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Modeling present and future distribution of plankton populations in a coastal upwelling zone: the copepod Calanus chilensis as a study case

  • Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101 (2010).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • González, C. E., Medellín-Mora, J. & Escribano, R. Environmental gradients and spatial patterns of calanoid copepods in the southeast pacific. Front. Ecol. Evol. 8, 1–16 (2020).

    Article 

    Google Scholar 

  • Rombouts, I. et al. Global latitudinal variations in marine copepod diversity and environmental factors. Proc. R. Soc. B Biol. Sci. 276, 3053–3062 (2009).

    Article 

    Google Scholar 

  • Brandão, M. C. et al. Macroscale patterns of oceanic zooplankton composition and size structure. Sci. Rep. 11, 1–19 (2021).

    Google Scholar 

  • Mcclain, C. R. & Barry, J. P. Habitat heterogeneity, disturbance, and productivity work in concert to regulate biodiversity in deep submarine canyons. Ecology 91, 964–976 (2010).

    Article 
    PubMed 

    Google Scholar 

  • Escribano, R. & Rodriguez, L. Life cycle of Calanus chilensis Brodsky in Bay of San Jorge, Antofagasta Chile. Hydrobiologia 292–293, 289–294 (1994).

    Article 

    Google Scholar 

  • Strub, P. T., Mesías, M. J., Montecino, V., Rutllant, J. & Salinas, S. Coastal ocean circulation off western South America coastal segment. Sea 11, 273–313 (1998).

    Google Scholar 

  • Montecino, V. & Lange, C. The Humboldt current system: Ecosystem components and processes, fisheries, and sediment studies. Prog. Oceanogr. 83, 65–79 (2009).

    Article 
    ADS 

    Google Scholar 

  • Miloslavich, P. et al. Marine biodiversity in the Atlantic and Pacific coasts of South America: Knowledge and gaps. PLoS ONE 6, e14631 (2011).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Marín, V., Espinoza, S. & Fleminger, A. Morphometric study of Calanus chilensis males along the Chilean coast. Hydrobiologia 292, 75–80 (1994).

    Article 

    Google Scholar 

  • Escribano, R. & McLaren, I. Production of Calanus chilensis in the upwelling area of Antofagasta Northern Chile. Mar. Ecol. Prog. Ser. 177, 147–156 (1999).

    Article 
    ADS 

    Google Scholar 

  • Escribano, R. & Hidalgo, P. Spatial distribution of copepods in the north of the Humboldt Current region off Chile during coastal upwelling. J. Mar. Biol. Assoc. U. K. 80, 283–290 (2000).

    Article 

    Google Scholar 

  • Hirche, H. J., Barz, K., Ayon, P. & Schulz, J. High resolution vertical distribution of the copepod Calanus chilensis in relation to the shallow oxygen minimum zone off northern Peru using LOKI, a new plankton imaging system. Deep Res. I Oceanogr. Res. Pap. 88, 63–73 (2014).

    Article 
    ADS 
    CAS 

    Google Scholar 

  • Sabatini, M., rez, F. & Martos, P. Distribution pattern and population structure of Calanus australis Brodsky, 1959 over the southern Patagonian Shelf off Argentina in summer. ICES J. Mar. Sci. 57, 1856–1866 (2000).

    Article 

    Google Scholar 

  • Escribano, R. Population dynamics of Calanus chilensis in the Chilean Eastern Boundary Humboldt Current. Fish. Oceanogr. 7, 245–251 (1998).

    Article 

    Google Scholar 

  • Hidalgo, P. et al. Patterns of copepod diversity in the Chilean coastal upwelling system. Deep Sea Res. Part II Top. Stud. Oceanogr. 57, 2089–2097 (2010).

    Article 
    ADS 

    Google Scholar 

  • Hidalgo, P., Escribano, R., Fuentes, M., Jorquera, E. & Vergara, O. How coastal upwelling influences spatial patterns of size-structured diversity of copepods off central-southern Chile (summer 2009). Prog. Oceanogr. 92–95, 134–145 (2012).

