zoon_app.bib

@article{Barbosa2015,
  abstract = {In this study, we analyzed the characteristics of the most cited papers regarding species distribution predictive models (SDPMs). We found 173 papers on SDPMs that received at least 100 citations until 2013, according to the Thomson Reuters Web of Science database. These papers were published between 1991 and 2012, with the majority published between 2002 and 2012, indicating the rapid development of this field of research. The papers were published mainly in journals listed in the ecology category on the Web of Science. Almost half of the top-cited papers were methodological, introducing novel modeling methods and software. Applied papers on species conservation and biodiversity management, climate change, phylogeography, and biosecurity also figured out among the top-cited papers. Researchers from 174 institutions in 27 countries, with 51{\%} of the papers being internationally collaborative and 69{\%} inter-institutionally collaborative, published the papers. Among all 173 papers, seven papers stood out as having a great impact on the field, receiving more than 1000 citations each. Finally, the results found by analyzing the top-cited SDPMs papers support the view of a growing interest and rapid development of this research field over the past two decades. The top-cited papers primarily focused on the development and evaluation of novel methods to improve the performance of the models, and thus, to better predict the environmental suitability for species in applied studies.},
  author = {Barbosa, Fabiana G. and Schneck, Fabiana},
  doi = {10.1016/j.ecolmodel.2015.06.014},
  file = {:Users/nick/Dropbox/papers/characteristics of SDM papers.pdf:pdf},
  isbn = {0304-3800},
  issn = {03043800},
  journal = {Ecological Modelling},
  keywords = {Citation analysis,Highly-cited papers,Scientific productivity,Species distribution model},
  pages = {77--83},
  publisher = {Elsevier B.V.},
  title = {{Characteristics of the top-cited papers in species distribution predictive models}},
  volume = {313},
  year = {2015}
}
@article{Elith2006,
abstract = {Prediction of species’ distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species’ distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species’ occurrence data. Presence-only data were effective for modelling species’ distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve. J.},
author = {Elith, J. and Graham, C. H. and Anderson, R. P. and Dudik, M. and Ferrier, S. and Guisan, a. and Hijmans, R. J. and Huettmann, F. and Leathwick, J. R. and Lehmann, a. and Li, J. and Lohmann, L. G. and Loiselle, B. a. and Manion, G. and Moritz, C. and Nakamura, M. and Nakazawa, Y. and McC., Overton J. and Peterson, a. T. and Phillips, S. J. and Richardson, K. S. and Scachetti-Pereira, R. and Schapire, R. E. and Soberon, J. and Williams, S. and Wisz, M. S. and Zimmermann, N. E.},
doi = {10.1111/j.2006.0906-7590.04596.x},
file = {:Users/nick/Dropbox/papers/Ecography{\_}29{\_}129{\_}Elith.pdf:pdf},
isbn = {0906-7590},
issn = {0906-7590},
journal = {Ecography},
number = {January},
pages = {129--151},
pmid = {1891},
title = {{Novel methods improve prediction of species' distributions from occurrence data}},
volume = {29},
year = {2006}
}
@article{Araujo2009,
author = {Ara{\'{u}}jo, Miguel Bastos and Thuiller, Wilfried and Yoccoz, Nigel G.},
doi = {10.1073/pnas.0813294106},
file = {:Users/nick/Dropbox/papers/PNAS-2009-Ara{\'{u}}jo-E45-6.pdf:pdf},
isbn = {1091-6490 (Electronic)$\backslash$r0027-8424 (Linking)},
issn = {1091-6490},
journal = {Proceedings of the National Academy of Sciences},
number = {16},
pages = {E45--E46},
pmid = {19369203},
title = {{Reopening the climate envelope reveals macroscale associations with climate in European birds}},
volume = {106},
year = {2009}
}
@article{Beale2009,
author = {Beale, C M and Lennon, J J and Gimona, A},
doi = {10.1073/pnas.0902229106},
file = {:Users/nick/Dropbox/papers/PNAS-2009-Beale-E41-3.pdf:pdf},
isbn = {0027-8424},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences},
number = {16},
pages = {E41--E43},
title = {{European bird distributions still show few climate associations}},
volume = {106},
year = {2009}
}
@article{Beale2008,
abstract = {Predicting how species distributions might shift as global climate changes is fundamental to the successful adaptation of conservation policy. An increasing number of studies have responded to this challenge by using climate envelopes, modeling the association between climate variables and species distributions. However, it is difficult to quantify how well species actually match climate. Here, we use null models to show that species-climate associations found by climate envelope methods are no better than chance for 68 of 100 European bird species. In line with predictions, we demonstrate that the species with distribution limits determined by climate have more northerly ranges. We conclude that scientific studies and climate change adaptation policies based on the indiscriminate use of climate envelope methods irrespective of species sensitivity to climate may be misleading and in need of revision.},
archivePrefix = {arXiv},
arxivId = {arXiv:1408.1149},
author = {Beale, Colin M and Lennon, Jack J and Gimona, Alessandro},
doi = {10.1073/pnas.0803506105},
eprint = {arXiv:1408.1149},
file = {:Users/nick/Dropbox/papers/PNAS-2008-Beale-14908-12.pdf:pdf},
isbn = {289},
issn = {1091-6490},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {Acclimatization,Animals,Biological,Birds,Birds: classification,Birds: physiology,Conservation of Natural Resources,Europe,Extinction,Greenhouse Effect},
number = {39},
pages = {14908--14912},
pmid = {18815364},
title = {{Opening the climate envelope reveals no macroscale associations with climate in European birds.}},
volume = {105},
year = {2008}
}
@article{Soininen2014,
abstract = {ABSTRACT Aim We explored the effects ofmultiple species traits, spatial extent and ecosystem type on predictability in species distributions. Location Global. Methods We assembled over 4900 published AUC (area under the curve of a receiver operating characteristic plot) values from the species distribution model- ling literature. Our data covered broad variation in species characteristics such as body size or trophic position for taxa ranging from bacteria to mammals. Data covered ecosystems fromfreshwater to forests and encompassed geographical areas from the tropics to polar regions. We used generalized linear mixed models and boosted regression trees to analyse the AUC data. Results We found that most AUC values originated from large-sized terrestrial taxa while studies considering smaller taxa, especially from aquatic ecosystems, were rare.Predictability was highest in autotrophs and in active non-flying taxa and increased with body size and study extent. There were marginal differences in predictability between ectotherms and endotherms and between taxa originating from different ecosystems. Main conclusions Our results suggest that predictability in species distributions is related to organismal variables such as body size, dispersal mode and trophic position as well as to study extent.We also identified a gap in species distribution modelling studies for aquatic species and for small taxa},
author = {Soininen, Janne and Luoto, Miska},
doi = {10.1111/geb.12204},
file = {:Users/nick/Dropbox/papers/geb12204.pdf:pdf},
isbn = {1466-8238},
issn = {14668238},
journal = {Global Ecology and Biogeography},
keywords = {Body size,Dispersal mode,Distribution models,Mixed model,Species traits,Trophic position},
number = {11},
pages = {1264--1274},
title = {{Predictability in species distributions: A global analysis across organisms and ecosystems}},
volume = {23},
year = {2014}
}
@article{Naimi2016,
author = {Naimi, Babak and Ara{\'{u}}jo, Miguel B},
doi = {10.1111/ecog.01881},
file = {:Users/nick/Dropbox/papers/ecog1881.pdf:pdf},
isbn = {6503251521},
issn = {09067590},
journal = {Ecography},
number = {January},
pages = {1--8},
title = {{sdm: a reproducible and extensible R platform for species distribution modelling}},
volume = {39},
year = {2016}
}

