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Community management indicators can conflate divergent phenomena: two challenges and a decomposition‐based solution
Author(s) -
Adams Georgina L.,
Jennings Simon,
Reuman Daniel C.
Publication year - 2017
Publication title -
journal of applied ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.503
H-Index - 181
eISSN - 1365-2664
pISSN - 0021-8901
DOI - 10.1111/1365-2664.12787
Subject(s) - multivariate statistics , univariate , biomass (ecology) , abundance (ecology) , geography , sampling (signal processing) , community structure , ecological indicator , ecology , environmental resource management , environmental science , statistics , biology , computer science , mathematics , ecosystem , filter (signal processing) , computer vision
Summary 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.