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Modeling stream fish distributions using interval-censored detection times
Publication . Ferreira, Mário; Filipe, Ana Filipa; Bardos, David C.; Magalhães, Maria Filomena; Beja, Pedro
Controlling for imperfect detection is important for developing species distribution
models (SDMs). Occupancy-detection models based on the time needed to
detect a species can be used to address this problem, but this is hindered when
times to detection are not known precisely. Here, we extend the time-to-detection
model to deal with detections recorded in time intervals and illustrate the
method using a case study on stream fish distribution modeling. We collected
electrofishing samples of six fish species across a Mediterranean watershed in
Northeast Portugal. Based on a Bayesian hierarchical framework, we modeled
the probability of water presence in stream channels, and the probability of species
occupancy conditional on water presence, in relation to environmental and
spatial variables. We also modeled time-to-first detection conditional on occupancy
in relation to local factors, using modified interval-censored exponential
survival models. Posterior distributions of occupancy probabilities derived from
the models were used to produce species distribution maps. Simulations indicated
that the modified time-to-detection model provided unbiased parameter
estimates despite interval-censoring. There was a tendency for spatial variation
in detection rates to be primarily influenced by depth and, to a lesser extent,
stream width. Species occupancies were consistently affected by stream order,
elevation, and annual precipitation. Bayesian P-values and AUCs indicated that
all models had adequate fit and high discrimination ability, respectively. Mapping
of predicted occupancy probabilities showed widespread distribution by
most species, but uncertainty was generally higher in tributaries and upper
reaches. The interval-censored time-to-detection model provides a practical
solution to model occupancy-detection when detections are recorded in time
intervals. This modeling framework is useful for developing SDMs while controlling
for variation in detection rates, as it uses simple data that can be readily
collected by field ecologists
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Entidade financiadora
Fundação para a Ciência e a Tecnologia
Programa de financiamento
3599-PPCDT
Número da atribuição
LTER/BIA-BEC/0004/2009