Modeling stream fish distributions using interval‐censored detection times
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 t...
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| Format: | Online |
| Language: | English |
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John Wiley and Sons Inc.
2016
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4984523/ |
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pubmed-49845232016-08-22 Modeling stream fish distributions using interval‐censored detection times Ferreira, Mário Filipe, Ana Filipa Bardos, David C. Magalhães, Maria Filomena Beja, Pedro Original Research 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. John Wiley and Sons Inc. 2016-07-13 /pmc/articles/PMC4984523/ /pubmed/27551402 http://dx.doi.org/10.1002/ece3.2295 Text en © 2016 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Ferreira, Mário Filipe, Ana Filipa Bardos, David C. Magalhães, Maria Filomena Beja, Pedro |
| spellingShingle |
Ferreira, Mário Filipe, Ana Filipa Bardos, David C. Magalhães, Maria Filomena Beja, Pedro Modeling stream fish distributions using interval‐censored detection times |
| author_facet |
Ferreira, Mário Filipe, Ana Filipa Bardos, David C. Magalhães, Maria Filomena Beja, Pedro |
| author_sort |
Ferreira, Mário |
| title |
Modeling stream fish distributions using interval‐censored detection times |
| title_short |
Modeling stream fish distributions using interval‐censored detection times |
| title_full |
Modeling stream fish distributions using interval‐censored detection times |
| title_fullStr |
Modeling stream fish distributions using interval‐censored detection times |
| title_full_unstemmed |
Modeling stream fish distributions using interval‐censored detection times |
| title_sort |
modeling stream fish distributions using interval‐censored detection times |
| description |
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. |
| publisher |
John Wiley and Sons Inc. |
| publishDate |
2016 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4984523/ |
| _version_ |
1613627364895358976 |