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Species distribution models as a tool for forest management planning under climate change: risk evaluation of Abies alba in Bavaria

Authors


  • Co-ordinating Editor: Ingolf Kuehn

  • Falk, W. (corresponding author; wolfgang.falk@lwf.bayern.de): Bavarian State Institute of Forestry – LWF, Hans-Carl-v.-Carlowitz-Platz 1, D-85354 Freising, Germany
    Mellert, K.H. (karl.mellert@online.de): Agwa Umweltberatung, Planegger Str. 46, D-81241 München, Germany

Abstract

Questions: How can SDMs be adopted as a tool for forest management planning? Based on presence-absence data, which modelling techniques are appropriate to determine species potential distribution for forest management planning under climate change? Do species distribution models (SDMs) agree with expert knowledge about species distribution and species traits? How can forest researcher deal with distribution data of a species whose distribution is heavily affected by human impacts?

Location: Bavaria (Southern Germany).

Methods: We used SDMs based on the Second National Forest Inventory from 2002 (4 × 4 km grid) containing presence-absence data of tree species to identify species environment relationships (‘Grinnellian niche’). As an example, the distribution of silver fir (Abies alba Mill.) was modelled. Site condition data of the plots were derived from solar radiation, climate and soil maps. Models applied were boosted regression trees (BRT) and generalised additive models (GAM). Model predictions were compared with an expert based evaluation of the potential natural vegetation and were run with a climate change scenario (WETTREG B1) to project future distribution of silver fir.

Results: Both models discriminated well between presence and absence of silver fir but underestimated the potential distribution. The BRT model was more sensitive to local site conditions in the present data, but the GAM showed more generality. The truncated response curves and high uncertainties of predictions at the edge of the site spectrum indicated a low data density and that the data did not cover the whole niche space of silver fir. As indicated by validation with expert knowledge, the model output approached potential distribution by optimizing true positive predictions. The classification of SDMs output into risk classes allowed model evaluation and interpretation. Predictions of GAM and BRT under the climate change scenario showed high accordance and therefore, low uncertainty. Finally, large areas of Bavaria are described to have a high risk of silver fir cultivation in future.

Conclusions: SDMs are especially interesting as a decision basis for forest management because some of the general limitations of static modelling approaches are not relevant in this context. Limitations of forest inventory data can be partially overcome by using information on the potential distribution of species. The transferability of the models to future scenarios strongly depends on the spectrum and range of the training data sets and the depicted functional relationships. In order to improve the models and reduce the uncertainties, we need to improve the soil data and cover the whole niche space of silver fir.

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