Are winter and summer dormancy symmetrical seasonal adaptive strategies? The case of temperate herbaceous perennials.

BACKGROUND: Dormancy in higher plants is an adaptive response enabling plant survival during the harshest seasons and has been more explored in woody species than in herbaceous species. Nevertheless, winter and summer shoot meristem dormancy are adaptive strategies that could play a major role in enhancing seasonal stress tolerance and resilience of widespread herbaceous plant communities. SCOPE: This review outlines the symmetrical aspects of winter and summer dormancy in order to better understand plant adaptation to severe stress, and highlight research priorities in a changing climate. Seasonal dormancy is a good model to explore the growth-stress survival trade-off and unravel the relationships between growth potential and stress hardiness. Although photoperiod and temperature are known to play a crucial, though reversed, role in the induction and release of both types of dormancy, the thresholds and combined effects of these environmental factors remain to be identified. The biochemical compounds involved in induction or release in winter dormancy (abscisic acid, ethylene, sugars, cytokinins and gibberellins) could be a priority research focus for summer dormancy. To address these research priorities, herbaceous species, being more tractable than woody species, are excellent model plants for which both summer and winter dormancy have been clearly identified. CONCLUSIONS: Summer and winter dormancy, although responding to inverse conditions, share many characteristics. This analogous nature can facilitate research as well as lead to insight into plant adaptations to extreme conditions and the evolution of phenological patterns of species and communities under climate change. The development of phenotypes showing reduced winter and/or enhanced summer dormancy may be expected and could improve adaptation to less predictable environmental stresses correlated with future climates. To this end, it is suggested to explore the inter- and intraspecific genotypic variability of dormancy and its plasticity according to environmental conditions to contribute to predicting and mitigating global warming.

Leveraging research infrastructure co-location to evaluate constraints on terrestrial carbon cycling in northern European forests.

Integrated long-term, in-situ observations are needed to document ongoing environmental change, to "ground-truth" remote sensing and model outputs and to predict future Earth system behaviour. The scientific and societal value of in-situ observations increases with site representativeness, temporal duration, number of parameters measured and comparability within and across sites. Research Infrastructures (RIs) can support harmonised, cross-site data collection, curation and publication. Integrating RI networks through site co-location and standardised observation methods can help answers three questions about the terrestrial carbon sink: (i) What are present and future carbon sequestration rates in northern European forests? (ii) How are these rates controlled? (iii) Why do the observed patterns exist? Here, we present a conceptual model for RI co-location and highlight potential insights into the terrestrial carbon sink achievable when long-term in-situ Earth observation sites participate in multiple RI networks (e.g., ICOS and eLTER). Finally, we offer recommendations to promote RI co-location.

A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy.

The trait-based approach in plant ecology aims at understanding and classifying the diversity of ecological strategies by comparing plant morphology and physiology across organisms. The major drawback of the approach is that the time and financial cost of measuring the traits on many individuals and environments can be prohibitive. We show that combining near-infrared spectroscopy (NIRS) with deep learning resolves this limitation by quickly, non-destructively, and accurately measuring a suite of traits, including plant morphology, chemistry, and metabolism. Such an approach also allows to position plants within the well-known CSR triangle that depicts the diversity of plant ecological strategies. The processing of NIRS through deep learning identifies the effect of growth conditions on trait values, an issue that plagues traditional statistical approaches. Together, the coupling of NIRS and deep learning is a promising high-throughput approach to capture a range of ecological information on plant diversity and functioning and can accelerate the creation of extensive trait databases.

Higher tree diversity increases soil microbial resistance to drought.

Predicted increases in drought frequency and severity may change soil microbial functioning. Microbial resistance and recovery to drought depend on plant community characteristics, among other factors, yet how changes in plant diversity modify microbial drought responses is uncertain. Here, we assessed how repeated drying-rewetting cycles affect soil microbial functioning and whether tree species diversity modifies these effects with a microcosm experiment using soils from different European forests. Our results show that microbial aerobic respiration and denitrification decline under drought but are similar in single and mixed tree species forests. However, microbial communities from mixed forests resist drought better than those from mono-specific forests. This positive tree species mixture effect is robust across forests differing in environmental conditions and species composition. Our data show that mixed forests mitigate drought effects on soil microbial processes, suggesting greater stability of biogeochemical cycling in mixed forests should drought frequency increase in the future.