Models,species distribution model},
  file = {/Users/bbest/Zotero/storage/M8252XZC/ecy.html}
}

@article{fletcherjr.PracticalGuideCombining2019b,
  title = {A Practical Guide for Combining Data to Model Species Distributions},
  author = {Fletcher Jr., Robert J. and Hefley, Trevor J. and Robertson, Ellen P. and Zuckerberg, Benjamin and McCleery, Robert A. and Dorazio, Robert M.},
  year = {2019},
  journal = {Ecology},
  volume = {100},
  number = {6},
  pages = {e02710},
  issn = {1939-9170},
  doi = {10.1002/ecy.2710},
  urldate = {2023-06-26},
  abstract = {Understanding and accurately modeling species distributions lies at the heart of many problems in ecology, evolution, and conservation. Multiple sources of data are increasingly available for modeling species distributions, such as data from citizen science programs, atlases, museums, and planned surveys. Yet reliably combining data sources can be challenging because data sources can vary considerably in their design, gradients covered, and potential sampling biases. We review, synthesize, and illustrate recent developments in combining multiple sources of data for species distribution modeling. We identify five ways in which multiple sources of data are typically combined for modeling species distributions. These approaches vary in their ability to accommodate sampling design, bias, and uncertainty when quantifying environmental relationships in species distribution models. Many of the challenges for combining data are solved through the prudent use of integrated species distribution models: models that simultaneously combine different data sources on species locations to quantify environmental relationships for explaining species distribution. We illustrate these approaches using planned survey data on 24 species of birds coupled with opportunistically collected eBird data in the southeastern United States. This example illustrates some of the benefits of data integration, such as increased precision in environmental relationships, greater predictive accuracy, and accounting for sample bias. Yet it also illustrates challenges of combining data sources with vastly different sampling methodologies and amounts of data. We provide one solution to this challenge through the use of weighted joint likelihoods. Weighted joint likelihoods provide a means to emphasize data sources based on different criteria (e.g., sample size), and we find that weighting improves predictions for all species considered. We conclude by providing practical guidance on combining multiple sources of data for modeling species distributions.},
  copyright = {\textcopyright{} 2019 by the Ecological Society of America},
  langid = {english},
  keywords = {citizen science,data fusion,ecological niche model,habitat suitability model,integrated model,spatial point process,Special Feature: Data Integration for Population Models,species distribution model},
  file = {/Users/bbest/Zotero/storage/VI72AM45/Fletcher Jr. et al. - 2019 - A practical guide for combining data to model spec.pdf;/Users/bbest/Zotero/storage/WGXUHE73/ecy.html}
}

@article{fordererGlobalDiversityPatterns2023,
  title = {Global Diversity Patterns of Larger Benthic Foraminifera under Future Climate Change},
  author = {F{\"o}rderer, Esther-Meena and R{\"o}dder, Dennis and Langer, Martin R.},
  year = {2023},
  journal = {Global Change Biology},
  volume = {29},
  number = {4},
  pages = {969--981},
  issn = {1365-2486},
  doi = {10.1111/gcb.16535},
  urldate = {2023-06-22},
  abstract = {Global warming threatens the viability of tropical coral reefs and associated marine calcifiers, including symbiont-bearing larger benthic foraminifera (LBF). The impacts of current climate change on LBF are debated because they were particularly diverse and abundant during past warm periods. Studies on the responses of selected LBF species to changing environmental conditions reveal varying results. Based on a comprehensive review of the scientific literature on LBF species occurrences, we applied species distribution modeling using Maxent to estimate present-day and future species richness patterns on a global scale for the time periods 2040\textendash 2050 and 2090\textendash 2100. For our future projections, we focus on Representative Concentration Pathway 6.0 from the Intergovernmental Panel on Climate Change, which projects mean surface temperature changes of +2.2\textdegree C by the year 2100. Our results suggest that species richness in the Central Indo-Pacific is two to three times higher than in the Bahamian ecoregion, which we have identified as the present-day center of LBF diversity in the Atlantic. Our future predictions project a dramatic temperature-driven decline in low-latitude species richness and an increasing widening bimodal latitudinal pattern of species diversity. While the central Indo-Pacific, now the stronghold of LBF diversity, is expected to be most pushed outside of the currently realized niches of most species, refugia may be largely preserved in the Atlantic. LBF species will face large-scale non-analogous climatic conditions compared to currently realized climate space in the near future, as reflected in the extensive areas of extrapolation, particularly in the Indo-Pacific. Our study supports hypotheses that species richness and biogeographic patterns of LBF will fundamentally change under future climate conditions, possibly initiating a faunal turnover by the late 21st century.},
  copyright = {\textcopyright{} 2022 The Authors. Global Change Biology published by John Wiley \& Sons Ltd.},
  langid = {english},
  keywords = {climate change,coral reefs,Coral Triangle,global warming,larger benthic foraminifera,marine biodiversity,species distribution modeling},
  file = {/Users/bbest/Zotero/storage/T33ULIDF/Förderer et al. - 2023 - Global diversity patterns of larger benthic forami.pdf;/Users/bbest/Zotero/storage/Z6E24GP9/gcb.html}
}

@article{galanteChangeRangeRPackageReproducible2023,
  title = {{{changeRangeR}}: {{An R}} Package for Reproducible Biodiversity Change Metrics from Species Distribution Estimates},
  shorttitle = {{{changeRangeR}}},
  author = {Galante, Peter J. and Chang Triguero, Samuel and Paz, Andrea and {Aiello-Lammens}, Matthew and Gerstner, Beth E. and Johnson, Bethany A. and Kass, Jamie M. and Merow, Cory and {Noguera-Urbano}, Elkin A. and {Pinilla-Buitrago}, Gonzalo E. and Blair, Mary E.},
  year = {2023},
  journal = {Conservation Science and Practice},
  volume = {5},
  number = {1},
  pages = {e12863},
  issn = {2578-4854},
  doi = {10.1111/csp2.12863},
  urldate = {2023-06-22},
  abstract = {Conservation planning and decision-making rely on evaluations of biodiversity status and threats that are based upon species' distribution estimates. However, gaps exist regarding automated tools to delineate species' current ranges from distribution estimates and use those estimates to calculate both species- and community-level biodiversity metrics. Here, we introduce changeRangeR, an R package that facilitates workflows to reproducibly transform estimates of species' distributions into metrics relevant for conservation. For example, by combining predictions from species distribution models (SDMs) with other maps of environmental data (e.g., suitable forest cover), researchers can characterize the proportion of a species' range that is under protection, metrics used under the IUCN Criteria A and B guidelines (Area of Occupancy and Extent of Occurrence), and other more general metrics such as taxonomic and phylogenetic diversity and endemism. Further, changeRangeR facilitates temporal comparisons among biodiversity metrics to inform efforts toward complementarity and consideration of future scenarios in conservation decisions. changeRangeR also provides tools to determine the effects of modeling decisions through sensitivity tests. Transparent and repeatable workflows for calculating biodiversity change metrics from SDMs such as those provided by changeRangeR are essential to inform conservation decision-making efforts and represent key extensions for SDM methodology and associated metadata documentation.},
  copyright = {\textcopyright{} 2022 The Authors. Conservation Science and Practice published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.},
  langid = {english},
  keywords = {area of occupancy,conservation biogeography,endemism,extent of occurrence,niche model,range,Red List,software,spatial analysis,temporal trends},
  file = {/Users/bbest/Zotero/storage/CTYHRN98/Galante et al. - 2023 - changeRangeR An R package for reproducible biodiv.pdf;/Users/bbest/Zotero/storage/7TLSXADC/csp2.html}
}

@article{garcia-reyesCaliforniaMultivariateOcean2017,
  title = {California {{Multivariate Ocean Climate Indicator}} ({{MOCI}}) and Marine Ecosystem Dynamics},
  author = {{Garc{\'i}a-Reyes}, Marisol and Sydeman, William J.},
  year = {2017},
  month = jan,
  journal = {Ecological Indicators},
  volume = {72},
  pages = {521--529},
  issn = {1470-160X},
  doi = {10.1016/j.ecolind.2016.08.045},
  urldate = {2023-06-28},
  abstract = {Marine ecosystems are complex adaptive systems with physical and biological processes operating on multiple spatial and temporal scales. Here, we present an operational regional indicator for California's continental shelf system and investigate its skill in predicting a variety of biological responses across trophic levels. This updated Multivariate Ocean Climate Indicator (MOCI) version 2 includes data that are readily available from the Internet so the indicator can be automatically updated and shared regularly. MOCIv.2 is a simplified version of MOCIv.1, but it captures ocean-climate variability similarly. MOCIv.2 illustrates all major ENSO events that occurred over the past 25 years as well as the phasing and magnitude of the most recent North Pacific marine heat wave, dubbed `The Blob'. It also shows differences in the magnitude and timing of ocean-climate variability in different regions off California. MOCIv.2 has skill in nowcasting marine ecosystem dynamics, from zooplankton to top predators, and therefore may be useful in establishing bio-physical relationships important to ecosystem-based fisheries and wildlife management in California.},
  langid = {english},
  keywords = {Biophysical relationships,California marine ecosystem,Climate variability,Coastal upwelling,Temperature},
  file = {/Users/bbest/Zotero/storage/WKU5LTQZ/García-Reyes and Sydeman - 2017 - California Multivariate Ocean Climate Indicator (M.pdf;/Users/bbest/Zotero/storage/XA4JXZZ9/S1470160X16305064.html}
}

@misc{GdmPackageGeneralized,
  title = {Gdm {{R}} Package: {{Generalized Dissimilarity Modeling}}},
  urldate = {2023-06-22},
  abstract = {A toolkit with functions to fit, plot, summarize, and apply Generalized Dissimilarity Models. Mokany K, Ware C, Woolley SNC, Ferrier S, Fitzpatrick MC (2022) {$<$}doi:10.1111/geb.13459{$>$} Ferrier S, Manion G, Elith J, Richardson K (2007) {$<$}doi:10.1111/j.1472-4642.2007.00341.x{$>$}.},
  file = {/Users/bbest/Zotero/storage/QITX5A59/gdm.html}
}

@article{goldingZoonPackageReproducible2018,
  title = {The Zoon r Package for Reproducible and Shareable Species Distribution Modelling},
  author = {Golding, Nick and August, Tom A. and Lucas, Tim C. D. and Gavaghan, David J. and {van Loon}, E. Emiel and McInerny, Greg},
  year = {2018},
  journal = {Methods in Ecology and Evolution},
  volume = {9},
  number = {2},
  pages = {260--268},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.12858},
  urldate = {2023-06-22},
  abstract = {The rapid growth of species distribution modelling (SDM) as an ecological discipline has resulted in a large and diverse set of methods and software for constructing and evaluating SDMs. The disjointed nature of the current SDM research environment hinders evaluation of new methods, synthesis of current knowledge and the dissemination of new methods to SDM users. The zoon r package aims to overcome these problems by providing a modular framework for constructing reproducible SDM workflows. zoon modules are interoperable snippets of r code, each carrying a SDM method that zoon combines into a single analysis object. Rather than defining these modules, zoon draws modules from an open, version-controlled online repository. zoon makes it easy for SDM researchers to contribute modules to this repository, enabling others to rapidly deploy new methods in their own workflows or to compare alternative methods. Each workflow object created by zoon is a rerunnable record of the data, code and results of an entire SDM analysis. This can then be easily shared, scrutinised, reproduced and extended by the whole SDM research community. We explain how zoon works and demonstrate how it can be used to construct a completely reproducible SDM analyses, create and share a new module, and perform a methodological comparison study.},
  copyright = {\textcopyright{} 2017 The Authors. Methods in Ecology and Evolution \textcopyright{} 2017 British Ecological Society},
  langid = {english},
  keywords = {modelling,population ecology,software,species distribution modelling,zoon},
  file = {/Users/bbest/Zotero/storage/4JH6VFQE/Golding et al. - 2018 - The zoon r package for reproducible and shareable .pdf;/Users/bbest/Zotero/storage/U7PFQT29/2041-210X.html}
}

@article{gonzalezFrameworkDetectionAttribution2023,
  title = {A Framework for the Detection and Attribution of Biodiversity Change},
  author = {Gonzalez, Andrew and Chase, Jonathan M. and O'Connor, Mary I.},
  year = {2023},
  month = may,
  journal = {Philosophical Transactions of the Royal Society B: Biological Sciences},
  volume = {378},
  number = {1881},
  pages = {20220182},
  publisher = {{Royal Society}},
  doi = {10.1098/rstb.2022.0182},
  urldate = {2023-06-24},
  abstract = {The causes of biodiversity change are of great scientific interest and central to policy efforts aimed at meeting biodiversity targets. Changes in species diversity and high rates of compositional turnover have been reported worldwide. In many cases, trends in biodiversity are detected, but these trends are rarely causally attributed to possible drivers. A formal framework and guidelines for the detection and attribution of biodiversity change is needed. We propose an inferential framework to guide detection and attribution analyses, which identifies five steps\textemdash causal modelling, observation, estimation, detection and attribution\textemdash for robust attribution. This workflow provides evidence of biodiversity change in relation to hypothesized impacts of multiple potential drivers and can eliminate putative drivers from contention. The framework encourages a formal and reproducible statement of confidence about the role of drivers after robust methods for trend detection and attribution have been deployed. Confidence in trend attribution requires that data and analyses used in all steps of the framework follow best practices reducing uncertainty at each step. We illustrate these steps with examples. This framework could strengthen the bridge between biodiversity science and policy and support effective actions to halt biodiversity loss and the impacts this has on ecosystems. This article is part of the theme issue `Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.},
  keywords = {anthropocene,causal inference,diversity,global change,monitoring,time series},
  file = {/Users/bbest/Zotero/storage/CWX5LK45/Gonzalez et al. - 2023 - A framework for the detection and attribution of b.pdf}
}

@article{hallgrenSpeciesDistributionModels2019,
  title = {Species Distribution Models Can Be Highly Sensitive to Algorithm Configuration},
  author = {Hallgren, W. and Santana, F. and {Low-Choy}, S. and Zhao, Y. and Mackey, B.},
  year = {2019},
  month = sep,
  journal = {Ecological Modelling},
  volume = {408},
  pages = {108719},
  issn = {0304-3800},
  doi = {10.1016/j.ecolmodel.2019.108719},
  urldate = {2023-06-22},
  abstract = {In pursuit of a more robust provenance in the field of species distribution modelling, an extensive literature search was undertaken to find the typical default values, and the range of values, for configuration settings of a large number of the most commonly used statistical algorithms available for constructing species distribution models (SDM), as implemented in the R script packages (such as Dismo and Biomod2) or other species distribution modelling programs like MaxEnt. We found that documentation of SDM algorithm configuration option settings in the SDM literature is, overall, very uncommon, and the justifications for these settings were minimal, when present. Such settings were often the R default values, or were the result of trial and error. This is potentially concerning since: (i) it detracts from the robustness of the provenance for such SDM studies; (ii) a lack of documentation of configuration option settings in a paper prevents the replication of an experiment, which contravenes one of the main tenets of the scientific method; (iii) inappropriate or uninformed configuration option settings are particularly concerning if they represent a poorly understood ecological variable or process, and if the algorithm is sensitive to such settings, this could result in erroneous and/or unrealistic SDMs. Therefore, this study sets out to comprehensively test the sensitivity of eight widely used SDM algorithms to variation in configuration options settings: MaxEnt, Artificial Neural Network (ANN), Generalized Linear Model (GLM), Generalized Additive Model (GAM), Multivariate Adaptive Regression Splines (MARS), Flexible Discriminant Analysis (FDA), Surface Range Envelope (SRE) and Classification tree analysis (CTA). A process of expert elicitation was used to derive a range of appropriate values with which to test the sensitivity of our algorithms. We chose to use species occurrence records for two species - Koala (Phascolartos cinereus) and Thorny Devil (Moloch horridus) - in order to investigate how algorithm sensitivity depends on the species being modelled. Results were assessed by comparing the modelled distribution of the control SDM (default settings) to the modelled distribution from each sensitivity test SDM (i.e. non-default configuration settings). This was done using the visual and statistical measures of predictive performance available in the Biodiversity and Climate Change Virtual Laboratory (BCCVL), including the area under the (receiver operating characteristic) curve. The aim of our study was to be able to draw conclusions as to how the sensitivity of SDM algorithms to their configuration option settings may detract from the reliability of SDM results, given the often unjustified and unscrutinized use of the default settings, and generally infrequent and largely perfunctory attendance to this issue in most of the published SDM literature. Our results indicate that all of the algorithms tested showed sensitivity to alternative (non-default) values for some of their configuration settings and that often this sensitivity is species-dependent. Therefore we can conclude that the choice of configuration settings in these widely used SDM algorithms can have a large impact on the resulting projected distribution. This has important ramifications for decision-making and policy outcomes wherever SDMs are used to inform species and biodiversity management plans and policy settings. This study demonstrates that assigning suitable values for these settings is a very important consideration and as such should always be published along with the model. Documenting all configuration settings is necessary to increase the scientific robustness, transparency and reproducibility of species distribution modelling studies.},
  langid = {english},
  keywords = {ANN GLM,Configuration option settings,CTA,FDA,GAM,Koala,MARS,MaxEnt,Provenance,SRE,Thorny devil,Transparency},
  file = {/Users/bbest/Zotero/storage/YD4PMNAB/Hallgren et al. - 2019 - Species distribution models can be highly sensitiv.pdf;/Users/bbest/Zotero/storage/82F9QF37/S0304380019302194.html}
}

@article{hammondEstimatingAbundanceMarine2021,
  title = {Estimating the {{Abundance}} of {{Marine Mammal Populations}}},
  author = {Hammond, Philip S. and Francis, Tessa B. and Heinemann, Dennis and Long, Kristy J. and Moore, Jeffrey E. and Punt, Andr{\'e} E. and Reeves, Randall R. and Sep{\'u}lveda, Maritza and Sigur{\dh}sson, Gu{\dh}j{\'o}n M{\'a}r and Siple, Margaret C. and V{\'i}kingsson, G{\'i}sli and Wade, Paul R. and Williams, Rob and Zerbini, Alexandre N.},
  year = {2021},
  journal = {Frontiers in Marine Science},
  volume = {8},
  issn = {2296-7745},
  urldate = {2023-06-22},
  abstract = {Motivated by the need to estimate the abundance of marine mammal populations to inform conservation assessments, especially relating to fishery bycatch, this paper provides background on abundance estimation and reviews the various methods available for pinnipeds, cetaceans and sirenians. We first give an ``entry-level'' introduction to abundance estimation, including fundamental concepts and the importance of recognizing sources of bias and obtaining a measure of precision. Each of the primary methods available to estimate abundance of marine mammals is then described, including data collection and analysis, common challenges in implementation, and the assumptions made, violation of which can lead to bias. The main method for estimating pinniped abundance is extrapolation of counts of animals (pups or all-ages) on land or ice to the whole population. Cetacean and sirenian abundance is primarily estimated from transect surveys conducted from ships, small boats or aircraft. If individuals of a species can be recognized from natural markings, mark-recapture analysis of photo-identification data can be used to estimate the number of animals using the study area. Throughout, we cite example studies that illustrate the methods described. To estimate the abundance of a marine mammal population, key issues include: defining the population to be estimated, considering candidate methods based on strengths and weaknesses in relation to a range of logistical and practical issues, being aware of the resources required to collect and analyze the data, and understanding the assumptions made. We conclude with a discussion of some practical issues, given the various challenges that arise during implementation.},
  file = {/Users/bbest/Zotero/storage/2X4ARKYU/Hammond et al. - 2021 - Estimating the Abundance of Marine Mammal Populati.pdf}
}

