Occupancy versus colonisation-extinction models for projecting
population trends at different spatial scales
Abstract
Understanding spatiotemporal population trends and their drivers is a
key aim in population ecology. We further need to be able to predict how
the dynamics and sizes of populations are affected in the long term by
changing landscapes and climate. However, predictions of future
population trends are sensitive to a range of modelling assumptions.
Deadwood-dependent fungi are an excellent system for testing the
performance of different predictive models of sessile species as these
species have different rarity and spatial population dynamics, the
populations are structured at different spatial scales and they utilize
distinct substrates. We tested how the projected large scale occupancies
of species with differing landscape-scale occupancies are affected over
the coming century by different modelling assumptions. We compared
projections based on occupancy models against colonization-extinction
models, conducting the modelling at alternative spatial scales, and
using fine or coarse resolution deadwood data. We also tested effects of
key explanatory variables on species occurrence and
colonization-extinction dynamics. The hierarchical Bayesian models
applied were fitted to an extensive repeated survey of deadwood and
fungi at 174 patches. We projected higher occurrence probabilities and
more positive trends using the occupancy models compared to the
colonisation-extinction models, with greater difference for the species
with lower occupancy, colonization rate and colonization:extinction
ratio than for the species with higher estimates of these statistics.
The magnitude of future increase in occupancy depended strongly on the
spatial modelling scale and resource resolution. We encourage using
colonisation-extinction models over occupancy models, modelling the
process at the finest resource-unit resolution that is utilizable by the
species, and conducting projections for the same spatial scale and
resource resolution at which the model fitting is conducted. Further,
the models applied should include key variables driving the
metapopulation dynamics, such as the availability of suitable resource
units, habitat quality and spatial connectivity.