资源与生态学报 ›› 2021, Vol. 12 ›› Issue (1): 30-42.DOI: 10.5814/j.issn.1674-764x.2021.01.004
Kaushalendra K. JHA1,*(), Radhika JHA2
收稿日期:
2020-06-16
接受日期:
2020-08-15
出版日期:
2021-01-30
发布日期:
2021-03-30
通讯作者:
Kaushalendra K. JHA
Kaushalendra K. JHA1,*(), Radhika JHA2
Received:
2020-06-16
Accepted:
2020-08-15
Online:
2021-01-30
Published:
2021-03-30
Contact:
Kaushalendra K. JHA
摘要:
秃鹫提供了宝贵的生态系统服务,在生态系统平衡中发挥着重要作用,但印度本土秃鹫数量在过去几年有所下降。掌握秃鹫栖息地的分布现状对于管理和防止秃鹫数量继续下降至关重要。可以预见,目前的气候危机可能会进一步导致秃鹫生境适宜性的变化,并影响现存的秃鹫种群。因此,本研究利用物种分布模型,对印度中部一个秃鹫栖息地的短期和长期变化进行预测,并以统计和图形的方式呈现数据。选择MaxEnt软件进行预测,是因为它与其他模型相比具有一定的优势,如只使用现有数据,在数据不完整、样本量小、样本间隙小等情况下表现良好。采用全球气候模式集成学习算法(CCSM4、HadGEM2AO和MIROC5)以获得更好的预测结果。14个稳健模型(AUC 0.864-0.892)是利用7个秃鹫种群(长喙、白臀、红头、银灰色、埃及秃鹫、喜马拉雅和欧亚狮鹫)在两个季节共1000多个地点的数据建立的。选定的气候(温度和降水)和环境变量(NDVI、海拔和土地利用/土地覆盖)被用于预测当前栖息地,未来的预测只基于气候变量。影响秃鹫栖息地分布的最重要变量是降水量(bio 15,bio 18, bio19)和温度(bio 3,bio 5)。在目前的预测中,森林和水体是影响土地利用的主要因素。在较小尺度上,随着时间的推移,极端适宜的栖息地面积减少,高度适宜的栖息地面积增加,总适宜栖息地面积在2050年略有增加,但到2070年有所减少。在更大的尺度上考虑,2050年适宜栖息地的净损失为5%,2070年为7.17% (RCP4.5)。相似的, 在RCP8.5下,2050年适宜栖息地的净损失为6%,2070年为7.3%。 研究结果可用于制定秃鹫的保护规划和管理,从而保护其免受未来的气候变化等威胁。
Kaushalendra K. JHA, Radhika JHA. 利用MaxEnt研究印度中部秃鹫栖息地适宜性及气候变化的影响[J]. 资源与生态学报, 2021, 12(1): 30-42.
Kaushalendra K. JHA, Radhika JHA. Study of Vulture Habitat Suitability and Impact of Climate Change in Central India Using MaxEnt[J]. Journal of Resources and Ecology, 2021, 12(1): 30-42.
Fig. 1 Location of vulture species in Central India surveyed in 2016 Note: This presence only record has been used in Species Distribution Model, MaxEnt, as sample input. Proximity of vulture locations may be noted to forested landscape in most cases.