@article{Yackulic2013,
abstract = {* Recently, interest in species distribution modelling has increased following the development of new methods for the analysis of presence-only data and the deployment of these methods in user-friendly and powerful computer programs. However, reliable inference from these powerful tools requires that several assumptions be met, including the assumptions that observed presences are the consequence of random or representative sampling and that detectability during sampling does not vary with the covariates that determine occurrence probability. * Based on our interactions with researchers using these tools, we hypothesized that many presence-only studies were ignoring important assumptions of presence-only modelling. We tested this hypothesis by reviewing 108 articles published between 2008 and 2012 that used the MAXENT algorithm to analyse empirical (i.e. not simulated) data. We chose to focus on these articles because MAXENT has been the most popular algorithm in recent years for analysing presence-only data. * Many articles (87{\%}) were based on data that were likely to suffer from sample selection bias; however, methods to control for sample selection bias were rarely used. In addition, many analyses (36{\%}) discarded absence information by analysing presence–absence data in a presence-only framework, and few articles (14{\%}) mentioned detection probability. We conclude that there are many misconceptions concerning the use of presence-only models, including the misunderstanding that MAXENT, and other presence-only methods, relieve users from the constraints of survey design. * In the process of our literature review, we became aware of other factors that raised concerns about the validity of study conclusions. In particular, we observed that 83{\%} of articles studies focused exclusively on model output (i.e. maps) without providing readers with any means to critically examine modelled relationships and that MAXENT's logistic output was frequently (54{\%} of articles) and incorrectly interpreted as occurrence probability. * We conclude with a series of recommendations foremost that researchers analyse data in a presence–absence framework whenever possible, because fewer assumptions are required and inferences can be made about clearly defined parameters such as occurrence probability.},
author = {Yackulic, Charles B. and Chandler, Richard and Zipkin, Elise F. and Royle, J. Andrew and Nichols, James D. and {Campbell Grant}, Evan H. and Veran, Sophie},
doi = {10.1111/2041-210x.12004},
file = {:Users/nick/Dropbox/papers/mee312004.pdf:pdf},
isbn = {2041-210X},
issn = {2041210X},
journal = {Methods in Ecology and Evolution},
keywords = {AUC,Detection,Occurrence,Prevalence,Sample selection bias},
number = {3},
pages = {236--243},
title = {{Presence-only modelling using MAXENT: When can we trust the inferences?}},
volume = {4},
year = {2013}
}
@article{maxlike,
abstract = {1.Understanding the factors affecting species occurrence is a pre-eminent focus of applied ecological research. However, direct information about species occurrence is lacking for many species. Instead, researchers sometimes have to rely on so-called presence-only data (i.e. when no direct information about absences is available), which often results from opportunistic, unstructured sampling. maxent is a widely used software program designed to model and map species distribution using presence-only data. 2.We provide a critical review of maxent as applied to species distribution modelling and discuss how it can lead to inferential errors. A chief concern is that maxent produces a number of poorly defined indices that are not directly related to the actual parameter of interest – the probability of occurrence ($\psi$). This focus on an index was motivated by the belief that it is not possible to estimate $\psi$ from presence-only data; however, we demonstrate that $\psi$ is identifiable using conventional likelihood methods under the assumptions of random sampling and constant probability of species detection. 3.The model is implemented in a convenient r package which we use to apply the model to simulated data and data from the North American Breeding Bird Survey. We demonstrate that maxent produces extreme under-predictions when compared to estimates produced by logistic regression which uses the full (presence/absence) data set. We note that maxent predictions are extremely sensitive to specification of the background prevalence, which is not objectively estimated using the maxent method. 4.As with maxent, formal model-based inference requires a random sample of presence locations. Many presence-only data sets, such as those based on museum records and herbarium collections, may not satisfy this assumption. However, when sampling is random, we believe that inference should be based on formal methods that facilitate inference about interpretable ecological quantities instead of vaguely defined indices.},
author = {Royle, J. Andrew and Chandler, Richard B. and Yackulic, Charles and Nichols, James D.},
doi = {10.1111/j.2041-210X.2011.00182.x},
file = {:Users/nick/Dropbox/papers/j.2041-210X.2011.00182.x.pdf:pdf},
isbn = {2041-210X},
issn = {2041210X},
journal = {Methods in Ecology and Evolution},
keywords = {Bayes' rule,Detection probability,Logistic regression,Occupancy model,Occurrence probability,Presence-only data,Species distribution model,maxent},
number = {3},
pages = {545--554},
title = {{Likelihood analysis of species occurrence probability from presence-only data for modelling species distributions}},
volume = {3},
year = {2012}
}
@article{Fitzpatrick2013,
abstract = {MaxEnt is one of the most widely used tools in ecology, biogeography, and evolution for modeling and mapping species distributions using presence-only occurrence records and associated environmental covariates. Despite its popularity, the exponential model implemented by MaxEnt does not directly estimate occurrence probability, the natural quantity of interest when modeling species distributions. Instead, MaxEnt generates an index of relative habitat suitability. MaxLike, a newly introduced maximum-likelihood technique, has been shown to overcome the problem of directly estimating the probability of occurrence using presence-only data. However, the performance and relative merits of MaxEnt and MaxLike remain largely untested, especially when modeling species with relatively few occurrence data that encompass only a portion of the geographic range of the species. Using geo-referenced occurrence records for six species of ants in New England, we provide comparisons of MaxEnt and MaxLike. We show that by mos...},
author = {Fitzpatrick, Matthew C. and Gotelli, Nicholas J. and Ellison, Aaron M.},
doi = {10.1890/ES13-00066.1},
file = {:Users/nick/Dropbox/papers/es13-00066{\%}2E1.pdf:pdf},
isbn = {2150-8925},
issn = {2150-8925},
journal = {Ecosphere},
keywords = {New England,ecological niche modeling,myrmecology,occurrence probability,presence-only data,species distribution modeling},
number = {5},
pages = {Article 55},
title = {{MaxEnt versus MaxLike: empirical comparisons with ant species distributions}},
volume = {4},
year = {2013}
}
@article{Merow2014,
abstract = {1. Understanding species spatial occurrence patterns and their environmental dependence is one of the fundamental goals in ecology and evolution. Often, occurrence models are built with presence-only data because absence data are unavailable. We compare the strengths and limitations of the recently developed presence-only modelling method, Maxlike, with the more widely used Maxent. 2. In spite of disparities highlighted by the developers of Maxlike and Maxent, we show approximate formal relationships between the parameters of Maxlike and Maxent for two scenarios to illustrate their similarity. Using case studies based on real and simulated data, we show how these similarities manifest in practice. 3. We found more similarities than differences between Maxlike and Maxent, including coefficient values, predicted spatial distributions, similarity to presence–absence models, predictive performance and ranking the pre- dicted suitability of cells. Maxlike reliably predicted absolute occurrence probabilities for very large data sets on landscapes where occurrence probability approximately spanned [0,1]. For smaller data sets, the uncertainty in predicted occurrence probability by Maxlike was very large, due to the inherent limitations of presence-only data. In contrast, Maxent is constrained to predicting relative occurrence probabilities or relative occurrence rates unless it is provided with additional information from presence–absence data. Both models can reliably pre- dict relative differences in occurrence probability. 4. The choice of which model to use depends partly on sampling assumptions, which we discuss in detail. Due to limitations of presence-only data, ecologists should typically focus on interpretations relying on relative differences in occurrence probability or relative occurrence rates. We discuss how to remedy a number of concerns about the use of Maxent and how to avoid some potential pitfalls with Maxlike – particularly related to high variance predictions. We conclude that both methods are similarly valuable for understanding and predicting species’ distributions in terms of relative differences in occurrence probability when the models are specified carefully.},
author = {Merow, Cory and Silander, John a.},
doi = {10.1111/2041-210X.12152},
file = {:Users/nick/Dropbox/papers/mee312152.pdf:pdf},
isbn = {2041-210X},
issn = {2041210X},
journal = {Methods in Ecology and Evolution},
keywords = {occurrence model,presence-only data,probability of presence,resource selection function,species distribution modelling},
number = {3},
pages = {215--225},
title = {{A comparison of Maxlike and Maxent for modelling species distributions}},
volume = {5},
year = {2014}
}
@article{Hastie2013,
abstract = {Presence-only data abounds in ecology, often accompanied by a background sample. Although many interesting aspects of the species’ distribution can be learned from such data, one cannot learn the overall species occurrence probability, or prevalence, without making unjustified simplifying assumptions. In this forum article we question the approach of Royle et al. (2012) that claims to be able to do this},
archivePrefix = {arXiv},
arxivId = {NIHMS150003},
author = {Hastie, Trevor and Fithian, Will},
doi = {10.1111/j.1600-0587.2013.00321.x},
eprint = {NIHMS150003},
file = {:Users/nick/Dropbox/papers/j.1600-0587.2013.00321.x.pdf:pdf},
isbn = {0906-7590},
issn = {09067590},
journal = {Ecography},
number = {8},
pages = {864--867},
pmid = {1000000221},
title = {{Inference from presence-only data; the ongoing controversy}},
volume = {36},
year = {2013}
}
@article{Phillips2013,
abstract = {PMID: 23923504},
author = {Phillips, S J and Elith, J},
doi = {10.1890/12-1520.1},
file = {:Users/nick/Dropbox/papers/ecy20139461409.pdf:pdf},
isbn = {0012-9658},
issn = {0012-9658},
journal = {Ecology},
keywords = {Folder - My Collection,Prevalence,availability,background,identifiability,logistic,measuring use vs,non-use,presence,resource selection,species distribution model},
number = {6},
pages = {1409--1419},
pmid = {23923504},
title = {{On estimating probability of presence from use-availability or presence-background data}},
volume = {94},
year = {2013}
}
@Manual{spocc,
  title = {spocc: Interface to Species Occurrence Data Sources},
  author = {Scott Chamberlain and Karthik Ram and Ted Hart},
  year = {2016},
  note = {R package version 0.4.5},
}
@Manual{raster,
  title = {raster: Geographic Data Analysis and Modeling},
  author = {Robert J. Hijmans},
  year = {2015},
  note = {R package version 2.5-2},
}
@misc{Feng2015,
abstract = {The distribution of the nine banded arma- dillo (Dasypus novemcinctus), the only spe- cies in the family Dasypodidae found in the USA, has expanded greatly since the species was first recorded in southern Texas in 1849. Currently, the range of D. novemcinctus includes 15 states in the USA. Previous studies on the geographical expansion of this species, based on physio- logical experiments and distribution sur- veys, revealed a possible western moisture limit, a northern temperature limit, and potential north-eastward range expansion in the USA. We applied an ecological niche modelling approach and produced a potential distribution map of D. novem- cinctus with comparable western (102 °W) and northern (40 °N) limits, and con- firmed the possibility of further north-east range expansion to climatically suitable areas in the USA.},
author = {Feng, Xiao and Papeş, Monica},
booktitle = {Journal of Biogeography},
doi = {10.1111/jbi.12427},
file = {:Users/nick/Dropbox/papers/jbi12427.pdf:pdf},
isbn = {1365-2699},
issn = {13652699},
keywords = {Climatically suitable area,Invasive species,Maxent,Potential distribution,Precipitation limit,Temperature limit,USA},
number = {4},
pages = {803--807},
title = {{Ecological niche modelling confirms potential north-east range expansion of the nine-banded armadillo (Dasypus novemcinctus) in the USA}},
volume = {42},
year = {2015}
}


