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Species-Distribution Modeling: Advantages and Limitations of Its Application. 1. General Approaches

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Abstract

For a long time, studies of the distribution of living beings in a geographical space were performed only with empirical methods. A change in the view of a species distribution as a projection of a Hutchinsonian ecological niche led to the formation of the discipline of ecological modeling of the species distribution, which switched faunistics/floristics from data accumulation to a full-fledged scientific industry with experiment planning and result verification. The various methods of species-distribution modeling make it possible to analyze the patterns of the geographical distributional of organisms in the presence of methodological challenges: nonrandomness of the occurrence data, inhomogeneity of the collection efforts, landscape heterogeneity in different scales, etc. The results of species-distribution modeling represent spatially continuous data of habitat suitability and are valuable not only for studies of the habitats themselves but also for a number of disciplines that involve species distributions.

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REFERENCES

  1. Abolmaali, S.M.-R., Tarkesh, M., and Bashari, H., MaxEnt modeling for predicting suitable habitats and identifying the effects of climate change on a threatened species, Daphne mucronata, in central Iran, Ecol. Inf., 2018, vol. 43, pp. 116–123.

    Article  Google Scholar 

  2. Afanas’ev, A.V., Zoogeografiya Kazakhstana (na osnove rasprostraneniya mlekopitayushchikh) (Zoogeography of Kazakhstan Based on Distribution of Mammals), Alma-Ata: Akad. Nauk KazSSR, 1960.

  3. Amatulli, G., Domisch, S., Tuanmu, M.N., Parmentier, B., Ranipeta, A., et al., Data Descriptor: a suite of global, cross-scale topographic variables for environmental and biodiversity modeling, Sci. Data, 2018, vol. 5, pp. 1–15.

    Article  Google Scholar 

  4. Araújo, M.B. and Peterson, A.T., Uses and misuses of bioclimatic envelope modeling, Ecology, 2012, vol. 93, no. 7, pp. 1527–1539.

    Article  PubMed  Google Scholar 

  5. Araújo, M.B., Anderson, R.P., Barbosa, A.M., Beale, C.M., Dormann, C.F., et al., Standards for distribution models in biodiversity assessments, Sci. Adv., 2019, vol. 5, no. 1, p. eaat4858.

  6. Assis, J., Tyberghein, L., Bosch, S., Verbruggen, H., Serrão, E.A., and de Clerck, O., Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modeling, Global Ecol. Biogeogr., 2018, vol. 27, no. 3, pp. 277–284.

    Article  Google Scholar 

  7. Austin, M.P., Spatial prediction of species distribution: an interface between ecological theory and statistical modeling, Ecol. Model., 2002, vol. 157, no. 2, pp. 101–118.

    Article  Google Scholar 

  8. Bell, D.M., Bradford, J.B., and Lauenroth, W.K., Mountain landscapes offer few opportunities for high-elevation tree species migration, Global Change Biol., 2014, vol. 20, no. 5, pp. 1441–1451.

    Article  Google Scholar 

  9. Bosso, L., Smeraldo, S., Rapuzzi, P., Sama, G., Garonna, A.P., and Russo, D., Nature protection areas of Europe are insufficient to preserve the threatened beetle Rosalia alpine (Coleoptera: Cerambycidae): evidence from species distribution models and conservation gap analysis, Ecol. Entomol., 2018, vol. 43, no. 2, pp. 192–203.

    Article  Google Scholar 

  10. Borisova, N.G. and Starkov, A.I., Distribution of the Daurian pika: climatic factors, Vestn. Buryat. Gos. Univ., Biol., Geogr., 2015, no. 4, pp. 130–136.

  11. Braconnot, P., Harrison, S.P., Kageyama, M., Bartlein, P.J., Masson-Delmotte, V., et al., Evaluation of climate models using palaeoclimatic data, Nat. Clim. Change, 2012, vol. 2, no. 6, pp. 417–424.

    Article  Google Scholar 

  12. Brown, J.L., SDMtoolbox: a python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses, Methods Ecol. Evol., 2014, vol. 5, no. 7, pp. 694–700.

