Tilker, A., Niedballa, J., Viet, H. L., Abrams, J. F., Marescot, L., Wilkinson, N., Rawson, B. M., Sollmann, R., & Wilting, A. (2024). Addressing the Southeast Asian snaring crisis: Impact of 11 years of snare removal in a biodiversity hotspot. Conservation Letters, e13021. https://doi.org/10.1111/conl.13021 .
Unsustainable snaring is causing biodiversity declines across tropical protected areas, resulting in species extinctions and jeopardizing the health of forest ecosystems. Here, we used 11 years of ranger-collected data to assess the impact of intensive snare removal on snaring levels in two protected areas in Viet Nam. Snare removal resulted in significant declines in snare occupancy (36.9, 95% Bayesian credible interval [4.6, 59.0] reduction in percent area occupied), but snaring levels nonetheless remained high (31.4, [23.6, 40.8] percent area occupied), and came with a substantial financial cost. Our results indicate that snare removal remains an important component of efforts to protect tropical protected areas but by itself is likely insufficient to address this threat. To stop snaring in protected areas, a multifaceted approach will be necessary that combines short-term reactive snare removal with long-term proactive programs that address the underlying drivers behind snaring.
The methodological framework we present here applies a site-occupancy model to estimate the occurrence probability of animal traps in the Saola Nature Reserves, Vietnam. The models are fitted in a Bayesian framework using the ubms package in R. Sampling sites are 200x200m grid cells. Primary occasion are semesters, secondary occasions are months. We use random effects of year-semester to account for variation in detection probability, and random effects of year to account for variation in occupancy probability and the effect of site covariates on occupancy between years. We include a MacKenzie-Bailey chi-square goodness-of-fit test and calculate percentage of area occupied (PAO) in each year.
The model input data and a covariate data frame for predictions are located in the data subfolder.
covariates.RData: covariate data frame for predictions
modelInput.RData: unmarkedFrameOccu for occupancy model.
Numeric site covariates and observation covariate effort were standardized to mean = 0 and SD = 1. Spatial information were removed in this public data.
The R script is located in the R subfolder.
1_snare_occupancy_model.R: fit the occupancy model in ubms, goodness-of-fit test, calculation of annual percentage of area occupied (PAO)