پژوهش‌های تغییرات آب و هوایی

پژوهش‌های تغییرات آب و هوایی

مدل‌سازی مبتنی بر یادگیری عمیق توزیع مکانی دوزیستان برای ارزیابی اثر بخشی مناطق تحت حفاظت

نوع مقاله : مقاله پژوهشی

نویسندگان
فراهم احمدزاده 1
الهام ابراهیمی 1
اصغر عبدلی 1
بابک نعیمی 2
1 گروه تنوع زیستی و مدیریت اکوسیستم، پژوهشکده علوم محیطی، دانشگاه شهید بهشتی، تهران، ایران
2 گروه پویایی کمی تنوع زیستی، گروه زیست شناسی، دانشگاه اوترخت، هلند
10.30488/ccr.2024.469810.1232
چکیده
آنالیزگپ مبتنی بر مدل‌سازی توزیع گونه‌ای (SDMs)، ابزاری کلیدی برای ارزیابی عملکرد مناطق تحت حفاظت و شناسایی گپ‌های حفاظتی است. بنابراین بهبود دقت و اعتبار SDMs می‌تواند به تحقق استراتژی‌های چارچوب جهانی حفاظت از تنوع‌زیستی کمک کند. هدف از این مطالعه، ارتقاء روش­های آنالیزگپ با استفاده از مدل‌سازی برپایه هوش مصنوعی با استفاده از یادگیری عمیق و رفع برخی از محدودیت­های روش­های سنتی مدل­سازی در آنالیزگپ با استفاده از رویکرد فازی در محاسبه درجه مطلوبیت زیستگاه و نهایتاً ارزیابی کارایی آنالیزگپ در شناسایی گپ­های حفاظتی می­باشد. لذا از روش CNN-SDM، مبتنی بر یادگیری عمیق، برای شناسایی روابط پیچیده بین توزیع مکانی گونه‌ها و شرایط محیطی استفاده شد. داده‌های اقلیمی WorldClim و حضور 45 گونه دوزیست در جنوب­غرب آسیا از پایگاه GBIF برای کالیبره کردن مدل استفاده شد. پس از تولید نقشه مطلوبیت زیستگاهی، گونه‌ها براساس درجه تهدید در فهرست سرخ IUCN وزن‌دهی شدند و نقشه غنای گونه‌ای وزنی ایجاد شد. برای رفع محدودیت‌های داده‌های باینری، از مقیاس‌گذاری فازی استفاده شد که تغییرات کیفیت زیستگاه را پیوسته در بازه‌ای بین 0 و 1 نمایش می‌دهد. برای پیش‌بینی آینده، سناریوهای اقلیمی SSP126 و SSP585 برای سال‌های ۲۰۶۰ و ۲۱۰۰ بررسی شدند. مقایسه نقشه‌های غنای گونه‌ای وزنی و رویهم‌گذاری مرسوم نشان داد که 43% از زیستگاه‌های دوزیستان در معرض تهدید در روش رویهم‌گذاری نادیده گرفته می‌شود. نتایج تایید کرد در بدترین سناریو اقلیمی، اگرچه 85% از زیستگاه‌های مطلوب در مناطق تحت حفاظت حفظ می‌شود اما میانگین ارزش پیکسل‌های مطلوب در این مناطق به‌طور معناداری کاهش می‌یابد بطوریکه مطلوبیت زیستگاهی فازی برای 97% از گونه‌ها کاهش خواهد یافت. مدل CNN-SDM نشان داد با AUC برابر با 94/0و TSS برابر با 89/0دقت بالایی در پیش‌بینی توزیع گونه‌ها دارد. این مطالعه تأکید بر ارتقاء روش‌های آنالیزگپ و رفع محدودیت‌های روش‌های سنتی دارد.
