Data from: On the sampling design of spatially explicit integrated population models

It is important to understand metapopulation dynamics and underlying
demographic processes in heterogeneous landscapes. Traditionally
demographic parameters are estimated using capture-recapture data that can
be difficult to collect. Spatially explicit dynamic N-mixture models allow
inference for demographic parameters, including dispersal, using count
data of unmarked animals, but these models have only been shown effective
under constant demographic parameters and dispersal between adjacent local
populations. In this study I aimed to compensate the weakness of spatially
explicit dynamic N-mixtures and multistate capture-recapture models by
jointly analyzing count and capture-recapture data. This spatially
explicit integrated population model allows for spatiotemporal variation
of demographic parameters in relation to environmental and density
covariates and dispersal between any local populations. I conducted
simulations to evaluate this model (1) for species with distinct life
histories under different detection and capture probabilities, (2) when
spatial sampling intensity varied, (3) when the length of survey period
varied, (4) when the robust sampling design was adopted or not, and (5)
when auxiliary information is partially available. I also provided an
empirical example of Gadwall (Mareca strepera) metapopulation dynamics in
North American prairies. The results showed that the model provided
unbiased parameter estimates under a variety of ecological and sampling
conditions, even when the spatial sampling intensity of capture-recapture
survey was low (20% of the patches) with the complement of count data (≥
60% of the patches). Also, the model only required a relatively short
survey period (6~8 years) to provide unbiased inferences. The robust
sampling design was not necessary for the model to provide unbiased
inferences when spatial counts were intense, but became critical when
spatial counts were sparse. Parameter estimates remain unbiased when
auxiliary information is partially available. The model showed that
Gadwall had low emigration probability (11.8%) but could disperse more
than 200 km. Based on the results, I provide recommendations about the
tradeoff between spatial sampling intensity, length of survey period, and
the use of the robust sampling design when applying this model in real
world studies. The model could have wide applications in the interface of
metapopulation ecology and landscape ecology.