Geoscience Reference
In-Depth Information
sis of index data should be considered in light of potential limitations imposed
by the index-abundance relationship. For example, saturated indices could be
the cause of a failure to detect population changes. Most importantly, animal
ecologists must be cautious about concluding that a lack of trend in a time series
of index data indicates population stability. Often an index may be unable to
capture population change because of a flawed index-abundance relationship
or simply excessive noise caused by sampling error in the index.
Spatial Aspects of Measuring Changes in Indices
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Many animal ecologists are concerned with monitoring multiple local popula-
tions with the intent of extrapolating changes observed in those populations to
larger, regional populations. In such a case, the sample of areas monitored
must be representative of areas in a region that are not sampled if observed
trends are to be extrapolated to regional populations. Selection of sites for
monitoring is therefore a key consideration for animal ecologists concerned
with identifying change in regional populations.
Balancing sampling needs and logistical constraints in the design of
regional monitoring programs can be problematic, however. For sampling
areas to be representative, random selection of sites for surveying is advised,
but a purely random scheme for site selection is often unworkable in practice.
For example, sites near roadsides and those on public lands are generally easier
to access by survey personnel than are randomly selected sites. Also, monitor-
ing sites that occur in clusters minimize unproductive time traveling among
survey sites. Time is generally at a premium in monitoring efforts not only
because of the costs of supporting survey personnel but also because the survey
window each day or season for many animals is brief.
A simple random sample of sites may also produce unacceptably low
encounter rates for the organisms being monitored (too many zero counts to
be useful). This could be overcome by stratifying sampling according to habi-
tat types frequented by the species being monitored. However, information on
habitat distributions in a region from which a stratified random sampling
scheme might be developed often is not available to researchers. Furthermore,
prior knowledge of habitat associations of most species that can be used as a
basis for stratification often is not available. Finally, ecologists often monitor
multiple species for which a single optimal sampling strategy may simply not
be identifiable.
These difficulties in implementing random sampling schemes imply that
