Spatially explicit ecological modeling improves empirical characterization of plant pathogen dispersal
Abstract Dispersal is a key ecological process, but it remains difficult to measure. By recording numbers of dispersed individuals at different distances from the source, one acquires a dispersal gradient. Dispersal gradients contain information on dispersal, but they are influenced by the spatial e...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-04-01
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| Series: | Plant-Environment Interactions |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/pei3.10104 |
| _version_ | 1797838507393155072 |
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| author | Petteri Karisto Frédéric Suffert Alexey Mikaberidze |
| author_facet | Petteri Karisto Frédéric Suffert Alexey Mikaberidze |
| author_sort | Petteri Karisto |
| collection | DOAJ |
| description | Abstract Dispersal is a key ecological process, but it remains difficult to measure. By recording numbers of dispersed individuals at different distances from the source, one acquires a dispersal gradient. Dispersal gradients contain information on dispersal, but they are influenced by the spatial extent of the source. How can we separate the two contributions to extract knowledge about dispersal? One could use a small, point‐like source for which a dispersal gradient represents a dispersal kernel, which quantifies the probability of an individual dispersal event from a source to a destination. However, the validity of this approximation cannot be established before conducting measurements. This represents a key challenge hindering progress in characterization of dispersal. To overcome it, we formulated a theory that incorporates the spatial extent of sources to estimate dispersal kernels from dispersal gradients. Using this theory, we re‐analyzed published dispersal gradients for three major plant pathogens. We demonstrated that the three pathogens disperse over substantially shorter distances compared to conventional estimates. This method will allow the researchers to re‐analyze a vast number of existing dispersal gradients to improve our knowledge about dispersal. The improved knowledge has potential to advance our understanding of species' range expansions and shifts, and inform management of weeds and diseases in crops. |
| first_indexed | 2024-04-09T15:41:58Z |
| format | Article |
| id | doaj.art-637f21fcca7d4c79b2d65f048b7d8daa |
| institution | Directory Open Access Journal |
| issn | 2575-6265 |
| language | English |
| last_indexed | 2024-04-09T15:41:58Z |
| publishDate | 2023-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Plant-Environment Interactions |
| spelling | doaj.art-637f21fcca7d4c79b2d65f048b7d8daa2023-04-27T07:40:06ZengWileyPlant-Environment Interactions2575-62652023-04-0142869610.1002/pei3.10104Spatially explicit ecological modeling improves empirical characterization of plant pathogen dispersalPetteri Karisto0Frédéric Suffert1Alexey Mikaberidze2Plant Pathology Group, Institute of Integrative Biology ETH Zurich Zurich SwitzerlandUniversité Paris‐Saclay, INRAE, AgroParisTech, UMR BIOGER 78850 Thiverval‐Grignon FrancePlant Pathology Group, Institute of Integrative Biology ETH Zurich Zurich SwitzerlandAbstract Dispersal is a key ecological process, but it remains difficult to measure. By recording numbers of dispersed individuals at different distances from the source, one acquires a dispersal gradient. Dispersal gradients contain information on dispersal, but they are influenced by the spatial extent of the source. How can we separate the two contributions to extract knowledge about dispersal? One could use a small, point‐like source for which a dispersal gradient represents a dispersal kernel, which quantifies the probability of an individual dispersal event from a source to a destination. However, the validity of this approximation cannot be established before conducting measurements. This represents a key challenge hindering progress in characterization of dispersal. To overcome it, we formulated a theory that incorporates the spatial extent of sources to estimate dispersal kernels from dispersal gradients. Using this theory, we re‐analyzed published dispersal gradients for three major plant pathogens. We demonstrated that the three pathogens disperse over substantially shorter distances compared to conventional estimates. This method will allow the researchers to re‐analyze a vast number of existing dispersal gradients to improve our knowledge about dispersal. The improved knowledge has potential to advance our understanding of species' range expansions and shifts, and inform management of weeds and diseases in crops.https://doi.org/10.1002/pei3.10104dispersal ecologydispersal gradientdispersal kerneldispersal theoryexperimental designmathematical modeling |
| spellingShingle | Petteri Karisto Frédéric Suffert Alexey Mikaberidze Spatially explicit ecological modeling improves empirical characterization of plant pathogen dispersal Plant-Environment Interactions dispersal ecology dispersal gradient dispersal kernel dispersal theory experimental design mathematical modeling |
| title | Spatially explicit ecological modeling improves empirical characterization of plant pathogen dispersal |
| title_full | Spatially explicit ecological modeling improves empirical characterization of plant pathogen dispersal |
| title_fullStr | Spatially explicit ecological modeling improves empirical characterization of plant pathogen dispersal |
| title_full_unstemmed | Spatially explicit ecological modeling improves empirical characterization of plant pathogen dispersal |
| title_short | Spatially explicit ecological modeling improves empirical characterization of plant pathogen dispersal |
| title_sort | spatially explicit ecological modeling improves empirical characterization of plant pathogen dispersal |
| topic | dispersal ecology dispersal gradient dispersal kernel dispersal theory experimental design mathematical modeling |
| url | https://doi.org/10.1002/pei3.10104 |
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