@article{hartogForecastsMarineHeatwaves2023,
  title = {Forecasts of Marine Heatwaves for Marine Industries: {{Reducing}} Risk, Building Resilience and Enhancing Management Responses},
  shorttitle = {Forecasts of Marine Heatwaves for Marine Industries},
  author = {Hartog, Jason R. and Spillman, Claire M. and Smith, Grant and Hobday, Alistair J.},
  year = {2023},
  month = jun,
  journal = {Deep Sea Research Part II: Topical Studies in Oceanography},
  volume = {209},
  pages = {105276},
  issn = {0967-0645},
  doi = {10.1016/j.dsr2.2023.105276},
  urldate = {2023-06-22},
  abstract = {Ocean use has always been risky because of uncertain and dramatic ocean conditions and modern businesses continue to experience risk due to environmental extremes. A changing physical environment due to anthropogenic climate change and increased frequency of extreme events such as marine heatwaves makes past experience less valuable. This risk can be reduced by utilising seasonal forecasts that provide early warning of climate events several months ahead of time. However, to benefit from a forecast, a marine business will need to be agile to respond to changing information and response options. We define a set of seven attributes that can influence and enhance this management agility; leadership, social expectations, signal strength, system manipulation, regulatory environment, market forces, and value of the operations. The management agility of different marine businesses in fisheries, aquaculture, and tourism can influence their ability to use seasonal forecast information effectively, and potentially modify the usual negative relationship between resilience and the frequency of the stress event, thus reducing the impact of extreme events. Engagement between forecast developers and marine users can also improve responses, while at the same time, improving the agility of businesses can enhance overall resilience to extreme events and lower their risk.},
  langid = {english},
  keywords = {Aquaculture,Climate risk,Management agility,Marine heatwaves,Marine industries,Marine management,Sea surface temperature},
  file = {/Users/bbest/Zotero/storage/V3SWQKG2/Hartog et al. - 2023 - Forecasts of marine heatwaves for marine industrie.pdf;/Users/bbest/Zotero/storage/4EUXADGV/S0967064523000267.html}
}

@article{heberlingDataIntegrationEnables2021,
  title = {Data Integration Enables Global Biodiversity Synthesis},
  author = {Heberling, J. Mason and Miller, Joseph T. and Noesgaard, Daniel and Weingart, Scott B. and Schigel, Dmitry},
  year = {2021},
  month = feb,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {118},
  number = {6},
  pages = {e2018093118},
  publisher = {{Proceedings of the National Academy of Sciences}},
  doi = {10.1073/pnas.2018093118},
  urldate = {2023-06-22},
  abstract = {The accessibility of global biodiversity information has surged in the past two decades, notably through widespread funding initiatives for museum specimen digitization and emergence of large-scale public participation in community science. Effective use of these data requires the integration of disconnected datasets, but the scientific impacts of consolidated biodiversity data networks have not yet been quantified. To determine whether data integration enables novel research, we carried out a quantitative text analysis and bibliographic synthesis of {$>$}4,000 studies published from 2003 to 2019 that use data mediated by the world's largest biodiversity data network, the Global Biodiversity Information Facility (GBIF). Data available through GBIF increased 12-fold since 2007, a trend matched by global data use with roughly two publications using GBIF-mediated data per day in 2019. Data-use patterns were diverse by authorship, geographic extent, taxonomic group, and dataset type. Despite facilitating global authorship, legacies of colonial science remain. Studies involving species distribution modeling were most prevalent (31\% of literature surveyed) but recently shifted in focus from theory to application. Topic prevalence was stable across the 17-y period for some research areas (e.g., macroecology), yet other topics proportionately declined (e.g., taxonomy) or increased (e.g., species interactions, disease). Although centered on biological subfields, GBIF-enabled research extends surprisingly across all major scientific disciplines. Biodiversity data mobilization through global data aggregation has enabled basic and applied research use at temporal, spatial, and taxonomic scales otherwise not possible, launching biodiversity sciences into a new era.},
  file = {/Users/bbest/Zotero/storage/Y9JBIX9C/Heberling et al. - 2021 - Data integration enables global biodiversity synth.pdf}
}

@misc{hillWorldwideImpactsProjected2018,
  title = {Worldwide Impacts of Past and Projected Future Land-Use Change on Local Species Richness and the {{Biodiversity Intactness Index}}},
  author = {Hill, Samantha L. L. and Gonzalez, Ricardo and {Sanchez-Ortiz}, Katia and Caton, Emma and Espinoza, Felipe and Newbold, Tim and Tylianakis, Jason and Scharlemann, J{\"o}rn P. W. and Palma, Adriana De and Purvis, Andy},
  year = {2018},
  month = may,
  primaryclass = {New Results},
  pages = {311787},
  publisher = {{bioRxiv}},
  doi = {10.1101/311787},
  urldate = {2023-07-21},
  abstract = {Although people have modified the world around us throughout human history, the `Great Acceleration' has seen drivers such as land conversion, exploitation of natural populations, species introductions, pollution and human-induced climate change placing biodiversity under increasing pressure. In this paper we examine 1) how terrestrial species communities have been impacted over the last thousand years of human development and 2) how plausible futures defined by alternative socio-economic scenarios are expected to impact species communities in the future. We use the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) database to model impacts of land-use change and human population on local species richness, community abundance, and biodiversity intactness using a mixed-effects modelling structure. Historical impacts are inferred through projection of model results onto maps of historical land use, provided by the land-use harmonization project, and gridded human population density (HYDE 3.1). Future impacts are explored using the Shared Socio-economic Pathway (SSP) scenarios. These scenarios detail five plausible global futures based upon socio-economic factors such as wealth, population, education, technology, and reliance on fossil fuels, and can be combined with Representative Concentration Pathway (RCP) scenarios to consider climate mitigation strategies. We project model results onto the gridded outputs of six SSP/RCP scenario combinations: SSP1/RCP2.6, SSP2/RCP4.5, SSP3/RCP7.0, SSP4/RCP3.4, SSP4/RCP6.0, and SSP5/RCP8.5. Historical trend lines show that most losses in local biodiversity are relatively recent, with 75\% of all loss in both abundance-based Biodiversity Intactness Index and species richness occurring post-1800. Stark regional differences emerge in all future scenarios, with biodiversity in African regions undergoing greater losses than Oceania, North America and the European regions. Although climate change is expected to have severe detrimental impacts to biodiversity \textendash{} which are not quantified in these results \textendash{} it is important to consider how the climate change mitigation itself may also impact biodiversity. Our results suggest that strong climate change mitigation through biofuel production will detrimentally impact biodiversity: SSP4/RCP3.4 (with high biofuel mitigation) is predicted to see two times the decrease in abundance-based biodiversity intactness and three times the decrease in local species richness between 2015\textendash 2100 as is predicted for SSP4/RCP6.0 (with lower levels of mitigation). SSP4/RCP3.4 forecasts the greatest impact to average local species richness of all the SSP/RCP combinations with an average loss of 13\% of local species richness projected to have occurred by 2100. SSP3/RCP7.0 \textendash{} a scenario describing a globally segregated, and economically protectionist future with low climate change mitigation \textendash{} has the worst impacts on abundance-based biodiversity intactness with an average loss of 26\% of intactness by 2100. However, a brighter future is possible; SSP1/RCP2.6 describes a more sustainable future, where human populations are provided for without further jeopardising environmental integrity \textendash{} in this scenario we project that biodiversity will recover somewhat, with gains in biodiversity intactness and species richness in many regions of the world by 2100.},
  archiveprefix = {bioRxiv},
  chapter = {New Results},
  copyright = {\textcopyright{} 2018, Posted by Cold Spring Harbor Laboratory. The copyright holder for this pre-print is the author. All rights reserved. The material may not be redistributed, re-used or adapted without the author's permission.},
  langid = {english},
  file = {/Users/bbest/Zotero/storage/2G8JQ9ZK/Hill et al. - 2018 - Worldwide impacts of past and projected future lan.pdf}
}

@article{hudson2016ReleasePREDICTS2016,
  title = {The 2016 Release of the {{PREDICTS}} Database},
  author = {Hudson, Lawrence and Newbold, Tim and Contu, Sara and Hill, Samantha L. L. and Lysenko, Igor and Palma, Adriana De and Phillips, Helen and Alhusseini, Tamera I. and Bedford, Felicity E. and Bennett, Dominic J. and Booth, Hollie and Burton, Victoria and Chng, Wen Ting Charlotte and Choimes, Argyrios and Correia, David L. P. and Day, Julie and {Echeverr{\'i}a-Londo{\~n}o}, Susy and Emerson, Susan R. and Gao, Di and Garon, Morgan and Harrison, Michelle L. K. and Ingram, Daniel J. and Jung, Martin and Kemp, Victoria and Kirkpatrick, Lucinda and Martin, Callum and Pan, Yuan and {Pask-Hale}, Gwilym and Pynegar, Edwin L. and Robinson, Alexandra N. and {Sanchez-Ortiz}, Katia and Senior, Rebecca A. and Simmons, Benno and White, Hannah J. and Zhang, Hanbin and Aben, Job and Abrahamczyk, Stefan and Adum, Gilbert B. and {Aguilar-Barquero}, Virginia and Aizen, Marcelo A. and Albertos, Bel{\'e}n and Alcala, E. L. and Alguacil, Maria del Mar and Alignier, Audrey and Ancrenaz, Marc and Andersen, Alan N. and {Arbel{\'a}ez-Cort{\'e}s}, Enrique and Armbrecht, Inge and {Arroyo-Rodr{\'i}guez}, V{\'i}ctor and Aumann, Tom and Axmacher, Jan C. and Azhar, Badrul and Azpiroz, Adri{\'a}n B. and Baeten, Lander and Bakayoko, Adama and B{\'a}ldi, Andr{\'a}s and Banks, John E. and Baral, Sharad K. and Barlow, Jos and Barratt, Barbara I. P. and Barrico, Lurdes and Bartolommei, Paola and Barton, Diane M. and Basset, Yves and Bat{\'a}ry, P{\'e}ter and Bates, Adam J. and Baur, Bruno and Bayne, Erin M. and Beja, Pedro and Benedick, Suzan and Berg, {\AA}ke and Bernard, Henry and Berry, Nicholas J. and Bhatt, Dinesh and Bicknell, Jake E. and Bihn, Jochen H. and Blake, Robin J. and Bobo, Kadiri S. and B{\'o}{\c c}on, Roberto and Boekhout, Teun and {B{\"o}hning-Gaese}, Katrin and Bonham, Kevin J. and Borges, Paulo A. V. and Borges, S{\'e}rgio H. and Boutin, C{\'e}line and Bouyer, J{\'e}r{\'e}my and Bragagnolo, Cibele and Brandt, Jodi S. and Brearley, Francis Q. and Brito, Isabel and Bros, Vicen{\c c} and Brunet, J{\"o}rg and Buczkowski, Grzegorz and Buddle, Christopher M. and Bugter, Rob and Buscardo, Erika and Buse, J{\"o}rn and {Cabra-Garc{\'i}a}, Jimmy and C{\'a}ceres, Nilton C. and Cagle, Nicolette L. and {Calvi{\~n}o-Cancela}, Mar{\'i}a and Cameron, Sydney A. and Cancello, Eliana M. and Caparr{\'o}s, Rut and Cardoso, Pedro and Carpenter, Dan and Carrijo, Tiago F. and Carvalho, Anelena L. and Cassano, Camila R. and Castro, Helena and {Castro-Luna}, Alejandro A. and B, Rolando Cerda and Cerezo, Alexis and Chapman, Kim Alan and Chauvat, Matthieu and Christensen, Morten and Clarke, Francis M. and Cleary, Daniel F. R. and Colombo, Giorgio and Connop, Stuart P. and Craig, Michael D. and {Cruz-L{\'o}pez}, Leopoldo and Cunningham, Saul A. and D'Aniello, Biagio and D'Cruze, Neil and da Silva, Pedro Giov{\^a}ni and Dallimer, Martin and Danquah, Emmanuel and Darvill, Ben and Dauber, Jens and Davis, Adrian L. V. and Dawson, Jeff and de Sassi, Claudio and de Thoisy, Benoit and Deheuvels, Olivier and Dejean, Alain and Devineau, Jean-Louis and Diek{\"o}tter, Tim and Dolia, Jignasu V. and Dom{\'i}nguez, Erwin and {Dominguez-Haydar}, Yamileth and Dorn, Silvia and Draper, Isabel and Dreber, Niels and Dumont, Bertrand and Dures, Simon G. and Dynesius, Mats and Edenius, Lars and Eggleton, Paul and Eigenbrod, Felix and Elek, Zolt{\'a}n and Entling, Martin H. and Esler, Karen J. and de Lima, Ricardo F. and Faruk, Aisyah and Farwig, Nina and Fayle, Tom M. and Felicioli, Antonio and Felton, Annika M. and Fensham, Roderick J. and Fernandez, Ignacio C. and Ferreira, Catarina C. and Ficetola, Gentile F. and Fiera, Cristina and Filgueiras, Bruno K. C. and F{\i}r{\i}nc{\i}o{\u g}lu, H{\"u}seyin K. and Flaspohler, David and Floren, Andreas and Fonte, Steven J. and Fournier, Anne and Fowler, Robert E. and Franz{\'e}n, Markus and Fraser, Lauchlan H. and Fredriksson, Gabriella M. and {Freire-Jr}, Geraldo B. and Frizzo, Tiago L. M. and Fukuda, Daisuke and Furlani, Dario and Gaigher, Ren{\'e} and Ganzhorn, J{\"o}rg U. and Garc{\'i}a, Karla P. and {Garcia-R}, Juan C. and Garden, Jenni G. and Garilleti, Ricardo and Ge, Bao-Ming and {Gendreau-Berthiaume}, Benoit and Gerard, Philippa J. and {Gheler-Costa}, Carla and Gilbert, Benjamin and Giordani, Paolo and Giordano, Simonetta and Golodets, Carly and Gomes, Laurens G. L. and Gould, Rachelle K. and Goulson, Dave and Gove, Aaron D. and Granjon, Laurent and Grass, Ingo and Gray, Claudia L. and Grogan, James and Gu, Weibin and Guardiola, Mois{\`e}s and Gunawardene, Nihara R. and Gutierrez, Alvaro G. and {Guti{\'e}rrez-Lamus}, Doris L. and Haarmeyer, Daniela H. and Hanley, Mick E. and Hanson, Thor and Hashim, Nor R. and Hassan, Shombe N. and Hatfield, Richard G. and Hawes, Joseph E. and Hayward, Matt W. and H{\'e}bert, Christian and Helden, Alvin J. and Henden, John-Andr{\'e} and Henschel, Philipp and Hern{\'a}ndez, Lionel and Herrera, James P. and Herrmann, Farina and Herzog, Felix and {Higuera-Diaz}, Diego and Hilje, Branko and H{\"o}fer, Hubert and Hoffmann, Anke and Horgan, Finbarr G. and Hornung, Elisabeth and Horv{\'a}th, Roland and Hylander, Kristoffer and {Isaacs-Cubides}, Paola and Ishida, Hiroaki and Ishitani, Masahiro and Jacobs, Carmen T. and Jaramillo, V{\'i}ctor J. and Jauker, Birgit and Hern{\'a}ndez, F. Jim{\'e}nez and Johnson, McKenzie and Jolli, Virat and Jonsell, Mats and S, Nur Juliani and Jung, Thomas S. and Kapoor, Vena and Kappes, Heike and Kati, Vassiliki and Katovai, Eric and Kellner, Klaus and Kessler, Michael and Kirby, Kathryn R. and Kittle, Andrew M. and Knight, Mairi E. and Knop, Eva and Kohler, Florian and Koivula, Matti and Kolb, Annette and Kone, Mouhamadou and K{\H o}r{\"o}si, {\'A}d{\'a}m and Krauss, Jochen and Kumar, Ajith and Kumar, Raman and Kurz, David J. and Kutt, Alex S. and Lachat, Thibault and Lantschner, Victoria and Lara, Francisco and Lasky, Jesse R. and Latta, Steven C. and Laurance, William F. and Lavelle, Patrick and F{\'e}on, Violette Le and LeBuhn, Gretchen and L{\'e}gar{\'e}, Jean-Philippe and Lehouck, Val{\'e}rie and Lencinas, Mar{\'i}a V. and Lentini, Pia E. and Letcher, Susan G. and Li, Qi and Litchwark, Simon A. and Littlewood, Nick and Liu, Yunhui and Hung, Nancy Fran{\c c}a Lo Man and {L{\'o}pez-Quintero}, Carlos A. and Louhaichi, Mounir and L{\"o}vei, Gabor L. and {Lucas-Borja}, Manuel Esteban and Luja, Victor H. and Luskin, Matthew S. and G, M. Cristina MacSwiney and Maeto, Kaoru and Magura, Tibor and Mallari, Neil Aldrin and Malone, Louise A. and Malonza, Patrick K. and {Malumbres-Olarte}, Jagoba and Salvador, Andreia and M{\aa}ren, Inger E. and {Marin-Spiotta}, Erika and Marsh, Charles J. and Marshall, E. J. P. and Mart{\'i}nez, Eliana and Pastur, Guillermo Mart{\'i}nez and Mateos, David Moreno and Mayfield, Margaret M. and Mazimpaka, Vicente and McCarthy, Jennifer L. and McCarthy, Kyle P. and McFrederick, Quinn S. and McNamara, Sean and Medina, Nagore G. and Medina, Rafael and Mena, Jose L. and Mico, Estefania and Mikusinski, Grzegorz and Milder, Jeffrey C. and Miller, James R. and {Miranda-Esquivel}, Daniel R. and Moir, Melinda L. and Morales, Carolina L. and Muchane, Mary N. and Muchane, Muchai and {Mudri-Stojnic}, Sonja and A, Nur Munira and {Mu{\~n}oz-Alonso}, Antonio and Munyekenye, B. F. and Naidoo, Robin and Naithani, A. and Nakagawa, Michiko and Nakamura, Akihiro and Nakashima, Yoshihiro and Naoe, Shoji and {Nates-Parra}, Guiomar and Gutierrez, Dario A. Navarrete and {Navarro-Iriarte}, Luis and Ndang'ang'a, Paul K. and Neuschulz, Eike L. and Ngai, Jacqueline T. and Nicolas, Violaine and Nilsson, Sven G. and Holstein, Norbert and Norfolk, Olivia and Noriega, Jorge Ari and Norton, David A. and N{\"o}ske, Nicole M. and Nowakowski, A. Justin and Numa, Catherine and O'Dea, Niall and O'Farrell, Patrick J. and Oduro, William and Oertli, Sabine and {Ofori-Boateng}, Caleb and Oke, Christopher Omamoke and Oostra, Vicencio and Osgathorpe, Lynne M. and Otavo, Samuel Eduardo and Page, Navendu V. and Paritsis, Juan and {Parra-H}, Alejandro and Parry, Luke and Pe'er, Guy and Pearman, Peter B. and Pelegrin, Nicol{\'a}s and P{\'e}lissier, Rapha{\"e}l and Peres, Carlos A. and Peri, Pablo L. and Persson, Anna S. and Hayes, Peta Angela and Peters, Marcell K. and Pethiyagoda, Rohan S. and Phalan, Ben and Philips, T. Keith and Pillsbury, Finn C. and {Pincheira-Ulbrich}, Jimmy and Pineda, Eduardo and Pino, Joan and {Pizarro-Araya}, Jaime and Plumptre, A. J. and Poggio, Santiago L. and Politi, Natalia and Pons, Pere and Poveda, Katja and Power, Eileen F. and Presley, Steven J. and Proen{\c c}a, V{\^a}nia and Quaranta, Marino and Quintero, Carolina and Rader, Romina and Ramesh, B. R. and {Ramirez-Pinilla}, Martha P. and Ranganathan, Jai and Rasmussen, Claus and {Redpath-Downing}, Nicola A. and Reid, J. Leighton and Reis, Yana T. and Benayas, Jos{\'e} M. Rey and {Rey-Velasco}, Juan Carlos and Reynolds, Chevonne and Ribeiro, Danilo Bandini and Richards, Miriam H. and Richardson, Barbara A. and Richardson, Michael J. and R{\'i}os, Rodrigo Macip and Robinson, Richard and Robles, Carolina A. and R{\"o}mbke, J{\"o}rg and {Romero-Duque}, Luz Piedad and R{\"o}s, Matthias and Rosselli, Loreta and Rossiter, Stephen J. and Roth, Dana S. and Roulston, T'ai H. and Rousseau, Laurent and Rubio, Andr{\'e} V. and Ruel, Jean-Claude and Sadler, Jonathan and S{\'a}fi{\'a}n, Szabolcs and {Salda{\~n}a-V{\'a}zquez}, Romeo A. and Sam, Katerina and Samneg{\aa}rd, Ulrika and Santana, Joana and Santos, Xavier and Savage, Jade and Schellhorn, Nancy A. and Schilthuizen, Menno and Schmiedel, Ute and Schmitt, Christine B. and Schon, Nicole L. and Sch{\"u}epp, Christof and Schumann, Katharina and Schweiger, Oliver and Scott, Dawn M. and Scott, Kenneth A. and Sedlock, Jodi L. and Seefeldt, Steven S. and Shahabuddin, Ghazala and Shannon, Graeme and Sheil, Douglas and Sheldon, Frederick H. and Shochat, Eyal and Siebert, Stefan J. and Silva, Fernando A. B. and Simonetti, Javier A. and Slade, Eleanor M. and Smith, Jo and {Smith-Pardo}, Allan H. and Sodhi, Navjot S. and Somarriba, Eduardo J. and Sosa, Ram{\'o}n A. and Quiroga, Grimaldo Soto and {St-Laurent}, Martin-Hugues and Starzomski, Brian M. and Stefanescu, Constanti and {Steffan-Dewenter}, Ingolf and Stouffer, Philip C. and Stout, Jane C. and Strauch, Ayron M. and Struebig, Matthew J. and Su, Zhimin and {Suarez-Rubio}, Marcela and Sugiura, Shinji and Summerville, Keith S. and Sung, Yik-Hei and Sutrisno, Hari and Svenning, Jens-Christian and Teder, Tiit and Threlfall, Caragh G. and Tiitsaar, Anu and Todd, Jacqui H. and Tonietto, Rebecca K. and Torre, Ignasi and T{\'o}thm{\'e}r{\'e}sz, B{\'e}la and Tscharntke, Teja and Turner, Edgar C. and Tylianakis, Jason and {Uehara-Prado}, Marcio and {Urbina-Cardona}, J. Nicolas and Vallan, Denis and Vanbergen, Adam and Vasconcelos, Heraldo L. and Vassilev, Kiril and Verboven, Hans A. F. and Verdasca, Maria Jo{\~a}o and Verd{\'u}, Jos{\'e} R. and Vergara, Carlos H. and Vergara, Pablo M. and Verhulst, Jort and Virgilio, Massimiliano and Vu, Lien Van and Waite, Edward M. and Walker, Tony R. and Wang, Hua-Feng and Wang, Yanping and Watling, James I. and Weller, Britta and Wells, Konstans and Westphal, Catrin and Wiafe, Edward D. and Williams, Christopher and Willig, Michael R. and Woinarski, John C. Z. and Wolf, Jan H. D. and Wolters, Volkmar and Woodcock, Ben A. and Wu, Jihua and Wunderle, Joseph M. and Yamaura, Yuichi and Yoshikura, Satoko and Yu, Douglas W. and Zaitsev, Andrey S. and Zeidler, Juliane and Zou, Fasheng and Collen, Ben and Ewers, Rob M. and Mace, Georgina M. and Purves, Drew W. and Scharlemann, Jorn and Purvis, Andy},
  year = {2016},
  month = dec,
  publisher = {{Natural History Museum}},
  doi = {10.5519/0066354},
  urldate = {2023-07-21},
  abstract = {A dataset of 3,250,404 measurements, collated from 26,114 sampling locations in 94 countries and representing 47,044 species. The data were collated from 480 existing spatial comparisons of...},
  langid = {english},
  file = {/Users/bbest/Zotero/storage/C5W8E9IS/the-2016-release-of-the-predicts-database.html}
}

