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

Abstract

Accurate estimations of animal populations are necessary for management, conservation, and policy decisions. However, methods for surveying animal communities disproportionately represent specific groups or guilds. For example, transect surveys can provide robust data for large arboreal species but underestimate cryptic or small‐bodied terrestrial species, whereas camera traps have the inverse tendency. The integration of information from multiple methodologies would provide the most complete inference on population size or responses to putative covariates, yet a simple, robust framework that allows integration and comparison of multiple data sources has been lacking. We use 27,813 counts of 35 species or species groups derived from concurrent visual transects, dung transects, and camera trap surveys in tropical forests and compare them within a generalized joint attribute modeling framework (GJAM) that both compares and integrates field‐collected dung, visual, and camera trap data to quantify the species‐ and trait‐specific differences in detection for each method. The effectiveness of survey method was strongly dependent on species, as well as animal traits. These differences in effectiveness contributed to meaningful differences in the reported strength of a known important covariate for animal communities (distance to nearest village). Data fusion through GJAM allows clear and unambiguous comparisons of the counts provided from each different methodology, the incorporation of trait information, and fusion of all three data streams to generate a more complete estimate of the effects of an anthropogenic disturbance covariate. Research and conservation resources are extremely limited, which often means that field campaigns attempt to maximize the amount of information gathered especially in remote, hard‐to‐access areas. Advances in these understudied areas will be accelerated by analytical methods that can fully leverage the total body of diverse biodiversity field data, even when they are collected using different methods. We demonstrate that survey methods vary in their effectiveness for counting species based on biological traits, but more importantly that generative models like GJAM can integrate data from multiple sources in one cohesive statistical framework to make improved inference in understudied environments.

High abundance and large proportion of medium and large duikers in an intact and unhunted afrotropical protected area: Insights into monitoring methods

“…Usage of direct observations along line transects during the day reveals lower duiker estimates (Poulsen et al., 2011) compared to night surveys (Jost Robinson et al., 2017; Kamgaing et al., 2018). Ideally, the usage of a set of methods should be applied at the same site (Nuñez et al., 2019; Pfeffer et al., 2018). Incorporating local knowledge and promoting local participation in monitoring can be extremely important.…”

Distinct Community-Wide Responses to Forecasted Climate Change in Afrotropical Forests

“…Although such mechanistic understanding is needed, the type and resolution of data currently available make this impossible. These results do not consider other types of disturbance that are likely to affect biodiversity, but are less clearly measurable, like the loss of medium and large animals from hunting (Koerner et al, 2017;Poulsen et al, 2017bPoulsen et al, , 2018Poulsen et al, , 2021Beirne et al, 2019;Nuñez et al, 2019c). Finally, although the geographic scope of this analysis is uncommonly expansive, covering the entire country of Gabon (267,667 km 2 ), it may not be directly comparable to the diverse terrain contained in the 2,250 km 2 assessed in the Neotropics (Feeley and Silman, 2010;Maicher et al, 2021).…”

Coupling paraecology and hunter GPS self‐follows to quantify village bushmeat hunting dynamics across the landscape scale

“…Estimates of duiker density can vary widely across methods, introducing uncertainty to assessments of sustainability (van Vliet & Nasi, 2008b). Comparisons from multiple methods at the same site are useful but rare (see though Breuer et al, 2021; Jost Robinson et al, 2017; Nakashima et al, 2017; Nuñez et al, 2019; van Vliet et al, 2007; Waltert et al, 2006). Using data from eight villages with estimated hunting catchments (Figure 3; we excluded the village with the largest catchment due to a small number of ceremonial hunts causing the inclusion of large areas of mostly unhunted land), we compared “offtake density” as the number of individual blue and medium‐sized duikers hunted per km 2 across villages to (a) density estimates of live populations from line transects conducted in 2014 (Koerner et al, 2017) and (b) offtake density estimates from a single village in the area via researcher‐observed data from 2005 (van Vliet & Nasi, 2008a).…”