A standard protocol for reporting species distribution models

Species distribution models (SDMs) constitute the most common class of models across ecology, evolution and conservation. The advent of ready-to-use software pack - ages 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 model - ing 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.

[1]  Barry W. Brook,et al.  Modelling range dynamics under global change: which framework and why? , 2015 .

[2]  Jennifer A. Miller,et al.  Mapping Species Distributions: Spatial Inference and Prediction , 2010 .

[3]  Helen M. Regan,et al.  Big data for forecasting the impacts of global change on plant communities , 2017 .

[4]  N. Nagelkerke,et al.  A note on a general definition of the coefficient of determination , 1991 .

[5]  Steven J. Phillips,et al.  Point process models for presence‐only analysis , 2015 .

[6]  Jane Elith,et al.  Forecasting species range dynamics with process-explicit models: matching methods to applications. , 2019, Ecology letters.

[7]  Scott L. Powell,et al.  Bringing an ecological view of change to Landsat‐based remote sensing , 2014 .

[8]  Antoine Guisan,et al.  Optimizing ensembles of small models for predicting the distribution of species with few occurrences , 2018 .

[9]  R. Newcombe.  Two-sided confidence intervals for the single proportion: comparison of seven methods. , 1998, Statistics in medicine.

[10]  M. Araújo,et al.  BIOMOD – a platform for ensemble forecasting of species distributions , 2009 .

[11]  A. Peterson,et al.  An evaluation of transferability of ecological niche models , 2018, Ecography.

[12]  Guidelines for Using the IUCN Red List Categories and Criteria , 2005 .

[13]  Kendall R. Jones,et al.  Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation , 2016, Nature Communications.

[14]  A. Townsend Peterson,et al.  kuenm: an R package for detailed development of ecological niche models using Maxent , 2019, PeerJ.

[15]  Jane Elith,et al.  blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models , 2018, bioRxiv.

[16]  Damaris Zurell,et al.  Collinearity: a review of methods to deal with it and a simulation study evaluating their performance , 2013 .

[17]  Wilfried Thuiller,et al.  Uncertainty in ensembles of global biodiversity scenarios , 2019, Nature Communications.

[18]  J. Lennon,et al.  Incorporating uncertainty in predictive species distribution modelling , 2012, Philosophical Transactions of the Royal Society B: Biological Sciences.

[19]  J Elith,et al.  A working guide to boosted regression trees. , 2008, The Journal of animal ecology.

[20]  Rodney X. Sturdivant,et al.  Applied Logistic Regression: Hosmer/Applied Logistic Regression , 2005 .

[21]  Brendan A. Wintle,et al.  Imperfect detection impacts the performance of species distribution models , 2014 .

[22]  David J. Gavaghan,et al.  The zoon r package for reproducible and shareable species distribution modelling , 2017 .

[23]  R. G. Davies,et al.  Methods to account for spatial autocorrelation in the analysis of species distributional data : a review , 2007 .

[24]  Jessica J. Meeuwig,et al.  Drifting baited stereo‐videography: a novel sampling tool for surveying pelagic wildlife in offshore marine reserves , 2015 .

[25]  R. Peng.  Reproducible Research in Computational Science , 2011, Science.

[26]  M. Boyce,et al.  Evaluating resource selection functions , 2002 .

[27]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[28]  Josep M. Serra-Diaz,et al.  Big data of tree species distributions: how big and how good? , 2017, Forest Ecosystems.

[29]  N. Zimmermann,et al.  Habitat Suitability and Distribution Models: With Applications in R , 2017 .

[30]  M. Boyce,et al.  WOLVES INFLUENCE ELK MOVEMENTS: BEHAVIOR SHAPES A TROPHIC CASCADE IN YELLOWSTONE NATIONAL PARK , 2005 .

[31]  M. Austin.  Spatial prediction of species distribution: an interface between ecological theory and statistical modelling , 2002 .

[32]  Mark S Boyce,et al.  Applications of step-selection functions in ecology and conservation , 2014, Movement Ecology.

[33]  John P. A. Ioannidis,et al.  What does research reproducibility mean? , 2016, Science Translational Medicine.

[34]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[35]  Bruce L. Webber,et al.  Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models , 2014 .