    Article 
    ADS 

    Google Scholar 

  • Giraldo, A., Escribano, R. & Marin, V. Spatial distribution of Calanus chilensis off Mejillones Peninsula (northern Chile): Ecological consequences upon coastal upwelling. Mar. Ecol. Prog. Ser. 230, 225–234 (2002).

    Article 
    ADS 

    Google Scholar 

  • Gonzalez, A. & Marin, V. Distribution and life cycle of Calanus chilensis and Centropages brachiatus (Copepoda) in Chilean coastal waters: A GIS approach. Mar. Ecol. Prog. Ser. 165, 109–117 (1998).

    Article 
    ADS 

    Google Scholar 

  • Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Parmesan, C. Influences of species, latitudes and methodologies on estimates of phenological response to global warming. Glob. Change Biol. 13, 1860–1872 (2007).

    Article 
    ADS 

    Google Scholar 

  • Visser, M. E. & Both, C. Shifts in phenology due to global climate change: The need for a yardstick. Proc. R. Soc. B Biol. Sci. 272, 2561–2569 (2005).

    Article 

    Google Scholar 

  • Chaudhary, C., Richardson, A. J., Schoeman, D. S. & Costello, M. J. Global warming is causing a more pronounced dip in marine species richness around the equator. Proc. Natl. Acad. Sci. 118, e2015094118 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ferrier, S., Drielsma, M., Manion, G. & Watson, G. Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. II. Community-level modelling. Biodivers. Conserv. 11, 2309–2338 (2002).

    Article 

    Google Scholar 

  • Jetz, W., Wilcove, D. S. & Dobson, A. P. Projected impacts of climate and land-use change on the global diversity of birds. PLoS Biol. 5, e157 (2007).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Peterson, A. T. et al. Ecological Niches and Geographic Distributions (MPB-49) (Princeton University Press, 2011). https://doi.org/10.2307/j.ctt7stnh.

    Book 

    Google Scholar 

  • Franklin, J. Spatial Inference and Prediction. Mapping Species Distributions Vol. 141 (Cambridge University Press, 2010).

    Book 

    Google Scholar 

  • Guisan, A., Thuiller, W. & Zimmermann, N. E. Habitat Suitability and Distribution Models: With Applications in R. Ecology Biodiversity and Conservation (Cambridge University Press, 2017). https://doi.org/10.1017/9781139028271.

    Book 

    Google Scholar 

  • Freer, J. J., Partridge, J. C., Tarling, G. A., Collins, M. A. & Genner, M. J. Predicting ecological responses in a changing ocean: The effects of future climate uncertainty. Mar. Biol. 165, 7 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Robinson, N. M., Nelson, W. A., Costello, M. J., Sutherland, J. E. & Lundquist, C. J. A systematic review of marine-based species distribution models (SDMs) with recommendations for best practice. Front. Mar. Sci. 4, 421 (2017).

    Article 

    Google Scholar 

  • Pennino, M. G. et al. Accounting for preferential sampling in species distribution models. Ecol. Evol. 9, 653–663 (2019).

    Article 
    PubMed 

    Google Scholar 

  • Coll, M., Pennino, M. G., Steenbeek, J., Sole, J. & Bellido, J. M. Predicting marine species distributions: Complementarity of food-web and Bayesian hierarchical modelling approaches. Ecol. Model. 405, 86–101 (2019).

    Article 

    Google Scholar 

  • Stock, B. C. et al. Comparing predictions of fisheries bycatch using multiple spatiotemporal species distribution model frameworks. Can. J. Fish. Aquat. Sci. 77, 146–163 (2019).

    Article 

    Google Scholar 

  • Lezama-Ochoa, N. et al. Spatio-temporal distribution of the spinetail devil ray mobula mobular in the Eastern tropical Atlantic ocean. Endanger. Species Res. 43, 447–460 (2020).

    Article 

    Google Scholar 

  • Marshall, C. E., Glegg, G. A. & Howell, K. L. Species distribution modelling to support marine conservation planning: The next steps. Mar. Policy 45, 330–332 (2014).