کلیدواژه‌ها
آنالیزگپ
یادگیری عمیق
غنای گونه‌ای وزنی
روش فازی
مناطق تحت حفاظت
دوزیستان
موضوعات

عنوان مقاله English

Deep Learning-Based Modeling of Amphibian Spatial Distribution for Assessing the Effectiveness of Protected Areas

نویسندگان English

Faraham Ahmadzadeh 1
Elham Ebrahimi 1
Asghar Abdoli 1
Babak Naimi 2
1 Department of Biodiversity and Ecosystem Management, Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran
2 Quantitative Biodiversity Dynamics (QBD), Department of Biology, University of Utrecht, The Netherlands
چکیده English

The gap analysis based on species distribution modeling (SDMs) is a essential tool for evaluating the performance of Protected Areas and identifying conservation gaps. Improving the accuracy and reliability of SDMs can significantly aid in implementing strategies under the Global Biodiversity Framework. This study aimed to enhance gap analysis methods through deep learning-based modeling and to overcome some limitations of traditional gap analysis approaches by using a fuzzy approach to calculate habitat suitability, ultimately assessing the efficacy of gap analysis in identifying conservation gaps. The study employed the CNN-SDM deep learning-based method to identify complex relationships between species' spatial distributions and environmental conditions. WorldClim data and records of 45 amphibian species from the GBIF in Southwest Asia were used to calibrate the model. After generating habitat suitability maps, species were weighted according to their threat levels in the IUCN Red List, creating a weighted species richness map. To address the limitations of binary data, a fuzzy approach was used to represent habitat quality on a continuous scale from 0 to 1. Future predictions were made using the SSP126 and SSP585 climatic scenarios for the years 2060 and 2100. The comparison of weighted species richness maps and stacked richness revealed that 43% of threatened amphibian habitats were overlooked by the stack binary method. Results confirmed that under the worst climate scenario, while 85% of suitable habitats remain within protected areas, the average pixel value of habitat suitability within these areas significantly decreases, with fuzzy habitat suitability declining for 97% of the species. The CNN-SDM model demonstrated high accuracy, with an AUC of 0.94 and a TSS of 0.89. This study highlights the need for enhancing gap analysis methods and addressing the limitations of traditional approaches

کلیدواژه‌ها English

Gap analysis
Deep Learning
Weighted Species Richness
Fuzzy Approach
Protected Areas
Amphibians
  1. عزیزی جلیلیان، منا.، دانه­کار، افشین.، سلمان ماهینی، عبدالرسول.، شایسته، کامران، بالی، علی. (1402). آنالیز گپ: ابزاری حفاظتی برای مدیریت سیستم پارک­های ملی در ایران. محیط زیست طبیعی. 76. 14-1.
  2. قیومی، راضیه.، ابراهیمی، الهام.، حسینی طایفه، فرهاد.، و کشتکار، مصطفی. (1398). پیش­بینی اثرات تغییرات اقلیمی بر توزیع جنگل­های مانگرو در ایران با استفاده از مدل حداکثر آنتروپی. سنجش‌ازدور و سامانه اطلاعات جغرافیایی در منابع طبیعی. 10(2)، 34-47.
  3. Abbaspour, M., Mahiny, A. S., Arjmandy, R., & Naimi, B. (2011). Integrated approach for land use suitability analysis. International Agrophysics, 25(4). https://agro.icm.edu.pl/agro/element/bwmeta1.element.agro-babd4e54-f64e-4413-9f46-3df1073f8d02
  4. Abellán, P., & Sánchez-Fernández, D. (2015). A gap analysis comparing the effectiveness of Natura 2000 and national protected area networks in representing European amphibians and reptiles. Biodiversity and Conservation, 24(6), 1377–1390. https://doi.org/10.1007 /s10531-015-0862-3
  5. Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B., & Anderson, R. P. (2015). spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography, 38(5), 541–545. https://doi.org/10.1111/ecog.01132