[36]  Hugh P Possingham,et al.  Changes in human footprint drive changes in species extinction risk , 2018, Nature Communications.

[37]  M. Araújo,et al.  Validation of species–climate impact models under climate change , 2005 .

[38]  Damaris Zurell,et al.  Outstanding Challenges in the Transferability of Ecological Models. , 2018, Trends in ecology & evolution.

[39]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[40]  Omri Allouche,et al.  Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS) , 2006 .

[41]  F. G. Barbosa,et al.  Characteristics of the top-cited papers in species distribution predictive models , 2015 .

[42]  Matthew J. Smith,et al.  Protected areas network is not adequate to protect a critically endangered East Africa Chelonian: Modelling distribution of pancake tortoise, Malacochersus tornieri under current and future climates , 2013, bioRxiv.

[43]  Damaris Zurell,et al.  Predicting to new environments: tools for visualizing model behaviour and impacts on mapped distributions , 2012 .

[44]  Daniel S. Park,et al.  A checklist for maximizing reproducibility of ecological niche models , 2019, Nature Ecology & Evolution.

[45]  William K. Michener,et al.  NONGEOSPATIAL METADATA FOR THE ECOLOGICAL SCIENCES , 1997 .

[46]  J. Elith,et al.  Species Distribution Models: Ecological Explanation and Prediction Across Space and Time , 2009 .

[47]  Wilfried Thuiller,et al.  Integrating correlation between traits improves spatial predictions of plant functional composition , 2018 .

[48]  Eve McDonald-Madden,et al.  Predicting species distributions for conservation decisions , 2013, Ecology letters.

[49]  Antoine Guisan,et al.  Predictive habitat distribution models in ecology , 2000 .

[50]  J. Franklin.  Predicting the distribution of shrub species in southern California from climate and terrain‐derived variables , 1998 .

[51]  Philip B. Holden,et al.  Modeling the ecology and evolution of biodiversity: Biogeographical cradles, museums, and graves , 2018, Science.

[52]  Daniel S. Park,et al.  Implications and alternatives of assigning climate data to geographical centroids , 2017 .

[53]  Robert P. Anderson,et al.  Standards for distribution models in biodiversity assessments , 2019, Science Advances.

[54]  John P. A. Ioannidis,et al.  A manifesto for reproducible science , 2017, Nature Human Behaviour.

[55]  Damaris Zurell,et al.  Benchmarking novel approaches for modelling species range dynamics , 2016, Global change biology.

[56]  S. Lavorel,et al.  Effects of restricting environmental range of data to project current and future species distributions , 2004 .

[57]  C. Rahbek,et al.  Spatial predictions at the community level: from current approaches to future frameworks , 2017, Biological reviews of the Cambridge Philosophical Society.

[58]  Robert P. Anderson,et al.  Are species occurrence data in global online repositories fit for modeling species distributions? The case of the Global Biodiversity Information Facility (GBIF). Final Report of the Task Group on GBIF Data Fitness for Use in Distribution Modelling. , 2016 .

[59]  Miguel Lurgi Rivera,et al.  A concise guide to developing and using quantitative models in conservation management , 2019, Conservation science and practice.

[60]  Timothy J. S. Whitfeld,et al.  Widespread sampling biases in herbaria revealed from large-scale digitization , 2017, bioRxiv.

[61]  G. Guillera‐Arroita.  Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities , 2017 .

[62]  Walter Jetz,et al.  Species' range model metadata standards: RMMS , 2019, Global Ecology and Biogeography.

[63]  Matthew E. Aiello-Lammens,et al.  Wallace: A flexible platform for reproducible modeling of species niches and distributions built for community expansion , 2017 .

[64]  A. Hirzel,et al.  Evaluating the ability of habitat suitability models to predict species presences , 2006 .

[65]  Jane Elith,et al.  The evaluation strip: A new and robust method for plotting predicted responses from species distribution models , 2005 .

[66]  Jane Elith,et al.  POC plots: calibrating species distribution models with presence-only data. , 2010, Ecology.

[67]  Johan Ehrlén,et al.  Predicting changes in the distribution and abundance of species under environmental change , 2015, Ecology letters.

[68]  Pedro J. Leitão,et al.  Modelling species distributions with remote sensing data: bridging disciplinary perspectives , 2013 .

