Establishing large mammal population trends from heterogeneous count data
Abstract Monitoring population trends is pivotal to effective wildlife conservation and management. However, wildlife managers often face many challenges when analyzing time series of census data due to heterogeneities in sampling methodology, strategy, or frequency. We present a three‐step method f...
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| Format: | Article |
| Language: | English |
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Wiley
2024-08-01
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| Series: | Ecology and Evolution |
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| Online Access: | https://doi.org/10.1002/ece3.70193 |
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| author | R. Pradel P.‐C. Renaud O. Pays P. Scholte J. O. Ogutu F. Hibert N. Casajus F. Mialhe H. Fritz |
| author_facet | R. Pradel P.‐C. Renaud O. Pays P. Scholte J. O. Ogutu F. Hibert N. Casajus F. Mialhe H. Fritz |
| author_sort | R. Pradel |
| collection | DOAJ |
| description | Abstract Monitoring population trends is pivotal to effective wildlife conservation and management. However, wildlife managers often face many challenges when analyzing time series of census data due to heterogeneities in sampling methodology, strategy, or frequency. We present a three‐step method for modeling trends from time series of count data obtained through multiple census methods (aerial or ground census and expert estimates). First, we design a heuristic for constructing credible intervals for all types of animal counts including those which come with no precision measure. Then, we define conversion factors for rendering aerial and ground counts comparable and provide values for broad classes of animals from an extant series of parallel aerial and ground censuses. Lastly, we construct a Bayesian model that takes the reconciled counts as input and estimates the relative growth rates between successive dates while accounting for their precisions. Importantly, we bound the rate of increase to account for the demographic potential of a species. We propose a flow chart for constructing credible intervals for various types of animal counts. We provide estimates of conversion factors for 5 broad classes of species. We describe the Bayesian model for calculating trends, annual rates of population increase, and the associated credible intervals. We develop a bespoke R CRAN package, popbayes, for implementing all the calculations that take the raw counts as input. It produces consistent and reliable estimates of population trends and annual rates of increase. Several examples from real populations of large African mammals illustrate the different features of our method. The approach is well‐suited for analyzing population trends for heterogeneous time series and allows a principled use of all the available historical census data. The method is general and flexible and applicable to various other animal species besides African large mammals. It can readily be adapted to test predictions of various hypotheses about drivers of rates of population increase. |
| format | Article |
| id | doaj-art-c26efb82e3ea4100805c61b6816b61ce |
| institution | OA Journals |
| issn | 2045-7758 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecology and Evolution |
| spelling | doaj-art-c26efb82e3ea4100805c61b6816b61ce2025-08-20T01:55:49ZengWileyEcology and Evolution2045-77582024-08-01148n/an/a10.1002/ece3.70193Establishing large mammal population trends from heterogeneous count dataR. Pradel0P.‐C. Renaud1O. Pays2P. Scholte3J. O. Ogutu4F. Hibert5N. Casajus6F. Mialhe7H. Fritz8CEFE, Univ Montpellier, CNRS, EPHE, IRD Montpellier FranceSustainability Research Unit, Faculty of Science, George Campus Nelson Mandela University George South AfricaUniv Angers, BIODIVAG Angers FranceGerman Development Cooperation (GIZ) Addis Ababa EthiopiaBiostatistics Unit, Institute of Crop Science University of Hohenheim Stuttgart GermanyUniversité de Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Evolutive UMR 5558 Villeurbanne FranceFRB‐CESAB Montpellier FranceDepartment of Geography, CNRS 5600 EVS University Lumière Lyon 2 Bron FranceSustainability Research Unit, Faculty of Science, George Campus Nelson Mandela University George South AfricaAbstract Monitoring population trends is pivotal to effective wildlife conservation and management. However, wildlife managers often face many challenges when analyzing time series of census data due to heterogeneities in sampling methodology, strategy, or frequency. We present a three‐step method for modeling trends from time series of count data obtained through multiple census methods (aerial or ground census and expert estimates). First, we design a heuristic for constructing credible intervals for all types of animal counts including those which come with no precision measure. Then, we define conversion factors for rendering aerial and ground counts comparable and provide values for broad classes of animals from an extant series of parallel aerial and ground censuses. Lastly, we construct a Bayesian model that takes the reconciled counts as input and estimates the relative growth rates between successive dates while accounting for their precisions. Importantly, we bound the rate of increase to account for the demographic potential of a species. We propose a flow chart for constructing credible intervals for various types of animal counts. We provide estimates of conversion factors for 5 broad classes of species. We describe the Bayesian model for calculating trends, annual rates of population increase, and the associated credible intervals. We develop a bespoke R CRAN package, popbayes, for implementing all the calculations that take the raw counts as input. It produces consistent and reliable estimates of population trends and annual rates of increase. Several examples from real populations of large African mammals illustrate the different features of our method. The approach is well‐suited for analyzing population trends for heterogeneous time series and allows a principled use of all the available historical census data. The method is general and flexible and applicable to various other animal species besides African large mammals. It can readily be adapted to test predictions of various hypotheses about drivers of rates of population increase.https://doi.org/10.1002/ece3.70193Bayesian modelingheterogeneous wildlife censusespartial countspopbayes R packagepopulation rate of increasepopulation trend |
| spellingShingle | R. Pradel P.‐C. Renaud O. Pays P. Scholte J. O. Ogutu F. Hibert N. Casajus F. Mialhe H. Fritz Establishing large mammal population trends from heterogeneous count data Ecology and Evolution Bayesian modeling heterogeneous wildlife censuses partial counts popbayes R package population rate of increase population trend |
| title | Establishing large mammal population trends from heterogeneous count data |
| title_full | Establishing large mammal population trends from heterogeneous count data |
| title_fullStr | Establishing large mammal population trends from heterogeneous count data |
| title_full_unstemmed | Establishing large mammal population trends from heterogeneous count data |
| title_short | Establishing large mammal population trends from heterogeneous count data |
| title_sort | establishing large mammal population trends from heterogeneous count data |
| topic | Bayesian modeling heterogeneous wildlife censuses partial counts popbayes R package population rate of increase population trend |
| url | https://doi.org/10.1002/ece3.70193 |
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