On the use of neighboring habitats as predictors of species distributions

Collart, Flavien; Rey, Pierre‐Louis; Altermatt, Florian; Külling, Nathan; Guisan, Antoine; Adde, Antoine

doi:10.1002/oik.11963

Download

Article (Scientific journals)

On the use of neighboring habitats as predictors of species distributions

Collart, Flavien; Rey, Pierre‐Louis; Altermatt, Florian et al.

2025 • In Oikos

Peer Reviewed verified by ORBi

Permalink
https://hdl.handle.net/2268/339330

DOI
10.1002/oik.11963

Files (1)Send to Details Statistics Bibliography Similar publications

Files

Full Text

Oikos On the use of neighboring habitats.pdf

Author postprint (773.4 kB)

Download

All documents in ORBi are protected by a user license.

Send to

RIS BibTex APA Chicago Permalink X Linkedin

Details

Keywords :

animals; ecological niche modelling; focal predictor; plants; spatial scale; multi-scale process

Abstract :

[en] Choosing the appropriate scale for measuring environmental predictors is needed for accurately modelling species distributions. This need is becoming increasingly important with the use of high‐resolution species distribution models (SDMs), emphasizing the challenge of aligning predictors with the spatial and ecological scales at which species interact with their environments. Focal predictors, which summarize landscape information within a spatially moving window, are powerful to account for neighboring information and scale dependency but have remained overlooked in SDMs. Using an automated selection procedure to identify the best predictors and measurement scales from a high‐dimensional pool of candidates, including 13 nested circular focal sizes from 25 m to 5 km radius for each landscape feature, this study evaluated the use of focal predictors through a set of national‐scale, high‐resolution SDMs for more than 7000 species across 17 major taxonomic groups. It further examined whether focal selection depended on species' mobility or body size. Among all species, focal predictors were selected at least once in ≥ 94% of the SDMs, highlighting their important role. For mobile species, larger focal windows were selected for the land use and land cover category, whereas sessile species were associated with larger focal windows for topographic predictors. For small species, predictors with smaller focal windows were more often selected. Given the importance of focal predictors across all studied taxa, adjusting the optimal scale for each predictor and species is of utmost importance to improve model performance and account for species' scale dependency.

Research Center/Unit :

InBios - Integrative Biological Sciences - ULiège

Disciplines :

Environmental sciences & ecology
Phytobiology (plant sciences, forestry, mycology...)
Zoology

Author, co-author :

Collart, Flavien ^✱; Université de Liège - ULiège > Integrative Biological Sciences (InBioS)

Rey, Pierre‐Louis ^✱; Institute of Earth Surface Dynamics, University of Lausanne Lausanne Switzerland ; Interdisciplinary Center on Mountain Research, University of Lausanne Sion Switzerland

Altermatt, Florian ; Department of Aquatic Ecology, Eawag: Swiss Federal Institute of Aquatic Science and Technology Dübendorf Switzerland ; Department of Evolutionary Biology and Environmental Studies, University of Zurich Zurich Switzerland

Külling, Nathan ; EnviroSPACE Laboratory, Institute for Environmental Sciences, University of Geneva Bd. Carl‐Vogt Geneva Switzerland

Guisan, Antoine ^✱; Institute of Earth Surface Dynamics, University of Lausanne Lausanne Switzerland ; Interdisciplinary Center on Mountain Research, University of Lausanne Sion Switzerland ; Department of Ecology and Evolution, University of Lausanne Lausanne Switzerland

Adde, Antoine ^✱; Department of Aquatic Ecology, Eawag: Swiss Federal Institute of Aquatic Science and Technology Dübendorf Switzerland

^✱ These authors have contributed equally to this work.

Language :

English

Title :

On the use of neighboring habitats as predictors of species distributions

Publication date :

21 December 2025

Journal title :

Oikos

ISSN :

0030-1299

eISSN :

1600-0706

Publisher :

Wiley

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://nsojournals.onlinelibrary.wiley.com/doi/pdf/10.1002/oik.11963

Available on ORBi :

since 06 January 2026

Statistics

Number of views

34 (4 by ULiège)

