A major part of forest biodiversity is depending on dead wood. So-called ‘saproxylic’ (dead-wood dependent) beetles are an excellent indicator group for forest quality, because of their sensitivity to forest structure, forest age, management and climate. Saproxylic beetles have a wide distribution, across all European forests. Forestry has greatly impacted these communities over the past centuries. Saproxylic beetle communities are naturally changing over a latitudinal gradient because of climate, and a gradient from old to young forests because of niche availability.
Whether beetle species manage to persist in communities, or are likely to return after forest restoration, can to some degree be predicted from their traits. Trait-based approaches can provide insights in community functioning and distribution, and are therefore widely used in community ecology. Traits can correlate, forming ‘clusters’ that can be distinguished across species. These clusters are termed ‘trait syndromes’ and can correlate with ecological strategy or distribution.
Although both ecological and morphological traits have been determined for saproxylic beetles in the past, they haven’t been cross-correlated and correlated to distribution patterns. The question is if syndromes can be distinguished from an saproxylic morphological traits, and to what extend are these traits associated with ecological and distribution patterns?
The challenge is to identify syndromes that can predict distribution patterns and ecology of saproxylic beetle species. This will involved multivariate analysis. Hagge et al. (2021) described 37 traits for 1157 European saproxylic beetle species. They compared IUCN status to these traits, to extract 13 traits that were correlated with threatened beetles.
Available distribution data from species in Hagge’s database can be retrieved from the Global Biodiversity Information Facility. Data on ecological traits can be found in for instance Schmidl & Bußler (2004), Colijn & Burgers (2022) and Bouget et al. (2008). Check Sosiak and Barden (2021) for useful R-packages.
Hagge, J., Müller, J., Birkemoe, T., Buse, J., Gossner, M. M., Gruppe, A., … & Drag, L. (2021). What does a threatened saproxylic beetle look like? Modelling extinction risk using a new morphological trait database. Journal of Animal Ecology, 90(8), 1934-1947.
Hagge, J., Müller, J., Birkemoe, T., Buse, J., Christensen, R. H. B., Gossner, M. M., & Drag, L. (2021). Morphological trait database of saproxylic beetles. Dryad Digital Repository.
Global Biodiversity Information Facility (GBIF). Occurrence data for multiple species can be downloaded simultaneously via the GBIF API.
Bouget, C., Brustel, H., & Zagatti, P. (2008). The FRench Information system on Saproxylic BEetle Ecology (FRISBEE): An ecological and taxonomical database to help with the assessment of forest conservation status. Revue d’Ecologie (La Terre et La Vie), 63(SUPPL. 10), 33–36.
Colijn, E. O., & Burgers, J. (2022). De doodhoutbewonende kevers van Nederland ( Coleoptera ). Entomologische Berichten, 82(5), 150–177.
Schmidl, J., & Bußler, H. (2004). Ökologische Gilden xylobionter Käfer Deutschlands. Naturschutz Und Landschaftsplanung, 36(7), 202–218.
Sosiak, C. E., & Barden, P. (2021). Multidimensional trait morphology predicts ecology across ant lineages. Functional Ecology, 35, 139–152.