@article{Elith2010,
  title={The art of modelling range-shifting species},
  author={Elith, Jane and Kearney, Michael and Phillips, Steven},
  journal={Methods in ecology and evolution},
  volume={1},
  doi={10.1111/j.2041-210X.2010.00036.x},
  number={4},
  pages={330--342},
  year={2010},
  publisher={Wiley Online Library}
}

@article{Hijmans2005,
  title={Very high resolution interpolated climate surfaces for global land areas},
  author={Hijmans, Robert J and Cameron, Susan E and Parra, Juan L and Jones, Peter G and Jarvis, Andy},
  journal={International journal of climatology},
  volume={25},
  doi={10.1002/joc.1276},
  number={15},
  pages={1965--1978},
  year={2005},
  publisher={Wiley Online Library}
}
@Manual{spThin,
    title = {spThin: Functions for Spatial Thinning of Species Occurrence Records for
Use in Ecological Models},
    author = {Matthew E. Aiello-Lammens and Robert A. Boria and Aleksandar Radosavljevic and Bruno Vilela and Robert P. Anderson},
    year = {2014},
    note = {R package version 0.1.0},
    url = {https://CRAN.R-project.org/package=spThin},
}
@article{Aiello-Lammens2015,
abstract = {Spatial thinning of species occurrence records can help address problems associated with spatial sampling biases. Ideally, thinning removes the fewest records necessary to substantially reduce the effects of sampling bias, while simultaneously retaining the greatest amount of useful information. Spatial thinning can be done manually; however, this is prohibi- tively time consuming for large datasets. Using a randomization approach, the ‘thin' function in the spThin R package returns a dataset with the maximum number of records for a given thinning distance, when run for sufficient iterations. We here provide a worked example for the Caribbean spiny pocket mouse, where the results obtained match those of manual thinning.},
author = {Aiello-Lammens, Matthew E. and Boria, Robert A. and Radosavljevic, Aleksandar and Vilela, Bruno and Anderson, Robert P.},
doi = {10.1111/ecog.01132},
file = {:Users/nick/Dropbox/papers/ecog1132.pdf:pdf},
isbn = {1600-0587},
issn = {16000587},
journal = {Ecography},
number = {5},
pages = {541--545},
title = {{spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models}},
volume = {38},
year = {2015}
}
@article{Carpenter1993,
  title={DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals},
  author={Carpenter, G and Gillison, AN and Winter, J},
  journal={Biodiversity \& Conservation},
  volume={2},
  number={6},
  pages={667--680},
  year={1993},
  publisher={Springer}
}
@article{Brown2014,
  title={SDMtoolbox: a python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses},
  author={Brown, Jason L},
  journal={Methods in Ecology and Evolution},
  volume={5},
  number={7},
  pages={694--700},
  year={2014},
  publisher={Wiley Online Library}
}
@article{Cooper2016,
  title={The cardiac electrophysiology web lab},
  author={Cooper, Jonathan and Scharm, Martin and Mirams, Gary R},
  journal={Biophysical journal},
  volume={110},
  number={2},
  pages={292--300},
  year={2016},
  publisher={Elsevier}
}
@article{Corpas2014,
  title={BioJS: an open source standard for biological visualisation--its status in 2014},
  author={Corpas, Manuel and Jimenez, Rafael and Carbon, Seth J and Garc{\'\i}a, Alex and Garcia, Leyla and Goldberg, Tatyana and Gomez, John and Kalderimis, Alexis and Lewis, Suzanna E and Mulvany, Ian and others},
  journal={F1000Research},
  volume={3},
  year={2014}
}
@article{deSouza2011,
  title={openModeller: a generic approach to species’ potential distribution modelling},
  author={de Souza Mu{\~n}oz, Mauro Enrique and De Giovanni, Renato and de Siqueira, Marinez Ferreira and Sutton, Tim and Brewer, Peter and Pereira, Ricardo Scachetti and Canhos, Dora Ann Lange and Canhos, Vanderlei Perez},
  journal={GeoInformatica},
  volume={15},
  number={1},
  pages={111--135},
  year={2011},
  publisher={Springer}
}