Number of downloads

21 (0 by ULiège)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenCitations

OpenAlex citations

Bibliography

Adde, A., Dusfour, I., Roux, E., Girod, R. and Briolant, S. 2016. Anopheles fauna of coastal Cayenne, French Guiana: modelling and mapping of species presence using remotely sensed land cover data. – Mem. Inst. Oswaldo Cruz 111: 750–756.
Adde, A., Rey, P.-L., Brun, P., Külling, N., Fopp, F., Altermatt, F., Broennimann, O., Lehmann, A., Petitpierre, B., Zimmermann, N. E., Pellissier, L. and Guisan, A. 2023a. N-SDM: A high-performance computing pipeline for nested species distribution modelling. – Ecography 2023: e06540.
Adde, A., Rey, P.-L., Fopp, F., Petitpierre, B., Schweiger, A. K., Broennimann, O., Lehmann, A., Zimmermann, N. E., Altermatt, F., Pellissier, L. and Guisan, A. 2023b. Too many candidates: embedded covariate selection procedure for species distribution modelling with the covsel R package. – Ecol. Inform. 75: 102080.
Adde, A., Rey, P.-L., Külling, N., Chauvier-Mendes, Y., Fopp, F., Popp, M. R., Broennimann, O., Petitpierre, B., Strebel, N., Gross, A., Stofer, S., Lehmann, A., Zimmermann, N. E., Pellissier, L., Guisan, A. and Altermatt, F. 2025. SDMapCH: a Comprehensive database of >7500 modelled species habitat suitability maps for Switzerland. – Sci. Data 12: 1752.
Ancillotto, L., Budinski, I., Nardone, V., Di Salvo, I., Della Corte, M., Bosso, L., Conti, P. and Russo, D. 2018. What is driving range expansion in a common bat? Hints from thermoregulation and habitat selection. – Behav. Processes 157: 540–546.
Andriollo, T., Eggenberg, S., Gonseth, Y., Gross, A., Hofmann, H., Krättli, H., Schmid, H., Stofer, S., Zumbach, S. and Zumbach, P. 2021. Swiss species occurrence data for controlled access by Valpar.ch and SwissCatchment, v1, [version 1.5]. – Swiss National Biodiversity Data and Information Centres - InfoSpecies.
Araújo, M. B., Anderson, R. P., Márcia Barbosa, A., Beale, C. M., Dormann, C. F., Early, R., Garcia, R. A., Guisan, A., Maiorano, L., Naimi, B., O'Hara, R. B., Zimmermann, N. E. and Rahbek, C. 2019. Standards for distribution models in biodiversity assessments. – Sci. Adv. 5: eaat4858.
Araújo, M. B. and Guisan, A. 2006. Five (or so) challenges for species distribution modelling. – J. Biogeogr. 33: 1677–1688.
Austin, M. P. and Van Niel, K. P. 2011a. Impact of landscape predictors on climate change modelling of species distributions: a case study with Eucalyptus fastigata in southern New South Wales, Australia. – J. Biogeogr. 38: 9–19.
Austin, M. P. and Van Niel, K. P. 2011b. Improving species distribution models for climate change studies: variable selection and scale. – J. Biogeogr. 38: 1–8.
Bellamy, C. and Altringham, J. 2015. Predicting species distributions using record centre data: multi-scale modelling of habitat suitability for bat roosts. – PLoS One 10: e0128440.
Bellamy, C., Scott, C. and Altringham, J. 2013. Multiscale, presence-only habitat suitability models: fine-resolution maps for eight bat species. – J. Appl. Ecol. 50: 892–901.
Bellamy, C., Boughey, K., Hawkins, C., Reveley, S., Spake, R., Williams, C. and Altringham, J. 2020. A sequential multi-level framework to improve habitat suitability modelling. – Landsc. Ecol. 35: 1001–1020.
Benjamin, F. E., Reilly, R. and Winfree, R. 2014. Pollinator body size mediates the scale at which land use drives crop pollination services. – J. Appl. Ecol. 51: 440–449.
Brauner, N. and Shacham, M. 1998. Role of range and precision of the independent variable in regression of data. – AIChE J. 44: 603–611.
Braunisch, V. and Suchant, R. 2010. Predicting species distributions based on incomplete survey data: the tradeoff between precision and scale. – Ecography 33: 826–840.
