Ensemble modelling for smart conservation strategies for forest reptile species at their range edges in Europe amidst climate change

Abstract

Reptile fauna should be considered a conservation objective, especially in respect of the impacts of climate change on their distribution and range’s dynamics. Investigating the environmental drivers of reptile species richness and identifying their suitable habitats is a fundamental prerequisite to setting efficient long-term conservation measures. This study focused on geographical patterns and estimations of species richness for herpetofauna widely spread Z. vivipara, N. natrix, V. berus, A. colchica, and protected in Latvia C. austriaca, E. orbicularis, L. agilis inhabiting northern (model territory Latvia) and southern (model territory Ukraine) part of their European range. The ultimate goal was to designate a conservation network that will meet long-term goals for survival of the target species in the context of climate change. We used stacked species distribution models for creating maps depicting the distribution of species richness under current and future (by 2050) climates for marginal reptilepopulations. Using cluster analysis, we showed that this herpeto-complex can be divided into “widespread species” and “forest species”. For all forest species we predicted a climate-driven reduction in their distribution range both North (Latvia) and South (Ukraine). The most vulnerable populations of “forest species” tend to be located in the South of their range, as a consequence of northward shifts by 2050. By 2050 the greatest reduction in range is predicted for currently widely spread Z. vivipara (by 1.4 times) and V. berus (by 2.2 times). In terms of designing an effective protected-area network, these results permit to identify priority conservation areas where the full ensemble of selected reptile species can be found, and confirms the relevance of abiotic multi-factor GIS-modelling for achieving this goal.

Keywords

Edge of area / Stacked species distribution models / Suitable habitats / Priority conservation areas

Abbreviations

SDM - standard distribution model

ESDM - ensemble species distribution model

GIS-modelling - geographic information system modelling

SSDM - stacked species distribution model

RF - Random Forest

SVM - Support Vector Machines

GLM - Generalized Linear Models

MaxEnt - Maximum Entropy Modelling

AUC - area under the relative operating characteristic curve

SES - sensitivity-specificity equality

CRediT authorship contribution statement

Oksana Nekrasova: Writing - original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Mihails Pupins: Writing - review & editing, Supervision, Resources, Project administration, Funding acquisition, Formal analysis, Conceptualization. Volodymyr Tytar: Writing - review & editing, Validation, Software, Investigation, Formal analysis, Data curation. Andris Čeirāns: Writing - review & editing, Supervision, Project administration, Funding acquisition, Conceptualization. Oleksii Marushchak: Writing - review & editing, Writing - original draft, Visualization, Validation, Software, Formal analysis, Conceptualization. Arturs Škute: Writing - review & editing, Funding acquisition, Data curation, Conceptualization. Kathrin Theissinger: Writing - review & editing, Validation, Supervision, Resources. Jean-Yves Georges: Writing - review & editing, Validation, Supervision, Resources, Methodology, Funding acquisition, Conceptualization.

Declaration of competing interests

The authors declare that there are no known competing financial interests or personal relationships that influenced the work reported in this paper.

Acknowledgements

We acknowledge the Emys-R project (https://emysr.cnrs.fr) funded through the 2020–2021 Biodiversa+ and Water JPI joint call for research projects, under the BiodivRestore ERA-NET Cofund (GA N°101003777), with the EU and the funding organizations Agence Nationale de la Recherche (ANR, France, grant ANR-21-BIRE-0005), Bundesministerium für Bildung und Forschung (BMBF, Germany, grant 16LW015), the State Education Development Agency (VIAA, Latvia, grant ES RTD/2022/2), and the National Science Center (NSC, Poland, grant 2021/03/Y/NZ8/00101). We are also thankful for the support from the Collège de France and Agence Nationale de la Recherche (ANR) through the PAUSE ANR Ukraine program (Nekrasova O., grant ANR-23-PAUK-0074), and the project “Ecological and socioeconomic thresholds as a basis for defining adaptive management triggers in Latvian pond aquaculture” (lzp-2021/1–0247), project 16–00-F02201–000002 for the use of the mobile complex of scientific laboratories for research purposes.

