Climate, topography, and the 3D structure of forests are major drivers affecting local species communities. However, little is known about how the specific functional traits of saproxylic (wood-living) beetles, involved in the recycling of wood, might be affected by those environmental characteristics.

Here we combine ecological and morphological traits available for saproxylic beetles and airborne laser scanning (ALS) data in Bayesian trait-based joint species distribution models to study how traits drive the distributions of more than 230 species in temperate forests of Europe.

We found that elevation (as a proxy for temperature and precipitation) and the proportion of conifers played important roles in species occurrences while variables related to habitat heterogeneity and forest complexity were less relevant. Further, we showed that local communities were shaped by environmental variation primarily through their ecological traits whereas morphological traits were involved only marginally. As predicted, ecological traits influenced species’ responses to forest structure, and to other environmental variation, with canopy niche, wood decay niche, and host preference as the most important ecological traits. Conversely, no links between morphological traits and environmental characteristics were observed. Both models, however, revealed strong phylogenetic signal in species’ response to environmental characteristics.

These findings imply that alterations of climate and tree species composition have the potential to alter saproxylic beetle communities in temperate forests. Additionally, ecological traits help explain species’ responses to environmental characteristics and thus should prove useful in predicting their responses to future change. It remains challenging, however, to link simple morphological traits to species’ complex ecological niches.

See manuscript for methods details.

• Species traits - from references cited in manuscript (Hagge et al., 2021; Köhler, 2000; Seibold et al., 2015)
• Environmental variables - from multiple local and remote measurements
• Species detections - presence/absence of species captured in window traps at each plot

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