Brosse, S., Charpin, N., Su, G., Toussaint, A., Herrera-R, G. A., Tedesco, P. A. and Villéger, S. 2021. FISHMORPH: a global database on morphological traits of freshwater fishes. – Global Ecol. Biogeogr. 30: 2330–2336.
Brun, P., Thuiller, W., Chauvier, Y., Pellissier, L., Wüest, R. O., Wang, Z. and Zimmermann, N. E. 2020. Model complexity affects species distribution projections under climate change. – J. Biogeogr. 47: 130–142.
Buri, A., Cianfrani, C., Pinto-Figueroa, E., Yashiro, E., Spangenberg, J. E., Adatte, T., Verrecchia, E., Guisan, A. and Pradervand, J.-N. 2017. Soil factors improve predictions of plant species distribution in a mountain environment. – Prog. Phys. Geogr. 41: 703–722.
Cardinale, B. J., Duffy, J. E., Gonzalez, A., Hooper, D. U., Perrings, C., Venail, P., Narwani, A., Mace, G. M., Tilman, D., Wardle, D. A., Kinzig, A. P., Daily, G. C., Loreau, M., Grace, J. B., Larigauderie, A., Srivastava, D. S. and Naeem, S. 2012. Biodiversity loss and its impact on humanity. – Nature 486: 59–67.
Chamberlain, S. A. and Szöcs, E. 2013. taxize: taxonomic search and retrieval in R. – F1000Research 2: 191.
Chase, J. M., McGill, B. J., McGlinn, D. J., May, F., Blowes, S. A., Xiao, X., Knight, T. M., Purschke, O. and Gotelli, N. J. 2018. Embracing scale-dependence to achieve a deeper understanding of biodiversity and its change across communities. – Ecol. Lett. 21: 1737–1751.
Chevalier, M., Mod, H., Broennimann, O., Di Cola, V., Schmid, S., Niculita-Hirzel, H., Pradervand, J.-N., Schmidt, B. R., Ursenbacher, S., Pellissier, L., and Guisan, A. 2021. Low spatial autocorrelation in mountain biodiversity data and model residuals. – Ecosphere 12: e03403.
Chytrý, K., Helm, N., Hülber, K., Moser, D., Wessely, J., Hausharter, J., Kollert, A., Mayr, A., Rutzinger, M., Winkler, M., Pauli, H., Saccone, P., Paetzolt, M., Hietz, P. and Dullinger, S. 2024. Limited impact of microtopography on alpine plant distribution. – Ecography 2024: e06744.
Collart, F. and Guisan, A. 2023. Small to train, small to test: dealing with low sample size in model evaluation. – Ecol. Inform. 75: 102106.
Collart, F., Broennimann, O., Guisan, A. and Vanderpoorten, A. 2023. Ecological and biological indicators of the accuracy of species distribution models: lessons from European bryophytes. – Ecography 2023: e06721.
Collart, F., Kiebacher, T., Quetsch, M., Broennimann, O., Guisan, A. and Vanderpoorten, A. 2024. To what extent can we predict variation of bryophyte and tracheophyte community composition at fine spatial scale along an elevation gradient? – Sci. Total Environ. 926: 171741.
Damschen, E. I. 2018. Decoding plant communities across scales. – Nat. Ecol. Evol. 2: 1844–1845.
Deneu, B., Servajean, M., Bonnet, P., Botella, C., Munoz, F. and Joly, A. 2021. Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment. – PLoS Comput. Biol. 17: e1008856.
Deng, H. and Runger, G. 2013. Gene selection with guided regularized random forest. – Pattern Recognit. 46: 3483–3489.
Doney, S. C., Ruckelshaus, M., Duffy, J. E., Barry, J. P., Chan, F., English, C. A., Galindo, H. M., Grebmeier, J. M., Hollowed, A. B., Knowlton, N., Polovina, J., Rabalais, N. N., Sydeman, W. J. and Talley, L. D. 2012. Climate change impacts on marine ecosystems. – Annu. Rev. Mar. Sci. 4: 11–37.
Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J. R. G., Gruber, B., Lafourcade, B., Leitão, P. J., Münkemüller, T., McClean, C., Osborne, P. E., Reineking, B., Schröder, B., Skidmore, A. K., Zurell, D. and Lautenbach, S. 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. – Ecography 36: 27–46.
Eigenbrod, F., Hecnar, S. J. and Fahrig, L. 2008. Accessible habitat: an improved measure of the effects of habitat loss and roads on wildlife populations. – Landsc. Ecol. 23: 159–168.
Fahrig, L. 2013. Rethinking patch size and isolation effects: the habitat amount hypothesis. – J. Biogeogr. 40: 1649–1663.
Fahrig, L. 2017. Ecological responses to habitat fragmentation per se. – Annu. Rev. Ecol. Evol. Syst. 48: 1–23.
