2026-05-05T20:51:49Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/704152025-02-19T08:08:31Zcom_10324_7492com_10324_952com_10324_894col_10324_7493

00925njm 22002777a 4500 dc Martínez Rodrigo, Raquel author Agueda Hernández, Beatriz author Ágreda, Teresa author Altelarrea, José Miguel author Fernández Toirán, Luz Marina author Rodríguez Puerta, Francisco author 2024 The integral role of wild fungi in ecosystems, including provisioning, regulating, cultural, and supporting services, is well recognized. However, quantifying and predicting wild mushroom yields is challenging due to spatial and temporal variability. In Mediterranean forests, climate-change-induced droughts further impact mushroom production. Fungal fruiting is influenced by factors such as climate, soil, topography, and forest structure. This study aims to quantify and predict the mycological potential of Lactarius deliciosus in sustainably managed Mediterranean pine forests using machine learning models. We utilize a long-term dataset of Lactarius deliciosus yields from 17 Pinus pinaster plots in Soria, Spain, integrating forest-derived structural data, NASA Landsat mission vegetation indices, and climatic data. The resulting multisource database facilitates the creation of a two-stage ‘mycological exploitability’ index, crucial for incorporating anticipated mycological production into sustainable forest management, in line with what is usually done for other uses such as timber or game. Various Machine Learning (ML) techniques, such as classification trees, random forest, linear and radial support vector machine, and neural networks, were employed to construct models for classification and prediction. The sample was always divided into training and validation sets (70-30%), while the differences were found in terms of Overall Accuracy (OA). Neural networks, incorporating critical variables like climatic data (precipitation in January and humidity in November), remote sensing indices (Enhanced Vegetation Index, Green Normalization Difference Vegetation Index), and structural forest variables (mean height, site index and basal area), produced the most accurate and unbiased models (OAtraining = 0.8398; OAvalidation = 0.7190). This research emphasizes the importance of considering a diverse array of ecosystem variables for quantifying wild mushroom yields and underscores the pivotal role of Artificial Intelligence (AI) tools and remotely sensed observations in modeling non-wood forest products. Integrating such models into sustainable forest management plans is crucial for recognizing the ecosystem services provided by them. Sustainability, 2024, Vol. 16, Nº. 13, 5656 2071-1050 https://uvadoc.uva.es/handle/10324/70415 10.3390/su16135656 5656 13 Sustainability 16 2071-1050 Mushrooms Hongos Edible mushrooms Hongos comestibles Non-wood forest products Forest products Productos forestales Remote sensing Teledetección Neural networks (Computer science) Redes neuronales (Informática) Forests and forestry Bosques y silvicultura Forest management Bosques - Gestión Sustainable development Desarrollo sostenible Assessment of mycological possibility using machine learning models for effective inclusion in sustainable forest management