@article{Diniz2009,
  title={Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change},
  author={Diniz-Filho, Jos{\'e} Alexandre F and Mauricio Bini, Luis and Fernando Rangel, Thiago and Loyola, Rafael D and Hof, Christian and Nogu{\'e}s-Bravo, David and Ara{\'u}jo, Miguel B},
  journal={Ecography},
  volume={32},
  number={6},
  pages={897--906},
  year={2009},
  publisher={Wiley Online Library}
}

@article{Gentleman2004,
  title={Bioconductor: open software development for computational biology and bioinformatics},
  author={Gentleman, Robert C and Carey, Vincent J and Bates, Douglas M and Bolstad, Ben and Dettling, Marcel and Dudoit, Sandrine and Ellis, Byron and Gautier, Laurent and Ge, Yongchao and Gentry, Jeff and others},
  journal={Genome biology},
  volume={5},
  number={10},
  pages={1},
  year={2004},
  publisher={BioMed Central}
}

@article{Guo2010,
  title={ModEco: an integrated software package for ecological niche modeling},
  author={Guo, Qinghua and Liu, Yu},
  journal={Ecography},
  volume={33},
  number={4},
  pages={637--642},
  year={2010},
  publisher={Wiley Online Library}
}

@Manual{dismo,
    title = {dismo: Species Distribution Modeling},
    author = {Robert J. Hijmans and Steven Phillips and John Leathwick and Jane Elith},
    year = {2016},
    note = {R package version 1.1-1},
    url = {https://CRAN.R-project.org/package=dismo},
  }
  
@article{Lunn2000,
  title={WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility},
  author={Lunn, David J and Thomas, Andrew and Best, Nicky and Spiegelhalter, David},
  journal={Statistics and computing},
  volume={10},
  number={4},
  pages={325--337},
  year={2000},
  publisher={Springer}
}

@article{Mirams2013,
  title={Chaste: an open source C++ library for computational physiology and biology},
  author={Mirams, Gary R and Arthurs, Christopher J and Bernabeu, Miguel O and Bordas, Rafel and Cooper, Jonathan and Corrias, Alberto and Davit, Yohan and Dunn, Sara-Jane and Fletcher, Alexander G and Harvey, Daniel G and others},
  journal={PLoS Comput Biol},
  volume={9},
  number={3},
  pages={e1002970},
  year={2013},
  publisher={Public Library of Science}
}

@article{Phillips2006,
  title={Maximum entropy modeling of species geographic distributions},
  author={Phillips, Steven J and Anderson, Robert P and Schapire, Robert E},
  journal={Ecological modelling},
  volume={190},
  number={3},
  pages={231--259},
  year={2006},
  publisher={Elsevier}
}

@Manual{R,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2016},
  url = {https://www.R-project.org/},
}

@article{TerBraak2002,
  title={CANOCO reference manual and CanoDraw for Windows user's guide: software for canonical community ordination (version 4.5)},
  author={Ter Braak, Cajo JF and Smilauer, Petr},
  year={2002}
}

@article{Thuiller2009,
  title={BIOMOD--a platform for ensemble forecasting of species distributions},
  author={Thuiller, Wilfried and Lafourcade, Bruno and Engler, Robin and Ara{\'u}jo, Miguel B},
  journal={Ecography},
  volume={32},
  number={3},
  pages={369--373},
  year={2009},
  publisher={Wiley Online Library}
}

@article{Touchon2016,
  title={The mismatch between current statistical practice and doctoral training in ecology},
  author={Touchon, Justin C and McCoy, Michael W},
  journal={Ecosphere},
  volume={7},
  number={8},
  year={2016},
  publisher={Wiley Online Library}
}

@article{Beaumont2016,
  title={Which species distribution models are more (or less) likely to project broad-scale, climate-induced shifts in species ranges?},
  author={Beaumont, Linda J and Graham, Erin and Duursma, Daisy Englert and Wilson, Peter D and Cabrelli, Abigail and Baumgartner, John B and Hallgren, Willow and Esper{\'o}n-Rodr{\'\i}guez, Manuel and Nipperess, David A and Warren, Dan L and others},
  journal={Ecological Modelling},
  volume={342},
  pages={135--146},
  year={2016},
  publisher={Elsevier}
}

@inproceedings{Thomer2016,
  title={Co-designing Scientific Software: Hackathons for Participatory Interface Design},
  author={Thomer, Andrea K and Twidale, Michael B and Guo, Jinlong and Yoder, Matthew J},
  booktitle={Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems},
  pages={3219--3226},
  year={2016},
  organization={ACM}
}

@article{McNutt2016,
  title={Taking up {TOP}},
  author={McNutt, Marcia},
  journal={Science},
  volume={352},
  number={6290},
  pages={1147--1147},
  year={2016},
  publisher={American Association for the Advancement of Science}
}

@article{Pavelin2012,
  title={Bioinformatics meets user-centred design: a perspective},
  author={Pavelin, Katrina and Cham, Jennifer A and de Matos, Paula and Brooksbank, Cath and Cameron, Graham and Steinbeck, Christoph},
  journal={PLoS Comput Biol},
  volume={8},
  number={7},
  pages={e1002554},
  year={2012},
  publisher={Public Library of Science}
}

@article{EC2016,
  author = {{European Commission}},
  number = {3.1},
  title = {Open Access to Scientific Publications and Research Data in Horizon 2020},
  year = {2016}
}

@article{Ahmed2015,
  abstract = {Software use is ubiquitous in the species distribution modelling (SDM) domain; nearly every scientist working on SDM either uses or develops specialist SDM software; however, little is formally known about the prevalence or preference of one software over another. We seek to provide, for the first time, a ‘snapshot' of SDM users, the methods they use and the questions they answer.$\backslash$n$\backslash$n$\backslash$nLocation$\backslash$n$\backslash$nGlobal.$\backslash$n$\backslash$n$\backslash$nMethods$\backslash$n$\backslash$nWe conducted a survey of over 300 SDM scientists to capture a snapshot of the community and used an extensive literature search of SDM papers in order to investigate the characteristics of the SDM community and its interactions with software developers in terms of co-authoring research publications.$\backslash$n$\backslash$n$\backslash$nResults$\backslash$n$\backslash$nOur results show that those members of the community who develop software and who are directly connected with developers are among the most highly connected and published authors in the field. We further show that the two most popular softwares for SDM lie at opposite ends of the ‘use-complexity' continuum.$\backslash$n$\backslash$n$\backslash$nMain conclusion$\backslash$n$\backslash$nGiven the importance of SDM research in a changing environment, with its increasing use in the policy domain, it is vital to be aware of what software and methodologies are being implemented. Here, we present a snapshot of the SDM community, the software and the methods being used.$\backslash$n},
  author = {Ahmed, Sadia E. and Mcinerny, Greg and O'Hara, Kenton and Harper, Richard and Salido, Lara and Emmott, Stephen and Joppa, Lucas N.},
  doi = {10.1111/ddi.12305},
  isbn = {1472-4642},
  issn = {14724642},
  journal = {Diversity and Distributions},
  keywords = {Scientific software,Species distribution,Survey},
  number = {3},
  pages = {258--267},
  title = {{Scientists and software - surveying the species distribution modelling community}},
  volume = {21},
  year = {2015}
}