Fahrig, L., Baudry, J., Brotons, L., Burel, F. G., Crist, T. O., Fuller, R. J., Sirami, C., Siriwardena, G. M. and Martin, J.-L. 2011. Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. – Ecol. Lett. 14: 101–112.
Fahrig, L., Watling, J. I., Arnillas, C. A., Arroyo-Rodríguez, V., Jörger-Hickfang, T., Müller, J., Pereira, H. M., Riva, F., Rösch, V., Seibold, S., Tscharntke, T. and May, F. 2022. Resolving the SLOSS dilemma for biodiversity conservation: a research agenda. – Biol. Rev. 97: 99–114.
Fick, S. E. and Hijmans, R. J. 2017. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. – Int. J. Climatol. 37: 4302–4315.
Fisher, J. T., Anholt, B. and Volpe, J. P. 2011. Body mass explains characteristic scales of habitat selection in terrestrial mammals. – Ecol. Evol. 1: 517–528.
Fourcade, Y., Besnard, A. G. and Secondi, J. 2018. Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics. – Global Ecol. Biogeogr. 27: 245–256.
Fournier, A., Barbet-Massin, M., Rome, Q. and Courchamp, F. 2017. Predicting species distribution combining multi-scale drivers. – Global Ecol. Conserv. 12: 215–226.
Franklin, J. 1995. Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients. – Prog. Phys. Geogr. 19: 474–499.
Gomes, E., Inácio, M., Bogdzevič, K., Kalinauskas, M., Karnauskaitė, D. and Pereira, P. 2021. Future land-use changes and its impacts on terrestrial ecosystem services: a review. – Sci. Total Environ. 781: 146716.
Grimm, N. B., Chapin III, F. S., Bierwagen, B., Gonzalez, P., Groffman, P. M., Luo, Y., Melton, F., Nadelhoffer, K., Pairis, A., Raymond, P. A., Schimel, J. and Williamson, C. E. 2013. The impacts of climate change on ecosystem structure and function. – Front. Ecol. Environ. 11: 474–482.
Guisan, A. and Zimmermann, N. E. 2000. Predictive habitat distribution models in ecology. – Ecol. Modell. 135: 147–186.
Guisan, A. and Thuiller, W. 2005. Predicting species distribution: offering more than simple habitat models. – Ecol. Lett. 8: 993–1009.
Guisan, A., Lehmann, A., Ferrier, S., Austin, M., Overton, J. Mc. C., Aspinall, R. and Hastie, T. 2006. Making better biogeographical predictions of species' distributions. – J. Appl. Ecol. 43: 386–392.
Guisan, A. et al. 2013. Predicting species distributions for conservation decisions. – Ecol. Lett. 16: 1424–1435.
Guisan, A., Thuiller, W. and Zimmermann, N. E. 2017. Habitat suitability and distribution models. – Cambridge Univ. Press.
Guisan, A., Chevalier, M., Adde, A., Zarzo-Arias, A., Goicolea, T., Broennimann, O., Petitpierre, B., Scherrer, D., Rey, P.-L., Collart, F., Riva, F., Steen, B. and Mateo, R. G. 2025. Spatially nested species distribution models (N-SDM): an effective tool to overcome niche truncation for more robust inference and projections. – J. Ecol. 113: 1588–1605.
Haddad, N. M. et al. 2015. Habitat fragmentation and its lasting impact on Earth's ecosystems. – Sci. Adv. 1: e1500052.
Haesen, S., Lenoir, J., Gril, E., De Frenne, P., Lembrechts, J. J., Kopecký, M., Macek, M., Man, M., Wild, J. and Van Meerbeek, K. 2023. Microclimate reveals the true thermal niche of forest plant species. – Ecol. Lett. 26: 2043–2055.
Hale, R., Colton, M. A., Peng, P. and Swearer, S. E. 2019. Do spatial scale and life history affect fish–habitat relationships? – J. Anim. Ecol. 88: 439–449.
Halstead, K. E., Alexander, J. D., Hadley, A. S., Stephens, J. L., Yang, Z. and Betts, M. G. 2019. Using a species-centered approach to predict bird community responses to habitat fragmentation. – Landsc. Ecol. 34: 1919–1935.
Harrell, F. E. Jr., Lee, K. L. and Mark, D. B. 1996. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. – Stat. Med. 15: 361–387.
