Dung and nest surveys: estimating decay rates
S. T. Buckland, Centre for Research into Ecological and Environmental Modelling, The Observatory, Buchanan Gardens, St Andrews KY16 9LZ, UK. E-mail firstname.lastname@example.org
- 1Wildlife managers often require estimates of abundance. Direct methods of estimation are often impractical, especially in closed-forest environments, so indirect methods such as dung or nest surveys are increasingly popular.
- 2Dung and nest surveys typically have three elements: surveys to estimate abundance of the dung or nests; experiments to estimate the production (defecation or nest construction) rate; and experiments to estimate the decay or disappearance rate. The last of these is usually the most problematic, and was the subject of this study.
- 3The design of experiments to allow robust estimation of mean time to decay was addressed. In most studies to date, dung or nests have been monitored until they disappear. Instead, we advocate that fresh dung or nests are located, with a single follow-up visit to establish whether the dung or nest is still present or has decayed.
- 4Logistic regression was used to estimate probability of decay as a function of time, and possibly of other covariates. Mean time to decay was estimated from this function.
- 5Synthesis and applications. Effective management of mammal populations usually requires reliable abundance estimates. The difficulty in estimating abundance of mammals in forest environments has increasingly led to the use of indirect survey methods, in which abundance of sign, usually dung (e.g. deer, antelope and elephants) or nests (e.g. apes), is estimated. Given estimated rates of sign production and decay, sign abundance estimates can be converted to estimates of animal abundance. Decay rates typically vary according to season, weather, habitat, diet and many other factors, making reliable estimation of mean time to decay of signs present at the time of the survey problematic. We emphasize the need for retrospective rather than prospective rates, propose a strategy for survey design, and provide analysis methods for estimating retrospective rates.