Intraspecific genetic variation of a Fagus sylvatica population in a temperate forest derived from airborne imaging spectroscopy time series

1. 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.
2. 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.
3. 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.
4. 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.