Additional Resources

This course is a very brief introduction to the broad topic of SDMs / ENMs. There are lots of other excellent resources available online, some which are compiled here.

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Tutorials on SDM/ENMs

• ENM2020 course (YouTube playlist with lectures) organized by T. Peterson
  • Schedule with PDFs of presentations
• Intro to SDM in R by D. Zurell
• Best Practices in Species Distribution Modeling: A Workshop in R by A. Smith
• Species distribution modeling in R by R. Hijmans and J. Elith

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General spatial data analysis in R

• Spatial Data Science by R. Hijmans
• Spatial Data Science with applications in R by E. Pebesma and R. Bivand
• Geocomputation in R by R. Lovelace, J. Nowosad, J. Muenchow
• NEON data science tutorials

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Recommended Reading

Work in progress…

General

• Merow, C., Smith, M.J., Edwards, T.C., Jr, Guisan, A., McMahon, S.M., Normand, S., et al. (2014). What do we gain from simplicity versus complexity in species distribution models? Ecography , 37, 1267–1281.

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Best practices

• Zurell, D., Franklin, J., König, C., Bouchet, P.J., Dormann, C.F., Elith, J., et al. (2020). A standard protocol for reporting species distribution models. Ecography, 43, 1261–1277.

• Araújo, M. B., R. P. Anderson, A. M. Barbosa, C. M. Beale, C. F. Dormann, R. Early, R. A. Garcia, et al. 2019. “Standards for Distribution Models in Biodiversity Assessments.” Science Advances 5: eaat4858.

• Merow, C., Maitner, B.S., Owens, H.L., Kass, J.M., Enquist, B.J. et al. (2019) Species’ range model metadata standards: Rmms. Global Ecology and Biogeography, 28, 1912-1924.

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Incorporating adaptive potential

• Bush, A., Mokany, K., Catullo, R., Hoffmann, A., Kellermann, V. et al. (2016) Incorporating evolutionary adaptation in species distribution modelling reduces projected vulnerability to climate change. Ecology Letters, 19, 1468-1478.

• Chardon, N.I., Pironon, S., Peterson, M.L. & Doak, D.F. (2020) Incorporating intraspecific variation into species distribution models improves distribution predictions, but cannot predict species traits for a wide-spread plant species. Ecography, 43, 60-74.

• Chen, Q., Yin, Y., Zhao, R., Yang, Y., Teixeira da Silva, J.A. et al. (2020) Incorporating local adaptation into species distribution modeling of paeonia mairei, an endemic plant to china. Frontiers in Plant Science, 10, 1717.

• Hällfors, M.H., Liao, J., Dzurisin, J., Grundel, R., Hyvärinen, M. et al. (2016) Addressing potential local adaptation in species distribution models: Implications for conservation under climate change. Ecological Applications, 26, 1154-1169.

• Miller, J.A. & Holloway, P. (2015) Incorporating movement in species distribution models. Progress in Physical Geography: Earth and Environment, 39, 837-849.

• Peterson, M.L., Doak, D.F. & Morris, W.F. (2019) Incorporating local adaptation into forecasts of species’ distribution and abundance under climate change. Global Change Biology, 25, 775-793.

• Razgour, O., Forester, B., Taggart, J.B., Bekaert, M., Juste, J. et al. (2019) Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. Proceedings of the National Academy of Sciences, 116, 10418.

• Uden, D.R., Allen, C.R., Angeler, D.G., Corral, L. & Fricke, K.A. (2015) Adaptive invasive species distribution models: A framework for modeling incipient invasions. Biological Invasions, 17, 2831-2850.

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Citizen science data

• Bradsworth, N., White, J.G., Isaac, B. & Cooke, R. (2017) Species distribution models derived from citizen science data predict the fine scale movements of owls in an urbanizing landscape. Biological Conservation, 213, 27-35.

• Feldman, M.J., Imbeau, L., Marchand, P., Mazerolle, M.J., Darveau, M. et al. (2021) Trends and gaps in the use of citizen science derived data as input for species distribution models: A quantitative review. PLoS ONE, 16, e0234587.

