Community livelihoods depend upon accurate wildlife estimates

Evidence of wildlife passage, such as tracks, scat, fur, and disturbed surroundings, is a more accurate tool for assessing wildlife conservation status than actual encounters with animals, according to an international team of scientists. “Being too conservative, such as prohibiting hunting of a game species because there are few sightings, can be unnecessarily hard on communities that depend upon game for livelihoods and food security,” said Senior Research Associate Kirsten Silvius, a member of the team that spent three years in the Amazon comparing sign-based and encounter-based methods to estimate hunted wildlife populations. Their results were published in PLOS ONE.

Both animal sign (tracks, feces, hair, etc.) and direct encounter data were collected. Several hundred hunted and un-hunted species were detected using both methods, but the researchers found that the most important game animals, such as tapirs and the hog-like peccary, were significantly under-detected by the encounter method, likely as a result of behavior changes in response to being hunted. Sign-based methods proved more reliable.

“These animals are important to food security and still hunted successfully,” Silvius said. “Because the encounter-based method is considered the gold-standard for density estimations, the absence of sightings of tapirs near two villages would have resulted in a conclusion of tapir extinction, yet sign evidence was plentiful and kill rates were no different from those at villages where tapirs were visible.” Sustainable logging in a multiple-use protected area could also have been curtailed based on the low visual encounters of tapirs, resulting in unnecessary restrictions on sustainable forestry activities.

Surveys based on sign are an efficient tool because data can be continually collected and provide information on changes of numbers and location over time and in response to management strategies, the researchers report. Silvius emphasizes that sign data can be locally collected and interpreted as the basis for rapid, community-based decision-making and management.

Read the full press release.