Absence of the “Absences”: the Engler-Hengl Approach in Species Distribution Modelling

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The possibilities of creating artificial absence points (pseudo-absences) to build species distribution models are considered. An approach proposed by Robin Engler and adapted by Tomislav Hengl is described, which takes into account habitat suitability indices and distances to presence points to create pseudo-absences. Using the example of bilberries (Vaccinium myrtillus) in the Central Forest Nature Reserve and its buffer zone, generalized linear models based on the Engler-Hengl design, traveled tracks and distances to presence points are compared, as well as a model built using the maximum entropy method. The results obtained indicate the superiority of the model based on the Engler-Hengl approach both in terms of quality assessments and in terms of the realism of the constructed spatial distribution maps.

作者简介

S. Ogurtsov

Central Forest State Nature Biosphere Reserve; Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences

编辑信件的主要联系方式.
Email: etundra@mail.ru
Russia, 172521, Nelidovo District, Tver Region, Zapovednyi, 32; Russia, 119071, Moscow, Leninsky prospect, 33

参考

  1. Желтухин А.С., Пузаченко Ю.Г., Сандлерский Р.Б. Оценка качества местообитаний животных на основе учетов следовой активности и дистанционной информации // Сибирский экологический журн. 2009. № 3. С. 341–351.
  2. Огурцов С.С. Моделирование пригодности местообитаний бурого медведя Ursus arctos (Linnaeus, 1758) на основе функции выбора ресурсов в мозаичных ландшафтах южной тайги. Автореф. дис. канд. биол. наук. М.: Институт проблем экологии и эволюции им. А.Н. Северцова РАН, 2023. 26 с.
  3. Пузаченко Ю.Г., Желтухин А.С., Сандлерский Р.Б. Анализ пространственно-временной динамики экологической ниши на примере популяции лесной куницы (Martes martes) // Журн. общей биологии. 2010. Т. 71(6). С. 467–487.
  4. Пузаченко Ю.Г., Кузьмин С.Л., Сандлерский Р.Б. Количественная оценка параметров ареалов (на примере представителей рода Rana) // Журн. общей биологии. 2011. Т. 72(5). С. 339–354.
  5. Пузаченко Ю.Г., Желтухин А.С., Козлов Д.Н., Кораблев Н.П., Федяева М.В., Пузаченко М.Ю., Сиунова Е.В. Центрально-Лесной государственный природный биосферный заповедник. Научно-популярный очерк. Издание 2-е. Тверь: ООО “Печатня”, 2016. 80 с.
  6. Aiello-Lammens M.E., Boria R.A., Radosavljevic A., Vilela B., Anderson R.P. spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models // Ecography. 2015. V. 38. P. 541–545. https://doi.org/10.1111/ecog.01132
  7. Allouche O., Tsoar A., Kadmon R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS) // J. Applied Ecology. 2006. V. 43. P. 1223–1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x
  8. Araújo M.B., Anderson R., Barbosa A.M., Beale C., Dormann C., Early R., Garcia R., Guisan A., Maiorano L., Naimi B., O’Hara R., Zimmermann N., Rahbek C. Standards for distribution models in biodiversity assessments // Science Advances. 2019. V. 5. eaat4858. https://doi.org/10.1126/sciadv.aat4858
  9. Araújo M.B., Pearson R.G., Thuiller W., Erhard M. Validation of species-climate impact models under climate change // Global Change Biology. 2005. V. 11. P. 1504–1513. https://doi.org/10.1111/j.1365-2486.2005.01000.x
  10. Baig M.H.A., Zhang L., Shuai T., Tong Q. Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance // Remote Sensing Letters. 2014. V. 5(5). P. 423–431. https://doi.org/10.1080/2150704X.2014.915434
  11. Barbet-Massin M., Jiguet F., Albert C.H., Thuiller W. Selecting pseudo-absences for species distribution models: how, where and how many? // Methods in Ecology and Evolution. 2012. V. 3. P. 327–338. https://doi.org/10.1111/j.2041-210X.2011.00172.x
  12. Boyce M.S., Vernier P.R., Nielsen S.E., Schmiegelow F.K.A. Evaluating resource selection functions // Ecological Modelling. 2002. V. 157(2–3). P. 281–300. https://doi.org/10.1016/S0304-3800(02)00200-4
