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2. A standard protocol for reporting species distribution models

Author

Zurell, Damaris, Franklin, Janet, Koenig, Christian, Bouchet, Phil J, Dormann, Carsten F, Elith, Jane, Fandos, Guillermo, Feng, Xiao, Guillera-Arroita, Gurutzeta, Guisan, Antoine, Lahoz-Monfort, Jose J, Leitao, Pedro J, Park, Daniel S, Peterson, A Townsend, Rapacciuolo, Giovanni, Schmatz, Dirk R, Schroeder, Boris, Serra-Diaz, Josep M, Thuiller, Wilfried, Yates, Katherine L, Zimmermann, Niklaus E, and Merow, Cory

Subjects
biodiversity assessment, ecological niche model, habitat suitability model, reproducibility, Shiny, transparency, Ecology, Ecological Applications, Environmental Science and Management
Published
2020

3. A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels

Author

Norberg, Anna, Abrego, Nerea, Blanchet, F Guillaume, Adler, Frederick R, Anderson, Barbara J, Anttila, Jani, Araujo, Miguel B, Dallas, Tad, Dunson, David, Elith, Jane, Foster, Scott D, Fox, Richard, Franklin, Janet, Godsoe, William, Guisan, Antoine, O'Hara, Bob, Hill, Nicole A, Holt, Robert D, Hui, Francis KC, Husby, Magne, Kalas, John Atle, Lehikoinen, Aleksi, Luoto, Miska, Mod, Heidi K, Newell, Graeme, Renner, Ian, Roslin, Tomas, Soininen, Janne, Thuiller, Wilfried, Vanhatalo, Jarno, Warton, David, White, Matt, Zimmermann, Niklaus E, Gravel, Dominique, and Ovaskainen, Otso

Subjects
community assembly, community modeling, environmental filtering, joint species distribution model, model performance, prediction, predictive power, species interactions, stacked species distribution model, Physical Geography and Environmental Geoscience, Ecological Applications, Ecology
Published
2019

4. Outstanding Challenges in the Transferability of Ecological Models

Author

Yates, Katherine L, Bouchet, Phil J, Caley, M Julian, Mengersen, Kerrie, Randin, Christophe F, Parnell, Stephen, Fielding, Alan H, Bamford, Andrew J, Ban, Stephen, Barbosa, A Márcia, Dormann, Carsten F, Elith, Jane, Embling, Clare B, Ervin, Gary N, Fisher, Rebecca, Gould, Susan, Graf, Roland F, Gregr, Edward J, Halpin, Patrick N, Heikkinen, Risto K, Heinänen, Stefan, Jones, Alice R, Krishnakumar, Periyadan K, Lauria, Valentina, Lozano-Montes, Hector, Mannocci, Laura, Mellin, Camille, Mesgaran, Mohsen B, Moreno-Amat, Elena, Mormede, Sophie, Novaczek, Emilie, Oppel, Steffen, Crespo, Guillermo Ortuño, Peterson, A Townsend, Rapacciuolo, Giovanni, Roberts, Jason J, Ross, Rebecca E, Scales, Kylie L, Schoeman, David, Snelgrove, Paul, Sundblad, Göran, Thuiller, Wilfried, Torres, Leigh G, Verbruggen, Heroen, Wang, Lifei, Wenger, Seth, Whittingham, Mark J, Zharikov, Yuri, Zurell, Damaris, and Sequeira, Ana MM

Subjects
Ecology, Models, Biological, Predictive modeling, extrapolation, generality, habitat models, model transfers, species distribution models, uncertainty, Environmental Sciences, Biological Sciences, Evolutionary Biology
Abstract

Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions.

Published
2018

6. Bias Correction in Species Distribution Models: Pooling Survey and Collection Data for Multiple Species

Author

Fithian, William, Elith, Jane, Hastie, Trevor, and Keith, David A.

Subjects
Statistics - Applications
Abstract

Presence-only records may provide data on the distributions of rare species, but commonly suffer from large, unknown biases due to their typically haphazard collection schemes. Presence-absence or count data collected in systematic, planned surveys are more reliable but typically less abundant. We proposed a probabilistic model to allow for joint analysis of presence-only and survey data to exploit their complementary strengths. Our method pools presence-only and presence-absence data for many species and maximizes a joint likelihood, simultaneously estimating and adjusting for the sampling bias affecting the presence-only data. By assuming that the sampling bias is the same for all species, we can borrow strength across species to efficiently estimate the bias and improve our inference from presence-only data. We evaluate our model's performance on data for 36 eucalypt species in southeastern Australia. We find that presence-only records exhibit a strong sampling bias toward the coast and toward Sydney, the largest city. Our data-pooling technique substantially improves the out-of-sample predictive performance of our model when the amount of available presence-absence data for a given species is scarce. If we have only presence-only data and no presence-absence data for a given species, but both types of data for several other species that suffer from the same spatial sampling bias, then our method can obtain an unbiased estimate of the first species' geographic range.