References

[1] Akimov, I.A., 2009. Red Data Book of Ukraine. Animals, 3rd Global Consulting, Кyiv.

[2] Allouche, O., Tsoar, A., Kadmon, R., 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223-1232. doi: 10.1111/j.1365-2664.2006.01214.x.

[3] Araujo, M.B., New, M., 2007. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42-47. doi: 10.1016/j.tree.2006.09.010.

[4] Araujo, M.B., Thuiller, W., Pearson, R.G., 2006. Climate warming and the decline of amphibians and reptiles in Europe. J. Biogeogr. 33, 1712-1728. doi: 10.1111/j.1365-2699.2006.01482.x.

[5] Arino, O., Ramoz Perez, J.J., Kalogirou, V., Bontemps, S., Defourny, P., van Bogaert, E., 2012. Global Land Cover Map for 2009 (GlobCover 2009). European Space Agency (ESA) & Université Catholique de Louvain (UCL), Pangaea.

[6] Bahrenberg, G., Giese, E., Mevenkamp, N., Nipper, J., 2017. Statistical Methods in Geography. Gebrüder Borntraeger Verlagsbuchhandlung, Berlin, Germany.

[7] Böhm, M., Collen, B., Baillie, J.E.M., Bowles, P., Chanson, J., Cox, N., Hammerson, G., Hoffmann, M., Livingstone, S.R., Ram, M., Rhodin, A.G.J., Stuart, S.N., van Dijk, P.P., Young, B.E., Afuang, L.E., Aghasyan, A., García, A., Aguilar, C., Ajtic, R., Akarsu, F., Alencar, L.R.V., Allison, A., Ananjeva, N., Anderson, S., Andrén, C., Ariano-Sánchez, D., Arredondo, J.C., Auliya, M., Austin, C.C., Zug, G., 2013. The conservation status of the world’s reptiles. Biol. Conserv. 157, 372-385. doi: 10.1016/j.biocon.2012.07.015.

[8] Brandon, K., Gorenflo, L.J., Rodrigues, A.S.L., Waller, R.W., 2005. Reconciling biodiversity conservation, people, protected areas, and agricultural suitability in Mexico. World Dev. 33 (9), 1403-1418. doi: 10.1016/j.worlddev.2004.10.005.

[9] Braunisch, V., Coppes, J., Arlettaz, R., Suchant, R., Schmid, H., Bollmann, K., 2013. Selecting from correlated climate variables: a major source of uncertainty for predicting species distributions under climate change. Ecography 36 (9), 971-983. doi: 10.1111/j.1600-0587.2013.00138.x.

[10] Breiman, L., 2001. Random forests. Mach. Learn. 45, 5-32. doi: 10.1023/A:1010933404324.

[11] Burnham, K.P., Anderson, D.R., 1998. Model Selection and Inference: A Practical Information- theoretic Approach. Springer-Verlag, New York.

[12] Chen, I.-C., Hill, J.K., Ohlemüller, R., Roy, D.B., Thomas, C.D., 2011. Rapid range shifts of species associated with high levels of climate warming. Science ( 1979) 333, 1024-1026. doi: 10.1126/science.1206432.

[13] Čeirāns, A., 2004. Reptiles in sub-boreal forests of Eastern Europe: patterns of forest type preferences and habitat use in Anguis fragilis, Zootoca vivipara, and Natrix natrix. Herpetozoa 17, 65-74.

[14] Čeirāns, A., 2007. Microhabitat characteristics for reptiles Lacerta agilis, Zootoca vivipara, Anguis fragilis, Natrix natrix, and Vipera berus in Latvia. Russ. J. Herpetol. 14, 172-176.

[15] Čeirāns, A., Nikolajeva, L., 2017. Habitat ecology of the smooth snake Coronella austriaca and its reptilian prey in the degraded bog with implications for artificial refugee surveys. Zool. Ecol. 27, 19-29. doi: 10.1080/21658005.2016.1252125.