    Article 

    Google Scholar 

  • Hunt, T. N., Allen, S. J., Bejder, L. & Parra, G. J. Identifying priority habitat for conservation and management of Australian humpback dolphins within a marine protected area. Sci. Rep. 10, 1–14 (2020).

    Article 

    Google Scholar 

  • Champion, C., Brodie, S. & Coleman, M. A. Climate-driven range shifts are rapid yet variable among recreationally important coastal-pelagic fishes. Front. Mar. Sci. 8, 1–13 (2021).

    Article 

    Google Scholar 

  • Przeslawski, R., Falkner, I., Ashcroft, M. B. & Hutchings, P. Using rigorous selection criteria to investigate marine range shifts. Estuar. Coast. Shelf Sci. 113, 205–212 (2012).

    Article 
    ADS 

    Google Scholar 

  • Januario, S. M., Estay, S. A., Labra, F. A. & Lima, M. Combining environmental suitability and population abundances to evaluate the invasive potential of the tunicate Ciona intestinalis along the temperate South American coast. PeerJ 3, e1357. https://doi.org/10.7717/peerj.1357 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Pinochet, J., Rivera, R., Neill, P. E., Brante, A. & Hernández, C. E. Spread of the non-native anemone Anemonia alicemartinae Häussermann & Försterra, 2001 along the Humboldt-current large marine ecosystem: An ecological niche model approach. PeerJ https://doi.org/10.7717/peerj.7156 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lh, G., Rj, R. & Brante, A. One step ahead of sea anemone invasions with ecological niche modeling: Potential distributions and niche dynamics of three successful invasive species. Mar. Ecol. Prog. Ser. 690, 83–95 (2022).

    Article 

    Google Scholar 

  • Allynid, A. J. et al. Comparing and synthesizing quantitative distribution models and qualitative vulnerability assessments to project marine species distributions under climate change. PLoS ONE 15, 1–28 (2020).

    Google Scholar 

  • Pennino, M. G. et al. Current and future influence of environmental factors on small pelagic fish distributions in the northwestern mediterranean sea. Front. Mar. Sci. 7, 1–20 (2020).

    Article 

    Google Scholar 

  • Melo-Merino, S. M., Reyes-Bonilla, H. & Lira-Noriega, A. Ecological niche models and species distribution models in marine environments: A literature review and spatial analysis of evidence. Ecol. Model. 415, 108837 (2020).

    Article 

    Google Scholar 

  • Rosa, R., Dierssen, H. M., Gonzalez, L. & Seibel, B. A. Ecological biogeography of cephalopod molluscs in the Atlantic Ocean: Historical and contemporary causes of coastal diversity patterns. Glob. Ecol. Biogeogr. 17, 600–610 (2008).

    Article 

    Google Scholar 

  • Barton, A. D., Dutkiewicz, S., Flierl, G., Bragg, J. & Follows, M. J. Patterns of diversity in marine phytoplankton. Science 327, 1509–1511 (2010).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Rodríguez-Ramos, T., Marañón, E. & Cermeño, P. Marine nano- and microphytoplankton diversity: Redrawing global patterns from sampling-standardized data. Glob. Ecol. Biogeogr. 24, 527–538 (2015).

    Article 

    Google Scholar 

  • Righetti, D., Vogt, M., Gruber, N., Psomas, A. & Zimmermann, N. E. Global pattern of phytoplankton diversity driven by temperature and environmental variability. Sci. Adv. 5, eaau6253 (2022).

    Article 
    ADS 

    Google Scholar 

  • Busseni, G. et al. Large scale patterns of marine diatom richness: Drivers and trends in a changing ocean. Glob. Ecol. Biogeogr. 29, 1915–1928 (2020).

    Article 

    Google Scholar 

  • Ruz, P. M., Hidalgo, P., Yáñez, S., Escribano, R. & Keister, J. E. Egg production and hatching success of Calanus chilensis and Acartia tonsa in the northern Chile upwelling zone (23°S) Humboldt Current System. J. Mar. Syst. 148, 200–212 (2015).