Bio-climatic | Environmental | |
---|---|---|
Temperature | Precipitation | |
(i) bio 3 = Isothermality (bio 2/bio 7) ×100 | (v) bio 13 = Precipitation of wettest month | (x) LULC (Forest, water, rural and urban built-up area, agriculture, wasteland, scrubland) |
(ii) bio 5 = Max temperature of warmest month | (vi) bio 14 = Precipitation of driest month | (xi ) NDVI (January 2016) (xii) NDVI (May 2016) |
(iii) bio 9 = Mean temperature of driest quarter | (vii) bio 15 = Precipitation seasonality (coefficient of variation) | (xiii) Elevation |
(iv) bio 11 = Mean temperature of coldest quarter | (viii) bio 18 = Precipitation of warmest quarter | |
(ix) bio 19 = Precipitation of coldest quarter |
Table 1 Selected variables used in vulture habitat prediction model
Bio-climatic | Environmental | |
---|---|---|
Temperature | Precipitation | |
(i) bio 3 = Isothermality (bio 2/bio 7) ×100 | (v) bio 13 = Precipitation of wettest month | (x) LULC (Forest, water, rural and urban built-up area, agriculture, wasteland, scrubland) |
(ii) bio 5 = Max temperature of warmest month | (vi) bio 14 = Precipitation of driest month | (xi ) NDVI (January 2016) (xii) NDVI (May 2016) |
(iii) bio 9 = Mean temperature of driest quarter | (vii) bio 15 = Precipitation seasonality (coefficient of variation) | (xiii) Elevation |
(iv) bio 11 = Mean temperature of coldest quarter | (viii) bio 18 = Precipitation of warmest quarter | |
(ix) bio 19 = Precipitation of coldest quarter |
Fig. 2 Flow chart of Species Distribution Modelling using MaxEnt software Note: EV I to EV IV are environmental variables (Table 1); N indicates any number of EV. Flow is top to bottom along connectors.
Fig. 3 Jackknife charts of variable contribution in model prediction Note: The bars in light blue represent without variable; the bars in dark blue represent with only variable; the bars in red represent with all variables.
Fig. 4 The expanse of habitat suitability classes under different scenarios Note: Top two maps compare impact of LULC inclusion in terms of reduced suitable area in the top right map.
Habitat suitability category | Current (2016) | Short term (2050) | Long term (2070) | |||
---|---|---|---|---|---|---|
Environmental | Climatic | Climatic | Climatic | |||
RCP4.5 | RCP8.5 | RCP4.5 | RCP8.5 | |||
Least suitable | 242265 (79.3) | 189013 (61.1) | 187442 (60.6) | 188833 (61.0) | 191174 (61.8) | 190816 (61.7) |
Slightly suitable | 27733 (9.1) | 78313 (25.3) | 79883 (25.8) | 79138 (25.6) | 76827 (24.8) | 78066 (25.2) |
Moderately suitable | 28089 (9.2) | 33930 (11.0) | 33347 (10.8) | 33330 (10.8) | 32513 (10.5) | 31633 (10.2) |
Highly suitable | 7310 (2.4) | 7336 (2.4) | 8020 (2.6) | 7357 (2.4) | 8139 (2.6) | 8118 (2.6) |
Extremely suitable | 217 (0.1) | 829 (0.3) | 729 (0.2) | 764 (0.2) | 769 (0.2) | 788 (0.3) |
Table 2 Habitat suitability area (km2; average of three GCMs) distribution among different categories under varied scenario
Habitat suitability category | Current (2016) | Short term (2050) | Long term (2070) | |||
---|---|---|---|---|---|---|
Environmental | Climatic | Climatic | Climatic | |||
RCP4.5 | RCP8.5 | RCP4.5 | RCP8.5 | |||
Least suitable | 242265 (79.3) | 189013 (61.1) | 187442 (60.6) | 188833 (61.0) | 191174 (61.8) | 190816 (61.7) |
Slightly suitable | 27733 (9.1) | 78313 (25.3) | 79883 (25.8) | 79138 (25.6) | 76827 (24.8) | 78066 (25.2) |
Moderately suitable | 28089 (9.2) | 33930 (11.0) | 33347 (10.8) | 33330 (10.8) | 32513 (10.5) | 31633 (10.2) |
Highly suitable | 7310 (2.4) | 7336 (2.4) | 8020 (2.6) | 7357 (2.4) | 8139 (2.6) | 8118 (2.6) |