  13. Broennimann O. Package ‘ecospat’, version 3.0. Spatial Ecology Miscellaneous Methods. 2018. 107 p.
  14. Chefaoui R.M., Lobo J.M. Assessing the effects of pseudo-absences on predictive distribution model performance // Ecological Modelling. 2008. V. 210. P. 478–486. https://doi.org/10.1016/j.ecolmodel.2007.08.010
  15. Conrad O., Bechtel B., Bock M., Dietrich H., Fischer E., Gerlitz L., Wehberg J., Wichmann V., Boehner J. System for Automated Geoscientific Analyses (SAGA) v. 2.1.4 // Geoscientific Model Development. 2015. V. 8. P. 1991–2007. https://doi.org/10.5194/gmd-8-1991-2015
  16. Coudun C., Gégout J.-C. Quantitative prediction of the distribution and abundance of Vaccinium myrtillus with climatic and edaphic factors // J. Vegetation Science. 2007. V. 18. P. 517–524. https://doi.org/10.1658/1100-9233(2007)18[517:QPOTDA]-2.0.CO;2
  17. Dettmers R., Bart J. A GIS modeling method applied to predicting forest songbird habitat // Ecological Applications. 1999. V. 9. P. 152–163. https://doi.org/10.1890/1051-0761(1999)009[0152: AGMMAT]2.0.CO;2
  18. Duque-Lazo J., Navarro-Cerrillo R.M. What to save, the host or the pest? The spatial distribution of xylophage insects within the Mediterranean oak woodlands of Southwestern Spain // Forest Ecology and Management. 2017. V. 392. P. 90–104. https://doi.org/10.1016/j.foreco.2017.02.047
  19. Duque-Lazo J., van Gils H., Groen T.A., Navarro-Cerrillo R.M. Transferability of species distribution models: The case of Phytophthora cinnamomi in Southwest Spain and Southwest Australia // Ecological Modelling. 2016. V. 320. P. 62–70. https://doi.org/10.1016/j.ecolmodel.2015.09.019
  20. Elith J., Graham C.H., Anderson R.P., Dudík M., Ferrier S., Guisan A., Hijmans R.J., Huettmann F., Leathwick J.R., Lehman A., Li J., Lohmann L.G., Loiselle B.A., Manion G., Moritz C., Nakamura M., Nakazawa Y., Overton J.M.M., Peterson A.T., Phillips S.J., Richardson K., Scachetti-Pereira R., Schapire R.E., Soberón J., Williams S., Wisz M.S., Zimmermann N.E. Novel methods improve prediction of species’ distributions from occurrence data // Ecography. 2006. V. 29 (2). P. 129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.x
  21. Elith J., Phillips S.J., Hastie T., Dudík M., Chee Y.E., Yates C.J. A statistical explanation of MaxEnt for Ecologists // Diversity and Distributions. 2011. V. 17. P. 43–57. https://doi.org/10.1111/j.1472-4642.2010.00725.x
  22. Elith J., Kearney M., Phillips S. The art of modelling range-shifting species // Methods in Ecology and Evolution. 2010. V. 1. P. 330–342. https://doi.org/10.1111/j.2041-210X.2010.00036.x
  23. Engler R., Guisan A., Rechsteiner L. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudoabsence data // J. Applied Ecology. 2004. V. 41(2). P. 263–274. https://doi.org/10.1111/j.0021-8901.2004.00881.x
  24. Evans J.S., Murphy M.A., Ram K. Package ‘spatialEco’. Spatial analysis and modelling utilities. Version 1.3-7. 2021. 181 p.
  25. Fox J., Weisberg S. An R Companion to Applied Regression, Third Edition. Thousand Oaks CA: Sage Publications Inc., 2018. 608 p.
  26. Franklin J. Mapping species distributions. Cambridge: Cambridge University Press, 2009. 320 p. https://doi.org/10.1017/CBO9780511810602
  27. Freeman E.A., Moisen G. PresenceAbsence: An R package for presence-absence model analysis // J. Statistical Software. 2008. V. 23(11). P. 1–31. https://doi.org/10.18637/JSS.V023.I11
  28. Ghoddousi A. Habitat suitability modelling of the Brown bear Ursus arctos in Croatia and Slovenia using telemetry data. Master of Science thesis. Imperial College London, 2010. 71 p.
