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bioRxiv · 10.1101/2022.05.05.490326

The characteristics of high-dark-diversity habitats derived from lidar

Abstract

A key aspect of nature conservation is knowledge of which aspects of nature to conserve or restore to favor the characteristic diversity of plants in a given area. Here, we used a large plant dataset with > 40.000 plots combined with airborne laser scanning (lidar) data to reveal the local characteristics of habitats having a high plant dark diversity - i.e., absence of suitable species - at national extent (> 43.000 km2). Such habitats have potential for reaching high realized diversity levels and hence are important in a conservation context. We calculated 10 different lidar based metrics (both terrain and vegetation structure) and combined these with 7 different field-based measures (soil chemistry and species indicators). We then used Integrated Nested Laplace Approximation for modelling plant dark diversity across 33 North European habitat types (open landscapes and forests) selected by the European communities to be important. In open habitat types high-dark-diversity habitats had relatively low pH, high nitrogen content, tall homogenous vegetation and overall relatively homogenous terrains (high terrain openness) although with a rather high degree of local microtopographical variations. High-dark-diversity habitats in forests had relatively tall vegetation, few natural-forest indicators, low potential solar radiation input and a low cover of small woody plants. Our results highlight important vegetation, terrain and soil related factors that managers and policymakers should be aware of in conservation and restoration projects to ensure a natural plant diversity, for example low nutrient loads, natural microtopography and open forests with old-growth elements such as dead wood and rot attacks.

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BibTeXRIS

Moeslund, J. E., Clausen, K. K., Dalby, L., Floejgaard, C., Partel, M., Pfeifer, N., Hollaus, M., Brunbjerg, A. K.. 2022-05-05. The characteristics of high-dark-diversity habitats derived from lidar. https://doi.org/10.1101/2022.05.05.490326

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