Predicting the spatial expansion of an animal population with presence‐only data
Abstract Predictive models can improve the efficiency of wildlife management by guiding actions at the local, landscape and regional scales. In recent decades, a vast range of modelling techniques have been developed to predict species distributions and patterns of population spread. However, data l...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-11-01
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| Series: | Ecology and Evolution |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ece3.10778 |
| _version_ | 1827630903523803136 |
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| author | Owain Barton John R. Healey Line S. Cordes Andrew J. Davies Graeme Shannon |
| author_facet | Owain Barton John R. Healey Line S. Cordes Andrew J. Davies Graeme Shannon |
| author_sort | Owain Barton |
| collection | DOAJ |
| description | Abstract Predictive models can improve the efficiency of wildlife management by guiding actions at the local, landscape and regional scales. In recent decades, a vast range of modelling techniques have been developed to predict species distributions and patterns of population spread. However, data limitations often constrain the precision and biological realism of models, which make them less useful for supporting decision‐making. Complex models can also be challenging to evaluate, and the results are often difficult to interpret for wildlife management practitioners. There is therefore a need to develop techniques that are appropriately robust, but also accessible to a range of end users. We developed a hybrid species distribution model that utilises commonly available presence‐only distribution data and minimal demographic information to predict the spread of roe deer (Capreolus caprelous) in Great Britain. We take a novel approach to representing the environment in the model by constraining the size of habitat patches to the home‐range area of an individual. Population dynamics are then simplified to a set of generic rules describing patch occupancy. The model is constructed and evaluated using data from a populated region (England and Scotland) and applied to predict regional‐scale patterns of spread in a novel region (Wales). It is used to forecast the relative timing of colonisation events and identify important areas for targeted surveillance and management. The study demonstrates the utility of presence‐only data for predicting the spread of animal species and describes a method of reducing model complexity while retaining important environmental detail and biological realism. Our modelling approach provides a much‐needed opportunity for users without specialist expertise in computer coding to leverage limited data and make robust, easily interpretable predictions of spread to inform proactive population management. |
| first_indexed | 2024-03-09T14:13:34Z |
| format | Article |
| id | doaj.art-3ccc3ec8e47645d1b4711e23b92911de |
| institution | Directory Open Access Journal |
| issn | 2045-7758 |
| language | English |
| last_indexed | 2024-03-09T14:13:34Z |
| publishDate | 2023-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecology and Evolution |
| spelling | doaj.art-3ccc3ec8e47645d1b4711e23b92911de2023-11-29T05:44:08ZengWileyEcology and Evolution2045-77582023-11-011311n/an/a10.1002/ece3.10778Predicting the spatial expansion of an animal population with presence‐only dataOwain Barton0John R. Healey1Line S. Cordes2Andrew J. Davies3Graeme Shannon4School of Natural Sciences Bangor University Bangor UKSchool of Natural Sciences Bangor University Bangor UKNorwegian Institute for Nature Research Trondheim NorwayDepartment of Biological Sciences University of Rhode Island Kingston Rhode Island USASchool of Natural Sciences Bangor University Bangor UKAbstract Predictive models can improve the efficiency of wildlife management by guiding actions at the local, landscape and regional scales. In recent decades, a vast range of modelling techniques have been developed to predict species distributions and patterns of population spread. However, data limitations often constrain the precision and biological realism of models, which make them less useful for supporting decision‐making. Complex models can also be challenging to evaluate, and the results are often difficult to interpret for wildlife management practitioners. There is therefore a need to develop techniques that are appropriately robust, but also accessible to a range of end users. We developed a hybrid species distribution model that utilises commonly available presence‐only distribution data and minimal demographic information to predict the spread of roe deer (Capreolus caprelous) in Great Britain. We take a novel approach to representing the environment in the model by constraining the size of habitat patches to the home‐range area of an individual. Population dynamics are then simplified to a set of generic rules describing patch occupancy. The model is constructed and evaluated using data from a populated region (England and Scotland) and applied to predict regional‐scale patterns of spread in a novel region (Wales). It is used to forecast the relative timing of colonisation events and identify important areas for targeted surveillance and management. The study demonstrates the utility of presence‐only data for predicting the spread of animal species and describes a method of reducing model complexity while retaining important environmental detail and biological realism. Our modelling approach provides a much‐needed opportunity for users without specialist expertise in computer coding to leverage limited data and make robust, easily interpretable predictions of spread to inform proactive population management.https://doi.org/10.1002/ece3.10778Capreolus capreolushybrid modelmechanisticpopulation managementpresence‐only datarange expansion |
| spellingShingle | Owain Barton John R. Healey Line S. Cordes Andrew J. Davies Graeme Shannon Predicting the spatial expansion of an animal population with presence‐only data Ecology and Evolution Capreolus capreolus hybrid model mechanistic population management presence‐only data range expansion |
| title | Predicting the spatial expansion of an animal population with presence‐only data |
| title_full | Predicting the spatial expansion of an animal population with presence‐only data |
| title_fullStr | Predicting the spatial expansion of an animal population with presence‐only data |
| title_full_unstemmed | Predicting the spatial expansion of an animal population with presence‐only data |
| title_short | Predicting the spatial expansion of an animal population with presence‐only data |
| title_sort | predicting the spatial expansion of an animal population with presence only data |
| topic | Capreolus capreolus hybrid model mechanistic population management presence‐only data range expansion |
| url | https://doi.org/10.1002/ece3.10778 |
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