To improve the predictive capability of ecosystem biogeochemical models (EBMs), we discuss the feasibility of formulating biogeochemical processes using physical rules that have underpinned the many successes in computational physics and chemistry. We argue that the currently popular empirically based approaches, such as multiplicative empirical response functions and the law of the minimum, will not lead to EBM formulations that can be continuously refined to incorporate improved mechanistic understanding and empirical observations of biogeochemical processes. Instead, we propose that EBM parameterizations, as a lossy data compression problem, can be better formulated using established physical rules widely used in computational physics and chemistry, and different biogeochemical processes can be more robustly integrated within a reactive-transport framework. Through several examples, we demonstrate how mathematical representations derived from physical rules can improve understanding of relevant biogeochemical processes and enable more effective communication between modelers, observationalists, and experimentalists regarding essential questions, such as what measurements are needed to meaningfully inform models and how can models generate new process-level hypotheses to test in empirical studies. Finally, while empirical models with more parameters are often less robust, physical rules-based models can be more robust and show lower predictive equifinality, stemming from their enhanced consistency in representations of processes, interactions and spatial scaling.

Although substantial advances in predicting the ecological impacts of global change have been made, predictions of the evolutionary impacts have lagged behind. In soil ecosystems, microbes act as the primary energetic drivers of carbon cycling; however, microbes are also capable of evolving on timescales comparable to rates of global change. Given the importance of soil ecosystems in global carbon cycling, we assess the potential impact of microbial evolution on carbon-climate feedbacks in this system. We begin by reviewing the current state of knowledge concerning microbial evolution in response to global change and its specific effect on soil carbon dynamics. Through this integration, we synthesize a roadmap detailing how to integrate microbial evolution into ecosystem biogeochemical models. Specifically, we highlight the importance of microscale mechanistic soil carbon models, including choosing an appropriate evolutionary model (e.g., adaptive dynamics, quantitative genetics), validating model predictions with 'omics' and experimental data, scaling microbial adaptations to ecosystem level processes, and validating with ecosystem-scale measurements. The proposed steps will require significant investment of scientific resources and might require 10-20 years to be fully implemented. However, through the application of multi-scale integrated approaches, we will advance the integration of microbial evolution into predictive understanding of ecosystems, providing clarity on its role and impact within the broader context of environmental change.

Many recent studies on drought-induced vegetation mortality have explored how plant functional traits, and classifications of such traits along axes of, for example, isohydry-anisohydry, might contribute to predicting drought survival and recovery. As these studies proliferate, the consistency and predictive value of such classifications need to be carefully examined. Here, we outline the basis for a systematic classification of plant drought responses that accounts for both environmental conditions and functional traits. We use non-dimensional analysis to integrate plant traits and metrics of environmental variation into groups that can be associated with alternative drought stress pathways (hydraulic failure and carbon limitation), and demonstrate that these groupings predict physiological drought outcomes using both synthetic and measured data. In doing so, we aim to untangle some confounding effects of environment and trait variations that undermine current classification schemes, advocate for more careful treatment of the environmental context within which plants experience and respond to drought, and outline a pathway towards a general classification of drought vulnerability.

Soil moisture controls microbial activity and soil carbon cycling. Because microbial activity decreases as soils dry, decomposition of soil organic matter (SOM) is thought to decrease with increasing drought length. Yet, microbial biomass and a pool of water-extractable organic carbon (WEOC) can increase as soils dry, perhaps implying microbes may continue to break down SOM even if drought stressed. Here, we test the hypothesis that WEOC increases as soils dry because exoenzymes continue to break down litter, while their products accumulate because they cannot diffuse to microbes. To test this hypothesis, we manipulated field plots by cutting off litter inputs and by irrigating and excluding precipitation inputs to extend or shorten the length of the dry season. We expected that the longer the soils would remain dry, the more WEOC would accumulate in the presence of litter, whereas shortening the length of the dry season, or cutting off litter inputs, would reduce WEOC accumulation. Lastly, we incubated grass roots in the laboratory and measured the concentration of reducing sugars and potential hydrolytic enzyme activities, strictly to understand the mechanisms whereby exoenzymes break down litter over the dry season. As expected, extending dry season length increased WEOC concentrations by 30% above the 108 μg C/g measured in untreated plots, whereas keeping soils moist prevented WEOC from accumulating. Contrary to our hypothesis, excluding plant litter inputs actually increased WEOC concentrations by 40% above the 105 μg C/g measured in plots with plants. Reducing sugars did not accumulate in dry senesced roots in our laboratory incubation. Potential rates of reducing sugar production by hydrolytic enzymes ranged from 0.7 to 10 μmol·g^-1 ·h^-1 and far exceeded the rates of reducing sugar accumulation (~0.001 μmol·g^-1 ·h^-1 ). Our observations do not support the hypothesis that exoenzymes continue to break down litter to produce WEOC in dry soils. Instead, we develop the argument that physical processes are more likely to govern short-term WEOC dynamics via slaking of microaggregates that stabilize SOM and through WEOC redistribution when soils wet up, as well as through less understood effects of drought on the soil mineral matrix.

