Community management indicators can conflate divergent phenomena: two challenges and a decomposition-based solution
Community management indicators can conflate divergent phenomena: two challenges and a decomposition-based solution
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社区管理指标可能会合并不同的现象：两个挑战和基于分解的解决方案
DOI：
10.1111/1365-2664.12787
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发表时间：
2017
影响因子：
5.7
通讯作者：
Frid, Chris
中科院分区：
文献类型：
--
作者：
Adams, Georgina L.;Jennings, Simon;Reuman, Daniel C.;Frid, Chris
社区指标用于评估生态社区的状况并指导管理。它们通常是根据监测数据计算出来的，这些数据通常是每年收集的。由于任何特定的社区指标都是复杂的多变量现象的单变量摘要，社区的不同变化可能导致指标的相同反应。采样变化也可以掩盖生态上重要的trends.This研究解决这些挑战的社区指标，重点是大型鱼类指标（LFI），国际上用来报告海洋鱼类群落的状态。LFI表示“大型”鱼类生物量占鱼类总生物量的比例，并根据拖网调查收集的物种大小丰度数据计算。我们开发了新的方法来分解物种，采样地点和季节的贡献，随着时间的推移，在LFI的趋势，并强调后果的评估和management.Our的结果表明，物种和地点作出了不同的贡献，在LFI指标的整体趋势，不同的贡献由几个数量级和符号。只有一小部分物种和地点推动了整体LFI趋势，其贡献随季节（春季和秋季调查）而变化。为了评估组分趋势的重要性，开发了一种重新排序方法。我们的方法可以推广应用到许多其他社区指标的基础上调查数据。合成和应用。我们用于分解社区指标和生成置信区间的新方法可以提取更多关于驱动“标题”指标的信息，为解决指标变化的多种可能解释和抽样变化带来的挑战提供了解决方案。建议分析指标组成部分对标题指标值的影响，因为分析结果使评估人员和管理人员能够确定和解释不同因素（例如物种、取样地点和季节）对标题指标值的影响。
Community indicators are used to assess the state of ecological communities and to guide management. They are usually calculated from monitoring data, often collected annually. Since any given community indicator provides a univariate summary of complex multivariate phenomena, different changes in the community may lead to the same response in the indicator. Sampling variation can also mask ecologically important trends.This study addresses these challenges for community indicators, with a focus on the large fish indicator (LFI), internationally used to report status of marine fish communities. The LFI expresses ‘large’ fish biomass as a proportion of total fish biomass and is calculated from species–size–abundance data collected on trawl surveys. We develop new methods to decompose the contributions of species, sampling locations and season to trends over time in the LFI, and highlight consequences for assessment and management.Our results showed that both species and locations made divergent contributions to overall trends in the LFI indicator, with contributions differing by several orders of magnitude and in sign. Only small proportions of species and locations drove overall LFI trends, and their contributions changed with season (spring and autumn surveys). To assess significance of component trends, a resampling method was developed. Our method can be generalized and applied to many other community indicators based on survey data.Synthesis and applications. Our new method for decomposing community indicators and generating confidence intervals makes it possible to extract much more information on what drives a ‘headline’ indicator, providing a solution to challenges arising from multiple possible interpretations of changes in the indicator and from sampling variation. Analysis of the effects of indicator components on headline indicator values is recommended, because the results allow assessors and managers to identify and interpret how divergent factors (e.g. species, sampling locations and seasons) contribute to the headline indicator value.