Extremely suitable | 217 (0.1) | 829 (0.3) | 729 (0.2) | 764 (0.2) | 769 (0.2) | 788 (0.3) |
Category | Bioclimatic | RCP4.5 | RCP8.5 | ||
---|---|---|---|---|---|
2016 | 2050 | 2070 | 2050 | 2070 | |
Low habitat suitability | 266641 | 264391 | 264294 | 264599 | 265111 |
Gain | 0 | 2250 | 2347 | 2042 | 1530 |
Loss | 0 | 2249 | 3020 | 2685 | 3082 |
High habitat suitability | 41987 | 39738 | 38967 | 39302 | 38905 |
Table 3 Data representing the expanse of habitat suitability and possible future change in km2
Category | Bioclimatic | RCP4.5 | RCP8.5 | ||
---|---|---|---|---|---|
2016 | 2050 | 2070 | 2050 | 2070 | |
Low habitat suitability | 266641 | 264391 | 264294 | 264599 | 265111 |
Gain | 0 | 2250 | 2347 | 2042 | 1530 |
Loss | 0 | 2249 | 3020 | 2685 | 3082 |
High habitat suitability | 41987 | 39738 | 38967 | 39302 | 38905 |
Fig. 5 The changes in vulture habitat area suitability from low to high and vice versa in short term (2050) and long term (2070) with respect to present (2016)
[1] |
Abdelaal M, Fois M, Fenu G, et al. 2019. Using MaxEnt modeling to predict the potential distribution of the endemic plant Rosa arabica Crép. in Egypt. Ecological Informatics, 50:68-75.
DOI URL |
[2] | Abolmaali M R S, 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. |
[3] |
Ahmad R, Khuroo A A, Hamid M, et al. 2019. Predicting invasion potential and niche dynamics of Parthenium hysterophorus (Congress grass) in India under projected climate change. Biodiversity and Conservation, 28:2319-2344.
DOI URL |
[4] | Allen C D. 2009. Climate-induced forest dieback: An escalating global phenomenon? Unasylva, 60:43-49. |
[5] | Ali S, Ripley R. 1987. Compact handbook if the birds of India and Pakistan. Delhi, India: Oxford University Press. |
[6] |
Angelieri C C, Adams-Hosking C, Ferraz K M, et al. 2016. Using species distribution models to predict potential landscape restoration effects on Puma conservation. Plos One, 11(1):e0145232. DOI. 10.1371/journal.p one.0145232.
URL PMID |
[7] | Anon. 2004. Report of the international south Asian vulture recovery plan workshop. Parwanoo, India. |
[8] | Anon. 2006. Action plan for vulture conservation in India. Ministry of Environment and Forests, Government of India. |
[9] | Aragon P, Lobo J M, Olalla-Tarraga M A, et al. 2010. The contribution of contemporary climate to ectothermic and endothermic vertebrate distributions in a glacial refuge. Global Ecology and Biogeography, 19: 40-49. |
[10] |
Araújo M B, Pearson R G, Thuiller W, et al. 2005. Validation of species-climate impact models under climate change. Global Change Biology, 11: 1504-1513.
DOI URL |
[11] |
Ashraf U, Ali H, Chaudry M N, et al. 2016. Predicting the potential distribution of Olea ferruginea in Pakistan incorporating climate change by using MaxEnt model. Sustainability, 8:722. DOI: 10.3390/su8080722.
DOI URL |
[12] |
Atzeni L, Cushman S A, Bai D, et al. 2020. Meta-replication, sampling bias, and multi-scale model selection: A case study on snow leopard (Panthera uncia) in western China. Ecology and Evolution, 10:7686-7712.
DOI URL PMID |
[13] |
Baldwin R A. 2009. Use of maximum entropy modelling in wildlife research. Entropy, 11(11):854-866.
DOI URL |
[14] | Bamford A J, Monadjem A, Hardy I W. 2009. Nesting habitat preference of the African white backed vulture Gyps africanus and the effects of anthropogenic disturbance. Ibis, 151(151):51-62. |
[15] |
Banag C, Thrippleton T, Alejandro G J, et al. 2015. Bioclimatic niches of selected endemic Ixora species on the Philippines: Predicting habitat suitability due to climate change. Plant Ecology, 216:1325-1340.
