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Including indigenous knowledge in species distribution modeling for increased ecological insights.

Authors :
Skroblin, Anja
Carboon, Tracy
Bidu, Gladys
Chapman, Nganjapayi
Miller, Minyawu
Taylor, Karnu
Taylor, Waka
Game, Edward T.
Wintle, Brendan A.
Source :
Conservation Biology. Apr2021, Vol. 35 Issue 2, p587-597. 11p.
Publication Year :
2021

Abstract

Indigenous knowledge systems hold detailed information on current and past environments that can inform ecological understanding as well as contemporary environmental management. Despite its applicability, there are limited examples of indigenous knowledge being incorporated in species distribution models, which are widely used in the ecological sciences. In a collaborative manner, we designed a structured elicitation process and statistical framework to combine indigenous knowledge with survey data to model the distribution of a threatened and culturally significant species (greater bilby or mankarr [Macrotis lagotis]). We used Martu (Aboriginal people of the Australian western deserts) occurrence knowledge and presence data from trackā€based surveys to create predictive species distribution models with the Maxent program. Predictions of species distribution based on Martu knowledge were broader than those created with survey data. Together the Martu and survey models showed potential local declines, which were supported by Martu observation. Both data types were influenced by sampling bias that appeared to affect model predictions and performance. Martu provided additional information on habitat associations and locations of decline and descriptions of the ecosystem dynamics and disturbance regimes that influence occupancy. We concluded that intercultural approaches that draw on multiple sources of knowledge and information types may improve species distribution modeling and inform management of threatened or culturally significant species. Article impact statement: Indigenous knowledge systems hold detailed environmental data that can be used to model species' distributions to assist with management. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
08888892
Volume :
35
Issue :
2
Database :
Academic Search Index
Journal :
Conservation Biology
Publication Type :
Academic Journal
Accession number :
149552296
Full Text :
https://doi.org/10.1111/cobi.13373