One size need not fit all: dealing with elephants in the high-conflict Alur area in southern India

One size need not fit all: dealing with elephants in the high-conflict Alur area in southern India

Presented by
Subhankar Chakraborty
Indian Institute Of ScienceAuthors
Subhankar Chakraborty, D Bhoominathan, Ajay A Desai, T N C Vidya

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Introduction: What conservation problem or question does your study address?
The Alur area in Karnataka (India), where a tiny habitat available for elephants in human-dominated landscape, has witnessed acute HEC over the last two decades and does not appear to support elephants over long-term. We carried out a molecular genetic study of elephants in Alur area to estimate population size, sex ratio and social organisation, contributing to informed management decisions.

Methods: What were the main research methods you used?
Over 100 freshly collected dung samples, which covered extensively the area elephants were using, were genotyped for 12 microsatellite loci. Genetic relatedness between individuals was calculated and pedigrees were constructed to infer parentage and sibships by taking the likelihood of the entire pedigree into account. Genetic structure with regard to the larger population in the landscape was also examined. Molecular sexing was carried out to ascertain the sex of the individuals. Age classes of the individuals were assigned by calculating the mean circumference of the intact dung boli of each animal.

Results: What are your most important results?
We found 29 unique individuals in the population, comprising of 17 females and 12 males of different age classes. Relatedness between females suggested independent colonisations by discrete, small groups rather than one cohesive clan of related females.
Discussion: What are your important discussion points and what is the relevance of your results to conservation (if any)?
Our study obviates the need for a single solution for dealing with all the females in the area in order to maintain social integrity. We demonstrate how social organization inferred through molecular data from non-invasive sampling can inform management decisions.

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