Quantifying ecological memory in plant and ecosystem processes
The role of time in ecology has a long history of investigation, but ecologists have largely restricted their attention to the influence of concurrent abiotic conditions on rates and magnitudes of important ecological processes. Recently, however, ecologists have improved their understanding of ecol...
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| Veröffentlicht in: | Ecology letters 2015-03, Vol.18 (3), p.221-235 |
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| container_title | Ecology letters |
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| creator | Ogle, Kiona Barber, Jarrett J. Barron-Gafford, Greg A. Bentley, Lisa Patrick Young, Jessica M. Huxman, Travis E. Loik, Michael E. Tissue, David T. |
| description | The role of time in ecology has a long history of investigation, but ecologists have largely restricted their attention to the influence of concurrent abiotic conditions on rates and magnitudes of important ecological processes. Recently, however, ecologists have improved their understanding of ecological processes by explicitly considering the effects of antecedent conditions. To broadly help in studying the role of time, we evaluate the length, temporal pattern, and strength of memory with respect to the influence of antecedent conditions on current ecological dynamics. We developed the stochastic antecedent modelling (SAM) framework as a flexible analytic approach for evaluating exogenous and endogenous process components of memory in a system of interest. We designed SAM to be useful in revealing novel insights promoting further study, illustrated in four examples with different degrees of complexity and varying time scales: stomatal conductance, soil respiration, ecosystem productivity, and tree growth. Models with antecedent effects explained an additional 18–28% of response variation compared to models without antecedent effects. Moreover, SAM also enabled identification of potential mechanisms that underlie components of memory, thus revealing temporal properties that are not apparent from traditional treatments of ecological time‐series data and facilitating new hypothesis generation and additional research. |
| doi_str_mv | 10.1111/ele.12399 |
| format | Article |
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Recently, however, ecologists have improved their understanding of ecological processes by explicitly considering the effects of antecedent conditions. To broadly help in studying the role of time, we evaluate the length, temporal pattern, and strength of memory with respect to the influence of antecedent conditions on current ecological dynamics. We developed the stochastic antecedent modelling (SAM) framework as a flexible analytic approach for evaluating exogenous and endogenous process components of memory in a system of interest. We designed SAM to be useful in revealing novel insights promoting further study, illustrated in four examples with different degrees of complexity and varying time scales: stomatal conductance, soil respiration, ecosystem productivity, and tree growth. Models with antecedent effects explained an additional 18–28% of response variation compared to models without antecedent effects. Moreover, SAM also enabled identification of potential mechanisms that underlie components of memory, thus revealing temporal properties that are not apparent from traditional treatments of ecological time‐series data and facilitating new hypothesis generation and additional research.</description><identifier>ISSN: 1461-023X</identifier><identifier>EISSN: 1461-0248</identifier><identifier>DOI: 10.1111/ele.12399</identifier><identifier>PMID: 25522778</identifier><language>eng</language><publisher>England: Blackwell Publishing Ltd</publisher><subject>Antecedent conditions ; Bayes Theorem ; Ecological and Environmental Phenomena ; Ecologists ; Ecology ; Ecosystem ; Ecosystems ; hierarchical Bayesian model ; lag effects ; legacy effects ; Models, Biological ; Models, Statistical ; net primary production ; Soil ; soil respiration ; Stochastic Processes ; Stomata ; Stomatal conductance ; Time ; time-series ; tree growth ; tree rings ; Trees</subject><ispartof>Ecology letters, 2015-03, Vol.18 (3), p.221-235</ispartof><rights>2014 John Wiley & Sons Ltd/CNRS</rights><rights>2014 John Wiley & Sons Ltd/CNRS.</rights><rights>Copyright © 2015 John Wiley & Sons Ltd/CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4859-64dcecef54047deebc593fd4270d58f48460ab1d4ccf83ef738bbc70cc27c42b3</citedby><cites>FETCH-LOGICAL-c4859-64dcecef54047deebc593fd4270d58f48460ab1d4ccf83ef738bbc70cc27c42b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fele.12399$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fele.12399$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25522778$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Cleland, Elsa</contributor><creatorcontrib>Ogle, Kiona</creatorcontrib><creatorcontrib>Barber, Jarrett J.</creatorcontrib><creatorcontrib>Barron-Gafford, Greg A.</creatorcontrib><creatorcontrib>Bentley, Lisa Patrick</creatorcontrib><creatorcontrib>Young, Jessica M.</creatorcontrib><creatorcontrib>Huxman, Travis E.</creatorcontrib><creatorcontrib>Loik, Michael E.</creatorcontrib><creatorcontrib>Tissue, David T.</creatorcontrib><title>Quantifying ecological memory in plant and ecosystem processes</title><title>Ecology letters</title><addtitle>Ecol Lett</addtitle><description>The role of time in ecology has a long history of investigation, but ecologists have largely restricted their attention to the influence of concurrent abiotic conditions on rates and magnitudes of important ecological processes. 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Moreover, SAM also enabled identification of potential mechanisms that underlie components of memory, thus revealing temporal properties that are not apparent from traditional treatments of ecological time‐series data and facilitating new hypothesis generation and additional research.</description><subject>Antecedent conditions</subject><subject>Bayes Theorem</subject><subject>Ecological and Environmental Phenomena</subject><subject>Ecologists</subject><subject>Ecology</subject><subject>Ecosystem</subject><subject>Ecosystems</subject><subject>hierarchical Bayesian model</subject><subject>lag effects</subject><subject>legacy effects</subject><subject>Models, Biological</subject><subject>Models, Statistical</subject><subject>net primary production</subject><subject>Soil</subject><subject>soil respiration</subject><subject>Stochastic Processes</subject><subject>Stomata</subject><subject>Stomatal conductance</subject><subject>Time</subject><subject>time-series</subject><subject>tree growth</subject><subject>tree rings</subject><subject>Trees</subject><issn>1461-023X</issn><issn>1461-0248</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kMtKw0AUhgdRrFYXvoAE3Ogi7VwzyUaQUqtQlIpicTMkkzMlNZeaadC8vVN7WQg6mzMw3_nmnB-hM4J7xJ0-5NAjlEXRHjoiPCA-pjzc393ZtIOOrZ1jTGgkySHqUCEolTI8QteTJi6XmWmzcuaBrvJqluk49wooqrr1stJb5A7w4jJdPdvWLqHwFnWlwVqwJ-jAxLmF003topfb4fPgzh8_ju4HN2Nf81BEfsBTDRqM4JjLFCDRImIm5VTiVISGhzzAcUJSrrUJGRjJwiTREmtNpeY0YV10ufa6nz8asEtVZFZD7maDqrGKBGK1EsbMoRe_0HnV1KWbTjEcCMoZJeI_yrm4FBQH1FFXa0rXlbU1GLWosyKuW0WwWkWvXPTqJ3rHnm-MTVJAuiO3WTugvwY-sxzav01qOB5ulf66I3Opf-064vpdBZJJoV4fRmoylYM3OnpSAfsGcfybaQ</recordid><startdate>201503</startdate><enddate>201503</enddate><creator>Ogle, Kiona</creator><creator>Barber, Jarrett J.</creator><creator>Barron-Gafford, Greg A.</creator><creator>Bentley, Lisa Patrick</creator><creator>Young, Jessica M.</creator><creator>Huxman, Travis E.</creator><creator>Loik, Michael E.</creator><creator>Tissue, David T.</creator><general>Blackwell Publishing Ltd</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7SS</scope><scope>7U9</scope><scope>C1K</scope><scope>H94</scope><scope>M7N</scope><scope>7X8</scope></search><sort><creationdate>201503</creationdate><title>Quantifying ecological memory in plant and ecosystem processes</title><author>Ogle, Kiona ; Barber, Jarrett J. ; Barron-Gafford, Greg A. ; Bentley, Lisa Patrick ; Young, Jessica M. ; Huxman, Travis E. ; Loik, Michael E. ; Tissue, David T.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4859-64dcecef54047deebc593fd4270d58f48460ab1d4ccf83ef738bbc70cc27c42b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Antecedent conditions</topic><topic>Bayes Theorem</topic><topic>Ecological and Environmental Phenomena</topic><topic>Ecologists</topic><topic>Ecology</topic><topic>Ecosystem</topic><topic>Ecosystems</topic><topic>hierarchical Bayesian model</topic><topic>lag effects</topic><topic>legacy effects</topic><topic>Models, Biological</topic><topic>Models, Statistical</topic><topic>net primary production</topic><topic>Soil</topic><topic>soil respiration</topic><topic>Stochastic Processes</topic><topic>Stomata</topic><topic>Stomatal conductance</topic><topic>Time</topic><topic>time-series</topic><topic>tree growth</topic><topic>tree rings</topic><topic>Trees</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ogle, Kiona</creatorcontrib><creatorcontrib>Barber, Jarrett J.</creatorcontrib><creatorcontrib>Barron-Gafford, Greg A.</creatorcontrib><creatorcontrib>Bentley, Lisa Patrick</creatorcontrib><creatorcontrib>Young, Jessica M.</creatorcontrib><creatorcontrib>Huxman, Travis E.</creatorcontrib><creatorcontrib>Loik, Michael E.</creatorcontrib><creatorcontrib>Tissue, David T.</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Virology and AIDS Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>MEDLINE - Academic</collection><jtitle>Ecology letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ogle, Kiona</au><au>Barber, Jarrett J.</au><au>Barron-Gafford, Greg A.</au><au>Bentley, Lisa Patrick</au><au>Young, Jessica M.</au><au>Huxman, Travis E.</au><au>Loik, Michael E.</au><au>Tissue, David T.</au><au>Cleland, Elsa</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quantifying ecological memory in plant and ecosystem processes</atitle><jtitle>Ecology letters</jtitle><addtitle>Ecol Lett</addtitle><date>2015-03</date><risdate>2015</risdate><volume>18</volume><issue>3</issue><spage>221</spage><epage>235</epage><pages>221-235</pages><issn>1461-023X</issn><eissn>1461-0248</eissn><abstract>The role of time in ecology has a long history of investigation, but ecologists have largely restricted their attention to the influence of concurrent abiotic conditions on rates and magnitudes of important ecological processes. Recently, however, ecologists have improved their understanding of ecological processes by explicitly considering the effects of antecedent conditions. To broadly help in studying the role of time, we evaluate the length, temporal pattern, and strength of memory with respect to the influence of antecedent conditions on current ecological dynamics. We developed the stochastic antecedent modelling (SAM) framework as a flexible analytic approach for evaluating exogenous and endogenous process components of memory in a system of interest. We designed SAM to be useful in revealing novel insights promoting further study, illustrated in four examples with different degrees of complexity and varying time scales: stomatal conductance, soil respiration, ecosystem productivity, and tree growth. Models with antecedent effects explained an additional 18–28% of response variation compared to models without antecedent effects. Moreover, SAM also enabled identification of potential mechanisms that underlie components of memory, thus revealing temporal properties that are not apparent from traditional treatments of ecological time‐series data and facilitating new hypothesis generation and additional research.</abstract><cop>England</cop><pub>Blackwell Publishing Ltd</pub><pmid>25522778</pmid><doi>10.1111/ele.12399</doi><tpages>15</tpages></addata></record> |
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| source | MEDLINE; Wiley Online Library Journals Frontfile Complete |
| subjects | Antecedent conditions Bayes Theorem Ecological and Environmental Phenomena Ecologists Ecology Ecosystem Ecosystems hierarchical Bayesian model lag effects legacy effects Models, Biological Models, Statistical net primary production Soil soil respiration Stochastic Processes Stomata Stomatal conductance Time time-series tree growth tree rings Trees |
| title | Quantifying ecological memory in plant and ecosystem processes |
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