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| Main Authors: | , , , , , , , , , , |
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| Format: | Artículo científico |
| Language: | en |
| Published: |
Ecology letters
2025
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| Subjects: | |
| Online Access: | https://pubmed.ncbi.nlm.nih.gov/40269596/ |
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| _version_ | 1868266214102401026 |
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| author | Reuman, Daniel C Walter, Jonathan A Sheppard, Lawrence W Karatayev, Vadim A Kadiyala, Ethan S Lohmann, Amanda C Anderson, Thomas L Coombs, Nat J Haynes, Kyle J Hallett, Lauren M Castorani, Max C N |
| author_facet | Reuman, Daniel C Walter, Jonathan A Sheppard, Lawrence W Karatayev, Vadim A Kadiyala, Ethan S Lohmann, Amanda C Anderson, Thomas L Coombs, Nat J Haynes, Kyle J Hallett, Lauren M Castorani, Max C N Reuman, Daniel C Walter, Jonathan A Sheppard, Lawrence W Karatayev, Vadim A Kadiyala, Ethan S Lohmann, Amanda C Anderson, Thomas L Coombs, Nat J Haynes, Kyle J Hallett, Lauren M Castorani, Max C N |
| collection | PubMed - marine biology |
| contents | Insights Into Spatial Synchrony Enabled by Long-Term Data. Reuman, Daniel C Walter, Jonathan A Sheppard, Lawrence W Karatayev, Vadim A Kadiyala, Ethan S Lohmann, Amanda C Anderson, Thomas L Coombs, Nat J Haynes, Kyle J Hallett, Lauren M Castorani, Max C N Ecosystem Animals Models, Biological Ecology Population Dynamics Time Factors Spatial synchrony, the tendency for temporal fluctuations in an ecological variable to be positively associated in different locations, is a widespread and important phenomenon in ecology. Understanding of the nature and mechanisms of synchrony, and how synchrony is changing, has developed rapidly over the past 2 decades. Many recent developments have taken place through the study of long-term data sets. Here, we review and synthesise some important recent advances in spatial synchrony, with a focus on how long-term data have facilitated new understanding. Longer time series do not just facilitate better testing of existing ideas or more precise statistical results; more importantly, they also frequently make possible the expansion of conceptual paradigms. We discuss several such advances in our understanding of synchrony, how long-term data led to these advances, and how future studies can continue to improve the state of knowledge. |
| format | Artículo científico |
| id | pubmed_40269596 |
| institution | PubMed |
| language | en |
| publishDate | 2025 |
| publisher | Ecology letters |
| record_format | pubmed |
| spellingShingle | Insights Into Spatial Synchrony Enabled by Long-Term Data. Reuman, Daniel C Walter, Jonathan A Sheppard, Lawrence W Karatayev, Vadim A Kadiyala, Ethan S Lohmann, Amanda C Anderson, Thomas L Coombs, Nat J Haynes, Kyle J Hallett, Lauren M Castorani, Max C N Ecosystem Animals Models, Biological Ecology Population Dynamics Time Factors Insights Into Spatial Synchrony Enabled by Long-Term Data. Reuman, Daniel C Walter, Jonathan A Sheppard, Lawrence W Karatayev, Vadim A Kadiyala, Ethan S Lohmann, Amanda C Anderson, Thomas L Coombs, Nat J Haynes, Kyle J Hallett, Lauren M Castorani, Max C N Ecosystem Animals Models, Biological Ecology Population Dynamics Time Factors Spatial synchrony, the tendency for temporal fluctuations in an ecological variable to be positively associated in different locations, is a widespread and important phenomenon in ecology. Understanding of the nature and mechanisms of synchrony, and how synchrony is changing, has developed rapidly over the past 2 decades. Many recent developments have taken place through the study of long-term data sets. Here, we review and synthesise some important recent advances in spatial synchrony, with a focus on how long-term data have facilitated new understanding. Longer time series do not just facilitate better testing of existing ideas or more precise statistical results; more importantly, they also frequently make possible the expansion of conceptual paradigms. We discuss several such advances in our understanding of synchrony, how long-term data led to these advances, and how future studies can continue to improve the state of knowledge. |
| title | Insights Into Spatial Synchrony Enabled by Long-Term Data. |
| topic | Ecosystem Animals Models, Biological Ecology Population Dynamics Time Factors |
| url | https://pubmed.ncbi.nlm.nih.gov/40269596/ |