DOI URL |
[16] |
Bosch J, Mardones F, Pérez A, et al. 2012. A maximum entropy model for predicting wild boar distribution in Spain. Spanish Journal of Agricultural Research, 12(12):984-999.
DOI URL |
[17] |
Boshoff A F, Vernon C J. 1980. The past and present distribution of Cape vulture in the Cape Province. Ostrich, 51:230-250.
DOI URL |
[18] | Botella C, Joly A, Bonnet P, et al. 2018. A deep learning approach to species distribution modelling. In: Joly A, Vrochidis S, Karatzas K, et al. (eds.). Multimedia tools and applications for environmental & biodiversity informatics. New York, USA: Springer, 169-199. |
[19] | Campbell M. 2015. Vultures: Their evolution, ecology and conservation. London and New York: CRC Press, Taylor and Francis Group. |
[20] |
Cao B, Bai C, Zhang L, et al. 2016. Modeling habitat distribution of Cornus officinalis with Maxent modeling and fuzzy logics in China. Journal of Plant Ecology, 9(6):742-751.
DOI URL |
[21] | Cavaliere C. 2009. The effects of climate change on medicinal and aromatic plants. Herbal Gram (American Botanical Council), 81:44-57. |
[22] |
Chomba C, M’Simuko E. 2013. Nesting pattern of raptors: White backed vulture (Gyps africanus) and African fish eagle (Haliaeetus vocifer) in Lochinvar National Park on the kafue flats Zambia. Open Journal of Ecology, 3(3):325-330.
DOI URL |
[23] | D’Addario M, Monroy-Vilchis O, Zarco-González M M, et al. 2019. Potential distribution of Aquila chrysaetos in Mexico: Implications for conservation. Avian Biology Research, 12(1):1-9. |
[24] |
de Frutos A, Olea P P, Vera R. 2007. Analyzing and modelling spatial distribution of summering lesser kestrel: The role of spatial autocorrelation. Ecological Modelling, 200(200):33-44.
DOI URL |
[25] |
Dormann C F, Calabrese J M, Guillera-Arroita G, et al. 2018. Model averaging in ecology: A review of Bayesian, information-theoretic, and tactical approaches for predictive inference. Ecological Monographs, 88(88):1-20.
DOI URL |
[26] |
Elith J, Graham C H, Anderson R P, et al. 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29:129-151.
DOI URL |
[27] |
Elith J, Leathwick J R. 2009. Species distribution models: Ecological explanation and prediction across space and time. Annual Review of Ecology and Evolution Systematics, 40:677-697.
DOI URL |
[28] |
Elith J, Phillips S J, Hastie T, et al. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 1:43-57.
DOI URL |
[29] | EPCO-Report MP. 2017. Climate change vulnerability assessment for Madhya Pradesh. Technical report on climate change impacts on select sectors (water, forestry, agriculture and health). Government of Madhya Pradesh, Department of Environment, Bhopal. http://www.climateac tions.in/UploadedDocuments/ResourceFiles/16123300.pdf. |
[30] |
Gama M, Crespo D, Dolbeth M, et al. 2015. Predicting global habitat suitability for Corbicula fluminea using species distribution models: The importance of different environmental datasets. Ecological Modelling, 319:163-169.
DOI URL |
[31] |
Genero F, Franchini M, Fanin Y, et al. 2020. Spatial ecology of non-breeding Eurasian Griffon vultures Gyps fulvus in relation to natural and artificial food availability. Bird Study, 67(67):53-70.
DOI URL |
[32] | Groff L A, Marks S B, Hayes M P. 2014. Using ecological niche models to direct rare amphibian surveys: A case study using the oregon spotted frog (Rana pretiosa). Herpetological Conservation and Biology, 9(2):354-368. |
[33] |
Guisan A, Thuiller W. 2005. Predicting species distribution: Offering more than simple habitat models. Ecology Letters, 8:993-1009.
