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Species distribution modelling of the Southern Ocean benthos: a review on methods, cautions and solutions

Published online by Cambridge University Press:  21 June 2021

Charlène Guillaumot Open the ORCID record for Charlène Guillaumot [Opens in a new window] ,
Bruno Danis Open the ORCID record for Bruno Danis [Opens in a new window]  and
Thomas Saucède Open the ORCID record for Thomas Saucède [Opens in a new window]
Charlène Guillaumot*
Affiliation:
Université Libre de Bruxelles, Marine Biology Lab, Avenue F.D. Roosevelt, 50. CP 160/15 1050Bruxelles, Belgium UMR 6282 Biogéosciences, Univ. Bourgogne Franche-Comté, CNRS, 6 bd Gabriel F-21000, Dijon, France
Bruno Danis
Affiliation:
Université Libre de Bruxelles, Marine Biology Lab, Avenue F.D. Roosevelt, 50. CP 160/15 1050Bruxelles, Belgium
Thomas Saucède
Affiliation:
UMR 6282 Biogéosciences, Univ. Bourgogne Franche-Comté, CNRS, 6 bd Gabriel F-21000, Dijon, France
*
[email protected]
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Abstract

Species distribution modelling studies the relationship between species occurrence records and their environmental setting, providing a valuable approach to predicting species distribution in the Southern Ocean (SO), a challenging region to investigate due to its remoteness and extreme weather and sea conditions. The specificity of SO studies, including restricted field access and sampling, the paucity of observations and difficulties in conducting biological experiments, limit the performance of species distribution models. In this review, we discuss some issues that may influence model performance when preparing datasets and calibrating models, namely the selection and quality of environmental descriptors, the spatial and temporal biases that may affect the quality of occurrence data, the choice of modelling algorithms and the spatial scale and limits of the projection area. We stress the importance of evaluating and communicating model uncertainties, and the most common evaluation metrics are reviewed and discussed accordingly. Based on a selection of case studies on SO benthic invertebrates, we highlight important cautions to take and pitfalls to avoid when modelling the distribution of SO species, and we provide some guidelines along with potential methods and original solutions that can be used for improving model performance.

Keywords

Antarcticabiaseslimitsmarine benthic invertebratesmodelling performance
Type
Biological Sciences
Information
Antarctic Science , Volume 33 , Issue 4 , August 2021 , pp. 349 - 372
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Copyright
Copyright © Antarctic Science Ltd 2021

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References

Addison, P.F., Rumpff, L., Bau, S.S., Carey, J.M., Chee, Y.E., Jarrad, F.C., et al. 2013. Practical solutions for making models indispensable in conservation decision-making. Diversity and Distributions, 19, 490–502.CrossRefGoogle Scholar
Aguiar, L.M., da Rosa, R.O., Jones, G. & Machado, R.B. 2015. Effect of chronological addition of records to species distribution maps: the case of Tonatia saurophila maresi (Chiroptera, Phyllostomidae) in South America. Austral Ecology, 40, 836–844.CrossRefGoogle Scholar
Aguirre-Gutiérrez, J., Carvalheiro, L.G., Polce, C., van Loon, E.E., Raes, N., Reemer, M. & Biesmeijer, J.C. 2013. Fit-for-purpose: species distribution model performance depends on evaluation criteria - Dutch hoverflies as a case study. PLoS One, 8, e63708.CrossRefGoogle ScholarPubMed
Aiello-Lammens, M.E., Boria, R.A., Radosavljevic, A., Vilela, B. & Anderson, R.P. 2015. spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography, 38, 541–545.CrossRefGoogle Scholar
Allan, M.G. 2014. Remote sensing, numerical modelling and ground truthing for analysis of lake water quality and temperature. Doctoral dissertation, University of Waikato.Google Scholar
Allouche, O., Tsoar, A. & Kadmon, R. 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology, 43, 1223–1232.CrossRefGoogle Scholar
Anderson, R.P. 2017. When and how should biotic interactions be considered in models of species niches and distributions? Journal of Biogeography, 44, 8–17.CrossRefGoogle Scholar
Anderson, R.P. & Gonzalez, I. Jr. 2011. Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent. Ecological Modelling, 222, 2796–2811.CrossRefGoogle Scholar
Anderson, R.P. & Raza, A. 2010. The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela. Journal of Biogeography, 37, 1378–1393.CrossRefGoogle Scholar
Araújo, M.B. & Guisan, A. 2006. Five (or so) challenges for species distribution modelling. Journal of Biogeography, 33, 1677–1688.CrossRefGoogle Scholar
Araújo, M.B. & New, M. 2007. Ensemble forecasting of species distributions. Trends in Ecology & Evolution, 22, 42–47.CrossRefGoogle ScholarPubMed
Araújo, M.B., Anderson, R.P., Barbosa, A.M., Beale, C.M., Dormann, C.F., Early, R., et al. 2019. Standards for distribution models in biodiversity assessments. Science Advances, 5, eaat4858.CrossRefGoogle ScholarPubMed
Arthur, B., Hindell, M., Bester, M., De Bruyn, P.N., Trathan, P., Goebel, M. & Lea, M.A. 2017. Winter habitat predictions of a key Southern Ocean predator, the Antarctic fur seal (Arctocephalus gazella). Deep-Sea Research II: Topical Studies in Oceanography, 140, 171–181.CrossRefGoogle Scholar
Ashton, G.V., Morley, S.A., Barnes, D.K., Clark, M.S. & Peck, L.S. 2017. Warming by 1°C drives species and assemblage level responses in Antarctica's marine shallows. Current Biology, 27, 2698–2705.CrossRefGoogle Scholar
Assis, J., Tyberghein, L., Bosch, S., Verbruggen, H., Serrão, E.A. & De Clerck, O. 2018. BioORACLE v2.0: extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27, 277–284.CrossRefGoogle Scholar
Aubry, K.B., Raley, C.M. & McKelvey, K.S. 2017. The importance of data quality for generating reliable distribution models for rare, elusive, and cryptic species. PLoS One, 12, e0179152.CrossRefGoogle ScholarPubMed
Austin, M.P. 2002. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling, 157, 101–118.CrossRefGoogle Scholar
Austin, M.P. & Van Niel, K.P. 2011. Improving species distribution models for climate change studies: variable selection and scale. Journal of Biogeography, 38, 1–8.CrossRefGoogle Scholar
Bahn, V. & McGill, B.J. 2007. Can niche-based distribution models outperform spatial interpolation? Global Ecology and Biogeography, 16, 733–742.CrossRefGoogle Scholar
Bahn, V. & McGill, B.J. 2013. Testing the predictive performance of distribution models. Oikos, 122, 321–331.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Barve, N., Barve, V., Jiménez-Valverde, A., Lira-Noriega, A., Maher, S.P., Peterson, A.T., et al. 2011. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecological Modelling, 222, 1810–1819.CrossRefGoogle Scholar
Basher, Z. & Costello, M.J. 2016. The past, present and future distribution of a deep-sea shrimp in the Southern Ocean. PeerJ, 4, e1713.CrossRefGoogle ScholarPubMed
Beale, C.M. & Lennon, J.J. 2012. Incorporating uncertainty in predictive species distribution modelling. Philosophical Transactions of the Royal Society B: Biological Sciences, 367, 247–258.CrossRefGoogle ScholarPubMed
Bean, W.T., Stafford, R. & Brashares, J.S. 2012. The effects of small sample size and sample bias on threshold selection and accuracy assessment of species distribution models. Ecography, 35, 250–258.CrossRefGoogle Scholar
Beaumont, L.J., Hughes, L. & Pitman, A.J. 2008. Why is the choice of future climate scenarios for species distribution modelling important? Ecology Letters, 11, 1135–1146.CrossRefGoogle ScholarPubMed
Beaumont, L.J., Graham, E., Duursma, D.E., Wilson, P.D., Cabrelli, A., Baumgartner, J.B., et al. 2016. Which species distribution models are more (or less) likely to project broad-scale, climate-induced shifts in species ranges? Ecological Modelling, 342, 135–146.CrossRefGoogle Scholar
Bertrand, A., Habasque, J., Hattab, T., Hintzen, N.T., Oliveros-Ramos, R., Gutiérrez, M., et al. 2016. 3-D habitat suitability of jack mackerel Trachurus murphyi in the southeastern Pacific, a comprehensive study. Progress in Oceanography, 146, 199–211.CrossRefGoogle Scholar
