توسعۀ مدل مکانی‌– زمانی مطلوبیت زیستگاه برای ارزیابی تغییرات تنوع زیستی در پاسخ به تغییر اقلیم

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه تنوع زیستی و مدیریت اکوسیستم، پژوهشکدة علوم محیطی، دانشگاه شهید بهشتی، تهران، ایران

2 دانشیار، گروه تنوع زیستی و مدیریت اکوسیستم، پژوهشکدة علوم محیطی، دانشگاه شهید بهشتی، تهران، ایران

3 استاد، گروه تنوع زیستی و مدیریت اکوسیستم، پژوهشکدة علوم محیطی، دانشگاه شهید بهشتی، تهران، ایران

4 استادیار، گروه پویایی کمّی تنوع زیستی، گروه زیست‌شناسی، دانشگاه اوترخت، هلند

چکیده

سابقه و هدف: ازدست‌دادن تنوع‌زیستی تهدیدی جهانی برای انسان‌ها محسوب می‌شود. برای مقابله با این تهدیدها، سازمان‌های جهانی محیط‌زیستی اهداف و برنامه‌های استراتژیک ویژه‌ای را در قالب چارچوب جهانی تنوع‌زیستی کونمینگ‌– مونترال، به‌منظور درک چگونگی تغییرات تنوع‌زیستی در طول زمان و شناسایی عوامل تأثیرگذار در آنها، تصویب کردند. بدین‌منظور ابزارهای مدل‌سازی تنوع زیستی، به‌ویژه مدل‌های مطلوبیت زیستگاه (توزیع گونه‌ای) که از پرکاربردترین روش‌های شناخته‌شده برای مطالعات تنوع زیستی محسوب می‌شوند، نقشی اساسی دارند. با وجود پیشرفت‌های شگرف در توسعۀ روش‌های مدل‌سازی در کنار دسترسی روزافزون به داده‌های مکانی، مدل‌های فعلی در پیش‌بینی دقیق وضعیت و تغییرات تنوع‌زیستی محدودیت‌های جدی دارند؛ این مسئله به ناممکن‌بودن ایجاد چارچوبی مناسب برای پایش تنوع زیستی در طول زمان منجر می‌شود. یکی از مهم‌ترین محدودیت‌های کاربردی این مدل‌ها ناسازگاری داده‌های ثبت‌شده، به‌لحاظ فراوانی و گسترۀ مکانی حضور گونه‌ها در بازه‌های زمانی متفاوت است. این شرایط امکان توسعۀ مدل‌های مکانی‌– زمانی را که در درک پویایی و تغییرات توزیع جغرافیایی گونه‌ها طی زمان ضروری‌اند، محدود می‌کند. هدف از این مطالعه بیان راه‌حلی برای غلبه بر مشکلات ناسازگاری داده‌های تنوع زیستی در طول زمان، و توسعۀ فرایند محاسباتی برای مدل‌سازی مکانی‌– زمانی مطلوبیت زیستگاه و سپس کمّی‌سازی تغییرات در توزیع مکانی است.
مواد و روش‌ها: در این مطالعه، برای توسعۀ مدل مکانی‌– زمانی تنوع زیستی، از داده‌های حضور گونۀ نوعی بز کوهی (Hippotragus equinus) استفاده شد. داده‌های سری زمانی بلندمدت حضور این گونه بین سال‌های ۱۹۰۱ تا ۲۰۲۰ از پایگاه‌ دادۀ جهانی GBIF، به‌منظور توسعۀ مدل‌های مکانی‌– زمانی، و همچنین داده‌های سری زمانی تنوع زیستی (فراوانی گونه) در پایگاه‌های اطلاعاتی LPI و BioTime برای اعتبارسنجی ارزیابی تغییرات تنوع زیستی به کار رفت. به‌علاوه، داده‌های اقلیمی از پایگاه‌ دادۀ CRUTS استخراج شدند که