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. 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. 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.

中文翻译：

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预测西班牙北部樟子松和Pinus pinaster林分地上外生菌根真菌产量的产量模型

预测模型揭示了地上真菌的产量动态，并且可以通过将这种有价值的非木材林产品整合到森林管理规划中来协助林业决策。但是，当前存在的模型是基于相当本地的数据，因此，缺乏用于大规模监控蘑菇产量的预测工具。这项工作提出了第一个经验模型，用于预测西班牙北部樟子松和Pinus pinaster林分中的外生菌根蘑菇和相关生态系统服务的年产量，使用适合于考虑气象条件和林分结构综合影响的长期数据集。分别为以下几类真菌配备了模型：所有外生菌根蘑菇，食用蘑菇和市售蘑菇。