Bloom, T.D., Flower, A. & DeChaine, E.G. 2018. Why georeferencing matters: introducing a practical protocol to prepare species occurrence records for spatial analysis. Ecology and Evolution, 8, 765–777.CrossRefGoogle ScholarPubMed
Boria, R.A., Olson, L.E., Goodman, S.M. & Anderson, R.P. 2014. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecological Modelling, 275, 73–77.CrossRefGoogle Scholar
Boulanger, Y., Parisien, M.A. & Wang, X. 2018. Model-specification uncertainty in future area burned by wildfires in Canada. International Journal of Wildland Fire, 27, 164–175.CrossRefGoogle Scholar
Bradie, J. & Leung, B. 2017. A quantitative synthesis of the importance of variables used in MaxEnt species distribution models. Journal of Biogeography, 44, 1344–1361.CrossRefGoogle Scholar
Braunisch, V., Coppes, J., Arlettaz, R., Suchant, R., Schmid, H. & Bollmann, K. 2013. Selecting from correlated climate variables: a major source of uncertainty for predicting species distributions under climate change. Ecography, 36, 971–983.CrossRefGoogle Scholar
Breiman, L. 2001. Random forests. Machine Learning, 45, 5–32.CrossRefGoogle Scholar
Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. 1984. Classification and regression trees. Belmont, CA: Wadsworth International Group, 368 pp.Google Scholar
Breiner, F.T., Guisan, A., Bergamini, A. & Nobis, M.P. 2015. Overcoming limitations of modelling rare species by using ensembles of small models. Methods in Ecology and Evolution, 6, 1210–1218.CrossRefGoogle Scholar
Breiner, F.T., Nobis, M.P., Bergamini, A. & Guisan, A. 2018. Optimizing ensembles of small models for predicting the distribution of species with few occurrences. Methods in Ecology and Evolution, 9, 802–808.CrossRefGoogle Scholar
Breitzke, M. 2014. Overview of seismic research activities in the Southern Ocean-quantifying the environmental impact. Antarctic Science, 26, 80–92.CrossRefGoogle Scholar
Briscoe, N.J., Kearney, M.R., Taylor, C.A. & Wintle, B.A. 2016. Unpacking the mechanisms captured by a correlative species distribution model to improve predictions of climate refugia. Global Change Biology, 22, 2425–2439.CrossRefGoogle ScholarPubMed
Brotons, L., Thuiller, W., Araújo, M.B. & Hirzel, A.H. 2004. Presence-absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography, 27, 437–448.CrossRefGoogle Scholar
Buckley, L.B., Urban, M.C., Angilletta, M.J., Crozier, L.G., Rissler, L.J. & Sears, M.W. 2010. Can mechanism inform species' distribution models? Ecology Letters, 13, 1041–1054.Google ScholarPubMed
Bucklin, D.N., Basille, M., Benscoter, A.M., Brandt, L.A., Mazzotti, F.J., Romanach, S.S., et al. 2015. Comparing species distribution models constructed with different subsets of environmental predictors. Diversity and Distributions, 21, 23–35.CrossRefGoogle Scholar
Buisson, L., Thuiller, W., Casajus, N., Lek, S. & Grenouillet, G. 2010. Uncertainty in ensemble forecasting of species distribution. Global Change Biology, 16, 1145–1157.CrossRefGoogle Scholar
Byrne, M., Gall, M., Wolfe, K., & Agüera, A. 2016. From pole to pole: the potential for the Arctic seastar Asterias amurensis to invade a warming Southern Ocean. Global Change Biology, 22, 3874–3887.CrossRefGoogle ScholarPubMed
Cacciapaglia, C. & van Woesik, R. 2017. Marine species distribution modelling and the effects of genetic isolation under climate change. Journal of Biogeography, 45, 154–163.CrossRefGoogle Scholar
Carvalho, B.M., Rangel, E.F., Ready, P.D. & Vale, M.M. 2015. Ecological niche modelling predicts southward expansion of Lutzomyia (Nyssomyia) flaviscutellata (Diptera: Psychodidae: Phlebotominae), vector of Leishmania (Leishmania) amazonensis in South America, under climate change. PLoS One, 10, e0143282.CrossRefGoogle ScholarPubMed
Cavanagh, R.D., Murphy, E.J., Bracegirdle, T.J., Turner, J., Knowland, C.A., Corney, S.P., et al. 2017. A synergistic approach for evaluating climate model output for ecological applications. Frontiers in Marine Science, 4, 308.CrossRefGoogle Scholar
Chefaoui, R.M. & Lobo, J.M. 2008. Assessing the effects of pseudo-absences on predictive distribution model performance. Ecological Modelling, 210, 478–486.CrossRefGoogle Scholar
Chevin, L.M., Lande, R. & Mace, G.M. 2010. Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory. PLoS Biology, 8, e1000357.CrossRefGoogle Scholar
Clark, M.S., Nieva, L.V., Hoffman, J.I., Davies, A.J., Trivedi, U.H., Turner, F., et al. 2019. Lack of long-term acclimation in Antarctic encrusting species suggests vulnerability to warming. Nature Communications, 10, 1–10.CrossRefGoogle ScholarPubMed
Clarke, A., Griffiths, H.J., Linse, K., Barnes, D.K. & Crame, J.A. 2007. How well do we know the Antarctic marine fauna? A preliminary study of macroecological and biogeographical patterns in Southern Ocean gastropod and bivalve molluscs. Diversity and Distributions, 13, 620–632.CrossRefGoogle Scholar
Conlisk, E., Syphard, A.D., Franklin, J., Flint, L., Flint, A. & Regan, H. 2013. Uncertainty in assessing the impacts of global change with coupled dynamic species distribution and population models. Global Change Biology, 19, 858–869.CrossRefGoogle ScholarPubMed
Connor, T., Hull, V., Viña, A., Shortridge, A., Tang, Y., Zhang, J., et al. 2018. Effects of grain size and niche breadth on species distribution modeling. Ecography, 41, 1270–1282.CrossRefGoogle Scholar
Costa, H., Foody, G.M., Jiménez, S. & Silva, L. 2015. Impacts of species misidentification on species distribution modeling with presence-only data. ISPRS International Journal of Geo-Information, 4, 2496–2518.CrossRefGoogle Scholar
Crase, B., Liedloff, A.C. & Wintle, B.A. 2012. A new method for dealing with residual spatial autocorrelation in species distribution models. Ecography, 35, 879–888.CrossRefGoogle Scholar
Crimmins, S.M., Dobrowski, S.Z. & Mynsberge, A.R. 2013. Evaluating ensemble forecasts of plant species distributions under climate change. Ecological Modelling, 266, 126–130.CrossRefGoogle Scholar
Currie, D.J. 2007. Disentangling the roles of environment and space in ecology. Journal of Biogeography, 34, 2009–2011.CrossRefGoogle Scholar
Dhingra, M.S., Artois, J., Robinson, T.P., Linard, C., Chaiban, C., Xenarios, I., et al. 2016. Global mapping of highly pathogenic avian influenza H5N1 and H5Nx clade 2.3. 4.4 viruses with spatial cross-validation. Elife, 5, e19571.CrossRefGoogle ScholarPubMed
De'ath, G. & Fabricius, K.E. 2000. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology, 81, 3178–3192.CrossRefGoogle Scholar
De Broyer, C. & Danis, B. 2011. How many species in the Southern Ocean? Towards a dynamic inventory of the Antarctic marine species. Deep-Sea Research II: Topical Studies in Oceanography, 58, 5–17.CrossRefGoogle Scholar
De Broyer, C., Koubbi, P., Griffiths, H.J., Raymond, B., d'Udekem d'Acoz, C., Van de Putte, A.P., et al. 2014. Biogeographic atlas of the Southern Ocean. Cambridge: Scientific Committee on Antarctic Research, 498 pp.Google Scholar
De la Hoz, C.F., Ramos, E., Puente, A. & Juanes, J.A. 2019. Temporal transferability of marine distribution models: the role of algorithm selection. Ecological Indicators, 106, 105499.CrossRefGoogle Scholar
De la Hoz, C.F., Ramos, E., Acevedo, A., Puente, A., Losada, Í.J. & Juanes, J.A. 2018. OCLE: a European open access database on climate change effects on littoral and oceanic ecosystems. Progress in Oceanography, 168, 222–231.CrossRefGoogle Scholar
De Villiers, M., Hattingh, V. & Kriticos, D.J. 2013. Combining field phenological observations with distribution data to model the potential distribution of the fruit fly Ceratitis rosa Karsch (Diptera: Tephritidae). Bulletin of Entomological Research, 103, 60–73.CrossRefGoogle Scholar
Deleersnijder, E., Van Ypersele, J.P., & Campin, J.M. 1993. An orthogonal curvilinear coordinate system for a world ocean model. Ocean Modelling, 100, 7–10.Google Scholar