براساس آنها، لایه‌های اطلاعاتی نوزده‌گانۀ زیست‌اقلیمی به‌صورت سالیانه تولید شد. پس از پاک‌سازی و آماده‌سازی داده‌های گونه‌ و انتخاب متغیرهای مناسب در حین آزمایش چندخطی‌گری، داده‌های سری زمانی به‌صورت یکپارچه در جدول دادۀ واحد یا استخر داده گردآوری شدند. در این رویکرد، داده‌های حضور گونه متعلق به هر سال به اطلاعات اقلیمی همان سال و مکان پیوند داده شد. سپس برای توسعۀ مدل مکانی‌– زمانی و به‌منظور بهبود کارآیی و کاهش عدم‌قطعیت مدل‌ها، ده الگوریتم مرسوم یادگیری ماشینی انتخاب و مدل‌های مکانی‌– زمانی کالیبره شدند. پس از اعتبارسنجی مدل‌ها، الگوی مکانی توزیع گونه با ترکیب پیش‌بینی‌های حاصل از مدل‌های گوناگون، با استفاده از میانگین‌گیری وزنی (انسمبل) برای هر سال، پیش‌بینی شد؛ این کار به تولید سری زمانی ۱۲۰ساله شامل پیش‌بینی‌های توزیع مکانی گونه‌ای منجر شد. در گام بعدی، با استفاده از تابع تخمینگر شیب سن، روند تغییرات مطلوبیت زیستگاهی در طول ۱۲۰ سال در هر پیکسل محاسبه شد.
نتایج و بحث: نتایج اعتبارسنجی مدل‌ها نشان داد که تمامی روش‌های مدل‌سازی از عملکرد بسیار خوبی برخوردار بودند؛ به‌گونه‌ای‌که شاخص AUC برای این مدل‌ها بین 996/0 و 926/0 محاسبه شد. تحلیل نقشه‌های روند تغییرات تنوع زیستی در طول زمان نشان داد که در عرض‌های جنوبی، احتمال حضور گونه به‌تدریج کاهش یافته است؛ درحالی‌که در کمربند میانی افریقا، افزایش احتمال حضور این گونه مشاهده می‌شود. صحت‌سنجی نتایج، با استفاده از داده‌های سری زمانی توزیع و فراوانی گونه‌ از منابع BioTime و LPI، نشان داد که به‌ترتیب در 88% و 84% نقاط کاهش مطلوبیت زیستگاهی، مدل با داده‌های واقعی هم‌پوشانی دارد؛ ‌این مسئله دقت بالای مدل را در پیش‌بینی توزیع و تغییرات گونه‌ای در طول زمان تأیید می‌کند.
نتیجه‌گیری: راه‌حل ارائه‌شده در این مطالعه، افزون بر اینکه امکان مدل‌سازی مکانی‌– زمانی برای تحلیل الگوهای توزیع گونه‌ها و تغییرات آنها را فراهم می‌آورد، با افزایش دقت در کمّی‌سازی آشیان بوم‌شناختی، به بهبود پیش‌بینی الگوهای توزیع مکانی و کاهش عدم اطمینان در ارزیابی‌ها کمک می‌کند. این رویکرد با ارتقای پوشش و اندازۀ نمونه‌ها، چالش‌های در زمینۀ ناسازگاری زمانی داده‌ها را حل می‌کند و استفادۀ بهینه از تمامی رکوردهای اطلاعاتی موجود را ممکن می‌سازد. این مطالعه بر اهمیت بُعد زمانی در مدل‌های توزیع گونه‌ای تأکید دارد و این رویکرد، به‌ویژه در مناطقی با تغییرات اقلیمی بیشتر، از اهمیت فراوانی برخوردار است و می‌تواند در راستای اهداف توسعۀ پایدار، حفاظت از تنوع زیستی و چارچوب جهانی KM-GBF به مدیران برای تصمیم‌گیری حفاظتی کمک کند.
 