DOI URL |
[34] | Habibzadeh N, Ludwig T. 2019. Ensemble of small models for estimating potential abundance of Caucasian grouse (Lyrurus mlokosiewiczi) in Iran. Ornis Fennica, 96:77-79. |
[35] |
Hayes M A, Piaggio A J. 2018. Assessing the potential impacts of a changing climate on the distribution of a rabies virus vector. Plos One, 13(2):e0192887. DOI: 10.1371/journal.pone.0192887.
DOI URL PMID |
[36] |
Henriques M, Granadeiro J P, Monteiro H, et al. 2018. Not in wilderness: African vulture strongholds remain in areas with high human densities. Plos One, 13(1):e0190594. DOI: 10.1371/journal.pone.0190594.
DOI URL PMID |
[37] |
Hernandez P A, Graham C H, Master L L, et al. 2006. The effect of sample size and species characteristics on performance of different species distribution modelling methods. Ecography, 29:773-785.
DOI URL |
[38] |
Hiraldo F, Blanco J C, Bustamante J. 1991. Unspecialised exploitation of small carcasses by birds. Bird Study, 38(38):200-207.
DOI URL |
[39] |
Holland A E, Byrne M E, Hepinstall-Cymerman J, et al. 2019. Evidence of niche differentiation for two sympatric vulture species in the Southeastern United States. Movement Ecology, 7:31. DOI: 10.1186/s40462-019 -0179-z.
DOI URL PMID |
[40] | Howard A M, Bernardes S, Nibbelink N, et al. 2012. A maximum entropy model of the bearded capuchin monkey habitat incorporating topography and spectral unmixing analysis. ISPRS annals of the photogrammetry, remote sensing and spatial information sciences, Volume I-2, 2012. XXII ISPRS Congress, 25 August-1 September 2012, Melbourne, Australia. |
[41] |
Jafari A, Mirzaei R, Zamani-Ahmadmahmoodi R. 2016. Species distribution modeling of wild sheep based on improving bias of occurrence records and selecting appropriate environmental predictors using MaxEnt. Iranian Journal of Applied Ecology, 5(5):39-49.
DOI URL |
[42] | Jha K K. 2015. Distribution of vultures in Uttar Pradesh, India. Journal of Threatened Taxa, 7(7):6750-6763. |
[43] | Jha K K. 2017. Vulture atlas of central India—MP. Bhopal, India: Indian Institute of Forest Management, Bhopal. |
[44] | Jha K K. 2018. Mapping and management of vultures in an Indian stronghold. In: Campbell M O (ed.). Geomatics and conservation biology. New York, USA: Nova Science Publishers, 45-75. |
[45] | Jha K K, Campbell M O, Jha R. 2020. Vultures, their population status and some ecological aspects in an Indian stronghold. Notulae Scientia Biologicae, 12(12):124-142. |
[46] | Jha K K, 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(4):91-111. |
[47] |
Jiao S, Zeng Q, Sun G, et al. 2016. Improving conservation of cranes by modeling potential wintering distributions in China. Journal of Resources and Ecology, 7(1):44-50.
DOI URL |
[48] |
Joshi M K, Chalise M K, Chaudhry A, et al. 2015. Himalayan vultures in Khopde, far west Nepal: Is there any threat? Journal of Threatened Taxa, 7(7):8128-8133.
DOI URL |
[49] | Kambale A A. 2011. A study on breeding behaviour of oriental white backed vulture (Gyps bengalensis) in Anjarle and Deobag, Maharashtra. Wildlife Institute of India, Dehradun. |
[50] | Khatri P C. 2013. Home range use of migratory vultures in and around Jorbeer, Bikner (Rajasthan) India. Bioscience Discovery, 4(4):96-99. |
[51] |
Kumar P. 2012. Assessment of impact of climate change on Rhododendrons in Sikkim Himalayas using MaxEnt modelling: Limitations and challenges. Biodiversity and Conservation, 21:1251-1266.