  6. Alagador, D., Martins, M. J., Cerdeira, J. O., Cabeza, M., & Araújo, M. B. (2011). A probability-based approach to match species with reserves when data are at different resolutions. Biological Conservation, 144(2), 811-820. https://doi.org/10.1016/j.biocon.2010.11.011
  7. Alves-Ferreira, G., Talora, D. C., Solé, M., Cervantes-López, M. J., & Heming, N. M. (2022). Unraveling global impacts of climate change on amphibians distributions: A life-history and biogeographic-based approach. Frontiers in Ecology and Evolution, 10, 1–12. https://doi.org/10.3389/fevo.2022.987237
  8. Amiri, N., Vaissi, S., Aghamir, F., Saberi-Pirooz, R., Rödder, D., Ebrahimi, E., & Ahmadzadeh, F. (2021). Tracking climate change in the spatial distribution pattern and the phylogeographic structure of Hyrcanian wood frog, Rana pseudodalmatina (Anura: Ranidae). Journal of Zoological Systematics and Evolutionary Research, 59(7), 1604–1619. https://doi.org/10.1111/jzs.12503
  9. Araújo, M. B., Alagador, D., Cabeza, M., Nogués-Bravo, D., & Thuiller, W. (2011). Climate change threatens European conservation areas. Ecology Letters, 14(5), 484–492. https://doi.org/10.1111/j.1461-0248.2011.01610.x
  10. Araújo, M. B., Lobo, J. M., & Moreno, J. C. (2007). The effectiveness of Iberian protected areas in conserving terrestrial biodiversity. Conservation Biology, 21(6), 1423-1432. https://doi.org/10.1016 /j.pecon.2018.03.001
  11. Arponen, A. (2012). Prioritizing species for conservation planning. Biodiversity and Conservation, 21(4), 875–893. https://doi.org /10.1007/s10531-012-0242-1
  12. Borowiec, M. L., Dikow, R. B., Frandsen, P. B., McKeeken, A., Valentini, G., & White, A. E. (2022). Deep learning as a tool for ecology and evolution. Methods in Ecology and Evolution, 13(8), 1640-1660. https://doi.org/10.1111/2041-210X.13901
  13. Brun, P., Karger, D. N., Zurell, D., Descombes, P., De Witte, L. C., De Lutio, R., Wegner, J. D., & Zimmermann, N. E. (2023). Rank-based deep learning from citizen-science data to model plant communities. BioRxiv, 05. https://doi.org/10.1101/2023.05.30.542843
  14. Bueno, A.S., & Peres, C.A. (2019). Patch-scale biodiversity retention in fragmented landscapes: Reconciling the habitat amount hypothesis with the island biogeography theory. Journal of Biogeography, 46(3), 621–632. https://doi.org/10.1111 /jbi.13499
  15. Case, M. J., Lawler, J. J., & Tomasevic, J. A. (2015). Relative sensitivity to climate change of species in northwestern North America. Biological Conservation, 187, 127–133. https://doi.org/10.1016 /j.biocon.2015.04.013
  16. Catullo, G., Masi, M., Falcucci, A., Maiorano, L., Rondinini, C., & Boitani, L. (2008). A gap analysis of Southeast Asian mammals based on habitat suitability models. Biological Conservation, 141(11), 2730-2744. https://doi.org/10.1016 /j.biocon.2008.08.019
  17. Chen, Y., Zhang, J., Jiang, J., Nielsen, S. E., & He, F. (2016). Assessing the effectiveness of China's protected areas to conserve current and future amphibian diversity. Diversity and Distributions,  23(2), 146-157. https://doi.org/ 10.1111/ddi.12508
  18. Convetion on Biological Diversity, (CBD). (2020). Global Biodiversity Outlook 5 - full Report. emdashdesign.ca
  19. Deneu, B., Servajean, M., Bonnet, P., Botella, C., Munoz, F., & Joly, A. (2021). Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment. Plos Computational Biology, 17(4). https://doi.org /10.1371/journal.pcbi.1008856
  20. Dudley, N., Stolton, S., & Elliott, W. (2013). Wildlife crime poses unique challenges to protected areas. 19, 1–125. https://doi.org/10.2305/IUCN.CH.2013.PARKS-19-1.ND.en
  21. Dziba, L., Erpul, G., Fazel, A., Fischer, M., & Hernández, A. M. (2019). Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. IPBES Secretariat: Bonn, Germany, May 2019, 22–47.