@article{hudsonDatabasePREDICTSProjecting2017,
  title = {The Database of the {{PREDICTS}} ({{Projecting Responses}} of {{Ecological Diversity In Changing Terrestrial Systems}}) Project},
  author = {Hudson, Lawrence N. and Newbold, Tim and Contu, Sara and Hill, Samantha L. L. and Lysenko, Igor and De Palma, Adriana and Phillips, Helen R. P. and Alhusseini, Tamera I. and Bedford, Felicity E. and Bennett, Dominic J. and Booth, Hollie and Burton, Victoria J. and Chng, Charlotte W. T. and Choimes, Argyrios and Correia, David L. P. and Day, Julie and {Echeverr{\'i}a-Londo{\~n}o}, Susy and Emerson, Susan R. and Gao, Di and Garon, Morgan and Harrison, Michelle L. K. and Ingram, Daniel J. and Jung, Martin and Kemp, Victoria and Kirkpatrick, Lucinda and Martin, Callum D. and Pan, Yuan and {Pask-Hale}, Gwilym D. and Pynegar, Edwin L. and Robinson, Alexandra N. and {Sanchez-Ortiz}, Katia and Senior, Rebecca A. and Simmons, Benno I. and White, Hannah J. and Zhang, Hanbin and Aben, Job and Abrahamczyk, Stefan and Adum, Gilbert B. and {Aguilar-Barquero}, Virginia and Aizen, Marcelo A. and Albertos, Bel{\'e}n and Alcala, E. L. and {del Mar Alguacil}, Maria and Alignier, Audrey and Ancrenaz, Marc and Andersen, Alan N. and {Arbel{\'a}ez-Cort{\'e}s}, Enrique and Armbrecht, Inge and {Arroyo-Rodr{\'i}guez}, V{\'i}ctor and Aumann, Tom and Axmacher, Jan C. and Azhar, Badrul and Azpiroz, Adri{\'a}n B. and Baeten, Lander and Bakayoko, Adama and B{\'a}ldi, Andr{\'a}s and Banks, John E. and Baral, Sharad K. and Barlow, Jos and Barratt, Barbara I. P. and Barrico, Lurdes and Bartolommei, Paola and Barton, Diane M. and Basset, Yves and Bat{\'a}ry, P{\'e}ter and Bates, Adam J. and Baur, Bruno and Bayne, Erin M. and Beja, Pedro and Benedick, Suzan and Berg, {\AA}ke and Bernard, Henry and Berry, Nicholas J. and Bhatt, Dinesh and Bicknell, Jake E. and Bihn, Jochen H. and Blake, Robin J. and Bobo, Kadiri S. and B{\'o}{\c c}on, Roberto and Boekhout, Teun and {B{\"o}hning-Gaese}, Katrin and Bonham, Kevin J. and Borges, Paulo A. V. and Borges, S{\'e}rgio H. and Boutin, C{\'e}line and Bouyer, J{\'e}r{\'e}my and Bragagnolo, Cibele and Brandt, Jodi S. and Brearley, Francis Q. and Brito, Isabel and Bros, Vicen{\c c} and Brunet, J{\"o}rg and Buczkowski, Grzegorz and Buddle, Christopher M. and Bugter, Rob and Buscardo, Erika and Buse, J{\"o}rn and {Cabra-Garc{\'i}a}, Jimmy and C{\'a}ceres, Nilton C. and Cagle, Nicolette L. and {Calvi{\~n}o-Cancela}, Mar{\'i}a and Cameron, Sydney A. and Cancello, Eliana M. and Caparr{\'o}s, Rut and Cardoso, Pedro and Carpenter, Dan and Carrijo, Tiago F. and Carvalho, Anelena L. and Cassano, Camila R. and Castro, Helena and {Castro-Luna}, Alejandro A. and Rolando, Cerda B. and Cerezo, Alexis and Chapman, Kim Alan and Chauvat, Matthieu and Christensen, Morten and Clarke, Francis M. and Cleary, Daniel F.R. and Colombo, Giorgio and Connop, Stuart P. and Craig, Michael D. and {Cruz-L{\'o}pez}, Leopoldo and Cunningham, Saul A. and D'Aniello, Biagio and D'Cruze, Neil and {da Silva}, Pedro Giov{\^a}ni and Dallimer, Martin and Danquah, Emmanuel and Darvill, Ben and Dauber, Jens and Davis, Adrian L. V. and Dawson, Jeff and {de Sassi}, Claudio and {de Thoisy}, Benoit and Deheuvels, Olivier and Dejean, Alain and Devineau, Jean-Louis and Diek{\"o}tter, Tim and Dolia, Jignasu V. and Dom{\'i}nguez, Erwin and {Dominguez-Haydar}, Yamileth and Dorn, Silvia and Draper, Isabel and Dreber, Niels and Dumont, Bertrand and Dures, Simon G. and Dynesius, Mats and Edenius, Lars and Eggleton, Paul and Eigenbrod, Felix and Elek, Zolt{\'a}n and Entling, Martin H. and Esler, Karen J. and {de Lima}, Ricardo F. and Faruk, Aisyah and Farwig, Nina and Fayle, Tom M. and Felicioli, Antonio and Felton, Annika M. and Fensham, Roderick J. and Fernandez, Ignacio C. and Ferreira, Catarina C. and Ficetola, Gentile F. and Fiera, Cristina and Filgueiras, Bruno K. C. and F{\i}r{\i}nc{\i}o{\u g}lu, H{\"u}seyin K. and Flaspohler, David and Floren, Andreas and Fonte, Steven J. and Fournier, Anne and Fowler, Robert E. and Franz{\'e}n, Markus and Fraser, Lauchlan H. and Fredriksson, Gabriella M. and Freire Jr, Geraldo B. and Frizzo, Tiago L. M. and Fukuda, Daisuke and Furlani, Dario and Gaigher, Ren{\'e} and Ganzhorn, J{\"o}rg U. and Garc{\'i}a, Karla P. and {Garcia-R}, Juan C. and Garden, Jenni G. and Garilleti, Ricardo and Ge, Bao-Ming and {Gendreau-Berthiaume}, Benoit and Gerard, Philippa J. and {Gheler-Costa}, Carla and Gilbert, Benjamin and Giordani, Paolo and Giordano, Simonetta and Golodets, Carly and Gomes, Laurens G. L. and Gould, Rachelle K. and Goulson, Dave and Gove, Aaron D. and Granjon, Laurent and Grass, Ingo and Gray, Claudia L. and Grogan, James and Gu, Weibin and Guardiola, Mois{\`e}s and Gunawardene, Nihara R. and Gutierrez, Alvaro G. and {Guti{\'e}rrez-Lamus}, Doris L. and Haarmeyer, Daniela H. and Hanley, Mick E. and Hanson, Thor and Hashim, Nor R. and Hassan, Shombe N. and Hatfield, Richard G. and Hawes, Joseph E. and Hayward, Matt W. and H{\'e}bert, Christian and Helden, Alvin J. and Henden, John-Andr{\'e} and Henschel, Philipp and Hern{\'a}ndez, Lionel and Herrera, James P. and Herrmann, Farina and Herzog, Felix and {Higuera-Diaz}, Diego and Hilje, Branko and H{\"o}fer, Hubert and Hoffmann, Anke and Horgan, Finbarr G. and Hornung, Elisabeth and Horv{\'a}th, Roland and Hylander, Kristoffer and {Isaacs-Cubides}, Paola and Ishida, Hiroaki and Ishitani, Masahiro and Jacobs, Carmen T. and Jaramillo, V{\'i}ctor J. and Jauker, Birgit and Hern{\'a}ndez, F. Jim{\'e}nez and Johnson, McKenzie F. and Jolli, Virat and Jonsell, Mats and Juliani, S. Nur and Jung, Thomas S. and Kapoor, Vena and Kappes, Heike and Kati, Vassiliki and Katovai, Eric and Kellner, Klaus and Kessler, Michael and Kirby, Kathryn R. and Kittle, Andrew M. and Knight, Mairi E. and Knop, Eva and Kohler, Florian and Koivula, Matti and Kolb, Annette and Kone, Mouhamadou and K{\H o}r{\"o}si, {\'A}d{\'a}m and Krauss, Jochen and Kumar, Ajith and Kumar, Raman and Kurz, David J. and Kutt, Alex S. and Lachat, Thibault and Lantschner, Victoria and Lara, Francisco and Lasky, Jesse R. and Latta, Steven C. and Laurance, William F. and Lavelle, Patrick and Le F{\'e}on, Violette and LeBuhn, Gretchen and L{\'e}gar{\'e}, Jean-Philippe and Lehouck, Val{\'e}rie and Lencinas, Mar{\'i}a V. and Lentini, Pia E. and Letcher, Susan G. and Li, Qi and Litchwark, Simon A. and Littlewood, Nick A. and Liu, Yunhui and {Lo-Man-Hung}, Nancy and {L{\'o}pez-Quintero}, Carlos A. and Louhaichi, Mounir and L{\"o}vei, Gabor L. and {Lucas-Borja}, Manuel Esteban and Luja, Victor H. and Luskin, Matthew S. and MacSwiney G, M Cristina and Maeto, Kaoru and Magura, Tibor and Mallari, Neil Aldrin and Malone, Louise A. and Malonza, Patrick K. and {Malumbres-Olarte}, Jagoba and Mandujano, Salvador and M{\aa}ren, Inger E. and {Marin-Spiotta}, Erika and Marsh, Charles J. and Marshall, E. J. P. and Mart{\'i}nez, Eliana and Mart{\'i}nez Pastur, Guillermo and Moreno Mateos, David and Mayfield, Margaret M. and Mazimpaka, Vicente and McCarthy, Jennifer L. and McCarthy, Kyle P. and McFrederick, Quinn S. and McNamara, Sean and Medina, Nagore G. and Medina, Rafael and Mena, Jose L. and Mico, Estefania and Mikusinski, Grzegorz and Milder, Jeffrey C. and Miller, James R. and {Miranda-Esquivel}, Daniel R. and Moir, Melinda L. and Morales, Carolina L. and Muchane, Mary N. and Muchane, Muchai and {Mudri-Stojnic}, Sonja and Munira, A. Nur and {Muo{\~n}z-Alonso}, Antonio and Munyekenye, B. F. and Naidoo, Robin and Naithani, A. and Nakagawa, Michiko and Nakamura, Akihiro and Nakashima, Yoshihiro and Naoe, Shoji and {Nates-Parra}, Guiomar and Navarrete Gutierrez, Dario A. and {Navarro-Iriarte}, Luis and Ndang'ang'a, Paul K. and Neuschulz, Eike L. and Ngai, Jacqueline T. and Nicolas, Violaine and Nilsson, Sven G. and Noreika, Norbertas and Norfolk, Olivia and Noriega, Jorge Ari and Norton, David A. and N{\"o}ske, Nicole M. and Nowakowski, A. Justin and Numa, Catherine and O'Dea, Niall and O'Farrell, Patrick J. and Oduro, William and Oertli, Sabine and {Ofori-Boateng}, Caleb and Oke, Christopher Omamoke and Oostra, Vicencio and Osgathorpe, Lynne M. and Otavo, Samuel Eduardo and Page, Navendu V. and Paritsis, Juan and {Parra-H}, Alejandro and Parry, Luke and Pe'er, Guy and Pearman, Peter B. and Pelegrin, Nicol{\'a}s and P{\'e}lissier, Rapha{\"e}l and Peres, Carlos A. and Peri, Pablo L. and Persson, Anna S. and Petanidou, Theodora and Peters, Marcell K. and Pethiyagoda, Rohan S. and Phalan, Ben and Philips, T. Keith and Pillsbury, Finn C. and {Pincheira-Ulbrich}, Jimmy and Pineda, Eduardo and Pino, Joan and {Pizarro-Araya}, Jaime and Plumptre, A. J. and Poggio, Santiago L. and Politi, Natalia and Pons, Pere and Poveda, Katja and Power, Eileen F. and Presley, Steven J. and Proen{\c c}a, V{\^a}nia and Quaranta, Marino and Quintero, Carolina and Rader, Romina and Ramesh, B. R. and {Ramirez-Pinilla}, Martha P. and Ranganathan, Jai and Rasmussen, Claus and {Redpath-Downing}, Nicola A. and Reid, J. Leighton and Reis, Yana T. and Rey Benayas, Jos{\'e} M. and {Rey-Velasco}, Juan Carlos and Reynolds, Chevonne and Ribeiro, Danilo Bandini and Richards, Miriam H. and Richardson, Barbara A. and Richardson, Michael J. and R{\'i}os, Rodrigo Macip and Robinson, Richard and Robles, Carolina A. and R{\"o}mbke, J{\"o}rg and {Romero-Duque}, Luz Piedad and R{\"o}s, Matthias and Rosselli, Loreta and Rossiter, Stephen J. and Roth, Dana S. and Roulston, T'ai H. and Rousseau, Laurent and Rubio, Andr{\'e} V. and Ruel, Jean-Claude and Sadler, Jonathan P. and S{\'a}fi{\'a}n, Szabolcs and {Salda{\~n}a-V{\'a}zquez}, Romeo A. and Sam, Katerina and Samneg{\aa}rd, Ulrika and Santana, Joana and Santos, Xavier and Savage, Jade and Schellhorn, Nancy A. and Schilthuizen, Menno and Schmiedel, Ute and Schmitt, Christine B. and Schon, Nicole L. and Sch{\"u}epp, Christof and Schumann, Katharina and Schweiger, Oliver and Scott, Dawn M. and Scott, Kenneth A. and Sedlock, Jodi L. and Seefeldt, Steven S. and Shahabuddin, Ghazala and Shannon, Graeme and Sheil, Douglas and Sheldon, Frederick H. and Shochat, Eyal and Siebert, Stefan J. and Silva, Fernando A. B. and Simonetti, Javier A. and Slade, Eleanor M. and Smith, Jo and {Smith-Pardo}, Allan H. and Sodhi, Navjot S. and Somarriba, Eduardo J. and Sosa, Ram{\'o}n A. and Soto Quiroga, Grimaldo and {St-Laurent}, Martin-Hugues and Starzomski, Brian M. and Stefanescu, Constanti and {Steffan-Dewenter}, Ingolf and Stouffer, Philip C. and Stout, Jane C. and Strauch, Ayron M. and Struebig, Matthew J. and Su, Zhimin and {Suarez-Rubio}, Marcela and Sugiura, Shinji and Summerville, Keith S. and Sung, Yik-Hei and Sutrisno, Hari and Svenning, Jens-Christian and Teder, Tiit and Threlfall, Caragh G. and Tiitsaar, Anu and Todd, Jacqui H. and Tonietto, Rebecca K. and Torre, Ignasi and T{\'o}thm{\'e}r{\'e}sz, B{\'e}la and Tscharntke, Teja and Turner, Edgar C. and Tylianakis, Jason M. and {Uehara-Prado}, Marcio and {Urbina-Cardona}, Nicolas and Vallan, Denis and Vanbergen, Adam J. and Vasconcelos, Heraldo L. and Vassilev, Kiril and Verboven, Hans A. F. and Verdasca, Maria Jo{\~a}o and Verd{\'u}, Jos{\'e} R. and Vergara, Carlos H. and Vergara, Pablo M. and Verhulst, Jort and Virgilio, Massimiliano and Vu, Lien Van and Waite, Edward M. and Walker, Tony R. and Wang, Hua-Feng and Wang, Yanping and Watling, James I. and Weller, Britta and Wells, Konstans and Westphal, Catrin and Wiafe, Edward D. and Williams, Christopher D. and Willig, Michael R. and Woinarski, John C. Z. and Wolf, Jan H. D. and Wolters, Volkmar and Woodcock, Ben A. and Wu, Jihua and Wunderle Jr, Joseph M. and Yamaura, Yuichi and Yoshikura, Satoko and Yu, Douglas W. and Zaitsev, Andrey S. and Zeidler, Juliane and Zou, Fasheng and Collen, Ben and Ewers, Rob M. and Mace, Georgina M. and Purves, Drew W. and Scharlemann, J{\"o}rn P. W. and Purvis, Andy},
  year = {2017},
  journal = {Ecology and Evolution},
  volume = {7},
  number = {1},
  pages = {145--188},
  issn = {2045-7758},
  doi = {10.1002/ece3.2579},
  urldate = {2023-07-21},
  abstract = {The PREDICTS project\textemdash Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)\textemdash has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity.},
  copyright = {\textcopyright{} 2016 The Authors. Ecology and Evolution published by John Wiley \& Sons Ltd.},
  langid = {english},
  keywords = {data sharing,global biodiversity modeling,global change,habitat destruction,land use},
  file = {/Users/bbest/Zotero/storage/XPJSQKZW/Hudson et al. - 2017 - The database of the PREDICTS (Projecting Responses.pdf;/Users/bbest/Zotero/storage/K569A6VG/ece3.html}
}

