Inventory and characterization of new populations through ecological niche modelling improve threat assessment

被引：32

作者：

Adhikari, D. ^{[1
]}

Reshi, Z. ^{[2
]}

Datta, B. K. ^{[3
]}

Samant, S. S. ^{[4
]}

Chettri, A. ^{[5
]}

Upadhaya, K. ^{[6
]}

Shah, M. A. ^{[2
]}

Singh, P. P. ^{[1
]}

Tiwary, R. ^{[1
]}

Majumdar, K. ^{[3
]}

Pradhan, A. ^{[5
]}

Thakur, M. L. ^{[4
]}

Salam, N. ^{[2
]}

Zahoor, Z. ^{[2
]}

Mir, S. H. ^{[2
]}

Kaloo, Z. A. ^{[2
]}

Barik, S. K. ^{[1
,7
]}

机构：

[1] North Eastern Hill Univ, Dept Bot, Shillong 793022, Meghalaya, India

[2] Univ Kashmir, Dept Bot, Srinagar 190006, Jammu & Kashmir, India

[3] Tripura Univ, Dept Bot, Agartala 799022, India

[4] GB Pant Natl Inst Himalayan Environm & Sustaina D, Himachal Unit, Mohal Kullu 175101, India

[5] Sikkim Univ, Dept Bot, Gangtok 737102, India

[6] North Eastern Hill Univ, Sch Technol, Dept Basic Sci & Social Sci, Shillong 793022, Meghalaya, India

[7] CSIR Natl Bot Res Inst, Lucknow 226001, Uttar Pradesh, India

来源：

CURRENT SCIENCE | 2018年 / 114卷 / 03期

关键词：

Niche modelling; population characterization; threatened plants; threat assessment; SPECIES DISTRIBUTION MODELS; CONSERVATION; KNOWLEDGE; PREDICT; RARE;

D O I：

10.18520/cs/v114/i03/519-531

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Categorization of species under different threat classes is a pre-requisite for planning, management and monitoring of any species conservation programme. However, data availability, particularly at the population level, has been a major bottleneck in the correct categorization of threatened species. Till date, threat assessments have been mostly based on expert opinion and/or herbarium records. The availability of primary data on distribution of species and their population attributes is limited in India because of inadequate field survey, which has been ascribed to resource constraints and inaccessibility. In this study, we demonstrate that ecological niche modelling (ENM) can be an economical and effective tool to guide surveys overcoming the above two constraints leading to the discovery of new populations of threatened species. Such data lead to improved threat assessment and more accurate categorization. We selected 14 threatened plants comprising 5 trees (Acer hookeri Miq., Bhesa robusta (Roxb.) Ding Hou, Gynocardia odorata Roxb., Ilex venulosa Hook. f. and Lagerstroemia minu-ticarpa Debb. ex P.C. Kanjilal), 8 herbs (Angelica glauca Edgew., Aquilegia nivalis Falc. ex Jackson, Artemisia amygdalina DC., Begonia satrapis C.B. Clarke, Corydalis cashmeriana Royle, Dactylorhiza hatagirea (D. Don) Soo, Podophyllum hexandrum Royle, and Rheum australe D. Don), and 1 pteridophyte (Angiopteris evecta (Forst.) Hoffm.) having distribution range in North East India, Eastern and Western Himalaya, and Jammu and Kashmir. The study was carried out between 2012 and 2016. ENM-based survey led to the discovery and characterization of 348 new populations. The data so obtained helped in assigning conservation status to 10 species, which earlier were never classified due to data deficiency. Using the new population and distribution data of the remaining four species, only one was confirmed regarding its existing status and two species were classified as 'Critically endangered' instead of the present classification as 'Endangered'. The fourth species was classified as 'Critically endangered' against the earlier category of 'Least concerned'.

引用

页码：519 / 531

页数：13

共 43 条

[21]

Kataki S. K., 1986, ORCHIDS MEGHALAYA, P258

[22]

Kumar Sunil., 2009, J ECOLOGY NATURAL SC, V1, P094, DOI DOI 10.5897/JENE.9000071

[23] AUC:: a misleading measure of the performance of predictive distribution models [J].