Identifying important connectivity areas for the wide-ranging Asian elephant across conservation landscapes of Northeast India

• Published in: Diversity & distributions Vol. 27; no. 12; pp. 2510 - 2523
• Main Authors: Vasudev, Divya, Goswami, Varun R., Srinivas, Nishanth, La Nam Syiem, Biang, Sarma, Aishanya
• Format: Journal Article
• Language: English
• Published: Oxford Wiley 01-12-2021; John Wiley & Sons, Inc
• Subjects: Animal models; Animals; Anthropogenic factors; Assam; Climate change; Conservation; corridor; crowd‐sourced data; Dispersal; Elephants; Elephas maximus; Endangered species; Environmental protection; Forest conservation; forests; fragmentation; Human influences; Human motion; Human population density; Human populations; Infrastructure; Land cover; Land use; Landscape; Landscape preservation; linear infrastructure; movement models; Plantations; Population density; Questionnaires; randomised shortest path; Remote sensing; RESEARCH ARTICLE; resistance mapping; Vegetation cover; Wildlife conservation; India
• DOI: 10.1111/ddi.13419
• EISSN: 1472-4642
• ISSN: 1366-9516

Aim
Connectivity is increasingly important for landscape‐scale conservation programmes. Yet there are obstacles to developing reliable connectivity maps, including paucity of data on animal use of the non‐habitat matrix. Our aim was to identify important connectivity areas for the endangered Asian elephant Elephas maximus across a 21,210 km2 region using empirical data and recently developed animal movement models.
Location
Northeast India.
Methods
We interviewed 1,184 respondents, primarily farmers, residing across our study region, to collect crowd‐sourced data on elephant use of the matrix. We generated a classified land use/land cover map and collated remotely sensed data on environmental and anthropogenic covariates. We used logistic regression to estimate the influence of these covariates on resistance, based on elephant detections recorded via interviews. We modelled elephant movement within the randomised shortest path framework, which allows for scenarios ranging from optimal movement with complete information on the landscape to random movement with no information on the landscape. We calculated the passage of elephants through pixels in our study region, a parameter that denotes the expected number of elephant movements through a particular pixel across movement routes. We overlaid linear infrastructure sourced from secondary data, and human–elephant conflict hotspots generated from our interview data, on passage maps.
Results
Elephants preferred locations with high vegetation cover, close to forests and with low human population density. We mapped important connectivity areas across the study region, including in three important conservation landscapes. Whilst forests facilitated connectivity, the matrix also played an important contributory role to elephant dispersal. Incorporating information on environmental and anthropogenic drivers of elephant movement added value to connectivity predictions.
Main conclusions
Fine‐scale mapping of connectivity, using empirical data and realistic movement models, such as the approach we use, can provide for informed and more effective landscape‐scale conservation.