Point counts are commonly used to monitor bird populations, and a substanti
al amount of research has investigated how conducting counts for different
lengths of time affects the accuracy of these counts and the subsequent abi
lity to monitor changes in population trends. However, little work has been
done to assess how changes in count duration affect bird-habitat models de
veloped from point count data. In this paper, we present an empirical compa
rison of the performance of bird-habitat models, which were developed via l
ogistic regression analyses based on point count data from 3-, 5-, 10-, and
20-min counts. We also investigated the effect of die number of visits to
each sun ey point on model performance. We assessed model performance on th
e basis of R-2-values and percent concordant pairs. A positive relation bet
ween model performance and count duration was most apparent for species wit
h relatively low detection probabilities, whereas performance of models for
species with relatively high detectability was fairly consistent or even d
ecreased as count duration increased. Our results suggest that while some i
mprovement in bird-habitat models for species with low detection rates can
be achieved via longer point counts, the modest gains in model performance
should be weighed against the increased time and effort required to conduct
longer counts. Models based on data from a single visit to each point did
not performed as well as models based on multiple visits. However. we found
little or no improvement in model performance when the number of visits pe
r point increased from 2 to 3. We suggest that current recommendations on p
oint count durations (5 or 10 min) will provide adequate data for modeling
bird-habitat relations.