Estimating abundance and phenology from transect count data with GLMs

Estimating population abundance is central to population ecology. With
increasing concern over declining insect populations, estimating trends in
abundance has become even more urgent. At the same time, there is an
emerging interest in quantifying phenological patterns, in part because
phenological shifts are one of the most conspicuous signs of climate
change. Existing techniques to fit activity curves (and thus both
abundance and phenology) to repeated transect counts of insects (a common
form of data for these taxa) frequently fail for sparse data, and often
require advanced knowledge of statistical computing. These limitations
prevent us from understanding both population trends and phenological
shifts, especially in the at-risk species for which this understanding is
most vital. Here we present a method to fit repeated transect count data
with Gaussian curves using linear models and show how robust abundance and
phenological metrics can be obtained using standard regression tools. We
then apply this method to nine years of Baltimore checkerspot data using
generalized linear models (GLMs). This case study illustrates the ability
of our method to fit even years with only a few non-zero survey counts,
and identifies a significant negative relationship between population size
and growing degree days (GDD) each year. We believe our new method
provides a key tool to unlock previously-unusable data sets, and may
provide a useful middle ground between ad hoc metrics of abundance and
phenology, and custom-coded mechanistic models.