[16] Cochard, R., 2011. Consequences of deforestation and climate change on biodiversity. In: TrisuratY., ShresthaR.P., AlkemadeR. ( LandUse,Eds.), Climate Change and Biodiversity Modelling:Perspectives and Applications. IGI Global, Hershey, pp. 24-51.

[17] Cortes, C., Vapnik, V., 1995. Support-vector networks. Mach. Learn. 20 (3), 273-297. doi: 10.1007/BF00994018.

[18] Di Marco, M., 2022. Reptile research shows new avenues and old challenges for extinction risk modelling. PLoS Biol. 20 (7), e3001719. doi: 10.1371/journal. pbio.3001719.

[19] Dupin, M., Reynaud, P., Jarosik, V., Baker, R., Brunel, S., Eyre, D., Pergal, J., Makowski, D., 2011. Effects of the training dataset characteristics on the performance of nine species distribution models: application to Diabrotica virgifera virgifera. PLoS One 6, e20957. doi: 10.1371/journal.pone.0020957.

[20] Elith, J., Phillips, S.J., Hastie, T., Dudik, M., En Chee, Y., Yates, C.J., 2011. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17 (1), 43-57. doi: 10.1111/j.1472-4642.2010.00725.x.

[21] Fischer, G., Nachtergaele, F., Prieler, S., van Velthuizen, H.T., Verelst, L., Wiberg, D., 2012. Global Agro-Ecological Zones Assessment for Agriculture (GAEZ 2008). IIASA, Laxenburg, Austria and FAO, Rome.

[22] Gaston, K.J., Williams, P.H., 1996. Spatial patterns in taxonomic diversity. In: GastonK.J. (Ed.), Biodiversity: A Biology of Numbers and Difference. Blackwell Science Ltd., Oxford.

[23] [ dataset, GBIF.org (23 September 2021) GBIF Occurrence Download https://doi.org/10.15468/dl.xqbjy2.]

[24] Gibson, L., Wilson, B., Cahill, D., Hill, J., 2004. Spatial prediction of rufous bristlebird habitat in a coastal heathland: a GIS based approach. J. Appl. Ecol. 41 (2), 213-223. doi: 10.1111/j.0021-8901.2004.00896.x.

[25] Hardie, D.C., Hutchings, J.A., 2010. Evolutionary ecology at the extremes of species’ ranges. Environ. Rev. 18, 1-20. doi: 10.1139/A09-014.

[26] Hawkins, B.A., Field, R., Cornell, H.V., Currie, D.J., Guegan, J.F., Kaufman, D.M., Kerr, J.T., Mittelbach, G.G., Oberdorf, T., O’Brien, E.M., Porter, E.E., Turner, J.R.G., 2003. Energy, water, and broad-scale geographic patterns of species richness. Ecology 84, 3105-3117. doi: 10.1890/03-8006.

[27] Hijmans, R.J., van Etten, J., Cheng, J., Mattiuzzi, M., Sumner, M., Greenberg, J.A., Lamiguiro, O.P., Bevan, A., Racine, E.B., Shortridge, A., Hijmans, M.R.J., 2015. Package ‘raster’. R package. 734. Huey, R.B., 1982. Temperature, physiology, and the ecology of reptiles. Biol. Rep. 12, 25-91.

[28] Joos, J., Kirchner, M., Vamberger, M., Kaviani, M., Rahimibashar, M.R., Fritz, U., Müller, J., 2017. Climate and patterns of body size variation in the European pond turtle, Emys orbicularis. Biol. J. Linn. Soc. 122 (2), 351-365. doi: 10.1093/biolinnean/blx056.

[29] Kriticos, D.J., Webber, B.L., Leriche, A., Ota, N., Macadam, I., Bathols, J., Scott, J.K., 2012. CliMond: global high resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods Ecol. Evol. 3 (1), 53-64. doi: 10.1111/j.2041-210X.2011.00134.x.

[30] Kyubida, V.V., Nekrasova, O.D., Kutsokon, Y.K., Lopatynska, V.V., Truskavetska, I.Y., 2019. Summer fish kills in the Kaniv reservoir. Hydrobiol. J. 55 (1), 103-106. doi: 10.1615/HydrobJ.v55.i1.110.