@article{Araujo2012,
  title={Uses and misuses of bioclimatic envelope modeling},
  author={Ara{\'u}jo, Miguel B and Peterson, A Townsend},
  journal={Ecology},
  volume={93},
  number={7},
  pages={1527--1539},
  year={2012},
  publisher={Wiley Online Library}
}

@article{Obama2013,
  title={Executive Order--Making Open and Machine Readable the New Default for Government Information},
  author={Obama, Barack},
  journal={The White House},
  year={2013}
}

@misc{Roysoc2012,
  title={Science as an Open Enterprise},
  author={Royal Society},
  year={2012},
  publisher={Royal Society London}
}
@article{DeGiovanni2016,
abstract = {Ecological niche modelling (ENM) Components are a set of reusable workflow components specialized for performing ENM tasks within the Taverna workflow management system. Each component encapsulates specific functionality and can be combined with other components to facilitate the creation of larger and more complex workflows. One key distinguishing feature of {\{}ENM{\}} Components is that most tasks are performed remotely by calling web services, simplifying software setup and maintenance on the client side and allowing more powerful computing resources to be exploited. This paper presents the current set of ENM Components in the context of the Taverna family of tools for creating, publishing and sharing workflows. An example is included showing how the components can be used in a preliminary investigation of the effects of mixing different spatial resolutions in ENM experiments.},
author = {{De Giovanni}, Renato and Williams, Alan R. and Ernst, Vera Hern{\'{a}}ndez and Kulawik, Robert and Fernandez, Francisco Quevedo and Hardisty, Alex R.},
doi = {10.1111/ecog.01552},
file = {:Users/nick/Dropbox/papers/ecog1552 (1).pdf:pdf},
isbn = {1600-0587},
issn = {16000587},
journal = {Ecography},
number = {4},
pages = {376--383},
title = {{ENM Components: A new set of web service-based workflow components for ecological niche modelling}},
volume = {39},
year = {2016}
}

