A note on investigating cooccurrence patterns and dynamics for many
species, with imperfect detection and a log-linear modelling
parameterisation
Abstract
1. Patterns in, and the underlying dynamics of, species cooccurrence is
of interest in many ecological applications. Unaccounted for, imperfect
detection of the species can lead to misleading inferences about the
nature and magnitude of any interaction. A range of different
parameterisations have been published that could be used with the same
fundamental modelling framework that accounts for imperfect detection,
although each parameterisation has different advantages and
disadvantages. 2. We propose a parameterisation based on log-linear
modelling that does not require a species hierarchy to be defined (in
terms of dominance), and enables a numerically robust approach for
estimating covariate effects. 3. Conceptually the parameterisation is
equivalent to using the presence of species in the current, or a
previous, time period as predictor variables for the current occurrence
of other species. This leads to natural, ’symmetric’, interpretations of
parameter estimates. 4. The parameterisation can be applied to many
species, in either a maximum-likelihood or Bayesian estimation
framework. We illustrate the method using camera trapping data collected
on three mesocarnivore species in South Texas.