Diniz-Filho, J.A., Bini, L.M. & Hawkins, B.A. 2003. Spatial autocorrelation and red herrings in geographical ecology. Global Ecology and Biogeography, 12, 53–64.CrossRefGoogle Scholar
Diniz-Filho, J.A., Mauricio Bini, L., Fernando Rangel, T., Loyola, R.D., Hof, C., Nogués-Bravo, D. & Araújo, M.B. 2009. Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change. Ecography, 32, 897–906.CrossRefGoogle Scholar
Dixon, G.B., Davies, S.W., Aglyamova, G.V., Meyer, E., Bay, L.K. & Matz, M.V. 2015. Genomic determinants of coral heat tolerance across latitudes. Science, 348, 1460–1462.CrossRefGoogle ScholarPubMed
Dormann, C.F. 2007. Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Global Ecology and Biogeography, 16, 129–138.CrossRefGoogle Scholar
Dormann, C.F., Purschke, O., Márquez, J.R., Lautenbach, S. & Schröder, B. 2008. Components of uncertainty in species distribution analysis: a case study of the great grey shrike. Ecology, 89, 3371–3386.CrossRefGoogle ScholarPubMed
Dormann, C.F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., et al. 2012. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36, 27–46.CrossRefGoogle Scholar
Eby, M., & Holloway, G. 1994. Grid transformation for incorporating the Arctic in a global ocean model. Climate Dynamics, 10, 241–247.CrossRefGoogle Scholar
El-Gabbas, A. & Dormann, C.F. 2018. Wrong, but useful: regional species distribution models may not be improved by range-wide data under biased sampling. Ecology and Evolution, 8, 2196–2206.CrossRefGoogle Scholar
El Mahrad, B., Newton, A., Icely, J.D., Kacimi, I., Abalansa, S. & Snoussi, M. 2020. Contribution of remote sensing technologies to a holistic coastal and marine environmental management framework: a review. Remote Sensing, 12, 2313.CrossRefGoogle Scholar
Elith, J. & Graham, C.H. 2009. Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models. Ecography, 32, 66–77.CrossRefGoogle Scholar
Elith, J. & Leathwick, J.R. 2009. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics, 40, 677–697.CrossRefGoogle Scholar
Elith, J., Kearney, M. & Phillips, S. 2010. The art of modelling range-shifting species. Methods in Ecology and Evolution, 1, 330–342.CrossRefGoogle Scholar
Elith, J., Leathwick, J.R. & Hastie, T. 2008. A working guide to boosted regression trees. Journal of Animal Ecology, 77, 802–813.CrossRefGoogle ScholarPubMed
Elith, J., Phillips, S.J., Hastie, T., Dudík, M., Chee, Y.E. & Yates, C.J. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17, 43–57.CrossRefGoogle Scholar
Elith, J., Graham, C., Anderson, R., Dudík, M., Ferrier, S., Guisan, A., et al. 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography, 29, 129–151.CrossRefGoogle Scholar
Ensing, D.J., Moffat, C.E. & Pither, J. 2012. Taxonomic identification errors generate misleading ecological niche model predictions of an invasive hawkweed. Botany, 91, 137–147.CrossRefGoogle Scholar
Fabri-Ruiz, S. 2018. Modèles de distribution et changements environnementaux: application aux faunes d'échinides de l'océan Austral et écorégionalisation. Doctoral dissertation, Université de Bourgogne Franche-Comté, Dijon, France.Google Scholar
Fabri-Ruiz, S., Saucède, T., Danis, B. & David, B. 2017. Southern Ocean Echinoids database–An updated version of Antarctic, Sub-Antarctic and cold temperate echinoid database. ZooKeys, 697, 1–20.CrossRefGoogle Scholar
Fabri-Ruiz, S., Danis, B., David, B. & Saucède, T. 2019. Can we generate robust species distribution models at the scale of the Southern Ocean? Diversity and Distributions, 25, 21–37.CrossRefGoogle Scholar
Feng, X. & Papeş, M. 2017. Can incomplete knowledge of species' physiology facilitate ecological niche modelling? A case study with virtual species. Diversity and Distributions, 23, 1157–1168.CrossRefGoogle Scholar
Fielding, A.H. & Bell, J.F. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24, 38–49.CrossRefGoogle Scholar
Fitzpatrick, M.C. & Hargrove, W.W. 2009. The projection of species distribution models and the problem of non-analog climate. Biodiversity and Conservation, 18, 2255.CrossRefGoogle Scholar
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S.C., Collins, W., et al. 2014. Evaluation of climate models. In Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, 741–866.Google Scholar
Fois, M., Cuena-Lombraña, A., Fenu, G. & Bacchetta, G. 2018. Using species distribution models at local scale to guide the search of poorly known species: review, methodological issues and future directions. Ecological Modelling, 385, 124–132.CrossRefGoogle Scholar
Fordham, D.A., Mellin, C., Russell, B.D., Akçakaya, R.H., Bradshaw, C.J., Aiello-Lammens, M.E., et al. 2013. Population dynamics can be more important than physiological limits for determining range shifts under climate change. Global Change Biology, 19, 3224–3237.CrossRefGoogle ScholarPubMed
Fourcade, Y., Engler, J.O., Rödder, D. & Secondi, J. 2014. Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS One, 9, e97122.CrossRefGoogle ScholarPubMed
Franklin, J. 2009. Mapping species distributions: spatial inference and prediction. Cambridge: Cambridge University Press, 340 pp.Google Scholar
Franklin, J. 2010. Moving beyond static species distribution models in support of conservation biogeography. Diversity and Distributions, 16, 321–330.CrossRefGoogle Scholar
Fraser, C.I., Morrison, A.K., Hogg, A.M., Macaya, E.C., van Sebille, E., Ryan, P.G., et al. 2018. Antarctica's ecological isolation will be broken by storm-driven dispersal and warming. Nature Climate Change, 8, 704–708.CrossRefGoogle Scholar
Freer, J.J., Partridge, J.C., Tarling, G.A., Collins, M.A. & Genner, M.J. 2018. Predicting ecological responses in a changing ocean: the effects of future climate uncertainty. Marine Biology, 165, 7.CrossRefGoogle Scholar
Friedman, J.H. 2001. Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29, 1189–1232.CrossRefGoogle Scholar
Frölicher, T.L., Rodgers, K.B., Stock, C.A. & Cheung, W.W. 2016. Sources of uncertainties in 21st century projections of potential ocean ecosystem stressors. Global Biogeochemical Cycles, 30, 1224–1243.CrossRefGoogle Scholar
Gage, J.D. 2004. Diversity in deep-sea benthic macrofauna: the importance of local ecology, the larger scale, history and the Antarctic. Deep-Sea Research II: Topical Studies in Oceanography, 51, 1689–1708.CrossRefGoogle Scholar
Galante, P.J., Alade, B., Muscarella, R., Jansa, S.A., Goodman, S.M. & Anderson, R.P. 2018. The challenge of modeling niches and distributions for data-poor species: a comprehensive approach to model complexity. Ecography, 41, 726–736.CrossRefGoogle Scholar
Gallego, R., Dennis, T.E., Basher, Z., Lavery, S. & Sewell, M.A. 2017. On the need to consider multiphasic sensitivity of marine organisms to climate change: a case study of the Antarctic acorn barnacle. Journal of Biogeography, 44, 2165–2175.CrossRefGoogle Scholar
Gamliel, I., Buba, Y., Guy-Haim, T., Garval, T., Willette, D., Rilov, G. & Belmaker, J. 2020. Incorporating physiology into species distribution models moderates the projected impact of warming on selected Mediterranean marine species. Ecography, 43, 1090–1106.CrossRefGoogle Scholar
García-Callejas, D. & Araújo, M.B. 2016. The effects of model and data complexity on predictions from species distributions models. Ecological Modelling, 326, 4–12.CrossRefGoogle Scholar
González-Wevar, C.A., Hüne, M., Rosenfeld, S., Nakano, T., Saucède, T., Spencer, H. & Poulin, E. 2019. Systematic revision of Nacella (Patellogastropoda: Nacellidae) based on a complete phylogeny of the genus, with the description of a new species from the southern tip of South America. Zoological Journal of the Linnean Society, 186, 303–336.CrossRefGoogle Scholar
Gotelli, N.J. & Stanton-Geddes, J. 2015. Climate change, genetic markers and species distribution modelling. Journal of Biogeography, 42, 1577–1585.CrossRefGoogle Scholar
Graham, C.H., Elith, J., Hijmans, R.J., Guisan, A., Townsend Peterson, A., Loiselle, B.A. & NCEAS Predicting Species Distributions Working Group. 2008. The influence of spatial errors in species occurrence data used in distribution models. Journal of Applied Ecology, 45, 239–247.CrossRefGoogle Scholar