@article{Roberts2016,
abstract = {Community assembly rules have been extensively studied, but its association with regional environmental variation, while land use history remains largely unexplored. Land use history might be especially important in Mediterranean forests, considering their historical deforestation and recent afforestation. Using forest inventories and historical (1956) and recent (2000) land cover maps, we explored the following hypotheses: 1) woody species assembly is driven by environmental factors, but also by historical landscape attributes; 2) recent forests exhibit lower woody species richness than pre-existing due to the existence of colonization credits; 3) these credits are modulated by species' life-forms and dispersal mechanisms. We examined the association of forest historical type (pre-existing versus recent) with total species richness and that of diverse life-forms and dispersal groups, also considering the effects of current environment and past landscape factors. When accounting for these effects, no significant differences in woody species richness were found between forest historical types except for vertebrate-dispersed species. Species richness of this group was affected by the interaction of forest historical type with distance to coast and rainfall: vertebrate-dispersed species richness increased with rainfall and distance to the coast in recent forests, while it was higher in dryer sites in pre-existing forests. In addition, forest historical types showed differences in woody species composition associated to diverse environmental and past landscape factors. In view of these results we can conclude that: 1) community assembly in terms of species richness is fast enough to exhaust most colonization credit in recent Mediterranean forests except for vertebrate-dispersed species; 2) for these species, colonization credit is affected by the interplay of forest history and a set of proxies of niche and landscape constraints of species dispersal and establishment; 3) woody species assemblage is mostly shaped by the species' ecological niches in these forests.},
author = {Roberts, David R. and Bahn, Volker and Ciuti, Simone and Boyce, Mark S. and Elith, Jane and Guillera-Arroita, Gurutzeta and Hauenstein, Severin and Lahoz-Monfort, Jos{\'{e}} J. and Schr{\"{o}}der, Boris and Thuiller, Wilfried and Warton, David I. and Wintle, Brendan A. and Hartig, Florian and Dormann, Carsten F.},
doi = {10.1111/ecog.02881},
file = {:Users/nick/Dropbox/papers/ecog2881 (2).pdf:pdf},
isbn = {6503251521},
issn = {09067590},
journal = {Ecography},
title = {{Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure}},
year = {In Press}
}
@article{Hardisty2016,
abstract = {Making forecasts about biodiversity and giving support to policy relies increasingly on large collections of data held electronically, and on substantial computational capability and capacity to analyse, model, simulate and predict using such data. However, the physically distributed nature of data resources and of expertise in advanced analytical tools creates many challenges for the modern scientist. Across the wider biological sciences, presenting such capabilities on the Internet (as “Web services”) and using scientific workflow systems to compose them for particular tasks is a practical way to carry out robust “in silico” science. However, use of this approach in biodiversity science and ecology has thus far been quite limited. BioVeL is a virtual laboratory for data analysis and modelling in biodiversity science and ecology, freely accessible via the Internet. BioVeL includes functions for accessing and analysing data through curated Web services; for performing complex in silico analysis through exposure of R programs, workflows, and batch processing functions; for on-line collaboration through sharing of workflows and workflow runs; for experiment documentation through reproducibility and repeatability; and for computational support via seamless connections to supporting computing infrastructures. We developed and improved more than 60 Web services with significant potential in many different kinds of data analysis and modelling tasks. We composed reusable workflows using these Web services, also incorporating R programs. Deploying these tools into an easy-to-use and accessible ‘virtual laboratory', free via the Internet, we applied the workflows in several diverse case studies. We opened the virtual laboratory for public use and through a programme of external engagement we actively encouraged scientists and third party application and tool developers to try out the services and contribute to the activity. Our work shows we can deliver an operational, scalable and flexible Internet-based virtual laboratory to meet new demands for data processing and analysis in biodiversity science and ecology. In particular, we have successfully integrated existing and popular tools and practices from different scientific disciplines to be used in biodiversity and ecological research.},
author = {Hardisty, Alex R. and Bacall, Finn and Beard, Niall and Balc{\'{a}}zar-Vargas, Maria-Paula and Balech, Bachir and Barcza, Zolt{\'{a}}n and Bourlat, Sarah J. and {De Giovanni}, Renato and de Jong, Yde and {De Leo}, Francesca and Dobor, Laura and Donvito, Giacinto and Fellows, Donal and Guerra, Antonio Fernandez and Ferreira, Nuno and Fetyukova, Yuliya and Fosso, Bruno and Giddy, Jonathan and Goble, Carole and G{\"{u}}ntsch, Anton and Haines, Robert and Ernst, Vera Hern{\'{a}}ndez and Hettling, Hannes and Hidy, D{\'{o}}ra and Horv{\'{a}}th, Ferenc and Ittz{\'{e}}s, D{\'{o}}ra and Ittz{\'{e}}s, P{\'{e}}ter and Jones, Andrew and Kottmann, Renzo and Kulawik, Robert and Leidenberger, Sonja and Lyytik{\"{a}}inen-Saarenmaa, P{\"{a}}ivi and Mathew, Cherian and Morrison, Norman and Nenadic, Aleksandra and de la Hidalga, Abraham Nieva and Obst, Matthias and Oostermeijer, Gerard and Paymal, Elisabeth and Pesole, Graziano and Pinto, Salvatore and Poign{\'{e}}, Axel and Fernandez, Francisco Quevedo and Santamaria, Monica and Saarenmaa, Hannu and Sipos, Gergely and Sylla, Karl-Heinz and T{\"{a}}htinen, Marko and Vicario, Saverio and Vos, Rutger Aldo and Williams, Alan R. and Yilmaz, Pelin and Evans, MR and Evans, MR and Bithell, M and Cornell, SJ and Dall, SRX and D{\'{i}}az, S and Emmott, S and Purves, D and Scharlemann, J and Harfoot, M and Newbold, T and Tittensor, DP and Hutton, J and D{\'{i}}az, S and Demissew, S and Carabias, J and Joly, C and Lonsdale, M and Ash, N and Hampton, SE and Strasser, CA and Tewksbury, JJ and Gram, WK and Budden, AE and Batcheller, AL and Michener, WK and Jones, MB and Koureas, D and Arvanitidis, C and Belbin, L and Berendsohn, W and Damgaard, C and Groom, Q and Deelman, E and Vahi, K and Juve, G and Rynge, M and Callaghan, S and Maechling, PJ and Wolstencroft, K and Haines, R and Fellows, D and Williams, A and Withers, D and Owen, S and Goecks, J and Nekrutenko, A and Taylor, J and Hofmann, M and Klinkenberg, R and Fisher, P and Hedeler, C and Bentley, RD and Csillaghy, A and Aboudarham, J and Jacquey, C and Hapgood, MA and Bocchialini, K and Hardy, B and Douglas, N and Helma, C and Rautenberg, M and Jeliazkova, N and Jeliazkov, V and Rex, DE and Ma, JQ and Toga, AW and Kr{\"{u}}ger, F and Clare, EL and Greif, S and Siemers, BM and Symondson, WOC and Sommer, RS and Pennington, D and Higgins, D and Peterson, A and Jones, M and Lud{\"{a}}scher, B and Bowers, S and Jarnevich, CS and Holcombe, TR and Bella, EM and Carlson, ML and Graziano, G and Lamb, M and Dou, L and Cao, G and Morris, P and Morris, R and Lud{\"{a}}scher, B and Macklin, J and Papazoglou, MP and Georgakopoulos, D and Giovanni, R and Torres, E and Amaral, R and Blanquer, I and Rebello, V and Canhos, V and Bhagat, J and Tanoh, F and Nzuobontane, E and Laurent, T and Orlowski, J and Roos, M and Ihaka, R and Gentleman, R and Racine, JS and Leidenberger, S and Obst, M and Kulawik, R and Stelzer, K and Heyer, K and Hardisty, A and Giovanni, R and Williams, AR and Hern{\'{a}}ndez, E Vera and Kulawik, R and Fernandez, FQ and Hardisty, AR and Laugen, AT and Hollander, J and Obst, M and Strand, {\AA} and Hidy, D and Barcza, Z and Haszpra, L and Churkina, G and Pint{\'{e}}r, K and Nagy, Z and S{\'{a}}ndor, R and Ma, S and Acutis, M and Barcza, Z and Touhami, H Ben and Doro, L and S{\'{a}}ndor, R and Barcza, Z and Hidy, D and Lellei-Kov{\'{a}}cs, E and Ma, S and Bellocchi, G and Kopf, A and Bicak, M and Kottmann, R and Schnetzer, J and Kostadinov, I and Lehmann, K and Manzari, C and Fosso, B and Marzano, M and Annese, A and Caprioli, R and D'Erchia, AM and Fosso, B and Santamaria, M and Marzano, M and Alonso-Alemany, D and Valiente, G and Donvito, G and Sandionigi, A and Vicario, S and Prosdocimi, EM and Galimberti, A and Ferri, E and Bruno, A and Antonelli, A and Hettling, H and Condamine, FL and Vos, K and Nilsson, RH and Sanderson, MJ and Balech, B and Vicario, S and Donvito, G and Monaco, A and Notarangelo, P and Pesole, G and Deli{\'{c}}, D and Balech, B and Radulovi{\'{c}}, M and Loli{\'{c}}, B and Kara{\v{c}}i{\'{c}}, A and Vukosavljevi{\'{c}}, V and Leidenberger, S and Giovanni, R and Kulawik, R and Williams, AR and Bourlat, SJ and Roure, D and Goble, C and Stevens, R and Wolstencroft, K and Owen, S and Krebs, O and Nguyen, Q and Stanford, NJ and Golebiewski, M and Mathew, C and G{\"{u}}ntsch, A and Obst, M and Vicario, S and Haines, R and Williams, AR and Baker, E and Price, BW and Rycroft, SD and Hill, J and Smith, VS and Hampton, SE and Anderson, SS and Bagby, SC and Gries, C and Han, X and Hart, EM and Mislan, KAS and Heer, JM and White, EP and Kenall, A and Harold, S and Foote, C and Rigoni, R and Fontana, E and Guglielmetti, S and Fosso, B and D'Erchia, AM and Maina, V and Pereira, HM and Ferrier, S and Walters, M and Geller, GN and Jongman, RHG and Scholes, RJ and Verbruggen, H and Tyberghein, L and Pauly, K and Vlaeminck, C and Van, Nieuwenhuyze K and Kooistra, WHCF and Vilhena, DA and Antonelli, A and Kearney, MR and Wintle, BA and Porter, WP and White, RL and Sutton, AE and Salguero-G{\'{o}}mez, R and Bray, TC and Campbell, H and Cieraad, E and Michener, W and Beach, J and Jones, M and Lud{\"{a}}scher, B and Pennington, D and Pereira, R and Smith, VS and Rycroft, SD and Brake, I and Scott, B and Baker, E and Livermore, L and P{\'{e}}rez, F and Granger, B and G{\"{a}}rdenfors, U and J{\"{o}}nsson, M and Obst, M and Wremp, AM and Kindvall, O and Nilsson, J and Goble, C and Roure, D},
doi = {10.1186/s12898-016-0103-y},
file = {:Users/nick/Dropbox/papers/art{\%}3A10.1186{\%}2Fs12898-016-0103-y.pdf:pdf},
issn = {1472-6785},
journal = {BMC Ecology},
keywords = {Ecology,Life Sciences,general},
number = {1},
pages = {49},
publisher = {BioMed Central},
title = {{BioVeL: a virtual laboratory for data analysis and modelling in biodiversity science and ecology}},
volume = {16},
year = {2016}
}
@article{Wolstencroft2013a,
abstract = {The Taverna workflow tool suite (http://www.taverna.org.uk) is designed to combine distributed Web Services and/or local tools into complex analysis pipelines. These pipelines can be executed on local desktop machines or through larger infrastructure (such as supercomputers, Grids or cloud environments), using the Taverna Server. In bioinformatics, Taverna workflows are typically used in the areas of high-throughput omics analyses (for example, proteomics or transcriptomics), or for evidence gathering methods involving text mining or data mining. Through Taverna, scientists have access to several thousand different tools and resources that are freely available from a large range of life science institutions. Once constructed, the workflows are reusable, executable bioinformatics protocols that can be shared, reused and repurposed. A repository of public workflows is available at http://www.myexperiment.org. This article provides an update to the Taverna tool suite, highlighting new features and developments in the workbench and the Taverna Server.},
author = {Wolstencroft, Katherine and Haines, Robert and Fellows, Donal and Williams, Alan and Withers, David and Owen, Stuart and Soiland-Reyes, Stian and Dunlop, Ian and Nenadic, Aleksandra and Fisher, Paul and Bhagat, Jiten and Belhajjame, Khalid and Bacall, Finn and Hardisty, Alex and {Nieva de la Hidalga}, Abraham and {Balcazar Vargas}, Maria P. and Sufi, Shoaib and Goble, Carole},
doi = {10.1093/nar/gkt328},
file = {:Users/nick/Dropbox/papers/Nucl. Acids Res.-2013-Wolstencroft-nar-gkt328.pdf:pdf},
isbn = {1362-4962 (Electronic)$\backslash$r0305-1048 (Linking)},
issn = {13624962},
journal = {Nucleic acids research},
number = {Web Server issue},
pages = {1--5},
pmid = {23640334},
title = {{The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud.}},
volume = {41},
year = {2013}
}