Harvey, E., Marleau, J. N., Gounand, I., Leroux, S. J., Firkowski, C. R., Altermatt, F., Guillaume Blanchet, F., Cazelles, K., Chu, C., D'Aloia, C. C., Donelle, L., Gravel, D., Guichard, F., McCann, K., Ruppert, J. L. W., Ward, C. and Fortin, M.-J. 2023. A general meta-ecosystem model to predict ecosystem functions at landscape extents. – Ecography 2023: e06790.
Hasan, S. S., Zhen, L., Miah, M. G., Ahamed, T. and Samie, A. 2020. Impact of land use change on ecosystem services: a review. – Environ. Dev. 34: 100527.
Heegaard, E., Økland, H., Bratli, H., Bratli, H., Engan, G., Pedersen, O. and Solstad, H. 2007. Regularity of species richness relationships to patch size and shape. – Ecography 30: 589–597.
Hirzel, A. H., Le Lay, G., Helfer, V., Randin, C. and Guisan, A. 2006. Evaluating the ability of habitat suitability models to predict species presences. – Ecol. Modell. 199: 142–152.
Holland, J. D., Bert, D. G. and Fahrig, L. 2004. Determining the spatial scale of species' response to habitat. – BioScience 54: 227–233.
Holland, J. D., Fahrig, L. and Cappuccino, N. 2005. Body size affects the spatial scale of habitat–beetle interactions. – Oikos 110: 101–108.
Holling, C. S. 1992. Cross-scale morphology, geometry and dynamics of ecosystems. – Ecol. Monogr. 62: 447–502.
IPBES 2019. Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on biodiversity and Ecosystem Services. – Zenodo, https://doi.org/10.5281/zenodo.3831673.
IPCC (ed.) 2023. Summary for policymakers. – In: Climate Change 2022 – Impacts, Adaptation and Vulnerability: Working Group II Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge Univ. Press, pp. 3–34.
Isaac, N. J. B., Jarzyna, M. A., Keil, P., Dambly, L. I., Boersch-Supan, P. H., Browning, E., Freeman, S. N., Golding, N., Guillera-Arroita, G., Henrys, P. A., Jarvis, S., Lahoz-Monfort, J., Pagel, J., Pescott, O. L., Schmucki, R., Simmonds, E. G. and O'Hara, R. B. 2020. Data integration for large-scale models of species distributions. – Trends Ecol. Evol. 35: 56–67.
Jaberg, C. and Guisan, A. 2001. Modelling the distribution of bats in relation to landscape structure in a temperate mountain environment. – J. Appl. Ecol. 38: 1169–1181.
Jackson, H. B. and Fahrig, L. 2012. What size is a biologically relevant landscape? – Landsc. Ecol. 27: 929–941.
Jackson, H. B. and Fahrig, L. 2015. Are ecologists conducting research at the optimal scale? – Global Ecol. Biogeogr. 24: 52–63.
Júnior, P. D. M. and Nóbrega, C. C. 2018. Evaluating collinearity effects on species distribution models: an approach based on virtual species simulation. – PLoS One 13: e0202403.
Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P. and Kessler, M. 2017. Climatologies at high resolution for the earth's land surface areas. – Sci. Data 4: 170122.
Kattge, J. et al. 2020. TRY plant trait database – enhanced coverage and open access. – Global Change Biol. 26: 119–188.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. and Liu, T.-Y. 2017. LightGBM: a highly efficient gradient boosting decision tree. – In: Proc. 31st Int. Conf. on neural information processing systems, pp. 3149–3157.
Keck, F., Peller, T., Alther, R., Barouillet, C., Blackman, R., Capo, E., Chonova, T., Couton, M., Fehlinger, L., Kirschner, D., Knüsel, M., Muneret, L., Oester, R., Tapolczai, K., Zhang, H. and Altermatt, F. 2025. The global human impact on biodiversity. – Nature 641: 395–400.
Kemppinen, J. et al. 2024. Microclimate, an important part of ecology and biogeography. – Global Ecol. Biogeogr. 33: e13834.
Knüsel, M., Alther, R. and Altermatt, F. 2024. Terrestrial land use signals on groundwater fauna beyond current protection buffers. – Ecol. Appl. 34: e3040.
Kotowska, D., Pärt, T., Skórka, P., Auffret, A. G. and Żmihorski, M. 2022. Scale dependence of landscape heterogeneity effects on plant invasions. – J. Appl. Ecol. 59: 1313–1323.
Krauel, J. J., Ratcliffe, J. M., Westbrook, J. K. and McCracken, G. F. 2018. Brazilian free-tailed bats (Tadarida brasiliensis) adjust foraging behaviour in response to migratory moths. – Can. J. Zool. 96: 513–520.