[69]  Jorge Soberón,et al.  Integrating fundamental concepts of ecology, biogeography, and sampling into effective ecological niche modeling and species distribution modeling , 2012 .

[70]  Marc Kéry,et al.  Towards the modelling of true species distributions , 2011 .

[71]  Hans Ekkehard Plesser,et al.  Reproducibility vs. Replicability: A Brief History of a Confused Terminology , 2018, Front. Neuroinform..

[72]  Pedro J. Leitão,et al.  Improving Models of Species Ecological Niches: A Remote Sensing Overview , 2019, Front. Ecol. Evol..

[73]  Guillermo Fandos,et al.  Range compression of migratory passerines in wintering grounds of the Western Mediterranean: conservation prospects , 2017, Bird Conservation International.

[74]  Pedro J. Leitão,et al.  Breeding habitat selection of steppe birds in Castro Verde: a remote sensing and advanced statistics approach , 2010 .

[75]  T. Carter,et al.  Climate and socio-economic scenarios for climate change research and assessment: reconciling the new with the old , 2014, Climatic Change.

[76]  Trevor Hastie,et al.  A statistical explanation of MaxEnt for ecologists , 2011 .

[77]  C. Ricotta,et al.  Accounting for uncertainty when mapping species distributions: The need for maps of ignorance , 2011 .

[78]  Damaris Zurell,et al.  Macroecology in the age of Big Data – Where to go from here? , 2019, Journal of Biogeography.

[79]  Antoine Guisan,et al.  What we use is not what we know: environmental predictors in plant distribution models , 2016 .

[80]  M. White,et al.  Selecting thresholds for the prediction of species occurrence with presence‐only data , 2013 .

[81]  Kerrie Mengersen,et al.  Transferring biodiversity models for conservation: Opportunities and challenges , 2018 .

[82]  Jane Elith,et al.  What do we gain from simplicity versus complexity in species distribution models , 2014 .

[83]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.

[84]  J. Franklin.  Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients , 1995 .

[85]  Damaris Zurell,et al.  Testing species assemblage predictions from stacked and joint species distribution models , 2019, Journal of Biogeography.

[86]  Jason L. Brown.  SDMtoolbox: a python‐based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses , 2014 .

[87]  F. Jiguet,et al.  Selecting pseudo‐absences for species distribution models: how, where and how many? , 2012 .

[88]  M. Austin.  Species distribution models and ecological theory: A critical assessment and some possible new approaches , 2007 .

[89]  Steven J. Phillips,et al.  Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. , 2009, Ecological applications : a publication of the Ecological Society of America.

[90]  Jill P Mesirov,et al.  Accessible Reproducible Research , 2010, Science.

[91]  Volker Grimm,et al.  Ecological models supporting environmental decision making: a strategy for the future. , 2010, Trends in ecology & evolution.

[92]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[93]  Mark New,et al.  Ensemble forecasting of species distributions. , 2007, Trends in ecology & evolution.

[94]  W. Hargrove,et al.  The projection of species distribution models and the problem of non-analog climate , 2009, Biodiversity and Conservation.

[95]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[96]  P. Leitão,et al.  Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna , 2018, Carbon Balance and Management.

[97]  J. Diniz‐Filho,et al.  Spatial autocorrelation and red herrings in geographical ecology , 2003 .

[98]  Steven J. Phillips,et al.  The art of modelling range‐shifting species , 2010 .

[99]  E. Steyerberg,et al.  [Regression modeling strategies]. , 2011, Revista espanola de cardiologia.

[100]  Robert A. Boria,et al.  ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models , 2014 .

[101]  K. Walker,et al.  Climatic Associations of British Species Distributions Show Good Transferability in Time but Low Predictive Accuracy for Range Change , 2012, PloS one.

[102]  Carsten Meyer,et al.  Multidimensional biases, gaps and uncertainties in global plant occurrence information. , 2016, Ecology letters.

[103]  Brendan A. Wintle,et al.  Is my species distribution model fit for purpose? Matching data and models to applications , 2015 .

[104]  Carsten F. Dormann,et al.  Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure , 2017 .

[105]  David B. Dunson,et al.  A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels , 2019, Ecological Monographs.

