Dung beetles as indicators for rapid impact assessments: evaluating best practice forestry in the neotropics

Dung beetles (Scarabaeidae: Scarabaeinae) are sensitive to habitat perturbations and are easily studied, making them an ideal taxonomic group with which to evaluate the effects of low-intensity anthropogenic disturbances such as Reduced-Impact Logging. Here we examine the effect of a certified Reduced-Impact Logging operation on dung beetles, and demonstrate their suitability for use in rapid ecological impact studies. We sampled dung beetle assemblages, environmental variables and timber extraction rates across four treatment groups in closed canopy and canopy gaps in logged and unlogged forest in Guyana. Community analysis revealed that logged forest supported a more uniform dung beetle assemblage compared to unlogged forest. Differences in assemblage structure were driven by dissimilarity between closed canopy treatments, as plots in artificial and natural canopy gaps supported comparable assemblages. Indicator analyses were conducted across treatments, using a new approach (CLAM) and two well-established methods (IndVal, SIMPER). Two species respectively were classified as indicators of logged (Hansreia affinis and Eurysternus caribaeus) and unlogged forest (Canthidium aff. centrale and Deltochilum (Calhyboma) carinatum). BIO-ENV analysis demonstrated that tree extraction intensity, bare ground cover, and ground cover by leaf material were key factors influencing dung beetle assemblages. Despite the relatively low-impact of Reduced-Impact Logging reported by previous studies, we find that dung beetles are sensitive to even small changes in environmental conditions as a result of this form of anthropogenic disturbance. As dung beetles are a highly responsive taxonomic group, we illustrate that they represent a valuable taxon that can be used to critically evaluate best practice forestry operations and other disturbance activities, particularly in time constrained studies (e.g., rapid monitoring and environmental impact assessments). However, we recommend the use of multiple indicator analyses to monitor potential changes in assemblage composition, due to a lack of congruence between methods.