Data from: A multi-state dynamic occupancy model to estimate local colonization-extinction rates and patterns of co-occurrence between two or more interacting species

1. Although ecology is rife with theory that explores how multiple species
co-occur through space and time, the field lacks robust statistical models
to parameterize this theory with empirical data, particularly when species
are detected imperfectly and data are collected as a time-series. 2. We
address this need by developing an occupancy model that estimates local
colonization and extinction rates for two or more interacting species when
data are collected across multiple sampling occasions. This model
estimates how community composition at a site may change across sampling
occasions by assuming the latent occupancy state is a categorical random
variable. We used a multinomial-logit model to parameterize
species-specific parameters and pairwise interactions between species, both
of which can be made a function of covariates. These transition
probabilities between community states can then be converted to occupancy
or co-occurrence probabilities to determine how community composition
varies along an environmental gradient or through time. 3. As an example,
we estimate patterns of co-occurrence between coyote (Canis latrans),
Virginia opossum (Didelphis virginiana), and raccoon (Procyon lotor) in
Chicago, Illinois, USA with data from a multi-year camera trapping study.
Models with pairwise interactions between species greatly out performed
models that assumed independence between species. Opossum and raccoon, for
example, were far less likely to go extinct in habitat patches where
coyotes were present. 4. Community composition at a site depends on
species interactions and the local environment. Our model can separate
such effects by estimating the underlying processes that define species
occurrence patterns. As a result, our model can more explicitly quantify a
wide range of ecological dynamics and therefore be used to empirically
test ecological theory, such as estimating priority effects at a site or
turnover rates between species, both of which can be made to vary as a
function of covariates.