  22. Eastman, J. R. (1999). Multi-criteria evaluation and GIS. Geographical information systems, 1(1), 493-502.
  23. Ebrahimi, E., & Ahmadzadeh, F. (2022). Dynamics of habitat changes as a result of climate change in Zagros Mountains Range (Iran), a case study on Amphibians. Nova Biologica Reperta, 9(1), 29–39. https://doi.org/10.29252/nbr.9.1.29
  24. Ebrahimi, E., Araújo, M. B., & Naimi, B. (2023). Flood susceptibility mapping to improve models of species distributions. Ecological Indicators, 157, 111250. https://doi.org/10.1016 /j.ecolind.2023.111250
  25. Ebrahimi, E., Ranjbaran, Y., Sayahnia, R., & Ahmadzadeh, F. (2022). Assessing the climate change effects on the distribution pattern of the Azerbaijan Mountain Newt (Neurergus crocatus). Ecological Complexity, 50 (100997). https://doi.org/ 10.1016/j.ecocom.2022.100997
  26. Ebrahimi, E., Sayahnia, R., Ranjbaran, Y., Vaissi, S., & Ahmadzadeh, F. (2021). Dynamics of threatened mammalian distribution in Iran’s protected areas under climate change. Mammalian Biology, 101, 759–774. https://doi.org/10.1007/s42991-021-00136-z
  27. Dormann, C., M. McPherson, J., B. Araújo, M., Bivand, R., Bolliger, J., Carl, G., G. Davies, R., Hirzel, A., Jetz, W., Daniel Kissling, W., Kühn, I., Ohlemüller, R., R. Peres-Neto, P., Reineking, B., Schröder, B., M. Schurr, F., & Wilson, R. (2007). Methods to account for spatial autocorrelation in the analysis of species distributional data: A review. Ecography, 30(5), 609–628. https://doi.org/10.1111/j.2007.0906-7590.05171.x
  28. Fajardo, J., Lessmann, J., Bonaccorso, E., Devenish, C., & Muñoz, J. (2014). Combined use of systematic conservation planning, species distribution modelling, and connectivity analysis reveals severe conservation gaps in a megadiverse country (Peru). Plos one, 9(12), 1–23. https://doi.org/10.1371/journal.pone.0114367
  29. Farashi, A., & Shariati, M. (2017). Biodiversity hotspots and conservation gaps in Iran. Journal for nature conservation, 39, 37-57. https://doi.org/10.1016/j.jnc.2017.06.003
  30. Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. International journal of climatology, 37(12), 4302-4315. https://doi.org/10.1002/joc.5086
  31. Garcia, C. A., Savilaakso, S., Verburg, R. W., Stoudmann, N., Fernbach, P., Sloman, S. A., ... & Waeber, P. O. (2022). Strategy games to improve environmental policymaking. Nature Sustainability, 5(6), 464-471. https://doi.org/10.1038/s41893-022-00881-0
  32. Ghane-ameleh, S., Khosravi, M., & Saberi-pirooz, R. (2021). Mid-Pleistocene Transition as a trigger for diversification in the Irano-Anatolian region : Evidence revealed by phylogeography and distribution pattern of the eastern three-lined lizard. Global Ecology and Conservation, 31(e01839). https://doi.org/ 10.1016/j.gecco.2021.e01839
  33. Ghyoumi, R., Ebrahimi, E., & Mousavi, S. M. (2022). Dynamics of mangrove forest distribution changes in Iran. Journal of Water and Climate Change, 13(6), 2479–2489. https://doi.org/10.2166/wcc.2022.069
  34. Gorenflo, L. J., & Brandon, K. (2006). Key human dimensions of gaps in global biodiversity conservation. BioScience, 56(9), 723-731. https://doi.org/10.1641/0006-3568(2006)56[723:KHDOGI]2.0.CO;2
  35. Guillera‐Arroita, G., Lahoz‐Monfort, J. J., Elith, J., Gordon, A., Kujala, H., Lentini, P. E., ... & Wintle, B. A. (2015). Is my species distribution model fit for purpose? Matching data and models to applications. Global ecology and biogeography, 24(3), 276-292. https://doi.org/10.1111/geb.12268
