Ecological niche modeling of Lactarius deliciosus using kuenm R package: Insights into habitat preferences.

Understanding species habitat preferences is essential for conservation and management efforts, as it enables the identification of areas with a higher likelihood of species presence. Lactarius deliciosus (L.) Gray, an economically important edible mushroom, is influenced by various environmental variables, yet information regarding its ecological niche remains elusive. Therefore, in this study, we aim to address this gap by modeling the fundamental niche of L. deliciosus. Specifically, we explore its distribution patterns in response to large-scale environmental factors, including long-term temperature averages and topography. We employed 242 presence-only georeferenced points in Europe obtained from the Global Biodiversity Information Facility (GBIF). Utilizing the Kuenm R package, we constructed 210 models incorporating five sets of environmental variables, 14 regularization multiplier values, and three feature class combinations. Evaluation metrics included statistical significance, predictive power, and model complexity. The final model was transferred to Turkiye, with careful consideration of extrapolation risk using MESS (multivariate similarity surface) and MoD (most dissimilar variable) metrics. In alignment with all three evaluation criteria, the algorithm implemented in Kuenm identified the best model as the linear-quadratic combination with a regularization multiplier of 0.2, based on variables selected by the contribution importance method. Results underscore temperature-related variables as critical determinants of L. deliciosus habitat preferences within the calibration area, with solar radiation also playing a significant role in the final model. These results underscored the effectiveness of ecological niche modeling (ENM) in understanding how climatic patterns may alter the distribution of species like L. deliciosus. The findings contribute to the development of informed conservation strategies and decision-making in dynamic environments. Emphasizing a comprehensive approach to ecological modeling is crucial for promoting sustainable forest management. • Kuenm R package enhances prediction reliability through rigorous evaluation. • Response curves emphasize the role of climate and topography. • Species presence databases such as GBIF help identify spatial distribution patterns. • Extrapolation risk metrics MESS and MoD are important in model transferability. • Model transferability is crucial particularly in data-scarce regions such as Turkiye. [ABSTRACT FROM AUTHOR]