Snail Kites (Rostrhamus sociabilis) in Florida were monitored between 1969
and 1994 using a quasi-systematic annual survey. We analyzed data from the
annual Snail Kite survey using a generalized linear model where counts were
regarded as overdispersed Poisson random variables. This approach allowed
us to investigate covariates that might have obscured temporal patterns of
population change or induced spurious patterns in count data by influencing
detection rates. We selected a model that distinguished effects related to
these covariates from other temporal effects, allowing us to identify patt
erns of population change in count data. Snail Kite counts were influenced
by observed differences, site effects, effort, and water levels. Because th
ere was no temporal overlap of the primary observers who collected count da
ta, patterns of change could be estimated within time intervals covered by
an observed, but not for the intervals among observers. Modeled population
change was quite different from the change in counts, suggesting that analy
ses based on unadjusted counts do not accurately model Snail Kite populatio
n change. Results from this analysis were consistent with previous reports
of an association between water levels and counts, although further work is
needed to determine whether water levels affect actual population size as
well as detection rates of Snail Kites. Although the effects of variation i
n detection rates can sometimes be mitigated by including controls for fact
ors related to detection rates, it is often difficult to distinguish factor
s wholly related to detection rates from factors related to population size
. For factors related to both, count survey data cannot be adequately analy
zed without explicit estimation of detection rates, using procedures such a
s capture-recapture.