کلیدواژه‌ها

عنوان مقاله [English]

Developing a Spatiotemporal Habitat Suitability Model to Assess Biodiversity Changes in Response to Climate Change

نویسندگان [English]

  • Elham Ebrahimi 1
  • Faraham Ahmadzadeh 2
  • Asghar Abdoli 3
  • Babak Naimi 4
1 PHD Student, Dep. of Biodiversity and Ecosystem Management, Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran
2 Associate Professor, Dep. of Biodiversity and Ecosystem Management, Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran
3 Professor, Dep. of Biodiversity and Ecosystem Management, Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran
4 Assistant Professor, Quantitative Biodiversity Dynamics, Dep. of Biology, University of Utrecht, The Netherlands
چکیده [English]

Introduction: Biodiversity loss is a global threat to humanity. To address these challenges, international environmental organizations have adopted specific strategic goals and plans within the Kunming-Montreal Global Biodiversity Framework to understand how biodiversity changes over time and to identify the factors influencing these changes. Biodiversity modeling tools, particularly habitat suitability models (species distribution models), play a critical role in this effort. Despite significant advances in modeling techniques and the growing availability of spatial data, current models face serious limitations in accurately predicting biodiversity status and changes, hindering the development of an effective framework for monitoring biodiversity over time. One of the most significant practical limitations of these models is the inconsistency of recorded data in terms of species presence frequency and spatial extent across different time periods. This inconsistency limits the development of spatiotemporal models necessary for understanding species distribution dynamics over time. The objective of this study is to propose a solution to overcome the inconsistencies in biodiversity data over time and to develop a computational process for spatiotemporal habitat suitability modeling. Subsequently, spatiotemporal models were employed to quantify changes in species distribution.
Materials and Methods: In this study, a spatiotemporal biodiversity model was developed using presence data of the Roan antelope (Hippotragus equinus). Long-term species presence data spanning from 1901 to 2020 were sourced from the global biodiversity database GBIF to develop the spatiotemporal models. Additionally, species time series data (abundance data) from the LPI and BioTime databases were used to validate the assessment of biodiversity changes. Climatic data were extracted from the CRU TS database, which was used to generate 19 annual environmental layers. After data cleaning and preparation, and selecting appropriate climatic variables by testing for multicollinearity, the time series data were integrated into a single data table or data pool. In this approach, species presence data for each year were linked to the corresponding climatic data for that year and location. To improve model efficiency and reduce uncertainty, 10 common machine learning algorithms were selected to calibrate the spatiotemporal models. After model validation, spatial distribution predictions for each year were obtained by combining predictions from different models using weighted averaging (ensemble), resulting in a 120-year time series of species distribution predictions. Next, the Sen’s slope estimator function was used to calculate the trend of habitat suitability changes over 120 years for each pixel.
Results and Discussion: The results of model validation demonstrated that all modeling approaches performed exceptionally well, with AUC values ranging from 0.926 to 0.996, indicating high predictive accuracy. Analysis of the biodiversity trend maps over time revealed a gradual decline in the probability of species presence in southern latitudes. In contrast, an increase in presence probability was observed in the central African belt, suggesting shifts in species distribution patterns. Further validation of the results was carried out using time series data on species distribution and abundance from BioTime and LPI sources. This validation showed that the model accurately matched real data in 88% and 84% of the cases where habitat suitability had decreased. These findings confirm the high accuracy of the model in predicting both species distribution and changes over time. This strong correlation between model predictions and actual data underscores the effectiveness of the proposed spatiotemporal models in capturing and reflecting real-world biodiversity trends.
Conclusion: The proposed solution in this study not only enables spatiotemporal modeling for analyzing species distribution patterns and their changes but also improves the accuracy of ecological niche quantification, enhancing spatial distribution predictions and reducing uncertainty in assessments. This approach addresses temporal data inconsistency challenges by increasing sample size and coverage, allowing optimal use of all available records. This study emphasizes the importance of the temporal dimension in species distribution models, particularly in regions with significant climatic changes, and can assist managers in making conservation decisions aligned with sustainable development goals, biodiversity conservation, and the KM-GBF global framework.

کلیدواژه‌ها [English]

  • Spatio-temporal species distribution model
  • Data inconsistency
  • Biodiversity changes
  • Long-term time series of climate data
  • Machine learning

مراجع

Abbaspour, M., Mahiny, A.S., Arjmandy, R. & Naimi, B., 2011, Integrated Approach for Land Use Suitability Analysis, International Agrophysics, 25(4). bwmeta1.element.agro-babd4e54-f64e-4413-9f46-3df1073f8d02.
Abrahms, B., Welch, H., Brodie, S., Jacox, M.G., Becker, E.A., Bograd, S.J., ... & Hazen, E.L., 2019, Dynamic Ensemble Models to Predict Distributions and Anthropogenic Risk Exposure for Highly Mobile Species, Diversity and Distributions, 25(8), PP. 1182-1193. https://doi.org/10.1111/ddi.12940.
Ahmadzadeh, F., Ebrahimi, E. & Naimi, B., 2017, Species Distribution Potential of Striped Hyaena (Hyaena Hyaena) in Response to Climate Change in Iran, Environmental Sciences, 15(4), PP. 215–232 (In Persian). https://envs.sbu.ac.ir/article_97888.html.
Ahmadzadeh, F., Amiri, N. & Ebrahimi, E., 2018, Spatial Modeling of Species Distribution and Predicting Potential Distribution of the Iranian Long-Legged Wood Frog, Iranian Journal of Remote Sensing & GIS, 10(2), PP. 95–108 (In Persian).
ـــــــــــــــــــــــــــــــــــــــــــــــــــــــــــ
[1]. Species-Environment Relationship Theory
 