    Article 

    Google Scholar 

  • Ashlock, L., García-Reyes, M., Gentemann, C., Batten, S. & Sydeman, W. Temperature and patterns of occurrence and abundance of key copepod taxa in the Northeast Pacific. Front. Mar. Sci. 8, 1–10 (2021).

    Article 
    ADS 

    Google Scholar 

  • Campbell, M. D. et al. Testing Bergmann’s rule in marine copepods. Ecography 44, 1283–1295 (2021).

    Article 

    Google Scholar 

  • Soberón, J. Grinnellian and Eltonian niches and geographic distributions of species. Ecol. Lett. 10, 1115–1123 (2007).

    Article 
    PubMed 

    Google Scholar 

  • Soberón, J. & Nakamura, M. Niches and distributional areas: Concepts, methods, and assumptions. Proc. Natl. Acad. Sci. U. S. A. 106, 19644–19650 (2009).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Morales, C. E. et al. Mesoscale structure of copepod assemblages in the coastal transition zone and oceanic waters off central-southern Chile. Prog. Oceanogr. 84, 158–173 (2010).

    Article 
    ADS 

    Google Scholar 

  • Gonzalez, R. R. & Quiñones, R. A. Ldh activity in Euphausia mucronata and Calanus chilensis: Implications for vertical migration behaviour. J. Plankton Res. 24, 1349–1356 (2002).

    Article 
    CAS 

    Google Scholar 

  • Escribano, R., Hidalgo, P. & Krautz, C. Zooplankton associated with the oxygen minimum zone system in the northern upwelling region of Chile during March 2000. Deep Sea Res. Part II Top. Stud. Oceanogr. 56, 1083–1094 (2009).

    Article 
    ADS 

    Google Scholar 

  • Fernández-Urruzola, I. et al. Plankton respiration in the Atacama Trench region: Implications for particulate organic carbon flux into the hadal realm. Limnol. Oceanogr. 66, 3134–3148 (2021).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Steinberg, D. K. & Landry, M. R. Zooplankton and the ocean carbon cycle. Ann. Rev. Mar. Sci. 9, 413–444 (2017).

    Article 
    PubMed 

    Google Scholar 

  • Tutasi, P. & Escribano, R. Zooplankton diel vertical migration and downward~C flux into the oxygen minimum zone in the highly productive upwelling region off northern Chile. Biogeosciences 17, 455–473 (2020).

    Article 
    ADS 
    CAS 

    Google Scholar 

  • Gonzalez, A. & Marín, V. H. Distribution and life cycle of Calanus chilensis and Centropages brachiatus (Copepoda) in chilean coastal waters: A GIS approach. Mar. Ecol. Prog. Ser. 165, 109–117 (1998).

    Article 
    ADS 

    Google Scholar 

  • Pulliam, H. R. Sources, sinks, and population regulation. Am. Nat. 132, 652–661 (1988).

    Article 

    Google Scholar 

  • Dias, P. C. Sources and sinks in population biology. Trends Ecol. Evol. 11, 326–330 (1996).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Ding, M., Lin, P., Liu, H., Hu, A. & Liu, C. Lagrangian eddy kinetic energy of ocean mesoscale eddies and its application to the Northwestern Pacific. Sci. Rep. 10, 12791 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Morales, C. E. et al. The distribution of chlorophyll-a and dominant planktonic components in the coastal transition zone off Concepción, central Chile, during different oceanographic conditions. Prog. Oceanogr. 75, 452–469 (2007).

    Article 
    ADS 

    Google Scholar 

  • Escribano, R. & Rodriguez, L. Life cycle of Calanus chilensis Brodsky in Bay of San Jorge, Antofagasta Chile. Hydrobiologia 292, 289–294 (1994).

    Article 

    Google Scholar 

  • Hidalgo, P. & Escribano, R. Coupling of life cycles of the copepods Calanus chilensis and Centropages brachiatus to upwelling induced variability in the central-southern region of Chile. Prog. Oceanogr. 75, 501–517 (2007).

