Wintering habitat modelling for conservation of Eurasian vultures in northern India

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DOI:

https://doi.org/10.55779/ng2122

Keywords:

eco-zones, ensemble models, habitat suitability, species distribution modelling, vital parameters

Abstract

Eurasian Black Vulture (EBV) and Eurasian Griffon Vulture (EGV), while residents elsewhere, winter in Uttar Pradesh, India. Knowledge of the habitat and regulating factors is obligatory for protection and better management of these vultures. Therefore, different types of habitats were mapped using eight species distribution models. Presence records from field survey, published data and citizen science, and 23 bioenvironmental raster layers were the model inputs. Eighteen models were developed whose strength varied greatly. As per the performance indicators, GBM and GLM were found to be superior models for EGV. For EBV all models were acceptable. MARS, with good model strength, was rejected on the grounds of field verification. However, the Ensemble model, overall, was found the best. As per this model, good habitat was restricted mostly in the Tarai ecozone. The top two vital variables were NDVI, and bio13 for both the vultures. The most vital temperature variable for EGV was bio08 while bio09 for EBV. Tarai ecozone showed the largest expanse of suitable area for both the vultures followed by Vindhyan-Bundelkhand, Gangetic plains and Semi-arid ecozones. Among the two, EBV (49000 km2) had more suitable area than EGV (37000 km2). Agricultural areas were found to be largely unsuitable. As per land cover, good habitat was mostly confined in forests. For better management of these wintering vultures which need only roosting and foraging, it is proposed that destruction of forested habitat and decrease in foraging materials needed immediate attention and control.

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References

Abolmaali M-RS, Tarkesh M, Bashari H (2018). MaxEnt modeling for predicting suitable habitats and identifying the effects of climate change on a threatened species, Daphne mucronata in central Iran. Ecological Informatics 43:116-123. https://doi.org/10.1016/j.ecoinf.2017.10.002

Aguilar GD, Waqa-Sakiti H, Winder L (2016). Using predicted locations and an ensemble approach to address sparse data sets for species distribution modelling: Long-horned beetles (Cerambycidae) of the Fiji Islands. Unitec ePress. Retrieved from http://www.unitec.ac.nz/epress/

Allouche O, Tsoar A, Kadmon R (2006). Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology 43:1223-1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x

Anderson MG, Rhymer JM, Rohwer F (1992). Philopatry, dispersal, and the genetic structure of waterfowl populations. In: Batt BDJ, Afton AD, Anderson MG, Ankney CD, Johnson DH, Kadlec JA, Krapu GL (Eds). Ecology and management of breeding waterfowl. MN: University of Minnesota Press, Minneapolis pp 365-395.

Anoop NR, Babu S, Nagarajan R, Sen S (2020). Identifying suitable reintroduction sites for the White-rumped Vulture (Gyps bengalensis) in India’s Western Ghats using niche models and habitat requirements. Ecological Engineering 158:106034. https://doi.org/10.1016/j. ecoleng. 2020.106034

Araújo MB, New M (2007). Ensemble forecasting of species distributions. Trends in Ecology and Evolution 22:42-47. https://doi.org/10.1016/j.tree.2006.09.010

Bahadur KCK, Koju NP, Bhusal KP, Low M, Ghimire SK, Ranabhat R, Panthi S (2019). Factors influencing the presence of the endangered Egyptian vulture Neophron percnopterus in Rukum, Nepal. Global Ecology and Conservation 20:e00727. https://doi.org/10.1016/j.gecco. 2019. e00727

Banda LB, Tassie N (2018). Modeling the distribution of four-bird species under climate change in Ethiopia. Ethiopian Journal of Biological Science 17(1):1-17. https://www.ajol.info/index.php/ejbs/article/view/199170

BirdLife International (2021a). Aegypius monachus. The IUCN Red List of Threatened Species 2021:e.T22695231A154915043. Accessed on 24 January 2022. https://dx.doi.org/10.2305/IUCN.UK.20213.RLTS.T22695231A154915043.en

BirdLife International (2021b). Gyps fulvus. The IUCN Red List of Threatened Species 2021:e.T22695219A157719127. Accessed on 24 January 2022. https://dx.doi.org/10.2305/IUCN.UK.20213.RLTS.T22695219A157719127.en

Botha AJ, Andevski J, Bowden CG, Gudka M, Safford RJ, Tavares J, Williams NP (2017). Multi species action plan to conserve African-Eurasian vultures. Abu Dhabi, United Arab Emirates: CMS Raptors MOU Technical Publication No. 4. CMS Technical Series No. 33. Coordinating Unit of the CMS Raptors MOU.