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), PP. 541–545. https://doi.org/10.1111/ecog.01132.
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), PP. 1604–1619.
     https://doi.org/10.1111/jzs.12503.
Andrew, M.E. & Fox, E., 2020, Modelling Species Distributions in Dynamic Landscapes: The Importance of the Temporal Dimension, Journal of Biogeography, 47(7), 1510–1529. https://doi.org/10.1111/jbi.13832.
Araújo, M.B. & Naimi, B., 2020, Spread of SARS-CoV-2 Coronavirus likely Constrained by Climate, MedRxiv, 1–26. https://doi.org/10.1101/2020.03.12.20034728.
Araújo, M.B. & New, M., 2007, Ensemble Forecasting of Species Distributions, Trends in Ecology and Evolution, 22(1), PP. 42–47. https://doi.org/10.1016/j.tree.2006.09.010.
Araújo, M.B., Cabeza, M., Thuiller, W., Hannah, L. & Williams, P.H., 2004, Would Climate Change Drive Species out of Reserves? An Assessment of Existing Reserve-Selection Methods, Global Change Biology, 10(9), 1618–1626. https://doi.org/10.1111/j.1365-2486.2004.00828.x.
Araújo, M.B., Mestre, F. & Naimi, B., 2020, Ecological and Epidemiological Models are Both Useful for SARS-CoV-2, Nature Ecology & Evolution, 4(9), PP. 1153–1154. https://doi.org/10.1038/s41559-020-1246-y.
Bateman, B.L., VanDerWal, J. & Johnson, C.N., 2012, Nice Weather for Bettongs: Using Weather Events, not Climate Means, in Species Distribution Models, Ecography, 35(4), PP. 306–314. https://doi.org/10.1111/ j.1600-0587.2011.06871.x.
Beudels, R.C., Durant, S.M. & Harwood, J., 1992, Assessing the Risks of Extinction for Local Populations of Roan Antelope (Hippotragus Equinus), Biological Conservation, 61(2), PP. 107–116.
     https://doi.org/10.1016/0006-3207(92)91100-7.
Boali, A., Asgari, H.R., Mohammadian Behbahani, A., Salmanmahiny, A. & Naimi, B., 2024, Remotely Sensed Desertification Modeling Using Ensemble of Machine Learning Algorithms, Remote Sensing Applications: Society and Environment, 34. https://doi.org/10.1016/j.rsase.2024.101149.
Booth, T.H., Nix, H.A., Busby, J.R. & Hutchinson, M.F., 2014, Bioclim: The First Species Distribution Modelling Package, Its Early Applications and Relevance to Most Current MaxEnt Studies, Diversity and Distributions, 20(1), PP. 1–9. https://doi.org/10.1111/ddi.12144.
Breiman, L., 2001, Random Forests, Machine Learning, 45(1), PP. 5–32. https://doi.org/ 10.1023/A:1010933404324
Breiman, L., Friedman, J., Stone, C. & Olshen, R., 1984, Classification and Regression Trees, Routledge. https://doi.org/10.1201/ 9781315139470.
Brun, P., Karger, D.N., Zurell, D., Descombes, P., DeWitte, 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 2023.05.30.542843. https://doi.org/10.1101/2023.05.30.542843.
Brun, P., Karger, D.N., Zurell, D., Descombes, P., de Witte, L.C., de Lutio, R., Wegner, J.D. & Zimmermann, N.E., 2024, Multispecies Deep Learning Using Citizen Science Data Produces More Informative Plant Community Models, Nature Communications, 15(1), P. 4421. https://doi.org/10.1038/ s41467-024-48559-9.
Butchart, S.H., Walpole, M., Collen, B., Van Strien, A., Scharlemann, J.P., Almond, R.E., ... & Watson, R., 2010, Global Biodiversity: Indicators of Recent Declines, Science, 328(5982), PP. 1164–1168. https://doi.org/ 10.1126/science.1187512.
CBD; Secretariat of the Convention on Biological Diversity, 2020, Global Biodiversity Outlook 5, United Nations Convention on Biological Diversity, Montreal. https://www.cbd.int/gbo5.
Ceballos, G., Ehrlich, P.R. & Dirzo, R., 2017, Biological Annihilation via the Ongoing Sixth Mass Extinction Signaled by Vertebrate Population Losses and Declines, Proceedings of the National Academy of Sciences of the United States of America, 114(30), PP. E6089–E6096. https://doi.org/10.1073/pnas.1704949114.