    Article  Google Scholar 

  13. Busby, J.R., BIOCLIM: a bioclimate analysis and prediction system, Plant Prot. Q., 1991, vol. 6, no. 1, pp. 8–9.

    Google Scholar 

  14. Ceccarelli, S. and Rabinovich, J.E., Global climate change effects on Venezuela’s vulnerability to Chagas disease is kinked to the geographic distribution of five triatomine species, J. Med. Entomol., 2015, vol. 52, no. 6, pp. 1333–1343.

    Article  PubMed  Google Scholar 

  15. Chel’tsov-Bebutov, A.M., Zoogeographic mapping: principles and statements, Vestn. Mosk. Gos. Univ., Ser. Geogr., 1976, no. 2, pp. 50–56.

  16. Choe, H., Thorne, J.H., Hijmans, R., Kim, J., Kwon, H., and Seo, C., Meta-corridor solutions for climate-vulnerable plant species groups in South Korea, J. Appl. Ecol., 2017, vol. 54, no. 6, pp. 1742–1754.

    Article  Google Scholar 

  17. Cobos, M.E., Peterson, A.T., Barve, N., and Osorio-Olvera, L., kuenm: an R package for detailed development of ecological niche models using Maxent, PeerJ, 2019, vol. 7, p. e6281.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., et al., System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 2015, vol. 8, no. 7, pp. 1991–2007.

    Article  Google Scholar 

  19. Cord, A.F., Klein, D., Gernandt, D.S., de la Rosa, J.A.P., and Dech, S., Remote sensing data can improve predictions of species richness by stacked species distribution models: a case study for Mexican pines, J. Biogeogr., 2014, vol. 41, no. 4, pp. 736–748.

    Article  Google Scholar 

  20. Daru, B.H., Park, D.S., Primack, R.B., Willis, C.G., Barrington, D.S., et al., Widespread sampling biases in herbaria revealed from large-scale digitization, New Phytol., 2018, vol. 217, no. 2, pp. 939–955.

    Article  PubMed  Google Scholar 

  21. de Araújo, C.B., Marcondes-Machado, L.O., and Costa, G.C., The importance of biotic interactions in species distribution models: a test of the Eltonian noise hypothesis using parrots, J. Biogeogr., 2014, vol. 41, no. 3, pp. 513–523.

    Article  Google Scholar 

  22. Deblauwe, V., Droissart, V., Bose, R., and Sonké, B., Remotely sensed temperature and precipitation data improve species, Global Ecol. Biogeogr., 2016, vol. 25, no. 4, pp. 443–454.

    Article  Google Scholar 

  23. de Souza Muñoz M.E., de Giovanni R., de Siqueira M.F., Sutton T., Brewer, P., et al., openModeller: a generic approach to species’ potential distribution modeling, GeoInformatica, 2011, vol. 15, no. 1, pp. 111–135.

    Article  Google Scholar 

  24. Domisch, S., Amatulli, G., and Jetz, W., Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution, Sci. Data, 2015, vol. 2, pp. 1–13.

    Article  CAS  Google Scholar 

  25. Dormann, C.F., Bobrowski, M., Dehling, D.M., Harris, D.J., Hartig, F., et al., Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions, Global Ecol. Biogeogr., 2018, vol. 27, no. 9, pp. 1004–1016.

    Article  Google Scholar 

  26. Doronin, I.V., Mazanaeva, L.F., and Doronina, M.A., Use of GIS modeling to analyze the distribution of lizard Lacerta media (Lantz et Cyren, 1920) in Dagestan (Russia), Tr. Zool. Inst., Ross. Akad. Nauk, 2018, vol. 322, no. 4, pp. 463–480.

    Google Scholar 

  27. Dubinin, M.Yu. and Kostikova, A.A., Introduction into GIS systems, Vector and raster data, 2008. http://gis-lab.info/docs/giscourse/11-vector-raster.html.

  28. Duisebaeva, T.N., Doronin, I.V., Malakhov, D.V., Kukushkin, O.V., and Bakiev, A.G., GIS analysis of distribution of habitat conditions of Emysorbicularis orbicularis (Testudines, Emydidae): methodological aspects, Izv. Vyssh. Uchebn. Zaved., Povolzh. Reg., Estestv. Nauki, 2019, no. 1 (25), pp. 28–40.