Brown JL, Bennett JR, French CM (2017). SDM toolbox 2.0: the next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. PeerJ 5:e4095. https://doi.org/10.7717/peerj.4095

Buchhorn M, Smets B, Bertels L, De Roo B, Lesiv M, Tsendbazar N-E, … Fritz S (2020). Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe 2020. https://zenodo.org/record/3939050

Bucklin DN, Basille M, Benscoter AM, Brandt LA, Mazzotti FJ, Romanach SS, … Watling JI (2015). Comparing species distribution models constructed with different subsets of environmental predictors. Diversity and Distributions 21:23-35. https://doi.org/10.1111/ddi.12247

Campbell MO (2015). Vultures: Their Evolution, Ecology and Conservation. CRC Press.

Chalghaf B, Chemkhi J, Mayala B, Harrabi M, Benie GB, Michael E, Salah AB (2018). Ecological niche modelling predicting the potential distribution of Leishmania vectors in the Mediterranean basin: impact of climate change. Parasites and Vectors 11:461. https://doi.org/10.1186/s13071-018-3019-x.

Clausen KK, Madsen J, Cottaar F, Kuijken E, Verscheure C (2018). Highly dynamic wintering strategies in migratory geese: Coping with environmental change. Global Change Biology 24:3214-3225. https://doi.org/10.1111/gcb.14061

D’Addario M, Monroy-Vilchis O, Zarco-González MM, Santos-Fita D (2019). Potential distribution of Aquila chrysaetos in Mexico: Implications for conservation. Avian Biology Research 12:33-41. https://doi.org/10.1177/1758155918823424

De K, Ali SZ, Dasgpta N, Uniyal VP, Johnson JA, Hussain SA (2020). Evaluating performance of four species distribution models using Blue-tailed Green Darner Anax guttatus (Insecta: Odonata) as model organism from the Gangetic riparian zone. Journal of Threatened Taxa 12(14):16962-16970. https://doi.org/10.11609/jott.6106.12.14.16962-16970

D’Elia J, Haig SM, Johnson M, Marcot BG, Young R (2015). Activity-specific ecological niche models for planning reintroductions of California condors (Gymnogyps californianus). Biological Conservation 184:90-99. https://doi.org/10.1016/j.biocon.2015.01.002

Didan K (2015). MOD13A3 MODIS/Terra vegetation Indices Monthly L3 Global 1km SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC. Retrieved 2019 August 08 from doi:10.5067/MODIS/MOD13A3.006.

Dormann CF, Schweiger O, Arens P, Augenstein I, Aviron S, Bailey D, … Zobel M (2008). Prediction uncertainty of environmental change effects on temperate European biodiversity. Ecology Letters 11:235-244. https://doi.org/10.1111/j.1461-0248.2007.01142.x

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):e112764. https://doi.org/10.1371/journal.pone.0112764

Earth Resources Observation and Science (EROS) Center (2017). Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global [Data set]. U.S. Geological Survey. https://doi.org/10.5066/F7PR7TFT

Elith J, Burgman MA, Regan HM (2002). Mapping epistemic uncertainties and vague concepts in predictions of species distributions. Ecological Modelling 157:313-329. https://doi.org/10.1016/S0304-3800(02)00202-8

Farashi A, Alizadeh-Noughani M (2018). Niche modelling of the potential distribution of the Egyptian Vulture Neophron percnopterus during summer and winter in Iran, to identify gaps in protected area coverage. Bird Conservation International 29(3)423-436. https://doi.org/10.1017/S0959270918000278

Farrell A, Wang G, Rush SA, Martin JA, Belant JL, Butler AB, Godwin D (2019). Machine learning of large‐scale spatial distributions of wild turkeys with high‐dimensional environmental data. Ecology and Evolution 9:5938-5949. https://doi.org/10.1002/ece3.5177

Fick SE, Hijmans RJ (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

Fourcade Y, Engler J, Rödder D, Secondi J (2014). Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. Plos One 9(5):e97122. https://doi.org/10.1371/journal.pone.0097122

Fox AD, Mitchell C, Stewart A, Fletcher JD, Turner JV, Boyd H, … Tomlinson C (1994). Winter movements and site-fidelity of Pink-footed Geese Anser brachyrhynchus ringed in Britain, with particular emphasis on those marked in Lancashire. Bird Study 41:221-234. https://doi.org/10.1080/00063659409477222.