  36. Guisan, A., & Thuiller, W. (2005). Predicting species distribution: offering more than simple habitat models. Ecology letters, 8(9), 993-1009. https://doi.org /10.1111/j.1461-0248.2005.00792.x
  37. Harris, D. J., Taylor, S. D., & White, E. P. (2018). Forecasting biodiversity in breeding birds using best practices. PeerJ, 2018 (e4278). https://doi.org/10.7717/peerj.4278
  38. Hof, C., Araújo, M. B., Jetz, W., & Rahbek, C. (2011). Additive threats from pathogens, climate and land-use change for global amphibian diversity. Nature, 480(7378), 516–519. https://doi.org/10.1038/nature10650
  39. Hoffmann, S., Irl, S. D. H., & Beierkuhnlein, C. (2019). Predicted climate shifts within terrestrial protected areas worldwide. Nature Communications, 10(1), 1–10. https://doi.org/10.1038/s41467-019-12603-w
  40. Hofmeister, J., Hošek, J., Brabec, M., Dvořák, D., Beran, M., Deckerová, H., Burel, J., Kříž, M., Borovička, J., Bě͗ák, J., Vašutová, M., Malíček, J., Palice, Z., Syrovátková, L., Steinová, J., Černajová, I., Holá, E., Novozámská, E., Čížek, L., Svoboda, T. (2015). Value of old forest attributes related to cryptogam species richness in temperate forests: A quantitative assessment. Ecological Indicators, 57, 497–504. https://doi.org/10.1016/j.ecolind.2015.05.015
  41. Holt, B. G., Lessard, J. P., Borregaard, M. K., Fritz, S. A., Araújo, M. B., Dimitrov, D., Fabre, P. H., Graham, C. H., Graves, G. R., Jønsson, K. A., Nogués-Bravo, D., Wang, Z., Whittaker, R. J., Fjeldså, J., & Rahbek, C. (2013). An update of Wallace’s zoogeographic regions of the world. Science, 339(6115), 74–78. https://doi.org/10.1126/science.1228282
  42. Hughes, A. C. (2023). The Post‐2020 Global Biodiversity Framework: How did we get here, and where do we go next?. Integrative Conservation, 2(1), 1-9. https://doi.org/10.1002/inc3.16
  43. Ilanloo, S. S., Ebrahimi, E., Valizadegan, N., Ashrafi, S., Rezaei, H. R., & Yousefi, M. (2020). Little owl (Athene noctua) around human settlements and agricultural lands: Conservation and management enlightenments. Acta Ecologica Sinica, 40(5), 347-352. https://doi.org/ 10.1016/j.chnaes.2020.06.001
  44. Imbach, P., Molina, L., Locatelli, B., Roupsard, O., Ciais, P., Corrales, L., & Mahe, G. (2010). Climatology-based regional modelling of potential vegetation and average annual long-term runoff for Mesoamerica. Hydrology and Earth System Sciences, 14(10), 1801-1817. https://doi.org/10.5194/hess-14-1801-2010
  45. Jenkins, C. N., Pimm, S. L., & Joppa, L. N. (2013). Global patterns of terrestrial vertebrate diversity and conservation. Proceedings of the National Academy of Sciences of the United States of America, 110(28), E2603–E2610. https://doi.org/ 10.1073/pnas.1302251110
  46. Kafash, A., Ashrafi, S., Ohler, A., Yousefi, M., Malakoutikhah, S., Koehler, G., & Schmidt, B. R. (2018). Climate change produces winners and losers: Differential responses of amphibians in mountain forests of the Near East. Global Ecology and Conservation, 16, e00471. https://doi.org/10.1016/j.gecco.2018.e00471
  47. Karimi, A., Yazdandad, H., & Reside, A. E. (2023). Spatial conservation prioritization for locating protected area gaps in Iran. Biological Conservation, 279, 109902. https://doi.org/10.1016/j.biocon.2023.109902
  48. Lelieveld, J., Hadjinicolaou, P., Kostopoulou, E., Chenoweth, J., El Maayar, M., Giannakopoulos, C., Hannides, C., Lange, M. A., Tanarhte, M., Tyrlis, E., & Xoplaki, E. (2012). Climate change and impacts in the Eastern Mediterranean and the Middle East. Climatic Change, 114(3–4), 667–687. https://doi.org/10.1007/s10584-012-0418-4