Griffiths, H.J., Van de Putte, A.P. & Danis, B. 2014. Data distribution: patterns and implications. In De Broyer, C., Koubbi, P., Griffiths, H.J., Raymond, B., Udekem d'Acoz, C.d', et al. , eds. Biogeographic atlas of the Southern Ocean. Cambridge: Scientific Committee on Antarctic Research, 16–26.Google Scholar
Grimm, V. & Berger, U. 2016. Robustness analysis: deconstructing computational models for ecological theory and applications. Ecological Modelling, 326, 162–167.CrossRefGoogle Scholar
Grimm, V., Augusiak, J., Focks, A., Frank, B.M., Gabsi, F., Johnston, A.S., et al. 2014. Towards better modelling and decision support: documenting model development, testing, and analysis using TRACE. Ecological Modelling, 280, 129–139.CrossRefGoogle Scholar
Guillaumot, C., Danis, B. & Saucède, T. 2020a. Selecting environmental descriptors is critical for modelling the distribution of Antarctic benthic species. Polar Biology, 43, 1363–1381.Google Scholar
Guillaumot, C., Martin, A., Eléaume, M. & Saucède, T. 2018a. Methods for improving species distribution models in data-poor areas: example of sub-Antarctic benthic species on the Kerguelen Plateau. Marine Ecology Progress Series, 594, 149–164.CrossRefGoogle Scholar
Guillaumot, C., Moreau, C., Danis, B. & Saucède, T. 2020b. Extrapolation in species distribution modelling. Application to Southern Ocean marine species. Progress in Oceanography, 188, 102438.CrossRefGoogle Scholar
Guillaumot, C., Martin, A., Fabri-Ruiz, S., Eléaume, M. & Saucède, T. 2016. Echinoids of the Kerguelen Plateau - occurrence data and environmental setting for past, present, and future species distribution modelling. ZooKeys, 630, 1–17.CrossRefGoogle Scholar
Guillaumot, C., Fabri-Ruiz, S., Martin, A., Eléaume, M., Danis, B., Féral, J.P. & Saucède, T. 2018b. Benthic species of the Kerguelen Plateau show contrasting distribution shifts in response to environmental changes. Ecology and Evolution, 8, 6210–6225.CrossRefGoogle Scholar
Guillaumot, C., Artois, J., Saucède, T., Demoustier, L., Moreau, C., Eléaume, M., et al. 2019. Broad-scale species distribution models applied to data-poor areas. Progress in Oceanography, 175, 198–207.CrossRefGoogle Scholar
Guillera-Arroita, G. 2016. Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities. Ecography, 40, 281–295.CrossRefGoogle Scholar
Guisan, A. & Zimmermann, N.E. 2000. Predictive habitat distribution models in ecology. Ecological Modelling, 135, 147–186.CrossRefGoogle Scholar
Guisan, A., Thuiller, W. & Zimmermann, N.E. 2017. Habitat suitability and distribution models: with applications in R. Cambridge: Cambridge University Press, 478 pp.CrossRefGoogle Scholar
Guisan, A., Graham, C. H., Elith, J., Huettmann, F. & NCEAS Species Distribution Modelling Group. 2007a. Sensitivity of predictive species distribution models to change in grain size. Diversity and Distributions, 13, 332–340.CrossRefGoogle Scholar
Guisan, A., Tingley, R., Baumgartner, J.B., Naujokaitis-Lewis, I., Sutcliffe, P.R., Tulloch, A.I., et al. 2013. Predicting species distributions for conservation decisions. Ecology Letters, 16, 1424–1435.CrossRefGoogle ScholarPubMed
Gutt, J., Isla, E., Bertler, A.N., Bodeker, G.E., Bracegirdle, T., Cavanagh, R.D., et al. 2017. Cross-disciplinarity in the advance of Antarctic ecosystem research. Marine Genomics, 37, 1–17.CrossRefGoogle ScholarPubMed
Gutt, J., Zurell, D., Bracegridle, T., Cheung, W., Clark, M., Convey, P., et al. 2012. Correlative and dynamic species distribution modelling for ecological predictions in the Antarctic: a cross-disciplinary concept. Polar Research, 31, 11091.CrossRefGoogle Scholar
Halvorsen, R., Mazzoni, S., Dirksen, J.W., Næsset, E., Gobakken, T. & Ohlson, M. 2016. How important are choice of model selection method and spatial autocorrelation of presence data for distribution modelling by MaxEnt? Ecological Modelling, 328, 108–118.CrossRefGoogle Scholar
Hand, D.J. 2009. Measuring classifier performance: a coherent alternative to the area under the ROC curve. Machine Learning, 77, 103–123.CrossRefGoogle Scholar
Hansen, L.K. & Salamon, P. 1990. Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, 993–1001.CrossRefGoogle Scholar
Hao, T., Elith, J., Guillera-Arroita, G. & Lahoz-Monfort, J.J. 2019. A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD. Diversity and Distributions, 25, 839–852.CrossRefGoogle Scholar
Hao, T., Elith, J., Lahoz-Monfort, J.J. & Guillera-Arroita, G. 2020. Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models. Ecography, 43, 549–558.CrossRefGoogle Scholar
Hare, J.A., Wuenschel, M.J. & Kimball, M.E. 2012. Projecting range limits with coupled thermal tolerance-climate change models: an example based on gray snapper (Lutjanus griseus) along the US east coast. PLoS One, 7, e52294.CrossRefGoogle Scholar
Hayhoe, K., Edmonds, J., Kopp, R., LeGrande, A., Sanderson, B., Wehner, M. & Wuebbles, D. 2017. Climate models, scenarios, and projections. In Wuebbles, D.J., Fahey, D.W., Hibbard, K.A., Dokken, D.J., Stewart, B.C. & Maycock, T.K. eds. Climate science special report: a sustained assessment activity of the U.S. Global Change Research Program. Washington, DC: US Global Change Research Program, 186–227.Google Scholar
Hengl, T., Sierdsema, H., Radovic´, A. & Dilo, A. 2009. Spatial prediction of species' distributions from occurrence-only records: combining point pattern analysis, ENFA and regression-kriging. Ecological Modelling, 220, 3499–3511.CrossRefGoogle Scholar
Henson, S.A., Yool, A. & Sanders, R. 2015. Variability in efficiency of particulate organic carbon export: A model study. Global Biogeochemical Cycles, 29, 33–45.CrossRefGoogle Scholar
Hernandez, P.A., Graham, C.H., Master, L.L., & Albert, D.L. 2006. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography, 29, 773–785.CrossRefGoogle Scholar
Hibberd, T. 2016. Describing and predicting the spatial distribution of benthic biodiversity in the sub-Antarctic and Antarctic. Doctoral dissertation, University of Tasmania.Google Scholar
Hijmans, R.J. 2012. Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model. Ecology, 93, 679–688.CrossRefGoogle ScholarPubMed
Hindell, M.A., Reisinger, R.R., Ropert-Coudert, Y., Hückstädt, L.A., Trathan, P.N., Bornemann, H., et al. 2020. Tracking of marine predators to protect Southern Ocean ecosystems. Nature, 580, 87–92.CrossRefGoogle ScholarPubMed
Hortal, J., Lobo, J.M. & Jiménez-Valverde, A. 2007. Limitations of biodiversity databases: case study on seed-plant diversity in Tenerife, Canary Islands. Conservation Biology, 21, 853–863.CrossRefGoogle ScholarPubMed
Hortal, J., Jiménez-Valverde, A., Gómez, J.F., Lobo, J.M. & Baselga, A. 2008. Historical bias in biodiversity inventories affects the observed environmental niche of the species. Oikos, 117, 847–858.CrossRefGoogle Scholar
Hutchinson, G.E. 1957. Concluding remarks. Cold Spring Harbor Symposia on Quantitative Biology, 22, 415–427.CrossRefGoogle Scholar
Ikeda, D.H., Max, T.L., Allan, G.J., Lau, M.K., Shuster, S.M. & Whitham, T.G. 2017. Genetically informed ecological niche models improve climate change predictions. Global Change Biology, 23, 164–176.CrossRefGoogle ScholarPubMed
Iturbide, M., Bedia, J. & Gutiérrez, J.M. 2018. Background sampling and transferability of species distribution model ensembles under climate change. Global and Planetary Change, 166, 19–29.CrossRefGoogle Scholar
Iturbide, M., Bedia, J., Herrera, S., del Hierro, O., Pinto, M. & Gutiérrez, J.M. 2015. A framework for species distribution modelling with improved pseudo-absence generation. Ecological Modelling, 312, 166–174.CrossRefGoogle Scholar
Jarnevich, C.S., Stohlgren, T.J., Kumar, S., Morisette, J.T. & Holcombe, T.R. 2015. Caveats for correlative species distribution modeling. Ecological Informatics, 29, 6–15.CrossRefGoogle Scholar
Jarnevich, C.S., Talbert, M., Morisette, J., Aldridge, C., Brown, C.S., Kumar, S., et al. 2017. Minimizing effects of methodological decisions on interpretation and prediction in species distribution studies: an example with background selection. Ecological Modelling, 363, 48–56.CrossRefGoogle Scholar
Jerosch, K., Scharf, F.K., Deregibus, D., Campana, G.L., Zacher, K., Pehlke, H., et al. 2019. Ensemble modelling of Antarctic macroalgal habitats exposed to glacial melt in a polar fjord. Frontiers in Ecology and Evolution, 7, 207.CrossRefGoogle Scholar
Jiménez-Valverde, A. 2012. Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling. Global Ecology and Biogeography, 21, 498–507.CrossRefGoogle Scholar
Jiménez-Valverde, A. & Lobo, J.M. 2007. Threshold criteria for conversion of probability of species presence to either-or presence-absence. Acta Oecologica, 31, 361–369.CrossRefGoogle Scholar
Jiménez-Valverde, A., Rodríguez-Rey, M. & Peña-Aguilera, P. 2021. Climate data source matters in species distribution modelling: the case of the Iberian Peninsula. Biodiversity and Conservation, 30, 67–84.CrossRefGoogle Scholar
Kaiser, S., Brandão, S.N., Brix, S., Barnes, D.K.A., Bowden, D.A., Ingels, J., et al. 2013. Patterns, processes and vulnerability of Southern Ocean benthos: a decadal leap in knowledge and understanding. Marine Biology, 160, 2295–2317.CrossRefGoogle Scholar
Kamgar-Parsi, B. & Sander, W.A. 1989. Quantization error in spatial sampling: comparison between square and hexagonal pixels. In Proceedings CVPR'89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 604–611.Google Scholar
Kampichler, C. & Sierdsema, H. 2018. On the usefulness of prediction intervals for local species distribution model forecasts. Ecological Informatics, 47, 67–72.CrossRefGoogle Scholar
Kearney, M. & Porter, W. 2009. Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges. Ecology Letters, 12, 334–350.CrossRefGoogle ScholarPubMed
Kearney, M.R., Wintle, B.A. & Porter, W.P. 2010. Correlative and mechanistic models of species distribution provide congruent forecasts under climate change. Conservation Letters, 3, 203–213.CrossRefGoogle Scholar
Kennicutt, M.C., Chown, S.L., Cassano, J.J., Liggett, D., Massom, R., Peck, L.S., et al. 2014. Six priorities for Antarctic science. Nature, 512, 23–25.CrossRefGoogle ScholarPubMed
Knowles, L.L., Carstens, B.C. & Keat, M.L. 2007. Coupling genetic and ecological-niche models to examine how past population distributions contribute to divergence. Current Biology, 17, 940–946.CrossRefGoogle ScholarPubMed
Knutti, R. 2010. The end of model democracy? Climatic Change, 102, 395–404.CrossRefGoogle Scholar
Kramer-Schadt, S., Niedballa, J., Pilgrim, J.D., Schröder, B., Lindenborn, J., Reinfelder, V., et al. 2013. The importance of correcting for sampling bias in MaxEnt species distribution models. Diversity and Distributions, 19, 1366–1379.CrossRefGoogle Scholar
Kühn, I. 2007. Incorporating spatial autocorrelation may invert observed patterns. Diversity and Distributions, 13, 66–69.Google Scholar
Lahoz-Monfort, J.J., Guillera-Arroita, G. & Wintle, B.A. 2014. Imperfect detection impacts the performance of species distribution models. Global Ecology and Biogeography, 23, 504–515.CrossRefGoogle Scholar
Lauzeral, C., Grenouillet, G. & Brosse, S. 2013. Spatial range shape drives the grain size effects in species distribution models. Ecography, 36, 778–787.CrossRefGoogle Scholar
Leach, K., Montgomery, W.I. & Reid, N. 2016. Modelling the influence of biotic factors on species distribution patterns. Ecological Modelling, 337, 96–106.CrossRefGoogle Scholar
Lieske, D.J., Schmid, M.S. & Mahoney, M. 2018. Ensembles of ensembles: combining the predictions from multiple machine learning methods. In Machine Learning for Ecology and Sustainable Natural Resource Management. Cham: Springer, 109–121.CrossRefGoogle Scholar
Liu, C., Newell, G. & White, M. 2019. The effect of sample size on the accuracy of species distribution models: considering both presences and pseudo-absences or background sites. Ecography, 42, 535–548.CrossRefGoogle Scholar
Lobo, J.M. 2008. More complex distribution models or more representative data? Biodiversity Informatics, 5, 10.17161/bi.v5i0.40.CrossRefGoogle Scholar
Lobo, J.M., Jiménez-Valverde, A. & Hortal, J. 2010. The uncertain nature of absences and their importance in species distribution modelling. Ecography, 33, 103–114.CrossRefGoogle Scholar
Lomba, A., Pellissier, L., Randin, C., Vicente, J., Moreira, F., Honrado, J. & Guisan, A. 2010. Overcoming the rare species modelling paradox: a novel hierarchical framework applied to an Iberian endemic plant. Biological Conservation, 143, 2647–2657.CrossRefGoogle Scholar
Loots, C., Koubbi, P. & Duhamel, G. 2007. Habitat modelling of Electrona antarctica (Myctophidae, Pisces) in Kerguelen by generalized additive models and geographic information systems. Polar Biology, 30, 951–959.CrossRefGoogle Scholar
López-Farrán, Z., Guillaumot, C., Vargas-Chacoff, L., Paschke, K., Dulière, V., Danis, B., et al. 2021. Is the southern crab Halicarcinus planatus (Fabricius, 1775) the next invader of Antarctica? Global Change Biology 10.1111/gcb.15674.CrossRefGoogle ScholarPubMed
Lorena, A.C., Jacintho, L.F., Siqueira, M.F., De Giovanni, R., Lohmann, L.G., De Carvalho, A.C. & Yamamoto, M. 2011. Comparing machine learning classifiers in potential distribution modelling. Expert Systems with Applications, 38, 5268–5275.CrossRefGoogle Scholar
Lozier, J.D., Aniello, P. & Hickerson, M.J. 2009. Predicting the distribution of Sasquatch in western North America: anything goes with ecological niche modelling. Journal of Biogeography, 36, 1623–1627.CrossRefGoogle Scholar
Luedeling, E., Kindt, R., Huth, N.I. & Koenig, K. 2014. Agroforestry systems in a changing climate - challenges in projecting future performance. Current Opinion in Environmental Sustainability, 6, 1–7.CrossRefGoogle Scholar
Luoto, M., Pöyry, J., Heikkinen, R.K. & Saarinen, K. 2005. Uncertainty of bioclimate envelope models based on the geographical distribution of species. Global Ecology and Biogeography, 14, 575–584.CrossRefGoogle Scholar
Maldonado, C., Molina, C.I., Zizka, A., Persson, C., Taylor, C.M., Albán, J., et al. 2015. Estimating species diversity and distribution in the era of Big Data: to what extent can we trust public databases? Global Ecology and Biogeography, 24, 973–984.CrossRefGoogle ScholarPubMed
Manel, S., Williams, H.C. & Ormerod, S.J. 2001. Evaluating presence-absence models in ecology: the need to account for prevalence. Journal of Applied Ecology, 38, 921–931.CrossRefGoogle Scholar
Marcer, A., Méndez-Vigo, B., Alonso-Blanco, C. & Picó, F.X. 2016. Tackling intraspecific genetic structure in distribution models better reflects species geographical range. Ecology and Evolution, 6, 2084–2097.CrossRefGoogle ScholarPubMed
Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R.K. & Thuiller, W. 2009. Evaluation of consensus methods in predictive species distribution modelling. Diversity and Distributions, 15, 59–69.CrossRefGoogle Scholar
Massada, A.B., Syphard, A.D., Stewart, S.I. & Radeloff, V.C. 2013. Wildfire ignition-distribution modelling: a comparative study in the Huron-Manistee National Forest, Michigan, USA. International Journal of Wildland Fire, 22, 174–183.CrossRefGoogle Scholar
Mathewson, P.D., Moyer-Horner, L., Beever, E.A., Briscoe, N.J., Kearney, M., Yahn, J.M. & Porter, W.P. 2017. Mechanistic variables can enhance predictive models of endotherm distributions: the American pika under current, past, and future climates. Global Change Biology, 23, 1048–1064.CrossRefGoogle ScholarPubMed
McPherson, J.M., Jetz, W. & Rogers, D.J. 2004. The effects of species' range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact? Journal of Applied Ecology, 41, 811–823.CrossRefGoogle Scholar
Mearns, L.O., Hulme, M., Carter, T.R., Leemans, R., Lal, M., Whetton, P., et al. 2001. Climate scenario development. In Climate change 2001: the science of climate change. Cambridge: Cambridge University Press, 739–768.Google Scholar
Meier, E.S., Edwards, T.C. Jr, Kienast, F., Dobbertin, M. & Zimmermann, N.E. 2011. Co-occurrence patterns of trees along macro-climatic gradients and their potential influence on the present and future distribution of Fagus sylvatica L. Journal of Biogeography, 38, 371–382.CrossRefGoogle Scholar
Merow, C., Smith, M.J. & Silander, J.A. Jr. 2013. A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter. Ecography, 36, 1058–1069.CrossRefGoogle Scholar
Merow, C., Wilson, A.M. & Jetz, W. 2017. Integrating occurrence data and expert maps for improved species range predictions. Global Ecology and Biogeography, 26, 243–258.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Mesgaran, B.M., Cousens, R.D. & Webber, B.L. 2014. Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models. Diversity and Distributions, 20, 1147–1159.CrossRefGoogle Scholar
Meynard, C.N. & Quinn, J.F. 2007. Predicting species distributions: a critical comparison of the most common statistical models using artificial species. Journal of Biogeography, 34, 1455–1469.CrossRefGoogle Scholar
Mitchell, P.J., Monk, J. & Laurenson, L. 2017. Sensitivity of fine-scale species distribution models to locational uncertainty in occurrence data across multiple sample sizes. Methods in Ecology and Evolution, 8, 12–21.CrossRefGoogle Scholar
Molloy, S.W., Davis, R.A., Dunlop, J.A. & van Etten, E. 2017. Applying surrogate species presences to correct sample bias in species distribution models: a case study using the Pilbara population of the Northern Quoll. Nature Conservation, 18, 27–46.CrossRefGoogle Scholar
Monk, J. 2014. How long should we ignore imperfect detection of species in the marine environment when modelling their distribution? Fish and Fisheries, 15, 352–358.CrossRefGoogle Scholar
Moore, J.M., Carvajal, J.I., Rouse, G.W. & Wilson, N.G. 2018. The Antarctic Circumpolar Current isolates and connects: structured circumpolarity in the sea star Glabraster antarctica. Ecology and Evolution, 8, 10621–10633.CrossRefGoogle ScholarPubMed
Morales, N.S., Fernández, I.C. & Baca-González, V. 2017. MaxEnt's parameter configuration and small samples: are we paying attention to recommendations? A systematic review. PeerJ, 5, e3093.CrossRefGoogle ScholarPubMed
Moreau, C., Saucède, T., Jossart, Q., Agüera, A., Brayard, A. & Danis, B. 2017. Reproductive strategy as a piece of the biogeographic puzzle: a case study using Antarctic sea stars (Echinodermata, Asteroidea). Journal of Biogeography, 44, 848–860.CrossRefGoogle Scholar
Moreau, C., Jossart, Q., Danis, B., Eléaume, M., Christiansen, H., Guillaumot, C., et al. 2021. The overlooked diversity of Southern Ocean sea stars (Asteroidea) reveals original evolutionary pathways. Progress in Oceanography, 190, 102472.CrossRefGoogle Scholar
Moreau, C., Mah, C., Agüera, A., Améziane, N., Barnes, D., Crokaert, G., et al. 2018. Antarctic and sub-Antarctic Asteroidea database. ZooKeys, 747, 141–156.CrossRefGoogle Scholar
Moreno-Amat, E., Mateo, R.G., Nieto-Lugilde, D., Morueta-Holme, N., Svenning, J.C. & García-Amorena, I. 2015. Impact of model complexity on cross-temporal transferability in Maxent species distribution models: An assessment using paleobotanical data. Ecological Modelling, 312, 308–317.CrossRefGoogle Scholar
Mormède, S., Irisson, J.O. & Raymond, B. 2014. Distribution modelling. In De Broyer, C., Koubbi, P., Griffiths, H.J., Raymond, B., Udekem d'Acoz, C.D', et al. , eds. Biogeographic atlas of the Southern Ocean. Cambridge: Scientific Committee on Antarctic Research, 27–29.Google Scholar
Mulcahy, K.A. & Clarke, K.C. 2001. Symbolization of map projection distortion: a review. Cartography and Geographic Information Science, 28, 167–182.CrossRefGoogle Scholar
Murphey, P.C., Guralnick, R P., Glaubitz, R., Neufeld, D. & Ryan, J.A. 2004. Georeferencing of museum collections: A review of problems and automated tools, and the methodology developed by the Mountain and Plains Spatio-Temporal Database-Informatics Initiative (Mapstedi). PhyloInformatics, 3, 1–29.Google Scholar
Murray, R.J. 1996. Explicit generation of orthogonal grids for ocean models. Journal of Computational Physics, 126, 251–273.CrossRefGoogle Scholar
Muscarella, R., Galante, P.J., Soley-Guardia, M., Boria, R.A., Kass, J.M., Uriarte, M. & Anderson, R.P. 2014. ENM eval: an R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in Ecology and Evolution, 5, 1198–1205.CrossRefGoogle Scholar
Nachtsheim, D.A., Jerosch, K., Hagen, W., Plötz, J. & Bornemann, H. 2017. Habitat modelling of crabeater seals (Lobodon carcinophaga) in the Weddell Sea using the multivariate approach Maxent. Polar Biology, 40, 961–976.CrossRefGoogle Scholar
Naimi, B., Skidmore, A.K., Groen, T.A. & Hamm, N.A. 2011. Spatial autocorrelation in predictors reduces the impact of positional uncertainty in occurrence data on species distribution modelling. Journal of Biogeography, 38, 1497–1509.CrossRefGoogle Scholar
Newbold, T. 2010. Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models. Progress in Physical Geography, 34, 3–22.CrossRefGoogle Scholar
Ocaranza-Barrera, P., González-Wevar, C.A., Guillemin, M.L., Rosenfeld, S. & Mansilla, A. 2019. Molecular divergence between Iridaea cordata (Turner) Bory de Saint-Vincent from the Antarctic Peninsula and the Magellan Region. Journal of Applied Phycology, 31, 939–949.CrossRefGoogle Scholar
Olden, J.D., Lawler, J.J. & Poff, N.L. 2008. Machine learning methods without tears: a primer for ecologists. Quartely Review of Biology, 83, 171–193.CrossRefGoogle ScholarPubMed
Osborne, P.E. & Leitão, P.J. 2009. Effects of species and habitat positional errors on the performance and interpretation of species distribution models. Diversity and Distributions, 15, 671–681.CrossRefGoogle Scholar
Owens, H.L., Campbell, L.P., Dornak, L.L., Saupe, E.E., Barve, N., Soberón, J., et al. 2013. Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas. Ecological Modelling, 263, 10–18.CrossRefGoogle Scholar
Pagel, J. & Schurr, F.M. 2012. Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics. Global Ecology and Biogeography, 21, 293–304.CrossRefGoogle Scholar
Pardo-Gandarillas, M.C., Ibáñez, C.M., Torres, F.I., Sanhueza, V., Fabres, A., Escobar-Dodero, J., et al. 2018. Phylogeography and species distribution modelling reveal the effects of the Pleistocene ice ages on an intertidal limpet from the south-eastern Pacific. Journal of Biogeography, 45, 1751–1767.CrossRefGoogle Scholar
Pearson, R.G. 2007. Species' distribution modeling for conservation educators and practitioners. Synthesis. American Museum of Natural History, 50, 54–89.Google Scholar
Pearson, R.G., Raxworthy, C.J., Nakamura, M. & Townsend Peterson, A. 2007. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. Journal of Biogeography, 34, 102–117.CrossRefGoogle Scholar
Pearson, R.G., Thuiller, W., Araújo, M.B., Martinez-Meyer, E., Brotons, L., McClean, C., et al. 2006. Model-based uncertainty in species range prediction. Journal of Biogeography, 33, 1704–1711.CrossRefGoogle Scholar
Peel, S.L., Hill, N.A., Foster, S.D., Wotherspoon, S.J., Ghiglione, C. & Schiaparelli, S. 2019. Reliable species distributions are obtainable with sparse, patchy and biased data by leveraging over species and data types. Methods in Ecology and Evolution. 10, 1002–1014.CrossRefGoogle Scholar
Pellissier, L., Rohr, R.P., Ndiribe, C., Pradervand, J.N., Salamin, N., Guisan, A. & Wisz, M. 2013. Combining food web and species distribution models for improved community projections. Ecology and Evolution, 3, 4572–4583.CrossRefGoogle ScholarPubMed
Pellissier, L., Anne Bråthen, K., Pottier, J., Randin, C.F., Vittoz, P., Dubuis, A., et al. 2010. Species distribution models reveal apparent competitive and facilitative effects of a dominant species on the distribution of tundra plants. Ecography, 33, 1004–1014.CrossRefGoogle Scholar
Perrault-Hébert, M., Girard, F. Boucher, Fournier, Y., Mansuy, R., , N. & Valeria, O. 2019. Evaluation of spatiotemporal transferability of wildfire probability across eastern boreal forest of North America. PhD dissertation, University of Montréal.Google Scholar
Peterson, A.T., Soberón, J., Pearson, R.G., Anderson, R.P., Martínez-Meyer, E., Nakamura, M. & Araújo, M.B. 2011. Ecological niches and geographic distributions (MPB-49) (Vol. 56). Princeton, NJ: Princeton University Press, 328 pp.CrossRefGoogle Scholar