Früh L, Kampen H, Kerkow A, Schaub GA, Walther D, Wieland R (2018). Modelling the potential distribution of an invasive mosquito species: comparative evaluation of four machine learning methods and their combinations. Ecological Modelling 388:136-144. https://doi.org/10.1016/j.ecolmodel.2018.08.011.

FSI (2017). India State of Forest Report. Dehradun: Ministry of Environment Forests and Climate Change.

Grenouillet G, Buisson L, Casajus N, Lek S (2011). Ensemble modelling of species distribution: the effects of geographical and environmental ranges. Ecography 34:9-17. https://onlinelibrary.wiley.com/doi/epdf/10.1111/j.1600-0587.2010.06152.x

Gschweng M, Kalko EKV, Berthold P, Fiedler W, Fahr J (2012). Multi-temporal distribution modelling with satellite tracking data: predicting responses of a long distance migrant to changing environmental conditions. Journal of Applied Ecology 49:803-813. https://doi.org/10.1111/j.1365-2664.2012.02170.x

Guisan A, Thuiller W (2005). Predicting species distribution: offering more than simple habitat models. Ecology Letters 8:993-1009. https://onlinelibrary.wiley.com/doi/epdf/10.1111/j.1461-0248.2005.00792.x

Guisan A, Tingley R, Baumgartner JB, Naujokaitis-Lewis I, Sutcliffe PR, Tulloch AIT, … Martin TG (2013). Predicting species distributions for conservation decisions. Ecology Letters 16:1424-1435. https://doi.org/10.1111/ele.12189

Habibzadeh N, Ludwig T (2019). Ensemble of small models for estimating potential abundance of Caucasian grouse (Lyrurus mlokosiewiczi) in Iran. Ornis Fennica 96(2):77-89.

Heikkinen RK, Luoto M, Araújo MB, Virkkala R, Thuiller W, Sykes MT (2006). Methods and uncertainties in bioclimatic envelope modelling under climate change. Progress in Physical Geography 30(6):1-27. https://doi.org/10.1177/0309133306071957

Hirzel AH, Hausser J, Chessel D, Perrin N (2002). Ecological niche factor analysis: How to compute habitat suitability maps without absence data? Ecology 83:2027-2036. https://doi.org/10.1890/0012-9658(2002)083[2027:ENFAHT]2.0.CO;2

iNaturalist users, Ueda K (2020). iNaturalist Research-grade Observations. iNaturalist.org. Occurrence dataset. Retrieved 2020 October 23 from https://doi.org/10.15468/ab3s5x

India 2019 A Reference Annual (2019). Publications division, Ministry of Information and Broadcasting, Government of India.

Jha KK (2015). Distribution of vultures in Uttar Pradesh, India. Journal of Threatened Taxa 7(1):6750-6763. http://dx.doi.org/10.11609/JoTT.o3319.6750-63

Jha KK (2021). Geoinformatics, Climate Change, Habitat Dynamics and A Case of Vultures in Central India. In: Jha KK, Campbell MO (Eds). Critical Research Techniques in Animal and Habitat Ecology: Examples from India. Nova Science Publisher New York pp. 65-126.

Jha KK, Jha R (2020). Habitat suitability mapping for migratory and resident vultures: A case of Indian stronghold and species distribution model. Journal of Wildlife and Biodiversity 4(3):91-111. http://www.wildlife-biodiversity.com/article_39788.html

Jha KK, Campbell MO, Jha R (2020). Vultures, their population status and some ecological aspects in an Indian stronghold. Notulae Scientia Biologicae 12(1):124-142. https://doi.org/10.15835/nsb12110547

Jha R, Jha KK (2021a). Habitat prediction modelling for vulture conservation in Gangetic‑Thar‑Deccan region of India. Environmental Monitoring and Assessment 193(8):532. https://doi.org/10.1007/s10661-021-09323-4

Jha KK, Jha R (2021b). Study of vulture habitat suitability and impact of climate change in Central India using MaxEnt. Journal of Resources and Ecology 12(1):30-42. https://doi.org/10.5814/j.issn.1674-764x.2021.01.004