  29. Eklundh, L. and Jönsson, P., TIMESAT 3.3 with Seasonal Trend Decomposition and Parallel Processing: Software Manual, Lund: Lund Univ., 2017.

    Google Scholar 

  30. El-Gabbas, A. and Dormann, C.F., Improved species-occurrence predictions in data-poor regions: using large-scale data and bias correction with down-weighted Poisson regression and Maxent, Ecography, 2018, vol. 41, no. 7, pp. 1161–1172.

    Article  Google Scholar 

  31. Elith, J., Phillips, S.J., Hastie, T., Dudík, M., Chee, Y.E., and Yates, C.J., A statistical explanation of MaxEnt for ecologists, Diversity Distrib., 2011, vol. 17, no. 1, pp. 43–57.

    Article  Google Scholar 

  32. Fick, S.E. and Hijmans, R.J., WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas, Int. J. Climatol., 2017, vol. 37, no. 12, pp. 4302–4315.

    Article  Google Scholar 

  33. Fois, M., Cuena-Lombraña, A., Fenu, G., and Bacchetta, G., Using species distribution models at local scale to guide the search of poorly known species: review, methodological issues and future directions, Ecol. Model., 2018, vol. 385, pp. 124–132.

    Article  Google Scholar 

  34. Fourcade, Y., Besnard, A.G., and Secondi, J., Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics, Global Ecol. Biogeogr., 2018, vol. 27, no. 2, pp. 245–256.

    Article  Google Scholar 

  35. Franklin, J., Species distribution models in conservation biogeography: Developments and challenges, Diversity Distrib., 2013, vol. 19, no. 10, pp. 1217–1223.

    Article  Google Scholar 

  36. Gaston, K.J., The Structure and Dynamics of Geographic Ranges, Oxford: Oxford Univ. Press, 2003.

    Google Scholar 

  37. Gaston, K.J. and Fuller, R.A., The sizes of species’ geographic ranges, J. Appl. Ecol., 2009, vol. 46, no. 1, pp. 1–9.

    Article  Google Scholar 

  38. Gavin, D.G., Fitzpatrick, M.C., Gugger, P.F., Heath, K.D., Rodríguez-Sánchez, F., et al., Climate refugia: joint inference from fossil records, species distribution models and phylogeography, New Phytol., 2014, vol. 204, no. 1, pp. 37–54.

    Article  PubMed  Google Scholar 

  39. Giannini, T.C., Chapman, D.S., Saraiva, A.M., Alves-dos-Santos, I., and Biesmeijer, J.C., Improving species distribution models using biotic interactions: a case study of parasites, pollinators and plants, Ecography, 2013, vol. 36, no. 6, pp. 649–656.

    Article  Google Scholar 

  40. Gilfillan, D., Joyner, T.A., and Scheuerman, P., Maxent estimation of aquatic Escherichia coli stream impairment, PeerJ, 2018, vol. 6, p. e5610.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Goloshchapova, S.S. and Prokof’ev, I.L., Forecasting of dynamics of spatial distribution of daytime Rhopalocera (Lepidoptera) in Bryansk oblast using Cgcm3.1_cccma model of climate changes, Sovrem. Probl. Nauki Obraz., 2013, no. 2, p. 416.

  42. Guillera-Arroita, G., Lahoz-Monfort, J.J., Elith, J., Gordon, A., Kujala, H., et al., Is my species distribution model fit for purpose? Matching data and models to applications: matching distribution models to applications, Global Ecol. Biogeogr., 2015, vol. 24, no. 3, pp. 276–292.

    Article  Google Scholar 

  43. Guisan, A. and Thuiller, W., Predicting species distribution: offering more than simple habitat models, Ecol. Lett., 2005, vol. 8, no. 9, pp. 993–1009.

    Article  PubMed  Google Scholar 

  44. Guisan, A., Thuiller, W., and Zimmermann, N.E., Habitat Suitability and Distribution Models: With Applications in R, Cambridge: Cambridge Univ. Press, 2017.

    Book  Google Scholar 

  45. Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., et al., High-resolution global maps of 21st-century forest cover change, Science, 2013, vol. 342, no. 6160, pp. 850–853.