DOI URL |
[52] | Kumar S, Stohlgren T J. 2009. MaxEnt modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia. Journal of Ecology and Natural Environment, 1(1):94-98. |
[53] | Kushwaha S. 2016. Utilisation of green plant material in nests of Long-billed Vultures Gyps indicus in Bundelkhand Region, India. Vulture News, 7-21. |
[54] | Lane J E. 2018. Climate crisis and the “We”: An essay in deconstruction. International Journal of Managerial Studies and Research, 6(6):34-43. |
[55] | Liberatori F, Penteriani V. 2001. A long-term analysis of the declining population of the Egyptian vulture in the Italian peninsula: Distribution, habitat preference, productivity and conservation implications. Biological Conservation, 101(3):381-389. |
[56] | Liu L, Zhao Z, Zhang Y, et al. 2017. Using MaxEnt model to predict suitable habitat changes for key protected species in Koshi Basin, Central Himalayas. Journal of Resource Ecology, 8(8):77-87. |
[57] | Luoto M, Heikkinen R K, Pöyry J, et al. 2006. Determinants of the biogeographical distribution of butterflies in boreal regions. Journal of Biogeography, 33:1764-1778. |
[58] | Lv W, Li Z, Wu X, et al. 2012. Maximum entropy based niche modelling (MaxEnt) of potential geographical distributions of Lobesia botrana (Lepidoptera: Tortricidae) in China. International Conference on Computer and Computing Technologies in Agriculture, 239-246. |
[59] | Manning M R, Edmonds J, Emori S, et al. 2010. Misrepresentation of the IPCC CO2 emission scenarios. Nature Geoscience, 3:376-377. |
[60] | Markandya A, Taylor T, Longo A, et al. 2013. Counting the cost of vulture declines—Economic appraisal of the benefits of Gyps Vulture in India. Internet material, accessed on 08 February 2020. https://pdfs.semanticscholar.org/82d6/2781f86d962b294c5f524fd4f65936db28f4.pdf. |
[61] | Marshal J P, Bleich V C, Andrew N G. 2008. Evidence for interspecific competition between feral ass Equus asinus and mountain sheep Ovis canadensis in a desert environment. Wildlife Biology, 14:228-236. |
[62] |
Mateo-Tomas P, Olea P P. 2009. Combining scales in habitat models to improve conservation planning in an endangered vulture. Acta Oecologica, 35:489-498.
DOI URL |
[63] | McClure C W, Westrip J S, Johnson J A, et al. 2018. State of the world’s raptors: Distributions, threats, and conservation recommendations. Biological Conservation, 227:390-402. |
[64] |
McDonald M M, Johnson S M, Henry E R, et al. 2019. Differences between ecological niches in northern and southern populations of Angolan black and white colobus monkeys (Colobus angolensis palliatus and Colobus angolensis sharpei) throughout Kenya and Tanzania. American Journal of Primatology, 81(6):e22975. DOI: 10.1002/ajp.22975.
DOI URL PMID |
[65] | Meinshausen M, Smith S J, Calvin K, et al. 2011. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change, 109(1-2):213-241. |
[66] | Mohammadi S, Ebrahimi E, Shahriari M M, et al. 2019. Modelling current and future potential distributions of two desert jerboas under climate change in Iran. Ecological Informatics, 52:7-13. |
[67] |
Monteith K L, Klaver R W, Hersey K R, et al. 2015. Effects of climate and plant phenology on recruitment of moose at the southern extent of their range. Oecologia, 178:1137-1148.
DOI URL PMID |
[68] |
Morales N, Fernández I C, Baca-González V. 2017. MaxEnt’s parameter configuration and small samples: Are we paying attention to recommendations? A systematic review. Peer J, 5:e3093. DOI: 10.7717/peerj.3093.
DOI URL PMID |
[69] |
Mwakapeje E R, Ndimuligo S A, Mosomtai G, et al. 2019. Ecological niche modeling as a tool for prediction of the potential geographic distribution of Bacillus anthracis spores in Tanzania. International Journal of Infectious Diseases, 79:142-151.