Külling, N., Adde, A., Fopp, F., Schweiger, A. K., Broennimann, O., Rey, P.-L., Giuliani, G., Goicolea, T., Petitpierre, B., Zimmermann, N. E., Pellissier, L., Altermatt, F., Lehmann, A. and Guisan, A. 2024. SWECO25: a cross-thematic raster database for ecological research in Switzerland. – Sci. Data 11: 21.
Leibold, M. A., Holyoak, M., Mouquet, N., Amarasekare, P., Chase, J. M., Hoopes, M. F., Holt, R. D., Shurin, J. B., Law, R., Tilman, D., Loreau, M. and Gonzalez, A. 2004. The metacommunity concept: a framework for multi-scale community ecology. – Ecol. Lett. 7: 601–613.
Lembrechts, J. J. 2023. Microclimate alters the picture. – Nat. Clim. Change 13: 423–424.
Lembrechts, J. J., Lenoir, J., Roth, N., Hattab, T., Milbau, A., Haider, S., Pellissier, L., Pauchard, A., Ratier Backes, A., Dimarco, R. D., Nuñez, M. A., Aalto, J. and Nijs, I. 2019. Comparing temperature data sources for use in species distribution models: from in-situ logging to remote sensing. – Global Ecol. Biogeogr. 28: 1578–1596.
Li, L., Teng, S. N., Zhang, Y., Li, Y., Wang, H., Santana, J., Reino, L., Abades, S., Svenning, J.-C. and Xu, C. 2023. Neighbourhood landscape context shapes local species richness patterns across continents. – Global Ecol. Biogeogr. 32: 867–880.
Liu, J., Wilson, M., Hu, G., Liu, J., Wu, J. and Yu, M. 2018. How does habitat fragmentation affect the biodiversity and ecosystem functioning relationship? – Landsc. Ecol. 33: 341–352.
Luan, L., Jiang, Y., Cheng, M., Dini-Andreote, F., Sui, Y., Xu, Q., Geisen, S. and Sun, B. 2020. Organism body size structures the soil microbial and nematode community assembly at a continental and global scale. – Nat. Commun. 11: 6406.
Man, M., Wild, J., Macek, M. and Kopecký, M. 2022. Can high-resolution topography and forest canopy structure substitute microclimate measurements? Bryophytes say no. – Sci. Total Environ. 821: 153377.
Marra, G. and Wood, S. N. 2011. Practical variable selection for generalized additive models. – Comput. Stat. Data Anal. 55: 2372–2387.
Mazerolle, M. J. and Villard, M.-A. 1999. Patch characteristics and landscape context as predictors of species presence and abundance: a review1. – Écoscience 6: 117–124.
McGill, B. J. 2010. Matters of scale. – Science 328: 575–576.
Mod, H. K., Scherrer, D., Luoto, M. and Guisan, A. 2016. What we use is not what we know: environmental predictors in plant distribution models. – J. Veg. Sci. 27: 1308–1322.
Morse, D. H. 1974. Niche breadth as a function of social dominance. – Am. Nat. 108: 818–830.
Nelder, J. A. and Wedderburn, R. W. M. 1972. Generalized linear models. – J. R. Stat. Soc. A 135: 370–384.
Oliveira, B. F., São-Pedro, V. A., Santos-Barrera, G., Penone, C. and Costa, G. C. 2017. AmphiBIO, a global database for amphibian ecological traits. – Sci. Data 4: 170123.
O'Reilly-Nugent, A., Palit, R., Lopez-Aldana, A., Medina-Romero, M., Wandrag, E. and Duncan, R. P. 2016. Landscape effects on the spread of invasive species. – Curr. Landsc. Ecol. Rep. 1: 107–114.
Patiño, J., Collart, F., Vanderpoorten, A., Martin-Esquivel, J. L., Naranjo-Cigala, A., Mirolo, S. and Karger, D. N. 2023. Spatial resolution impacts projected plant responses to climate change on topographically complex islands. – Divers. Distrib. 29: 1245–1262.
Pekár, S. et al. 2021. The World Spider Trait database: a centralized global open repository for curated data on spider traits. – Database 2021: baab064.
Pellet, J., Guisan, A. and Perrin, N. 2004. A concentric analysis of the impact of urbanization on the threatened European tree frog in an agricultural landscape. – Conserv. Biol. 18: 1599–1606.
Pereira, H. M. et al. 2024. Global trends and scenarios for terrestrial biodiversity and ecosystem services from 1900 to 2050. – Science 384: 458–465.
