Monitoring mammal populations with line transect techniques in African forests.

Published online
12 Jul 2000
Content type
Journal article
Journal title
Journal of Applied Ecology

Plumptre, A. J.

Publication language
Africa South of Sahara & Africa & Uganda


Line transect survey techniques have been used to estimate population density for a variety of mammal species in tropical forests. This paper investigates how variation in conversion factors and measures of effort affect the error of density estimate, using results from published studies by the authors and others in African forests. In many cases, indirect methods surveying signs of animals such as counts of dung or nests, have been used because of the poor visibility in these forests. The estimates of the production and decomposition rates of these signs each have their associated errors; however, for the majority of published studies these errors have not been incorporated into the estimate of the standard errors or confidence limits of the density estimate. An equation is given showing how this should be done. An equation is also given relating the resolution (R) of a density estimate to the coefficient of variation (CV) of the estimate. This shows that to detect a 10% change in a population the CV must be 3.6% (with a power of 50%) or 2.4% (with 80% power). Using this equation and data from studies in Africa, it is shown that differences of <10-30% change in the population are unlikely to be detected between 2 surveys where visual sightings of animals are made. When indirect methods of estimating the population are used, it is unlikely that <30-50% change in the population could be detected. Some studies have surveyed primate groups using estimates of an average group spread. Data from primate groups in Budongo Forest, Uganda, show that group spread is highly variable and varies at different times of day and between months. This survey technique is not recommended. If line transects are used for monitoring populations, conversion factors should be minimized as each contributes to an increase in the CV and a reduction in the ability to detect small changes in population density. Monitoring trends in abundance over several survey periods can improve the detection of change, although this is costly and requires several surveys before any conclusions can be reached. Re-using transects in subsequent surveys can also reduce the variation around the estimate and will improve the resolution. Focusing survey efforts in areas of high density is an alternative strategy, but one that could lead to other errors as high-density areas may be the safest and hence the last to show change. Using biased survey methods is also a promising technique that can increase the precision of surveys. It is concluded that a combination of different survey methods will ensure that changes in abundance are identified.

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