Strong observer effect on tree microhabitats inventories: A case study in a French lowland forest

Validating biodiversity indicators requires an analysis of their applicability, their range of validity and their 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. 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. an 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. trong observer effects weaken the usefulness of microhabitats as biodiversity indicators. If microhabitat 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.