Improved cost-effectiveness of species monitoring programs through data integration.

Conservation initiatives strive for reliable and cost-effective species monitoring. However, resource constraints mean management decisions are overly reliant on data derived from single methodologies, resulting in taxonomic or geographic biases. We introduce a data integration framework to optimize species monitoring in terms of spatial representation, the reliability of biodiversity metrics, and the cost of implementation, focusing on tigers and their principal prey (sambar deer and wild pigs).

Population status of a cryptic top predator: an island-wide assessment of tigers in Sumatran rainforests.

Large carnivores living in tropical rainforests are under immense pressure from the rapid conversion of their habitat. In response, millions of dollars are spent on conserving these species. However, the cost-effectiveness of such investments is poorly understood and this is largely because the requisite population estimates are difficult to achieve at appropriate spatial scales for these secretive species.