  29. Glenz C., Massolo A., Kuonen D., Schlaepfer R. A wolf habitat suitability prediction study in Valais (Switzerland) // Landscape and Urban Planning. 2001. V. 55. P. 55–65. https://doi.org/10.1016/S0169-2046(01)00119-0
  30. Guisan A., Thuiller W., Zimmermann N.E. Habitat suitability and distribution models. Cambridge: Cambridge University Press, 2017. 462 p. https://doi.org/10.1017/9781139028271
  31. Guisan A., Zimmermann N. Predictive habitat distribution models in ecology // Ecological Modelling. 2000. V. 135. P. 147–186. https://doi.org/10.1016/S0304-3800(00)00354-9
  32. Guisan A., Zimmermann N.E., Elith J., Graham C.H., Phillips S., Peterson A.T. What matters for predicting the occurrences of trees: techniques, data, or species’ characteristics? // Ecological Monographs. 2007. V. 77(4). P. 615–630. https://doi.org/10.1890/06-1060.1
  33. Hengl T., Sierdsema H., Radovic A., Dilo A. Spatial prediction of species’ distributions from occurrence-only records: combining point pattern analysis, ENFA and regression-kriging // Ecological Modelling. 2009. V. 220. P. 3499–3511. https://doi.org/10.1016/j.ecolmodel.2009.06.038
  34. Hijmans R.J., Phillips S., Leathwick J., Elith J. Package ‘dismo’. Species Distribution Modeling. Version 1.1-4. 2017. 68 p.
  35. Hirzel A.H., Hausser J., Chessel D., Perrin N. Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data? // Ecology. 2002. V. 83(7). P. 2027–2036. https://doi.org/10.1890/0012-9658(2002)083[2027:ENFAHT]2.0.CO;2
  36. Hirzel A.H., Le Lay G. Habitat suitability modelling and niche theory // Journal of Applied Ecology. 2008. V. 45. P. 1372–1381. https://doi.org/10.1111/j.1365-2664.2008.01524.x
  37. Hirzel A.H., Helfer V., Metral F. Assessing habitat-suitability models with a virtual species // Ecological modelling. 2001. V. 145. P. 111–121. https://doi.org/10.1016/S0304-3800(01)00396-9
  38. Huete A., Didan K., Miura T., Rodriguez E., Gao X., Ferreira L.G. Overview of the radiometric and biophysical performance of the MODIS Vegetation Indices // Remote Sensing of Environment. 2002. V. 83. P. 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2
  39. Jerina K., Debeljak M., Dzeroske S., Kobler A., Adamic M. Modeling the Brown bear population in Slovenia. A tool in the conservation management of a threatened species // Ecological modelling. 2003. V. 170. P. 453–469. https://doi.org/10.1016/S0304-3800(03)00245-X
  40. Jiménez-Valverde A., Gómez J.F., Lobo J.M., Baselga A., Hortal J. Challenging species distribution models: the case of Maculinea nausithous in the Iberian Peninsula // Annales Zoologici Fennici. 2008. V. 45. P. 200–210. https://doi.org/10.5735/086.045.0305
  41. Johnson C.J., Nielsen S.E., Merrill E.H., McDonald T.L., Boyce M.S. Resource selection functions based on use-availability data: theoretical motivation and evaluation methods // J. Wildlife Management. 2006. V. 70(2). P. 347–357. https://doi.org/10.2193/0022-541X(2006)70[347:RSFBOU]2.0.CO;2
  42. Kauth R.J., Thomas G.S. The Tasseled Cap – a graphic description of the spectral- temporal development of agricultural crops as seen by Landsat // Proceedings of the Symposium on Machine Processing of Remotely Sensed Data. US, Indiana, West Lafayette: Purdue University, 1976. P. 4B41–4B51.
  43. Keating K., Cherry S. Use and interpretation of logistic regression in habitat selection studies // Journal of Wildlife Management. 2004. V. 68. P. 774–789. https://doi.org/10.2193/0022-541X(2004)068[0774: UAIOLR]2.0.CO;2
  44. Kéry M., Gardner B., Stoeckle T., Weber D., Royle J.A. Use of spatial capture-recapture modeling and DNA data to estimate densities of elusive animals // Conservation Biology. 2010. V. 25. P. 356–364. https://doi.org/10.1111/j.1523-1739.2010.01616.x
  45. Kobler A., Adamic M. Identifying brown bear habitat by a combined GIS and machine learning method // Ecological Modelling. 2000. V. 135. P. 291–300. https://doi.org/10.1016/S0304-3800(00)00384-7