DOI URL PMID |
[70] | Naidoo V, Wolter K, Espie I, et al. 2011. Vulture rescue and rehabilitation in South Africa: An urban perspective. Journal of the South African Veterinary Association, 82(82):24-31. |
[71] |
Newbold T, Gilbert F, Zalat S, et al. 2009. Climate based models of spatial patterns of species richness in Egypt’s butterfly and mammal fauna. Journal of Biogeography, 36(11): 2085-2095.
DOI URL |
[72] | Ng W T, Silva A C O, Rima P, et al. 2018. Ensemble approach for potential habitat mapping of invasive Prosopis spp. in Turkana, Kenya. Ecology and Evolution, 8(8):11921-11931. |
[73] |
Passadore C, Moller L M, Diaz-Aguirre F, et al. 2018. Modelling dolphin distribution to inform future spatial conservation decisions in a marine protected area. Scientific Reports, 8:1-14.
DOI URL PMID |
[74] | Pearce J, Ferrier S. 2000. Evaluating the predictive performance of habitat models developed using logistic regression. Ecological Modelling, 133(133):225-245. |
[75] | Phillips S J, Dudík M. 2008. Modeling of species distributions with MaxEnt: New extensions and a comprehensive evaluation. Ecography, 31:161-175. |
[76] | Phillips S J, Dudík M, Schapire R E. 2004. A maximum entropy approach to species distribution modeling. In: Greiner R, Schuurmans D (eds.). Proceedings of the 21st international conference on machine learning. Alberta, Canada, 655-662. |
[77] | Phillips S J, Anderson R P, Schapire R E. 2006. Maximum entropy modelling of species geographic distribution. Ecological Modelling, 190:231-259. |
[78] | Prakash V, Green R E, Pain D J, et al. 2007. Recent changes in populations of resident Gyps vultures in India. Journal of the Bombay Natural History Society, 104:129-135. |
[79] | Prakash V, Pain D J, Cunningham A A, et al. 2003. Catastrophic collapse of Indian white-backed Gyps bengalensis and long-billed Gyps indicus vulture populations. Biological Conservation, 109:381-390. |
[80] |
Qin A, Liu B, Guo Q, et al. 2017. MaxEnt modeling for predicting impacts of climate change on the potential distribution of Thuja sutchuenensis Franch., an extremely endangered conifer from southwestern China. Global Ecology and Conservation, 10:139-146.
DOI URL |
[81] | Ramakrishnan B, Kannan G, Samson A, et al. 2014. Nesting of White- Rumped vulture (Gyps Bengalensis) in the Segur Plateau of the Nilgiri north forest division, Tamilnadu, India. Indian Forester, 140(140):1014 -1018. |
[82] | Ravindranath N H, Joshi N V, Sukumar R, et al. 2006. Impact of climate change on forest in India. Current Science, 90(90):354-361. |
[83] |
Santangeli A, Spiegel O, Bridgeford P, et al. 2018. Synergistic effect of land-use and vegetation greenness on vulture nestling body condition in arid ecosystems. Scientific Reports, 8:13027. DOI: 10.1038/s41598- 018-31344-2.
DOI URL PMID |
[84] |
Schabo D G, Heuner S, Neethling M V, et al. 2016. Long-term data indicates that supplementary food enhances the number of breeding pairs in Cape Vulture Gyps coprotheres colony. Bird Conservation International, 27(27):140-152.
DOI URL |
[85] |
Sen B, Tavers J P, Bilgin C C. 2017. Nest site selection patterns of a local Egyptian vulture Neophron percnopterus population in Turkey. Bird Conservation International, 27:568-581.
DOI URL |
[86] |
Sercioglu C H, Primack B, Wormworth J. 2012. The effects of climate change on on tropical birds. Biological Conservation, 148(148):1-18.
DOI URL |
[87] |
Shabani F, Kumar L, Al Shidi R H S. 2018. Impacts of climate change on infestations of Dubas bug (Ommatissus lybicus Bergevin) on date palms in Oman. Peer J, 6:e5545. DOI: 10.7717/peerj.5545.