    Article  CAS  PubMed  Google Scholar 

  46. He, K.S., Bradley, B.A., Cord, A.F., Rocchini, D., Tuanmu, M.N., et al., Will remote sensing shape the next generation of species distribution models? Remote Sens. Ecol. Conserv., 2015, vol. 1, no. 1, pp. 4–18.

    Article  Google Scholar 

  47. Hengl, T., Mendes de Jesus, J., Heuvelink, G.B.M., Ruiperez Gonzalez, M., Kilibarda, M., et al., SoilGrids250m: Global gridded soil information based on machine learning, PLoS One, 2017, vol. 12, no. 2, p. e0169748.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., and Jarvis, A., Very high resolution interpolated climate surfaces for global land areas, Int. J. Climatol., 2005, vol. 25, no. 15, pp. 1965–1978.

    Article  Google Scholar 

  49. Hijmans, R.J., Phillips, S., Leathwick, J., Elith, J., and Hijmans, M.R.J., Package ‘dismo,’ Circles, 2017, vol. 9, no. 1, pp. 1–68.

    Google Scholar 

  50. Hijmans, R.J., Guarino, L., Bussink, C., Mathur, P., Cruz, M., et al., DIVA-GIS: a geographic information system for the analysis of species distribution data, Versão, 2012, vol. 7, pp. 476–486.

    Google Scholar 

  51. Hirzel, A.H., Hausser, J., Chessel, D., and Perrin, N., Ecological niche factor analysis: How to compute habitat suitability maps without absence data? Ecology, 2002, vol. 83, no. 7, pp. 2027–2036.

    Article  Google Scholar 

  52. Hutchinson, G.E., Concluding remarks, Cold Spring Harb. Symp. Quant. Biol., 1957, vol. 22, pp. 415–427.

    Article  Google Scholar 

  53. IPCC, Climate Change 2013: The Physical Science Basis, Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., et al., Eds., New York: Cambridge Univ. Press, 2013.

    Google Scholar 

  54. Kafanov, A.I., Istoriko-metodologicheskie aspekty obshchei i morskoi biogeografii (Historical and Methodological Aspects of General and Marine Biogeography), Vladivostok: Dal’nevost. Gos. Univ., 2005.

  55. Kalashnikova, Y.A., Karnaukhov, A.S., Dubinin, M.Y., Poyarkov, A.D., and Rozhnov, V.V., Potential habitat of snow leopard (Panthera uncial, Felinae) in south Siberia and adjacent territories based on the maximum entropy distribution model, Zool. Zh., 2019, vol. 98, no. 3, pp. 332–342.

    Google Scholar 

  56. Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., et al., Climatologies at high resolution for the Earth’s land surface areas, Sci. Data, 2017, vol. 4, pp. 1–20.

    Article  Google Scholar 

  57. Kearney, M.R., Isaac, A.P., and Porter, W.P., Microclim: Global estimates of hourly microclimate based on long-term monthly climate averages, Sci. Data, 2014, vol. 1, p. 140006.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Kerr, J.T. and Ostrovsky, M., From space to species: ecological applications for remote sensing, Trends Ecol. Evol., 2003, vol. 18, no. 6, pp. 299–305.

    Article  Google Scholar 

  59. Kohli, B.A., Fedorov, V.B., Waltari, E., and Cook, J.A., Phylogeography of a Holarctic rodent (Myodes rutilus): testing high-latitude biogeographical hypotheses and the dynamics of range shifts, J. Biogeogr., 2015, vol. 42, no. 2, pp. 377–389.

    Article  Google Scholar 

  60. Komarova, A.F., Zhuravleva, I.V., and Yablokov, V.M., Study of vegetation cover using open multispectral data and general remote sensing methods, Printsypy Ekol., 2016, no. 1, pp. 40–74.

  61. Krenke, A.N. and Puzachenko, Yu.G., Compilation of the map of landscape cover based on remote data, Ekol. Plan. Upr., 2008, vol. 2, no. 7, pp. 10–25.

    Google Scholar 

  62. Kucheruk, V.V., Steppe faunistic complex of mammals and its place in Palearctic fauna, in Geografiya naseleniya nazemnykh zhivotnykh i metody ikh izucheniya (Geography of Population of Terrestrial Animals and Its Study Methods), Moscow: Akad. Nauk SSSR, 1959, pp. 45–87.