@Manual{maxnet,
  title = {maxnet: Fitting `Maxent' Species Distribution Models with `glmnet'},
  author = {Steven Phillips},
  year = {2016},
  note = {R package version 0.1.0},
  url = {https://CRAN.R-project.org/package=maxnet} 
}
@article{Peng2011,
author = {Peng, RD},
file = {:Users/nick/Dropbox/papers/1226.full.pdf:pdf},
journal = {Science},
number = {6060},
pages = {1226},
title = {{Reproducible Research in Computational Science}},
volume = {334},
year = {2011}
}
@article{Royle2012,
abstract = {1.Understanding the factors affecting species occurrence is a pre-eminent focus of applied ecological research. However, direct information about species occurrence is lacking for many species. Instead, researchers sometimes have to rely on so-called presence-only data (i.e. when no direct information about absences is available), which often results from opportunistic, unstructured sampling. maxent is a widely used software program designed to model and map species distribution using presence-only data. 2.We provide a critical review of maxent as applied to species distribution modelling and discuss how it can lead to inferential errors. A chief concern is that maxent produces a number of poorly defined indices that are not directly related to the actual parameter of interest – the probability of occurrence ($\psi$). This focus on an index was motivated by the belief that it is not possible to estimate $\psi$ from presence-only data; however, we demonstrate that $\psi$ is identifiable using conventional likelihood methods under the assumptions of random sampling and constant probability of species detection. 3.The model is implemented in a convenient r package which we use to apply the model to simulated data and data from the North American Breeding Bird Survey. We demonstrate that maxent produces extreme under-predictions when compared to estimates produced by logistic regression which uses the full (presence/absence) data set. We note that maxent predictions are extremely sensitive to specification of the background prevalence, which is not objectively estimated using the maxent method. 4.As with maxent, formal model-based inference requires a random sample of presence locations. Many presence-only data sets, such as those based on museum records and herbarium collections, may not satisfy this assumption. However, when sampling is random, we believe that inference should be based on formal methods that facilitate inference about interpretable ecological quantities instead of vaguely defined indices.},
author = {Royle, J. Andrew and Chandler, Richard B. and Yackulic, Charles and Nichols, James D.},
doi = {10.1111/j.2041-210X.2011.00182.x},
file = {:Users/nick/Dropbox/papers/j.2041-210X.2011.00182.x.pdf:pdf},
isbn = {2041-210X},
issn = {2041210X},
journal = {Methods in Ecology and Evolution},
keywords = {Bayes' rule,Detection probability,Logistic regression,Occupancy model,Occurrence probability,Presence-only data,Species distribution model,maxent},
number = {3},
pages = {545--554},
title = {{Likelihood analysis of species occurrence probability from presence-only data for modelling species distributions}},
volume = {3},
year = {2012}
}
@article{Renner2013,
abstract = {Modeling the spatial distribution of a species is a fundamental problem in ecology. A number of modeling methods have been developed, an extremely popular one being MAXENT, a maximum entropy modeling approach. In this article, we show that MAXENT is equivalent to a Poisson regression model and hence is related to a Poisson point process model, differing only in the intercept term, which is scale-dependent in MAXENT. We illustrate a number of improvements to MAXENT that follow from these relations. In particular, a point process model approach facilitates methods for choosing the appropriate spatial resolution, assessing model adequacy, and choosing the LASSO penalty parameter, all currently unavailable to MAXENT. The equivalence result represents a significant step in the unification of the species distribution modeling literature.},
author = {Renner, Ian W. and Warton, David I.},
doi = {10.1111/j.1541-0420.2012.01824.x},
file = {:Users/nick/Dropbox/papers/biom1824.pdf:pdf},
isbn = {1541-0420},
issn = {0006341X},
journal = {Biometrics},
keywords = {Habitat modeling,Location-only,Maximum entropy,Poisson likelihood,Presence-only data,Use-availability},
number = {1},
pages = {274--281},
pmid = {23379623},
title = {{Equivalence of MAXENT and Poisson Point Process Models for Species Distribution Modeling in Ecology}},
volume = {69},
year = {2013}
}
@article{Warton2010,
abstract = {Presence-only data, point locations where a species has been recorded as being present, are often used in modeling the distribution of a species as a function of a set of explanatory variables---whether to map species occurrence, to understand its association with the environment, or to predict its response to environmental change. Currently, ecologists most commonly analyze presence-only data by adding randomly chosen "pseudo-absences" to the data such that it can be analyzed using logistic regression, an approach which has weaknesses in model specification, in interpretation, and in implementation. To address these issues, we propose Poisson point process modeling of the intensity of presences. We also derive a link between the proposed approach and logistic regression---specifically, we show that as the number of pseudo-absences increases (in a regular or uniform random arrangement), logistic regression slope parameters and their standard errors converge to those of the corresponding Poisson point process model. We discuss the practical implications of these results. In particular, point process modeling offers a framework for choice of the number and location of pseudo-absences, both of which are currently chosen by ad hoc and sometimes ineffective methods in ecology, a point which we illustrate by example.},
archivePrefix = {arXiv},
arxivId = {1011.3319},
author = {Warton, David I. and Shepherd, Leah C.},
doi = {10.1214/10-AOAS331},
eprint = {1011.3319},
file = {:Users/nick/Dropbox/papers/1011.3319.pdf:pdf},
isbn = {1932-6157},
issn = {19326157},
journal = {Annals of Applied Statistics},
keywords = {Habitat modeling,Occurrence data,Pseudo-absences,Quadrature points,Species distribution modeling},
number = {3},
pages = {1383--1402},
title = {{Poisson point process models solve the "pseudo-absence problem" for presence-only data in ecology}},
volume = {4},
year = {2010}
}
@article{Renner2015,
abstract = {1. Presence-only data are widely used for species distribution modelling, and point process regression models are a flexible tool that has considerable potential for this problem, when data arise as point events. 2. In this paper, we review point process models, some of their advantages and some common methods of fitting them to presence-only data. 3. Advantages include (and are not limited to) clarification of what the response variable is that is modelled; a framework for choosing the number and location of quadrature points (commonly referred to as pseudo-absences or 'background points') objectively; clarity of model assumptions and tools for checking them; models to handle spatial dependence between points when it is present; and ways forward regarding difficult issues such as accounting for sampling bias. 4. Point process models are related to some common approaches to presence-only species distribution model-ling, which means that a variety of different software tools can be used to fit these models, including MAXENT or generalised linear modelling software.},
author = {Renner, Ian W. and Elith, Jane and Baddeley, Adrian and Fithian, William and Hastie, Trevor and Phillips, Steven J. and Popovic, Gordana and Warton, David I.},
doi = {10.1111/2041-210X.12352},
file = {:Users/nick/Dropbox/papers/mee312352.pdf:pdf},
isbn = {2041210X},
issn = {2041210X},
journal = {Methods in Ecology and Evolution},
keywords = {Cox processes,Gibbs processes,Pseudo-absences,Species distribution modelling,maxent},
number = {4},
pages = {366--379},
title = {{Point process models for presence-only analysis}},
volume = {6},
year = {2015}
}
@article{Fithian2013,
abstract = {Statistical modeling of presence-only data has attracted much recent attention in the ecological literature, leading to a proliferation of meth- ods, including the inhomogeneous poisson process (IPP) model [15], maxi- mum entropy (Maxent) modeling of species distributions [12] [9] [10], and logistic regression models. Several recent articles have shown the close relationships between these methods [1] [15]. We explain why the IPP intensity function is a more natural object of inference in presence-only studies than occurrence probability (which is only defined with reference to quadrat size), and why presence-only data only allows estimation of relative, and not absolute intensities. All three of the above techniques amount to parametric density esti- mation under the same exponential family model. We show that the IPP and Maxent models give the exact same estimate for this density, but lo- gistic regression in general produces a different estimate in finite samples. When the model is misspecified, logistic regression and the IPP may have substantially different asymptotic limits with large data sets. We propose “infinitely weighted logistic regression,” which is exactly equivalent to the IPP in finite samples. Consequently, many already-implemented methods extending logistic regression can also extend the Maxent and IPP models in directly analogous ways using this technique. Finally, we address the issue of observer bias, modeling the presence- only data set as a thinned IPP.We discuss when the observer bias problem can solved by regression adjustment, and additionally propose a novel method for combining presence-only and presence-absence records from one or more species to account for it.},
archivePrefix = {arXiv},
arxivId = {arXiv:1207.6950v2},
author = {Fithian, William and Hastie, Trevor},
doi = {10.1214/13-AOAS667},
eprint = {arXiv:1207.6950v2},
file = {:Users/nick/Dropbox/papers/secondRev1.pdf:pdf},
isbn = {1932-6157},
issn = {19326157},
journal = {Annals of Applied Statistics},
keywords = {Case-control sampling,Logistic regression,Maximum entropy,Poisson process models,Presence-only data,Species modeling},
number = {4},
pages = {1917--1939},
pmid = {25493106},
title = {{Finite-sample equivalence in statistical models for presence-only data}},
volume = {7},
year = {2013}
}

