Leveraging remote sensing to distinguish closely related beech species in assisted gene flow scenarios

Abstract

European beech (Fagus sylvatica L.) forests are suffering under increasingly severe and frequent drought. Three closely related, hybridizing beech species, ranging from Bulgaria through Asia Minor and the Caucasus to Iran, offer potential resources for assisted gene flow (AGF) with the aim of increasing the adaptive capacity of European beech forests. However, due to similar morphology and leaf color, as well as hybridization, it is challenging to track the fate of introduced beech genotypes from these related species. Traditional identification methods relying on detailed morphological characterization and genetic testing are labor-intensive and costly, making them impractical for large-scale applications. Using multispectral data from PlanetScope SuperDove, we developed a classification approach that captures phenological differences between the European beech F. sylvatica and co-planted Caucasian beech (Fagus hohenackeriana Palibin). The approach focuses on key temporal windows and spectral features to optimize classification performance. We evaluated various machine learning algorithms with stratified spatial and temporal cross-validation on data from more than 200 genetically classified individuals in two well-studied sites in France and Switzerland, where Caucasian beech was introduced over a century ago. Our approach was then tested on three different study areas in Germany, where Caucasian beech was also planted, but without specific tree coordinates. Our results reveal consistent temporal and spectral differences during spring and autumn, aligning with budbreak and senescence periods. Most algorithms achieved classification accuracies of 90% and above. The algorithms effectively identified candidate zones for Caucasian beech within or near areas indicated by local foresters. This study demonstrates the potential of high-resolution multispectral satellite imagery and machine learning for scalable classification of closely related and hybridizing species, thereby facilitating forest management in the face of global change. HighlightsO_LIRemote sensing of unique phenological differences between closely related beech species C_LIO_LIModels with optimized classification parameters differentiated highly similar, introduced trees C_LIO_LIReal-world application to assisted gene flow sites yielded high performance and testable predictions C_LI