  46. Landsat 8 (L8) Data Users Handbook. Version 5.0. Department of the Interior U.S. Geological Survey. 2019. 106 p.
  47. Le Maitre D.C., Thuiller W., Schonegevel L. Developing an approach to defining the potential distributions of invasive plant species: a case study of Hakea species in South Africa // Global Ecology and Biogeography. 2008. V. 17. P. 569–584. https://doi.org/10.1111/j.1466-8238.2008.00407.x
  48. Liu C., White M., Newell G. Selecting thresholds for the prediction of species occurrence with presence-only data // J. Biogeography. 2013. V. 40. P. 778–789. https://doi.org/10.1111/jbi.12058
  49. Lobo J., Jimenez-Valverde A., Hortal J. The uncertain nature of absences and their importance in species distribution modelling // Ecography. 2010. V. 33 (1). P. 103–114. https://doi.org/10.1111/j.1600-0587.2009.06039.x
  50. MacKenzie D.I., Royle J.A. Designing occupancy studies: general advice and allocating survey effort // J. Applied Ecology. 2005. V. 42. P. 1105–1114. https://doi.org/10.1111/j.1365-2664.2005.01098.x
  51. Maiorano L., Boitani L., Monaco A., Tosoni E., Ciucci P. Modeling the distribution of Apennine brown bears during hyperphagia to reduce the impact of wild boar hunting // European J. Wildlife Research. 2015. V. 61(2). P. 241–253. https://doi.org/10.1007/s10344-014-0894-0
  52. Manly B.F.J., McDonald L.L., Thomas D.L., McDonald T.L., Erickson W.P. Resource Selection by Animals. Statistical Design and Analysis for Field Studies. Second Edition. Kluwer Academic Publishers, 2002. 221 p. https://doi.org/10.2307/5247
  53. Martin T.G., Wintle B.A., Rhodes J.R., Kuhnert P.M., Field S.A., Low-Choy S.J., Tyre A.J., Possingham H.P. Zero tolerance ecology: improving ecological inference by modelling the source of zero observations // Ecology Letters. 2005. V. 8. P. 1235–1246. https://doi.org/10.1111/j.1461-0248.2005.00826.x
  54. McClelland C.J.R., Coops N.C., Kearney S.P., Burton A.C., Nielsen S.E., Stenhouse G.B. Variations in grizzly bear habitat selection in relation to the daily and seasonal availability of annual plant-food resources // Ecological Informatics. 2020. V. 58. P. 101116. https://doi.org/10.1016/j.ecoinf.2020.101116
  55. McDonald T.L. Estimation of resource selection functions when used and available samples overlap // Resource selection methods and applications / Ed. Huzurbazar S. Madison, Wisconsin: Omnipress, 2003. P. 35–39.
  56. Merow C., Smith M.J., Silander J.A.Jr. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter // Ecography. 2013. V. 36. P. 1058–1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x
  57. Mertzanis G., Kallimanis A.S., Kanellopoulos N., Sgardelis S.P., Tragos A., Aravidis I. Brown bear (Ursus arctos) habitat use patterns in two regions of northern Pindos, Greece – management implications // J. Natural History. 2008. V. 42. P. 301–315. https://doi.org/10.1080/00222930701835175
  58. Mladenoff D.J., Sickley T.A., Wydeven A.P. Predicting grey wolf landscape colonization: logistic regression models vs. new field data // Ecological Applications. 1999. V. 9. P. 37–44. https://doi.org/10.1890/1051-0761(1999)009[0037: PGWLRL]2.0.CO;2
  59. Moore I.D., Grayson R.B., Ladson A.R. Digital terrain modelling: A review of hydrological, geomorphological, and biological applications // Hydrological Processes. 1991. V. 5(1). P. 3–30. https://doi.org/10.1002/hyp.3360050103
  60. Naimi B., Hamm N., Groen T.A., Skidmore A.K., Toxopeus A.G. Where is positional uncertainty a problem for species distribution modelling // Ecography. 2014. V. 37. P. 191–203. https://doi.org/10.1111/j.1600-0587.2013.00205.x
