Biology subjects

Langan, L.

Publications and source records attributed to Langan, L..

3 recordsLinked to original sources

bioRxiv2025

Predicting forest tree leaf phenology under climate change using satellite monitoring and population-based GWAS

Leaf phenology, a critical determinant of plant fitness and ecosystem function, is undergoing rapid shifts due to climate change, yet its complex genetic and environmental drivers remain incompletely understood. Understanding the genetic basis of phenological adaptation is crucial for forecasting forest responses to a changing climate. Here, we integrate multi-year satellite-derived phenology from 46 Fagus sylvatica (European beech) populations across Germany with a population-based genome-wide association study to dissect the environmental and genetic drivers of leaf-out day (LOD) and leaf shedding day (LSD). We show that environmental factors, particularly temperature forcing and water availability, are the primary drivers of LOD variation, while LSD is influenced by a more complex suite of climatic cues. Our genomic analysis identifies candidate genes associated with LOD and LSD, primarily linked to circadian rhythms and dormancy pathways, respectively. Furthermore, genomic prediction models incorporating these loci accurately reconstruct past phenological dynamics, providing a powerful framework to forecast forest vulnerability and adaptation to future climate change.

evolutionary biology↗

bioRxiv2025

Prediction in trait-based ecology: global simulations of specific leaf area using a trait-based dynamic vegetation model

AbstractPredicting plant community functional traits is considered a Holy Grail of trait-based ecology because traits underpin ecosystem processes. Previous statistical, machine learning, and optimality approaches have produced global plant trait predictions. However, the utility of trait-based vegetation models, which include demographic processes and can represent trait diversity, remains unexplored at this scale. We use aDGVM2-LL, a trait- and individual-based dynamic global vegetation model (DGVM). aDGVM2-LL simulates community assembly, which is driven by natural selection, biotic, and abiotic conditions; simulated specific leaf area (SLA) is an emergent outcome of community assembly. We examine: 1) how well aDGVM2-LL can simulate global SLA by examining deviations from trait data, and 2) explore drivers of strong deviations. Compared to GBIF-derived SLA data, aDGVM2-LL displays mean SLA differences of -2.9 (m2/kg)(GBIF range ca. 4 - 35 m2/kg), a root mean square error (RMSE) of 7.25, and normalised mean absolute error (nMAE) of 26.54%. Published statistical, machine learning, and optimality approaches displayed differences with GBIF-derived trait data which range between (mean : -4.83 - 2.67, RMSE: 4.41 - 6.68, nMAE: 13.41% - 25.20%). Thus, aDGVM2-LL mean differences are comparable with published predictions while RMSEs and nMAEs are higher. Large aDGVM2-LL mismatches occur in areas where the model incorrectly simulates the relative abundances of deciduous vs. evergreen leaf phenologies. Correcting mismatches in leaf phenological abundances strongly reduces the range of mean SLA differences (-0.14 - 0.43), RMSEs (5.85 - 5.90), and nMAEs (15.44% - 20.61%). These results show that an eco-evolutionary, process-based approach can reasonably simulate global SLA values, particularly when leaf phenological abundances are accurate. Our results highlight the general importance of the global drivers of leaf phenology for leaf traits. The correct simulation of the relative abundances of deciduous and evergreen leaf phenologies is crucial to predict contemporary and future SLA.

ecology↗

bioRxiv2023

Phenotypic drought stress prediction of European beech (Fagus sylvatica) by genomic prediction and remote sensing

Current climate change species response models usually not include evolution. We integrated remote sensing with population genomics to improve phenotypic response prediction to drought stress in the key forest tree European beech (Fagus sylvatica L.). We used whole-genome sequencing of pooled DNA from natural stands along an ecological gradient from humid-cold to warm-dry climate. We phenotyped stands for leaf area index (LAI) and moisture stress index (MSI) for the period 2016-2022. We predicted this data with matching meteorological data and a newly developed genomic population prediction score in a Generalised Linear Model. Model selection showed that addition of genomic prediction decisively increased the explanatory power. We then predicted the response of beech to future climate change under evolutionary adaptation scenarios. A moderate climate change scenario would allow persistence of adapted beech forests, but not worst-case scenarios. Our approach can thus guide mitigation measures, such as allowing natural selection or proactive evolutionary management.

evolutionary biology↗