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- phenotypic plasticity (2)
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Aim
Climate change challenges temperate forest trees by increasingly irregular precipitation and rising temperatures. Due to long generation cycles, trees cannot quickly adapt genetically. Hence, the persistence of tree populations in the face of ongoing climate change depends largely on phenotypic variation, that is the capability of a genotype to express variable phenotypes under different environmental conditions, known as plasticity. We aimed to quantify phenotypic variation of central Europe's naturally dominant forest tree across various intraspecific scales (individuals, mother trees (families), populations) to evaluate its potential to respond to changing climatic conditions.
Location
Europe.
Time Period
2016–2019.
Major Taxa Studied
European beech (Fagus sylvatica L.).
Methods
We conducted a fully reciprocal transplantation experiment with more than 9000 beech seeds from seven populations across a Europe-wide gradient. We compared morphological (Specific Leaf Area), phenological (leaf unfolding) and fitness-related (growth, survival) traits across various biological scales: within single mother trees, within populations and across different populations under the contrasting climates of the translocation sites.
Results
The experiment revealed significant phenotypic variation within the offspring of each mother tree, regardless of geographic origin. Initially, seedling height growth varied among mother trees and populations, likely due to maternal effects. However, the growth performance successively aligned after the first year. In summary, we observed a consistent growth response in different beech populations to diverse environments after initial maternal effects.
Main Conclusions
The study strikingly demonstrates the importance of considering intraspecific variation. Given the surprisingly broad spectrum of phenotypes each mother tree holds within its juvenile offspring, we conclude that Fagus sylvatica might have the potential for medium-term population persistence in face of climate change, provided that this pattern persists into later life stages. Hence, we also suggest further investigating the inclusion of passive adaptation and natural dynamics in the adaptive management of forests.
Plant roots influence many ecological and biogeochemical processes, such as carbon, water and nutrient cycling. Because of difficult accessibility, knowledge on plant root growth dynamics in field conditions, however, is fragmentary at best. Minirhizotrons, i.e. transparent tubes placed in the substrate into which specialized cameras or circular scanners are inserted, facilitate the capture of high-resolution images of root dynamics at the soil-tube interface with little to no disturbance after the initial installation. Their use, especially in field studies with multiple species and heterogeneous substrates, though, is limited by the amount of work that subsequent manual tracing of roots in the images requires. Furthermore, the reproducibility and objectivity of manual root detection is questionable. Here, we use a Convolutional Neural Network (CNN) for the automatic detection of roots in minirhizotron images and compare the performance of our RootDetector with human analysts with different levels of expertise. Our minirhizotron data come from various wetlands on organic soils, i.e. highly heterogeneous substrates consisting of dead plant material, often times mainly roots, in various degrees of decomposition. This may be seen as one of the most challenging soil types for root segmentation in minirhizotron images. RootDetector showed a high capability to correctly segment root pixels in minirhizotron images from field observations (F1 = 0.6044; r2 compared to a human expert = 0.99). Reproducibility among humans, however, depended strongly on expertise level, with novices showing drastic variation among individual analysts and annotating on average more than 13-times higher root length/cm2 per image compared to expert analysts. CNNs such as RootDetector provide a reliable and efficient method for the detection of roots and root length in minirhizotron images even from challenging field conditions. Analyses with RootDetector thus save resources, are reproducible and objective, and are as accurate as manual analyses performed by human experts.
Biological invasions pose global threats to biodiversity and ecosystem functions. Invasive species often display a high degree of phenotypic plasticity, enabling them to adapt to new environments. This study examines plasticity to water stress in native and invasive Opuntia ficus-indica populations, a prevalent invader in arid and semi-arid ecosystems. Through controlled greenhouse experiments, we evaluated three native and nine invasive populations. While all plants survived the dry treatment, natives exhibited lower plasticity to high water availability with only a 36% aboveground biomass increase compared to the invasives with a greater increase of 94%. In terms of belowground biomass, there was no significant response to increased water availability for native populations, but plants from the invasive populations showed a 75% increase from the dry to the wet treatment. Enhanced phenotypic plasticity observed in invasive populations of O. ficus-indica is likely a significant driver of their success and invasiveness across different regions, particularly with a clear environmental preference towards less arid conditions. Climate change is expected to amplify the invasion success due to the expansion of arid areas and desertification. Opuntia ficus-indica adapts to diverse environments, survives dry spells, and grows rapidly in times of high-water supply, making it a candidate for increased invasion potential with climate change.