Performance of statistical and machine learning-based methods for predicting biogeographical patterns of fungal productivity in forest ecosystems

Background:The prediction of biogeographical patterns from a large number of driving factors with complex interactions,correlations and non-linear dependences require advanced analytical methods and modeling tools.This study compares different statistical and machine learning-based models for predicting fungal productivity biogeographical patterns as a case study for the thorough assessment of the performance of alternative modeling approaches to provide accurate and ecologically-consistent predictions.Methods:We evaluated and compared the performance of two statistical modeling techniques,namely,generalized linear mixed models and geographically weighted regression,and four techniques based on different machine learning algorithms,namely,random forest,extreme gradient boosting,support vector machine and artificial neural network to predict fungal productivity.Model evaluation was conducted using a systematic methodology combining random,spatial and environmental blocking together with the assessment of the ecological consistency of spatially-explicit model predictions according to scientific knowledge.Results:Fungal productivity predictions were sensitive to the modeling approach and the number of predictors used.Moreover,the importance assigned to different predictors varied between machine learning modeling approaches.Decision tree-based models increased prediction accuracy by more than 10%compared to other machine learning approaches,and by more than 20%compared to statistical models,and resulted in higher ecological consistence of the predicted biogeographical patterns of fungal productivity.Conclusions:Decision tree-based models were the best approach for prediction both in sampling-like environments as well as in extrapolation beyond the spatial and climatic range of the modeling data.In this study,we show that proper variable selection is crucial to create robust models for extrapolation in biophysically differentiated areas.This allows for reducing the dimensions of the ecosystem space described by the predictors of the models,resulting in higher similarity between the modeling data and the environmental conditions over the whole study area.When dealing with spatial-temporal data in the analysis of biogeographical patterns,environmental blocking is postulated as a highly informative technique to be used in cross-validation to assess the prediction error over larger scales.

Yield models for predicting aboveground ectomycorrhizal fungal productivity in Pinus sylvestris and Pinus pinaster stands of northern Spain

Background: Predictive models shed light on aboveground fungal yield dynamics and can assist decision-making in forestry by integrating this valuable non-wood forest product into forest management planning. However, the currently existing models are based on rather local data and, thus, there is a lack of predictive tools to monitor mushroom yields on larger scales.Results: This work presents the first empirical models for predicting the annual yields of ectomycorrhizal mushrooms and related ecosystem services in Pinus sylvestris and Pinus pinaster stands in northern Spain, using a long-term dataset suitable to account for the combined effect of meteorological conditions and stand structure.Models were fitted for the following groups of fungi separately: all ectomycorrhizal mushrooms, edible mushrooms and marketed mushrooms. Our results show the influence of the weather variables(mainly precipitation) on mushroom yields as well as the relevance of the basal area of the forest stand that follows a right-skewed unimodal curve with maximum predicted yields at stand basal areas of 30–40 m2·ha-1.Conclusion: These models are the first empirical models for predicting the annual yields of ectomycorrhizal mushrooms in Pinus sylvestris and Pinus pinaster stands in northern Spain, being of the highest resolution developed to date and enable predictions of mushrooms productivity by taking into account weather conditions and forests’ location, composition and structure.