[106]  Miguel B. Araújo,et al.  sdm: a reproducible and extensible R platform for species distribution modelling , 2016 .

[107]  Aaron Christ,et al.  Mixed Effects Models and Extensions in Ecology with R , 2009 .

[108]  B. Schröder,et al.  Predictive species distribution modelling in butterflies , 2009 .

[109]  T. Dawson,et al.  Selecting thresholds of occurrence in the prediction of species distributions , 2005 .

[110]  Walter Jetz,et al.  Integrating biodiversity distribution knowledge: toward a global map of life. , 2012, Trends in ecology & evolution.

[111]  Birgit Müller,et al.  A standard protocol for describing individual-based and agent-based models , 2006 .

[112]  Antoine Guisan,et al.  Overcoming limitations of modelling rare species by using ensembles of small models , 2015 .

[113]  W. Thuiller,et al.  Predicting species distribution: offering more than simple habitat models. , 2005, Ecology letters.

[114]  John Wieczorek,et al.  Darwin Core: An Evolving Community-Developed Biodiversity Data Standard , 2012, PloS one.

[115]  Rui F. Fernandes,et al.  How to best threshold and validate stacked species assemblages? Community optimisation might hold the answer , 2018, Methods in Ecology and Evolution.

[116]  Andreas Focks,et al.  Towards better modelling and decision support: Documenting model development, testing, and analysis using TRACE , 2014 .

[117]  A. Peterson,et al.  Geographic potential of disease caused by Ebola and Marburg viruses in Africa. , 2016, Acta tropica.

[118]  Carsten F. Dormann,et al.  Model averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference , 2018, Ecological Monographs.

[119]  Steffen Fritz,et al.  A global dataset of crowdsourced land cover and land use reference data , 2016, Scientific Data.

[120]  Robert P. Anderson,et al.  Ecological Niches and Geographic Distributions , 2011 .

[121]  S. Richards,et al.  Prevalence, thresholds and the performance of presence–absence models , 2014 .

[122]  Achim Zeileis,et al.  Bias in random forest variable importance measures: Illustrations, sources and a solution , 2007, BMC Bioinformatics.

[123]  J. Gareth Polhill,et al.  The ODD protocol: A review and first update , 2010, Ecological Modelling.

[124]  Michael B. Morrissey,et al.  Multiple Regression Is Not Multiple Regressions: The Meaning of Multiple Regression and the Non-Problem of Collinearity , 2018, Philosophy, Theory, and Practice in Biology.

[125]  J. Elith,et al.  A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD , 2019, Diversity and Distributions.

[126]  Damaris Zurell,et al.  Long-distance migratory birds threatened by multiple independent risks from global change , 2018, Nature Climate Change.

[127]  Boris Schröder,et al.  The importance of correcting for sampling bias in MaxEnt species distribution models , 2013 .

[128]  John Bell,et al.  A review of methods for the assessment of prediction errors in conservation presence/absence models , 1997, Environmental Conservation.

[129]  G. Guillera‐Arroita,et al.  Analysing and mapping species range dynamics using occupancy models , 2013 .

[130]  Olaf Conrad,et al.  Climatologies at high resolution for the earth’s land surface areas , 2016, Scientific Data.

[131]  Giovanni Rapacciuolo,et al.  Strengthening the contribution of macroecological models to conservation practice , 2018, Global Ecology and Biogeography.

[132]  David D. Ackerly,et al.  Best practices for reporting climate data in ecology , 2018, Nature Climate Change.

[133]  C. Dormann,et al.  Components of uncertainty in species distribution analysis: a case study of the Great Grey Shrike. , 2008, Ecology.

[134]  R. Jansson,et al.  Predicting the Fate of Biodiversity Using Species’ Distribution Models: Enhancing Model Comparability and Repeatability , 2012, PloS one.

[135]  Pedro J. Leitão,et al.  Breeding habitat selection by steppe birds in Castro Verde = Selección de hábitat de reproducción de aves esteparias en Castro Verde: a remote sensing and advanced statistics approach = una aproximación con teledetección y estadística avanzada , 2010 .

[136]  J. Elith.  Predicting distributions of invasive species , 2013, 1312.0851.

[137]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[138]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .