Integrating presence-only and detection/non-detection data to estimate distributions and expected abundance of difficult-to-monitor species on a landscape-scale

Estimating species distribution and abundance is foundational to effective
management and conservation. Using an integrated species distribution
model that combines presence-only data from various sources with
detection/non-detection data from structured surveys, we estimated the
distribution and expected abundance of difficult-to-monitor mammals of
management concern across New York State, namely, coyotes, bobcats, and
black bears. Three distinct landscape-scale camera trap surveys provided
detection/non-detection data over nine years between 2013-2021, and we
augmented those data with incidental records of our focal species from
public repositories. We used an inhomogeneous Poisson point process to
construct an integrated model that fit both data types simultaneously. We
demonstrate a simple application of spatial point density of all species
records in the accessed public databases to inform the thinning process to
account for unknown spatial sampling in the presence-only data, often
referred to as the “magic covariate”. Using this approach, we examine
habitat associations and provide spatially explicit estimates of expected
abundance across the entirety of New York state for all three focal
species.  As expected, coyotes were the most widely distributed
and abundant species, with a strong positive association with agricultural
land uses. Bobcats exhibited low expected abundance throughout the state
and showed positive associations with deciduous forest and forest edge,
and a negative association with road density. Finally, we observed
considerable spatial variation in abundance of black bears with expected
numbers increasing in association with various forest cover and
composition covariates and decreasing with crop cover. We present insights
into habitat associations and provide management implications for each of
the species of interest.  Our integrated modelling method allows
for managers to use citizen sightings combined with
detection/non-detection surveys to estimate robust indices of abundance
for both high- and low-density, and wide-spread versus patchily
distributed species. Through comparison with previous studies, we
highlight how broad-scale programs, such as the statewide efforts to
estimate species distributions undertaken here, can benefit substantively
from integrated models that leverage additional data (here, incidental
records) from a larger region of space, and thus capture more landscape
heterogeneity than is plausible within formalized surveys.