Published
2014

9. Open access solutions for biodiversity journals : Do not replace one problem with another

Author

Peterson, A. Townsend, Anderson, Robert P., Beger, Maria, Bolliger, Janine, Brotons, Lluís, Burridge, Christopher P., Cobos, Marlon E., Cuervo-Robayo, Angela P., Di Minin, Enrico, Diez, Jeffrey, Elith, Jane, Embling, Clare B., Escobar, Luis E., Essl, Franz, Feeley, Kenneth J., Hawkes, Lucy, Jiménez-García, Daniel, Jimenez, Laura, Green, David M., Knop, Eva, Kühn, Ingolf, Lahoz-Monfort, José J., Lira-Noriega, Andres, Lobo, Jorge M., Loyola, Rafael, Nally, Ralph Mac, Machado-Stredel, Fernando, Martínez-Meyer, Enrique, McCarthy, Michael, Merow, Cory, Nori, Javier, Nuñez-Penichet, Claudia, Osorio-Olvera, Luis, Pyšek, Petr, Rejmánek, Marcel, Ricciardi, Anthony, Robertson, Mark, Soto, Octavio Rojas, Romero-Alvarez, Daniel, Roura-Pascual, Núria, Santini, Luca, Schoeman, David S., Schröder, Boris, Soberon, Jorge, Strubbe, Diederik, Thuiller, Wilfried, Traveset, Anna, Treml, Eric A., Václavík, Tomáš, Varela, Sara, Watson, James E. M., Wiersma, Yolanda, Wintle, Brendan, Yañez-Arenas, Carlos, and Zurell, Damaris

Published
2019

10. Predicting distributions of invasive species

Author

Elith, Jane

Subjects
Quantitative Biology - Populations and Evolution
Abstract

This chapter aims to inform a practitioner about current methods for predicting potential distributions of invasive species. It mostly addresses single species models, covering the conceptual bases, touching on mechanistic models, and then focusing on methods using species distribution records and environmental data to predict distributions. The commentary in this last section is oriented towards key issues that arise in fitting, and predicting with, these models (which include CLIMEX, MaxEnt and other regression methods). In other words, it is more about the process of thinking about the data and the modelling problem (which is a challenging one) than it is about one technique versus another. The discussion helps clarify the necessary steps and expertise for predicting distributions. Some researchers are optimistic that correlative models will predict with high precision; while that may be true for some species at some scales of evaluation, I believe that the issues discussed in this chapter show that substantial errors are reasonably likely. I am hopeful that ongoing developments will produce models better suited to the task and tools to help practitioners to better understand predictions and their uncertainties., Comment: 28 pages, this manuscript will later be published as a book chapter

Published
2013

11. Model averaging in ecology : a review of Bayesian, information-theoretic, and tactical approaches for predictive inference

Author

Dormann, Carsten F., Calabrese, Justin M., Guillera-Arroita, Gurutzeta, Matechou, Eleni, Bahn, Volker, Bartoń, Kamil, Beale, Colin M., Ciuti, Simone, Elith, Jane, Gerstner, Katharina, Guelat, Jérôme, Keil, Petr, Lahoz-Monfort, José J., Pollock, Laura J., Reineking, Björn, Roberts, David R., Schröder, Boris, Thuiller, Wilfried, Warton, David I., Wintle, Brendan A., Wood, Simon N., Wüest, Rafael O., and Hartig, Florian

Published
2018

12. Predicting species distributions for conservation decisions.

Author

Guisan, Antoine, Tingley, Reid, Baumgartner, John B, Naujokaitis-Lewis, Ilona, Sutcliffe, Patricia R, Tulloch, Ayesha IT, Regan, Tracey J, Brotons, Lluis, McDonald-Madden, Eve, Mantyka-Pringle, Chrystal, Martin, Tara G, Rhodes, Jonathan R, Maggini, Ramona, Setterfield, Samantha A, Elith, Jane, Schwartz, Mark W, Wintle, Brendan A, Broennimann, Olivier, Austin, Mike, Ferrier, Simon, Kearney, Michael R, Possingham, Hugh P, and Buckley, Yvonne M

