Integrating data from different survey types for population monitoring of an endangered species: the case of the Eld’s deer
Bowler, DIana E.; Nilsen, Erlend B.; Bischof, Richard; O'Hara, Robert B.; Yu, Thin Thin; Oo, Tun; Aug, Myint; Linnell, John D.C.
Peer reviewed, Journal article
Åpne
Permanent lenke
http://hdl.handle.net/11250/2599428Utgivelsesdato
2019Metadata
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- Scientific publications [1437]
Originalversjon
10.1038/s41598-019-44075-9Sammendrag
Despite its value for conservation decision-making, we lack information on population abundances
for most species. Because establishing large-scale monitoring schemes is rarely feasible, statistical
methods that combine multiple data sources are promising approaches to maximize use of available
information. We built a Bayesian hierarchical model that combined different survey data of the
endangered Eld’s deer in Shwesettaw Wildlife Sanctuary (SWS) in Myanmar and tested our approach
in simulation experiments. We combined spatially-restricted line-transect abundance data with more
spatially-extensive camera-trap occupancy data to enable estimation of the total deer abundance.
The integrated model comprised an ecological model (common to both survey types, based on the
equivalence between cloglog-transformed occurrence probability and log-transformed expected
abundance) and separate observation models for each survey type. We estimated that the population
size of Eld’s deer in SWS is c. 1519 (1061–2114), suggesting it is the world’s largest wild population.
The simulations indicated that the potential benefits of combining data include increased precision
and better sampling of the spatial variation in the environment, compared to separate analysis of
each survey. Our analytical approach, which integrates the strengths of different survey methods, has
widespread application for estimating species’ abundances, especially in information-poor regions of
the world.