Despite the appeal of the iso/anisohydric framework for classifying plant drought responses, recent studies have shown that such classifications can be strongly affected by a plant's environment. Here, we present measured in situ drought responses to demonstrate that apparent isohydricity can be conflated with environmental conditions that vary over space and time. In particular, we (a) use data from an oak species (Quercus douglasii) during the 2012-2015 extreme drought in California to demonstrate how temporal and spatial variability in the environment can influence plant water potential dynamics, masking the role of traits; (b) explain how these environmental variations might arise from climatic, topographic, and edaphic variability; (c) illustrate, through a "common garden" thought experiment, how existing trait-based or response-based isohydricity metrics can be confounded by these environmental variations, leading to Type-1 (false positive) and Type-2 (false negative) errors; and (d) advocate for the use of model-based approaches for formulating alternate classification schemes. Building on recent insights from greenhouse and vineyard studies, we offer additional evidence across multiple field sites to demonstrate the importance of spatial and temporal drivers of plants' apparent isohydricity. This evidence challenges the use of isohydricity indices, per se, to characterize plant water relations at the global scale.

Soil organic carbon management has the potential to aid climate change mitigation through drawdown of atmospheric carbon dioxide. To be effective, such management must account for processes influencing carbon storage and re-emission at different space and time scales. Achieving this requires a conceptual advance in our understanding to link carbon dynamics from the scales at which processes occur to the scales at which decisions are made. Here, we propose that soil carbon persistence can be understood through the lens of decomposers as a result of functional complexity derived from the interplay between spatial and temporal variation of molecular diversity and composition. For example, co-location alone can determine whether a molecule is decomposed, with rapid changes in moisture leading to transport of organic matter and constraining the fitness of the microbial community, while greater molecular diversity may increase the metabolic demand of, and thus potentially limit, decomposition. This conceptual shift accounts for emergent behaviour of the microbial community and would enable soil carbon changes to be predicted without invoking recalcitrant carbon forms that have not been observed experimentally. Functional complexity as a driver of soil carbon persistence suggests soil management should be based on constant care rather than one-time action to lock away carbon in soils.

Large areas in the tropics and at mid-latitudes experience pronounced seasonality and inter-annual variability in rainfall and hence water availability. Despite the importance of these seasonally dry ecosystems (SDEs) for the global carbon cycling and in providing ecosystem services, a unifying ecohydrological framework to interpret the effects of climatic variability on SDEs is still lacking. A synthesis of existing data about plant functional adaptations in SDEs, covering some 400 species, shows that leaf phenological variations, rather than physiological traits, provide the dominant control on plant-water-carbon interactions. Motivated by this result, the combined implications of leaf phenology and climatic variability on plant water use strategies are here explored with a minimalist model of the coupled soil water and plant carbon balances. The analyses are extended to five locations with different hydroclimatic forcing, spanning seasonally dry tropical climates (without temperature seasonality) and Mediterranean climates (exhibiting out of phase seasonal patterns of rainfall and temperature). The most beneficial leaf phenology in terms of carbon uptake depends on the climatic regime: evergreen species are favoured by short dry seasons or access to persistent water stores, whereas high inter-annual variability of rainy season duration favours the coexistence of multiple drought-deciduous phenological strategies. We conclude that drought-deciduousness may provide a competitive advantage in face of predicted declines in rainfall totals, while reduced seasonality and access to deep water stores may favour evergreen species. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Global vegetation and land-surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco-evolutionary optimality (EEO) principles can provide novel, parameter-sparse representations of plant and vegetation processes. We present case studies that demonstrate how EEO generates parsimonious representations of core, leaf-level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental change.

In most terrestrial ecosystems, plant growth is limited by nitrogen and phosphorus. Adding either nutrient to soil usually affects primary production, but their effects can be positive or negative. Here we provide a general stoichiometric framework for interpreting these contrasting effects. First, we identify nitrogen and phosphorus limitations on plants and soil microorganisms using their respective nitrogen to phosphorus critical ratios. Second, we use these ratios to show how soil microorganisms mediate the response of primary production to limiting and non-limiting nutrient addition along a wide gradient of soil nutrient availability. Using a meta-analysis of 51 factorial nitrogen-phosphorus fertilization experiments conducted across multiple ecosystems, we demonstrate that the response of primary production to nitrogen and phosphorus additions is accurately predicted by our stoichiometric framework. The only pattern that could not be predicted by our original framework suggests that nitrogen has not only a structural function in growing organisms, but also a key role in promoting plant and microbial nutrient acquisition. We conclude that this stoichiometric framework offers the most parsimonious way to interpret contrasting and, until now, unresolved responses of primary production to nutrient addition in terrestrial ecosystems.