Petitpierre, B., Broennimann, O., Kueffer, C., Daehler, C. and Guisan, A. 2017. Selecting predictors to maximize the transferability of species distribution models: lessons from cross-continental plant invasions. – Global Ecol. Biogeogr. 26: 275–287.
Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. and Blair, M. E. 2017. Opening the black box: an open-source release of Maxent. – Ecography 40: 887–893.
Pincebourde, S. and Woods, H. A. 2012. Climate uncertainty on leaf surfaces: the biophysics of leaf microclimates and their consequences for leaf-dwelling organisms. – Funct. Ecol. 26: 844–853.
Popovic, G., Mason, T. J., Drobniak, S. M., Marques, T. A., Potts, J., Joo, R., Altwegg, R., Burns, C. C. I., McCarthy, M. A., Johnston, A., Nakagawa, S., McMillan, L., Devarajan, K., Taggart, P. L., Wunderlich, A., Mair, M. M., Martínez-Lanfranco, J. A., Lagisz, M. and Pottier, P. 2024. Four principles for improved statistical ecology. – Methods Ecol. Evol. 15: 266–281.
Pottier, J., Dubuis, A., Pellissier, L., Maiorano, L., Rossier, L., Randin, C. F., Vittoz, P. and Guisan, A. 2013. The accuracy of plant assemblage prediction from species distribution models varies along environmental gradients. – Global Ecol. Biogeogr. 22: 52–63.
Pradervand, J.-N., Dubuis, A., Pellissier, L., Guisan, A. and Randin, C. 2014. Very high resolution environmental predictors in species distribution models: moving beyond topography? – Prog. Phys. Geogr. 38: 79–96.
Rapacciuolo, G. and Blois, J. L. 2019. Understanding ecological change across large spatial, temporal and taxonomic scales: integrating data and methods in light of theory. – Ecography 42: 1247–1266.
Razgour, O., Hanmer, J. and Jones, G. 2011. Using multi-scale modelling to predict habitat suitability for species of conservation concern: the grey long-eared bat as a case study. – Biol. Conserv. 144: 2922–2930.
Riva, F., Barbero, F., Balletto, E. and Bonelli, S. 2023. Combining environmental niche models, multi-grain analyses, and species traits identifies pervasive effects of land use on butterfly biodiversity across Italy. – Global Change Biol. 29: 1715–1728.
Riva, F. and Fahrig, L. 2023. Landscape-scale habitat fragmentation is positively related to biodiversity, despite patch-scale ecosystem decay. – Ecol. Lett. 26: 268–277.
Riva, F., Martin, C. J., Galán Acedo, C., Bellon, E. N., Keil, P., Morán-Ordóñez, A., Fahrig, L. and Guisan, A. 2024. Incorporating effects of habitat patches into species distribution models. – J. Ecol. 112: 2162–2182.
Scherrer, D. and Guisan, A. 2019. Ecological indicator values reveal missing predictors of species distributions. – Sci. Rep. 9: 3061.
Scherrer, D., Christe, P. and Guisan, A. 2019. Modelling bat distributions and diversity in a mountain landscape using focal predictors in ensemble of small models. – Divers. Distrib. 25: 770–782.
Schmeller, D. S., Courchamp, F. and Killeen, G. 2020. Biodiversity loss, emerging pathogens and human health risks. – Biodivers. Conserv. 29: 3095–3102.
Scott, J. M., Heglund, P. and Morrison, M. L. 2002. Predicting species occurrences: issues of accuracy and scale. – Island Press.
Shen, G., He, F., Waagepetersen, R., Sun, I.-F., Hao, Z., Chen, Z.-S. and Yu, M. 2013. Quantifying effects of habitat heterogeneity and other clustering processes on spatial distributions of tree species. – Ecology 94: 2436–2443.
Shirley, S. M., Yang, Z., Hutchinson, R. A., Alexander, J. D., McGarigal, K. and Betts, M. G. 2013. Species distribution modelling for the people: unclassified landsat TM imagery predicts bird occurrence at fine resolutions. – Divers. Distrib. 19: 855–866.
Shmida, A. and Whittaker, R. H. 1981. Pattern and biological microsite effects in two shrub communities, Southern California. – Ecology 62: 234–251.
Silander, J. A. and Pacala, S. W. 1985. Neighborhood predictors of plant performance. – Oecologia 66: 256–263.