Codron, D., Codron, J., Lee-Thorp, J.A., Sponheimer, M., Grant, C.C. & Brink, J.S., 2009, Stable Isotope Evidence for Nutritional Stress, Competition, and Loss of Functional Habitat as Factors Limiting Recovery of Rare Antelope in Southern Africa, Journal of Arid Environments, 73(4–5), PP. 449–457. https://doi.org/10.1016/ j.jaridenv.2008.12.003.
Dobson, R., Challinor, A.J., Cheke, R.A., Jennings, S., Willis, S.G. & Dallimer, M., 2023, Dynamic SDM: An R Package for Species Geographical Distribution and Abundance Modelling at High Spatiotemporal Resolution, Methods in Ecology and Evolution, 14(5), PP. 1190–1199. https://doi.org/10.1111/2041-210X.14101
Duan, R.Y., Kong, X.Q., Huang, M.Y., Fan, W.Y. & Wang, Z.G., 2014, The Predictive Performance and Stability of Six Species Distribution Models, PLoS ONE, 9(11), P. e112764. https://doi.org/10.1371/journal.pone.0112764.
Dunn, P.K. & Smyth, G.K., 2018, Generalized Linear Models with Examples in R, New York: Springer. https://doi.org/10.1007/978-1-4419-0118-7. ISBN 978-1-4419-0118-7.
East, R., 1999, African Antelope Database 1998. International Union for Conservation of Nature/Species Survival Commission Antelope Specialist Group, Gland, Switzerland.
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), PP. 29–39 (In Persian). https://doi.org/ 10.29252/nbr.9.1.29.
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, June. https://doi.org/10.1007/s42991-021-00136-z.
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(2), P. 100997. https://doi.org/10.1016/ j.ecocom.2022.100997.
Ebrahimi, E., Araújo, M.B. & Naimi, B., 2023, Flood Susceptibility Mapping to Improve Models of Species Distributions, Ecological Indicators, 157, P. 111250. https://doi.org/ 10.1016/j.ecolind.2023.111250.
Elith, J., Graham, C.H., Anderson, R.P., Dudík, M., Ferrier, S., Guisan, A., Hijmans, R.J., Huettmann, F., Leathwick, J.R., Lehmann, A., Li, J., Lohmann, L.G., Loiselle, B.A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J.McC.M., Townsend Peterson, A., … Zimmermann, N.E., 2006, Novel Methods Improve Prediction of Species’ Distributions from Occurrence Data, Ecography, 29(2), PP. 129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.x.
Elith, J., Leathwick, J.R. & Hastie, T., 2008, A Working Guide to Boosted Regression Trees, Journal of Animal Ecology, 77(4), PP. 802–813. https://doi.org/10.1111/j.1365-2656. 2008.01390.x.
Elith, J., Kearney, M. & Phillips, S., 2010, The Art of Modelling Range‐Shifting Species, Methods in Ecology and Evolution, 1(4), PP. 330-342. https://doi.org/10.1111/j.2041-210X. 2010.00036.x.
Fernandez, M., Yesson, C., Gannier, A., Miller, P.I. & Azevedo, J.M., 2017, The Importance of Temporal Resolution for Niche Modelling in Dynamic Marine Environments, Journal of Biogeography, 44(12), PP. 2816–2827. https://doi.org/ 10.1111/jbi.13080.
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), PP. 4302–4315. https://doi.org/10.1002/JOC. 5086.
Freeman, E.A. & Moisen, G.G., 2008, A Comparison of the Performance of Threshold Criteria for Binary Classification in Terms of Predicted Prevalence and Kappa, Ecological Modelling, 217(1–2), PP. 48–58. https://doi.org/10.1016/j.ecolmodel. 2008.05.015.
Friedman, J., 1991, Multivariate Adaptive Regression Splines, The Annals of Statistics, 19(1), PP. 1–67. https://doi.org/10.1214/aos/ 1176347963.
Ghane-Ameleh, S., Khosravi, M., Saberi-Pirooz, R., Ebrahimi, E., Aghbolaghi, M.A. & Ahmadzadeh, F., 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(May), P. e01839. https://doi.org/10.1016/j.gecco.2021.e01839.
Ghayoumi, R., Ebrahimi, E., Hosseini Tayefeh, F. & Keshtkar, M., 2019, Predicting the Effects Of Climate Change on the Distribution of Mangrove Forests in Iran Using the Maximum Entropy Model, Journal of RS and GIS for Natural Resources, 10(2), PP. 34–47. (In Persian). https://dorl.net/dor/http://dorl.net/dor/20.1001.1.26767082.1398.10.2.3.2.