Petitpierre, B., Broennimann, O., Kueffer, C., Daehler, C. & Guisan, A. 2017. Selecting predictors to maximize the transferability of species distribution models: lessons from cross-continental plant invasions. Global Ecology and Biogeography, 26, 275–287.CrossRefGoogle Scholar
Phillips, N.D., Reid, N., Thys, T., Harrod, C., Payne, N. L., Morgan, C.A., et al. 2017. Applying species distribution modelling to a data poor, pelagic fish complex: the ocean sunfishes. Journal of Biogeography, 44, 2176–2187.CrossRefGoogle Scholar
Phillips, S.J., Anderson, R.P. & Schapire, R.E. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231–259.CrossRefGoogle Scholar
Phillips, S.J., Dudík, M., Elith, J., Graham, C.H., Lehmann, A., Leathwick, J. & Ferrier, S. 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications, 19, 181–197.CrossRefGoogle ScholarPubMed
Pierrat, B., Saucède, T., Laffont, R., De Ridder, C., Festeau, A. & David, B. 2012. Large-scale distribution analysis of Antarctic echinoids using ecological niche modelling. Marine Ecology Progress Series, 463, 215–230.CrossRefGoogle Scholar
Pinkerton, M.H., Smith, A.N., Raymond, B., Hosie, G.W., Sharp, B., Leathwick, J.R. & Bradford-Grieve, J.M. 2010. Spatial and seasonal distribution of adult Oithona similis in the Southern Ocean: predictions using boosted regression trees. Deep-Sea Research I: Oceanographic Research Papers, 57, 469–485.CrossRefGoogle Scholar
Pittman, S.J., ed. 2017. Seascape ecology. Hoboken, NJ: John Wiley & Sons, 501 pp.Google Scholar
Pope, A., Wagner, P., Johnson, R., Shutler, J.D., Baeseman, J. & Newman, L. 2017. Community review of Southern Ocean satellite data needs. Antarctic Science, 29, 97–138.CrossRefGoogle Scholar
Porfirio, L.L., Harris, R.M., Lefroy, E.C., Hugh, S., Gould, S.F., Lee, G., et al. 2014. Improving the use of species distribution models in conservation planning and management under climate change. PLoS One, 9, e113749.CrossRefGoogle ScholarPubMed
Qiao, H., Soberón, J. & Peterson, A.T. 2015. No silver bullets in correlative ecological niche modelling: insights from testing among many potential algorithms for niche estimation. Methods in Ecology and Evolution, 6, 1126–1136.CrossRefGoogle Scholar
Radosavljevic, A. & Anderson, R.P. 2014. Making better Maxent models of species distributions: complexity, overfitting and evaluation. Journal of Biogeography, 41, 629–643.CrossRefGoogle Scholar
Raes, N. 2012. Partial versus full species distribution models. Natureza & Conservação, 10, 127–138.CrossRefGoogle Scholar
Raes, N. & ter Steege, H. 2007. A null-model for significance testing of presence-only species distribution models. Ecography, 30, 727–736.CrossRefGoogle Scholar
Ranc, N., Santini, L., Rondinini, C., Boitani, L., Poitevin, F., Angerbjörn, A. & Maiorano, L. 2017. Performance tradeoffs in target-group bias correction for species distribution models. Ecography, 40, 1076–1087.CrossRefGoogle Scholar
Randin, C.F., Dirnböck, T., Dullinger, S., Zimmermann, N.E., Zappa, M. & Guisan, A. 2006. Are niche-based species distribution models transferable in space? Journal of Biogeography, 33, 1689–1703.CrossRefGoogle Scholar
Raymond, B., Lea, M.A., Patterson, T., Andrews-Goff, V., Sharples, R., Charrassin, J.B., et al. 2015. Important marine habitat off east Antarctica revealed by two decades of multi-species predator tracking. Ecography, 38, 121–129.CrossRefGoogle Scholar
Ready, J., Kaschner, K., South, A.B., Eastwood, P.D., Rees, T., Rius, J., et al. 2010. Predicting the distributions of marine organisms at the global scale. Ecological Modelling, 221, 467–478.CrossRefGoogle Scholar
Reiss, H., Cunze, S., König, K., Neumann, H. & Kröncke, I. 2011. Species distribution modelling of marine benthos: a North Sea case study. Marine Ecology Progress Series, 442, 71–86.CrossRefGoogle Scholar
Roberts, D.R., Bahn, V., Ciuti, S., Boyce, M.S., Elith, J., Guillera-Arroita, G., et al. 2017. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography, 40, 913–929.CrossRefGoogle Scholar
Robinson, L.M., Elith, J., Hobday, A.J., Pearson, R.G., Kendall, B.E., Possingham, H.P. & Richardson, A.J. 2011. Pushing the limits in marine species distribution modelling: lessons from the land present challenges and opportunities. Global Ecology and Biogeography, 20, 789–802.CrossRefGoogle Scholar
Robinson, N.M., Nelson, W.A., Costello, M.J., Sutherland, J.E. & Lundquist, C.J. 2017. A systematic review of marine-based species distribution models (SDMs) with recommendations for best practice. Frontiers in Marine Science, 4, 421.CrossRefGoogle Scholar
Rocchini, D., Hortal, J., Lengyel, S., Lobo, J.M., Jimenez-Valverde, A., Ricotta, C., et al. 2011. Accounting for uncertainty when mapping species distributions: the need for maps of ignorance. Progress in Physical Geography, 35, 211–226.CrossRefGoogle Scholar
Rodda, G.H., Jarnevich, C.S. & Reed, R.N. 2011. Challenges in identifying sites climatically matched to the native ranges of animal invaders. PLoS One, 6, e14670.CrossRefGoogle ScholarPubMed
Rodríguez, L., García, J.J., Carreño, F. & Martínez, B. 2019. Integration of physiological knowledge into hybrid species distribution modelling to improve forecast of distributional shifts of tropical corals. Diversity and Distributions, 25, 715–728.CrossRefGoogle Scholar
Ropert-Coudert, Y., Van de Putte, A.P., Reisinger, R.R., Bornemann, H., Charrassin, J.B., Costa, D.P., et al. 2020. The retrospective analysis of Antarctic tracking data project. Scientific Data, 7, 1–11.CrossRefGoogle ScholarPubMed
Sánchez-Fernández, D., Lobo, J.M. & Hernández-Manrique, O.L. 2011. Species distribution models that do not incorporate global data misrepresent potential distributions: a case study using Iberian diving beetles. Diversity and Distributions, 17, 163–171.CrossRefGoogle Scholar
Scales, K.L., Miller, P.I., Ingram, S.N., Hazen, E.L., Bograd, S.J. & Phillips, R.A. 2016. Identifying predictable foraging habitats for a wide-ranging marine predator using ensemble ecological niche models. Diversity and Distributions, 22, 212–224.CrossRefGoogle Scholar
Schapire, R.E. 1990. The strength of weak learnability. Machine Learning, 5, 197–227.CrossRefGoogle Scholar
Segurado, P., Araujo, M.B. & Kunin, W.E. 2006. Consequences of spatial autocorrelation for niche-based models. Journal of Applied Ecology, 43, 433–444.CrossRefGoogle Scholar
Seo, C., Thorne, J.H., Hannah, L. & Thuiller, W. 2009. Scale effects in species distribution models: implications for conservation planning under climate change. Biology Letters, 5, 39–43.CrossRefGoogle ScholarPubMed
Sillero, N. 2011. What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods. Ecological Modelling, 222, 1343–1346.CrossRefGoogle Scholar
Sillero, N. & Barbosa, A.M. 2021. Common mistakes in ecological niche models. International Journal of Geographical Information Science, 35, 213–226.CrossRefGoogle Scholar
Smith, A.B. 2013. On evaluating species distribution models with random background sites in place of absences when test presences disproportionately sample suitable habitat. Diversity and Distributions, 19, 867–872.CrossRefGoogle Scholar
Snickars, M., Gullström, M., Sundblad, G., Bergström, U., Downie, A.L., Lindegarth, M. & Mattila, J. 2014. Species-environment relationships and potential for distribution modelling in coastal waters. Journal of Sea Research, 85, 116–125.CrossRefGoogle Scholar
Staveley, T.A., Perry, D., Lindborg, R. & Gullström, M. 2017. Seascape structure and complexity influence temperate seagrass fish assemblage composition. Ecography, 40, 936–946.CrossRefGoogle Scholar
Støa, B., Halvorsen, R., Mazzoni, S. & Gusarov, V.I. 2018. Sampling bias in presence-only data used for species distribution modelling: theory and methods for detecting sample bias and its effects on models. Sommerfeltia, 38, 1–53.CrossRefGoogle Scholar
Stock, A., Subramaniam, A., Van Dijken, G.L., Wedding, L.M., Arrigo, K.R., Mills, M.M., et al. 2020. Comparison of cloud-filling algorithms for marine satellite data. Remote Sensing, 12, 3313.CrossRefGoogle Scholar
Stockwell, D.R. & Peterson, A.T. 2002. Effects of sample size on accuracy of species distribution models. Ecological Modelling, 148, 1–13.CrossRefGoogle Scholar