  61. Nath A., Sinha A., Lahkar B.P., Brahma N. In search of Aliens: Factors influencing the distribution of Chromolaena odorata L. and Mikania micrantha Kunth in the Terai grasslands of Manas National Park, India // Ecological Engineering journal. 2019. V. 131. P. 16–26. https://doi.org/10.1016/j.ecoleng.2019.02.012
  62. Nielsen S.E., Boyce M.S., Stenhouse G.B., Munro R.H.M. Development and testing of phenologically driven grizzly bear habitat models // Écoscience. 2003. V. 10. P. 1–10. https://doi.org/10.1080/11956860.2003.11682743
  63. Nielsen S.E., Larsen T.A., Stenhouse G.B., Coogan S.C.P. Complementary food resources of carnivory and frugivory affect local abundance of an omnivorous carnivore // Oikos. 2016. V. 126(3). P. 1–12. https://doi.org/10.1111/oik.03144
  64. Pal M., Mather P.M. An assessment of the effectiveness of decision tree methods for land cover classification // Remote Sensing of Environment. 2003. V. 86(4). P. 554–565. https://doi.org/10.1016/S0034-4257(03)00132-9
  65. Penteriani V., Zarzo-Arias A., Novo-Fernández A., Bombieri G., López-Sánchez C.A. Responses of an endangered brown bear population to climate change based on predictable food resource and shelter alterations // Global Change Biology. 2019. V. 25(3). P. 1133–1151. https://doi.org/10.1111/gcb.14564
  66. Peterson A.T., Soberón J., Pearson R.G., Anderson R.P., Martínez-Meyer E., Nakamura M., Araújo M.B. Ecological Niches and Geographic Distributions. Princeton: Princeton University Press, 2011. 316 p.
  67. Petrosyan V., Osipov F., Bobrov V., Dergunova N., Nazarenko E., Omelchenko A., Danielyan F., Arakelyan M. Analysis of geographical distribution of the parthenogenetic rock lizard Darevskia armeniaca and its parental species (D. mixta, D. valentini) based on ecological modelling // Salamandra. 2019. V. 55(3). P. 173–190.
  68. Petrosyan V., Dinets V., Osipov F., Dergunova N., Khlyap L. Range Dynamics of Striped Field Mouse (Apodemus agrarius) in Northern Eurasia under Global Climate Change Based on Ensemble Species Distribution Models // Biology. 2023. V. 12(7). P. 1–30. https://doi.org/10.3390/biology12071034
  69. Phillips S.J., Anderson R.P., Dudík M., Schapire R.E., Blair M.E. Opening the black-box: an open-source release of Maxent // Ecography. 2017. V. 40. P. 887–893. https://doi.org/10.1111/ecog.03049
  70. Phillips S.J., Anderson R.P., Schapire R.E. Maximum entropy modeling of species geographic distributions // Ecological Modelling. 2006. V. 190. P. 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
  71. Phillips S.J., Dudík M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation // Ecography. 2008. V. 31. P. 161–175. https://doi.org/10.1111/j.2007.0906-7590.05203.x
  72. Phillips S.J., Dudík M., Elith J., Graham C.H., Lehmann A., Leathwick J., Ferrier S. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data // Ecological Applications. 2009. V. 19(1). P. 181–197. https://doi.org/10.1890/07-2153.1
  73. Phillips S.J., Dudík M., Schapire R.E. Maxent software for modeling species niches and distributions (Version 3.4.1). 2018. Accessed at http://biodiversityinformatics.amnh.org/open_source/maxent/.
  74. Phillips S.J., Elith J. POC plots: calibrating species distribution models with presence- only data // Ecology. 2010. V. 91. P. 2476–2484. https://doi.org/10.1890/09-0760.1
  75. Piédallu B., Quenette P.Y., Bombillon N., Gastineau A., Miquel C., Gimenez O. Determinants and patterns of habitat use by the brown bear Ursus arctos in the French Pyrenees revealed by occupancy modelling // Oryx. Fauna & Flora International. 2017. V. 53(2). P. 1–10. https://doi.org/10.1017/s0030605317000321
  76. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria, 2020. Accessed at https://www.R-project.org/.