  63. Kulik, I.L., Taiga complex of mammals in Eurasia, Byull. Mosk. O-va. Ispyt. Prir., Otd. Biol., 1972, vol. 77, no. 4, pp. 11–24.

    Google Scholar 

  64. Kummerow, C., Barnes, W., Kozu, T., Shiue, J., and Simpson, J., The tropical rainfall measuring mission (TRMM) sensor package, J. Atmos. Ocean. Technol., 1998, vol. 15, no. 3, pp. 809–817.

    Article  Google Scholar 

  65. Lanier, H.C. and Olson, L.E., Deep barriers, shallow divergences: reduced phylogeographical structure in the collared pika (Mammalia: Lagomorpha: Ochotona collaris), J. Biogeogr., 2013, vol. 40, no. 3, pp. 466–478.

    Article  Google Scholar 

  66. Levushkin, S.I., Problem of island faunas in terms of biogeography and ecology, in Morskaya biogeografiya: predmet, metody, printsipy raionirovaniya (Marine Biogeography: Subject, Methods, and Zoning Principles), Kusakin, O.G., Ed., Moscow: Nauka, 1982, pp. 26–52.

  67. Lissovsky, A.A., A new subspecies of Manchurian pika Ochotona mantchurica (Lagomorpha, Ochotonidae) from the Lesser Khinggan Range, China, Russ. J. Theriol., 2015, vol. 14, no. 2, pp. 145–152.

    Article  Google Scholar 

  68. Lissovsky, A.A. and Dudov, S.V., Advantages and limitations of application of the species distribution modeling methods. 2. MaxEnt, Zh. Obshch. Biol., 2020, vol. 81, no. 2, pp. 135–146.

    Google Scholar 

  69. Lissovsky, A.A. and Obolenskaya, E.V., A study of the distribution ranges of small mammals in southeastern Transbaikalia using ecological niche-based modeling methods, Biol. Bull. Rev., 2015, vol. 5, no. 3, pp. 233–248.

    Article  Google Scholar 

  70. Lissovsky, A.A., Obolenskaya, E.V., Deyan, G., and Yang, Q., Phylogeny and distribution of Palaearctic chipmunks Eutamias (Rodentia: Sciuridae), Hystrix, 2017, vol. 28, no. 1, pp. 107–109.

    Google Scholar 

  71. MacArthur, R.H. and Wilson, E.O., The Theory of Island Biogeography, Princeton: Princeton Univ. Press, 1967.

    Google Scholar 

  72. Mackey, B.G. and Lindenmayer, D.B., Towards a hierarchical framework for modeling the spatial distribution of animals, J. Biogeogr., 2001, vol. 28, no. 9, pp. 1147–1166.

    Article  Google Scholar 

  73. Maksimov, A.A., Landscape-ecological structure of habitat, in Problemy zoogeografii i istorii fauny (Problems of Zoogeography and History of Fauna), Novosibirsk: Nauka, 1980, pp. 5–13.

  74. Mateo, R.G., Broennimann, O., Normand, S., Petitpierre, B., Araújo, M.B., et al., The mossy north: an inverse latitudinal diversity gradient in European bryophytes, Sci. Rep., 2016, vol. 6, p. 25546.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Matyushkin, E.N., Zoogeographical peculiarities of Central Sikhote-Alin, in Rastitel’nyi i zhivotnyi mir Sikhote-Alin’skogo zapovednika (Flora and Fauna of Sikhote-Alin Nature Reserve), Moscow: Nauka, 1982, pp. 166–174.

  76. Merow, C., Smith, M.J., Edwards, T.C., Guisan, A., Mcmahon, S.M., et al., What do we gain from simplicity versus complexity in species distribution models? Ecography, 2014, vol. 37, no. 12, pp. 1267–1281.

    Article  Google Scholar 

  77. Miller, J.A. and Holloway, P., Incorporating movement in species distribution models, Prog. Phys. Geogr. Earth Environ., 2015, vol. 39, no. 6, pp. 837–849.