@article{hunsickerTrackingForecastingCommunity2022,
  title = {Tracking and Forecasting Community Responses to Climate Perturbations in the {{California Current Ecosystem}}},
  author = {Hunsicker, Mary E. and Ward, Eric J. and Litzow, Michael A. and Anderson, Sean C. and Harvey, Chris J. and Field, John C. and Gao, Jin and Jacox, Michael G. and Melin, Sharon and Thompson, Andrew R. and Warzybok, Pete},
  year = {2022},
  month = mar,
  journal = {PLOS Climate},
  volume = {1},
  number = {3},
  pages = {e0000014},
  publisher = {{Public Library of Science}},
  issn = {2767-3200},
  doi = {10.1371/journal.pclm.0000014},
  urldate = {2023-06-28},
  abstract = {Ocean ecosystems are vulnerable to climate-driven perturbations, which are increasing in frequency and can have profound effects on marine social-ecological systems. Thus, there is an urgency to develop tools that can detect the response of ecosystem components to these perturbations as early as possible. We used Bayesian Dynamic Factor Analysis (DFA) to develop a community state indicator for the California Current Ecosystem (CCE) to track the system's response to climate perturbations, and to forecast future changes in community state. Our key objectives were to (1) summarize environmental and biological variability in the southern and central regions of the CCE during a recent and unprecedented marine heatwave in the northeast Pacific Ocean (2014\textendash 2016) and compare these patterns to past variability, (2) examine whether there is evidence of a shift in the community to a new state in response to the heatwave, (3) identify relationships between community variability and climate variables; and (4) test our ability to create one-year ahead forecasts of individual species responses and the broader community response based on ocean conditions. Our analysis detected a clear community response to the marine heatwave, although it did not exceed normal variability over the past six decades (1951\textendash 2017), and we did not find evidence of a shift to a new community state. We found that nitrate flux through the base of the mixed layer exhibited the strongest relationship with species and community-level responses. Furthermore, we demonstrated skill in creating forecasts of species responses and community state based on estimates of nitrate flux. Our indicator and forecasts of community state show promise as tools for informing ecosystem-based and climate-ready fisheries management in the CCE. Our modeling framework is also widely applicable to other ecosystems where scientists and managers are faced with the challenge of managing and protecting living marine resources in a rapidly changing climate.},
  langid = {english},
  keywords = {California,Ecosystems,Fish biology,Larvae,Marine biology,Marine ecosystems,Marine fish,Nitrates},
  file = {/Users/bbest/Zotero/storage/8TWUMRTP/Hunsicker et al. - 2022 - Tracking and forecasting community responses to cl.pdf}
}

@article{isaacDataIntegrationLargeScale2020,
  title = {Data {{Integration}} for {{Large-Scale Models}} of {{Species Distributions}}},
  author = {Isaac, Nick J. B. and Jarzyna, Marta A. and Keil, Petr and Dambly, Lea I. and {Boersch-Supan}, Philipp H. and Browning, Ella and Freeman, Stephen N. and Golding, Nick and {Guillera-Arroita}, Gurutzeta and Henrys, Peter A. and Jarvis, Susan and {Lahoz-Monfort}, Jos{\'e} and Pagel, J{\"o}rn and Pescott, Oliver L. and Schmucki, Reto and Simmonds, Emily G. and O'Hara, Robert B.},
  year = {2020},
  month = jan,
  journal = {Trends in Ecology \& Evolution},
  volume = {35},
  number = {1},
  pages = {56--67},
  issn = {0169-5347},
  doi = {10.1016/j.tree.2019.08.006},
  urldate = {2023-06-26},
  abstract = {With the expansion in the quantity and types of biodiversity data being collected, there is a need to find ways to combine these different sources to provide cohesive summaries of species' potential and realized distributions in space and time. Recently, model-based data integration has emerged as a means to achieve this by combining datasets in ways that retain the strengths of each. We describe a flexible approach to data integration using point process models, which provide a convenient way to translate across ecological currencies. We highlight recent examples of large-scale ecological models based on data integration and outline the conceptual and technical challenges and opportunities that arise.},
  langid = {english},
  keywords = {citizen science,integrated distribution model,occupancy model,point process,species distribution model,state-space model},
  file = {/Users/bbest/Zotero/storage/HDJITH6D/Isaac et al. - 2020 - Data Integration for Large-Scale Models of Species.pdf;/Users/bbest/Zotero/storage/TZ4V39UC/S0169534719302551.html}
}

@article{jansenStopIgnoringMap2022,
  title = {Stop Ignoring Map Uncertainty in Biodiversity Science and Conservation Policy},
  author = {Jansen, Jan and Woolley, Skipton N. C. and Dunstan, Piers K. and Foster, Scott D. and Hill, Nicole A. and Haward, Marcus and Johnson, Craig R.},
  year = {2022},
  month = jul,
  journal = {Nature Ecology \& Evolution},
  volume = {6},
  number = {7},
  pages = {828--829},
  publisher = {{Nature Publishing Group}},
  issn = {2397-334X},
  doi = {10.1038/s41559-022-01778-z},
  urldate = {2023-06-22},
  copyright = {2022 The Author(s), under exclusive licence to Springer Nature Limited},
  langid = {english},
  keywords = {Biodiversity,Conservation biology,Publication characteristics},
  file = {/Users/bbest/Zotero/storage/X49JLQIZ/Jansen et al. - 2022 - Stop ignoring map uncertainty in biodiversity scie.pdf}
}

@article{jetzEssentialBiodiversityVariables2019,
  title = {Essential Biodiversity Variables for Mapping and Monitoring Species Populations},
  author = {Jetz, Walter and McGeoch, Melodie A. and Guralnick, Robert and Ferrier, Simon and Beck, Jan and Costello, Mark J. and Fernandez, Miguel and Geller, Gary N. and Keil, Petr and Merow, Cory and Meyer, Carsten and {Muller-Karger}, Frank E. and Pereira, Henrique M. and Regan, Eugenie C. and Schmeller, Dirk S. and Turak, Eren},
  year = {2019},
  month = apr,
  journal = {Nature Ecology \& Evolution},
  volume = {3},
  number = {4},
  pages = {539--551},
  publisher = {{Nature Publishing Group}},
  issn = {2397-334X},
  doi = {10.1038/s41559-019-0826-1},
  urldate = {2023-06-26},
  abstract = {Species distributions and abundances are undergoing rapid changes worldwide. This highlights the significance of reliable, integrated information for guiding and assessing actions and policies aimed at managing and sustaining the many functions and benefits of species. Here we synthesize the types of data and approaches that are required to achieve such an integration and conceptualize `essential biodiversity variables' (EBVs) for a unified global capture of species populations in space and time. The inherent heterogeneity and sparseness of raw biodiversity data are overcome by the use of models and remotely sensed covariates to inform predictions that are contiguous in space and time and global in extent. We define the species population EBVs as a space\textendash time\textendash species\textendash gram (cube) that simultaneously addresses the distribution or abundance of multiple species, with its resolution adjusted to represent available evidence and acceptable levels of uncertainty. This essential information enables the monitoring of single or aggregate spatial or taxonomic units at scales relevant to research and decision-making. When combined with ancillary environmental or species data, this fundamental species population information directly underpins a range of biodiversity and ecosystem function indicators. The unified concept we present links disparate data to downstream uses and informs a vision for species population monitoring in which data collection is closely integrated with models and infrastructure to support effective biodiversity assessment.},
  copyright = {2019 The Author(s)},
  langid = {english},
  keywords = {Ecology,Evolution},
  file = {/Users/bbest/Zotero/storage/FLB23YJH/Jetz et al. - 2019 - Essential biodiversity variables for mapping and m.pdf}
}

@article{jungIntegratedSpeciesDistribution2023,
  title = {An Integrated Species Distribution Modelling Framework for Heterogeneous Biodiversity Data},
  author = {Jung, Martin},
  year = {2023},
  month = sep,
  journal = {Ecological Informatics},
  volume = {76},
  pages = {102127},
  issn = {1574-9541},
  doi = {10.1016/j.ecoinf.2023.102127},
  urldate = {2023-06-26},
  abstract = {Most knowledge about species and habitats is in-homogeneously distributed, with biases existing in space, time and taxonomic and functional knowledge. Yet, controversially the total amount of biodiversity data has never been greater. A key challenge is thus how to make effective use of the various sources of biodiversity data in an integrated manner. Particularly for widely used modelling approaches, such as species distribution models (SDMs), the need for integration is urgent, if spatial and temporal predictions are to be accurate enough in addressing global challenges. Here, I present a modelling framework that brings together several ideas and methodological advances for creating integrated species distribution models (iSDM). The ibis.iSDM R-package is a set of modular convenience functions that allows the integration of different data sources, such as presence-only, community survey, expert ranges or species habitat preferences, in a single model or ensemble of models. Further it supports convenient parameter transformations and tuning, data preparation helpers and allows the creation of spatial-temporal projections and scenarios. Ecological constraints such as projection limits, dispersal, connectivity or adaptability can be added in a modular fashion thus helping to prevent unrealistic estimates of species distribution changes. The ibis.iSDM R-package makes use of a series of methodological advances and is aimed to be a vehicle for creating more realistic and constrained spatial predictions. Besides providing convenience functions for a range of different statistical models as well as an increasing number of wrappers for mechanistic modules, ibis.iSDM also introduces several innovative concepts such as sequential or weighted integration, or thresholding by prediction uncertainty. The overall framework will be continued to be improved and further functionalities be added.},
  langid = {english},
  keywords = {Bayesian,Data integration,Environmental niche,Offset,Point-process-model,R-package,Species distribution model},
  file = {/Users/bbest/Zotero/storage/V5R9WWY8/Jung - 2023 - An integrated species distribution modelling frame.pdf;/Users/bbest/Zotero/storage/9CYMQBLD/S1574954123001565.html}
}

@misc{kaschnerAquaMapsPredictedRange2023,
  title = {{{AquaMaps}}: {{Predicted}} Range Maps for Aquatic Species. {{Retrieved}} from {{https://www.aquamaps.org.}}},
  author = {Kaschner, K. and {Kesner-Reyes}, K. and Garilao, C. and Segschneider, J. and {Rius-Barile}, J. and Rees, T. and Froese, R.},
  year = {2023}
}

@article{kaschnerMappingWorldwideDistributions2006,
  title = {Mapping World-Wide Distributions of Marine Mammal Species Using a Relative Environmental Suitability ({{RES}}) Model},
  author = {Kaschner, K. and Watson, R. and Trites, A. W. and Pauly, D.},
  year = {2006},
  month = jul,
  journal = {Marine Ecology Progress Series},
  volume = {316},
  pages = {285--310},
  doi = {10.3354/meps316285},
  urldate = {2008-07-02},
  abstract = {ABSTRACT: The lack of comprehensive sighting data sets precludes the application of standard habitat suitability modeling approaches to predict distributions of the majority of marine mammal species on very large scales. As an alternative, we developed an ecological niche model to map global distributions of 115 cetacean and pinniped species living in the marine environment using more readily available expert knowledge about habitat usage. We started by assigning each species to broad-scale niche categories with respect to depth, sea-surface temperature, and ice edge association based on synopses of published information. Within a global information system framework and a global grid of 0.5\textdegree{} latitude/longitude cell dimensions, we then generated an index of the relative environmental suitability (RES) of each cell for a given species by relating known habitat usage to local environmental conditions. RES predictions closely matched published maximum ranges for most species, thus representing useful, more objective alternatives to existing sketched distributional outlines. In addition, raster-based predictions provided detailed information about heterogeneous patterns of potentially suitable habitat for species throughout their range. We tested RES model outputs for 11 species (northern fur seal, harbor porpoise, sperm whale, killer whale, hourglass dolphin, fin whale, humpback whale, blue whale, Antarctic minke, and dwarf minke whales) from a broad taxonomic and geographic range, using data from dedicated surveys. Observed encounter rates and species-specific predicted environmental suitability were significantly and positively correlated for all but 1 species. In comparison, encounter rates were correlated with {$<$}1\% of 1000 simulated random data sets for all but 2 species. Mapping of large-scale marine mammal distributions using this environmental envelope model is helpful for evaluating current assumptions and knowledge about species' occurrences, especially for data-poor species. Moreover, RES modeling can help to focus research efforts on smaller geographic scales and usefully supplement other, statistical, habitat suitability models.},
  keywords = {Distribution,GIS,Global,Habitat suitability modeling,Marine mammals,Niche model,Relative environ-mental suitability},
  file = {/Users/bbest/Zotero/storage/6NJTME7Q/Kaschner et al. - 2006 - Mapping world-wide distributions of marine mammal .pdf;/Users/bbest/Zotero/storage/7EIMDY6M/Kaschner2006.pdf;/Users/bbest/Zotero/storage/TMB4U5LT/Kaschner et al. - 2006 - Mapping world-wide distributions of marine mammal .pdf;/Users/bbest/Zotero/storage/5DQJAUK3/p285-310.html}
}

@article{kassENMevalRedesignedCustomizable2021,
  title = {{{ENMeval}} 2.0: {{Redesigned}} for Customizable and Reproducible Modeling of Species' Niches and Distributions},
  shorttitle = {{{ENMeval}} 2.0},
  author = {Kass, Jamie M. and Muscarella, Robert and Galante, Peter J. and Bohl, Corentin L. and {Pinilla-Buitrago}, Gonzalo E. and Boria, Robert A. and {Soley-Guardia}, Mariano and Anderson, Robert P.},
  year = {2021},
  journal = {Methods in Ecology and Evolution},
  volume = {12},
  number = {9},
  pages = {1602--1608},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.13628},
  urldate = {2023-06-22},
  abstract = {Quantitative evaluations to optimize complexity have become standard for avoiding overfitting of ecological niche models (ENMs) that estimate species' potential geographic distributions. ENMeval was the first R package to make such evaluations (often termed model tuning) widely accessible for the Maxent algorithm. It also provided multiple methods for partitioning occurrence data and reported various performance metrics. Requests by users, recent developments in the field, and needs for software compatibility led to a major redesign and expansion. We additionally conducted a literature review to investigate trends in ENMeval use (2015\textendash 2019). ENMeval 2.0 has a new object-oriented structure for adding other algorithms, enables customizing algorithmic settings and performance metrics, generates extensive metadata, implements a null-model approach to quantify significance and effect sizes, and includes features to increase the breadth of analyses and visualizations. In our literature review, we found insufficient reporting of model performance and parameterization, heavy reliance on model selection with AICc and low utilization of spatial cross-validation; we explain how ENMeval 2.0 can help address these issues. This redesigned and expanded version can promote progress in the field and improve the information available for decision-making. \hspace{0pt}},
  copyright = {\textcopyright{} 2021 British Ecological Society},
  langid = {english},
  keywords = {cross-validation,ecological niche model,metadata,model evaluation,model tuning,software,spatial,species distribution model},
  file = {/Users/bbest/Zotero/storage/7A6D6EX5/Kass et al. - 2021 - ENMeval 2.0 Redesigned for customizable and repro.pdf;/Users/bbest/Zotero/storage/A3HT4WEK/2041-210X.html}
}

@misc{kassENMevalVersion2023,
  title = {{{ENMeval}} Version 2.0.4},
  author = {Kass, Jamie M.},
  year = {2023},
  month = jun,
  urldate = {2023-06-22},
  abstract = {R package for automated runs and evaluations of ecological niche models.}
}

@article{kassWallaceFlexiblePlatform2018,
  title = {Wallace: {{A}} Flexible Platform for Reproducible Modeling of Species Niches and Distributions Built for Community Expansion},
  shorttitle = {Wallace},
  author = {Kass, Jamie M. and Vilela, Bruno and {Aiello-Lammens}, Matthew E. and Muscarella, Robert and Merow, Cory and Anderson, Robert P.},
  year = {2018},
  journal = {Methods in Ecology and Evolution},
  volume = {9},
  number = {4},
  pages = {1151--1156},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.12945},
  urldate = {2023-06-22},
  abstract = {Scientific research increasingly calls for open-source software that is flexible, interactive, and expandable, while providing methodological guidance and reproducibility. Currently, many analyses in ecology are implemented with ``black box'' graphical user interfaces (GUIs) that lack flexibility or command-line interfaces that are infrequently used by non-specialists. To help remedy this situation in the context of species distribution modeling, we created Wallace, an open and modular application with a richly documented GUI with underlying R scripts that is flexible and highly interactive. Wallace guides users from acquiring and processing data to building models and examining predictions. Additionally, it is designed to grow via community contributions of new modules to expand functionality. All results are downloadable, along with code to reproduce the analysis. Wallace provides an example of an innovative platform to increase access to cutting-edge methods and encourage plurality in science and collaboration in software development.},
  copyright = {\textcopyright{} 2017 The Authors. Methods in Ecology and Evolution \textcopyright{} 2017 British Ecological Society},
  langid = {english},
  keywords = {biogeography,range,reproducibility,software,spatial analysis,species distribution model},
  file = {/Users/bbest/Zotero/storage/BWNITJUR/Kass et al. - 2018 - Wallace A flexible platform for reproducible mode.pdf;/Users/bbest/Zotero/storage/2TMY7JVA/2041-210X.html}
}

@article{kassWallaceShinyApp2023,
  title = {Wallace 2: A Shiny App for Modeling Species Niches and Distributions Redesigned to Facilitate Expansion via Module Contributions},
  shorttitle = {Wallace 2},
  author = {Kass, Jamie M. and {Pinilla-Buitrago}, Gonzalo E. and Paz, Andrea and Johnson, Bethany A. and {Grisales-Betancur}, Valentina and Meenan, Sarah I. and Attali, Dean and Broennimann, Olivier and Galante, Peter J. and Maitner, Brian S. and Owens, Hannah L. and Varela, Sara and {Aiello-Lammens}, Matthew E. and Merow, Cory and Blair, Mary E. and Anderson, Robert P.},
  year = {2023},
  journal = {Ecography},
  volume = {2023},
  number = {3},
  pages = {e06547},
  issn = {1600-0587},
  doi = {10.1111/ecog.06547},
  urldate = {2023-06-22},
  abstract = {Released 4 years ago, the Wallace EcoMod application (R package wallace) provided an open-source and interactive platform for modeling species niches and distributions that served as a reproducible toolbox and educational resource. wallace harnesses R package tools documented in the literature and makes them available via a graphical user interface that runs analyses and returns code to document and reproduce them. Since its release, feedback from users and partners helped identify key areas for advancement, leading to the development of wallace 2. Following the vision of growth by community expansion, the core development team engaged with collaborators and undertook a major restructuring of the application to enable: simplified addition of custom modules to expand methodological options, analyses for multiple species in the same session, improved metadata features, new database connections, and saving/loading sessions. wallace 2 features nine new modules and added functionalities that facilitate data acquisition from climate-simulation, botanical and paleontological databases; custom data inputs; model metadata tracking; and citations for R packages used (to promote documentation and give credit to developers). Three of these modules compose a new component for environmental space analyses (e.g., niche overlap). This expansion was paired with outreach to the biogeography and biodiversity communities, including international presentations and workshops that take advantage of the software's extensive guidance text. Additionally, the advances extend accessibility with a cloud-computing implementation and include a suite of comprehensive unit tests. The features in wallace 2 greatly improve its expandability, breadth of analyses, and reproducibility options, including the use of emerging metadata standards. The new architecture serves as an example for other modular software, especially those developed using the rapidly proliferating R package shiny, by showcasing straightforward module ingestion and unit testing. Importantly, wallace 2 sets the stage for future expansions, including those enabling biodiversity estimation and threat assessments for conservation.},
  copyright = {\textcopyright{} 2023 The Authors. Ecography published by John Wiley \& Sons Ltd on behalf of Nordic Society Oikos},
  langid = {english},
  keywords = {ecological niche model,modular,R,reproducibility,shiny,software,species distribution model},
  file = {/Users/bbest/Zotero/storage/722K4X9A/Kass et al. - 2023 - wallace 2 a shiny app for modeling species niches.pdf;/Users/bbest/Zotero/storage/6BALPQ6E/ecog.html}
}

