globalchange  > 气候减缓与适应
DOI: 10.1111/ele.12399
Scopus记录号: 2-s2.0-84922576994
论文题名:
Quantifying ecological memory in plant and ecosystem processes
作者: Ogle K.; Barber J.J.; Barron-Gafford G.A.; Bentley L.P.; Young J.M.; Huxman T.E.; Loik M.E.; Tissue D.T.
刊名: Ecology Letters
ISSN: 1461023X
EISSN: 1461-0248
出版年: 2015
卷: 18, 期:3
起始页码: 221
结束页码: 235
语种: 英语
英文关键词: Antecedent conditions ; Hierarchical Bayesian model ; Lag effects ; Legacy effects ; Net primary production ; Soil respiration ; Stomatal conductance ; Time-series ; Tree growth ; Tree rings
Scopus关键词: antecedent conditions ; ecosystem dynamics ; growth rate ; memory ; net primary production ; soil respiration ; stomatal conductance ; temporal variation ; time series ; woody plant ; soil ; Bayes theorem ; biological model ; ecosystem ; environmental aspects and related phenomena ; soil ; statistical model ; statistics ; time ; tree ; Bayes Theorem ; Ecological and Environmental Processes ; Ecosystem ; Models, Biological ; Models, Statistical ; Soil ; Stochastic Processes ; Time ; Trees
英文摘要: 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. © 2014 John Wiley & Sons Ltd/CNRS.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/107919
Appears in Collections:气候减缓与适应

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作者单位: School of Life Sciences, Arizona State University, Tempe, AZ, United States; School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, United States; School of Geography and Development and B2 Earthscience, University of Arizona, Tucson, AZ, United States; Environmental Change Institute, Oxford University Centre for the Environment, University of Oxford, Oxford, United Kingdom; International Arctic Research Center, University of Alaska, Fairbanks, AK, United States; Ecology and Evolutionary Biology and Center for Environmental Biology, University of California, Irvine, CA, United States; Department of Environmental Studies, University of California, Santa Cruz, CA, United States; Hawkesbury Institute for the Environment, University of Western Sydney, Richmond, NSW, Australia

Recommended Citation:
Ogle K.,Barber J.J.,Barron-Gafford G.A.,et al. Quantifying ecological memory in plant and ecosystem processes[J]. Ecology Letters,2015-01-01,18(3)
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