title: Modeling stream fish distributions using interval-censored detection times.
abstract: 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.
authors.affiliation: EDP Biodiversity Chair CIBIO/InBIO Centro de Investigação em Biodiversidade e Recursos Genéticos da Universidade do Porto Campus Agrário de Vairão, R. Padre Armando Quintas 4485-661 Vairão Portugal; CEABN/InBIO Centro de Ecologia Aplicada "Professor Baeta Neves" Instituto Superior de Agronomia Universidade de Lisboa Tapada da Ajuda 1349-017 Lisboa Portugal.
authors.forename: Mário
authors.identifier: 0000-0002-0595-620X
authors.initials: M
authors.lastname: Ferreira
authors.affiliation: EDP Biodiversity Chair CIBIO/InBIO Centro de Investigação em Biodiversidade e Recursos Genéticos da Universidade do Porto Campus Agrário de Vairão, R. Padre Armando Quintas 4485-661 Vairão Portugal; CEABN/InBIO Centro de Ecologia Aplicada "Professor Baeta Neves" Instituto Superior de Agronomia Universidade de Lisboa Tapada da Ajuda 1349-017 Lisboa Portugal.
authors.forename: Ana Filipa
authors.initials: AF
authors.lastname: Filipe
authors.affiliation: School of Physics The University of Melbourne Parkville Victoria 3010 Australia.
authors.forename: David C
authors.initials: DC
authors.lastname: Bardos
authors.affiliation: Departamento de Biologia Animal, Faculdade de Ciências de Lisboa cE3c, Centro de Ecologia, Evolução e Alterações Ambientais Campo Grande, Bloco C2 1749-016 Lisboa Portugal.
authors.forename: Maria Filomena
authors.initials: MF
authors.lastname: Magalhães
authors.affiliation: EDP Biodiversity Chair CIBIO/InBIO Centro de Investigação em Biodiversidade e Recursos Genéticos da Universidade do Porto Campus Agrário de Vairão, R. Padre Armando Quintas 4485-661 Vairão Portugal; CEABN/InBIO Centro de Ecologia Aplicada "Professor Baeta Neves" Instituto Superior de Agronomia Universidade de Lisboa Tapada da Ajuda 1349-017 Lisboa Portugal.
authors.forename: Pedro
authors.identifier: 0000-0001-8164-0760
authors.initials: P
authors.lastname: Beja
keywords: Distribution modeling; hierarchical Bayesian models; imperfect detection; occupancy‐detection modeling; stream fish; survival analysis; time to first detection