@article{leclereBendingCurveTerrestrial2020,
  title = {Bending the Curve of Terrestrial Biodiversity Needs an Integrated Strategy},
  author = {Lecl{\`e}re, David and Obersteiner, Michael and Barrett, Mike and Butchart, Stuart H. M. and Chaudhary, Abhishek and De Palma, Adriana and DeClerck, Fabrice A. J. and Di Marco, Moreno and Doelman, Jonathan C. and D{\"u}rauer, Martina and Freeman, Robin and Harfoot, Michael and Hasegawa, Tomoko and Hellweg, Stefanie and Hilbers, Jelle P. and Hill, Samantha L. L. and Humpen{\"o}der, Florian and Jennings, Nancy and Krisztin, Tam{\'a}s and Mace, Georgina M. and Ohashi, Haruka and Popp, Alexander and Purvis, Andy and Schipper, Aafke M. and Tabeau, Andrzej and Valin, Hugo and {van Meijl}, Hans and {van Zeist}, Willem-Jan and Visconti, Piero and Alkemade, Rob and Almond, Rosamunde and Bunting, Gill and Burgess, Neil D. and Cornell, Sarah E. and Di Fulvio, Fulvio and Ferrier, Simon and Fritz, Steffen and Fujimori, Shinichiro and Grooten, Monique and Harwood, Thomas and Havl{\'i}k, Petr and Herrero, Mario and Hoskins, Andrew J. and Jung, Martin and Kram, Tom and {Lotze-Campen}, Hermann and Matsui, Tetsuya and Meyer, Carsten and Nel, Deon and Newbold, Tim and {Schmidt-Traub}, Guido and Stehfest, Elke and Strassburg, Bernardo B. N. and {van Vuuren}, Detlef P. and Ware, Chris and Watson, James E. M. and Wu, Wenchao and Young, Lucy},
  year = {2020},
  month = sep,
  journal = {Nature},
  volume = {585},
  number = {7826},
  pages = {551--556},
  publisher = {{Nature Publishing Group}},
  issn = {1476-4687},
  doi = {10.1038/s41586-020-2705-y},
  urldate = {2023-07-21},
  abstract = {Increased efforts are required to prevent further losses to terrestrial~biodiversity and the ecosystem services that it~ provides1,2. Ambitious targets have been proposed, such as reversing the declining trends in biodiversity3; however, just feeding the growing human population will make this a challenge4. Here we use an ensemble of land-use and biodiversity models to assess whether\textemdash and how\textemdash humanity can reverse the declines in terrestrial biodiversity caused by habitat conversion, which is a major threat to biodiversity5. We show that immediate efforts, consistent with the broader sustainability agenda but of unprecedented ambition and coordination, could enable the provision of food for the growing human population while reversing the global terrestrial biodiversity trends caused by habitat conversion. If we decide to increase the extent of land under conservation management, restore degraded land and generalize landscape-level conservation planning, biodiversity trends from habitat conversion could become positive by~the mid-twenty-first century on average across models (confidence interval, 2042\textendash 2061), but this was not the case for all models. Food prices could increase and, on average across models, almost half (confidence interval, 34\textendash 50\%) of the future biodiversity losses could not be avoided. However, additionally tackling the drivers of land-use change could avoid conflict with affordable food provision and reduces the environmental effects of the food-provision system. Through further sustainable intensification and trade, reduced food waste and more plant-based human diets, more than two thirds of future biodiversity losses are avoided and the biodiversity trends from habitat conversion are reversed by 2050 for almost all of the models. Although limiting further loss will remain challenging in several biodiversity-rich regions, and other threats\textemdash such as climate change\textemdash must be addressed to truly reverse the declines in biodiversity, our results show that ambitious conservation efforts and food system transformation are central to an effective post-2020 biodiversity strategy.},
  copyright = {2020 The Author(s), under exclusive licence to Springer Nature Limited},
  langid = {english},
  keywords = {Agriculture,Biodiversity,Environmental impact},
  file = {/Users/bbest/Zotero/storage/QIMAF229/Leclère et al. - 2020 - Bending the curve of terrestrial biodiversity need.pdf}
}

@article{martinBiodiversityIntactnessIndex2019,
  title = {The Biodiversity Intactness Index May Underestimate Losses},
  author = {Martin, Philip A. and Green, Rhys E. and Balmford, Andrew},
  year = {2019},
  month = jun,
  journal = {Nature Ecology \& Evolution},
  volume = {3},
  number = {6},
  pages = {862--863},
  publisher = {{Nature Publishing Group}},
  issn = {2397-334X},
  doi = {10.1038/s41559-019-0895-1},
  urldate = {2023-07-21},
  copyright = {2019 Springer Nature Limited},
  langid = {english},
  keywords = {Biodiversity,Policy},
  file = {/Users/bbest/Zotero/storage/QG66W34X/Martin et al. - 2019 - The biodiversity intactness index may underestimat.pdf}
}

@article{mateoLookingOptimalHierarchical2019,
  title = {Looking for an Optimal Hierarchical Approach for Ecologically Meaningful Niche Modelling},
  author = {Mateo, Rub{\'e}n G. and {Aroca-Fern{\'a}ndez}, Mar{\'i}a Jos{\'e} and Gast{\'o}n, Aitor and {G{\'o}mez-Rubio}, Virgilio and Saura, Santiago and {Garc{\'i}a-Vi{\~n}as}, Juan Ignacio},
  year = {2019},
  month = oct,
  journal = {Ecological Modelling},
  volume = {409},
  pages = {108735},
  issn = {0304-3800},
  doi = {10.1016/j.ecolmodel.2019.108735},
  urldate = {2023-06-22},
  abstract = {Ecological niche models are powerful tools in ecology. Factors operating at different spatial scales are known to jointly influence species distributions, but their integration in meaningful and reliable niche models is still methodologically complex and requires further research and validation. Here, we compare six different hierarchical niche models (HNMs): two ensemble, two Bayesian, and two penalized logistic regression approaches. The six HNMs were applied to produce high-resolution (25\,m) predictions for five tree species in a Biosphere Reserve in Central Spain, combining information from two spatial scales. At the regional scale (mainland Spain) climatic variables were used as predictors, and presence/absence data were derived from the Spanish Forest Inventory (76,347 plots). At the landscape scale (Biosphere Reserve) environmental variables were used as predictors while presence/absence data were derived from a local vegetation sampling (346 plots). We compared and evaluated the six HNMs using the AUC, MaxKappa, and MaxTSS statistics and the Pearson's correlation coefficient. We obtained reliable high-resolution HNMs at the landscape scale (AUC values were greater than 0.8), although with variable performance and scope of application. Ensemble approaches delivered reliable models particularly when the sample size was not a limiting modelling factor. However, Bayesian modelling allowed considering a spatially correlated random effect that outperformed the results of all other approaches for species with a low sample size, possibly derived from a strong spatial structure in their distribution. HNMs succeeded in generating high-resolution predictions and manage to identify a significantly greater part of the climatic niche of the species than non-hierarchical models. This allows more accurate projections both in space and time, which is essential in climate change and invasive species modelling projections. The usefulness of these models for decision support in local conservation programs is therefore highlighted. Our methodological comparison is valuable to inform modellers and decision makers of the performance and implications of these approaches regarding the support they can provide for the implementation of conservation management measures at the landscape scale.},
  langid = {english},
  keywords = {Ecological niche models,Ensemble ecological models,Hierarchical bayesian modelling,Multiscale approach,Penalized logistic regression,Species distribution models},
  file = {/Users/bbest/Zotero/storage/N2IDAB34/Mateo et al. - 2019 - Looking for an optimal hierarchical approach for e.pdf;/Users/bbest/Zotero/storage/VX46RC32/S0304380019302431.html}
}

@article{melo-merinoEcologicalNicheModels2020,
  title = {Ecological Niche Models and Species Distribution Models in Marine Environments: {{A}} Literature Review and Spatial Analysis of Evidence},
  shorttitle = {Ecological Niche Models and Species Distribution Models in Marine Environments},
  author = {{Melo-Merino}, Sara M. and {Reyes-Bonilla}, H{\'e}ctor and {Lira-Noriega}, Andr{\'e}s},
  year = {2020},
  month = jan,
  journal = {Ecological Modelling},
  volume = {415},
  pages = {108837},
  issn = {0304-3800},
  doi = {10.1016/j.ecolmodel.2019.108837},
  urldate = {2023-06-22},
  abstract = {In recent years, the use of ecological niche models (ENMs) and species distribution models (SDMs) to explore the patterns and processes behind observed distribution of species has experienced an explosive growth. Although the use of these methods has been less common and more recent in marine ecosystems than in a terrestrial context, they have shown significant increases in use and applications. Herein, we provide a systematic review of 328 articles on marine ENMs and SDMs published between 1990 and 2016, aiming to identify their main applications and the diversity of methodological frameworks in which they are developed, including spatial scale, geographic realm, taxonomic groups assessed, algorithms implemented, and data sources. Of the 328 studies, 48 \% were at local scales, with a hotspot of research effort in the North Atlantic Ocean. Most studies were based on correlative approaches and were used to answer ecological or biogeographic questions about mechanisms underlying geographic ranges (64 \%). A few attempted to evaluate impacts of climate change (19 \%) or to develop strategies for conservation (11 \%). Several correlative techniques have been used, but most common was the machine-learning approach Maxent (46 \%) and statistical approaches such as generalized additive models GAMs (22 \%) and generalized linear models, GLMs (14 \%). The groups most studied were fish (23 \%), molluscs (16 \%), and marine mammals (14 \%), the first two with commercial importance and the last important for conservation. We noted a lack of clarity regarding the definitions of ENMs versus SDMs, and a rather consistent failure to differentiate between them. This review exposed a need to know, reduce, and report error and uncertainty associated with species' occurrence records and environmental data. In addition, particular to marine realms, a third dimension should be incorporated into the modelling process, referring to the vertical position of the species, which will improve the precision and utility of these models. So too is of paramount importance the consideration of temporal and spatial resolution of environmental layers to adequately represent the dynamic nature of marine ecosystems, especially in the case of highly mobile species.},
  langid = {english},
  keywords = {Biogeography,Coastal,Correlative modelling,Geographic distribution,Ocean,Process-based modelling,Sea,Suitability},
  file = {/Users/bbest/Zotero/storage/9SN4L4QE/Melo-Merino et al. - 2020 - Ecological niche models and species distribution m.pdf;/Users/bbest/Zotero/storage/G2DQ62MR/S030438001930345X.html}
}

@misc{merowChangeRangeR2023,
  title = {{{changeRangeR}}},
  author = {Merow, Cory},
  year = {2023},
  month = jan,
  urldate = {2023-06-22},
  abstract = {R package for calculating metrics of species distributions}
}

@article{merowIntegratingOccurrenceData2017,
  title = {Integrating Occurrence Data and Expert Maps for Improved Species Range Predictions},
  author = {Merow, Cory and Wilson, Adam M. and Jetz, Walter},
  year = {2017},
  journal = {Global Ecology and Biogeography},
  volume = {26},
  number = {2},
  pages = {243--258},
  issn = {1466-8238},
  doi = {10.1111/geb.12539},
  urldate = {2023-06-26},
  abstract = {Aim Knowledge of species geographical distributions is critical for many ecological and evolutionary questions and underpins effective conservation decision-making, yet it is usually limited in spatial resolution or reliability. Over large spatial extents, range predictions are typically derived from expert knowledge or, increasingly, species distribution models based on individual occurrence records. Expert maps are useful at coarse resolution, where they are suitable for delineating unoccupied regions. In contrast, point records typically provide finer-scale occurrence information that can be characterized for its environmental association, but usually suffers from observer biases and does not representatively or fully address the geographical or environmental range occupied by a species. Innovation We develop a new modelling methodology to combine the complementary informative attributes of both data types to improve fine-scale, large-extent predictions. We use expert delineations to constrain predictions of a species distribution model parameterized with incidental point occurrence records. We introduce a maximum entropy approach for combining the two data types and generalize it to Poisson point process models. We illustrate critical decision making during model construction using two detailed case studies and illustrate features more generally with applications to species with vastly different range and data attributes. Our methods are illustrated in the Supporting Information and with a new R package, bossMaps, that integrates with existing generalized linear modelling and Maxent software. Main conclusions Our modelling strategy flexibly accommodates expert maps with different levels of bias and precision. The approach can also be useful with other coarse sources of spatially explicit information, including habitat associations, elevational bands or vegetation types. The flexible nature of this methodological innovation can support improved characterization of species distributions for a variety of applications and is being implemented as a standard element underpinning integrative species distribution predictions in the Map of Life (https://mol.org/).},
  copyright = {\textcopyright{} 2016 John Wiley \& Sons Ltd},
  langid = {english},
  keywords = {Ecological niche model,maximum entropy,Poisson point process,species distribution model},
  file = {/Users/bbest/Zotero/storage/2A7KHZ4Y/Merow et al. - 2017 - Integrating occurrence data and expert maps for im.pdf;/Users/bbest/Zotero/storage/5BBELIBV/geb.html}
}

@article{merowOperationalizingExpertKnowledge2022,
  title = {Operationalizing Expert Knowledge in Species' Range Estimates Using Diverse Data Types},
  author = {Merow, Cory and Galante, Peter J. and Kass, Jamie M. and {Aiello-Lammens}, Matthew E. and Babich Morrow, Cecina and Gerstner, Beth E. and Grisales Betancur, Valentina and Moore, Alex C. and {Noguera-Urbano}, Elkin A. and {Pinilla-Buitrago}, Gonzalo E. and {Vel{\'a}squez-Tibat{\'a}}, Jorge and Anderson, Robert P. and Blair, Mary E.},
  year = {2022},
  journal = {Frontiers of Biogeography},
  volume = {14},
  number = {2},
  doi = {10.21425/F5FBG53589},
  urldate = {2023-06-26},
  abstract = {Estimates of species' ranges can inform many aspects of biodiversity research and conservation-management decisions. Many practical applications need high-precision range estimates that are sufficiently reliable to use as input data in downstream applications. One solution has involved expert-generated maps that reflect on-the-ground field information and implicitly capture various processes that may limit a species' geographic distribution. However, expert maps are often subjective and rarely reproducible. In contrast, species distribution models (SDMs) typically have finer resolution and are reproducible because of explicit links to data. Yet, SDMs can have higher uncertainty when data are sparse, which is an issue for most species. Also, SDMs often capture only a subset of the factors that determine species distributions (e.g., climate) and hence can require significant post-processing to better estimate species' current realized distributions. Here, we demonstrate how expert knowledge, diverse data types, and SDMs can be used together in a transparent and reproducible modeling workflow. Specifically, we show how expert knowledge regarding species' habitat use, elevation, biotic interactions, and environmental tolerances can be used to make and refine range estimates using SDMs and various data sources, including high-resolution remotely sensed products. This range-refinement approach is primed to use various data sources, including many with continuously improving spatial or temporal resolution. To facilitate such analyses, we compile a comprehensive suite of tools in a new R package, maskRangeR, and provide worked examples. These tools can facilitate a wide variety of basic and applied research that requires high-resolution maps of species' current ranges, including quantifications of biodiversity and its change over time.},
  langid = {english},
  file = {/Users/bbest/Zotero/storage/BQZIXTUZ/Merow et al. - 2022 - Operationalizing expert knowledge in species' rang.pdf}
}

@misc{merowRangeModelMetadata2022,
  title = {{{rangeModelMetadata}}},
  author = {Merow, Cory},
  year = {2022},
  month = jun,
  urldate = {2023-06-22},
  copyright = {GPL-3.0},
  keywords = {ecological-metadata-language,ecological-modelling,ecological-models,ecology,species-distribution-modelling,species-distributions}
}

@article{merowSpeciesRangeModel2019,
  title = {Species' Range Model Metadata Standards: {{RMMS}}},
  shorttitle = {Species' Range Model Metadata Standards},
  author = {Merow, Cory and Maitner, Brian S. and Owens, Hannah L. and Kass, Jamie M. and Enquist, Brian J. and Jetz, Walter and Guralnick, Rob},
  year = {2019},
  journal = {Global Ecology and Biogeography},
  volume = {28},
  number = {12},
  pages = {1912--1924},
  issn = {1466-8238},
  doi = {10.1111/geb.12993},
  urldate = {2023-06-22},
  abstract = {Aim The geographic range and ecological niche of species are widely used concepts in ecology, evolution and conservation and many modelling approaches have been developed to quantify each. Niche and distribution modelling methods require a litany of design choices; differences among subdisciplines have created communication barriers that increase isolation of scientific advances. As a result, understanding and reproducing the work of others is difficult, if not impossible. It is often challenging to evaluate whether a model has been built appropriately for its intended application or subsequent reuse. Here, we propose a standardized model metadata framework that enables researchers to understand and evaluate modelling decisions while making models fully citable and reproducible. Such reproducibility is critical for both scientific and policy reports, while international standardization enables better comparison between different scenarios and research groups. Innovation Range modelling metadata (RMMS) address three challenges: they (a) are designed for convenience to encourage use, (b) accommodate a wide variety of applications, and (c) are extensible to allow the research community to steer them as needed. RMMS are based on a metadata dictionary that specifies a hierarchical structure to catalogue different aspects of the range modelling process. The dictionary balances a constrained, minimalist vocabulary to improve standardization with flexibility for users to modify and extend. To facilitate use, we have developed an R package, rangeModelMetaData, to build templates, automatically fill values from common modelling objects, check for inconsistencies with standards, and suggest values. Main conclusions Range Modelling Metadata tools foster cross-disciplinary advances in biogeography, conservation and allied disciplines by improving evaluation, model sharing, model searching, comparisons and reproducibility among studies. Our initially proposed standards here are designed to be modified and extended to evolve with research trends and needs.},
  copyright = {\textcopyright{} 2019 John Wiley \& Sons Ltd},
  langid = {english},
  keywords = {abundance,Maxent,niche model,R package,reproducible research,species distribution model},
  file = {/Users/bbest/Zotero/storage/V9R35WRC/Merow et al. - 2019 - Species' range model metadata standards RMMS.pdf;/Users/bbest/Zotero/storage/F3ZBRF3Q/geb.html}
}

@article{millerRecentPromisingFuture2019,
  title = {The Recent Past and Promising Future for Data Integration Methods to Estimate Species' Distributions},
  author = {Miller, David A. W. and Pacifici, Krishna and Sanderlin, Jamie S. and Reich, Brian J.},
  year = {2019},
  journal = {Methods in Ecology and Evolution},
  volume = {10},
  number = {1},
  pages = {22--37},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.13110},
  urldate = {2023-06-26},
  abstract = {With the advance of methods for estimating species distribution models has come an interest in how to best combine datasets to improve estimates of species distributions. This has spurred the development of data integration methods that simultaneously harness information from multiple datasets while dealing with the specific strengths and weaknesses of each dataset. We outline the general principles that have guided data integration methods and review recent developments in the field. We then outline key areas that allow for a more general framework for integrating data and provide suggestions for improving sampling design and validation for integrated models. Key to recent advances has been using point-process thinking to combine estimators developed for different data types. Extending this framework to new data types will further improve our inferences, as well as relaxing assumptions about how parameters are jointly estimated. These along with the better use of information regarding sampling effort and spatial autocorrelation will further improve our inferences. Recent developments form a strong foundation for implementation of data integration models. Wider adoption can improve our inferences about species distributions and the dynamic processes that lead to distributional shifts.},
  copyright = {\textcopyright{} 2019 The Authors. Methods in Ecology and Evolution \textcopyright{} 2019 British Ecological Society},
  langid = {english},
  keywords = {data fusion,integrated distribution model,joint likelihood,spatial point process,species distribution modelling},
  file = {/Users/bbest/Zotero/storage/9EWUFVW7/Miller et al. - 2019 - The recent past and promising future for data inte.pdf;/Users/bbest/Zotero/storage/WMI8VYRX/2041-210X.html}
}

@article{modBioticInteractionsBoost2015,
  title = {Biotic Interactions Boost Spatial Models of Species Richness},
  author = {Mod, Heidi K. and {le Roux}, Peter C. and Guisan, Antoine and Luoto, Miska},
  year = {2015},
  journal = {Ecography},
  volume = {38},
  number = {9},
  pages = {913--921},
  issn = {1600-0587},
  doi = {10.1111/ecog.01129},
  urldate = {2023-06-22},
  abstract = {Biotic interactions are known to affect the composition of species assemblages via several mechanisms, such as competition and facilitation. However, most spatial models of species richness do not explicitly consider inter-specific interactions. Here, we test whether incorporating biotic interactions into high-resolution models alters predictions of species richness as hypothesised. We included key biotic variables (cover of three dominant arctic-alpine plant species) into two methodologically divergent species richness modelling frameworks \textendash{} stacked species distribution models (SSDM) and macroecological models (MEM) \textendash{} for three ecologically and evolutionary distinct taxonomic groups (vascular plants, bryophytes and lichens). Predictions from models including biotic interactions were compared to the predictions of models based on climatic and abiotic data only. Including plant\textendash plant interactions consistently and significantly lowered bias in species richness predictions and increased predictive power for independent evaluation data when compared to the conventional climatic and abiotic data based models. Improvements in predictions were constant irrespective of the modelling framework or taxonomic group used. The global biodiversity crisis necessitates accurate predictions of how changes in biotic and abiotic conditions will potentially affect species richness patterns. Here, we demonstrate that models of the spatial distribution of species richness can be improved by incorporating biotic interactions, and thus that these key predictor factors must be accounted for in biodiversity forecasts.},
  copyright = {\textcopyright{} 2015 The Authors},
  langid = {english},
  file = {/Users/bbest/Zotero/storage/8N3BUSKA/Mod et al. - 2015 - Biotic interactions boost spatial models of specie.pdf;/Users/bbest/Zotero/storage/YEMEE4L4/ecog.html}
}

@misc{ModleRWorkflowEcological2023,
  title = {{{modleR}}: A Workflow for Ecological Niche Models},
  shorttitle = {{{modleR}}},
  year = {2023},
  month = may,
  urldate = {2023-06-22},
  abstract = {An ecological niche model workflow based on dismo},
  howpublished = {modleR},
  keywords = {dismo,ecological-niche-models,r,r-package,rstats}
}