    Article  Google Scholar 

  78. Mordkovich, V.G., Osnovy biogeografii (Basic Biogeography), Moscow: KMK, 2005.

  79. Naumov, N.P., Ekologiya zhivotnykh (Ecology of Animals), Moscow: Vysshaya Shkola, 1963.

  80. Mori, E., Menchetti, M., Zozzoli, R., and Milanesi, P., The importance of taxonomy in species distribution models at a global scale: the case of an overlooked alien squirrel facing taxonomic revision, J. Zool., 2019, vol. 307, no. 1, pp. 43–52.

    Article  Google Scholar 

  81. Naimi, B. and Araújo, M.B., sdm: A reproducible and extensible R platform for species distribution modeling, Ecography, 2016, vol. 39, no. 4, pp. 368–375.

    Article  Google Scholar 

  82. Obolenskaya, E.V. and Lissovsky, A.A., Regional zoogeographical zoning using species distribution modeling by the example of small mammals of South-Eastern Transbaikalia, Russ. J. Theriol., 2015, vol. 14, no. 2, pp. 171–185.

    Article  Google Scholar 

  83. Padalia, H., Srivastava, V., and Kushwaha, S.P.S., Modeling potential invasion range of alien invasive species, Hyptis suaveolens (L.) Poit. in India: comparison of MaxEnt and GARP, Ecol. Inf., 2014, vol. 22, pp. 36–43.

    Article  Google Scholar 

  84. Paevskii, V.A., The development of ornithological researches over seventy years (1940–2009) in USSR and CIS countries, Zool. Zh., 2011, vol. 90, no. 7, pp. 891–901.

    Google Scholar 

  85. Pearson, R.G. and Dawson, T.P., Predicting the impacts of climate change on the distribution of species: Are bioclimate envelope models useful? Global Ecol. Biogeogr., 2003, vol. 12, no. 5, pp. 361–371.

    Article  Google Scholar 

  86. Peterson, A.T., Soberón, J., Pearson, R.G., Anderson, R.P., Martínez-Meyer, E., et al., Ecological Niches and Geographic Distributions (MPB-49), Princeton: Princeton Univ. Press, 2011.

    Book  Google Scholar 

  87. Phillips, S.J., Anderson, R.P., and Schapire, R.E., Maximum entropy modeling of species geographic distributions, Ecol. Model., 2006, vol. 190, nos. 3–4, pp. 231–259.

    Article  Google Scholar 

  88. Phillips, S.J., Anderson, R.P., Dudík, M., Schapire, R.E., and Blair, M.E., Opening the black box: An open-source release of Maxent, Ecography, 2017, vol. 40, no. 7, pp. 887–893.

    Article  Google Scholar 

  89. Porter, C., Morin, P., Howat, I., Noh, M.J., Bates, B., et al., ArcticDEM, Harvard Dataverse, 2018. https://doi.org/10.7910/DVN/OHHUKH

  90. Puzachenko, Yu.G., Zheltukhin, A.S., and Sandlerskiy, R.B., Analyzing space-time dynamics of the ecological niche: a case study with the pine marten (Martes martes) population, Biol. Bull. Rev., 2011, vol. 1, no. 3, pp. 245–264.

    Article  Google Scholar 

  91. Robinson, N., Regetz, J., and Guralnick, R.P., EarthEnv-DEM90: a nearly-global, void-free, multi-scale smoothed, 90m digital elevation model from fused ASTER and SRTM data, ISPRS J. Photogramm. Remote Sens., 2014, vol. 87, pp. 57–67.

    Article  Google Scholar 

  92. Rosauer D.F., Catullo, R.A., VanDerWal, J., Moussalli, A., and Moritz, C., Lineage range estimation method reveals fine-scale endemism linked to Pleistocene stability in Australian rainforest herpetofauna, PLoS One, 2015, vol. 10, no. 5, p. e0126274.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  93. Rozhnov, V.V., Yachmennikova, A.A., Naidenko, S.V., Ernandes-Blanko, Kh.A., Chistopolova, M.D., et al., Monitoring peredneaziatskogo leoparda i drugikh krupnykh koshek (Monitoring of Central Asian Leopard and Other Large Cats), Moscow: KMK, 2018.