Jiménez-Valverde A (2014). Threshold-dependence as a desirable attribute for discrimination assessment: implications for the evaluation of species distribution models. Biodiversity and Conservation 23(2):369-385. https://doi.org/10.1007/s10531-013-0606-1

Kafash A, Kaboli M, Koehler G, Yousefi M, Asadi A (2016). Ensemble distribution modeling of the Mesopotamian spiny-tailed lizard, Saara loricate (Blanford, 1874), in Iran: an insight into the impact of climate change. Turkish Journal of Zoology 40:262-271. https://journals.tubitak.gov.tr/zoology/issues/zoo-16-40-2/zoo-40-2-15-1504-10.pdf

Kanth DR, Narendra MK, Krishna BG, Ravali B (2020). MAXENT Modeling and mapping of Passer domesticus species distribution in Guntur Region. Indian Journal of Ecology 47(11):64-70.

Kanaujia A, Kushwaha S (2014). Relationship between water bodies and vulture populations in Bundelkhand, India. Species 11(27):6-19.

Kaky E, Nolan V, Alatawi A, Gilbert F (2020). A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants. Ecological Informatics 60:101150. https://doi.org/10.1016/j.ecoinf.2020.101150.

Konowalik K, Nosol A (2021). Evaluation metrics and validation of presence‑only species distribution models based on distributional maps with varying coverage. Scientific Reports 11:1482. https://doi.org/10.1038/s41598-020-80062-1

Landis J, Koch G (1977). The measurement of observer agreement for categorical data. Biometrics 33:159-74.

Li X, Wang Y (2013). Applying various algorithms for species distribution modelling. Integrative Zoology 8(2):124-135. https://doi.org/10.1111/1749-4877.12000

Liminana R, Soutullo A, Arroyo B, Urios V (2012). Protected areas do not fulfil the wintering habitat needs of the trans-Saharan migratory Montagu’s harrier. Biological Conservation 145:62–69.

Malabet FM, Peacock H, Razafitsalama J, Birkinshaw C, Colquhoun I (2020). Realized distribution patterns of crowned lemurs (Eulemur coronatus) within a human‐dominated forest fragment in northern Madagascar. American Journal of Primatology 82(4):e23125. https://doi.org/10.1002/ajp.23125

Marmion M, Parviainen M, Luoto M, Heikkinen RK, Thuiller W (2009). Evaluation of consensus methods in predictive species distribution modelling. Diversity and Distribution 15:59-69. https://doi.org/10.1111/j.1472-4642.2008.00491.x

MoEFCC (2020). Action Plan for Vulture Conservation in India, 2020‐2025. Ministry of Environment, Forest and Climate Change, Government of India, New Delhi.

Ng W-T, Silva ACO, Rima P, Atzberger C, Immitzer M (2018) Ensemble approach for potential habitat mapping of invasive Prosopis spp. in Turkana, Kenya. Ecology and Evolution 8(23):11921-11931. https://doi.org/10.1002/ece3.4649

Nursamsi I, Partasasmita R, Cundaningsih N, Ramadhani HS (2018). Modeling the predicted suitable habitat distribution of Javan hawk eagle Nisaetus bartelsi in the Java Island, Indonesia. Biodiversitas Journal of Biological Diversity 19(4):1539-1551. https://doi.org/10.13057/biodiv/d190447

Passadore C, Möller LM, Diaz-Aguirre F, Parra GJ (2018). Modelling dolphin distribution to inform future spatial conservation decisions in a marine protected area. Scientific Reports 8:15659. https://doi.org/10.1038/s41598-018-34095-2

Phipps WL, Diekmann M, MacTavish LM, Mendelsohn JM, Naidoo V, Wolter K, Yarnell RW (2017). Due South: A first assessment of the potential impacts of climate change on Cape vulture occurrence. Biological Conservation 210:16-25. https://doi.org/10.1016/j.biocon.2017.03.028

Prakash V, Pain DJ, Cunningham AA, Donald PF, Prakash N, Verma A, … Rahmani AR (2003). Catastrophic collapse of Indian white-backed Gyps bengalensis and long-billed Gyps indicus vulture populations. Biological Conservation 109:381-390.