@article{mokanyWorkingGuideHarnessing2022,
  title = {A Working Guide to Harnessing Generalized Dissimilarity Modelling for Biodiversity Analysis and Conservation Assessment},
  author = {Mokany, Karel and Ware, Chris and Woolley, Skipton N. C. and Ferrier, Simon and Fitzpatrick, Matthew C.},
  year = {2022},
  journal = {Global Ecology and Biogeography},
  volume = {31},
  number = {4},
  pages = {802--821},
  issn = {1466-8238},
  doi = {10.1111/geb.13459},
  urldate = {2023-06-22},
  abstract = {Aim Generalized dissimilarity modelling (GDM) is a powerful and unique method for characterizing and predicting beta diversity, the change in biodiversity over space, time and environmental gradients. The number of studies applying GDM is expanding, with increasing recognition of its value in improving our understanding of the drivers of biodiversity patterns and in implementing a wide variety of spatial assessments relevant to biodiversity conservation. However, apart from the original presentation of the GDM technique, there has been little guidance available to users on applying GDM to different situations or on the key modelling decisions required. Innovation We present an accessible working guide to GDM. We describe the context for the development of GDM, present a simple statistical explanation of how model fitting works, and step through key considerations involved in data preparation, model fitting, refinement and assessment. We then describe how several novel spatial biodiversity analyses can be implemented using GDM, with code to support broader implementation. We conclude by providing an overview of the range of GDM-based analyses that have been undertaken to date and identify priority areas for future research and development. Main conclusions Our vision is that this working guide will facilitate greater and more rigorous use of GDM as a powerful tool for undertaking biodiversity analyses and assessments.},
  copyright = {\textcopyright{} 2022 Commonwealth Scientific and Industrial Research Organisation. Global Ecology and Biogeography published by John Wiley \& Sons Ltd.},
  langid = {english},
  keywords = {beta diversity,compositional turnover,distance,GDM,generalized dissimilarity modelling,site-pair},
  file = {/Users/bbest/Zotero/storage/2VW6QZIL/Mokany et al. - 2022 - A working guide to harnessing generalized dissimil.pdf;/Users/bbest/Zotero/storage/8QZ6WYYV/geb.html}
}

@misc{mrmaxentMaxentJava2023,
  title = {Maxent - {{Java}}},
  author = {{mrmaxent}},
  year = {2023},
  month = jun,
  urldate = {2023-06-22},
  abstract = {Maximum entropy modelling of species geographic distributions.},
  copyright = {MIT}
}

@misc{mrmaxentMaxnetVersionMaxent2023,
  title = {Maxnet - {{R}} Version of Maxent},
  author = {{mrmaxent}},
  year = {2023},
  month = mar,
  urldate = {2023-06-22},
  abstract = {An R package that implements much of the functionality of the Maxent Java application}
}

@article{newboldReplyBiodiversityIntactness2019,
  title = {Reply to `{{The}} Biodiversity Intactness Index May Underestimate Losses'},
  author = {Newbold, Tim and {Sanchez-Ortiz}, Katia and De Palma, Adriana and Hill, Samantha L. L. and Purvis, Andy},
  year = {2019},
  month = jun,
  journal = {Nature Ecology \& Evolution},
  volume = {3},
  number = {6},
  pages = {864--865},
  publisher = {{Nature Publishing Group}},
  issn = {2397-334X},
  doi = {10.1038/s41559-019-0896-0},
  urldate = {2023-07-21},
  copyright = {2019 Springer Nature Limited},
  langid = {english},
  keywords = {Biodiversity,Policy},
  file = {/Users/bbest/Zotero/storage/6GP3AEDD/Newbold et al. - 2019 - Reply to ‘The biodiversity intactness index may un.pdf}
}

@misc{OccCite2023,
  title = {{{occCite}}},
  year = {2023},
  month = mar,
  urldate = {2023-06-22},
  abstract = {Querying database aggregators and citing primary sources of resulting occurrence records.},
  copyright = {GPL-3.0},
  howpublished = {rOpenSci},
  keywords = {biodiversity-data,biodiversity-informatics,biodiversity-standards,citations,museum-collection-specimens,museum-collections,museum-metadata}
}

@misc{ODMAPOverviewData2022,
  title = {{{ODMAP}} ({{Overview}}, {{Data}}, {{Model}}, {{Assessment}} and {{Prediction}}) for {{SDMs}} ({{Zurell}} et al, 2020)},
  year = {2022},
  month = sep,
  urldate = {2023-06-22},
  howpublished = {UP-Macroecology}
}

@article{oharaAligningMarineSpecies2017,
  title = {Aligning Marine Species Range Data to Better Serve Science and Conservation},
  author = {O'Hara, Casey C. and Afflerbach, Jamie C. and Scarborough, Courtney and Kaschner, Kristin and Halpern, Benjamin S.},
  year = {2017},
  month = may,
  journal = {PLOS ONE},
  volume = {12},
  number = {5},
  pages = {e0175739},
  publisher = {{Public Library of Science}},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0175739},
  urldate = {2023-06-22},
  abstract = {Species distribution data provide the foundation for a wide range of ecological research studies and conservation management decisions. Two major efforts to provide marine species distributions at a global scale are the International Union for Conservation of Nature (IUCN), which provides expert-generated range maps that outline the complete extent of a species' distribution; and AquaMaps, which provides model-generated species distribution maps that predict areas occupied by the species. Together these databases represent 24,586 species (93.1\% within AquaMaps, 16.4\% within IUCN), with only 2,330 shared species. Differences in intent and methodology can result in very different predictions of species distributions, which bear important implications for scientists and decision makers who rely upon these datasets when conducting research or informing conservation policy and management actions. Comparing distributions for the small subset of species with maps in both datasets, we found that AquaMaps and IUCN range maps show strong agreement for many well-studied species, but our analysis highlights several key examples in which introduced errors drive differences in predicted species ranges. In particular, we find that IUCN maps greatly overpredict coral presence into unsuitably deep waters, and we show that some AquaMaps computer-generated default maps (only 5.7\% of which have been reviewed by experts) can produce odd discontinuities at the extremes of a species' predicted range. We illustrate the scientific and management implications of these tradeoffs by repeating a global analysis of gaps in coverage of marine protected areas, and find significantly different results depending on how the two datasets are used. By highlighting tradeoffs between the two datasets, we hope to encourage increased collaboration between taxa experts and large scale species distribution modeling efforts to further improve these foundational datasets, helping to better inform science and policy recommendations around understanding, managing, and protecting marine biodiversity.},
  langid = {english},
  keywords = {Biodiversity,Conservation science,Corals,Data management,Marine conservation,Sequence alignment,Species delimitation,Taxonomy},
  file = {/Users/bbest/Zotero/storage/AJMPN7TB/O'Hara et al. - 2017 - Aligning marine species range data to better serve.pdf}
}

@article{osorio-olveraNtboxPackageGraphical2020,
  title = {Ntbox: {{An}} r Package with Graphical User Interface for Modelling and Evaluating Multidimensional Ecological Niches},
  shorttitle = {Ntbox},
  author = {{Osorio-Olvera}, Luis and {Lira-Noriega}, Andr{\'e}s and Sober{\'o}n, Jorge and Peterson, Andrew Townsend and Falconi, Manuel and {Contreras-D{\'i}az}, Rusby G. and {Mart{\'i}nez-Meyer}, Enrique and Barve, Vijay and Barve, Narayani},
  year = {2020},
  journal = {Methods in Ecology and Evolution},
  volume = {11},
  number = {10},
  pages = {1199--1206},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.13452},
  urldate = {2023-06-22},
  abstract = {Biodiversity studies rely heavily on estimates of species' distributions often obtained through ecological niche modelling. Numerous software packages exist that allow users to model ecological niches using machine learning and statistical methods. However, no existing package with a graphical user interface allows users to perform model calibration and selection based on convex forms such as ellipsoids, which may match fundamental ecological niche shapes better, incorporating tools for exploring, modelling, and evaluating niches and distributions that are intuitive for both novice and proficient users. Here we describe an r package, NicheToolBox (ntbox), that allows users to conduct all processing steps involved in ecological niche modelling: downloading and curating occurrence data, obtaining and transforming environmental data layers, selecting environmental variables, exploring relationships between geographic and environmental spaces, calibrating and selecting ellipsoid models, evaluating models using binomial and partial ROC tests, assessing extrapolation risk, and performing geographic information system operations via a graphical user interface. A summary of the entire workflow is produced for use as a stand-alone algorithm or as part of research reports. The method is explained in detail and tested via modelling the threatened feline species Leopardus wiedii. Georeferenced occurrence data for this species are queried to display both point occurrences and the IUCN extent of occurrence polygon (IUCN, 2007). This information is used to illustrate tools available for accessing, processing and exploring biodiversity data (e.g. number of occurrences and chronology of collecting) and transforming environmental data (e.g. a summary PCA for 19 bioclimatic layers). Visualizations of three-dimensional ecological niches modelled as minimum volume ellipsoids are developed with ancillary statistics. This niche model is then projected to geographic space, to represent a corresponding potential suitability map. Using ntbox allows a fast and straightforward means by which to retrieve and manipulate occurrence and environmental data, which can then be implemented in model calibration, projection and evaluation for assessing distributions of species in geographic space and their corresponding environmental combinations.},
  copyright = {\textcopyright{} 2020 British Ecological Society},
  langid = {english},
  keywords = {biodiversity informatics,ecological niche modelling,GIS tools,minimum volume ellipsoid,model evaluation,model selection,model uncertainty,species distribution},
  file = {/Users/bbest/Zotero/storage/SND7WWP2/Osorio-Olvera et al. - 2020 - ntbox An r package with graphical user interface .pdf;/Users/bbest/Zotero/storage/5MB63EQE/2041-210X.html}
}

@misc{osorioNtboxNicheToolBOX2021,
  title = {Ntbox: {{NicheToolBOX}}},
  author = {Osorio, Luis},
  year = {2021},
  month = nov,
  urldate = {2023-06-22},
  abstract = {The package aims to facilitate the process of building niche models and estimate the species distributions.}
}

@misc{owensHannahlowensVoluModelTransition2023,
  title = {Hannahlowens/{{voluModel}}: {{Transition}} to Active Spatial Dependencies Complete},
  shorttitle = {Hannahlowens/{{voluModel}}},
  author = {Owens, Hannah},
  year = {2023},
  month = apr,
  doi = {10.5281/zenodo.7813394},
  urldate = {2023-06-26},
  abstract = {scaleRange argument added to oneRasterPlot(). MESS3D now calculated using modEvA::MESS() instead of dismo::mess(). Removed dependency on raster and rgdal. Raster visualizations now done with terra and include optional graticule plotting. Added function to generate SpatRaster template with input SpatVector points centered on template cells.},
  howpublished = {Zenodo},
  file = {/Users/bbest/Zotero/storage/KC3BJVCN/7813394.html}
}

@article{owensOccCiteToolsQuerying2021,
  title = {{{occCite}}: {{Tools}} for Querying and Managing Large Biodiversity Occurrence Datasets},
  shorttitle = {{{occCite}}},
  author = {Owens, Hannah L. and Merow, Cory and Maitner, Brian S. and Kass, Jamie M. and Barve, Vijay and Guralnick, Robert P.},
  year = {2021},
  journal = {Ecography},
  volume = {44},
  number = {8},
  pages = {1228--1235},
  issn = {1600-0587},
  doi = {10.1111/ecog.05618},
  urldate = {2023-06-22},
  abstract = {The amount of observational and specimen-based biodiversity data available to researchers is increasing exponentially, yet the ability to manage and cite large, complex biodiversity datasets lags behind. This management and citation gap impedes reproducibility for data users and the ability for data publishers to track use and accumulate use citations, ultimately harming the longer-term sustainability of the still-emerging enterprise of research data-sharing. Here we present an R package, occCite (v. 0.4.7), to aid researchers in querying large species occurrence data aggregators (specifically, the Global Biodiversity Information Facility, GBIF, and the Botanical Information and Ecology Network, BIEN), and store metadata such as primary data providers, database accession dates, DOIs, and the taxonomic source used for search terms. occCite also includes tools to summarize and visualize query results and generate citation lists of all data providers and software packages used during the query process. We provide examples of a basic occurrence search and citation workflow as well as an advanced workflow using features for custom optimized searches, visualization, and summary procedures. occCite improves upon existing R packages by uniting data from powerful API-based query packages (rgbif and BIEN) into a unified object-based framework, while maintaining metadata vital to best-practice recommendations for documenting biodiversity analysis workflows. occCite aims to efficiently close the gap in the citation cycle between primary data providers and final research products, allowing researchers to meet dataset documentation standards without sacrificing time and resources to the demands of providing increasing levels of detail on their datasets.},
  copyright = {\textcopyright{} 2021 The Authors. Ecography published by John Wiley \& Sons Ltd on behalf of Nordic Society Oikos},
  langid = {english},
  keywords = {citations,database aggregation,metadata,presence-only data,R package},
  file = {/Users/bbest/Zotero/storage/PP2GZG36/Owens et al. - 2021 - occCite Tools for querying and managing large bio.pdf;/Users/bbest/Zotero/storage/9I8XQJ52/ecog.html}
}

@article{peclBiodiversityRedistributionClimate2017,
  title = {Biodiversity Redistribution under Climate Change: {{Impacts}} on Ecosystems and Human Well-Being},
  shorttitle = {Biodiversity Redistribution under Climate Change},
  author = {Pecl, Gretta T. and Ara{\'u}jo, Miguel B. and Bell, Johann D. and Blanchard, Julia and Bonebrake, Timothy C. and Chen, I-Ching and Clark, Timothy D. and Colwell, Robert K. and Danielsen, Finn and Eveng{\aa}rd, Birgitta and Falconi, Lorena and Ferrier, Simon and Frusher, Stewart and Garcia, Raquel A. and Griffis, Roger B. and Hobday, Alistair J. and {Janion-Scheepers}, Charlene and Jarzyna, Marta A. and Jennings, Sarah and Lenoir, Jonathan and Linnetved, Hlif I. and Martin, Victoria Y. and McCormack, Phillipa C. and McDonald, Jan and Mitchell, Nicola J. and Mustonen, Tero and Pandolfi, John M. and Pettorelli, Nathalie and Popova, Ekaterina and Robinson, Sharon A. and Scheffers, Brett R. and Shaw, Justine D. and Sorte, Cascade J. B. and Strugnell, Jan M. and Sunday, Jennifer M. and Tuanmu, Mao-Ning and Verg{\'e}s, Adriana and Villanueva, Cecilia and Wernberg, Thomas and Wapstra, Erik and Williams, Stephen E.},
  year = {2017},
  month = mar,
  journal = {Science},
  volume = {355},
  number = {6332},
  pages = {eaai9214},
  publisher = {{American Association for the Advancement of Science}},
  doi = {10.1126/science.aai9214},
  urldate = {2023-06-22},
  abstract = {Distributions of Earth's species are changing at accelerating rates, increasingly driven by human-mediated climate change. Such changes are already altering the composition of ecological communities, but beyond conservation of natural systems, how and why does this matter? We review evidence that climate-driven species redistribution at regional to global scales affects ecosystem functioning, human well-being, and the dynamics of climate change itself. Production of natural resources required for food security, patterns of disease transmission, and processes of carbon sequestration are all altered by changes in species distribution. Consideration of these effects of biodiversity redistribution is critical yet lacking in most mitigation and adaptation strategies, including the United Nation's Sustainable Development Goals.},
  file = {/Users/bbest/Zotero/storage/IZDE3MDW/Pecl et al. - 2017 - Biodiversity redistribution under climate change .pdf}
}

@article{petersonENM2020FreeOnline2022,
  title = {{{ENM2020}}: {{A Free Online Course}} and {{Set}} of {{Resources}} on {{Modeling Species}}' {{Niches}} and {{Distributions}}},
  shorttitle = {{{ENM2020}}},
  author = {Peterson, A. Townsend and {Aiello-Lammens}, Matthew and Amatulli, Giuseppe and Anderson, Robert and Cobos, Marlon and {Diniz-Filho}, Jos{\'e} Alexandre and Escobar, Luis and Feng, Xiao and Franklin, Janet and Gadelha, Luiz and Georges, Damien and Gu{\'e}guen, M. and Gueta, Tomer and Ingenloff, Kate and Jarvie, Scott and Jim{\'e}nez, Laura and Karger, Dirk and Kass, Jamie and Kearney, Michael and Loyola, Rafael and {Machado-Stredel}, Fernando and {Mart{\'i}nez-Meyer}, Enrique and Merow, Cory and Mondelli, Maria Luiza and Mortara, Sara and Muscarella, Robert and Myers, Corinne and Naimi, Babak and Noesgaard, Daniel and Ondo, Ian and {Osorio-Olvera}, Luis and Owens, Hannah and Pearson, Richard and {Pinilla-Buitrago}, Gonzalo and {S{\'a}nchez-Tapia}, Andrea and Saupe, Erin and Thuiller, Wilfried and Varela, Sara and Warren, Dan and Wieczorek, John and Yates, Katherine and Zhu, Gengping and Zuquim, Gabriela and Zurell, Damaris},
  year = {2022},
  month = mar,
  journal = {Biodiversity Informatics},
  volume = {17},
  pages = {1--9},
  issn = {1546-9735},
  doi = {10.17161/bi.v17i.15016},
  urldate = {2023-06-22},
  abstract = {The field of distributional ecology has seen considerable recent attention, particularly surrounding the theory, protocols, and tools for Ecological Niche Modeling (ENM) or Species Distribution Modeling (SDM). Such analyses have grown steadily over the past two decades\textemdash including a maturation of relevant theory and key concepts\textemdash but methodological consensus has yet to be reached. In response, and following an online course taught in Spanish in 2018, we designed a comprehensive English-language course covering much of the underlying theory and methods currently applied in this broad field. Here, we summarize that course, ENM2020, and provide links by which resources produced for it can be accessed into the future. ENM2020 lasted 43 weeks, with presentations from 52 instructors, who engaged with \&gt;2500 participants globally through \&gt;14,000 hours of viewing and \&gt;90,000 views of instructional video and question-and-answer sessions. Each major topic was introduced by an ``Overview'' talk, followed by more detailed lectures on subtopics. The hierarchical and modular format of the course permits updates, corrections, or alternative viewpoints, and generally facilitates revision and reuse, including the use of only the Overview lectures for introductory courses. All course materials are free and openly accessible (CC-BY license) to ensure these resources remain available to all interested in distributional ecology.},
  copyright = {Copyright (c) 2022 A. Townsend Peterson, Matthew Aiello-Lammens, Giuseppe Amatulli, Robert Anderson, Marlon Cobos, Jos\'e Alexandre Diniz-Filho, Luis Escobar, Xiao Feng, Janet Franklin, Luiz Gadelha, Damien Georges, M Gu\'eguen, Tomer Gueta, Kate Ingenloff, Scott Jarvie, Laura Jim\'enez, Dirk Karger, Jamie Kass, Michael Kearney, Rafael Loyola, Fernando Machado-Stredel, Enrique Mart\'inez-Meyer, Cory Merow, Maria Luiza Mondelli, Sara Mortara, Robert Muscarella, Corinne Myers, Babak Naimi, Daniel Noesgaard, Ian Ondo, Luis Osorio-Olvera, Hannah Owens, Richard Pearson, Gonzalo Pinilla-Buitrago, Andrea S\'anchez-Tapia, Erin Saupe, Wilfried Thuiller, Sara Varela, Dan Warren, John Wieczorek, Katherine Yates, Gengping Zhu, Gabriela Zuquim, Damaris Zurell},
  langid = {english},
  file = {/Users/bbest/Zotero/storage/7XU4ZDBA/Peterson et al. - 2022 - ENM2020 A Free Online Course and Set of Resources.pdf}
}

@article{phillipsOpeningBlackBox2017,
  title = {Opening the Black Box: An Open-Source Release of {{Maxent}}},
  shorttitle = {Opening the Black Box},
  author = {Phillips, Steven J. and Anderson, Robert P. and Dud{\'i}k, Miroslav and Schapire, Robert E. and Blair, Mary E.},
  year = {2017},
  journal = {Ecography},
  volume = {40},
  number = {7},
  pages = {887--893},
  issn = {1600-0587},
  doi = {10.1111/ecog.03049},
  urldate = {2023-06-22},
  abstract = {This software note announces a new open-source release of the Maxent software for modeling species distributions from occurrence records and environmental data, and describes a new R package for fitting such models. The new release (ver. 3.4.0) will be hosted online by the American Museum of Natural History, along with future versions. It contains small functional changes, most notably use of a complementary log-log (cloglog) transform to produce an estimate of occurrence probability. The cloglog transform derives from the recently-published interpretation of Maxent as an inhomogeneous Poisson process (IPP), giving it a stronger theoretical justification than the logistic transform which it replaces by default. In addition, the new R package, maxnet, fits Maxent models using the glmnet package for regularized generalized linear models. We discuss the implications of the IPP formulation in terms of model inputs and outputs, treating occurrence records as points rather than grid cells and interpreting the exponential Maxent model (raw output) as as an estimate of relative abundance. With these two open-source developments, we invite others to freely use and contribute to the software.},
  copyright = {\textcopyright{} 2017 The Authors},
  langid = {english},
  file = {/Users/bbest/Zotero/storage/IDRM89H9/Phillips et al. - 2017 - Opening the black box an open-source release of M.pdf;/Users/bbest/Zotero/storage/FSM6Q63M/ecog.html}
}

