2022
DOI: 10.1007/s10841-022-00391-6
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How to estimate carabid biomass?—an evaluation of size-weight models for ground beetles (Coleoptera: Carabidae) and perspectives for further improvement

Abstract: Biomass is an important metric for monitoring carabid populations and serves as an ecological indicator. Models that predict carabid weight based on body size represent a simple and straightforward method to estimate biomass and are therefore commonly used. However, such models are rarely evaluated against independent validation data. In this study, we evaluated the two widely used size-weight models by Szyszko (1983) and Booij et al. (1994) drawing on previously published independent data. Additionally, we de… Show more

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Cited by 3 publications
(6 citation statements)
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“…Smaller individuals of A. pacificum occurred at lower altitudes, larger ones occurred at 600 m asl, and individuals with intermediate body sizes were found at 900 m asl. Since body size and body mass are often correlated in carabids [ 61 ], we were expecting a quadratic relationship between altitude and body mass. However, the observed trend was not significant.…”
Section: Discussionmentioning
confidence: 99%
“…Smaller individuals of A. pacificum occurred at lower altitudes, larger ones occurred at 600 m asl, and individuals with intermediate body sizes were found at 900 m asl. Since body size and body mass are often correlated in carabids [ 61 ], we were expecting a quadratic relationship between altitude and body mass. However, the observed trend was not significant.…”
Section: Discussionmentioning
confidence: 99%
“…1). An additional crossed random intercept for year (factor) was used to account for temporal pseudoreplication and year effects (Chaves 2010, Knape 2016, Daskalova et al 2021, Weiss et al 2023a). We also fitted a negative‐binomial generalized additive mixed model (GAMM) to investigate non‐linear trends, which followed the same structure as the GLMM with the only difference that the main predictor (year) was fitted with a smoothing term.…”
Section: Methodsmentioning
confidence: 99%
“…In the AIC‐based model selection we fitted all candidate models with maximum likelihood instead of restricted maximum likelihood (Fox et al 2015). We ran a sensitivity analysis for abundance and biomass models to test the robustness of estimates by iteratively excluding data of single plots and years and refitting the models (Weiss et al 2023a). Predictions for all GLMMs and GAMMs (incl.…”
Section: Methodsmentioning
confidence: 99%
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