Co-occurrence patterns of tree-related microhabitats: A method to simplify routine monitoring

A Tree-related Microhabitat (TreM) is a distinct, well-delineated morphological singularity occurring on living or standing dead trees, which constitutes a crucial substrate or life site for various species. TreMs are widely recognized as key features for biodiversity. Current TreM typology identifies 47 TreM types according to their morphology and their associated taxa. In order to provide a range of resolutions and make the typology more user-friendly, these 47 TreM types have been pooled into 15 groups and seven forms. Depending on the accuracy required and the time available, a user can now choose to describe TreMs at resolution levels corresponding to type, group or form. Another way to more easily record TreMs during routine management work would be to use co-occurrence patterns to reduce the number of observed TreMs required. Based on a large international TreM database (2052 plots; 70,958 individual trees; 78 tree species), we evaluated both the significance and the magnitude of TreM co-occurrence on living trees for 11 TreM groups. We highlighted 33 significant co-occurrences for broadleaves and nine for conifers. Bark loss, rot hole, crack and polypore had the highest number of positive co-occurrences (N = 8) with other TreMs on broadleaves; bark loss (N = 4) had the highest number for conifers. We found mutually exclusive occurrences only for conifers: Exposed Heartwood excluded both dendrotelm and sap run. Among the four variables we tested for their positive contribution to significant co-occurrences, tree diameter at breast height was the most consistent. Based on our results and practical considerations, we selected three TreM groups for broadleaves, and nine for conifers, and formed useful short lists to reduce the number of TreM groups to assess during routine forest management work in the field. In addition, detecting potential similarities or associations between TreMs has potential theoretical value, e.g. it may help researchers identify common factors favouring TreM formation or help managers select trees with multiple TreMs as candidates for retention.