Ghyoumi, R., Ebrahimi, E. & Mousavi, S.M., 2022, Dynamics of Mangrove Forest Distribution Changes in Iran, Journal of Water and Climate Change, 13(6), PP. 2479–2489. https://doi.org/10.2166/wcc.2022.069.
Giovanelli, J.G., de Siqueira, M.F., Haddad, C.F. & Alexandrino, J., 2010, Modeling a Spatially Restricted Distribution in the Neotropics: How the Size of Calibration Area Affects the Performance of Five presence-Only Methods, Ecological Modelling, 221(2), PP. 215–224. https://doi.org/ 10.1016/j.ecolmodel.2009.10.009.
Gonçalves, J., Alves, P., Pôças, I., Marcos, B., Sousa-Silva, R., Lomba, Â. & Honrado, J.P., 2016, Exploring the Spatiotemporal Dynamics of Habitat Suitability to Improve Conservation Management of a Vulnerable Plant Species, Biodiversity and Conservation, 25(14), PP. 2867–2888. https://doi.org/10.1007/s10531-016-1206-7
Grant, C.C., Davidson, T., Funston, P.J. & Pienaar, D.J., 2002, Challenges Faced in the Conservation of Rare Antelope: A Case Study on the Northern Basalt Plains of the Kruger National Park, Koedoe, 45(2), PP. 45–66. https://doi.org/10.4102/koedoe.v45i2.26.
Guisan, A. & Zimmermann, N.E., 2000, Predictive Habitat Distribution Models in Ecology, Ecological Modelling, 135(2–3), PP. 147–186. https://doi.org/10.1016/S0304-3800(00)00354-9.
Guisan, A., Tingley, R., Baumgartner, J.B., Naujokaitis-Lewis, I., Sutcliffe, P.R., Tulloch, A.I.T., Regan, T.J., Brotons, L., Mcdonald-Madden, E., Mantyka-Pringle, C., Martin, T.G., Rhodes, J.R., Maggini, R., Setterfield, S.A., Elith, J., Schwartz, M.W., Wintle, B.A., Broennimann, O., Austin, M., … & Buckley, Y.M., 2013, Predicting Species Distributions for Conservation Decisions, Ecology Letters, 16(12), PP. 1424–1435. https://doi.org/10.1111/ ele.12189.
Guo, Q., 2005, Development of Geospatial Techniques for Ecological Analysis: A Case Study of Sudden Oak Death in California, Doctoral Thesis, University of California, Berkeley.
Hao, Y. & Tian, C., 2019, A Novel Two-Stage Forecasting Model Based on Error Factor and Ensemble Method for Multi-Step Wind Power Forecasting, Applied Energy, 238, PP. 368–383. https://doi.org/10.1016/ j.apenergy.2019.01.063.
Harrington, R., Owen-Smith, N., Viljoen, P.C., Biggs, H.C., Mason, D.R. & Funston, P., 1999, Establishing the Causes of the Roan Antelope Decline in the Kruger National Park, South Africa, Biological Conservation, 90(1), PP. 69–78. https://doi.org/ 10.1016/S0006-3207(98)00120-7.
Hastie, T. & Tibshirani, R., 1990, Generalized Additive Models, Routledge. https://doi.org/10.1201/9780203753781.
Hastie, T., Tibshirani, R. & Buja, A., 1994, Flexible Discriminant Analysis by Optimal Scoring, Journal of the American Statistical Association, 89(428). https://doi.org/10.1080/ 01621459.1994.10476866.
Havemann, C.P., Retief, T.A., Tosh, C.A. & de Bruyn, P.J.N., 2016, Roan Antelope (Hippotragus Equinus) in Africa: A Review of Abundance, Threats and Ecology, Mammal Review, 46(2), PP. 144–158. https://doi.org/10.1111/mam.12061.
Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. & Jarvis, A., 2005, Very High Resolution Interpolated Climate Surfaces for Global Land Areas, International Journal of Climatology: A Journal of the Royal Meteorological Society, 25(15), PP. 1965–1978. https://doi.org/10.1002/joc.1276.
Hijmans, R.J., Phillips, S., Leathwick, J. & Elith, J., 2023, Package "dismo": Methods for Species Distribution Modeling, That Is, Predicting the Environmental Similarity of Any Site to That of the Locations of Known Occurrences of a Species. https://cran.r-project.org/web/packages/ dismo/.
Hosseini Tayefeh, F., Izadian, M., Ashoori, A., Jolaee, L. & Ebrahimi, E., 2021, Trends of Waterbirds Population Changes in Fars Province Wetlands 1988-2018, Environmental Sciences, 19(1), PP. 177–196. (In Persian). https://doi.org/10.52547/ envs.33047.
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), PP. 347–352. https://doi.org/10.1016/ j.chnaes.2020.06.001.