[31] Liu, C., White, M., Newell, G., 2013. Selecting thresholds for the prediction of species occurrence with presence-only data. J. Biogeogr. 40, 778-789. doi: 10.1111/jbi.12058.

[32] Madden, R.A., Williams, J.,1978. The correlation between temperature and precipitation in the United States and Europe. Mon. Weather Rev. 106, 142-147. doi: 10.1175/1520-0493(1978)106<0142:TCBTAP>2.0.CO;2.

[33] Mader, S., Goldenberg, J., Massetti, F., Bisschop, K., D’Alba, L., Etienne, R.S., Clusella- Trullas, S., Shawkey, M.D., 2022. How melanism affects the sensitivity of lizards to climate change. Funct. Ecol. 36 (4), 812-825. doi: 10.1111/1365-2435.13993.

[34] Mantyka-Pringle, C.S., Martin, T.G., Rhodes, J.R., 2012. Interactions between climate and habitat loss effects on biodiversity: a systematic review and meta-analysis. Glob. Change Biol. 18, 1239-1252. doi: 10.1111/j.1365-2486.2011.02593.x.

[35] Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R.K., Thuiller, W., 2009. Evaluation of consensus methods in predictive species distribution modelling. Divers. Distrib. 15, 59-69. doi: 10.1111/j.1472-4642.2008.00491.x.

[36] Marushchak, O., Nekrasova, O., Pupins, M., Tytar, V., Ceirans, A.,20-22 June 2019. The role and importance of the protected areas’ (Emerald Network) development for amphibians and reptiles on the example of Ukraine in the context of various factors’ influence. Technology, Resources. Rezekne, Latvia, pp. 154-158.

[37] McCullagh, P., Nelder, J.A., 1989. Generalized Linear Models. Chapman and Hall, London.

[38] Merow, C., Smith, M.J., Edwards Jr., T.C., Guisan, A., McMahon, S.M., Normand, S., Thuiller, W., Wüest, R.O., Zimmermann, N.E., Elith, J., 2014. What do we gain from simplicity versus complexity in species distribution models? Ecography 37 (12), 1267-1281. doi: 10.1111/ecog.00845.

[39] Mi, C., Ma, L., Wang, Y., Wu, D., Du, W., Sun, B., 2022. Temperate and tropical lizards are vulnerable to climate warming due to increased water loss and heat stress. Proc. R. Soc. B-Biol. Sci. 289 ( 1980), 20221074. doi: 10.1098/rspb.2022.1074.

[40] Mi, C., Ma, L., Yang, M., Li, X., Meiri, S., Roll, U., Oskyrko, O., Pincheira-Donoso, D., Harvey, L.P., Jablonski, D., Safaei-Mahroo, B., Ghaffari, H., Smid, J., Jarvie, S., Kimani, R.M., Masroor, R., Kazemi, S.M., Nneji, L.M., Fokoua, A.M.T., Tasse Taboue, G.C., Bauer, A., Nogueira, C., Meirte, D., Chapple, D.G., Das, I., Grismer, L., Avila, L.J., Ribeiro Júnior, M.A., Tallowin, O.J.S., Torres-Carvajal, O., Wagner, P., Ron, S.R., Wang, Y., Itescu, Y., Nagy, Z.T., Wilcove, D.S., Liu, X., Du, W., 2023. Global protected areas as refuges for amphibians and reptiles under climate change. Nat. Commun. 14 (1), 1389. doi: 10.1038/s41467-023-36987-y.

[41] Nekrasova, O.D., 2018. Rare color variants in Lacertidae on the example of Zootoca vivipara (Jacquin, 1787) in Ukraine. In: The Second International Conference “Amphibian and Reptiles Anomalies and Pathology:Methodology, Evolutionary Significance, Monitoring and Environmental Health ”. KnE Life Sciences, pp. 108-116.

