Abstract
SDMs are not new to conservation, but their popularity has increased dramatically in recent years. This step-by-step review provides an overview of the efficacy of SDMs in guiding restoration and conservation strategies across a wide range of ecological realms. Numerous studies have demonstrated the applicability of SDMs to various fields; however, their effectiveness has not been evaluated for a variety of ecosystems. Therefore, a survey and analysis of published work on the use of SDM in ecological rejuvenation and conservation from 2002 to 2023 (May) is conducted. The analysis found a total of 739 papers and the number of papers increased after 2016. The United States of America (135) had the most SDM implementations in conservation planning, followed by China (59), Australia (40), and other nations, according to the classification of the research area by country. In the model, Maxent (341) and in the areas, Forest (252) outperformed contenders for the number of papers published. This review will create a framework to aid in the following: (1) information about taxa and realms in need of protection, (2) selection of the best SDM approach according to study aim, focused species, and study area, and (3) supplemental techniques useful for better SDM output. In addition, it will discuss the advantages and disadvantages of various fundamental SDM algorithms in the context of ecological conservation.
Graphical Abstract
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abad-Uribarren A, Prado E, Sierra S, Cobo A, Rodríguez-Basalo A, Gómez-Ballesteros M, Sánchez F (2022) Deep learning-assisted high resolution mapping of vulnerable habitats within the Capbreton Canyon system, Bay of Biscay. Estuar Coast Shelf Sci 275:107957. https://doi.org/10.1016/j.ecss.2022.107957
Acevedo P, Alzaga V, Cassinello J, Gortazar C (2007) Habitat suitability modelling reveals a strong niche overlap between two poorly known species, the broom hare and the Pyrenean grey partridge, in the north of Spain. Acta Oecologica. 31(2):174–184. https://doi.org/10.1016/j.actao.2006.09.003
Aitken SN, Whitlock MC (2013) Assisted gene flow to facilitate local adaptation to climate change. Annu Rev Ecol Evol Syst 44(1):367–388. https://doi.org/10.1146/annurev-ecolsys-110512-135747
Alsos IG, Ehrich D, Thuiller W, Eidesen PB, Tribsch A, Schönswetter P, Brochmann C et al (2012) Genetic consequences of climate change for northern plants. Proc Roy Soc B Biol Sci 279(1735):2042–2051. https://doi.org/10.1098/rspb.2011.2363
Andersen LH, Sunde P, Pellegrino I, Loeschcke V, Pertoldi C (2017) Using population viability analysis, genomics, and habitat suitability to forecast future population patterns of Little Owl Athene noctua across Europe. Ecol Evol 7(24):10987–11001. https://doi.org/10.1002/ece3.3629
Anderson K, Gaston KJ (2013) Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front Ecol Environ 11(3):138–146. https://doi.org/10.1890/120150
Araújo MB, New M (2007) Ensemble forecasting of species distributions. Trends Ecol Evol 22(1):42–47
Araújo MB, Peterson AT (2012) Uses and misuses of bioclimatic envelope modeling. Ecology 93(7):1527–1539. https://doi.org/10.1890/11-1930.1
Araújo MB, Williams PH (2000) Selecting areas for species persistence using occurrence data. Biol Cons 96(3):331–345. https://doi.org/10.1016/S0006-3207(00)00074-4
Arthur B, Hindell M, Bester M, De Bruyn PN, Goebel ME, Trathan P, Lea MA (2018) Managing for change: using vertebrate at sea habitat use to direct management efforts. Ecol Ind 91:338–349. https://doi.org/10.1016/j.ecolind.2018.04.019
Assunção ACR, Alexandrino RV, Caiafa AN, de Oliveira G (2019) The invasion of Artocarpus heterophyllus, jackfruit, in protected areas under climate change and across scales: from Atlantic forest to a natural heritage private reserve. Biol Invasions 21(2):481–492. https://doi.org/10.1007/s10530-018-1840-y
Aubry KB, Raley CM, McKelvey KS (2017) The importance of data quality for generating reliable distribution models for rare, elusive, and cryptic species. PLoS ONE 12(6):e0179152. https://doi.org/10.1371/journal.pone.0179152
Austin M (2007) Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecol Model 200(1–2):1–19. https://doi.org/10.1016/j.ecolmodel.2006.07.005
Austin RA, Hawkes LA, Doherty PD, Henderson SM, Inger R, Johnson L, Witt MJ et al (2019) Predicting habitat suitability for basking sharks (Cetorhinus maximus) in UK waters using ensemble ecological niche modelling. J Sea Res 153:101767. https://doi.org/10.1016/j.seares.2019.101767
Bai J, Hou P, Jin D, Zhai J, Ma Y, Zhao J (2022) Habitat suitability assessment of black-necked crane (Grus nigricollis) in the Zoige grassland wetland ecological function zone on the Eastern Tibetan Plateau. Diversity 14(7):579. https://doi.org/10.3390/d14070579
Ballard G, Jongsomjit D, Veloz SD, Ainley DG (2012) Coexistence of mesopredators in an intact polar ocean ecosystem: the basis for defining a Ross Sea marine protected area. Biol Cons 156:72–82. https://doi.org/10.1016/j.biocon.2011.11.017
Barbosa C, Otalora JM, Giehl EL, Villalobos F, Loyola R, Tessarolo G, Castellani TT et al (2017) Changes in the realized niche of the invasive succulent CAM plant Furcraea foetida. Austral Ecol 42(6):643–654. https://doi.org/10.1111/aec.12483
Barlow MM, Johnson CN, McDowell MC, Fielding MW, Amin RJ, Brewster R (2021) Species distribution models for conservation: identifying translocation sites for eastern quolls under climate change. Glob Ecol Conserv. 29:e01735. https://doi.org/10.1016/j.gecco.2021.e01735
Barnes JC, Delborne JA (2019) Rethinking restoration targets for American chestnut using species distribution modeling. Biodivers Conserv 28(12):3199–3220. https://doi.org/10.1007/s10531-019-01814-8
Basille M, Calenge C, Marboutin E, Andersen R, Gaillard JM (2008) Assessing habitat selection using multivariate statistics: some refinements of the ecological-niche factor analysis. Ecol Model 211(1–2):233–240. https://doi.org/10.1016/j.ecolmodel.2007.09.006
Bateman BL, Pidgeon AM, Radeloff VC, Flather CH, VanDerWal J, Akçakaya HR, Heglund PJ et al (2016) Potential breeding distributions of US birds predicted with both short-term variability and long-term average climate data. Ecol Appl 26(8):2720–2731. https://doi.org/10.1002/eap.1416
Beca-Carretero P, Varela S, Stengel DB (2020) A novel method combining species distribution models, remote sensing, and field surveys for detecting and mapping subtidal seagrass meadows. Aquat Conserv Mar Freshwat Ecosyst 30(6):1098–1110. https://doi.org/10.1002/aqc.3312
Bellamy C, Scott C, Altringham J (2013) Multiscale, presence-only habitat suitability models: fine-resolution maps for eight bat species. J Appl Ecol 50(4):892–901. https://doi.org/10.1111/1365-2664.12117
Benkendorf DJ, Hawkins CP (2020) Effects of sample size and network depth on a deep learning approach to species distribution modeling. Eco Inform 60:101137. https://doi.org/10.1016/j.ecoinf.2020.101137
Bergen KM, Gilboy AM, Brown DG (2007) Multi-dimensional vegetation structure in modeling avian habitat. Eco Inform 2(1):9–22. https://doi.org/10.1016/j.ecoinf.2007.01.001
Bernardes M, Rödder D, Nguyen TT, Pham CT, Nguyen TQ, Ziegler T (2013) Habitat characterization and potential distribution of Tylototriton vietnamensis in northern Vietnam. J Nat Hist 47(17–18):1161–1175. https://doi.org/10.1080/00222933.2012.743611
Bino G, Kingsford RT, Wintle BA (2020) A stitch in time–Synergistic impacts to platypus metapopulation extinction risk. Biol Conser 242:108399. https://doi.org/10.1016/j.biocon.2019.108399
Böhner J, Antonić O (2009) Land-surface parameters specific to topo-climatology. Devel soil sci, 33: 195-226. https://doi.org/10.1016/S0166-2481(08)00008-1
Braunisch V, Bollmann K, Graf RF, Hirzel AH (2008) Living on the edge—modelling habitat suitability for species at the edge of their fundamental niche. Ecol Model 214(2–4):153–167. https://doi.org/10.1016/j.ecolmodel.2008.02.001
Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324
Brimacombe C, Bodner K, Fortin MJ (2021) Inferred seasonal interaction rewiring of a freshwater stream fish network. Ecography 44(2):219–230. https://doi.org/10.1111/ecog.05452
Buckley LB, Urban MC, Angilletta MJ, Crozier LG, Rissler LJ, Sears MW (2010) Can mechanism inform species’ distribution models? Ecol Lett 13(8):1041–1054. https://doi.org/10.1111/j.1461-0248.2010.01479.x
Burnside NG, Smith RF, Waite S (2002) Habitat suitability modelling for calcareous grassland restoration on the South Downs, United Kingdom. J Environ Manage 65(2):209–221. https://doi.org/10.1006/jema.2002.0546
Busby JR (1986) A biogeoclimatic analysis of Nothofagus cunninghamii (Hook.) Oerst. in Southeastern Australia. Aust J Ecol 11(1):1–7. https://doi.org/10.1111/j.1442-9993.1986.tb00912.x
Butterfield BJ, Copeland SM, Munson SM, Roybal CM, Wood TE (2017) Prestoration: using species in restoration that will persist now and into the future. Restor Ecol 25:S155–S163. https://doi.org/10.1111/rec.12381
Cardoso P, Borges PA, Triantis KA, Ferrández MA, Martín JL (2012) The underrepresentation and misrepresentation of invertebrates in the IUCN Red List. Biol Cons 149(1):147–148. https://doi.org/10.1016/J.BIOCON.2012.02.011
Carneiro LR, Lima AP, Machado RB, Magnusson WE (2016) Limitations to the use of species-distribution models for environmental-impact assessments in the Amazon. PLoS ONE 11(1):e0146543. https://doi.org/10.1371/journal.pone.0146543
Castaño-Santamaría J, López-Sánchez CA, Obeso JR, Barrio-Anta M (2019) Modelling and mapping beech forest distribution and site productivity under different climate change scenarios in the Cantabrian Range (North-western Spain). Forest Ecol Manag 450:117488. https://doi.org/10.1016/j.foreco.2019.117488
Cavada N, Ciolli M, Rocchini D, Barelli C, Marshall AR, Rovero F (2017) Integrating field and satellite data for spatially explicit inference on the density of threatened arboreal primates. Ecol Appl 27(1):235–243. https://doi.org/10.1002/eap.1438
Chen Y, Shan X, Zeng D, Gorfine H, Xu Y, Wu Q, Jin X et al (2022) Estimating seasonal habitat suitability for migratory species in the Bohai Sea and Yellow Sea: a case study of Tanaka’s snailfish (Liparis tanakae). Acta Oceanol Sin 41(6):22–30. https://doi.org/10.1007/s13131-021-1912-1
Cheng R, Jiang N, Yang X, Xue D, Liu S, Han H (2016) The influence of geological movements on the population differentiation of Biston panterinaria (Lepidoptera: Geometridae). J Biogeogr 43(4):691–702. https://doi.org/10.1111/jbi.12676
Chiffard J, Marciau C, Yoccoz N, Mouillot F, Duchateau S, Nadeau I, Besnard A et al (2020) Adaptive niche-based sampling to improve ability to find rare and elusive species: simulations and field tests. Methods Ecol Evol. https://doi.org/10.1111/2041-210X.13399
Chowdhury MSN, Wijsman JW, Hossain MS, Ysebaert T, Smaal AC (2019) A verified habitat suitability model for the intertidal rock oyster, Saccostrea cucullata. PLoS ONE 14(6):e0217688. https://doi.org/10.1371/journal.pone.0217688
Coro G, Bove P, Ellenbroek A (2022) Habitat distribution change of commercial species in the Adriatic Sea during the COVID-19 pandemic. Ecol Inform. https://doi.org/10.1016/j.ecoinf.2022.101675
Costa HC, de Rezende DT, Molina FB, Nascimento LB, Leite FS, Fernandes APB (2015) New distribution records and potentially suitable areas for the threatened snake-necked turtle Hydromedusa maximiliani (Testudines: Chelidae). Chelonian Conserv Biol 14(1):88–94. https://doi.org/10.2744/ccab-14-01-88-94.1
Cottee-Jones HEW, Mittermeier JC, Redding DW (2013) The Moluccan Woodcock Scolopax rochussenii on Obi Island, North Moluccas, Indonesia: a ‘lost’species is less endangered than expected. Forktail 29:88–93
da Silva LB, Oliveira GL, Frederico RG, Loyola R, Zacarias D, Ribeiro BR, Mendes-Oliveira AC (2022) How future climate change and deforestation can drastically affect the species of monkeys endemic to the eastern Amazon, and priorities for conservation. Biodivers Conserv 31(3):971–988. https://doi.org/10.1007/s10531-022-02373-1
Dai L, Hodgdon C, Tian S, Chen J, Gao C, Han D, Wang X et al (2020) Comparative performance of modelling approaches for predicting fish species richness in the Yangtze River Estuary. Region Stud Mar Sci 35:101161. https://doi.org/10.1016/j.rsma.2020.101161
Dale JJ, Brodie S, Carlisle AB, Castleton M, Hazen EL, Bograd SJ, Block BA (2022) Global habitat loss of a highly migratory predator, the blue marlin (Makaira nigricans). Divers Distrib 28(9):2020–2034. https://doi.org/10.1111/ddi.13606
Dalponte M, Bruzzone L, Gianelle D (2012) Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data. Remote Sens Environ 123:258–270. https://doi.org/10.1016/j.rse.2012.03.013
De Cáceres M, Brotons L, Aquilué N, Fortin MJ (2013) The combined effects of land-use legacies and novel fire regimes on bird distributions in the Mediterranean. J Biogeogr 40(8):1535–1547. https://doi.org/10.1111/jbi.12111
de Faria Oshima JE, Jorge MLS, Sobral-Souza T, Börger L, Keuroghlian A, Peres CA, Ribeiro MC et al (2021) Setting priority conservation management regions to reverse rapid range decline of a key neotropical forest ungulate. Glob Ecol Conserv 31:e01796. https://doi.org/10.1016/j.gecco.2021.e01796
de Oliveira Teixeira K, Silveira TCL, Harter-Marques B (2018) Different responses in geographic range shifts and increase of niche overlap in future climate scenario of the subspecies of Melipona quadrifasciata Lepeletier. Sociobiology 65(4):630–639. https://doi.org/10.13102/sociobiology.v65i4.3375
Deflem IS, Bennetsen E, Opedal ØH, Calboli FC, Ovaskainen O, Van Thuyne G, Raeymaekers JA et al (2021) Predicting fish community responses to environmental policy targets. Biodivers Conserv 30(5):1457–1478. https://doi.org/10.1007/s10531-021-02154-2
DeMarche ML, Doak DF, Morris WF (2019) Incorporating local adaptation into forecasts of species’ distribution and abundance under climate change. Glob Change Biol 25(3):775–793. https://doi.org/10.1111/gcb.14562
Descombes P, Wisz MS, Leprieur F, Parravicini V, Heine C, Olsen SM, Pellissier L et al (2015) Forecasted coral reef decline in marine biodiversity hotspots under climate change. Glob Change Biol 21(7):2479–2487. https://doi.org/10.1111/gcb.12868
Dhyani S, Kadaverugu R, Pujari P (2020) Predicting impacts of climate variability on Banj oak (Quercus leucotrichophora A. Camus) forests: understanding future implications for Central Himalayas. Reg Environ Change 20(4):1–13. https://doi.org/10.1007/s10113-020-01696-5
Diengdoh VL, Ondei S, Hunt M, Brook BW (2022) Predicted impacts of climate change and extreme temperature events on the future distribution of fruit bat species in Australia. Glob Ecol Conserv. https://doi.org/10.1016/j.gecco.2022.e02181
Dietz M, Büchner S, Hillen J, Schulz B (2018) A small mammal’s map: identifying and improving the large-scale and cross-border habitat connectivity for the hazel dormouse Muscardinus avellanarius in a fragmented agricultural landscape. Biodivers Conserv 27(8):1891–1904. https://doi.org/10.1007/s10531-018-1515-0
Drake JM, Randin C, Guisan A (2006) Modelling ecological niches with support vector machines. J Appl Ecol 43(3):424–432. https://doi.org/10.1111/j.1365-2664.2006.01141.x
Du J, Ding L, Su S, Hu W, Wang Y, Loh KH, Chen B et al (2022) Setting conservation priorities for marine sharks in China and the Association of Southeast Asian Nations (ASEAN) seas: what are the benefits of a 30% conservation target? Front Marine Sci. https://doi.org/10.3389/fmars.2022.933291
Elith J, Leathwick JR (2009) Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40(1):677–697. https://doi.org/10.1146/annurev.ecolsys.110308.120159
Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol 77(4):802–813. https://doi.org/10.1111/j.1365-2656.2008.01390.x
Engler R, Guisan A, Rechsteiner L (2004) An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. J Appl Ecol 41(2):263–274. https://doi.org/10.1111/j.0021-8901.2004.00881.x
Fabbrizzi E, Scardi M, Ballesteros E, Benedetti-Cecchi L, Cebrian E, Ceccherelli G, Fraschetti S et al (2020) Modeling macroalgal forest distribution at Mediterranean scale: present status, drivers of changes and insights for conservation and management. Front Mar Sci 7:20. https://doi.org/10.3389/fmars.2020.00020
Falcucci A, Ciucci P, Maiorano L, Gentile L, Boitani L (2009) Assessing habitat quality for conservation using an integrated occurrence-mortality model. J Appl Ecol 46(3):600–609. https://doi.org/10.1111/j.1365-2664.2009.01634.x
Febriamansyah R, Nugroho S (2021) Land use change, climate change, and river basin management: a preliminary study in small river basin of Batang Paninggahan, West Sumatra, Indonesia. Nat Resour Gov Asia. https://doi.org/10.1016/B978-0-323-85729-1.00021-9
Fern RR, Morrison ML (2017) Mapping critical areas for migratory songbirds using a fusion of remote sensing and distributional modeling techniques. Eco Inform 42:55–60. https://doi.org/10.1016/j.ecoinf.2017.09.007
Fern RR, Morrison ML, Grant WE, Wang H, Campbell TA (2020) Modeling the influence of livestock grazing pressure on grassland bird distributions. Ecol Process 9(1):1–11. https://doi.org/10.1186/s13717-020-00244-7
Franklin J (2010) Mapping species distributions: spatial inference and prediction. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9780511810602
Fretwell PT, Staniland IJ, Forcada J (2014) Whales from space: counting southern right whales by satellite. PLoS ONE 9(2):e88655. https://doi.org/10.1371/journal.pone.0088655
Friedland KD, Bachman M, Davies A, Frelat R, McManus MC, Morse R, Tanaka K et al (2021) Machine learning highlights the importance of primary and secondary production in determining habitat for marine fish and macroinvertebrates. Aquat Conserv Mar Freshw Ecosyst 31(6):1482–1498. https://doi.org/10.1002/aqc.3527
Fukuda S, De Baets B, Waegeman W, Verwaeren J, Mouton AM (2013) Habitat prediction and knowledge extraction for spawning European grayling (Thymallus thymallus L.) using a broad range of species distribution models. Environ Model Softw 47:1–6. https://doi.org/10.1016/j.envsoft.2013.04.005
Garcı́a D, Zamora R, Hódar JA, Gómez JM (1999) Age structure of Juniperus communis L. in the Iberian peninsula: conservation of remnant populations in Mediterranean mountains. Biol Conserv 87(2):215–220. https://doi.org/10.1016/S0006-3207(98)00059-7
Gaston A, Garcia-Vinas JI (2013) Evaluating the predictive performance of stacked species distribution models applied to plant species selection in ecological restoration. Ecol Model 263:103–108. https://doi.org/10.1016/j.ecolmodel.2013.04.020
Genuer R, Poggi JM, Tuleau-Malot C (2010) Variable selection using random forests. Pattern Recognit Lett 31:2225. https://doi.org/10.1016/j.patrec.2010.03.014
Ghyoumi R, Ebrahimi E, Mousavi SM (2022) Dynamics of mangrove forest distribution changes in Iran. J Water Clim Change. https://doi.org/10.2166/wcc.2022.069
Glad A, Mallard F (2022) Alpine marmot (Marmota marmota) distribution evolution under climate change: the use of species distribution models at a local scale in the western Pyrenees massif (France). Ecol Inform 69:101646. https://doi.org/10.1016/j.ecoinf.2022.101646
Gobeyn S, Mouton AM, Cord AF, Kaim A, Volk M, Goethals PL (2019) Evolutionary algorithms for species distribution modelling: a review in the context of machine learning. Ecol Model 392:179–195. https://doi.org/10.1016/j.ecolmodel.2018.11.013
Grenouillet G, Buisson L, Casajus N, Lek S (2011) Ensemble modelling of species distribution: the effects of geographical and environmental ranges. Ecography 34(1):9–17
Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8(9):993–1009. https://doi.org/10.1111/j.1461-0248.2005.00792.x
Guo Q, Kelly M, Graham CH (2005) Support vector machines for predicting distribution of Sudden Oak Death in California. Ecol Model 182(1):75–90. https://doi.org/10.1016/j.ecolmodel.2004.07.012
Gutierres F, Gil A, Reis E, Lobo A, Neto C, Calado H, Costa JC (2011) Acacia saligna (Labill.) H. Wendl in the Sesimbra County: invaded habitats and potential distribution modeling. J Coastal Res 64:403–407
Hao T, Elith J, Guillera-Arroita G, Lahoz-Monfort JJ (2019) A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD. Divers Distrib 25(5):839–852
Harrison PA (2021) Climate change and the suitability of local and non-local species for ecosystem restoration. Ecol Manag Restor 22:75–91. https://doi.org/10.1111/emr.12520
Hastie T, Tibshirani R (1986) Generalized Additive Models. Statis Sci, 1(3). https://doi.org/10.1214/ss/1177013604
He Q, Silliman BR (2019) Climate change, human impacts, and coastal ecosystems in the Anthropocene. Curr Biol 29(19):R1021–R1035. https://doi.org/10.1016/j.cub.2019.08.042
He KS, Bradley BA, Cord AF, Rocchini D, Tuanmu MN, Schmidtlein S, Pettorelli N et al (2015) Will remote sensing shape the next generation of species distribution models? Remote Sens Ecol Conserv 1(1):4–18. https://doi.org/10.1002/rse2.7
Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B (1998) Support vector machines. IEEE Intell Syst Appl 13(4):18–28. https://doi.org/10.1109/5254.708428
Heming NM, Schroth G, Talora DC, Faria D (2022) Cabruca agroforestry systems reduce vulnerability of cacao plantations to climate change in southern Bahia. Agron Sustain Dev 42(3):1–16. https://doi.org/10.1007/s13593-022-00780-w
Higa M, Tsuyama I, Nakao K, Nakazono E, Matsui T, Tanaka N (2013) Influence of nonclimatic factors on the habitat prediction of tree species and an assessment of the impact of climate change. Landscape Ecol Eng 9(1):111–120. https://doi.org/10.1007/s11355-011-0183-y
Higgins SI, Richardson DM, Cowling RM, Trinder-Smith TH (1999) Predicting the landscape-scale distribution of alien plants and their threat to plant diversity. Conserv Biol 13(2):303–313. https://doi.org/10.1046/j.1523-1739.1999.013002303.x
Hijmans RJ, Elith J (2013) Species distribution modeling with R. R Cran project
Hirzel AH, Hausser J, Chessel D, Perrin N (2002) Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data? Ecology 83(7):2027–2036. https://doi.org/10.2307/3071784
Hope AG, Waltari E, Malaney JL, Payer DC, Cook JA, Talbot SL (2015) Arctic biodiversity: increasing richness accompanies shrinking refugia for a cold-associated tundra fauna. Ecosphere 6(9):1–67. https://doi.org/10.1890/ES15-00104.1
Hotta M, Tsuyama I, Nakao K, Ozeki M, Higa M, Kominami Y, Tanaka N et al (2019) Modeling future wildlife habitat suitability: serious climate change impacts on the potential distribution of the Rock Ptarmigan Lagopus muta japonica in Japan’s northern Alps. BMC Ecol 19(1):1–14. https://doi.org/10.1186/s12898-019-0238-8
Huettmann F, Franklin SE, Stenhouse GB (2005) Predictive spatial modelling of landscape change in the Foothills Model Forest. For Chron 81(4):525–537. https://doi.org/10.5558/tfc81525-4
Huerta MAO (2007) Fragmentation patterns and implications for biodiversity conservation in three biosphere reserves and surrounding regional environments, northeastern Mexico. Biol Conserv, 134(1): 83–95.
Huntley B, Collingham YC, Green RE, Hilton GM, Rahbek C, Willis SG (2006) Potential impacts of climatic change upon geographical distributions of birds. Ibis 148:8–28. https://doi.org/10.1111/j.1474-919X.2006.00523.x
Iwai N, Yasumiba K, Akasaka M (2018) Calling-site preferences of three co-occurring endangered frog species on Amami-Oshima Island. Herpetologica 74(3):199–206. https://doi.org/10.1655/HERPETOLOGICA-D-17-00064.1
Jain P, Coogan SC, Subramanian SG, Crowley M, Taylor S, Flannigan MD (2020) A review of machine learning applications in wildfire science and management. Environ Rev 28(4):478–505. https://doi.org/10.1139/er-2020-0019
Jantz SM, Pintea L, Nackoney J, Hansen MC (2016) Landsat ETM+ and SRTM data provide near real-time monitoring of Chimpanzee (Pan troglodytes) habitats in Africa. Remote Sens 8(5):e427. https://doi.org/10.3390/rs8050427
Jaynes ET (1957) Information theory and statistical mechanics. Phys Rev 106(4):620. https://doi.org/10.1103/PhysRev.106.620
Jiménez-Valverde A, Peterson AT, Soberón J, Overton JM, Aragón P, Lobo JM (2011) Use of niche models in invasive species risk assessments. Biol Invasions 13(12):2785–2797. https://doi.org/10.1007/s10530-011-9963-4
Jones MC, Cheung WW (2015) Multi-model ensemble projections of climate change effects on global marine biodiversity. ICES J Mar Sci 72(3):741–752. https://doi.org/10.1093/icesjms/fsu172
Kaky E, Nolan V, Alatawi A, Gilbert F (2020) A comparison between Ensemble and MaxEnt species distribution modeling approaches for conservation: a case study with Egyptian medicinal plants. Ecol Inform. https://doi.org/10.1016/j.ecoinf.2020.101150
Kanaji Y, Gerrodette T (2020) Estimating abundance of Risso’s dolphins using a hierarchical Bayesian habitat model: a framework for monitoring stocks of animals inhabiting a dynamic ocean environment. Deep Sea Res Part II Top Stud Oceanogr 175:104699. https://doi.org/10.1016/j.dsr2.2019.104699
Kiser AH, Cummings KS, Tiemann JS, Smith CH, Johnson NA, Lopez RR, Randklev CR (2022) Using a multi-model ensemble approach to determine biodiversity hotspots with limited occurrence data in understudied areas: an example using freshwater mussels in México. Ecol Evol 12(5):e8909. https://doi.org/10.1002/ece3.8909
Klunzinger MW, Beatty SJ, Morgan DL, Pinder AM, Lymbery AJ (2015) Range decline and conservation status of Westralunio carteri Iredale, 1934 (Bivalvia: Hyriidae) from South-western Australia. Aust J Zool 63(2):127–135. https://doi.org/10.1071/ZO15002
Krüger L, Ramos JA, Xavier JC, Grémillet D, González-Solís J, Kolbeinsson Y, Paiva VH et al (2017) Identification of candidate pelagic marine protected areas through a seabird seasonal-, multispecific-and extinction risk-based approach. Anim Conserv 20(5):409–424. https://doi.org/10.1111/acv.12339
Kuehne LM, Hayes MP, Tyson JA, Douville KA, Tabor RA, Olden JD (2022) A stakeholder-supported conservation assessment for a data-limited species: Olympic mudminnow (Novumbra hubbsi). Aquat Conserv Mar Freshwat Ecosyst 32(1):139–156. https://doi.org/10.1002/aqc.3744
LaRose SH, MacPherson MP, Lesmeister DB, Mundy Hackett H, Perry RW, Blake Sasse D, Gompper ME (2022) Predicted distribution of plains spotted skunk in Arkansas and Missouri. J Wildl Manag 86(2):e22165. https://doi.org/10.1002/jwmg.22165
Laszlo AM, Placyk JS, Williams LR, Williams MG, Banta JA (2022) A novel multivariate ecological approach to modeling freshwater mussel habitats verified by ground truthing. Hydrobiologia 849(14):3117–3133. https://doi.org/10.1007/s10750-022-04913-w
Law B, Caccamo G, Roe P, Truskinger A, Brassil T, Gonsalves L, Stanton M et al (2017) Development and field validation of a regional, management-scale habitat model: a koala Phascolarctos cinereus case study. Ecol Evol 7(18):7475–7489. https://doi.org/10.1002/ece3.3300
Law B, Kerr I, Gonsalves L, Brassil T, Eichinski P, Truskinger A, Roe P (2022) Mini-acoustic sensors reveal occupancy and threats to koalas Phascolarctos cinereus in private native forests. J Appl Ecol 59(3):835–846. https://doi.org/10.1111/1365-2664.14099
Lawler JJ, Wiersma YF, Huettmann F (2011) Using species distribution models for conservation planning and ecological forecasting. In: Drew CA, Wiersma YF, Huettmann F (eds) Predictive species and habitat modeling in landscape ecology. Springer, New York, pp 217–290. https://doi.org/10.1007/978-1-4419-7390-0_14
Leathwick JR (1998) Are New Zealand’s Nothofagus species in equilibrium with their environment? J Veg Sci 9(5):719–732. https://doi.org/10.2307/3237290
Leathwick JR, Elith J, Francis MP, Hastie T, Taylor P (2006) Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees. Mar Ecol Prog Ser 321:267–281. https://doi.org/10.3354/meps321267
Leathwick JR, Elith J, Rowe D, Julian K (2009) Robust planning for restoring diadromous fish species in New Zealand’s lowland rivers and streams. NZ J Mar Freshw Res 43(3):659–671. https://doi.org/10.1080/00288330909510032
Lehmann A, Overton JM, Leathwick JR (2002) GRASP: generalized regression analysis and spatial prediction. Ecol Model 157(2–3):189–207. https://doi.org/10.1016/S0304-3800(02)00354-X
Lentini PE, Stirnemann IA, Stojanovic D, Worthy TH, Stein JA (2018) Using fossil records to inform reintroduction of the kakapo as a refugee species. Biol Cons 217:157–165. https://doi.org/10.1016/j.biocon.2017.10.027
Lester SE, Dubel AK, Hernán G, McHenry J, Rassweiler A (2020) Spatial planning principles for marine ecosystem restoration. Front Mar Sci 7:328. https://doi.org/10.3389/fmars.2020.00328
Leston L, Bayne E, Dzus E, Sólymos P, Moore T, Andison D, Carlson M et al (2020) Quantifying long-term bird population responses to simulated harvest plans and cumulative effects of disturbance. Front Ecol Evol 8:252. https://doi.org/10.3389/fevo.2020.00252
Li X, Wang Y (2013) Applying various algorithms for species distribution modelling. Integr Zool 8(2):124–135. https://doi.org/10.1111/1749-4877.12000
Li Z, Ye Z, Wan R, Zhang C (2015) Model selection between traditional and popular methods for standardizing catch rates of target species: a case study of Japanese Spanish mackerel in the gillnet fishery. Fish Res 161:312–319. https://doi.org/10.1016/j.fishres.2014.08.021
Li X, Ma L, Hu D, Ma D, Li R, Sun Y, Gao E (2022) Potential range shift of snow leopard in future climate change scenarios. Sustainability 14(3):1115. https://doi.org/10.3390/su14031115
Lin L, He J, Lyu R, Luo Y, Yao M, Xie L, Cui G (2021) Targeted conservation management of white pines in China: integrating phylogeographic structure, niche modeling, and conservation gap analyses. For Ecol Manag 492:119211. https://doi.org/10.1016/j.foreco.2021.119211
Linero D, Cuervo-Robayo AP, Etter A (2020) Assessing the future conservation potential of the Amazon and Andes protected areas: using the woolly monkey (Lagothrix lagothricha) as an umbrella species. J Nat Conserv 58:125926. https://doi.org/10.1016/j.jnc.2020.125926
Lissovsky AA, Dudov SV, Obolenskaya EV (2021) Species-distribution modeling: advantages and limitations of its application. 1. General approaches. Biol Bull Rev 11(3):254–264. https://doi.org/10.1134/S2079086421030075
Liu D, Lei X, Gao W, Guo H, Xie Y, Fu L, Tang S et al (2022) Mapping the potential distribution suitability of 16 tree species under climate change in northeastern China using Maxent modelling. J for Res. https://doi.org/10.1007/s11676-022-01459-4
Lu T, Brandt M, Tong X, Hiernaux P, Leroux L, Ndao B, Fensholt R (2022) Mapping the abundance of multipurpose agroforestry Faidherbia albida trees in Senegal. Remote Sens 14(3):662. https://doi.org/10.3390/rs14030662
MacLaren CA (2016) Climate change drives decline of Juniperus seravschanica in Oman. J Arid Environ 128:91–100. https://doi.org/10.1016/j.jaridenv.2016.02.001
Mafuwe K, Broadley S, Moyo S (2022) Use of maximum entropy (Maxent) niche modelling to predict the occurrence of threatened freshwater species in a biodiversity hotspot of Zimbabwe. Afr J Ecol 60(3):557–565. https://doi.org/10.1111/aje.12928
Marmion M, Parviainen M, Luoto M, Heikkinen RK, Thuiller W (2009) Evaluation of consensus methods in predictive species distribution modelling. Divers Distrib 15(1):59–69
Marshall AR, Platts PJ, Gereau RE, Kindeketa W, Kang’ethe S, Marchant R (2012) The genus Acacia (Fabaceae) in East Africa: distribution, diversity and the protected area network. Plant Ecol Evol 145(3):289–301. https://doi.org/10.5091/plecevo.2012.597
Mata C, Fuentes-Allende N, Malo JE, Vielma A, González BA (2019) The mismatch between location of protected areas and suitable habitat for the Vulnerable taruka Hippocamelus antisensis. Oryx 53(4):752–756. https://doi.org/10.1017/S0030605317001740
McCullagh P, Nelder JA (2019) Generalized linear models. Routledge, Milton Park. https://doi.org/10.1201/9780203753736
McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133. https://doi.org/10.1007/BF02478259
McCune JL (2019) A new record of Stylophorum diphyllum (Michx.) Nutt. in Canada: a case study of the value and limitations of building species distribution models for very rare plants. J Torrey Botanical Soc 146(2):119–127. https://doi.org/10.3159/TORREY-D-18-00026.1
McGarvey DJ, Brown AL, Chen EB, Viverette CB, Tuley PA, Latham OC, Kaseloo EA et al (2021) Do fishes enjoy the view? A MaxEnt assessment of fish habitat suitability within scenic rivers. Biol Conserv 263:109357. https://doi.org/10.1016/j.biocon.2021.109357
McGuire JL, Davis EB (2013) Using the palaeontological record of Microtus to test species distribution models and reveal responses to climate change. J Biogeogr 40(8):1490–1500. https://doi.org/10.2307/23463669
McSHEA WJ (2014) What are the roles of species distribution models in conservation planning? Environ Conserv 41(2):93–96. https://doi.org/10.1017/S0376892913000581
Mert A, Kirac A (2019) GIS as a tool to map habitat suitability for two lizard species using environmental factors. Fresenius Environ Bull 28(2A):1330–1336
Meyer JY, Pouteau R, Vincent F (2022) Assessing habitat suitability for the translocation of Ochrosia tahitensis (Apocynaceae), a critically endangered endemic plant from the island of Tahiti (South Pacific). J Nat Conserv. https://doi.org/10.1016/j.jnc.2022.126198
Miller J (2010) Species distribution modeling. Geogr Compass 4(6):490–509. https://doi.org/10.1111/j.1749-8198.2010.00351.x
Mohammadi S, Ebrahimi E, Moghadam MS, Bosso L (2019) Modelling current and future potential distributions of two desert jerboas under climate change in Iran. Eco Inform 52:7–13. https://doi.org/10.1016/j.ecoinf.2019.04.003
Moraes MR, Ríos-Uzeda B, Moreno LR, Huanca-Huarachi G, Larrea-Alcázar D (2014) Using potential distribution models for patterns of species richness, endemism, and phytogeography of palm species in Bolivia. Trop Conserv Sci 7(1):45–60. https://doi.org/10.1177/194008291400700109
Moullec F, Barrier N, Drira S, Guilhaumon F, Hattab T, Peck MA, Shin YJ (2022) Using species distribution models only may underestimate climate change impacts on future marine biodiversity. Ecol Modell 464:109826. https://doi.org/10.1016/j.ecolmodel.2021.109826
Mukherjee T, Sharma LK, Kumar V, Sharief A, Dutta R, Kumar M, Chandra K et al (2021) Adaptive spatial planning of protected area network for conserving the Himalayan brown bear. Sci Total Environ 754:142416. https://doi.org/10.1016/j.scitotenv.2020.142416
Nelder JA, Wedderburn RW (1972) Generalized linear models. J Roy Stat Soc Ser A 135(3):370–384. https://doi.org/10.2307/2344614
Nelli L, Schehl B, Stewart RA, Scott C, Ferguson S, MacMillan S, McCafferty DJ (2022) Predicting habitat suitability and connectivity for management and conservation of urban wildlife: a real-time web application for grassland water voles. J Appl Ecol 59(4):1072–1085. https://doi.org/10.1111/1365-2664.14118
Neto JGDS, Sutton WB, Spear SF, Freake MJ, Kéry M, Schmidt BR (2020) Integrating species distribution and occupancy modeling to study hellbender (Cryptobranchus alleganiensis) occurrence based on eDNA surveys. Biol Conserv 251:108787. https://doi.org/10.1016/j.biocon.2020.108787
Niella Y, Butcher P, Holmes B, Barnett A, Harcourt R (2022) Forecasting intraspecific changes in distribution of a wide-ranging marine predator under climate change. Oecologia 198(1):111–124. https://doi.org/10.1007/s00442-021-05075-7
Nielsen, SE, Stenhouse GB, Beyer HL, Huettmann F, Boyce MS (2008) Can natural disturbance-based forestry rescue a declining population of grizzly bears?. Biol Conser, 141(9): 2193–2207.
Nix HA (1986) A biogeographic analysis of Australian elapid snakes, Atlas of elapid snakes of Australia, vol Australian Flora and Fauna Series 7. Bureau of Flora and Fauna, Canberra, pp 4–15
Noble WS (2006) What is a support vector machine? Nat Biotechnol 24(12):1565–1567. https://doi.org/10.1038/nbt1206-1565
Noce S, Caporaso L, Santini M (2020) A new global dataset of bioclimatic indicators. Sci Data 7(1):398
Noviello N, McGonigle C, Jacoby DM, Meyers EK, Jiménez-Alvarado D, Barker J (2021) Modelling critically endangered marine species: bias-corrected citizen science data inform habitat suitability for the angelshark (Squatina squatina). Aquat Conserv Mar Freshwat Ecosyst 31(12):3451–3465. https://doi.org/10.1002/aqc.3711
Olaya Marín EJ, Martinez-Capel F, García Bartual RL, Vezza P (2016) Modelling critical factors affecting the distribution of the vulnerable endemic Eastern Iberian barbel (Luciobarbus guiraonis) in Mediterranean rivers. Mediterr Mar Sci 17(1):264–279. https://doi.org/10.12681/mms.1351
Olden JD, Joy MK, Death RG (2006) Rediscovering the species in community-wide predictive modeling. Ecol Appl 16(4):1449–1460. https://doi.org/10.1890/1051-0761(2006)016[1449:rtsicp]2.0.co;2
Olden JD, Lawler JJ, Poff NL (2008) Machine learning methods without tears: a primer for ecologists. Q Rev Biol 83(2):171–193. https://doi.org/10.1086/587826
Olden JD, Vander Zanden MJ, Johnson PT (2011) Assessing ecosystem vulnerability to invasive rusty crayfish (Orconectes rusticus). Ecol Appl 21(7):2587–2599. https://doi.org/10.1890/10-2051.1
Omar K, Elgamal I (2021) IUCN Red List and species distribution models as tools for the conservation of poorly known species: a case study of endemic plants Micromeria serbaliana and Veronica kaiseri in South Sinai, Egypt. Kew Bull 76(3):477–496. https://doi.org/10.1007/s12225-021-09953-4
Ortega-Andrade HM, Rojas-Soto O, Paucar C (2013) Novel data on the ecology of Cochranella mache (Anura: Centrolenidae) and the importance of protected areas for this critically endangered glassfrog in the Neotropics. PLoS ONE 8(12):e81837. https://doi.org/10.1371/journal.pone.0081837
Pandit SN, Zhao Y, Ciborowski JJ, Gorman AM, Knight CT (2013) Suitable habitat model for walleye (Sander vitreus) in Lake Erie: implications for inter-jurisdictional harvest quota allocations. J Great Lakes Res 39(4):591–601. https://doi.org/10.1016/j.jglr.2013.09.011
Panthi S, Wang T, Sun Y, Thapa A (2019) An assessment of human impacts on endangered red pandas (Ailurus fulgens) living in the Himalaya. Ecol Evol 9(23):13413–13425. https://doi.org/10.1002/ece3.5797
Pasquaud S, Vasconcelos RP, França S, Henriques S, Costa MJ, Cabral H (2015) Worldwide patterns of fish biodiversity in estuaries: effect of global vs. local factors. Estuar Coast Shelf Sci 154:122–128. https://doi.org/10.1016/j.ecss.2014.12.050
Pearce JL, Boyce MS (2006) Modelling distribution and abundance with presence-only data. J Appl Ecol 43(3):405–412. https://doi.org/10.1111/j.1365-2664.2005.01112.x
Pearson RG, Raxworthy CJ, Nakamura M, Townsend Peterson A (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr 34(1):102–117. https://doi.org/10.1111/j.1365-2699.2006.01594.x
Penman TD, Keith DA, Elith J, Mahony MJ, Tingley R, Baumgartner JB, Regan TJ (2015) Interactive effects of climate change and fire on metapopulation viability of a forest-dependent frog in South-eastern Australia. Biol Cons 190:142–153. https://doi.org/10.1016/j.biocon.2015.05.020
Peterson AT, Robins CR (2003) Using ecological-niche modeling to predict barred owl invasions with implications for spotted owl conservation. Conserv Biol 17(4):1161–1165. https://doi.org/10.1046/j.1523-1739.2003.02206.x
Peterson AT, Sanchez-Cordero V, Martínez-Meyer E, Navarro-Sigüenza AG (2006) Tracking population extirpations via melding ecological niche modeling with land-cover information. Ecol Model 195(3–4):229–236. https://doi.org/10.1016/j.ecolmodel.2005.11.020
Pham CH, Price JJ, Tallant JM, Karowe DN (2022) Climate change is predicted to reduce sympatry among North American wood-warblers. Ornithol Appl. https://doi.org/10.1093/ornithapp/duac025
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Modell 190(3–4):231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Piekielek NB, Hansen AJ, Chang T (2015) Using custom scientific workflow software and GIS to inform protected area climate adaptation planning in the greater Yellowstone ecosystem. Eco Inform 30:40–48. https://doi.org/10.1016/j.ecoinf.2015.08.010
Plue J, Baeten L (2021) Soil phosphorus availability determines the contribution of small, individual grassland remnants to the conservation of landscape-scale biodiversity. Appl Veg Sci 24(2):e12590. https://doi.org/10.1111/avsc.12590
Pouteau R, Birnbaum P (2016) Island biodiversity hotspots are getting hotter: vulnerability of tree species to climate change in New Caledonia. Biol Cons 201:111–119. https://doi.org/10.1016/j.biocon.2016.06.031
Qi D, Zhang S, Zhang Z, Hu Y, Yang X, Wang H, Wei F (2011) Different habitat preferences of male and female giant pandas. J Zool 285(3):205–214. https://doi.org/10.1111/j.1469-7998.2011.00831.x
Qin A, Jin K, Batsaikhan ME, Nyamjav J, Li G, Li J, Xiao W et al (2020) Predicting the current and future suitable habitats of the main dietary plants of the Gobi Bear using MaxEnt modeling. Glob Ecol Conserv 22:e01032. https://doi.org/10.1016/j.gecco.2020.e01032
Questad EJ, Kellner JR, Kinney K, Cordell S, Asner GP, Thaxton J, Tucker B et al (2014) Mapping habitat suitability for at-risk plant species and its implications for restoration and reintroduction. Ecol Appl 24(2):385–395. https://doi.org/10.1890/13-0775.1
Ramalho CE, Byrne M, Yates CJ (2017) A climate-oriented approach to support decision-making for seed provenance in ecological restoration. Front Ecol Evol. https://doi.org/10.3389/fevo.2017.00095
Ramirez-Reyes C, Street G, Vilella FJ, Jones-Farrand DT, Wiggers MS, Evans KO (2021) Ensemble species distribution model identifies survey opportunities for at-risk bearded beaksedge (Rhynchospora crinipes) in the southeastern United States. Nat Areas J 41(1):55–63. https://doi.org/10.3375/043.041.0108
Rana SK, Rana HK, Luo D, Sun H (2021) Estimating climate-induced ‘Nowhere to go’range shifts of the Himalayan Incarvillea Juss. using multi-model median ensemble species distribution models. Ecol Indic 121:107127. https://doi.org/10.1016/j.ecolind.2020.107127
Randin CF, Ashcroft MB, Bolliger J, Cavender-Bares J, Coops NC, Dullinger S, Payne D (2020) Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models. Remote Sens Environ 239:111626. https://doi.org/10.1016/j.rse.2019.111626
Rather TA, Kumar S, Khan JA (2021) Using machine learning to predict habitat suitability of sloth bears at multiple spatial scales. Ecol Process 10(1):1–12. https://doi.org/10.1186/s13717-021-00323-3
Raxworthy CJ, Martinez-Meyer E, Horning N, Nussbaum RA, Schneider GE, Ortega-Huerta MA, Townsend Peterson A (2003) Predicting distributions of known and unknown reptile species in Madagascar. Nature 426(6968):837–841. https://doi.org/10.1038/nature02205
Reyna P, Nori J, Ballesteros ML, Hued AC, Tatian M (2018) Targeting clams: insights into the invasive potential and current and future distribution of Asian clams. Environ Conserv 45(4):387–395. https://doi.org/10.1017/S0376892918000139
Riquelme C, Estay SA, López R, Pastore H, Soto-Gamboa M, Corti P (2018) Protected areas’ effectiveness under climate change: a latitudinal distribution projection of an endangered mountain ungulate along the Andes Range. PeerJ 6:e5222. https://doi.org/10.7717/peerj.5222
Riva F, Acorn JH, Nielsen SE (2018) Distribution of cranberry blue butterflies (Agriades optilete) and their responses to forest disturbance from in situ oil sands and wildfires. Diversity 10(4):112. https://doi.org/10.3390/d10040112
Robinson NM, Nelson WA, Costello MJ, Sutherland JE, Lundquist CJ (2017) A systematic review of marine-based species distribution models (SDMs) with recommendations for best practice. Front Mar Sci 4:421. https://doi.org/10.3389/fmars.2017.00421
Robinson CL, Proudfoot B, Rooper CN, Bertram DF (2021) Comparison of spatial distribution models to predict subtidal burying habitat of the forage fish Ammodytes personatus in the Strait of Georgia, British Columbia, Canada. Aquat Conserv Mar Freshw Ecosyst 31(10):2855–2869. https://doi.org/10.1002/aqc.3593
Ross CH, Pendleton DE, Tupper B, Brickman D, Zani MA, Mayo CA, Record NR (2021) Projecting regions of North Atlantic right whale, Eubalaena glacialis, habitat suitability in the Gulf of Maine for the year 2050. Elem Sci Anth 9(1):00058. https://doi.org/10.1525/elementa.2020.20.00058
Salo J, Gage E, Katz G, Stoker J (2020) Assessing Preble’s meadow jumping mouse (Zapus hudsonius preblei) habitat and connectivity for conservation and restoration. Wetlands 40(6):1813–1827. https://doi.org/10.1007/s13157-020-01374-6
Scherrer D, Christe P, Guisan A (2019) Modelling bat distributions and diversity in a mountain landscape using focal predictors in ensemble of small models. Divers Distrib 25(5):770–782. https://doi.org/10.1111/ddi.12893
Schubert PR, Hukriede W, Karez R, Reusch TB (2015) Mapping and modeling eelgrass Zostera marina distribution in the western Baltic Sea. Mar Ecol Prog Ser 522:79–95. https://doi.org/10.3354/meps11133
Schwager P, Berg C (2021) Remote sensing variables improve species distribution models for alpine plant species. Basic Appl Ecol 54:1–13. https://doi.org/10.1016/j.baae.2021.04.002
Seidle KM, Kiss J, Attanayake AU, DeVink JM, Bedard-Haughn A, Westwood R, Lamb EG (2020) Extent of Dakota skipper, Hesperia dacotae, distribution in Southeastern Saskatchewan, Canada. J Insect Conserv 24(6):1073–1081. https://doi.org/10.1007/s10841-020-00276-6
Semerdjian AE, Butterfield HS, Stafford R, Westphal MF, Bean WT (2021) Combining occurrence and habitat suitability data improve conservation guidance for the giant kangaroo rat. J Wildl Manag 85(5):855–867. https://doi.org/10.1002/jwmg.22052
Sérgio C, Figueira R, Draper D, Menezes R, Sousa AJ (2007) Modelling bryophyte distribution based on ecological information for extent of occurrence assessment. Biol Cons 135(3):341–351. https://doi.org/10.1016/j.biocon.2006.10.018
Serra-Varela MJ, Alía R, Daniels RR, Zimmermann NE, Gonzalo-Jiménez J, Grivet D (2017) Assessing vulnerability of two Mediterranean conifers to support genetic conservation management in the face of climate change. Divers Distrib 23(5):507–516. https://doi.org/10.1111/ddi.12544
Shirk AJ, Cushman SA, Waring KM, Wehenkel CA, Leal-Sáenz A, Toney C, Lopez-Sanchez CA (2018) Southwestern white pine (Pinus strobiformis) species distribution models project a large range shift and contraction due to regional climatic changes. For Ecol Manage 411:176–186. https://doi.org/10.1016/j.foreco.2018.01.025
Shryock DF, Washburn LK, DeFalco LA, Esque TC (2021) Harnessing landscape genomics to identify future climate resilient genotypes in a desert annual. Mol Ecol 30(3):698–717. https://doi.org/10.1111/mec.15672
Shryock DF, DeFalco LA, Esque TC (2022) Seed Menus: an integrated decision-support framework for native plant restoration in the Mojave Desert. Ecol Evol 12(4):e8805. https://doi.org/10.1002/ece3.8805
Singh M (2020) Evaluating the impact of future climate and forest cover change on the ability of Southeast (SE) Asia’s protected areas to provide coverage to the habitats of threatened avian species. Ecol Indic 114:106307. https://doi.org/10.1016/j.ecolind.2020.106307
Sistri G, Menchetti M, Santini L, Pasquali L, Sapienti S, Cini A, Dapporto L et al (2022) The isolated Erebia pandrose Apennine population is genetically unique and endangered by climate change. Insect Conserv Divers 15(1):136–148. https://doi.org/10.1111/icad.12538
Soares FC, Panisi M, Sampaio H, Soares E, Santana A, Buchanan GM, de Lima RF et al (2020) Land-use intensification promotes non-native species in a tropical island bird assemblage. Anim Conserv 23(5):573–584. https://doi.org/10.1111/acv.12568
Soares de Oliveira I, Roedder D, Capinha C, Ahmadzadeh F, Cunha K, de Oliveira A, Toledo LF (2016) Assessing future habitat availability for coastal lowland anurans in the Brazilian Atlantic rainforest. Stud Neotropical Fauna Environ 51(1):45–55. https://doi.org/10.1080/01650521.2016.1160610
Steiner FM, Schlick-Steiner BC, VanDerWal J, Reuther KD, Christian E, Stauffer C, Crozier RH et al (2008) Combined modelling of distribution and niche in invasion biology: a case study of two invasive Tetramorium ant species. Divers Distrib 14(3):538–545. https://doi.org/10.1111/j.1472-4642.2008.00472.x
Stralberg D, Matsuoka SM, Hamann A, Bayne EM, Sólymos P, Schmiegelow FK, Song SJ (2015) Projecting boreal bird responses to climate change: the signal exceeds the noise. Ecol Appl 25(1):52–69. https://doi.org/10.1890/13-2289.1
Sunny A, González-Fernández A, D’Addario M (2017) Potential distribution of the endemic imbricate alligator lizard (Barisia imbricata imbricata) in highlands of central Mexico. Amphibia Reptilia 38(2):225–231. https://doi.org/10.1163/15685381-00003092
Suttidate N, Steinmetz R, Lynam AJ, Sukmasuang R, Ngoprasert D, Chutipong W, Radeloff VC (2021) Habitat connectivity for endangered Indochinese tigers in Thailand. Glob Ecol Conserv 29:e01718. https://doi.org/10.1016/j.gecco.2021.e01718
Syfert MM, Brummitt NA, Coomes DA, Bystriakova N, Smith MJ (2018) Inferring diversity patterns along an elevation gradient from stacked SDMs: a case study on Mesoamerican ferns. Glob Ecol Conserv 16:e00433. https://doi.org/10.1016/j.gecco.2018.e00433
Teitelbaum CS, Sirén AP, Coffel E, Foster JR, Frair JL, Hinton JW, Morelli TL et al (2021) Habitat use as indicator of adaptive capacity to climate change. Divers Distrib 27(4):655–667. https://doi.org/10.1111/ddi.13223
Thompson CA, Benson JF, Patterson BR (2022) A novel survey design for modeling species distribution of beavers in Algonquin Park, Canada. Wildl Soc Bull 46(3):e1322. https://doi.org/10.1002/wsb.1322
Torrejón-Magallanes J, Grados D, Lau-Medrano W (2019) Spatio-temporal distribution modeling of dolphinfish (Coryphaena hippurus) in the Pacific Ocean off Peru using artisanal longline fishery data. Deep Sea Res Part II Top Stud Oceanogr 169:104665. https://doi.org/10.1016/j.dsr2.2019.104665
Trisurat Y, Bhumpakphan N, Reed DH, Kanchanasaka B (2012) Using species distribution modeling to set management priorities for mammals in northern Thailand. J Nat Conserv 20(5):264–273. https://doi.org/10.1016/j.jnc.2012.05.002
Usio N (2007) Endangered crayfish in northern Japan: distribution, abundance and microhabitat specificity in relation to stream and riparian environment. Biol Cons 134(4):517–526. https://doi.org/10.1016/j.biocon.2006.09.002
Vaissi S (2021) Design of protected area by tracking and excluding the effects of climate and landscape change: a case study using Neurergus derjugini. Sustainability 13(10):5645. https://doi.org/10.3390/su13105645
Valencia J, Vaca-Guerrero G, Garzón K (2011) Natural History, Potential Distribution and Conservation Status of the Manabi Hognose Pitviper Porthidium Arcosae (SCHATTI & KRAMER, 1993),in Ecuador. Herpetozoa 23(3–4):31–43
Valle M, Borja Á, Chust G, Galparsoro I, Garmendia JM (2011) Modelling suitable estuarine habitats for Zostera noltii, using ecological niche factor analysis and bathymetric LiDAR. Estuar Coast Shelf Sci 94(2):144–154. https://doi.org/10.1016/j.ecss.2011.05.031
Valle M, Garmendia JM, Chust G, Franco J, Borja Á (2015) Increasing the chance of a successful restoration of Zostera noltii meadows. Aquat Bot 127:12–19. https://doi.org/10.1016/j.aquabot.2015.07.002
van Proosdij AS, Sosef MS, Wieringa JJ, Raes N (2016) Minimum required number of specimen records to develop accurate species distribution models. Ecography 39(6):542–552. https://doi.org/10.1111/ecog.01509
Vander Zanden MJ, Olden JD (2008) A management framework for preventing the secondary spread of aquatic invasive species. Can J Fish Aquat Sci 65(7):1512–1522. https://doi.org/10.1139/F08-099
VanDerWal J, Shoo LP, Johnson CN, Williams SE (2009) Abundance and the environmental niche: environmental suitability estimated from niche models predicts the upper limit of local abundance. Am Nat 174(2):282–291. https://doi.org/10.1086/600087
Vargas Soto JS, Beirne C, Whitworth A, Cruz Diaz JC, Flatt E, Pillco-Huarcaya R, Molnár PK et al (2022) Human disturbance and shifts in vertebrate community composition in a biodiversity hotspot. Conserv Biol 36(2):e13813. https://doi.org/10.1111/cobi.13813
Vega GC, Pertierra LR, Benayas J, Olalla-Tárraga MÁ (2021) Ensemble forecasting of invasion risk for four alien springtail (Collembola) species in Antarctica. Polar Biol 44(11):2151–2164. https://doi.org/10.1007/s00300-021-02949-7
Vetter VM, Tjaden NB, Jaeschke A, Buhk C, Wahl V, Wasowicz P, Jentsch A (2018) Invasion of a legume ecosystem engineer in a cold biome alters plant biodiversity. Front Plant Sci 9:715. https://doi.org/10.3389/fpls.2018.00715
Vincent C, Fernandes RF, Cardoso AR, Broennimann O, Di Cola V, D’Amen M, Guisan A et al (2019) Climate and land-use changes reshuffle politically-weighted priority areas of mountain biodiversity. Glob Ecol Conserv 17:e00589. https://doi.org/10.1016/j.gecco.2019.e00589
Vogeler JC, Yang Z, Cohen WB (2016) Mapping suitable Lewis’s Woodpecker nesting habitat in a post-fire landscape. Northwest Sci 90(4):421–432. https://doi.org/10.3955/046.090.0404
Walsworth TE, Budy P (2015) Integrating nonnative species in niche models to prioritize native fish restoration activity locations along a desert river corridor. Trans Am Fish Soc 144(4):667–681. https://doi.org/10.1080/00028487.2015.1024333
Wang CJ, Wan JZ, Zhang ZX (2017a) Expansion potential of invasive tree plants in ecoregions under climate change scenarios: an assessment of 54 species at a global scale. Scand J for Res 32(8):663–670. https://doi.org/10.1080/02827581.2017.1283049
Wang Q, Yang L, Ranjitkar S, Wang JJ, Wang XR, Zhang DX, Guan WB et al (2017b) Distribution and in situ conservation of a relic Chinese oil woody species Xanthoceras sorbifolium (yellowhorn). Can J for Res 47(11):1450–1456. https://doi.org/10.1139/cjfr-2017-0210
Wang F, Zhao Q, McShea WJ, Songer M, Huang Q, Zhang X, Zhou L (2018) Incorporating biotic interactions reveals potential climate tolerance of giant pandas. Conserv Lett 11(6):e12592. https://doi.org/10.1111/conl.12592
White RL, Baptiste TJ, Dornelly A, MortonO’ConnellYoung MNMJRP (2012) Population responses of the endangered white-breasted Thrasher Ramphocinclus brachyurus to a tourist development in Saint Lucia–conservation implications from a spatial modelling approach. Bird Conserv Int 22(4):468–485. https://doi.org/10.1017/S0959270912000184
Willems EP, Barton RA, Hill RA (2009) Remotely sensed productivity, regional home range selection, and local range use by an omnivorous primate. Behav Ecol 20(5):985–992. https://doi.org/10.1093/beheco/arp087
Williams AV, Nevill PG, Krauss SL (2014) Next generation restoration genetics: applications and opportunities. Trends Plant Sci 19(8):529–537. https://doi.org/10.1016/j.tplants.2014.03.011
Williams BA, Grantham HS, Watson JE, Shapiro AC, Plumptre AJ, Ayebare S, Tulloch AI (2022) Reconsidering priorities for forest conservation when considering the threats of mining and armed conflict. Ambio. https://doi.org/10.1007/s13280-022-01724-0
Wilson KA, Westphal MI, Possingham HP, Elith J (2005) Sensitivity of conservation planning to different approaches to using predicted species distribution data. Biol Cons 122(1):99–112. https://doi.org/10.1016/j.biocon.2004.07.004
Wilson CD, Roberts D, Reid N (2011) Applying species distribution modelling to identify areas of high conservation value for endangered species: a case study using Margaritifera margaritifera (L.). Biol Conserv 144(2):821–829. https://doi.org/10.1016/j.biocon.2010.11.014
Wilson KL, Skinner MA, Lotze HK (2019) Projected 21st-century distribution of canopy-forming seaweeds in the Northwest Atlantic with climate change. Divers Distrib 25(4):582–602. https://doi.org/10.1111/ddi.12897
Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan A, NCEAS Predicting Species Distributions Working Group (2008) Effects of sample size on the performance of species distribution models. Divers Distrib 14(5):763–773. https://doi.org/10.1111/j.1472-4642.2008.00482.x
Xie D, Du H, Xu WH, Ran JH, Wang XQ (2022) Effects of climate change on richness distribution patterns of threatened conifers endemic to China. Ecol Indicators 136:108594. https://doi.org/10.1016/j.ecolind.2022.108594
Yang Z, Wang T, Skidmore AK, De Leeuw J, Said MY, Freer J (2014) Spotting East African mammals in open savannah from space. PLoS ONE 9(12):e115989. https://doi.org/10.1371/journal.pone.0115989
Yang L, Li H, Li Q, Guo Q, Li J (2021) Genetic diversity analysis and potential distribution prediction of Sophora moorcroftiana endemic to Qinghai-Tibet Plateau, China. Forests 12(8):1106. https://doi.org/10.3390/f12081106
Yu J, Wang C, Wan J, Han S, Wang Q, Nie S (2014) A model-based method to evaluate the ability of nature reserves to protect endangered tree species in the context of climate change. For Ecol Manage 327:48–54. https://doi.org/10.1016/j.foreco.2014.04.020
Yu F, Wu Z, Shen J, Huang J, Groen TA, Skidmore AK, Wang T et al (2021) Low-elevation endemic Rhododendrons in China are highly vulnerable to climate and land use change. Ecol Indicators 126:107699. https://doi.org/10.1016/j.ecolind.2021.107699
Zellmer AJ, Claisse JT, Williams CM, Schwab S, Pondella DJ (2019) Predicting optimal sites for ecosystem restoration using stacked-species distribution modeling. Front Mar Sci 6:3. https://doi.org/10.3389/fmars.2019.00003
Zeng Y, Yeo DC (2018) Assessing the aggregated risk of invasive crayfish and climate change to freshwater crabs: a Southeast Asian case study. Biol Cons 223:58–67. https://doi.org/10.1016/j.biocon.2018.04.033
Zhang L, Liu S, Sun P, Wang T, Wang G, Wang L, Zhang X (2016) Using DEM to predict Abies faxoniana and Quercus aquifolioides distributions in the upstream catchment basin of the Min River in southwest China. Ecol Ind 69:91–99. https://doi.org/10.1016/j.ecolind.2016.04.008
Zhang L, Huettmann F, Liu S, Sun P, Yu Z, Zhang X, Mi C (2019) Classification and regression with random forests as a standard method for presence-only data SDMs: a future conservation example using China tree species. Eco Inform 52:46–56. https://doi.org/10.1016/j.ecoinf.2019.05.003
Zhang Z, Mammola S, Liang Z, Capinha C, Wei Q, Wu Y, Wang C et al (2020) Future climate change will severely reduce habitat suitability of the critically endangered Chinese giant salamander. Freshw Biol 65(5):971–980. https://doi.org/10.1111/fwb.13483
Zhang B, Yuan Y, Shu L, Grosholz E, Guo Y, Hastings A, Qiu J et al (2021a) Scaling up experimental stress responses of grass invasion to predictions of continental-level range suitability. Ecology 102(8):e03417. https://doi.org/10.1002/ecy.3417
Zhang HX, Wang Q, Wen ZB (2021b) Spatial genetic structure of Prunus mongolica in Arid Northwestern China based on RAD sequencing data. Diversity 13(8):397. https://doi.org/10.3390/d13080397
Zhang D, An B, Chen L, Sun Z, Mao R, Zhao C, Zhang L (2022) Camera trapping reveals spatiotemporal partitioning patterns and conservation implications for two sympatric pheasant species in the Qilian Mountains, Northwestern China. Animals 12(13):1657. https://doi.org/10.3390/ani12131657
Zhiyun O, Jianguo L, Han X, Yingchun T, Hemin Z (2001) An assessment of giant panda habitat in Wolong nature reserve. Acta Ecol Sin 21(11):1869–1874
Zhu K, Woodall CW, Ghosh S, Gelfand AE, Clark JS (2014) Dual impacts of climate change: forest migration and turnover through life history. Glob Change Biol 20(1):251–264. https://doi.org/10.1111/gcb.12382
Zielewska-Büttner K, Heurich M, Müller J, Braunisch V (2018) Remotely sensed single tree data enable the determination of habitat thresholds for the three-toed woodpecker (Picoides tridactylus). Remote Sens 10(12):1972. https://doi.org/10.3390/rs10121972
Zizka A, Azevedo J, Leme E, Neves B, da Costa AF, Caceres D, Zizka G (2020) Biogeography and conservation status of the pineapple family (Bromeliaceae). Divers Distrib 26(2):183–191. https://doi.org/10.1111/ddi.13004
Acknowledgements
The author wishes to acknowledge the support from the University Grants Commission (UGC) for providing funds under the CSIR-UGC NET-JRF fellowship (Ref no. 1017/ (CSIR-UGC NET-JUNE 2019). The Bibliometric analysis has been carried out on the software platform, i.e., VOSviewer, and ArcGIS is used to prepare Study area-wise publications distribution and are gratefully acknowledged.
Funding
This work was supported by the University Grants Commission (UGC) for providing funds under the CSIR-UGC NET-JRF fellowship (Ref no. 1017/ (CSIR-UGC NET-JUNE 2019).
Author information
Authors and Affiliations
Contributions
MKR: Conceptualization, Methodology, Software, Data curation, Writing—original draft. LKS: Conceptualization, Supervision, Writing – review & editing.
Corresponding author
Ethics declarations
Conflict of interest
The authors have no relevant financial or non-financial competing interests.
Additional information
Communicated by David Hawksworth.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Rathore, M.K., Sharma, L.K. Efficacy of species distribution models (SDMs) for ecological realms to ascertain biological conservation and practices. Biodivers Conserv 32, 3053–3087 (2023). https://doi.org/10.1007/s10531-023-02648-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10531-023-02648-1