@article{pontiImportanceIncludingPhenology2023,
  title = {The Importance of Including Phenology When Modelling Species Ecological Niche},
  author = {Ponti, Raquel and Sannolo, Marco},
  year = {2023},
  journal = {Ecography},
  volume = {2023},
  number = {4},
  pages = {e06143},
  issn = {1600-0587},
  doi = {10.1111/ecog.06143},
  urldate = {2023-06-22},
  abstract = {Species distribution models have grown in complexity by incorporating fine-scale variables, including data on microclimate, physiology and species interactions. Recent studies have acknowledged the importance of the spatial scale by including higher resolution maps and more complex climatic variables. However, models rarely consider the consequences of including data related to time. Indeed, species phenology \textendash{} and potential shifts in phenology due, for example, to climate change \textendash{} is potentially one of the most neglected aspects of ecological modelling. We present a literature review of relevant phenological aspects at different temporal scales and across several taxa. Such elements should be considered to define better the environmental niche and project present, future and past distribution models. We considered the available studies on plants, insects, reptiles, birds and mammals to evaluate how they dealt with the phenology of the investigated species, as well as the phenology of other resources and interacting species, to infer present, past and future projections. Here we focus on four main phenological aspects that, if not considered, may easily bias any projection, namely: 1) phenology can be accompanied by a shift in distribution within the year (e.g. migratory species); 2) activity may be restricted to a portion of the year (e.g. most ectotherms from temperate climates); 3) survival and reproduction success may depend on the synchrony with other species phenology (e.g. plants\textendash pollinators interactions); 4) changes in climatic conditions can lead to shifts in phenology (e.g. anticipated or delayed blooms or changes in migration timing). In this review, we show how neglecting such factors may quickly lead to project a biased distribution. Finally, we provide a guide on evaluating whether the case study may be affected by such factors and what actions may improve the models.},
  copyright = {\textcopyright{} 2022 The Authors. Ecography published by John Wiley \& Sons Ltd on behalf of Nordic Society Oikos},
  langid = {english},
  keywords = {bias,climate change,migration,phenology,species distribution models,species interactions},
  file = {/Users/bbest/Zotero/storage/NSF5PBBB/Ponti and Sannolo - 2023 - The importance of including phenology when modelli.pdf;/Users/bbest/Zotero/storage/FL9KIC9F/ecog.html}
}

@article{readyPredictingDistributionsMarine2010,
  title = {Predicting the Distributions of Marine Organisms at the Global Scale},
  author = {Ready, Jonathan and Kaschner, Kristin and South, Andy B. and Eastwood, Paul D. and Rees, Tony and Rius, Josephine and Agbayani, Eli and Kullander, Sven and Froese, Rainer},
  year = {2010},
  month = feb,
  journal = {Ecological Modelling},
  volume = {221},
  number = {3},
  pages = {467--478},
  issn = {0304-3800},
  doi = {10.1016/j.ecolmodel.2009.10.025},
  urldate = {2010-03-21},
  abstract = {We present and evaluate AquaMaps, a presence-only species distribution modelling system that allows the incorporation of expert knowledge about habitat usage and was designed for maximum output of standardized species range maps at the global scale. In the marine environment there is a significant challenge to the production of range maps due to large biases in the amount and location of occurrence data for most species. AquaMaps is compared with traditional presence-only species distribution modelling methods to determine the quality of outputs under equivalently automated conditions. The effect of the inclusion of expert knowledge to AquaMaps is also investigated. Model outputs were tested internally, through data partitioning, and externally against independent survey data to determine the ability of models to predict presence versus absence. Models were also tested externally by assessing correlation with independent survey estimates of relative species abundance. AquaMaps outputs compare well to the existing methods tested, and inclusion of expert knowledge results in a general improvement in model outputs. The transparency, speed and adaptability of the AquaMaps system, as well as the existing online framework which allows expert review to compensate for sampling biases and thus improve model predictions are proposed as additional benefits for public and research use alike.},
  keywords = {Expert review,Global marine biodiversity,Model comparison,Range maps,Species distribution modelling,Trawl surveys},
  file = {/Users/bbest/Zotero/storage/6UJ5XMN8/Ready et al. - 2010 - Predicting the distributions of marine organisms a.pdf;/Users/bbest/Zotero/storage/UMLHIHME/science.html}
}

@article{robinsonDecisionSupportEcosystemBased2023,
  title = {Decision Support for the {{Ecosystem-Based Management}} of a {{Range-Extending Species}} in a {{Global Marine Hotspot Presents Effective Strategies}} and {{Challenges}}},
  author = {Robinson, Lucy M. and Marzloff, Martin P. and {van Putten}, Ingrid and Pecl, Gretta and Jennings, Sarah and Nicol, Sam and Hobday, Alistair J. and Tracey, Sean and Hartmann, Klaas and Haward, Marcus and Frusher, Stewart},
  year = {2023},
  month = jan,
  journal = {Ecosystems},
  volume = {26},
  number = {1},
  pages = {232--251},
  issn = {1435-0629},
  doi = {10.1007/s10021-020-00560-1},
  urldate = {2023-06-22},
  abstract = {Climate-driven changes in ocean currents have facilitated the range extension of the long-spined sea urchin (Centrostephanus rodgersii) from Australia's mainland to eastern Tasmania over recent decades. Since its arrival, the destructive grazing of the urchin has led to widespread formation of sea urchin `barrens'. The loss of habitat, biodiversity and productivity for important commercial reef species in conjunction with the development of an urchin fishery has led to conflicting objectives among some stakeholders that pose complex challenges for regional management. Stakeholders representatives and managers were engaged via a participatory workshop and subsequent one-on-one surveys to trial a structured decision making process to identify effective ecosystem-based management strategies. We directly and indirectly elicited each preferences for nine alternative management strategies by presenting them with the 10-year consequences of each strategy estimated from an ecosystem model of Tasmanian reef communities. These preferences were included in cost-effectiveness scores that were averaged (across stakeholders) to enable strategy ranking from most to least cost-effective. Rankings revealed strategies that included sea urchin removal or translocation of predatory lobsters were the most cost-effective. However, assessment of stakeholders' individual cost-effectiveness scores showed some disparity among preferences in high ranking strategies. Additionally, inconsistencies in strategy preferences using alternative (direct or indirect) ranking scores reveal conflicting objectives as the most plausible explanation. Our study illustrates how structured decision making can effectively facilitate ecosystem-based management by engaging stakeholders step-by-step towards management strategy implementation and promoting collective learning.},
  langid = {english},
  keywords = {decision analysis,ecosystem-based management,global marine hotspot,keystone herbivore,multi-method elicitation,species range extension,stakeholder engagement,Structured decision making},
  file = {/Users/bbest/Zotero/storage/MLJSLTW3/Robinson et al. - 2023 - Decision support for the Ecosystem-Based Managemen.pdf}
}

@misc{sanchez-tapiaModleRModularWorkflow2020,
  title = {{{modleR}}: A Modular Workflow to Perform Ecological Niche Modeling in {{R}}},
  shorttitle = {{{modleR}}},
  author = {{S{\'a}nchez-Tapia}, Andrea and Mortara, Sara Ribeiro and Rocha, Diogo Souza Bezerra and Barros, Felipe Sodr{\'e} Mendes and Gall, Guilherme and de Siqueira, Marinez Ferreira},
  year = {2020},
  month = apr,
  primaryclass = {New Results},
  pages = {2020.04.01.021105},
  publisher = {{bioRxiv}},
  doi = {10.1101/2020.04.01.021105},
  urldate = {2023-06-22},
  abstract = {Ecological niche models (ENM) use the environmental variables associated with the currently known distribution of a species to model its ecological niche and project it into the geographic space. Widely used and misused, ENM has become a common tool for ecologists and decision-makers. Many ENM platforms have been developed over the years, first as standalone programs, later as packages within script-based programming languages and environments. The democratization of these programming tools and the advent of Open Science brought a growing concern regarding the reproducibility, transparency, robustness, portability, and interoperability in ENM workflows. ENM workflows have some core components that are replicated between projects. However, they have a large internal variation due to the variety of research questions and applications. Any ecological niche modeling platform should take into account this trade-off between stability and reproducibility on one hand, and flexibility and decision-making on the other. Here, we present modleR, a four-step workflow that wraps some of the common phases executed during an ecological niche model procedure. We have divided the process into (1) data setup, (2) model fitting and projection, (3) partition joining and (4) ensemble modeling (consensus between algorithms). modleR is highly adaptable and replicable depending on the user's needs and is open to deeper internal parametrization. It can be used as a testing platform due to its consistent folder structure and its capacity to control some sources of variation while changing others. It can be run in interactive local sessions and in high-performance or high-throughput computational (HPC/HTC) platforms and parallelized by species or algorithms. It can also communicate with other tools in the field, allowing the user to enter and exit the workflow at any phase, and execute complementary routines outside the package. Finally, it records metadata and session information at each step, ensuring reproducibility beyond the use of script-based applications.},
  archiveprefix = {bioRxiv},
  chapter = {New Results},
  copyright = {\textcopyright{} 2020, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/},
  langid = {english},
  file = {/Users/bbest/Zotero/storage/IH97FFCY/Sánchez-Tapia et al. - 2020 - modleR a modular workflow to perform ecological n.pdf}
}

@article{santiniAssessingReliabilitySpecies2021,
  title = {Assessing the Reliability of Species Distribution Projections in Climate Change Research},
  author = {Santini, Luca and {Ben{\'i}tez-L{\'o}pez}, Ana and Maiorano, Luigi and {\v C}engi{\'c}, Mirza and Huijbregts, Mark A. J.},
  year = {2021},
  journal = {Diversity and Distributions},
  volume = {27},
  number = {6},
  pages = {1035--1050},
  issn = {1472-4642},
  doi = {10.1111/ddi.13252},
  urldate = {2023-06-22},
  abstract = {Aim Forecasting changes in species distribution under future scenarios is one of the most prolific areas of application for species distribution models (SDMs). However, no consensus yet exists on the reliability of such models for drawing conclusions on species' distribution response to changing climate. In this study, we provide an overview of common modelling practices in the field and assess the reliability of model predictions using a virtual species approach. Location Global. Methods We first review papers published between 2015 and 2019. Then, we use a virtual species approach and three commonly applied SDM algorithms (GLM, MaxEnt and random forest) to assess the estimated and actual predictive performance of models parameterized with different modelling settings and violations of modelling assumptions. Results Most SDM papers relied on single models (65\%) and small samples (N {$<$} 50, 62\%), used presence-only data (85\%), binarized models' output (74\%) and used a split-sample validation (94\%). Our simulation reveals that the split-sample validation tends to be over-optimistic compared to the real performance, whereas spatial block validation provides a more honest estimate, except when datasets are environmentally biased. The binarization of predicted probabilities of presence reduces models' predictive ability considerably. Sample size is one of the main predictors of the real model accuracy, but has little influence on estimated accuracy. Finally, the inclusion of ecologically irrelevant predictors and the violation of modelling assumptions increases estimated accuracy but decreases real accuracy of model projections, leading to biased estimates of range contraction and expansion. Main conclusions Our ability to predict future species distribution is low on average, particularly when models' predictions are binarized. A robust validation by spatially independent samples is required, but does not rule out inflation of model accuracy by assumption violation. Our findings call for caution in the application and interpretation of SDM projections under different climates.},
  copyright = {\textcopyright{} 2021 The Authors. Diversity and Distributions published by John Wiley \& Sons Ltd.},
  langid = {english},
  keywords = {area under the curve,bias,climate change projections,equilibrium,geographic extent,niche modelling,sample size,spurious relationships,true skill statistics},
  file = {/Users/bbest/Zotero/storage/CF2QYK2D/Santini et al. - 2021 - Assessing the reliability of species distribution .pdf;/Users/bbest/Zotero/storage/D5RZWJ8H/ddi.html}
}

@article{scholesBiodiversityIntactnessIndex2005,
  title = {A Biodiversity Intactness Index},
  author = {Scholes, R. J. and Biggs, R.},
  year = {2005},
  month = mar,
  journal = {Nature},
  volume = {434},
  number = {7029},
  pages = {45--49},
  publisher = {{Nature Publishing Group}},
  issn = {1476-4687},
  doi = {10.1038/nature03289},
  urldate = {2023-07-21},
  abstract = {The nations of the world have set themselves a target of reducing the rate of biodiversity loss by 2010. Here, we propose a biodiversity intactness index (BII) for assessing progress towards this target that is simple and practical\textemdash but sensitive to important factors that influence biodiversity status\textemdash and which satisfies the criteria for policy relevance set by the Convention on Biological Diversity. Application of the BII is demonstrated on a large region (4 \texttimes{} 106\,km2) of southern Africa. The BII score in the year 2000 is about 84\%: in other words, averaged across all plant and vertebrate species in the region, populations have declined to 84\% of their presumed pre-modern levels. The taxonomic group with the greatest loss is mammals, at 71\% of pre-modern levels, and the ecosystem type with the greatest loss is grassland, with 74\% of its former populations remaining. During the 1990s, a population decline of 0.8\% is estimated to have occurred.},
  copyright = {2005 Macmillan Magazines Ltd.},
  langid = {english},
  keywords = {Humanities and Social Sciences,multidisciplinary,Science},
  file = {/Users/bbest/Zotero/storage/AAAKKQUI/Scholes and Biggs - 2005 - A biodiversity intactness index.pdf}
}

@misc{SdmpredictorsCompilationSpecies2023,
  title = {Sdmpredictors: A Compilation of Species Distribution Modelling Predictors Data},
  shorttitle = {Sdmpredictors},
  year = {2023},
  month = may,
  urldate = {2023-06-22},
  abstract = {A compilation of environmental data for species distribution modelling},
  howpublished = {LifeWatch.be},
  keywords = {bio-oracle,lifewatch,lifewatchvliz,r,rstats,species-distribution-modelling}
}

@misc{SDMtune2023,
  title = {{{SDMtune}}},
  year = {2023},
  month = jun,
  urldate = {2023-06-22},
  abstract = {Performs Variables selection and model tuning for Species Distribution Models (SDMs). It provides also several utilities to display results.},
  howpublished = {Conservation Biology - University of Bern},
  keywords = {hyperparameter-tuning,species-distribution-modelling,variable-selection}
}

@misc{ShinyBIOMODGitLab2021,
  title = {{{ShinyBIOMOD}} {$\cdot$} {{GitLab}}},
  year = {2021},
  month = dec,
  journal = {GitLab},
  urldate = {2023-06-22},
  abstract = {A new R application for Modelling Species Distributions},
  howpublished = {https://gitlab.com/IanOndo/shinybiomod},
  langid = {english},
  file = {/Users/bbest/Zotero/storage/FNWBVDXB/shinybiomod.html}
}

@article{silleroCuratedListPackages2023,
  title = {A Curated List of {{R}} Packages for Ecological Niche Modelling},
  author = {Sillero, Neftal{\'i} and Campos, Jo{\~a}o Carlos and {Arenas-Castro}, Salvador and Barbosa, A. M{\'a}rcia},
  year = {2023},
  month = feb,
  journal = {Ecological Modelling},
  volume = {476},
  pages = {110242},
  issn = {0304-3800},
  doi = {10.1016/j.ecolmodel.2022.110242},
  urldate = {2023-06-22},
  abstract = {The R language provides most applications (packages) currently available for ecological niche modelling. In the last few years, these packages have increased substantially. There are now numerous packages for computing ecological niche models (ENMs) with different algorithms, comparing species niches and niche models, creating virtual species, getting and preparing species and related environmental data, filtering species records, and evaluating models, amongst other tasks. This profusion of package options may be daunting for both beginners and more experienced R users. Here we provide an organised and annotated list of available packages for each task. We present a concise history of the creation of these packages, we briefly describe what each package does, and we discuss whether the current set of R packages covers all the necessities for modelling species' ecological niches. This list will be accessible and updated over time on a dedicated website.},
  langid = {english},
  keywords = {Biodiversity modelling,Biogeography,R language,R packages,Spatial analyses,Species distribution models},
  file = {/Users/bbest/Zotero/storage/STJTRW33/Sillero et al. - 2023 - A curated list of R packages for ecological niche .pdf;/Users/bbest/Zotero/storage/8J58RCMG/S0304380022003404.html}
}

@article{silleroWantModelSpecies2021,
  title = {Want to Model a Species Niche? {{A}} Step-by-Step Guideline on Correlative Ecological Niche Modelling},
  shorttitle = {Want to Model a Species Niche?},
  author = {Sillero, Neftal{\'i} and {Arenas-Castro}, Salvador and Enriquez-Urzelai, Urtzi and Vale, C{\^a}ndida Gomes and {Sousa-Guedes}, Diana and {Mart{\'i}nez-Freir{\'i}a}, Fernando and Real, Raimundo and Barbosa, A. M{\'a}rcia},
  year = {2021},
  month = sep,
  journal = {Ecological Modelling},
  volume = {456},
  pages = {109671},
  issn = {0304-3800},
  doi = {10.1016/j.ecolmodel.2021.109671},
  urldate = {2023-06-22},
  abstract = {The use of correlative ecological niche models has highly increased in the last decade. Despite all literature and textbooks in this field, few practical guidelines exist on the correct application of these techniques. We present here a step-by-step guideline explaining best practices for calculating correlative ecological niche models considering their conceptual and statistical assumptions and limitations. We divided the modelling process into four stages: 1) data collection and preparation; 2) model calculation; 3) model evaluation and validation; 4) and model application. Based on ecological niche theory, we review the concepts of ecological niche and how they can be modelled; classes of correlative models; modelling software; selection of study area; data sources for species records and environmental variables; types of species records and their influence on correlative models; errors in species records; minimum number of species records and environmental variables; effects of prevalence, sampling design, biases, and collinearity between variables; model calculation; model projection to different scenarios in time and space; ensemble modelling; model validation; classification, discrimination and calibration metrics; calculation of null models; analysis of model results; and model thresholding. This guideline is expected to help potential users to obtain better results when using correlative ecological niche models.},
  langid = {english},
  keywords = {Best practices,Model assumptions and limitations,Model performance,Model preparation and parametrization,Model validation,Species distribution models},
  file = {/Users/bbest/Zotero/storage/ECHWBT8J/Sillero et al. - 2021 - Want to model a species niche A step-by-step guid.pdf;/Users/bbest/Zotero/storage/YVFLZPUX/S0304380021002301.html}
}

@article{smithBiologicalImpactsMarine2023,
  title = {Biological {{Impacts}} of {{Marine Heatwaves}}},
  author = {Smith, Kathryn E. and Burrows, Michael T. and Hobday, Alistair J. and King, Nathan G. and Moore, Pippa J. and Sen Gupta, Alex and Thomsen, Mads S. and Wernberg, Thomas and Smale, Dan A.},
  year = {2023},
  journal = {Annual Review of Marine Science},
  volume = {15},
  number = {1},
  pages = {119--145},
  doi = {10.1146/annurev-marine-032122-121437},
  urldate = {2023-06-22},
  abstract = {Climatic extremes are becoming increasingly common against a background trend of global warming. In the oceans, marine heatwaves (MHWs)\textemdash discrete periods of anomalously warm water\textemdash have intensified and become more frequent over the past century, impacting the integrity of marine ecosystems globally. We review and synthesize current understanding of MHW impacts at the individual, population, and community levels. We then examine how these impacts affect broader ecosystem services and discuss the current state of research on biological impacts of MHWs. Finally, we explore current and emergent approaches to predicting the occurrence andimpacts of future events, along with adaptation and management approaches. With further increases in intensity and frequency projected for coming decades, MHWs are emerging as pervasive stressors to marine ecosystems globally. A deeper mechanistic understanding of their biological impacts is needed to better predict and adapt to increased MHW activity in the Anthropocene.},
  pmid = {35977411},
  keywords = {climate change,ecosystem services,environmental change,extreme events,marine ecosystems,ocean warming},
  file = {/Users/bbest/Zotero/storage/RSGF8BD5/Smith et al. - 2023 - Biological Impacts of Marine Heatwaves.pdf}
}