[42] Nekrasova, O.D., Gavris, G.G., Kuybida, V.V., 2013. Changes in the northern border of the home range of the dice snake, Natrix tessellata (Reptilia, Colubridae), in the Dnipro Basin (Ukraine). Vestn. Zool. 47 (5), 67-71. doi: 10.2478/vzoo-2013-0050.

[43] Nekrasova, O.D., Kostiushyn, V.A., 2016. Current distribution of the introduced rock lizards of the Darevskia ( Saxicola ) complex (Sauria, Lacertidae, Darevskia) in Zhytomyr region (Ukraine). Vestn. Zool. 50 (3), 225-230. doi: 10.1515/vzoo-2016-0026.

[44] Nekrasova, O., Marushchak, O., Pupins, M., Skute, A., Tytar, V., Ceirans, A., 2021. Distribution and potential limiting factors of the European pond turtle ( Emys orbicularis ) in Eastern Europe. Diversity 13 (7), 280. doi: 10.3390/d13070280.

[45] Nekrasova, O.D., Oskyrko, O.S., Marushchak, O.Y., 2018. Color features of sand lizards, Lacerta agilis (Sauria, Lacertidae), in Kyiv region (Ukraine). Vestn. Zool. 52 (6), 495-500. doi: 10.2478/vzoo-2018-0050.

[46] Nekrasova, O., Pupins, M., Marushchak, O., Tytar, V., Martinez-Silvestre, A., Škute, A., Čeirāns, A., Theissinger, K., Georges, J.-Y., 2024. Present and future distribution of the European pond turtle versus seven exotic freshwater turtles, with a focus on Eastern Europe. Sci. Rep. 14, 21149. doi: 10.1038/s41598-024-71911-4.

[47] Nekrasova, O.D., Tytar, V.M., 2014. Modelling and bioclimatic analysis of changes in the area of the dice snake, Natrix tessellata (Reptilia, Colubridae) in Ukraine. Proc. Ukr. Herp. Soc. 5, 80-83.

[48] Nekrasova, O.D., Tytar, V.M., Kuybida, V.V., 2019a. GIS Modelling of Climate Change Vulnerability of Amphibians and Reptiles in Ukraine. I.I. Schmalhausen Institute of Zoology, NAS of Ukraine, Kyiv Ukrainian.

[49] Nekrasova, O., Tytar, V., Pupins, M., Ceirans, A., 2022. Range expansion of the alien redeared slider Trachemys scripta (Thunberg in Schoepff, 1792) (Reptilia, Testudines) in Eastern Europe, with special reference to Latvia and Ukraine. Bioinvasions. Rec. 11 (1), 287-295. doi: 10.3391/bir.2022.11.1.29.

[50] Nekrasova, O., Tytar, V., Pupins, M., 2020. Local functional responses of the European pond turtle, Emys orbicularis, to bioclimatic habitat features:a comparison of populations from Latvia and Ukraine. In: OttonelloD., OnetoF., PiccardoP., SalvidioS. ( Proceedings of the II Congresso Nazionale Testuggini e Tartarughe,Eds.), pp.117-123.

[51] Nekrasova, O., Yanish, Y., Tytar, V., Pupins, M., 2019b. GIS-modelling of the range shifts of the sub-fossil and extant European pond turtle ( Emys orbicularis ) in Eastern Europe in Holocene. Diversity 11 (8), 121. doi: 10.3390/d11080121.

[52] Nekrasova, O., 2023. Records of Common Herpetofauna Species Widespread in Northern, Central and Southern Ukraine Ukrainian Nature Conservation Group (NGO). Version 1.8.Occurrence datasetaccessed via GBIF. org on 2023-01-05 doi: 10.15468/3t8srm.

[53] Nekrasova, O., Marushchak, O., 2023. Records of common species of amphibians and reptiles widespread in northern, central, western and southern Ukraine. Biodivers. Data J. 11, e99036. doi: 10.3897/BDJ.11.e99036.

[54] Osorio-Olvera, L., Lira-Noriega, A., Soberon, J., Peterson, A.T., Falconi, M., Contreras-Diaz, R.G., Martinez-Meyer, E., Barve, V., Barve, N., 2020. ntbox: an R package with graphical user interface for modelling and evaluating multidimensional ecological niches. Methods Ecol. Evol. 11 (10), 1199-1206. doi: 10.1111/2041-210X.13452.

