concept in wildlife surveys and we neglect it at our peril. Thus, comparison of
“unadjusted counts” will only be valid if the numbers represent a constant
proportion of the actual population present across space and time. This assump-
tion is often questionable and has been a matter of much debate (Buckland et al.
2001; Rosenstock et al. 2002; Thompson 2002). To be clear, this could affect
comparisons between different habitats surveyed at the same time, and between
the same or different habitats surveyed at different times.
The solution is to “adjust” counts to take account of detectability, and a number
of different methods have been proposed (Thompson 2002). For example, the
“double-observer” approach uses counts from primary and secondary observers,
who alternate roles, to model detection probabilities and adjust the counts
(Nichols et al. 2000). The “double-sampling” approach uses the findings from
an intensive census at a subsample of sites to correct the unadjusted counts from
a larger sample of sites (Bart and Earnst 2002). The “removal model” assesses the
detection probabilities of different species during the period of a point count and
adjusts the counts accordingly (Farnsworth et al. 2002). Finally, “distance sam-
pling” models the decline in the detectability of species with increasing distance
from an observer and corrects the counts appropriately.
Distance sampling is a specialized way of estimating bird densities from
transect data and of assessing the degree to which our ability to detect birds differs
in different habitats and at different times (Buckland et al. 2001; Rosenstock et al.
2002). The software and further information to undertake these analyses are
freely available at: http://www.ruwpa.st-and.ac.uk/distance/. Distance samplingtakes
account of the fact that the number of birds we see or hear declines with distance
from the observer. The shape of this decline, the distance function, differs among
species, among observers and, importantly, among habitats—birds within open
grassland are detectable over greater distances than those within dense forest—
even when they occur at the same densities. Distance samplingmodels the
“distance function” and estimates density taking into account both the birds that
were observed, plus those that were likely to be present but were not detected. This
method is strongly recommended.
Distance sampling provides an efficient and simple way of estimating bird
density from field data. It allows for differences in conspicuousness between
habitats and species (though not observers), enabling comparisons to be made
between and within species, and across different habitats at different times.
Density estimates improve with the number of birds recorded—a minimum
of about 80 records is recommended. The method relies on a number of assump-
tions which need to be evaluated carefully in the field and steps taken to lessen
their effects (Buckland et al. 2001). The key assumptions of distance methods
are that all the birds actually on the transect line or at the counting station are
Field methods| 43