Spatial and temporal non-stationarity in long-term population dynamics
of over-wintering birds of North America
Abstract
Understanding population change across long time scales and at fine
spatiotemporal resolutions is important for confronting a broad suite of
conservation challenges. However, this task is hampered by a lack of
quality long-term census data for multiple species collected across
large geographic regions. Here, we used century-long (1919-2018) data
from the Audubon Christmas Bird Count (CBC) survey to assess population
changes in over 300 avian species in North America and evaluate their
temporal non-stationarity. To estimate population sizes across the
entire century, we employed a Bayesian hierarchical model that accounts
for species detection probabilities, variable sampling effort, and
missing data. We evaluated population trends using generalized additive
models (GAMs) and assessed temporal non-stationarity in the rate of
population change by extracting the first derivatives from the fitted
GAM functions. We then summarized the population dynamics across
species, space, and time using a non-parametric clustering algorithm
that categorized individual population trends into four distinct trend
clusters. We found that species varied widely in their population
trajectories, with over 90% of species showing a considerable degree of
spatial and/or temporal non-stationarity, and many showing strong shifts
in the direction and magnitude of population trends throughout the past
century. Species were roughly equally distributed across the four
clusters of population trajectories, though grassland, forest, and
desert specialists more commonly showed declining trends. Interestingly,
for many species, region-wide population trends often differed from
those observed at individual sites, suggesting that conservation
decisions need to be tailored to fine spatial scales. Together, our
results highlight the importance of considering spatial and temporal
non-stationarity when assessing long-term population changes. More
generally, we demonstrate the promise of novel statistical techniques
for improving the utility and extending the temporal scope of existing
citizen science datasets.