Raina N, Khuman YSC, Rao KS, Sreekesh S (2014). Comparative analysis of species distribution modeling of Daphne papyracea in Dabka watershed Nainital district, Uttarakhand. Journal of Environment and Earth Science 4(17):9-29. https://www.researchgate.net/publication/266561109.

Rew J, Cho Y, Moon J, Hwang E (2020). Habitat suitability estimation using a two-stage ensemble approach. Remote Sensing 12:1475. https://doi.org/10.3390/rs12091475

Robertson G, Cooke F (1999). Winter philopatry in migratory waterfowl. Auk 116:20-34. https://doi.org/10.2307/4089450.

Rodriguez JP, Brotons L, Bustamante J, Seoane J (2007). The application of predictive modelling of species distribution to biodiversity conservation. Diversity and Distributions 13(3):243-251. https://doi.org/10.1111/j.1472-4642.2007.00356.x

Santangeli A, Spiegel O, Bridgeford P, Girardello M (2018). Synergistic effect of land-use and vegetation greenness on vulture nestling body condition in arid ecosystems. Scientific Reports 8(1):13027. https://doi.org/10.1038/s41598-018-31344-2

Schmitt S, Pouteau R, Justeau D, de Boisseu F, Birnbaum P (2017). SSDM: An R package to predict distribution of species richness and composition based on stacked species distribution models. Methods in Ecology and Evolution 8:1795-1803. https://doi.org/10.1111/2041-210X.12841

Shabani F, Kumar L, al Shidi RH (2018). Impacts of climate change on infestations of Dubas bug (Ommatissus lybicus Bergevin) on date palms in Oman. PeerJ 6:e5545. https://doi.org/10.7717/peerj.5545

Stockwell D, Peterson A (2002). Effects of sample size on accuracy of species distribution models. Ecological Modelling 148:1-13.

Stohlgren TJ, Ma P, Kumar S, Rocca M, Morisette JT, Jarnevich CS, Benson N (2010). Ensemble habitat mapping of invasive plant species. Risk Analysis 30(2):224-235. https://doi.org/10.1111/j.1539-6924.2009.01343.x

Sullivan BL, Wood CL, Iliff MJ, Bonney RE, Fink D, Kelling S (2009). eBird: a citizen-based bird observation network in the biological sciences. Biological Conservation 142:12282-2292. https://doi.org/10.1016/j.biocon.2009.05.006

Swets K (1988). Measuring the accuracy of diagnostic systems. Science 240:1285-1293. https://www.science.org/doi/10.1126/science.3287615

Thuiller W (2003). BIOMOD – optimizing predictions of species distributions and projecting potential future shifts under global change. Global Change Biology 9:1353-1362. https://doi.org/10.1046/j.1365-2486.2003.00666.x

Thuiller W, Münkemüller T (2010). Habitat Suitability Modelling. In: Møller AP, Fiedler W, Berthold P (Eds). Effect of Climate Change on Birds. Oxford University Press Oxford pp. 77-87.

UPFD and BNHS (2021). Determination of the status and distribution of vultures in Uttar Pradesh. Lucknow, Mumbai: Uttar Pradesh Forest Department, Bombay Natural History Society.

Yong DL, Heim W, Chowdhury SU, Choi C-Y, Ktitorov P, Kulikova O, … Szabo JK (2021). The state of migratory land birds in the East Asian flyway: Distributions, threats, and conservation needs. Frontiers in Ecology and Evolution 9:613172. https://doi.org/10.3389/fevo.2021.613172

Zhang J, Jiang F, Li G, Qin W, Li S, Gao H, … Zhang T (2019). MaxEnt modeling for predicting the spatial distribution of three raptors in the Sanjiangyuan National Park, China. Ecology and Evolution 9:6643-6654. https://doi.org/10.1002/ece3.5243

Zhang J, Li S (2017). A review of machine learning based species’ distribution modelling. International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration, pp. 119-206. https://ieeexplore.ieee.org/document/8328619

Zhang P, Dong X, Grenouillet G, Lek S, Zheng Y, Chang J (2020). Species range shifts in response to climate change and human pressure for the world’s largest amphibian. Science of the Total Environment 735(2):139543. https://doi.org/10.1016/j.scitotenv.2020.139543

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2022-03-05

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JHA, R., KANAUJIA, A., & JHA, K. K. (2022). Wintering habitat modelling for conservation of Eurasian vultures in northern India. Nova Geodesia, 2(1), 22. https://doi.org/10.55779/ng2122

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