[55] Pecl, G.T., Araujo, M.B., Bell, J.D., Blanchard, J., Bonebrake, T.C., Chen, I.C., Clark, T.D., Colwell, R.K., Danielsen, F., Evengård, B., Falconi, L., Ferrier, S., Frusher, S., Garcia, R.A., Griffis, R.B., Hobday, A.J., Janion-Scheepers, C., Jarzyna, M.A., Jennings, S., Lenoir, J., Linnetved, H.I., Martin, V.Y., McCormack, P.C., McDonald, J., Mitchell, N.J., Mustonen, T., Pandolfi, J.M., Pettorelli, N., Popova, E., Robinson, S.A., Scheffers, B.R., Shaw, J.D., Sorte, C.J.B., Strugnell, J.M., Sunday, J.M., Tuanmu, M. -, N., Vergés, A., Villanueva, C., Wernberg, T., Wapstra, E., Williams, S.E., 2017. Biodiversity redistribution under climate change: impacts on ecosystems and human wellbeing. Science 355 (6332), aai9214. doi: 10.1126/science.aai9214.

[56] Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modelling of species geographic distributions. Ecol. Model. 190, 231-259. doi: 10.1016/j.ecolmodel.2005.03.026.

[57] Pupina, A., Pupins, M., Nekrasova, O., Tytar, V., Kozynenko, I., Marushchak, O., 2018. Species distribution modelling: Bombina bombina (Linnaeus, 1761) and its important invasive threat Perccottus glenii (Dybowski, 1877) in Latvia under global climate change. Environ. Res. Eng. Manag. 74 (4), 79-86. doi: 10.5755/j01.erem.74.4.21093.

[58] Pupins, M., 2007. First report on recording of the invasive species Trachemys scripta elegans, a potential competitor of Emys orbicularis in Latvia. Acta Univ. Latviensis 273, 37-46.

[59] Pupins, M., Nekrasova, O., Marushchak, O., Dubyna, A., Neizhko, I., 2020. Morphological features of European pond turtles’ Emys orbicularis (Linnaeus, 1758) hatchlings at the northern edge (Latvia) and central part (Ukraine) of its distribution range. In: OttonelloD., OnetoF., PiccardoP., SalvidioS. ( Proceedings of the II Congresso Nazionale Testuggini e Tartarughe,Eds.), pp.144-150.

[60] Pupins, M., Pupina, A., Pupina, Ag., 2017. Updated distribution of the European pond turtle, Emys orbicularis (L., 1758) (Emydidae) on the extreme northern border of its European range in Latvia. Acta Zool. Bulg. 10, 133-137.

[61] Peterson, A.T., Vieglais, D.A., 2001. Predicting species invasions using ecological niche modelling: new approaches from bioinformatics attack a pressing problem: a new approach to ecological niche modelling, based on new tools drawn from biodiversity informatics, is applied to the challenge of predicting potential species’ invasions. Bioscience 51 (5), 363-371. doi: 10.1641/0006-3568(2001)051[0363:PSIUEN]2.0.CO;2.

[62] R, Core Team, 2020. R: a Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Available online at https://www.r-project.org/.

[63] Schmit, S., Pouteau, R., Justeau, D., de Boissieu, F., Birnbaum, P., 2017. SSDM: an R package to predict distribution of species richness and composition based on stacked species distribution models. Methods Ecol. Evol. 8, 1795-1803. doi: 10.1111/2041-210X.12841.

[64] Sillero, N., Campos, J., Bonardi, A., Corti, C., Creemers, R., Crochet, P.-A., Isailivich, J.C., Deno, M., Ficetola, G.F., Goncalves, J., Kuzmin, S., Lymberakis, P., De Pous, P., Rodriguez, A., Sindaco, R., Speybroeck, J., Toxopeus, B., Vieites, D.R., Vences, M., 2014. Updated distribution and biogeography of amphibians and reptiles of Europe. Amphibi. Reptil. 35 (1), 1-31. doi: 10.1163/15685381-00002935.

