Influence des caractéristiques des arbres et de la gestion forestière sur les microhabitats des arbres

International audienceHigher densities of tree microhabitats in unmanaged forests may explain biodiversity differences with managed forests. To better understand the determinants of this potential biodiversity indicator, we studied the influence of tree characteristics on a set of tree microhabitats (e.g. cavities, cracks, bark features)on 75 plots in managed and unmanaged French forests. We hypothesized that the number of different microhabitat types per tree and the occurrence of a given microhabitat type on a tree would be higher in unmanaged than in managed forests, and that this difference could be linked to individual tree characteristics: diameter, vitality and species. We show that unmanaged forests contained more trees likely to host microhabitats (i.e. large trees, snags) at the stand level. However, at the tree level, forest management did not influence microhabitats; only tree characteristics did: large trees and snags contained more microhabitats. The number and occurrence of microhabitats also varied with tree species: oaks and beech generally hosted more microhabitats, but occurrence of certain types of microhabitats was higher on fir and spruce. We conclude that, even though microhabitats are not equally distributed between managed and unmanaged forests, two trees with similar characteristics in similar site conditions have the same number and probability of occurrence of microhabitats, whatever the management type. In order to preserve biodiversity, foresters could reproduce unmanaged forest features in managed forests through the conservation of specific tree types (e.g. veteran trees, snags). Tree microhabitats could also be more often targeted in sustainable forest management monitoring

Vers la validation d'un nouvel indicateur de biodiversité forestière : effet observateur sur les relevés de microhabitats des arbres dans une forêt non exploitée française

International audienceA growing field of forest research deals with the improvement of forest biodiversity indicators. Validation of biodiversity indicators requires an analysis of their applicability, their range of validity and the magnitude of the correlation with the biodiversity they are supposed to represent. In this process, assessing the magnitude of observer effect is an essential step. In this context, we tested observer effects (probability of detection, probability of invention/false detection) on the censuses of tree microhabitats related to woodpecker cavities, cracks and bark characteristics. Within two 0.5ha plots in a forest reserve that has not been harvested for at least 150 years, 14 observers visually observed microhabitats on 106 Oak (Quercus petraea and Q. robur) and Beech (Fagus sylvatica) trees. We compared the censuses of these observers with an independent consensual census using parametric and Bayesian statistics. The mean number of microhabitats per tree varied widely between observers from 1.4 to over 3. Only three observers reported a mean number of microhabitats per tree statistically equivalent to the consensual census. The probability of detection also varied between observers among microhabitats (from to 0 to 1) as well as the probability of invention (from 0 to 0.7). These results show that microhabitats censuses are particularly prone to observer effects and should be used with caution. Such strong observer effects raise the question of the relevance of microhabitats as biodiversity indicator. However, we recommend to control for observer effects by (i) multiplying the number of training sessions and consensual censuses; (ii) recording microhabitats with two observers whenever possible, but the efficiency of this method remains to be tested; (iii) planning the fieldwork so that the factors of interest are not merely confused with observer effects and; (iv) integrating observer identity in statistical models whenever analysing such data

Un fort effet observateur dans les inventaires de microhabitats des arbres : un cas d'étude dans une forêt de plaine en France

International audienceValidating biodiversity indicators requires an analysis of their applicability, their range of validity andtheir degree of correlation with the biodiversity they are supposed to represent. In this process, assessing the magnitude of observer effect is an essential step, especially if non-specialist observers are involved.Tree microhabitats – woodpecker cavities, cracks and bark characteristics – are reputed to be easily detected by non-specialists as microhabitat observation does not require prior forestry or ecology knowledge. We therefore quantified the probabilities of true and false positive detections made by observers during inventories.Within two 0.5 ha plots in a forest reserve that has not been harvested for at least 150 years, 14 observers with various backgrounds visually inventoried microhabitats on 106 oak (Quercus petraea and Quercus robur) and beech (Fagus sylvatica) trees. We used parametric and Bayesian statistics to compare these observers’ recorded observations with results from an independent census.The mean number of microhabitats per tree varied widely among observers – from 1.4 to over 3. Only five observers reported a mean number of microhabitats per tree that was statistically equivalent to the reference census. The probability of true detection also varied among observers for each microhabitat(from to 0 to 1) as did the probability of false positive detection (from 0 to 0.7). These results show that microhabitat inventories are particularly prone to observer effects.Such strong observer effects weaken the usefulness of microhabitats as biodiversity indicators. If micro-habitat inventories are to be developed, we recommend controlling for observer effects by (i) defining standard operating procedures and multiplying the number of observer training sessions and of consensual standardization censuses; (ii) using pairs of observers to record microhabitats whenever possible (though the efficiency of this method remains to be tested); (iii) planning fieldwork so that the factors of interest are not confused with observer effects; and (iv) integrating observer profiles into the statistical models used to analyze the data