Integrating data from different survey types for population monitoring of an endangered species: the case of the Eld’s deer

Abstract

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.

Is more data always better? A simulation study of benefits and limitations of integrated distribution models

“…All of the models assumed that the intensity surface (λ( s )) resulted in one or more Poisson point patterns which could have been observed in different ways (Bowler et al 2019, Isaac et al 2019). For each quadrat ( i ) where the PA data ( Y i ) were sampled, presence was modelled as a single Bernoulli trial with probability of presence p i .…”

“…A cloglog link was used to link p i to the log intensity of the Poisson process (λ( s ) evaluated at γ i ) (Eq. 2 (Kery and Royle 2016, Bowler et al 2019)). The intercept is the baseline expected abundance when the environmental covariate equals zero. …”

“…including non‐detections or a standardized protocol) can be sufficient to overcome the spatial biases in presence‐only data. Bowler et al (2019) demonstrated that integrating datasets with different spatial footprints allowed predictions to be estimated over a wider spatial area, and with greater precision, than one dataset alone. Estimation of unbiased parameter values, higher precision of estimates and increased spatial coverage are some of the primary benefits suggested for IDMs.…”

Generalizability and comparability of prevalence estimates in the wild bird literature: methodological and epidemiological considerations

“…complete listing of all eligible study subjects), from which study subjects may then be randomly selected. In contrast, wildlife populations are challenging to delimit (Wobeser, 2007; Bowler et al ., 2019). The characterization of any wildlife sample as having been obtained in a truly random manner will likely be open to serious debate, as most sampling techniques may plausibly select for animals which are tame or otherwise amenable to capture or restraint.…”

Stronger together: comparing and integrating camera trap, visual, and dung survey data in tropical forest communities

“…Despite the consensus that multiple survey techniques should be used when censusing diverse wildlife communities, accessible analysis frameworks which allow simultaneous modeling of count data from different methodologies (from here onwards referred to as “data fusion”) are lacking to date (but see D. E. Bowler et al. for a single species example). As such, researchers choose to either focus on a single methodology or consider the data from different methods in separate analyses.…”