Subjects
Decision Making, Ecology, Conservation of Natural Resources, Decision Support Techniques, Models, Theoretical, Research Design, Endangered Species, Biological invasions, conservation planning, critical habitats, environmental suitability, reserve selection, species distribution model, structured decision making, translocation, Models, Theoretical, Ecological Applications, Evolutionary Biology
Abstract

Species distribution models (SDMs) are increasingly proposed to support conservation decision making. However, evidence of SDMs supporting solutions for on-ground conservation problems is still scarce in the scientific literature. Here, we show that successful examples exist but are still largely hidden in the grey literature, and thus less accessible for analysis and learning. Furthermore, the decision framework within which SDMs are used is rarely made explicit. Using case studies from biological invasions, identification of critical habitats, reserve selection and translocation of endangered species, we propose that SDMs may be tailored to suit a range of decision-making contexts when used within a structured and transparent decision-making process. To construct appropriate SDMs to more effectively guide conservation actions, modellers need to better understand the decision process, and decision makers need to provide feedback to modellers regarding the actual use of SDMs to support conservation decisions. This could be facilitated by individuals or institutions playing the role of 'translators' between modellers and decision makers. We encourage species distribution modellers to get involved in real decision-making processes that will benefit from their technical input; this strategy has the potential to better bridge theory and practice, and contribute to improve both scientific knowledge and conservation outcomes.

Published
2013

19. Novel Methods Improve Prediction of Species' Distributions from Occurrence Data

Author

Elith, Jane, Graham, Catherine H., Anderson, Robert P., Dudík, Miroslav, Ferrier, Simon, Guisan, Antoine, Hijmans, Robert J., Huettmann, Falk, Leathwick, John R., Lehmann, Anthony, Li, Jin, Lohmann, Lucia G., Loiselle, Bette A., Manion, Glenn, Moritz, Craig, Nakamura, Miguel, Nakazawa, Yoshinori, Peterson, A. Townsend, Phillips, Steven J., Richardson, Karen, Scachetti-Pereira, Ricardo, Schapire, Robert E., Soberón, Jorge, Williams, Stephen, Wisz, Mary S., Zimmermann, Niklaus E., and Araujo, Miguel

Published
2006

26. SDM_NCEAS

Author

Elith, Jane and Valavi, Roozbeh

Abstract

These are data described in this publication: Elith, J., Graham, C.H., Valavi, R., Abegg, M., Bruce, C., Ferrier, S., Ford, A., Guisan, A., Hijmans, R.J., Huettmann, F., Lohmann, L.G., Loiselle, B.A., Moritz, C., Overton, J.McC., Peterson, A.T., Phillips, S., Richardson, K., Williams, S., Wiser, S.K., Wohlgemuth, T., Zimmermann, N.E. (2020). Presence-only and presence-absence data for comparing species distribution modeling methods. Biodiversity Informatics 15:69-80

Published
2022
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27. Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale

Author

Kissling, W. Daniel, Ahumada, Jorge A., Bowser, Anne, Fernandez, Miguel, Fernández, Néstor, García, Enrique Alonso, Guralnick, Robert P., Isaac, Nick J. B., Kelling, Steve, Los, Wouter, McRae, Louise, Mihoub, Jean‐Baptiste, Obst, Matthias, Santamaria, Monica, Skidmore, Andrew K., Williams, Kristen J., Agosti, Donat, Amariles, Daniel, Arvanitidis, Christos, Bastin, Lucy, De Leo, Francesca, Egloff, Willi, Elith, Jane, Hobern, Donald, Martin, David, Pereira, Henrique M., Pesole, Graziano, Peterseil, Johannes, Saarenmaa, Hannu, Schigel, Dmitry, Schmeller, Dirk S., Segata, Nicola, Turak, Eren, Uhlir, Paul F., Wee, Brian, and Hardisty, Alex R.

Published
2018
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28. Detecting Extinction Risk from Climate Change by IUCN Red List Criteria

Author

KEITH, DAVID A., MAHONY, MICHAEL, HINES, HARRY, ELITH, JANE, REGAN, TRACEY J., BAUMGARTNER, JOHN B., HUNTER, DAVID, HEARD, GEOFFREY W., MITCHELL, NICOLA J., PARRIS, KIRSTEN M., PENMAN, TRENT, SCHEELE, BEN, SIMPSON, CHRISTOPHER C., TINGLEY, REID, TRACY, CHRISTOPHER R., WEST, MATT, and AKÇAKAYA, H. RESIT

Published
2014

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