Small datasets support conservation: Improving detectability for the elusive rusty‐spotted cat

Abstract

Fundamentally, science is the accumulation of knowledge. Yet, particularly in western circles, the value of small, locally specific data is increasingly under‐appreciated and undervalued by research metrics indexing research impact. Here we report novel data for the elusive, threatened rusty‐spotted cat ( Prionailurus rubiginosus ) whose fundamental ecology remains virtually unknown. We deployed camera traps within and around Wasgamuwa National Park, Sri Lanka, at 120 sites detecting 21 daily presences of rusty‐spotted cat across 17 sites. Although these data are few, they represent many detections compared to other studies, and we conduct the first occupancy analysis for the species on local‐scale variables. We use these data to show that medium‐tree density positively influences the detection probability of rusty‐spotted cats and that litter cover also influences site occupancy in positive ways. Our overall daily detection probability was just 1.5%, suggesting 202 (95% CI [79–522]) survey nights are required to be confident of a site‐specific absence when not considering microhabitat placement. Effective conservation of species requires effective survey methods. We recommend better micro‐habitat placement of camera traps, including the exploration of arboreal camera trapping to further our understanding of rusty‐spotted cats. Small datasets, due to their locally specific knowledge, can have significant value to conservation efforts but must be shared for this benefit to be realized.