Sirami, C., Caplat, P., Popy, S., Clamens, A., Arlettaz, R., Jiguet, F., Brotons, L. and Martin, J.-L. 2017. Impacts of global change on species distributions: obstacles and solutions to integrate climate and land use. – Global Ecol. Biogeogr. 26: 385–394.
Smith, A. B., Godsoe, W., Rodríguez-Sánchez, F., Wang, H.-H. and Warren, D. 2019. Niche estimation above and below the species level. – Trends Ecol. Evol. 34: 260–273.
Somers, R. H. 1962. A new asymmetric measure of association for ordinal variables. – Am. Sociol. Rev. 27: 799–811.
Soria, C. D., Pacifici, M., Di Marco, M., Stephen, S. M. and Rondinini, C. 2021. COMBINE: a coalesced mammal database of intrinsic and extrinsic traits. – Ecology 102: e03344.
Stein, A., Gerstner, K. and Kreft, H. 2014. Environmental heterogeneity as a universal driver of species richness across taxa, biomes and spatial scales. – Ecol. Lett. 17: 866–880.
Stuber, E. F., Gruber, F. and Fontaine, J. J. 2018. Predicting species-habitat relationships: does body size matter? – Landsc. Ecol. 33: 1049–1060.
Tessarolo, G., Lobo, J. M., Rangel, T. F. and Hortal, J. 2021. High uncertainty in the effects of data characteristics on the performance of species distribution models. – Ecol. Indic. 121: 107147.
Theux, C., Klein, N., Garibaldi, E., Jacot, A., Eichhorn, S., Guisan, A. and Pradervand, J.-N. 2022. Food and habitats requirements of the Eurasian scops owl (Otus scops) in Switzerland revealed by very high-resolution multi-scale models. – Ibis 164: 240–254.
Tonetti, V., Pena, J. C., Scarpelli, M. D., Sugai, L. S., Barros, F. M., Anunciação, P. R., Santos, P. M., Tavares, A. L. and Ribeiro, M. C. 2023. Landscape heterogeneity: concepts, quantification, challenges and future perspectives. – Environ. Conserv. 50: 83–92.
Van Niel, K. P., Laffan, S. W. and Lees, B. G. 2004. Effect of error in the DEM on environmental variables for predictive vegetation modelling. – J. Veg. Sci. 15: 747–756.
van Zuijlen, K., Nobis, M. P., Hedenäs, L., Hodgetts, N., Calleja Alarcón, J. A., Albertos, B., Bernhardt-Römermann, M., Gabriel, R., Garilleti, R., Lara, F., Preston, C. D., Simmel, J., Urmi, E., Bisang, I. and Bergamini, A. 2023. Bryophytes of Europe Traits (BET) data set: a fundamental tool for ecological studies. – J. Veg. Sci. 34: e13179.
Vicente, J. R., Gonçalves, J., Honrado, J. P., Randin, C. F., Pottier, J., Broennimann, O., Lomba, A. and Guisan, A. 2014. A framework for assessing the scale of influence of environmental factors on ecological patterns. – Ecol. Complexity 20: 151–156.
Villalobos-Chaves, D., Spínola-Parallada, M., Heer, K., Kalko, E. K. V. and Rodríguez-Herrera, B. 2017. Implications of a specialized diet for the foraging behavior of the Honduran white bat, Ectophylla alba (Chiroptera: Phyllostomidae). – J. Mammal. 98: 1193–1201.
Waller, J. T., Willink, B., Tschol, M. and Svensson, E. I. 2019. The odonate phenotypic database, a new open data resource for comparative studies of an old insect order. – Sci. Data 6: 316.
Watling, J. I., Arroyo-Rodríguez, V., Pfeifer, M., Baeten, L., Banks-Leite, C., Cisneros, L. M., Fang, R., Hamel-Leigue, A. C., Lachat, T., Leal, I. R., Lens, L., Possingham, H. P., Raheem, D. C., Ribeiro, D. B., Slade, E. M., Urbina-Cardona, J. N., Wood, E. M. and Fahrig, L. 2020. Support for the habitat amount hypothesis from a global synthesis of species density studies. – Ecol. Lett. 23: 674–681.
Wiens, J. A. 1989. Spatial scaling in ecology. – Funct. Ecol. 3: 385–397.
Zanini, F., Pellet, J. and Schmidt, B. R. 2009. The transferability of distribution models across regions: an amphibian case study. – Divers. Distrib. 15: 469–480.
Zou, H. and Hastie, T. 2005. Regularization and variable selection via the elastic net. – J. R. Stat. Soc. B 67: 301–320.
Zurell, D. et al. 2020. A standard protocol for reporting species distribution models. – Ecography 2020: ecog.04960.