DOI URL PMID |
[88] |
Sony R K, Sen S, Kumar S, et al. 2018. Niche models inform the effects of climate change on the endangered Nilgiri Tahr (Nilgiritragus hylocrius) populations in the southern Western Ghats, India. Ecological Engineering, 120:355-363.
DOI URL |
[89] | Stocker T. 2014. Climate change 2013: The physical science basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press. |
[90] |
Stockwell D, Peters D. 1999. The GARP modelling system: Problems and solutions to automated spatial prediction. International Journal of Geographic Information Science, 13(13):143-158.
DOI URL |
[91] | Stiels D, Schidelko K. 2018. Modeling avian distributions and niches: Insights into invasions and speciation in birds. In: Tietze D T (ed.). Bird Species, Fascinating Life Sciences. DOI: 10.1007/978-3-319-91689-7_9. |
[92] |
Straub M H, Kelly T R, Rideout B A, et al. 2015. Seroepidemiologic survey of potential pathogens in obligate and facultative scavenging avian species in California. Plos One, 10(11):e0143018. DOI: 10.1371/journal. pone.0143018.
DOI URL PMID |
[93] |
Summers D M, Bryan B A, Crossman N D, et al. 2012. Species vulnerability to climate change: Impacts on spatial conservation priorities and species representation. Global Change Biology, 18:2335-2348.
DOI URL |
[94] |
Sutton W B, Barrett K, Moody A T, et al. 2015. Predicted changes in climatic niche and climate refugia of conservation priority salamander species in the Northeastern United States. Forests, 6:1-26.
DOI URL |
[95] |
Swets J A. 1988. Measuring the accuracy of diagnostic systems. Science, 240(240):1285-1293.
DOI URL |
[96] |
Taylor A T, Hafen T, Holley C T, et al. 2019. Spatial sampling bias and model complexity in stream-based species distribution models: A case study of Paddlefish (Polyodon spathula) in the Arkansas River basin, USA. Ecology and Evolution, 10:705-717.
DOI URL PMID |
[97] | Thakur M L, Narang S K. 2012. Population status and habitat-use pattern of Indian white-backed vulture (Gyps bengalensis) in Himachal Pradesh, India. Journal of Ecology and the Natural Environment, 4(4):173-180. |
[98] |
Thomas C D, Cameron A, Green R E, et al. 2004. Extinction risk from climate change. Nature, 427:145-147.
DOI URL PMID |
[99] |
Tyrberg T. 2010. Avifaunal responses to warm climate: The message from last interglacial. Records of the Australian Museum, 62:193-205.
DOI URL |
[100] | Whittaker R J, Nogués-Bravo D, Araújo M B. 2007. Geographical gradients of species richness: A test of the water energy conjecture of Hawkins et al. (2003) using European data for five taxa. Global Change Biology, 16:76-89. |
[101] |
Wisz M S, Hijmans R J, Li J, et al. 2008. Effects of sample size on the performance of species distribution models. Diversity and Distributions, 14:763-773.
DOI URL |
[102] |
Yang X, Kushwaha S P S, Saran S, et al. 2013. MaxEnt modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills. Ecological Engineering, 51:83-87.
DOI URL |
[103] |
Yi Y J, Cheng X, Yang Z F, et al. 2016. MaxEnt modeling for predicting the potential distribution of endangered medicinal plant (H. riparia Lour) in Yunnan, China. Ecological Engineering, 92:260-269.
DOI URL |
[104] |
Zhang K, Zhang Y, Tao J. 2019. Predicting the potential distribution of Paeonia veitchii(Paeoniaceae) in China by incorporating climate change into a MaxEnt model. Forests, 10:190. DOI: 10.3390/f10020190.
DOI URL |
[105] |
Zhang K L, Yao L J, Meng J S, et al. 2018. MaxEnt modeling for predicting the potential geographical distribution of two peony species under climate change. Science of the Total Environment, 634:1326-1334.
DOI URL |
No related articles found! |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||