[65] Soberon, J., Nakamura, M., 2009. Niches and distributional areas: concepts, methods and assumptions. Proc. Natl. Acad. Sci. U.S.A. 106 (2), 19644-19650. doi: 10.1073/pnas.0901637106.

[66] IPCC, 2013. Climate change 2013:The physical science basis. Contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change.

[67] [ Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M. (Eds.)]. CambridgeUniversity Press, Cambridge. SuisleppK., RannapR., LõhmusA., 2011. Impacts of artificial drainage on amphibian breeding sites in hemiboreal forests. For. Ecol. Manag. 262 (6), 1078-1083. doi: 10.1016/j.foreco.2011.06.001.]

[68] Thuiller, W., Lafourcade, B., Engler, R., Araujo, M.B., 2009. BIOMOD -a platform for ensemble forecasting of species distributions. Ecography 32, 369-373. doi: 10.1111/j.1600-0587.2008.05742.x.

[69] Tytar, V.M., 2011. Analysis of home ranges in species: an approach based on modelling the ecological niche. Vestn. Zool. 25, 96.

[70] Tytar, V., Nekrasova, O., Pupina, A., Pupins, M., Oskyrko, O., 2018. Long-term bioclimatic modelling the distribution of the fire-bellied toad, Bombina bombina, under the influence of global climate change. Vestn. Zool. 52 (4), 553-556. doi: 10.2478/vzoo-2018-0036.

[71] Tytar, V., Sobolenko, L., Nekrasova, O., Mezhzherin, S., 2015. Using ecological niche modelling for biodiversity conservation guidance in the western Podillya (Ukraine): reptiles. Vestn. Zool. 49 (6), 551-558. doi: 10.1515/vzoo-2015-0015.

[72] Van Echelpoel, W., Boets, P., Landuyt, D., Gobeyt, S., Everaert, G., Bennetsen, E., Moutond, A., Goethals, P.L.M., 2015. Species distribution models for sustainable ecosystem management. Dev. Environ. Model. 27, 115-134. doi: 10.1016/B978-0-444-63536-5.00008-9.

[73] Vasyliuk, O., Marushchak, O., 2020. Developing of emerald network in Ukraine. In: ArdanovP.Ye., BrodtS.B., FontanaN.M., KazakovaI.V., MovchanV.O. ( Proceedings of Research and Practice Conference Eds.), “Polyculture and Permaculture ”, pp. 70-72.

[74] Vasyliuk, O.V., Nekrasova, O.D., Shyriaieva, D.V., Kolomytsev, G.O., 2015. A review of major impact factors of hostilities influencing biodiversity in the Eastern Ukraine (modeled on selected animal species). Vestn. Zool. 49 (2), 145-158. doi: 10.1515/vzoo-2015-0016.

[75] Winter, M., Fiedler, W., Hochachka, W.M., Koehncke, A., Meiri, S., De La Riva, I., 2016. Patterns and biases in climate change research on amphibians and reptiles: a systematic review. R. Soc. Open Sci. 3 (9), 160158. doi: 10.1098/rsos.160158.

[76] Zeng, Y., Low, B.W., Yeo, D.C., 2016. Novel methods to select environmental variables in MaxEnt: a case study using invasive crayfish. Ecol. Modell. 341, 5-13. doi: 10.1016/j.ecolmodel.2016.09.019.

[77] Zurell, D., Franklin, J., König, C., Bouchet, P.J., Dormann, C.F., Elith, J., Fandos, G., Feng, X., Guillera-Arroita, G., Guisan, A., Lahoz-Monfort, J.J., Leitao, P.J., Park, D.S., Peterson, A.T., Rapacciuolo, G., Schmatz, D.R., Schröder, B., Serra-Diaz, J.M., Thuiller, W., Yates, K.L., Zimmermann, N.E., Merow, C., 2020. A standard protocol for reporting species distribution models. Ecography 43 (9), 1261-1277. doi: 10.1111/ecog.04960.