@article{Borregaard2016,
author = {Borregaard, Michael Krabbe and Hart, Edmund M},
doi = {10.1111/ecog.02493},
file = {:Users/nick/Dropbox/papers/ecog2493.pdf:pdf},
issn = {09067590},
volume = {39},
pages = {349--353},
title = {{Towards a more reproducible ecology}},
year = {2016}
}
@article{Joppa2014,
author = {Joppa, Lucas N. and McInerny, Greg and Harper, Richard and Salido, Lara and Takeda, Kenji and O'Hara, Kenton and Gavaghan, David and Emmott, Stephen},
file = {:Users/nick/Dropbox/papers/814.full.pdf:pdf},
journal = {Science},
number = {6134},
pages = {814--815},
title = {{Troubling Trends in Scientific Software Use}},
volume = {340},
year = {2014}
}
@article{Cooper2016,
abstract = {Computational modeling of cardiac cellular electrophysiology has a long history, and many models are now available for different species, cell types, and experimental preparations. This success brings with it a challenge: how do we assess and compare the underlying hypotheses and emergent behaviors so that we can choose a model as a suitable basis for a new study or to characterize how a particular model behaves in different scenarios? We have created an online resource for the characterization and comparison of electrophysiological cell models in a wide range of experimental scenarios. The details of the mathematical model (quantitative assumptions and hypotheses formulated as ordinary differential equations) are separated from the experimental protocol being simulated. Each model and protocol is then encoded in computer-readable formats. A simulation tool runs virtual experiments on models encoded in CellML, and a website (https://chaste.cs.ox.ac.UK/WebLab) provides a friendly interface, allowing users to store and compare results. The system currently contains a sample of 36 models and 23 protocols, including current-voltage curve generation, action potential properties under steady pacing at different rates, restitution properties, block of particular channels, and hypo-/hyperkalemia. This resource is publicly available, open source, and free, and we invite the community to use it and become involved in future developments. Investigators interested in comparing competing hypotheses using models can make a more informed decision, and those developing new models can upload them for easy evaluation under the existing protocols, and even add their own protocols.},
author = {Cooper, Jonathan and Scharm, Martin and Mirams, Gary R.},
doi = {10.1016/j.bpj.2015.12.012},
file = {:Users/nick/Dropbox/papers/main.pdf:pdf},
issn = {15420086},
journal = {Biophysical Journal},
number = {2},
pages = {292--300},
publisher = {The Authors},
title = {{The Cardiac Electrophysiology Web Lab}},
url = {http://dx.doi.org/10.1016/j.bpj.2015.12.012},
volume = {110},
year = {2016}
}
@article{Guillera-Arroita2015,
abstract = {Species distribution models (SDMs) are used to inform a range of ecological, biogeographical and conservation applications. However, users often underesti- mate the strong links between data type, model output and suitability for end-use. We synthesize current knowledge and provide a simple framework that summarizes how interactions between data type and the sampling process (i.e. imperfect detec- tion and sampling bias) determine the quantity that is estimated by a SDM. We then draw upon the published literature and simulations to illustrate and evaluate the information needs of the most common ecological, biogeographical and conserva- tion applications of SDM outputs. We find that, while predictions of models fitted to the most commonly available observational data (presence records) suffice for some applications, others require estimates of occurrence probabilities, which are unattainable without reliable absence records. Our literature review and simula- tions reveal that, while converting continuous SDM outputs into categories of assumed presence or absence is common practice, it is seldom clearly justified by the application's objective and it usually degrades inference. Matching SDMs to the needs of particular applications is critical to avoid poor scientific inference and management outcomes. This paper aims to help modellers and users assess whether their intended SDM outputs are indeed fit for purpose.},
author = {Guillera-Arroita, Gurutzeta and Lahoz-Monfort, Jos?? J. and Elith, Jane and Gordon, Ascelin and Kujala, Heini and Lentini, Pia E. and Mccarthy, Michael A. and Tingley, Reid and Wintle, Brendan A.},
doi = {10.1111/geb.12268},
file = {:Users/nick/Dropbox/papers/geb12268.pdf:pdf},
isbn = {1466-8238},
issn = {14668238},
journal = {Global Ecology and Biogeography},
keywords = {Ecological niche model,Habitat model,Imperfect detection,Presence-absence,Presence-background,Presence-only,Prevalence,Sampling bias},
number = {3},
pages = {276--292},
title = {{Is my species distribution model fit for purpose? Matching data and models to applications}},
volume = {24},
year = {2015}
}