Abstract
The growing pace of environmental change has increased the need for large‐scale monitoring of biodiversity. Declining intraspecific genetic variation is likely a critical factor in biodiversity loss, but is especially difficult to monitor: assessments of genetic variation are commonly based on measuring allele pools, which requires sampling of individuals and extensive sample processing, limiting spatial coverage. Alternatively, imaging spectroscopy data from remote platforms may hold the potential to reveal genetic structure of populations. In this study, we investigated how differences detected in an airborne imaging spectroscopy time series correspond to genetic variation within a population of Fagus sylvatica under natural conditions.We used multi‐annual APEX (Airborne Prism Experiment) imaging spectrometer data from a temperate forest located in the Swiss midlands (Laegern, 47°28'N, 8°21'E), along with microsatellite data from F. sylvatica individuals collected at the site. We identified variation in foliar reflectance independent of annual and seasonal changes which we hypothesize is more likely to correspond to stable genetic differences. We established a direct connection between the spectroscopy and genetics data by using partial least squares (PLS) regression to predict the probability of belonging to a genetic cluster from spectral data.We achieved the best genetic structure prediction by using derivatives of reflectance and a subset of wavebands rather than full‐analyzed spectra. Our model indicates that spectral regions related to leaf water content, phenols, pigments, and wax composition contribute most to the ability of this approach to predict genetic structure of F. sylvatica population in natural conditions.This study advances the use of airborne imaging spectroscopy to assess tree genetic diversity at canopy level under natural conditions, which could overcome current spatiotemporal limitations on monitoring, understanding, and preventing genetic biodiversity loss imposed by requirements for extensive in situ sampling.
Highlights
It has long been recognized that declining genetic variation within species is a key factor in biodiversity loss (Wilson & Peter, 1988)
We investigated how differences detected in an airborne imaging spectroscopy time series correspond to genetic variation within a population of Fagus sylvatica under natural conditions
This study advances the use of airborne imaging spectroscopy to assess tree genetic diversity at canopy level under natural conditions, which could overcome current spatiotemporal limitations on monitoring, understanding, and preventing genetic biodiversity loss imposed by requirements for extensive in situ sampling
Summary
It has long been recognized that declining genetic variation within species is a key factor in biodiversity loss (Wilson & Peter, 1988). Continuous temporal, spatial, and spectral data derived from remote sensing platforms have the potential to overcome scale-induced limitations and are receiving increasing attention for achieving global biodiversity assessments (Navarro et al, 2017; O'Connor et al, 2015; Skidmore et al, 2015; Turner, 2014). Both passive and active remote sensing technologies have been used to estimate functional, taxonomic, and phylogenetic diversity of plants in a variety of ecosystems. Individual trees in the study area have been reconstructed in 3D using ground and airborne laser scanning, and modeled using 3D radiative transfer models (Schneider et al, 2014; Schneider et al 2017), allowing to model and validate airborne data with high accuracy
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