  77. Recio M.R., Knauer F., Molinari-Jobin A., Huber D., Filacorda S., Jerina K. Context-dependent behaviour and connectivity of recolonizing brown bear populations identify transboundary conservation challenges in Central Europe // Animal Conservation. 2020. V. 24(1). P. 73–83. https://doi.org/10.1111/acv.12624
  78. Renner I.W., Elith J., Baddeley A., Fithian W., Hastie T., Phillips S.J., Popovic G., Warton D.I. Point process models for presence-only analysis // Methods in Ecology and Evolution. 2015. V. 6. P. 366–379. https://doi.org/10.1111/2041-210X.12352
  79. Roberts D.R., Nielsen S.E., Stenhouse G.B. Idiosyncratic responses of grizzly bear habitat to climate change based on projected changes in their food resources // Ecological Applications. 2014. V. 24(5). P. 1144–1154. https://doi.org/10.1890/13-0829.1
  80. Robin X., Turck N., Hainard A., Tiberti N., Lisacek F., Sanchez J.-C., Müller M., Siegert S., Doering M. Package ‘pROC’, version 1.16.2. Display and Analyze ROC Curves. 2020. 95 p.
  81. Shores C.R., Mikle N., Graves T.A. Mapping a keystone shrub species, huckleberry (Vaccinium membranaceum), using seasonal colour change in the Rocky Mountains // International J. Remote Sensing. 2019. V. 40(15). P. 5695–5715. https://doi.org/10.1080/01431161.2019.1580819
  82. Sillero N., Barbosa A.M. Common mistakes in ecological niche models // International J. Geographical Information Science. 2021. V. 35(2). P. 213–226. https://doi.org/10.1080/13658816.2020.1798968
  83. Smulders M., Nelson T.A., Jelinski D.E., Nielsen S.E., Stenhouse G.B. A spatially explicit method for evaluating accuracy of species distribution models // Diversity and Distributions. 2010. V. 16. P. 996–1008. https://doi.org/10.1111/j.1472-4642.2010.00707.x
  84. Soberón J. Grinnellian and Eltonian niches and geographic distributions of species // Ecology Letters. 2007. V. 10(12). P. 1115–1123. https://doi.org/10.1111/j.1461-0248.2007.01107.x
  85. Soberón J., Peterson A.T. Interpretation of models of fundamental ecological niches and species’ distributional areas // Biodiversity Informatics. 2005. V. 2. P. 1–10. https://doi.org/10.17161/bi.v2i0.4
  86. Soberόn J., Nakamura M. Niches and distributional areas: concepts, methods, and assumptions // Proceedings of National Academy of Science. USA. 2009. V. 106. P. 19644e19650. https://doi.org/10.1073/pnas.0901637106
  87. Stockwell D., Peters D. The GARP modelling system: problems and solutions to automated spatial prediction // International J. Geographical Information Sciences. 1999. V. 2. P. 143–158. https://doi.org/10.1080/136588199241391
  88. Syfert M.M., Smith M.J., Coomes D.A. The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models // PLoS ONE. 2013. V. 8. P. e55158. https://doi.org/10.1371/journal.pone.0055158
  89. Thuiller W., Georges D., Engler R., Breiner F. biomod2: ensemble platform for species distribution modeling. R package version 3.4.6. 2020. 105 p.
  90. Valavi R., Elith J., Lahoz-Monfort J.J., Guillera-Arroita G. blockCV: An R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models // Methods in Ecology and Evolution. 2019. V. 10. P. 225–232. https://doi.org/10.1111/2041-210X.13107
  91. VanDerWal J., Shoo L.P., Johnson C.N., Williams S.E. Abundance and the environmental niche: Environmental suitability estimated from niche models predicts the upper limit of local abundance // American Naturalist. 2009. V. 174. P. 282–291. https://doi.org/10.1086/600087
  92. Vermote E., Justice C., Claverie M., Franch B. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product // Remote Sensing of Environment. 2016. V. 185. P. 46–56. https://doi.org/10.1016/j.rse.2016.04.008
  93. Vignali S., Barras A.G., Arlettaz R., Braunisch V. SDMtune: An R package to tune and evaluate species distribution models // Ecology and Evolution. 2020. V. 10(20). P. 11488–11506. https://doi.org/10.1002/ece3.6786
  94. Vignali S., Barras A., Braunisch V. Package ‘SDMtune’. Species distribution model selection. Version 1.1.3. 2020a. 70 p.
  95. Zaniewski A.E., Lehmann A., Overton J.McC. Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns // Ecological Modelling. 2002. V. 157. P. 261–280. https://doi.org/10.1016/S0304-3800(02)00199-0
  96. Zuur A.F., Ieno E.N., Walker N.J., Saveliev A.A., Smith G.M. Mixed Effects Models and Extensions in Ecology with R. New York: Springer, 2009. 574 p. https://doi.org/10.1007/978-0-387-87458-6

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