    Article 
    ADS 

    Google Scholar 

  • Sobarzo, M., Bravo, L., Donoso, D., Garcés-Vargas, J. & Schneider, W. Coastal upwelling and seasonal cycles that influence the water column over the continental shelf off central Chile. Prog. Oceanogr. 75, 363–382 (2007).

    Article 
    ADS 

    Google Scholar 

  • Carlson, C. J. embarcadero: Species distribution modelling with Bayesian additive regression trees in r. Methods Ecol. Evol. 11, 850–858 (2020).

    Article 

    Google Scholar 

  • Gelfand, A. et al. Explaining species distribution patterns through hierarchical modeling. Bayesian Anal. https://doi.org/10.1214/06-BA102 (2006).

    Article 
    MathSciNet 
    MATH 

    Google Scholar 

  • Hortal, J. et al. Seven shortfalls that beset large-scale knowledge of biodiversity. Annu. Rev. Ecol. Evol. Syst. 46, 523–549 (2015).

    Article 

    Google Scholar 

  • Wisz, M. S. et al. Effects of sample size on the performance of species distribution models. Divers. Distrib. 14, 763–773 (2008).

    Article 

    Google Scholar 

  • van Proosdij, A. S. J., Sosef, M. S. M., Wieringa, J. J. & Raes, N. Minimum required number of specimen records to develop accurate species distribution models. Ecography 39, 542–552 (2016).

    Article 

    Google Scholar 

  • Gaul, W. et al. Data quantity is more important than its spatial bias for predictive species distribution modelling. PeerJ 8, e10411 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Beck, J., Böller, M., Erhardt, A. & Schwanghart, W. Spatial bias in the GBIF database and its effect on modeling species’ geographic distributions. Ecol. Inform. 19, 10–15 (2014).

    Article 

    Google Scholar 

  • Breiner, F. T., Guisan, A., Bergamini, A. & Nobis, M. P. Overcoming limitations of modelling rare species by using ensembles of small models. Methods Ecol. Evol. 6, 1210–1218 (2015).

    Article 

    Google Scholar 

  • Breiner, F. T., Nobis, M. P., Bergamini, A. & Guisan, A. Optimizing ensembles of small models for predicting the distribution of species with few occurrences. Methods Ecol. Evol. 9, 802–808 (2018).

    Article 

    Google Scholar 

  • Yasuhara, M. et al. Past and future decline of tropical pelagic biodiversity. Proc. Natl. Acad. Sci. 117, 12891–12896 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Richardson, A., Schoeman, D., Richardson, A. J. & Schoeman, D. S. Climate impact on plankton ecosystems in the Northeast Atlantic. Science 305, 1609–1612 (2004).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Chiba, S., Sugisaki, H., Nonaka, M. & Saino, T. Geographical shift of zooplankton communities and decadal dynamics of the Kuroshio-Oyashio currents in the western North Pacific. Glob. Change Biol. 15, 1846–1858 (2009).

    Article 
    ADS 

    Google Scholar 

  • Reygondeau, G. & Beaugrand, G. Future climate-driven shifts in distribution of Calanus finmarchicus. Glob. Change Biol. 17, 756–766 (2011).

    Article 
    ADS 

    Google Scholar 

  • Beaugrand, G., Lindley, J. A., Helaouet, P. & Bonnet, D. Macroecological study of Centropages typicus in the North Atlantic Ocean. Prog. Oceanogr. 72, 259–273 (2007).

    Article 
    ADS 

    Google Scholar 

  • Hirche, H. J., Barz, K., Ayon, P. & Schulz, J. High resolution vertical distribution of the copepod Calanus chilensis in relation to the shallow oxygen minimum zone off northern Peru using LOKI, a new plankton imaging system. Deep Sea Res. I Oceanogr. Res. Pap. 88, 63–73 (2014).

    Article 
    ADS 
    CAS 

    Google Scholar 

  • Spalding, M. D. et al. Marine ecoregions of the world: A bioregionalization of coastal and shelf areas. Bioscience 57, 573–583 (2007).

