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Biology subjects

Schaepman, M.

Publications and source records attributed to Schaepman, M..

3 recordsLinked to original sources

Physiological signatures but no evidence for universal genomic predictors of extreme drought responses in European beech

Following a severe 2018 drought affecting much of Europe, European beech (Fagus sylvatica) exhibited patchy canopy dieback. To investigate the drivers of this mortality, we used a paired case-control design across Swiss forests, integrating phenotypic assessments and genomic analyses. Phenotypically, crown transparency and discoloration were used to classify tree damage state, while several other characteristics were measured to evaluate whether paired, neighboring damaged and undamaged trees were otherwise comparable, and to identify physiological stress signatures. Elevated leaf calcium content and red edge reflectance in dried leaves emerged as robust indicators of physiological stress. Genomic association models using reference-based SNP markers and reference-free k-mers were heavily confounded by micro-environmental variance and local population stratification even within our geographically limited study region. Our cross-validated genomic outliers showed no spatial overlap with predictive drought-associated loci previously identified in an independent German cohort. These findings indicate that drought survival in European beech is not governed by a universal set of major-effect alleles. Instead, tree performance appears highly context-dependent, likely shaped by micro-environmental heterogeneity and phenotypic condition. However, our results do not exclude polygenic drought responses involving multiple small-effect loci.

ecology↗

Individual tree-based vs pixel-based approaches to mapping forest functional traits and diversity by remote sensing

Trait-based approaches, focusing on the functional characteristics of vascular plants in a community, have been increasingly used in plant ecology and biodiversity research. Compared with traditional field survey (which typically samples individual trees), remote sensing enables quantifying functional traits over large contiguous areas, but assigning trait values to biological units such as species and individuals is difficult with pixel-based approaches. We used a subtropical forest landscape in China to compare an approach based on LiDAR-delineated individual tree crowns (ITCs) with a pixel-based approach for assessing functional traits from remote sensing data. We compared trait distributions, trait-trait relationships and functional diversity metrics obtained by the two approaches at changing grain and extent. We found that morphological traits derived from airborne laser scanning showed more differences between ITC- and pixel-based approaches than physiological traits estimated by imaging spectroscopy data. Pixel sizes approximating average tree crowns yielded similar results as ITCs, but 95th quantile height and foliage height diversity tended to be overestimated and leaf area index underestimated relative to ITC-based values. With increasing pixel size, the differences to ITC- based trait values became larger and less trait variance was captured, indicating information loss. The consistency of ITC- and pixel-based functional richness measures also decreased with increasing pixel grain, and changed with the observed extent for functional diversity monitoring. We conclude that whereas ITC-based approaches in principle allow partitioning of variation between individuals, genotypes and species, at high resolution, pixel-based approaches come close to this and can be suitable for assessing ecosystem-scale trait variation by weighting individuals and species according to coverage.

ecology↗

rasterdiv - an Information Theory tailored R package for measuring ecosystem heterogeneity from space: to the origin and back

O_LIEcosystem heterogeneity has been widely recognized as a key ecological feature, influencing several ecological functions, since it is strictly related to several ecological functions like diversity patterns and change, metapopulation dynamics, population connectivity, or gene flow. C_LIO_LIIn this paper, we present a new R package - rasterdiv - to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. C_LIO_LIThe rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open source algorithms. C_LI

ecology↗