Abstract
Forest biodiversity conservation requires precise, area-wide information on the abundance and distribution of key habitat structures at multiple spatial scales. We combined airborne laser scanning (ALS) data with color-infrared (CIR) aerial imagery for identifying individual tree characteristics and quantifying multi-scale habitat requirements using the example of the three-toed woodpecker (Picoides tridactylus) (TTW) in the Bavarian Forest National Park (Germany). This bird, a keystone species of boreal and mountainous forests, is highly reliant on bark beetles dwelling in dead or dying trees. While previous studies showed a positive relationship between the TTW presence and the amount of deadwood as a limiting resource, we hypothesized a unimodal response with a negative effect of very high deadwood amounts and tested for effects of substrate quality. Based on 104 woodpecker presence or absence locations, habitat selection was modelled at four spatial scales reflecting different woodpecker home range sizes. The abundance of standing dead trees was the most important predictor, with an increase in the probability of TTW occurrence up to a threshold of 44–50 dead trees per hectare, followed by a decrease in the probability of occurrence. A positive relationship with the deadwood crown size indicated the importance of fresh deadwood. Remote sensing data allowed both an area-wide prediction of species occurrence and the derivation of ecological threshold values for deadwood quality and quantity for more informed conservation management.
Highlights
Effective biodiversity conservation in managed forest landscapes requires knowledge about the distribution of key habitat features at relevant scales [1,2]
Habitat suitability models (HSMs) [3] and their spatially explicit variant, species distribution models (SDMs), have been widely employed in the last decades to predict species occurrence [4], abundance, or richness [5,6,7] based on environmental variables [8]
Altitude and the mean crown diameter of all standing deadwood were included in all models, but the former was only significant at the two smaller scales, while the latter was only significant at the two intermediate scales
Summary
Effective biodiversity conservation in managed forest landscapes requires knowledge about the distribution of key habitat features at relevant scales [1,2]. Habitat suitability models (HSMs) [3] and their spatially explicit variant, species distribution models (SDMs), have been widely employed in the last decades to predict species occurrence [4], abundance, or richness [5,6,7] based on environmental variables [8] Given their need for area-wide environmental information across large spatial scales, SDMs have mostly been based on publicly available topographic, climatic, or land-cover variables, which are often too coarse-grained and imprecise for reliably assessing habitat characteristics and quality for forest-dwelling species. These techniques and methods allow detailed and area-wide structural analyses, alleviating the trade-off between precision and extent [11,14]
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