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Occupancy models with autocorrelated detection heterogeneity.

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Title: Occupancy models with autocorrelated detection heterogeneity.
Authors: Hepler, Staci1 (AUTHOR) heplersa@wfu.edu, Yang, Biqing2 (AUTHOR)
Source: Environmental & Ecological Statistics. Sep2024, Vol. 31 Issue 3, p777-800. 24p.
Subject Terms: *National parks & reserves, *Gazelles, *Statistical models, *Zebras, *Data modeling
Abstract: Occupancy models are commonly used in statistical ecology to model binary detection/non-detection data. These hierarchical models make a distinction between detection/non-detection and presence/absence by treating true occupancy as a latent process. In this paper, we propose a multi-species, multi-season occupancy model to jointly model detection/non-detection data on multiple species. Existing literature has shown that models that account for various sources of dependence in the latent occupancy process improve estimation, especially in the single-survey setting. However, the detection process in the model has not received much attention, even though detectability of a species is expected to relate to the detectability of other species and to detectability in previous time periods. In this work, we propose a model to capture this phenomenon by incorporating a multivariate temporal random effect in the detection process. We perform a simulation study to show that the proposed model yields more accurate inference than models that only use covariates to quantify detection. We apply our model to detection/non-detection data on three species—Thomson's gazelle, zebra, and wildebeest—in Serengeti National Park of Tanzania, Africa. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 179295687
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
  Group: Ti
  Data: Occupancy models with autocorrelated detection heterogeneity.
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  Data: <searchLink fieldCode="AR" term="%22Hepler%2C+Staci%22">Hepler, Staci</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> heplersa@wfu.edu</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Biqing%22">Yang, Biqing</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Environmental+%26+Ecological+Statistics%22">Environmental & Ecological Statistics</searchLink>. Sep2024, Vol. 31 Issue 3, p777-800. 24p.
– Name: Subject
  Label: Subject Terms
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  Data: *<searchLink fieldCode="DE" term="%22National+parks+%26+reserves%22">National parks & reserves</searchLink><br />*<searchLink fieldCode="DE" term="%22Gazelles%22">Gazelles</searchLink><br />*<searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br />*<searchLink fieldCode="DE" term="%22Zebras%22">Zebras</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+modeling%22">Data modeling</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Occupancy models are commonly used in statistical ecology to model binary detection/non-detection data. These hierarchical models make a distinction between detection/non-detection and presence/absence by treating true occupancy as a latent process. In this paper, we propose a multi-species, multi-season occupancy model to jointly model detection/non-detection data on multiple species. Existing literature has shown that models that account for various sources of dependence in the latent occupancy process improve estimation, especially in the single-survey setting. However, the detection process in the model has not received much attention, even though detectability of a species is expected to relate to the detectability of other species and to detectability in previous time periods. In this work, we propose a model to capture this phenomenon by incorporating a multivariate temporal random effect in the detection process. We perform a simulation study to show that the proposed model yields more accurate inference than models that only use covariates to quantify detection. We apply our model to detection/non-detection data on three species—Thomson's gazelle, zebra, and wildebeest—in Serengeti National Park of Tanzania, Africa. [ABSTRACT FROM AUTHOR]
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      – Type: doi
        Value: 10.1007/s10651-024-00624-8
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      – Code: eng
        Text: English
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        PageCount: 24
        StartPage: 777
    Subjects:
      – SubjectFull: National parks & reserves
        Type: general
      – SubjectFull: Gazelles
        Type: general
      – SubjectFull: Statistical models
        Type: general
      – SubjectFull: Zebras
        Type: general
      – SubjectFull: Data modeling
        Type: general
    Titles:
      – TitleFull: Occupancy models with autocorrelated detection heterogeneity.
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            NameFull: Hepler, Staci
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            NameFull: Yang, Biqing
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            – D: 01
              M: 09
              Text: Sep2024
              Type: published
              Y: 2024
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            – TitleFull: Environmental & Ecological Statistics
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