• Fournier, A.M.V., Sullivan, A.R., Bump, J.K., Perkins, M., Shieldcastle, M.C. et al. (2017) Combining citizen science species distribution models and stable isotopes reveals migratory connectivity in the secretive virginia rail. Journal of Applied Ecology, 54, 618-627.

• Gaul, W., Sadykova, D., White, H.J., Leon-Sanchez, L., Caplat, P. et al. (2020) Data quantity is more important than its spatial bias for predictive species distribution modelling. PeerJ, 8, e10411.

• Matutini, F., Baudry, J., Pain, G., Sineau, M. & Pithon, J. (2021) How citizen science could improve species distribution models and their independent assessment. Ecology and Evolution, 11, 3028-3039.

• Milanesi, P., Mori, E. & Menchetti, M. (2020) Observer-oriented approach improves species distribution models from citizen science data. Ecology and Evolution, 10, 12104-12114.

• Steen, V.A., Elphick, C.S. & Tingley, M.W. (2019) An evaluation of stringent filtering to improve species distribution models from citizen science data. Diversity and Distributions, 25, 1857-1869.

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Model Evaluation

• Muscarella, R., Galante, P.J., Soley-Guardia, M., Boria, R.A., Kass, J.M., Uriarte, M., et al. (2014). ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods Ecol. Evol., 5, 1198–1205.

• Kass, J.M., Muscarella, R., Galante, P.J., Bohl, C.L., Pinilla‐Buitrago, G.E., Boria, R.A., Soley‐Guardia, M. and Anderson, R.P. (2021), ENMeval 2.0: redesigned for customizable and reproducible modeling of species’ niches and distributions. Methods in Ecology and Evolution.)

pROC

• Peterson, A. T., Papeş, M., & Soberón, J. (2008). Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecological modelling, 213(1), 63-72.

AUC

• Lobo, J. M., Jiménez‐Valverde, A., & Real, R. (2008). AUC: a misleading measure of the performance of predictive distribution models. Global ecology and Biogeography, 17(2), 145-151.

Continuous Boyce Index

• Hirzel, A. H., Le Lay, G., Helfer, V., Randin, C., & Guisan, A. (2006). Evaluating the ability of habitat suitability models to predict species presences. Ecological Modelling, 199(2), 142-152.

AIC for SDMs

• Warren, D. L., & Seifert, S. N. (2011). Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological applications, 21(2), 335-342.

• Velasco, J. A., & González-Salazar, C. (2019). Akaike information criterion should not be a “test” of geographical prediction accuracy in ecological niche modelling. Ecological Informatics, 51, 25-32.

Leave-one-out CV

• Shcheglovitova, M., & Anderson, R. P. (2013). Estimating optimal complexity for ecological niche models: a jackknife approach for species with small sample sizes. Ecological Modelling, 269, 9-17

Spatial blocks

• Radosavljevic, A., & Anderson, R. P. (2014). Making better Maxent models of species distributions: complexity, overfitting and evaluation. Journal of Biogeography, 41(4), 629-643.

Block partitions in general

• Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera‐Arroita, G., … & Dormann, C. F. (2017). Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography, 40(8), 913-929.

blockCV package

• Valavi, R., Elith, J., Lahoz‐Monfort, J. J., & Guillera‐Arroita, G. (2019). block CV: An r package for generating spatially or environmentally separated folds for k‐fold cross‐validation of species distribution models. Methods in Ecology and Evolution, 10(2), 225-232. and R package

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Background selection

• Barbet-Massin, M., Jiguet, F., Albert, C.H. & Thuiller, W. (2012) Selecting pseudo-absences for species distribution models: How, where and how many? Methods in Ecology and Evolution, 3, 327-338.

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SDMs as biotic variables

• Kass, J.M., Anderson, R.P., Espinosa-Lucas, A., Juárez-Jaimes, V., Martínez-Salas, E. et al. (2020) Biotic predictors with phenological information improve range estimates for migrating monarch butterflies in mexico. Ecography, 43, 341-352.

• Palacio, F.X. & Girini, J.M. (2018) Biotic interactions in species distribution models enhance model performance and shed light on natural history of rare birds: A case study using the straight-billed reedhaunter limnoctites rectirostris. Journal of Avian Biology, 49, e01743.

