Turning ghosts into dragons: improving camera monitoring outcomes for a cryptic low-density Komodo dragon population in eastern Indonesia

Deni Purwandana,Tim S Jessop,Achmad Ariefiandy,Muhammad Azmi,Sanggar A Nasu,Sahudin Sahudin,Andreas A Dos

doi:10.1071/wr21057

Abstract

Abstract Context Detection probability is a key attribute influencing population-level wildlife estimates necessary for conservation inference. Increasingly, camera traps are used to monitor threatened reptile populations and communities. Komodo dragon (Varanus komodoensis) populations have been previously monitored using camera traps; however, considerations for improving detection probability estimates for very low-density populations have not been well investigated. Aims Here we compare the effects of baited versus non-baited camera monitoring protocols to influence Komodo dragon detection and occupancy estimates alongside monitoring survey design and cost considerations for ongoing population monitoring within the Wae Wuul Nature Reserve on Flores Island, Indonesia. Methods Twenty-six camera monitoring stations (CMS) were deployed throughout the study area with a minimum of 400 m among CMS to achieve independent sampling units. Each CMS was randomly assigned as a baited or non-baited camera monitoring station and deployed for 6 or 30 daily sampling events. Key results Baited camera monitoring produced higher site occupancy estimates with reduced variance. Komodo dragon detection probability estimates were 0.15 ± 0.092–0.22 (95% CI), 0.01 ± 0.001–0.03, and 0.03 ± 0.01–0.04 for baited (6 daily survey sampling events), unbaited (6 daily survey sampling events) and long-unbaited (30 daily survey sampling events) sampling durations respectively. Additionally, the provision of baited lures at cameras had additional benefits for Komodo detection, survey design and sampling effort costs. Conclusions Our study indicated that baited cameras provide the most effective monitoring method to survey low-density Komodo dragon populations in protected areas on Flores. Implications We believe our monitoring approach now lends itself to evaluating population responses to ecological and anthropogenic factors, hence informing conservation efforts in this nature reserve.

Highlights

Large terrestrial predators are most often at risk from human actions and increasingly require effective conservation actions to ensure population persistence (Gittleman and Harvey 1982; Prowse et al 2015; Penjor et al 2019)
Our study indicated that baited cameras provide the most effective monitoring method to survey lowdensity Komodo dragon populations in protected areas on Flores
We believe our monitoring approach lends itself to evaluating population responses to ecological and anthropogenic factors, informing conservation efforts in this nature reserve

Summary

Introduction

Large terrestrial predators are most often at risk from human actions and increasingly require effective conservation actions to ensure population persistence (Gittleman and Harvey 1982; Prowse et al 2015; Penjor et al 2019). The increasing use of hierarchical models such as site occupancy and n-mixture models, which account for imperfect detection, are among the most common techniques used to provide population-level inference for apex predators (MacKenzie et al 2002, 2006; Royle 2004; Kery et al 2005) These methods are often well suited for threatened predator population studies (du Preez et al 2014; Tan et al 2017; Penjor et al 2019; Searle et al 2020), because threatened predators often persist at low densities where. Human activities can often disproportionately threaten large-bodied reptiles, causing their populations to be at much lower densities than normal and more difficult to monitor (Todd et al 2010; Tingley et al 2019) Addressing these factors by modifying camera sampling designs to increase detection probability is a key consideration to monitor threatened reptile populations effectively. There may be compelling reasons to improve camera-based detection using baits or lures (i.e. attractants) to increase detection probability (O’Connell et al 2010; Long et al 2012; Read et al 2015)

Methods

Results

Discussion

Conclusion