Drive counts as a method of estimating ungulate density in forests: mission impossible?

Abstract

Although drive counts are frequently used to estimate the size of deer populations in forests, little is known about how counting methods or the density and social organization of the deer species concerned influence the accuracy of the estimates obtained, and hence their suitability for informing management decisions. As these issues cannot readily be examined for real populations, we conducted a series of 'virtual experiments' in a computer simulation model to evaluate the effects of block size, proportion of forest counted, deer density, social aggregation and spatial auto-correlation on the accuracy of drive counts. Simulated populations of red and roe deer were generated on the basis of drive count data obtained from Polish commercial forests. For both deer species, count accuracy increased with increasing density, and decreased as the degree of aggregation, either demographic or spatial, within the population increased. However, the effect of density on accuracy was substantially greater than the effect of aggregation. Although improvements in accuracy could be made by reducing the size of counting blocks for low-density, aggregated populations, these were limited. Increasing the proportion of the forest counted led to greater improvements in accuracy, but the gains were limited compared with the increase in effort required. If it is necessary to estimate the deer population with a high degree of accuracy (e.g. within 10% of the true value), drive counts are likely to be inadequate whatever the deer density. However, if a lower level of accuracy (within 20% or more) is acceptable, our study suggests that at higher deer densities (more than ca. five to seven deer/100 ha) drive counts can provide reliable information on population size.

Fig. 1

Examples of randomly generated simulated deer populations and counting blocks in an 18,000 ha virtual forest: spatially unstructured, moderately aggregated populations of a red deer at 10/100 ha and b roe deer at 7.5/100 ha; spatially auto-correlated and demographically aggregated low-density populations of c red deer at 5/100 ha and d roe deer at 2.5/100 ha. Individual squares represent 20 ha forest compartments. The same randomly generated pattern of 30 counting blocks each comprising three adjacent compartments is superimposed on each population. The high frequency of zero counts for the highly aggregated, low-density populations c and d is clearly illustrated

Fig. 2

The accuracy of simulated estimated counts of a red deer and b roe deer in relation to density. The accuracy index shows the proportion of counts falling within a specified percentage of the true population total. Each count covered 10% of the forest using 30 blocks of 60 ha each. Means were derived from 20 replicate virtual forests

Fig. 3

The accuracy of estimated counts of a red deer at 10/100 ha and b roe deer at 7.5/100 ha in relation to demographic aggregation (low parameter values cause greater aggregation). The accuracy index shows the proportion of counts falling within a specified percentage of the true population total

Fig. 4

The accuracy of estimated counts of a red deer at 10/100 ha and b roe deer at 7.5/100 ha in relation to the degree of spatial auto-correlation (low parameter values cause greater auto-correlation). The accuracy index shows the proportion of counts falling within a specified percentage of the true population total

Fig. 5

The accuracy of estimated counts of low-density populations of a red deer (4/100 ha) and b roe deer (3/100 ha) and high-density populations of c red deer (12/100 ha) and d roe deer (20/100 ha) in relation to the size of counting blocks. Populations were demographically aggregated and spatially auto-correlated. The accuracy index shows the proportion of counts falling within a specified percentage of the true population total

Fig. 6

The accuracy of estimated counts of low-density spatially aggregated populations of a red deer (4/100 ha) and b roe deer (3/100 ha) and high-density spatially aggregated populations of c red deer (12/100 ha) and d roe deer (20/100 ha) in relation to the proportion of the forest counted. The accuracy index shows the proportion of counts falling within a specified percentage of the true population total. Means were averaged over 60 and 100 ha block sizes

Fig. 7

Aggregation at the block scale of drive counts of red deer (a, c) and roe deer (b, d) in four Polish forest districts (diamonds Iława, squares Pszczyna, triangles Rudy, and cross Strzałowo): the relationships of a, b the count variance and c, d the negative binomial aggregation parameter k with the mean count. Count data were standardised to a 60 ha block size