    Article 

    Google Scholar 

  • Barve, N. et al. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol. Model. 222, 1810–1819 (2011).

    Article 

    Google Scholar 

  • Riquelme-Bugueño, R. et al. The influence of upwelling variation on the spatially-structured euphausiid community off central-southern Chile in 2007–2008. Prog. Oceanogr. 92–95, 146–165 (2012).

    Article 
    ADS 

    Google Scholar 

  • Soberón, J. & Peterson, A. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers. Inform. https://doi.org/10.17161/bi.v2i0.4 (2005).

    Article 

    Google Scholar 

  • Provoost, P. & Bosch, S. robis: Ocean Biodiversity Information System (OBIS) Client (2020).

  • Chamberlain, S. & Oldoni, D. rgbif: Interface to the Global Biodiversity Information Facility API (2021).

  • R Core Team. R: A Language and Environment for Statistical Computing (2021).

  • Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).

    Article 

    Google Scholar 

  • ESRI. ArcGIS Desktop: Release 10.4.1 (Envrionmental Systems Research Institute, 2016).

    Google Scholar 

  • De Marco, P. & Nóbrega, C. C. Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation. PLoS ONE 13, e202403 (2018).

    Article 

    Google Scholar 

  • Feng, X. et al. A checklist for maximizing reproducibility of ecological niche models. Nat. Ecol. Evol. 3, 1382–1395 (2019).

    Article 
    PubMed 

    Google Scholar 

  • Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling?. Ecography 37, 191–203 (2014).

    Article 

    Google Scholar 

  • Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).

    Article 

    Google Scholar 

  • Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).

    Article 

    Google Scholar 

  • Pinto-Ledezma, J. N. & Cavender-Bares, J. Predicting species distributions and community composition using satellite remote sensing predictors. Sci. Rep. 11, 16448 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ellison, A. M. Bayesian inference in ecology. Ecol. Lett. 7, 509–520 (2004).

    Article 

    Google Scholar 

  • Pennino, M. G., Muñoz, F., Conesa, D., López-Quílez, A. & Bellido, J. M. Bayesian spatio-temporal discard model in a demersal trawl fishery. J. Sea Res. 90, 44–53 (2014).

    Article 

    Google Scholar 

  • Di Cola, V. et al. ecospat: An R package to support spatial analyses and modeling of species niches and distributions. Ecography 40, 774–787 (2017).

    Article 

    Google Scholar 

  • Engler, R., Guisan, A. & Rechsteiner, L. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. J. Appl. Ecol. 41, 263–274 (2004).

    Article 

    Google Scholar 

  • Pearce, J. & Ferrier, S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Model. 133, 225–245 (2000).

    Article 

    Google Scholar 

  • Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. A. Evaluating resource selection functions. Ecol. Model. 157, 281–300 (2002).

    Article 

    Google Scholar 

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

    Article 

    Google Scholar 

  • Warren, D. & Dinnage, R. ENMTools: Analysis of Niche Evolution using Niche and Distribution Models (2020).

  • Assis, J. et al. Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Glob. Ecol. Biogeogr. 27, 277–284 (2018).

    Article 

    Google Scholar 

  • Elith, J., Kearney, M. & Phillips, S. The art of modelling range-shifting species. Methods Ecol. Evol. 1, 330–342 (2010).

    Article 

    Google Scholar 

  • Osorio-Olvera, L. et al. ntbox: An r package with graphical user interface for modelling and evaluating multidimensional ecological niches. Methods Ecol. Evol. 11, 1199–1206 (2020).

    Article 

    Google Scholar 

  • Bosch, S., Tyberghein, L. & De Clerck, O. ‘sdmpredictors’: Species distribution modelling predictor datasets. R package version 0.2.6. R Packag. version 0.2.6 (2018).

  • Thuiller, W., Georges, D., Engler, R. & Breiner, F. biomod2: Ensemble platform for species distribution modeling (2020).

  • Zurell, D. et al. A standard protocol for reporting species distribution models. Ecography 43, 1261–1277 (2020).

    Article 

    Google Scholar 


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