  94. Rozhnov, V.V., Yachmennikova, A.A., Hernandez-Blanco, J.A., Naidenko, S.V., Chistopolova, M.D., et al., Study and Monitoring of Big Cats in Russia, Moscow: KMK, 2019.

    Google Scholar 

  95. Santoro, M., Kirches, G., Wevers, J., Boettcher, M., Brockmann, C., et al., Land cover CCI, Product User Guide, Version 2.0, 2017. http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf.

  96. Sbrocco, E.J. and Barber, P.H., MARSPEC: Ocean climate layers for marine spatial ecology, Ecology, 2013, vol. 94, no. 4, pp. 979–979.

    Article  Google Scholar 

  97. Shangguan, W., Hengl, T., Mendes de Jesus, J., Yuan, H., and Dai, Y., Mapping the global depth to bedrock for land surface modeling, J. Adv. Model. Earth Syst., 2017, vol. 9, no. 1, pp. 65–88.

    Article  Google Scholar 

  98. Shchipanov, N.A., Kuptsov, A.V., Kalinin, A.A., and Oleinichenko, V.Yu., Counting of red-tooth shrews (Insectivora, Soricidae), Zool. Zh., 2003, vol. 82, no. 10, pp. 1258–1265.

    Google Scholar 

  99. Siefert, A., Ravenscroft, C., Althoff, D., Alvarez-Yépiz, J.C., Carter, B.E., et al., Scale dependence of vegetation-environment relationships: a meta-analysis of multivariate data, J. Veg. Sci., 2012, vol. 23, no. 5, pp. 942–951.

    Article  Google Scholar 

  100. Staniczenko, P.P.A., Sivasubramaniam, P., Suttle, K.B., and Pearson, R.G., Linking macroecology and community ecology: refining predictions of species distributions using biotic interaction networks, Ecol. Lett., 2017, vol. 20, no. 6, pp. 693–707.

    Article  PubMed  PubMed Central  Google Scholar 

  101. Starobogatov, Ya.I., Fauna mollyuskov i zoogeograficheskoe raionirovanie kontinental’nykh vodoemov Zemnogo shara (Fauna of Mollusks and Zoogeographical Zoning of Global Continental Reservoirs), Leningrad: Nauka, 1970.

  102. Štípková, Z., Romportl, D., Černocká, V., and Kindlmann, P., Factors associated with the distributions of orchids in the Jeseníky mountains, Czech Republic, Eur. J. Environ. Sci., 2017, vol. 7, no. 2, pp. 135–145.

    Google Scholar 

  103. Sulla-Menashe, D., Friedl, M.A., Krankina, O.N., Baccini, A., Woodcock, C.E., et al., Hierarchical mapping of Northern Eurasian land cover using MODIS data, Remote Sens. Environ., 2011, vol. 115, no. 2, pp. 392–403.

    Article  Google Scholar 

  104. Thuiller, W., Georges, D., Engler, R., and Breiner, F., biomod2: ensemble platform for species distribution modeling, R package version 3.3-7.1, 2019. https://cran.r-project.org/web/packages/biomod2.

  105. Titar, V.M., Analysis of species habitats: an approach based on modeling of ecological niche, Vestn. Zool., 2011, no. 25, pp. 1–96.

  106. Title, P.O. and Bemmels, J.B., ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling, Ecography, 2018, vol. 41, no. 2, pp. 291–307.

    Article  Google Scholar 

  107. Tolmachev, A.I., Osnovy ucheniya ob arealakh (Vvedenie v khorologiyu rastenii) (Studies of Ranges: Introduction to the Plant Chorology), Leningrad: Leningr. Gos. Univ., 1962.

  108. Tuanmu, M.N. and Jetz, W., A global 1-km consensus land-cover product for biodiversity and ecosystem modeling, Global Ecol. Biogeogr., 2014, vol. 23, no. 9, pp. 1031–1045.

    Article  Google Scholar 

  109. Tuanmu, M.N. and Jetz, W., A global, remote sensing-based characterization of terrestrial habitat heterogeneity for biodiversity and ecosystem modeling, Global Ecol. Biogeogr., 2015, vol. 24, no. 11, pp. 1329–1339.