  49. Levin, S. A. (1992). The problem of pattern and scale in ecology. Ecology, 73(6), 1943–1967. https://doi.org/ 10.2307/1941447
  50. Luo, Z., Wang, X., Yang, S., Cheng, X., Liu, Y., & Hu, J. (2021). Combining the responses of habitat suitability and connectivity to climate change for an East Asian endemic frog. Frontiers in Zoology, 18(1), 1–15. https://doi.org/ 10.1186/s12983-021-00398-w
  51. Margules, C. R., & Pressey, R. L. (2000). Systematic conservation planning. Nature, 405(6783), 243-253. https://doi.org/10.1038/35012251
  52. Marquet, P. A. (2017). Integrating macroecology through a statistical mechanics of adaptive matter. Proceedings of the National Academy of Sciences of the United States of America, 114 (40), 10523–10525. https://doi.org/10.1073/pnas.1713971114
  53. Martínez-López, J., Martínez-Fernández, J., Naimi, B., Carreño, M. F., & Esteve, M. A. (2015). An open-source spatio-dynamic wetland model of plant community responses to hydrological pressures. Ecological Modelling, 306, 326-333.https://doi.org/ 10.1016/j.ecolmodel.2014.11.024
  54. Maxwell, S. L., Cazalis, V., Dudley, N., Hoffmann, M., Rodrigues, A. S. L., Stolton, S., Visconti, P., Woodley, S., Kingston, N., Lewis, E., Maron, M., Strassburg, B. B. N., Wenger, A., Jonas, H. D., Venter, O., & Watson, J. E. M. (2020). Area-based conservation in the twenty-first century. Nature, 586(7828), 217–227. https://doi.org/10.1038/s41586-020-2773-z
  55. 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., … Du, W. (2023). Global Protected Areas as refuges for amphibians and reptiles under climate change. Nature Communications, 14(1). https://doi.org/10.1038/s41467-023-36987-y
  56. Mohammadi, S., Ebrahimi, E., Shahriari Moghadam, M., & Bosso, L. (2019). Modelling current and future potential distributions of two desert jerboas under climate change in Iran. Ecol Inf 52, 7–13. https://doi.org/10.1016/j.ecoinf.2019.04.003
  57. Moreno, A., & Hasenauer, H. (2016). Spatial downscaling of European climate data. International Journal of Climatology, 36(3). https://doi.org/ 1002/joc.4436
  58. Naimi, B. (2015). usdm: Uncertainty analysis for species distribution models. Version 1.1-12. https://doi.org/10.32614/CRAN.package.usdm
  59. Naimi, B., Capinha, C., Ribeiro, J., Rahbek, C., Strubbe, D., Reino, L., & Araújo, M. B. (2022). Potential for invasion of traded birds under climate and land‐cover change. Global Change Biology, 28(19), 5654-5666. https://doi.org/10.1111/gcb.16310
  60. Naimi, B., Hamm, N. A., Groen, T. A., Skidmore, A. K., & Toxopeus, A. G. (2014). Where is positional uncertainty a problem for species distribution modelling?. Ecography, 37(2), 191-203. https://doi.org/10.1111/j.1600-0587.2013.00205.x
  61. Nenzén, H. K., & Araújo, M. B. (2011). Choice of threshold alters projections of species range shifts under climate change. Ecological Modelling, 222(18), 3346-3354. https://doi.org/10.1016/j.ecolmodel.2011.07.011
  62. Poggio, L., Simonetti, E., & Gimona, A. (2018). Enhancing the WorldClim data set for national and regional applications. Science of the Total Environment, 625, 1628-1643. https://doi.org/10.1016/j.scitotenv.2017.12.258
  63. Post-2020 Global Biodiversity Framework. (2021). First Draft of the Post-2020 Global Biodiversity. https://www.unep.org
  64. Rodrigues, A. S., Akcakaya, H. R., Andelman, S. J., Bakarr, M. I., Boitani, L., Brooks, T. M., ... & Yan, X. (2004). Global gap analysis: priority regions for expanding the global protected-area network. BioScience, 54(12), 1092-1100. https://doi.org/10.1641/0006-3568(2004)054[1092:GGAPRF]2.0.CO;2