@article{sofaerDevelopmentDeliverySpecies2019,
  title = {Development and {{Delivery}} of {{Species Distribution Models}} to {{Inform Decision-Making}}},
  author = {Sofaer, Helen R and Jarnevich, Catherine S and Pearse, Ian S and Smyth, Regan L and Auer, Stephanie and Cook, Gericke L and Edwards, Jr, Thomas C and Guala, Gerald F and Howard, Timothy G and Morisette, Jeffrey T and Hamilton, Healy},
  year = {2019},
  month = jul,
  journal = {BioScience},
  volume = {69},
  number = {7},
  pages = {544--557},
  issn = {0006-3568},
  doi = {10.1093/biosci/biz045},
  urldate = {2023-06-22},
  abstract = {Information on where species occur is an important component of conservation and management decisions, but knowledge of distributions is often coarse or incomplete. Species distribution models provide a tool for mapping habitat and can produce credible, defensible, and repeatable information with which to inform decisions. However, these models are sensitive to data inputs and methodological choices, making it important to assess the reliability and utility of model predictions. We provide a rubric that model developers can use to communicate a model's attributes and its appropriate uses. We emphasize the importance of tailoring model development and delivery to the species of interest and the intended use and the advantages of iterative modeling and validation. We highlight how species distribution models have been used to design surveys for new populations, inform spatial prioritization decisions for management actions, and support regulatory decision-making and compliance, tying these examples back to our model assessment rubric.},
  file = {/Users/bbest/Zotero/storage/X6CXFVAC/Sofaer et al. - 2019 - Development and Delivery of Species Distribution M.pdf;/Users/bbest/Zotero/storage/DR6VTAFX/5505326.html}
}

@article{sunEcologicalForecastingOperational2022,
  title = {Ecological {{Forecasting}} and {{Operational Information Systems Support Sustainable Ocean Management}}},
  author = {Sun, Chaojiao and Hobday, Alistair J. and Condie, Scott A. and Baird, Mark E. and Eveson, J. Paige and Hartog, Jason R. and Richardson, Anthony J. and Steven, Andrew D. L. and {Wild-Allen}, Karen and Babcock, Russell C. and Yang, Dezhou and Yu, Rencheng and Mongin, Mathieu},
  year = {2022},
  month = dec,
  journal = {Forecasting},
  volume = {4},
  number = {4},
  pages = {1051--1079},
  publisher = {{Multidisciplinary Digital Publishing Institute}},
  issn = {2571-9394},
  doi = {10.3390/forecast4040057},
  urldate = {2023-06-22},
  abstract = {In times of rapid change and rising human pressures on marine systems, information about the future state of the ocean can provide decision-makers with time to avoid adverse impacts and maximise opportunities. An ecological forecast predicts changes in ecosystems and its components due to environmental forcing such as climate variability and change, extreme weather conditions, pollution, or habitat change. Here, we summarise examples from several sectors and a range of locations. We describe the need, approach, forecast performance, delivery system, and end user uptake. This examination shows that near-term ecological forecasts are needed by end users, decisions are being made based on forecasts, and there is an urgent need to develop operational information systems to support sustainable ocean management. An operational information system is critical for connecting to decision makers and providing an enduring approach to forecasting and proactive decision making. These operational systems require significant investment and ongoing maintenance but are key to delivering ecological forecasts for societal benefits. Iterative forecasting practices could provide continuous improvement by incorporating evaluation and feedback to overcome the limitations of the imperfect model and incomplete observations to achieve better forecast outcomes and accuracy.},
  copyright = {http://creativecommons.org/licenses/by/3.0/},
  langid = {english},
  keywords = {aquaculture,climate change,coastal development,conservation,fisheries,prediction},
  file = {/Users/bbest/Zotero/storage/DI8WEYUT/Sun et al. - 2022 - Ecological Forecasting and Operational Information.pdf}
}

@article{taheriImprovementsReportsSpecies2021,
  title = {Improvements in Reports of Species Redistribution under Climate Change Are Required},
  author = {Taheri, Shirin and Naimi, Babak and Rahbek, Carsten and Ara{\'u}jo, Miguel B.},
  year = {2021},
  month = apr,
  journal = {Science Advances},
  volume = {7},
  number = {15},
  pages = {eabe1110},
  publisher = {{American Association for the Advancement of Science}},
  doi = {10.1126/sciadv.abe1110},
  urldate = {2023-06-22},
  abstract = {Studies have documented climate change\textendash induced shifts in species distributions but uncertainties associated with data and methods are typically unexplored. We reviewed 240 reports of climate-related species-range shifts and classified them based on three criteria. We ask whether observed distributional shifts are compared against random expectations, whether multicausal factors are examined on equal footing, and whether studies provide sufficient documentation to enable replication. We found that only \textasciitilde 12.1\% of studies compare distributional shifts across multiple directions, \textasciitilde 1.6\% distinguish observed patterns from random expectations, and \textasciitilde 19.66\% examine multicausal factors. Last, \textasciitilde 75.5\% of studies report sufficient data and results to allow replication. We show that despite gradual improvements over time, there is scope for raising standards in data and methods within reports of climate-change induced shifts in species distribution. Accurate reporting is important because policy responses depend on them. Flawed assessments can fuel criticism and divert scarce resources for biodiversity to competing priorities.},
  file = {/Users/bbest/Zotero/storage/QDSUXUZE/Taheri et al. - 2021 - Improvements in reports of species redistribution .pdf}
}

@misc{tarekBenRaisLasram2021,
  title = {Ben {{Rais Lasram}} (2020) {{SDM Rmarkdown}} Using {{BIOMOD}}},
  author = {Tarek, Hattab},
  year = {2021},
  month = may,
  urldate = {2023-06-22},
  abstract = {An open-source framework to project potential future distributions of marine species at local scale}
}

@article{tekwaDetectingAttributingCauses2023,
  title = {Detecting and Attributing the Causes of Biodiversity Change: Needs, Gaps and Solutions},
  shorttitle = {Detecting and Attributing the Causes of Biodiversity Change},
  author = {Tekwa, Eden and Gonzalez, Andrew and Zurell, Damaris and O'Connor, Mary},
  year = {2023},
  month = may,
  journal = {Philosophical Transactions of the Royal Society B: Biological Sciences},
  volume = {378},
  number = {1881},
  pages = {20220181},
  publisher = {{Royal Society}},
  doi = {10.1098/rstb.2022.0181},
  urldate = {2023-06-28},
  abstract = {This issue addresses the multifaceted problems of understanding biodiversity change to meet emerging international development and conservation goals, national economic accounting and diverse community needs. Recent international agreements highlight the need to establish monitoring and assessment programmes at national and regional levels. We identify an opportunity for the research community to develop the methods for robust detection and attribution of biodiversity change that will contribute to national assessments and guide conservation action. The 16 contributions of this issue address six major aspects of biodiversity assessment: connecting policy to science, establishing observation, improving statistical estimation, detecting change, attributing causes and projecting the future. These studies are led by experts in Indigenous studies, economics, ecology, conservation, statistics, and computer science, with representations from Asia, Africa, South America, North America and Europe. The results place biodiversity science in the context of policy needs and provide an updated roadmap for how to observe biodiversity change in a way that supports conservation action via robust detection and attribution science. This article is part of the theme issue `Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'},
  keywords = {assessment,attribution,biodiversity,detection,global biodiversity framework},
  file = {/Users/bbest/Zotero/storage/WNXJ3HPQ/Tekwa et al. - 2023 - Detecting and attributing the causes of biodiversi.pdf}
}

@article{urbanCodingLifeDesigning2022,
  title = {Coding for {{Life}}: {{Designing}} a {{Platform}} for {{Projecting}} and {{Protecting Global Biodiversity}}},
  shorttitle = {Coding for {{Life}}},
  author = {Urban, Mark C and Travis, Justin M J and Zurell, Damaris and Thompson, Patrick L and Synes, Nicholas W and Scarpa, Alice and {Peres-Neto}, Pedro R and Malchow, Anne-Kathleen and James, Patrick M A and Gravel, Dominique and De Meester, Luc and Brown, Calum and Bocedi, Greta and Albert, C{\'e}cile H and Gonzalez, Andrew and Hendry, Andrew P},
  year = {2022},
  month = jan,
  journal = {BioScience},
  volume = {72},
  number = {1},
  pages = {91--104},
  issn = {0006-3568},
  doi = {10.1093/biosci/biab099},
  urldate = {2023-05-10},
  abstract = {Time is running out to limit further devastating losses of biodiversity and nature's contributions to humans. Addressing this crisis requires accurate predictions about which species and ecosystems are most at risk to ensure efficient use of limited conservation and management resources. We review existing biodiversity projection models and discover problematic gaps. Current models usually cannot easily be reconfigured for other species or systems, omit key biological processes, and cannot accommodate feedbacks with Earth system dynamics. To fill these gaps, we envision an adaptable, accessible, and universal biodiversity modeling platform that can project essential biodiversity variables, explore the implications of divergent socioeconomic scenarios, and compare conservation and management strategies. We design a roadmap for implementing this vision and demonstrate that building this biodiversity forecasting platform is possible and practical.},
  file = {/Users/bbest/Zotero/storage/M87YQ6CP/Urban et al. - 2022 - Coding for Life Designing a Platform for Projecti.pdf}
}

@article{vaneupenImpactDataQuality2021,
  title = {The Impact of Data Quality Filtering of Opportunistic Citizen Science Data on Species Distribution Model Performance},
  author = {Van Eupen, Camille and Maes, Dirk and Herremans, Marc and Swinnen, Kristijn R. R. and Somers, Ben and Luca, Stijn},
  year = {2021},
  month = mar,
  journal = {Ecological Modelling},
  volume = {444},
  pages = {109453},
  issn = {0304-3800},
  doi = {10.1016/j.ecolmodel.2021.109453},
  urldate = {2023-06-22},
  abstract = {Opportunistically collected species occurrence data are often used for species distribution models (SDMs) when high-quality data collected through standardized recording protocols are unavailable. While opportunistic data are abundant, uncertainty is usually high, e.g. due to observer effects or a lack of metadata. To increase data quality and improve model performance, we filtered species records based on record attributes that provide information on the observation process or post-entry data validation. Data filtering does not only increase the quality of species records, it simultaneously reduces sample size, a trade-off that remains relatively unexplored. By controlling for sample size in a dataset of 255 species, we were able to explore the combined impact of data quality and sample size on model performance. We applied three data quality filters based on observers' activity, the validation status of a record in the database and the detail of a submitted record, and analyzed changes in AUC, Sensitivity and Specificity using Maxent with and without filtering. The impact of stringent filtering on model performance depended on (1) the quality of the filtered data: records validated as correct and more detailed records lead to higher model performance, (2) the proportional reduction in sample size caused by filtering and the remaining absolute sample size: filters causing small reductions that lead to sample sizes of more than 100 presences generally benefitted model performance and (3) the taxonomic group: plant and dragonfly models benefitted more from data quality filtering compared to bird and butterfly models. Our results also indicate that recommendations for quality filtering depend on the goal of the study, e.g. increasing Sensitivity and/or Specificity. Further research must identify what drives species' sensitivity to data quality. Nonetheless, our study confirms that large quantities of volunteer generated and opportunistically collected data can make a valuable contribution to ecological research and species conservation.},
  langid = {english},
  keywords = {Data quality filtering,Maxent,Opportunistic data,Presence-only,Sample size,Species distribution models},
  file = {/Users/bbest/Zotero/storage/5SS5A6L2/Van Eupen et al. - 2021 - The impact of data quality filtering of opportunis.pdf;/Users/bbest/Zotero/storage/RCTK9HSD/S0304380021000260.html}
}

@article{vignaliSDMtunePackageTune2020,
  title = {{{SDMtune}}: {{An R}} Package to Tune and Evaluate Species Distribution Models},
  shorttitle = {{{SDMtune}}},
  author = {Vignali, Sergio and Barras, Arnaud G. and Arlettaz, Rapha{\"e}l and Braunisch, Veronika},
  year = {2020},
  journal = {Ecology and Evolution},
  volume = {10},
  number = {20},
  pages = {11488--11506},
  issn = {2045-7758},
  doi = {10.1002/ece3.6786},
  urldate = {2023-06-22},
  abstract = {Balancing model complexity is a key challenge of modern computational ecology, particularly so since the spread of machine learning algorithms. Species distribution models are often implemented using a wide variety of machine learning algorithms that can be fine-tuned to achieve the best model prediction while avoiding overfitting. We have released SDMtune, a new R package that aims to facilitate training, tuning, and evaluation of species distribution models in a unified framework. The main innovations of this package are its functions to perform data-driven variable selection, and a novel genetic algorithm to tune model hyperparameters. Real-time and interactive charts are displayed during the execution of several functions to help users understand the effect of removing a variable or varying model hyperparameters on model performance. SDMtune supports three different metrics to evaluate model performance: the area under the receiver operating characteristic curve, the true skill statistic, and Akaike's information criterion corrected for small sample sizes. It implements four statistical methods: artificial neural networks, boosted regression trees, maximum entropy modeling, and random forest. Moreover, it includes functions to display the outputs and create a final report. SDMtune therefore represents a new, unified and user-friendly framework for the still-growing field of species distribution modeling.},
  copyright = {\textcopyright{} 2020 The Authors. Ecology and Evolution published by John Wiley \& Sons Ltd},
  langid = {english},
  keywords = {ecological niche model,fine-tuning,genetic algorithm,machine learning,model complexity,variable selection},
  file = {/Users/bbest/Zotero/storage/X2WTNZMS/Vignali et al. - 2020 - SDMtune An R package to tune and evaluate species.pdf;/Users/bbest/Zotero/storage/Y93RDJ7K/ece3.html}
}

@misc{WallaceV22023,
  title = {Wallace (v2.0.5)},
  year = {2023},
  month = mar,
  urldate = {2023-06-22},
  abstract = {an interactive, reproducible, expandible, instructional, and open-source GUI-based app for ecological niche modeling},
  howpublished = {Wallace Ecological Modeling App}
}

@article{watlingPerformanceMetricsVariance2015,
  title = {Performance Metrics and Variance Partitioning Reveal Sources of Uncertainty in Species Distribution Models},
  author = {Watling, James I. and Brandt, Laura A. and Bucklin, David N. and Fujisaki, Ikuko and Mazzotti, Frank J. and Roma{\~n}ach, Stephanie S. and Speroterra, Carolina},
  year = {2015},
  month = aug,
  journal = {Ecological Modelling},
  volume = {309--310},
  pages = {48--59},
  issn = {0304-3800},
  doi = {10.1016/j.ecolmodel.2015.03.017},
  urldate = {2023-06-22},
  abstract = {Species distribution models (SDMs) are widely used in basic and applied ecology, making it important to understand sources and magnitudes of uncertainty in SDM performance and predictions. We analyzed SDM performance and partitioned variance among prediction maps for 15 rare vertebrate species in the southeastern USA using all possible combinations of seven potential sources of uncertainty in SDMs: algorithms, climate datasets, model domain, species presences, variable collinearity, CO2 emissions scenarios, and general circulation models. The choice of modeling algorithm was the greatest source of uncertainty in SDM performance and prediction maps, with some additional variation in performance associated with the comprehensiveness of the species presences used for modeling. Other sources of uncertainty that have received attention in the SDM literature such as variable collinearity and model domain contributed little to differences in SDM performance or predictions in this study. Predictions from different algorithms tended to be more variable at northern range margins for species with more northern distributions, which may complicate conservation planning at the leading edge of species' geographic ranges. The clear message emerging from this work is that researchers should use multiple algorithms for modeling rather than relying on predictions from a single algorithm, invest resources in compiling a comprehensive set of species presences, and explicitly evaluate uncertainty in SDM predictions at leading range margins.},
  langid = {english},
  keywords = {Climate envelope,Niche model,Sensitivity analysis,Spatial analysis},
  file = {/Users/bbest/Zotero/storage/9VR49JHR/Watling et al. - 2015 - Performance metrics and variance partitioning reve.pdf;/Users/bbest/Zotero/storage/G7FN2K9K/S0304380015001283.html}
}

@misc{ZoonTutorials,
  title = {Zoon Tutorials},
  urldate = {2023-06-22},
  howpublished = {https://zoonproject.github.io/zoontutorials/articles/index.html},
  file = {/Users/bbest/Zotero/storage/G2Z4HBJV/index.html}
}

@article{zurellStandardProtocolReporting2020,
  title = {A Standard Protocol for Reporting Species Distribution Models},
  author = {Zurell, Damaris and Franklin, Janet and K{\"o}nig, Christian and Bouchet, Phil J. and Dormann, Carsten F. and Elith, Jane and Fandos, Guillermo and Feng, Xiao and {Guillera-Arroita}, Gurutzeta and Guisan, Antoine and {Lahoz-Monfort}, Jos{\'e} J. and Leit{\~a}o, Pedro J. and Park, Daniel S. and Peterson, A. Townsend and Rapacciuolo, Giovanni and Schmatz, Dirk R. and Schr{\"o}der, Boris and {Serra-Diaz}, Josep M. and Thuiller, Wilfried and Yates, Katherine L. and Zimmermann, Niklaus E. and Merow, Cory},
  year = {2020},
  journal = {Ecography},
  volume = {43},
  number = {9},
  pages = {1261--1277},
  issn = {1600-0587},
  doi = {10.1111/ecog.04960},
  urldate = {2023-06-22},
  abstract = {Species distribution models (SDMs) constitute the most common class of models across ecology, evolution and conservation. The advent of ready-to-use software packages and increasing availability of digital geoinformation have considerably assisted the application of SDMs in the past decade, greatly enabling their broader use for informing conservation and management, and for quantifying impacts from global change. However, models must be fit for purpose, with all important aspects of their development and applications properly considered. Despite the widespread use of SDMs, standardisation and documentation of modelling protocols remain limited, which makes it hard to assess whether development steps are appropriate for end use. To address these issues, we propose a standard protocol for reporting SDMs, with an emphasis on describing how a study's objective is achieved through a series of modeling decisions. We call this the ODMAP (Overview, Data, Model, Assessment and Prediction) protocol, as its components reflect the main steps involved in building SDMs and other empirically-based biodiversity models. The ODMAP protocol serves two main purposes. First, it provides a checklist for authors, detailing key steps for model building and analyses, and thus represents a quick guide and generic workflow for modern SDMs. Second, it introduces a structured format for documenting and communicating the models, ensuring transparency and reproducibility, facilitating peer review and expert evaluation of model quality, as well as meta-analyses. We detail all elements of ODMAP, and explain how it can be used for different model objectives and applications, and how it complements efforts to store associated metadata and define modelling standards. We illustrate its utility by revisiting nine previously published case studies, and provide an interactive web-based application to facilitate its use. We plan to advance ODMAP by encouraging its further refinement and adoption by the scientific community.},
  copyright = {\textcopyright{} 2020 The Authors. Ecography published by John Wiley \& Sons Ltd on behalf of Nordic Society Oikos},
  langid = {english},
  keywords = {biodiversity assessment,ecological niche model,habitat suitability model,reproducibility,Shiny,transparency},
  file = {/Users/bbest/Zotero/storage/4FE4R2L7/Zurell et al. - 2020 - A standard protocol for reporting species distribu.pdf;/Users/bbest/Zotero/storage/C93GYNC5/ecog.html}
}

@article{zurellStandardProtocolReporting2020a,
  title = {A Standard Protocol for Reporting Species Distribution Models},
  author = {Zurell, Damaris and Franklin, Janet and K{\"o}nig, Christian and Bouchet, Phil J. and Dormann, Carsten F. and Elith, Jane and Fandos, Guillermo and Feng, Xiao and Guillera-Arroita, Gurutzeta and Guisan, Antoine and Lahoz-Monfort, Jos{\'e} J. and Leit{\~a}o, Pedro J. and Park, Daniel S. and Peterson, A. Townsend and Rapacciuolo, Giovanni and Schmatz, Dirk R. and Schr{\"o}der, Boris and Serra-Diaz, Josep M. and Thuiller, Wilfried and Yates, Katherine L. and Zimmermann, Niklaus E. and Merow, Cory},
  year = {2020},
  month = sep,
  journal = {Ecography},
  volume = {43},
  number = {9},
  pages = {1261--1277},
  issn = {0906-7590, 1600-0587},
  doi = {10.1111/ecog.04960},
  urldate = {2023-06-22},
  langid = {english},
  file = {/Users/bbest/Zotero/storage/DN73TDPW/Zurell et al. - 2020 - A standard protocol for reporting species distribu.pdf}
}