    Article  Google Scholar 

  110. Tupikov, A.I. and Ukrainskii, P.A., Comparative analysis of various approaches to modeling of the species range in MaxEnt software (on the example of a steppes ratsnake and a venomous viper), Nauchn. Ved. Belgorod. Gos. Univ., Ser. Estestv. Nauki, 2016, no. 4 (225), pp. 71–84.

  111. Tupikova, N.V. and Komarova, L.V., Printsipy i metody zoologicheskogo kartografirovaniya (Principles and Methods of Zoological Mapping), Moscow: Mosk. Gos. Univ., 1979.

  112. Varela, S., Lobo, J.M., and Hortal, J., Using species distribution models in paleobiogeography: a matter of data, predictors and concepts, Palaeogeogr., Palaeoclimatol., Palaeoecol., 2011, vol. 310, nos. 3–4, pp. 451–463.

    Article  Google Scholar 

  113. Vasil’eva, V.K., Okhlopkov, I.M., and Borisov, B.Z., Distribution of voles of genus Alticola Blandford, 1881 in Yakutia and modeling of their range in MaxEnt software, Nauka Obraz., 2017, no. 4 (88), pp. 135–140.

  114. Velazco, S.J.E., Galvão, F., Villalobos, F., and de Marco, P., Using worldwide edaphic data to model plant species niches: an assessment at a continental extent, PLoS One, 2017, vol. 12, no. 10, pp. 1–24.

    Article  CAS  Google Scholar 

  115. Wallace, A.R., The Geographical Distribution of Animals with a Study of the Relations of Living and Extinct Fauna as Elucidating the Past Changes of the Earth’s Surface, New York: Harper and Brothers, 1876, 1st ed.

    Book  Google Scholar 

  116. Wan, J.-Z., Wang, C.-J., and Yu, F.-H., Effects of occurrence record number, environmental variable number, and spatial scales on MaxEnt distribution modeling for invasive plants, Biologia, 2019, vol. 74, no. 7, pp. 757–766.

    Article  Google Scholar 

  117. Wan, Z. and Dozier, J., A generalized split-window algorithm for retrieving land-surface temperature from space, IEEE Trans. Geosci. Remote Sens., 1996, vol. 34, no. 4, pp. 892–905.

    Article  Google Scholar 

  118. Ward, G., Hastie, T., Barry, S., Elith, J., and Leathwick, J.R., Presence-only data and the EM algorithm, Biometrics, 2009, vol. 65, no. 2, pp. 554–563.

    Article  PubMed  PubMed Central  Google Scholar 

  119. Williams, H.F., Bartholomew, D.C., Amakobe, B., and Githiru, M., Environmental factors affecting the distribution of African elephants in the Kasigau wildlife corridor, SE Kenya, Afr. J. Ecol., 2018, vol. 56, no. 2, pp. 244–253.

    Article  Google Scholar 

  120. Williams, J.N., Seo, C., Thorne, J., Nelson, J.K., Erwin, S., et al., Using species distribution models to predict new occurrences for rare plants, Diversity Distrib., 2009, vol. 15, no. 4, pp. 565–576.

    Article  Google Scholar 

  121. Wilson, A.M. and Jetz, W., Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions, PLoS Biol., 2016, vol. 14, no. 3, pp. 1–20.

    Article  Google Scholar 

  122. Yu, H., Zhang, Y., Liu, L., Qi, W., Li, S., and Hu, Z., Combining the least cost path method with population genetic data and species distribution models to identify landscape connectivity during the late Quaternary in Himalayan hemlock, Ecol. Evol., 2015, vol. 5, no. 24, pp. 5781–5791.

    Article  PubMed  PubMed Central  Google Scholar 

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ACKNOWLEDGMENTS

The authors are grateful to Yu.G. Puzachenko, who inspired us at different stages of work. The comments of two anonymous reviewers significantly improved the text of the manuscript.

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The work was financially supported by the Russian Science Foundation, project no. 18-14-00093.

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Correspondence to A. A. Lissovsky, S. V. Dudov or E. V. Obolenskaya.

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Lissovsky, A.A., Dudov, S.V. & Obolenskaya, E.V. Species-Distribution Modeling: Advantages and Limitations of Its Application. 1. General Approaches. Biol Bull Rev 11, 254–264 (2021). https://doi.org/10.1134/S2079086421030075

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