  65. Scott, J. M., Davis, F., Csuti, B., Noss, R., Butterfield, B., Groves, C., ... & Wright, R. G. (1993). Gap analysis: a geographic approach to protection of biological diversity. Wildlife Monographs, 3-41. https://www.jstor.org/stable/3830788
  66. Scott, J. M., Murray, M., Wright, R. G., Csuti, B., Morgan, P., & Pressey, R. L. (2001). Representation of natural vegetation in protected areas: capturing the geographic range. Biodiversity & Conservation, 10, 1297-1301. https://doi.org/10.1023/A:1016647726583
  67. Sheykhi Ilanloo, S., Khani, A., Kafash, A., Valizadegan, N., Ashrafi, S., Loercher, F., Ebrahimi, E., & Yousefi, M. (2021). Applying opportunistic observations to model current and future suitability of the Kopet Dagh Mountains for a Near Threatened avian scavenger. Avian Biology Research, 14(1), 18–26. https://doi.org/10.1177/1758155920962750
  68. Silva, A.F. da, Malhado, A. C. M., Correia, R.A., Ladle, R. J., Vital, M. V. C., & Mott, T. (2020). Taxonomic bias in amphibian research: Are researchers responding to conservation need? Journal for Nature Conservation, 56, 125829. https://doi.org/10.1016/j.jnc.2020.125829
  69. Thomas, C. D., Cameron, A., Green, R. E., Bakkenes, M., Beaumont, L. J., Collingham, Y. C., Erasmus, B. F. N., Ferreira De Siqueira, M., Grainger, A., Hannah, L., Hughes, L., Huntley, B., Van Jaarsveld, A. S., Midgley, G. F., Miles, L., Ortega-Huerta, M. A., Peterson, A. T., Phillips, O. L., & Williams, S. E. (2004). Extinction risk from climate change. Nature, 427(6970), 145–148. https://doi.org/10.1038/nature02121
  70. Titley, M. A., Snaddon, J. L., & Turner, E. C. (2017). Scientific research on animal biodiversity is systematically biased towards vertebrates and temperate regions. Plos One, 12(12). https://doi.org/ 10.1371/journal.pone.0189577
  71. Tittensor, D. P., Walpole, M., Hill, S. L. L., Boyce, D. G., Britten, G. L., Burgess, N. D., Butchart, S. H. M., Leadley, P. W., Regan, E. C., Alkemade, R., Baumung, R., Bellard, C., Bouwman, L., Bowles-newark, N. J., Chenery, A. M., & Cheung, W. W. L. (2014). A mid-term analysis of progress toward international biodiversity targets. Science, 346(6206), 241–243. https://doi.org/10.1126/science.1257484
  72. UNEP-WCMC, IUCN and NGS (2018). Protected Planet Report 2018. UNEP-WCMC, IUCN and NGS: Cambridge UK; Gland, Switzerland; and Washington, D.C., USA.
  73. Wüest, R. O., Zimmermann, N. E., Zurell, D., Alexander, J. M., Fritz, S. A., Hof, C., Kreft, H., Normand, S., Cabral, J. S., Szekely, E., Thuiller, W., Wikelski, M., & Karger, D. N. (2020). Macroecology in the age of Big Data – Where to go from here? Journal of Biogeography, 47(1), 1–12. https://doi.org/10.1111/jbi.13633
  74. Yigini, Y.; Panagos, P. (2016). Assessment of soil organic carbon stocks under future climate and land cover changes in Europe. Science of the Total Environment, 557, 838-850. https://doi.org/10.1016/j.scitotenv.2016.03.085
  75. Zhu, G., Papeş, M., Giam, X., Cho, S. H., & Armsworth, P. R. (2021). Are protected areas well-sited to support species in the future in a major climate refuge and corridor in the United States? Biological Conservation, 255 (108982). https://doi.org/10.1016/j.biocon.2021.108982
  76. Zizka, A., Silvestro, D., Andermann, T., Azevedo, J., Duarte Ritter, C., Edler, D., Farooq, H., Herdean, A., Ariza, M., Scharn, R., Svantesson, S., Wengström, N., Zizka, V., & Antonelli, A. (2019). CoordinateCleaner: Standardized cleaning of occurrence records from biological collection databases. Methods in Ecology and Evolution, 10(5), 744–751. https://doi.org/10.1111/2041-210X.13152