Jetz, W., McGeoch, M.A., Guralnick, R., Ferrier, S., Beck, J., Costello, M.J., Fernandez, M., Geller, G.N., Keil, P., Merow, C., Meyer, C., Muller-Karger, F.E., Pereira, H.M., Regan, E.C., Schmeller, D.S. & Turak, E., 2019, Essential Biodiversity Variables for Mapping and Monitoring Species Populations, Nature Ecology and Evolution, 3(4), PP. 539–551. https://doi.org/10.1038/ s41559-019-0826-1.
Kimanzi, J.K. & Wanyingi, J.N., 2014, The Declining Endangered Roan Antelope Population in Kenya: What Is the Way Forward?, Conference Papers in Science, 2014, PP. 1–6. https://doi.org/10.1155/2014/ 908628.
Kimathi, E., Tonnang, H.E., Subramanian, S., Cressman, K., Abdel-Rahman, E.M., Tesfayohannes, M., ... & Kelemu, S., 2020, Prediction of Breeding Regions for the Desert Locust Schistocerca Gregaria in East Africa, Scientific Reports, 10(1), P. 11937. https://doi.org/10.1038/s41598-020-68895-2.
Kissling, W.D., Ahumada, J.A., Bowser, A., Fernandez, M., Fernández, N., García, E.A., Guralnick, R.P., Isaac, N.J.B., Kelling, S., Los, W., McRae, L., Mihoub, J.B., Obst, M., Santamaria, M., Skidmore, A.K., Williams, K.J., Agosti, D., Amariles, D., Arvanitidis, C., … & Hardisty, A.R., 2018, Building Essential Biodiversity Variables (EBVs) of Species Distribution and Abundance at a Global Scale, Biological Reviews, 93(1), PP. 600–625. https://doi.org/10.1111/brv.12359.
Liu, Z., Peng, C., Work, T., Candau, J.-N., Desrochers, A. & Kneeshaw, D., 2018, Application of Machine-Learning Methods in Forest Ecology, Reviews, 26(4), PP. 339–350. https://doi.org/10.2307/90026557.
Martin, R.B., 2003, Species Report for Roan Hippotragus Equinus, Sable Hippotragus Niger and Tsessebe Damaliscus Lunatus, Ministry of Environment and Tourism, Windhoek, Namibia.
   http://www.rarespecies.org.na/library/speciesreports.htm.
McShea, W.J., 2014, What Are the Roles of Species Distribution Models in Conservation Planning?, Environmental Conservation, 41(2), PP. 93–96. https://doi.org/ 10.1017/S0376892913000581.
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, Ecological Informatics, 52, PP. 7–13. https://doi.org/ 10.1016/j.ecoinf.2019.04.003.
Naimi, B., 2022, Package "usdm": Uncertainty Analysis for Species Distribution Models, CRAN. https://cran.r-project.org/web/packages/usdm/.
Naimi, B. & Araújo, M.B., 2016, Sdm: A Reproducible and Extensible R Platform for Species Distribution Modelling, Ecography, 39(4), P. 368–375. https://doi.org/ 10.1111/ecog.01881.
Naimi, B. & Araujo, M.B., 2022, Package "sdm": Species Distribution Modelling, CRAN. https://doi.org/10.1111/ecog.01881.
Naimi, B., Hamm, N.A.S., Groen, T.A., Skidmore, A.K. & Toxopeus, A.G., 2014, Where is Positional Uncertainty a Problem for Species Distribution Modelling?, Ecography, 37(2), PP. 191–203. https://doi.org/ 10.1111/J.1600-0587.2013.00205.X.
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), PP. 5654–5666. https://doi.org/10.1111/gcb.16310.
Phillips, S.B., Aneja, V.P., Kang, D. & Arya, S.P., 2006, Maximum Entropy Modeling of Species Geographic Distributions, International Journal of Global Environmental Issues, 6(2–3), PP. 231–252. https://doi.org/ 10.1016/j.ecolmodel.2005.03.026.
Pichler, M. & Hartig, F., 2023, Machine Learning and Deep Learning—A Review for Ecologists, In Methods in Ecology and Evolution, 14(4), PP. 994–1016. British Ecological Society. https://doi.org/10.1111/ 2041-210X.14061
Pohlert, T., Pohlert, M.T. & Kendall, S., 2016, Package "Trend": Non-Parametric Trend Tests and Change-point Detection, CRAN. https://cran.r-project.org/web/packages/trend/.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N. & Prabhat, 2019, Deep Learning and Process Understanding for Data-Driven Earth System Science, Nature, 566(7743), PP. 195–204. https://doi.org/10.1038/s41586-019-0912-1.