Syfert, M.M., Smith, M.J. & Coomes, D.A. 2013. The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models. PLoS One, 8, e55158.CrossRefGoogle ScholarPubMed
Talluto, M.V., Boulangeat, I., Ameztegui, A., Aubin, I., Berteaux, D., Butler, A., et al. 2016. Cross-scale integration of knowledge for predicting species ranges: a metamodelling framework. Global Ecology and Biogeography, 25, 238–249.CrossRefGoogle Scholar
Telford, R.J. & Birks, H.J. 2009. Evaluation of transfer functions in spatially structured environments. Quaternary Science Reviews, 28, 1309–1316.CrossRefGoogle Scholar
Tessarolo, G., Ladle, R., Rangel, T. & Hortal, J. 2017. Temporal degradation of data limits biodiversity research. Ecology and Evolution, 7, 6863–6870.CrossRefGoogle ScholarPubMed
Thatje, S. 2012. Effects of capability for dispersal on the evolution of diversity in Antarctic benthos. Integrative and Comparative Ecology, 52, 470–482.CrossRefGoogle ScholarPubMed
Thiers, L., Delord, K., Bost, C.-A., Guinet, C. & Weimerskirch, H. 2017. Important marine sectors for the top predator community around Kerguelen Archipelago. Polar Biology, 40, 365–378.CrossRefGoogle Scholar
Thuiller, W., Brotons, L., Araújo, M.B. & Lavorel, S. 2004. Effects of restricting environmental range of data to project current and future species distributions. Ecography, 27, 165–172.CrossRefGoogle Scholar
Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M.B. 2009. BIOMOD - a platform for ensemble forecasting of species distributions. Ecography, 32, 369–373.CrossRefGoogle Scholar
Thuiller, W., Vayreda, J., Pino, J., Sabate, S., Lavorel, S. & Gracia, C. 2003. Large-scale environmental correlates of forest tree distributions in Catalonia (NE Spain). Global Ecology and Biogeography, 12, 313–325.CrossRefGoogle Scholar
Thyrring, J., Bundgaard, A. & Sejr, M.K. 2017. Seasonal acclimation and latitudinal adaptation are of the same magnitude in Mytilus edulis and Mytilus trossulus mitochondrial respiration. Polar Biology, 40, 1885–1891.CrossRefGoogle Scholar
Tirunelveli, G., Gordon, R. & Pistorius, S. 2002. Comparison of square-pixel and hexagonal-pixel resolution in image processing. In IEEE CCECE2002. Canadian Conference on Electrical and Computer Engineering. Conference Proceedings (Cat. No. 02CH37373), Vol. 2. Piscataway, NJ: IEEE, 867–872.CrossRefGoogle Scholar
Titeux, N., Maes, D., Van Daele, T., Onkelinx, T., Heikkinen, R.K., Romo, H., et al. 2017. The need for large-scale distribution data to estimate regional changes in species richness under future climate change. Diversity and Distributions, 23, 1393–1407.CrossRefGoogle Scholar
Trolle, D., Elliott, J.A., Mooij, W.M., Janse, J.H., Bolding, K., Hamilton, D.P. & Jeppesen, E. 2014. Advancing projections of phytoplankton responses to climate change through ensemble modelling. Environmental Modelling & Software, 61, 371–379.CrossRefGoogle Scholar
Trull, T.W., Passmore, A., Davies, D.M., Smit, T., Berry, K. & Tilbrook, B. 2018. Distribution of planktonic biogenic carbonate organisms in the Southern Ocean south of Australia: a baseline for ocean acidification impact assessment. Biogeosciences, 15, 31.CrossRefGoogle Scholar
Tsoar, A., Allouche, O., Steinitz, O., Rotem, D. & Kadmon, R. 2007. A comparative evaluation of presence-only methods for modelling species distribution. Diversity and Distributions, 13, 397–405.CrossRefGoogle Scholar
Turner, J., Barrand, N., Bracegirdle, T., Convey, P., Hodgson, D.A., Jarvis, M., et al. 2014. Antarctic climate change and the environment: an update. Polar Record, 50, 237–259.CrossRefGoogle Scholar
Valavi, R., Elith, J., Lahoz-Monfort, J.J. & Guillera-Arroita, G. 2019. blockCV: 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, 225–232.CrossRefGoogle Scholar
Vale, C.G., Tarroso, P. & Brito, J.C. 2014. Predicting species distribution at range margins: testing the effects of study area extent, resolution and threshold selection in the Sahara-Sahel transition zone. Diversity and Distributions, 20, 20–33.CrossRefGoogle Scholar
Van Proosdij, A.S., Sosef, M.S., Wieringa, J.J. & Raes, N. 2016. Minimum required number of specimen records to develop accurate species distribution models. Ecography, 39, 542–552.CrossRefGoogle Scholar
Vanden Berghe, E. 2013. Report of the project ‘Turning OBIS data into information'; project funded by the Census of Marine Life International Cosmos Prize Fund. New Brunswick, NJ: Rutgers University Press, 62 pp.Google Scholar
Vandersteen, W. 2011. Detecting gene expression profiles associated with environmental stressors within an ecological context. Molecular Ecology, 20, 1322–1323.CrossRefGoogle ScholarPubMed
Vapnik, V. 1998. Statistical learning theory. New York: Wiley. 624 pp.Google Scholar
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, 335–342.CrossRefGoogle ScholarPubMed
Watling, J.I., Brandt, L.A., Bucklin, D.N., Fujisaki, I., Mazzotti, F.J., Romañach, S.S. & Speroterra, C. 2015. Performance metrics and variance partitioning reveal sources of uncertainty in species distribution models. Ecological Modelling, 309, 48–59.CrossRefGoogle Scholar
Wenger, S.J. & Olden, J.D. 2012. Assessing transferability of ecological models: an underappreciated aspect of statistical validation. Methods in Ecology and Evolution, 3, 260–267.CrossRefGoogle Scholar
White-Newsome, J.L., Brines, S.J., Brown, D.G., Dvonch, J.T., Gronlund, C.J., Zhang, K., et al. 2013. Validating satellite-derived land surface temperature with in situ measurements: a public health perspective. Environmental Health Perspectives, 121, 925–931.CrossRefGoogle ScholarPubMed
Whittingham, M.J., Stephens, P.A., Bradbury, R.B. & Freckleton, R.P. 2006. Why do we still use stepwise modelling in ecology and behaviour? Journal of Animal Ecology, 75, 1182–1189.CrossRefGoogle ScholarPubMed
Wiley, E.O., McNyset, K.M., Peterson, A.T., Robins, C.R. & Stewart, A.M. 2003. Niche modeling perspective on geographic range predictions in the marine environment using a machine-learning algorithm. Oceonography, 16, 102–127.Google Scholar
Williams, J.W. & Jackson, S.T. 2007. Novel climates, no-analog communities, and ecological surprises. Frontiers in Ecology and the Environment, 5, 475–482.CrossRefGoogle Scholar
Williams, J.W., Jackson, S.T. & Kutzbach, J.E. 2007. Projected distributions of novel and disappearing climates by 2100 ad. Proceedings of the National Academy of Sciences of the United States of America, 104, 5738–5742.CrossRefGoogle ScholarPubMed
Wisz, M.S. & Guisan, A. 2009. Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data. BMC Ecology, 9, 8.CrossRefGoogle ScholarPubMed
Wisz, M.S., Hijmans, R.J., Li, J., Peterson, A.T., Graham, C.H., Guisan, A. & NCEAS Predicting Species Distributions Working Group. 2008. Effects of sample size on the performance of species distribution models. Diversity and Distributions, 14, 763–773.CrossRefGoogle Scholar
Xavier, J.C., Raymond, B., Jones, D.C. & Griffiths, H. 2016. Biogeography of Cephalopods in the Southern Ocean using habitat suitability prediction models. Ecosystems, 19, 220–247.CrossRefGoogle Scholar
Yackulic, C.B., Chandler, R., Zipkin, E.F., Royle, J.A., Nichols, J.D., Campbell Grant, E.H. & Veran, S. 2013. Presence-only modelling using MAXENT: when can we trust the inferences? Methods in Ecology and Evolution, 4, 236–243.CrossRefGoogle Scholar
Zaniewski, A.E., Lehmann, A. & Overton, J.M. 2002. Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns. Ecological Modelling, 157, 261–280.CrossRefGoogle Scholar
Zhang, X., & Mahadevan, S. 2019. Ensemble machine learning models for aviation incident risk prediction. Decision Support Systems, 116, 48–63.CrossRefGoogle Scholar
Zhou, Z.H. 2012. Ensemble methods: foundations and algorithms. London: Chapman and Hall/CRC Press, 236 pp.CrossRefGoogle Scholar
Zhu, G.P. & Peterson, A.T. 2017. Do consensus models outperform individual models? Transferability evaluations of diverse modeling approaches for an invasive moth. Biological Invasions, 19, 2519–2532.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar