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Strength of evidence for density dependence in abundance time series of 1198 species
Population limitation is a fundamental tenet of ecology, but the relative roles of exogenous and endogenous mechanisms remain unquantified for most species. Here we used multi-model inference (MMI), a form of model averaging, based on information theory (Akaike's Information Criterion) to evalu...
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Published in: | Ecology (Durham) 2006-06, Vol.87 (6), p.1445-1451 |
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description | Population limitation is a fundamental tenet of ecology, but the relative roles of exogenous and endogenous mechanisms remain unquantified for most species. Here we used multi-model inference (MMI), a form of model averaging, based on information theory (Akaike's Information Criterion) to evaluate the relative strength of evidence for density-dependent and density-independent population dynamical models in long-term abundance time series of 1198 species. We also compared the MMI results to more classic methods for detecting density dependence: Neyman-Pearson hypothesis-testing and best-model selection using the Bayesian Information Criterion or cross-validation. Using MMI on our large database, we show that density dependence is a pervasive feature of population dynamics (median MMI support for density dependence = 74.7-92.2%), and that this holds across widely different taxa. The weight of evidence for density dependence varied among species but increased consistently with the number of generations monitored. Best-model selection methods yielded similar results to MMI (a density-dependent model was favored in 66.2-93.9% of species time series), while the hypothesis-testing methods detected density dependence less frequently (32.6-49.8%). There were no obvious differences in the prevalence of density dependence across major taxonomic groups under any of the statistical methods used. These results underscore the value of using multiple modes of analysis to quantify the relative empirical support for a set of working hypotheses that encompass a range of realistic population dynamical behaviors. |
doi_str_mv | 10.1890/0012-9658(2006)87[1445:SOEFDD]2.0.CO;2 |
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A.</creator><creatorcontrib>Brook, Barry W. ; Bradshaw, Corey J. A.</creatorcontrib><description>Population limitation is a fundamental tenet of ecology, but the relative roles of exogenous and endogenous mechanisms remain unquantified for most species. Here we used multi-model inference (MMI), a form of model averaging, based on information theory (Akaike's Information Criterion) to evaluate the relative strength of evidence for density-dependent and density-independent population dynamical models in long-term abundance time series of 1198 species. We also compared the MMI results to more classic methods for detecting density dependence: Neyman-Pearson hypothesis-testing and best-model selection using the Bayesian Information Criterion or cross-validation. Using MMI on our large database, we show that density dependence is a pervasive feature of population dynamics (median MMI support for density dependence = 74.7-92.2%), and that this holds across widely different taxa. The weight of evidence for density dependence varied among species but increased consistently with the number of generations monitored. Best-model selection methods yielded similar results to MMI (a density-dependent model was favored in 66.2-93.9% of species time series), while the hypothesis-testing methods detected density dependence less frequently (32.6-49.8%). There were no obvious differences in the prevalence of density dependence across major taxonomic groups under any of the statistical methods used. These results underscore the value of using multiple modes of analysis to quantify the relative empirical support for a set of working hypotheses that encompass a range of realistic population dynamical behaviors.</description><identifier>ISSN: 0012-9658</identifier><identifier>EISSN: 1939-9170</identifier><identifier>DOI: 10.1890/0012-9658(2006)87[1445:SOEFDD]2.0.CO;2</identifier><identifier>PMID: 16869419</identifier><identifier>CODEN: ECGYAQ</identifier><language>eng</language><publisher>Washington, DC: Ecological Society of America</publisher><subject>Akaike information criterion ; Animal and plant ecology ; Animal populations ; Animal, plant and microbial ecology ; Biodiversity ; Biological and medical sciences ; Biological taxonomies ; community ecology ; Demecology ; Density ; density dependence ; Dependence ; Ecological modeling ; Ecosystem ; endogenous population dynamics ; Feedback ; Fundamental and applied biological sciences. Psychology ; General aspects ; Models, Biological ; multi-model inference ; negative feedback ; Plant Development ; Population density ; Population dynamics ; Population ecology ; population regulation ; Sampling errors ; Selection, Genetic ; statistical analysis ; Stochastic Processes ; strength of evidence ; Time dependence ; Time series ; time series analysis ; Time series models</subject><ispartof>Ecology (Durham), 2006-06, Vol.87 (6), p.1445-1451</ispartof><rights>Copyright 2006 Ecological Society of America</rights><rights>2006 by the Ecological Society of America</rights><rights>2006 INIST-CNRS</rights><rights>Copyright Ecological Society of America Jun 2006</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c6145-18006a06494eda2e77f3a59913bd986314a35210c206b40aa44a6f57dd545b2e3</citedby><cites>FETCH-LOGICAL-c6145-18006a06494eda2e77f3a59913bd986314a35210c206b40aa44a6f57dd545b2e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/20069094$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/20069094$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,58238,58471</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17887155$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16869419$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Brook, Barry W.</creatorcontrib><creatorcontrib>Bradshaw, Corey J. A.</creatorcontrib><title>Strength of evidence for density dependence in abundance time series of 1198 species</title><title>Ecology (Durham)</title><addtitle>Ecology</addtitle><description>Population limitation is a fundamental tenet of ecology, but the relative roles of exogenous and endogenous mechanisms remain unquantified for most species. Here we used multi-model inference (MMI), a form of model averaging, based on information theory (Akaike's Information Criterion) to evaluate the relative strength of evidence for density-dependent and density-independent population dynamical models in long-term abundance time series of 1198 species. We also compared the MMI results to more classic methods for detecting density dependence: Neyman-Pearson hypothesis-testing and best-model selection using the Bayesian Information Criterion or cross-validation. Using MMI on our large database, we show that density dependence is a pervasive feature of population dynamics (median MMI support for density dependence = 74.7-92.2%), and that this holds across widely different taxa. The weight of evidence for density dependence varied among species but increased consistently with the number of generations monitored. Best-model selection methods yielded similar results to MMI (a density-dependent model was favored in 66.2-93.9% of species time series), while the hypothesis-testing methods detected density dependence less frequently (32.6-49.8%). There were no obvious differences in the prevalence of density dependence across major taxonomic groups under any of the statistical methods used. These results underscore the value of using multiple modes of analysis to quantify the relative empirical support for a set of working hypotheses that encompass a range of realistic population dynamical behaviors.</description><subject>Akaike information criterion</subject><subject>Animal and plant ecology</subject><subject>Animal populations</subject><subject>Animal, plant and microbial ecology</subject><subject>Biodiversity</subject><subject>Biological and medical sciences</subject><subject>Biological taxonomies</subject><subject>community ecology</subject><subject>Demecology</subject><subject>Density</subject><subject>density dependence</subject><subject>Dependence</subject><subject>Ecological modeling</subject><subject>Ecosystem</subject><subject>endogenous population dynamics</subject><subject>Feedback</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General aspects</subject><subject>Models, Biological</subject><subject>multi-model inference</subject><subject>negative feedback</subject><subject>Plant Development</subject><subject>Population density</subject><subject>Population dynamics</subject><subject>Population ecology</subject><subject>population regulation</subject><subject>Sampling errors</subject><subject>Selection, Genetic</subject><subject>statistical analysis</subject><subject>Stochastic Processes</subject><subject>strength of evidence</subject><subject>Time dependence</subject><subject>Time series</subject><subject>time series analysis</subject><subject>Time series models</subject><issn>0012-9658</issn><issn>1939-9170</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><recordid>eNqdkV2L1DAUhoso7rj6E9QiKHrR8Zw0n3ol3RkVFgac3QsRCWmbrh067Zi0yvx7EzrsgjeCucnHefKejzdJlghLlAreAiDJFGfyNQHgb6T4hpSyd9vNan1x8Z0sYVls3pN7yQJVrjKFAu4ni9tPZ8kj73cQFlL5MDlDLrmiqBbJ1XZ0tr8Zf6RDk9pfbW37yqbN4NJw8u14DPvB9vNz26emnPraxMvY7m3qrWutj38RlUz9wVbh_jh50JjO2yen_Ty5Xq-uik_Z5ebj5-LDZVZxpCxDGVoxwKmitjbECtHkhimFeVkryXOkJmcEoSLASwrGUGp4w0RdM8pKYvPz5NWse3DDz8n6Ue9bX9muM70dJq9DlzLnAP8Ew9CIYpwE8MVf4G6YXB-a0AQVIAoR1dYzVLnBe2cbfXDt3rijRtDRLR0Hr-PgdXRLS6GjW3p2SxMNutjomO3ZKdtU7m19J3OyJwAvT4DxlekaFybf-jtOSCmQscB9mbnfbWeP_1mOXhVfY1wKHsNB9OksuvPj4G5FI6JA0RB_PscbM2hz40Jh11sCmAOInEmQ-R_tYseW</recordid><startdate>200606</startdate><enddate>200606</enddate><creator>Brook, Barry W.</creator><creator>Bradshaw, Corey J. A.</creator><general>Ecological Society of America</general><scope>FBQ</scope><scope>IQODW</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>7QG</scope><scope>7SN</scope><scope>7SS</scope><scope>7ST</scope><scope>7T7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>RC3</scope><scope>SOI</scope><scope>7U6</scope><scope>7X8</scope></search><sort><creationdate>200606</creationdate><title>Strength of evidence for density dependence in abundance time series of 1198 species</title><author>Brook, Barry W. ; Bradshaw, Corey J. A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c6145-18006a06494eda2e77f3a59913bd986314a35210c206b40aa44a6f57dd545b2e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Akaike information criterion</topic><topic>Animal and plant ecology</topic><topic>Animal populations</topic><topic>Animal, plant and microbial ecology</topic><topic>Biodiversity</topic><topic>Biological and medical sciences</topic><topic>Biological taxonomies</topic><topic>community ecology</topic><topic>Demecology</topic><topic>Density</topic><topic>density dependence</topic><topic>Dependence</topic><topic>Ecological modeling</topic><topic>Ecosystem</topic><topic>endogenous population dynamics</topic><topic>Feedback</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General aspects</topic><topic>Models, Biological</topic><topic>multi-model inference</topic><topic>negative feedback</topic><topic>Plant Development</topic><topic>Population density</topic><topic>Population dynamics</topic><topic>Population ecology</topic><topic>population regulation</topic><topic>Sampling errors</topic><topic>Selection, Genetic</topic><topic>statistical analysis</topic><topic>Stochastic Processes</topic><topic>strength of evidence</topic><topic>Time dependence</topic><topic>Time series</topic><topic>time series analysis</topic><topic>Time series models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Brook, Barry W.</creatorcontrib><creatorcontrib>Bradshaw, Corey J. 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A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Strength of evidence for density dependence in abundance time series of 1198 species</atitle><jtitle>Ecology (Durham)</jtitle><addtitle>Ecology</addtitle><date>2006-06</date><risdate>2006</risdate><volume>87</volume><issue>6</issue><spage>1445</spage><epage>1451</epage><pages>1445-1451</pages><issn>0012-9658</issn><eissn>1939-9170</eissn><coden>ECGYAQ</coden><abstract>Population limitation is a fundamental tenet of ecology, but the relative roles of exogenous and endogenous mechanisms remain unquantified for most species. Here we used multi-model inference (MMI), a form of model averaging, based on information theory (Akaike's Information Criterion) to evaluate the relative strength of evidence for density-dependent and density-independent population dynamical models in long-term abundance time series of 1198 species. We also compared the MMI results to more classic methods for detecting density dependence: Neyman-Pearson hypothesis-testing and best-model selection using the Bayesian Information Criterion or cross-validation. Using MMI on our large database, we show that density dependence is a pervasive feature of population dynamics (median MMI support for density dependence = 74.7-92.2%), and that this holds across widely different taxa. The weight of evidence for density dependence varied among species but increased consistently with the number of generations monitored. Best-model selection methods yielded similar results to MMI (a density-dependent model was favored in 66.2-93.9% of species time series), while the hypothesis-testing methods detected density dependence less frequently (32.6-49.8%). There were no obvious differences in the prevalence of density dependence across major taxonomic groups under any of the statistical methods used. These results underscore the value of using multiple modes of analysis to quantify the relative empirical support for a set of working hypotheses that encompass a range of realistic population dynamical behaviors.</abstract><cop>Washington, DC</cop><pub>Ecological Society of America</pub><pmid>16869419</pmid><doi>10.1890/0012-9658(2006)87[1445:SOEFDD]2.0.CO;2</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Akaike information criterion Animal and plant ecology Animal populations Animal, plant and microbial ecology Biodiversity Biological and medical sciences Biological taxonomies community ecology Demecology Density density dependence Dependence Ecological modeling Ecosystem endogenous population dynamics Feedback Fundamental and applied biological sciences. Psychology General aspects Models, Biological multi-model inference negative feedback Plant Development Population density Population dynamics Population ecology population regulation Sampling errors Selection, Genetic statistical analysis Stochastic Processes strength of evidence Time dependence Time series time series analysis Time series models |
title | Strength of evidence for density dependence in abundance time series of 1198 species |
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