Reiss, H., Cunze, S., König, K., Neumann, H. & Kröncke, I., 2011, Species Distribution Modelling of Marine Benthos: A North Sea Case Study, Marine Ecology Progress Series, 442, PP. 71–86. https://doi.org/10.3354/ meps09391.
Reside, A.E., Vanderwal, J.J., Kutt, A.S. & Perkins, G.C., 2010, Weather, Not Climate, Defines Distributions of Vagile Bird Species, PLoS ONE, 5(10). https://doi.org/ 10.1371/journal.pone.0013569.
Scherrer, D., D’Amen, M., Fernandes, R.F., Mateo, R.G. & Guisan, A., 2018, How to Best Threshold and Validate Stacked Species Assemblages? Community Optimisation Might Hold the Answer, Methods in Ecology and Evolution, 9(10), PP. 2155–2166. https://doi.org/10.1111/2041-210X.13041.
Schliep, E.M., Gelfand, A.E., Clark, J.S. & Kays, R., 2018, Joint Temporal Point Pattern Models for Proximate Species Occurrence in a Fixed Area Using Camera Trap Data, Journal of Agricultural, Biological, and Environmental Statistics, 23(3), PP. 334–357. https://doi.org/10.1007/s13253-018-0327-8.
Segurado, P. & Araújo, M.B., 2004, An Evaluation of Methods for Modelling Species Distributions, Journal of Biogeography, 31(10), PP. 1555–1568. https://doi.org/ 10.1111/j.1365-2699.2004.01076.x.
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), PP. 18–26. https://doi.org/10.1177/ 1758155920962750.
Smith, A.B. & Santos, M.J., 2020, Testing the Ability of Species Distribution Models to Infer Variable Importance, Ecography, 43(12), PP. 1801–1813. https://doi.org/10.1111/ ecog.05317.
Smith, R.L. & Lusseau, D., 2022, Modelling Habitat Suitability for a Potential Flagship Species, the Hooded Capuchin, of the Paraguayan Upper Paraná Atlantic Forest, Ecological Solutions and Evidence, 3(3), PP. 1–13. https://doi.org/10.1002/2688-8319.12146.
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), PP. 241–244. https://doi.org/ 10.1126/science.1257484.
Valkenborg, D., Rousseau, A.J., Geubbelmans, M. & Burzykowski, T., 2023, Support Vector Machines, American Journal of Orthodontics and Dentofacial Orthopedics, 164(5), PP. 754–757. https://doi.org/10.1016/ j.ajodo.2023.08.003.
Van Rooyen, J., 2009, Habitat and Seasonal Effects on the Nutrient Status of Selected Roan (Hippotragus Equinus) and Sable Antelope (Hippotragus Niger) Populations in South Africa, Master Thesis, University of Pretoria, (South Africa).
Villero, D., Pla, M., Camps, D., Ruiz-Olmo, J. & Brotons, L., 2017, Integrating Species Distribution Modelling into Decision-Making to Inform Conservation Actions, Biodiversity and Conservation, 26(2), PP. 251–271. https://doi.org/10.1007/s10531-016-1243-2.
Waltert, M., Meyer, B. & Kiffner, C., 2009, Habitat Availability, Hunting or Poaching: What Affects Distribution and Density of Large Mammals in Western Tanzanian Woodlands?, African Journal of Ecology, 47(4), PP. 737–746. https://doi.org/10.1111/ j.1365-2028.2009.01080.x.
Wieringa, J.G., Carstens, B.C. & Gibbs, H.L., 2021, Predicting Migration Routes for Three Species of Migratory Bats Using Species Distribution Models, PeerJ, 9, P. e11177. https://doi.org/10.1111/jav.01476.
Williams, H.M., Willemoes, M. & Thorup, K., 2017, A Temporally Explicit Species Distribution Model for a Long Distance Avian Migrant, the Common Cuckoo, Journal of Avian Biology, 48(12), PP. 1624–1636.
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), P. 744–751. https://doi.org/ 10.1111/2041-210X.13152.
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., Leitão, P.J., Park, D.S., Peterson, A.T., Rapacciuolo, G., Schmatz, D.R., Schröder, B., Serra-Diaz, J.M., Thuiller, W. & Merow, C., 2020, A Standard Protocol for Reporting Species Distribution Models, Ecography, 43(9), PP. 1261–1277. https://doi.org/10.1111/ecog.04960.
Zurell, D., Zimmermann, N.E. & Brun, P., 2024, The Niche through Time: Considering Phenology and Demographic Stages in Plant Distribution Models, Journal of Ecology. https://doi.org/10.1111/1365-2745.14361.
 
 

فایل ها

هم رسانی

ارجاع به این مقاله

آمار