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

doi:10.5814/j.issn.1674-764x.2021.01.004

摘要/Abstract

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。

关键词: 集成气候模型, 印度秃鹫据点, 长期影响, 短期影响, 物种分布模型, 秃鹫栖息地

Abstract:

Vultures provide invaluable ecosystem services and play an important role in ecosystem balancing. The number of native vultures in India has declined in the past. Acquiring present knowledge of their habitat spread is essential to manage and prevent such a decline. It is envisaged that ongoing climate crisis may further cause change in habitat suitability and impact the existing population. Therefore, this study in Central India—a vulture stronghold, is aimed at predicting habitat changes in the short and long term and present the data statistically and graphically by using Species Distribution Model. MaxEnt software was chosen for its advantages over other models, like using presence-only data and performing well with incomplete data, small sample sizes and gaps, etc. Global Climate Model ensemble (CCSM4, HadGEM2AO and MIROC5), was used to get better prediction. Fourteen robust models (AUC 0.864-0.892) were developed using data from over 1000 locations of seven vulture species over two seasons together. Selected climatic and other environmental variables were used to predict the current habitat. Future prediction was based on climatic variables only. The most important variables influencing the distribution were precipitation (bio 15, bio 18, bio 19) and temperature (bio 3, bio 5). Forest and water bodies were the major influencers within land use-landcover in the current prediction. At finer scale, while extremely suitable habitat area decreased and highly suitable area increased over time, the total suitable area marginally increased in 2050 but decreased in 2070. For broader consideration, net loss in suitable area was 5% in 2050 and 7.17% in 2070 (RCP4.5). Similarly, in the RCP8.5 this was 6% in 2050 and 7.3% in 2070. The data generated can be used in conservation planning and management and thus protecting the vultures from any future threat.

Key words: ensemble climate model, Indian vulture-stronghold, long term impact, short term impact, species distribution modelling, vulture habitats

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.

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Bio-climatic		Environmental
Temperature	Precipitation	Environmental
(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

Bio-climatic		Environmental
Temperature	Precipitation	Environmental
(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

Habitat suitability category	Current (2016)		Short term (2050)		Long term (2070)
	Environmental	Climatic	Climatic		Climatic
	Environmental	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)

Habitat suitability category	Current (2016)		Short term (2050)		Long term (2070)
	Environmental	Climatic	Climatic		Climatic
	Environmental	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
Category	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