• Heikkinen, R.K., Luoto, M., Virkkala, R., Pearson, R.G. & Körber, J.-H. (2007) Biotic interactions improve prediction of boreal bird distributions at macro-scales. Global Ecology and Biogeography, 16, 754-763.

• Araújo, M.B. & Luoto, M. (2007) The importance of biotic interactions for modelling species distributions under climate change. Global Ecology and Biogeography, 16, 743-753.

• Bateman, B.L., VanDerWal, J., Williams, S.E. & Johnson, C.N. (2012) Biotic interactions influence the projected distribution of a specialist mammal under climate change. Diversity and Distributions, 18, 861-872.

• Hof, A.R., Jansson, R. & Nilsson, C. (2012) How biotic interactions may alter future predictions of species distributions: Future threats to the persistence of the arctic fox in fennoscandia. Diversity and Distributions, 18, 554-562.

• Giannini, T.C., Chapman, D.S., Saraiva, A.M., Alves-dos-Santos, I. & Biesmeijer, J.C. (2013) Improving species distribution models using biotic interactions: A case study of parasites, pollinators and plants. Ecography, 36, 649-656.

• Atauchi, P.J., Peterson, A.T. & Flanagan, J. (2018) Species distribution models for peruvian plantcutter improve with consideration of biotic interactions. Journal of Avian Biology, 49, jav-01617.

• Wisz, M.S., Pottier, J., Kissling, W.D., Pellissier, L., Lenoir, J. et al. (2013) The role of biotic interactions in shaping distributions and realised assemblages of species: Implications for species distribution modelling. Biological Reviews, 88, 15-30.

• Belmaker, J., Zarnetske, P., Tuanmu, M.-N., Zonneveld, S., Record, S. et al. (2015) Empirical evidence for the scale dependence of biotic interactions. Global Ecology and Biogeography, 24, 750-761.

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Virtual species / simulation

• Meynard, C.N., Leroy, B. & Kaplan, D.M. (2019) Testing methods in species distribution modelling using virtual species: What have we learnt and what are we missing? Ecography, 42, 2021-2036.

• Inman, R., Franklin, J., Esque, T. & Nussear, K. (2021) Comparing sample bias correction methods for species distribution modeling using virtual species. Ecosphere, 12, e03422.

• Santini, L., Benítez-López, A., Maiorano, L., Čengić, M. & Huijbregts, M.A.J. (2021) Assessing the reliability of species distribution projections in climate change research. Diversity and Distributions, 27, 1035-1050.

• Grimmett, L., Whitsed, R. & Horta, A. (2021) Creating virtual species to test species distribution models: The importance of landscape structure, dispersal and population processes. Ecography, 44, 753-765.

• Smith, A.B. & Santos, M.J. (2020) Testing the ability of species distribution models to infer variable importance. Ecography, 43, 1801-1813.

• Leroy, B., Meynard, C.N., Bellard, C. & Courchamp, F. (2016) Virtualspecies, an r package to generate virtual species distributions. Ecography, 39, 599-607.

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Integrating dispersal

• Nobis, M.P. & Normand, S. (2014) Kissmig – a simple model for r to account for limited migration in analyses of species distributions. Ecography, 37, 1282-1287.

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Occurrence data cleaning

• Arlé, E., Zizka, A., Keil, P., Winter, M., Essl, F., Knight, T., Weigelt, P., Jiménez‐Muñoz, M. and Meyer, C. (2021), bRacatus: a method to estimate the accuracy and biogeographical status of georeferenced biological data. Methods in Ecology and Evolution.

• Zizka, A., Silvestro, D., Andermann, T., Azevedo, J., Duarte Ritter, C. et al. (2019) Coordinatecleaner: Standardized cleaning of occurrence records from biological collection databases. Methods in Ecology and Evolution, 10, 744-751.

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Modeling algorithms

• Merow, C., Smith, M. & Silander, J.A. (2013). A practical guide to Maxent: what it does, and why inputs and settings matter. Ecography , 36, 1–12.