Density estimation in tiger populations: combining information for strong inference

Abstract

A productive way forward in studies of animal populations is to efficiently make use of all the information available, either as raw data or as published sources, on critical parameters of interest. In this study, we demonstrate two approaches to the use of multiple sources of information on a parameter of fundamental interest to ecologists: animal density. The first approach produces estimates simultaneously from two different sources of data. The second approach was developed for situations in which initial data collection and analysis are followed up by subsequent data collection and prior knowledge is updated with new data using a stepwise process. Both approaches are used to estimate density of a rare and elusive predator, the tiger, by combining photographic and fecal DNA spatial capture-recapture data. The model, which combined information, provided the most precise estimate of density (8.5 +/- 1.95 tigers/100 km2 [posterior mean +/- SD]) relative to a model that utilized only one data source (photographic, 12.02 +/- 3.02 tigers/100 km2 and fecal DNA, 6.65 +/- 2.37 tigers/100 km2). Our study demonstrates that, by accounting for multiple sources of available information, estimates of animal density can be significantly improved.

Lion ( Panthera leo ) populations are declining rapidly across Africa, except in intensively managed areas

“…Theoretically, a hierarchical approach could be used to explain the growth rate of each population with hyperparameters (40,41) describing, for example, broad geographic location (southern, East, or West Central Africa), human population density, whether the reserve is fenced, conservation efforts, or governance scores (23). What is often referred to as "borrowing strength" by modeling parameters in the data model as random variables at the group level drawn from a hyper-distribution would allow a more informative posterior parameter estimate than a separate analysis of each dataset (42)(43)(44). However this approach was ill suited for this analysis, because populations were not exchangeable since populations with small amounts of data were not random draws from the overall distribution of lambda ( Fig.…”

Potential for camera-traps and spatial mark-resight models to improve monitoring of the critically endangered West African lion (Panthera leo)

“…Bayesian implementations of SCR models, such as the SMR model, provide a flexible platform to generate unbiased estimates of density comparable across different study sites and over time. The modeling approach allows for joint estimation with shared parameters, so survey types and can be alternated across sites and in different years depending on specific research objectives and costs constraints (Gopalaswamy et al 2012;Sollmann et al 2013c). For example, if it was desirable to collect DNA from fecal samples or hair snares, a method identified as a promising direction in African carnivore monitoring (Thorn et al 2010), the SCR structure can be used with fecal genotype mark-recapture data or incorporated as an additional survey type into the larger camera survey study design (Kéry et al 2011;Sollmann et al 2011;Gopalaswamy et al 2012;Sollmann et al 2013c).…”

“…As a result estimates from SCR models from different sites, times, and detection methods are comparable over the same unit of area. In addition, SCR models are easily integrated across sites or stratified populations (Royle and Converse 2014), and can integrate multiple monitoring methods within the same modeling framework (Gopalaswamy et al 2012;Sollmann et al 2011), making SCR models a flexible and versatile tool for effective monitoring across regions and over time.…”

Combining camera-trapping and noninvasive genetic data in a spatial capture–recapture framework improves density estimates for the jaguar

“…In the context of SCR, combining data from several surveys using the same technique (for example, camera-trapping) either at several sites, or repeatedly over time, has enabled researchers to estimate density of little studied species such as leopard cats (Mohamed et al, 2013) and Sunda clouded leopards (Wilting et al, 2012). Recently, Gopalaswamy et al (2012) developed an SCR model that combines data from a camera-trapping and a scat survey of a tiger population and showed that using both data sources led to a more precise estimate of population density.…”

“…In the year following this camera trap effort, a jaguar scat collection survey was carried out at the SCNP. Here, we modify the approach by Gopalaswamy et al (2012) to estimate separate population densities of jaguars in the SCNP in two subsequent years, by combining data from both the camera-trapping and the scat collection survey within a single SCR model. Using an improved analytical tool, the density estimates we produce are more reliable than earlier estimates from non-spatial capture-recapture models.…”