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Sampling saproxylic beetles with window flight traps: methodological insights. Rev Écol (Terre Vie)

Journal article published in 2008 by C. Bouget, H. Brustel, A. Brin, T. Noblecourt
This paper is available in a repository.
This paper is available in a repository.

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Abstract

Saproxylic beetles are species-rich, mostly small and cryptic, and difficult to sample. Different methods are traditionally used to collect saproxylic beetles. These are (i) direct active collection techniques, (ii) rearing techniques and (iii) mass trapping methods. Window-flight trapping is currently the most frequently used technique for catching flying active saproxylic beetles. Thanks to the combination of different trap principles, window-flight trap devices may differ by a large number of intrinsic parameters. This paper offers further insight into the influence of 3 trap factors on the catches of dead wood associated beetles, by comparing (i) cross-vanes or single-plane WFT (shape effect), (ii) black or transparent CWFT (silhouette effect), (iii) low or high CWFT (height effect). Six ecological data sets from French upland or lowland, deciduous or coniferous forests, with paired freely hanging window traps on each plot, were compiled in this study and analysed with a methodological point of view to compare the efficiency of sampling methods. Trap shape had a significant and strong effect on the abundance and species richness of saproxylic beetles. The single-plane traps caught a higher number of individuals and species. Nevertheless, given time/cost constraints, cross-vanes traps are recommended. Our study shows that black and transparent cross-vanes traps yielded similar saproxylic samples in terms of abundance, richness and overall composition. Our results confirm the vertical differentiation of saproxylic beetle assemblage. They suggest that low cross-vanes window traps yield more species-rich and individual-rich samples than canopy traps. Except Melyrids, no abundant species showed a strong association with top traps. Further optimisation analyses based on larger datasets are required to make sampling methods more reliable.