Linking dendroecology and association genetics in natural populations: Stress responses archived in tree rings associate with SNP genotypes in silver fir (Abies alba Mill.).

Genetic association studies in forest trees would greatly benefit from information on the response of trees to environmental stressors over time, which can be provided by dendroecological analysis. Here, we jointly analysed dendroecological and genetic data of surviving silver fir trees to explore the genetic basis of their response to the iconic stress episode of the 1970s and 1980s that led to large-scale forest dieback in Central Europe and has been attributed to air pollution. Specifically, we derived dendrophenotypic measures from 190 trees in the Bavarian Forest that characterize the resistance, resilience and recovery during this growth depression, and in the drought year in 1976. By focusing on relative growth changes of trees and by standardizing the dendrophenotypes within stands, we accounted for variation introduced by micro- and macroscale environmental differences. We associated the dendrophenotypes with single nucleotide polymorphisms (SNPs) in candidate genes using general linear models (GLMs) and the machine learning algorithm random forest with subsequent feature selection. Most trees at our study sites experienced a severe growth decline from 1974 until the mid-1980s with minimum values during the drought year. Fifteen genes were associated with the dendrophenotypes, including genes linked to photosynthesis and drought stress. With our study, we show that dendrophenotypes can be a powerful resource for genetic association studies that permit to account for micro- and macroenvironmental variation when data are derived from natural populations. We call for a wider collaboration of dendroecologists and forest geneticists to integrate individual tree-level dendrophenotypes in genetic association studies.

Spinning a laser web: predicting spider distributions using LiDAR.

LiDAR remote sensing has been used to examine relationships between vertebrate diversity and environmental characteristics, but its application to invertebrates has been limited. Our objectives were to determine whether LiDAR-derived variables could be used to accurately describe single-species distributions and community characteristics of spiders in remote forested and mountainous terrain. We collected over 5300 spiders across multiple transects in the Bavarian National Park (Germany) using pitfall traps. We examined spider community characteristics (species richness, the Shannon index, the Simpson index, community composition, mean body size, and abundance) and single-species distribution and abundance with LiDAR variables and ground-based measurements. We used the R2 and partial R2 provided by variance partitioning to evaluate the predictive power of LiDAR-derived variables compared to ground measurements for each of the community characteristics. The total adjusted R2 for species richness, the Shannon index, community species composition, and body size had a range of 25-57%. LiDAR variables and ground measurements both contributed >80% to the total predictive power. For species composition, the explained variance was approximately 32%, which was significantly greater than expected by chance. The predictive power of LiDAR-derived variables was comparable or superior to that of the ground-based variables for examinations of single-species distributions, and it explained up to 55% of the variance. The predictability of species distributions was higher for species that had strong associations with shade in open-forest habitats, and this niche position has been well documented across the European continent for spider species. The similar statistical performance between LiDAR and ground-based measures at our field sites indicated that deriving spider community and species distribution information using LiDAR data can provide not only high predictive power at relatively low cost, but may also allow unprecedented mapping of community- and species-level spider information at scales ranging from stands to landscapes. Therefore, LiDAR is a viable tool to assist species-specific conservation as well as broader biodiversity planning efforts not only for a growing list of vertebrates, but for invertebrates as well.