Leveraging plant hydraulics to yield predictive and dynamic plant leaf allocation in vegetation models with climate change

Plant functional traits provide a link in process‐based vegetation models between plant‐level physiology and ecosystem‐level responses. Recent advances in physiological understanding and computational efficiency have allowed for the incorporation of plant hydraulic processes in large‐scale vegetation models. However, a more mechanistic representation of water limitation that determines ecosystem responses to plant water stress necessitates a re‐evaluation of trait‐based constraints for plant carbon allocation, particularly allocation to leaf area. In this review, we examine model representations of plant allocation to leaves, which is often empirically set by plant functional type‐specific allometric relationships. We analyze the evolution of the representation of leaf allocation in models of different scales and complexities. We show the impacts of leaf allocation strategy on plant carbon uptake in the context of recent advancements in modeling hydraulic processes. Finally, we posit that deriving allometry from first principles using mechanistic hydraulic processes is possible and should become standard practice, rather than using prescribed allometries. The representation of allocation as an emergent property of scarce resource constraints is likely to be critical to representing how global change processes impact future ecosystem dynamics and carbon fluxes and may reduce the number of poorly constrained parameters in vegetation models.     

Cryptic phenology in plants: Case studies,implications, and recommendations

Plant phenology—the timing of cyclic or recurrent biological events in plants—offers insight into the ecology, evolution, and seasonality of plant‐mediated ecosystem processes. Traditionally studied phenologies are readily apparent, such as flowering events, germination timing, and season‐initiating budbreak. However, a broad range of phenologies that are fundamental to the ecology and evolution of plants, and to global biogeochemical cycles and climate change predictions, have been neglected because they are “cryptic”—that is, hidden from view (e.g., root production) or difficult to distinguish and interpret based on common measurements at typical scales of examination (e.g., leaf turnover in evergreen forests). We illustrate how capturing cryptic phenology can advance scientific understanding with two case studies: wood phenology in a deciduous forest of the northeastern USA and leaf phenology in tropical evergreen forests of Amazonia. Drawing on these case studies and other literature, we argue that conceptualizing and characterizing cryptic plant phenology is needed for understanding and accurate prediction at many scales from organisms to ecosystems. We recommend avenues of empirical and modeling research to accelerate discovery of cryptic phenological patterns, to understand their causes and consequences, and to represent these processes in terrestrial biosphere models.     

Observed and modelled historical trends in the water‐use efficiency of plants and ecosystems

Plant water‐use efficiency (WUE, the carbon gained through photosynthesis per unit of water lost through transpiration) is a tracer of the plant physiological controls on the exchange of water and carbon dioxide between terrestrial ecosystems and the atmosphere. At the leaf level, rising CO₂ concentrations tend to increase carbon uptake (in the absence of other limitations) and to reduce stomatal conductance, both effects leading to an increase in leaf WUE. At the ecosystem level, indirect effects (e.g. increased leaf area index, soil water savings) may amplify or dampen the direct effect of CO₂. Thus, the extent to which changes in leaf WUE translate to changes at the ecosystem scale remains unclear. The differences in the magnitude of increase in leaf versus ecosystem WUE as reported by several studies are much larger than would be expected with current understanding of tree physiology and scaling, indicating unresolved issues. Moreover, current vegetation models produce inconsistent and often unrealistic magnitudes and patterns of variability in leaf and ecosystem WUE, calling for a better assessment of the underlying approaches. Here, we review the causes of variations in observed and modelled historical trends in WUE over the continuum of scales from leaf to ecosystem, including methodological issues, with the aim of elucidating the reasons for discrepancies observed within and across spatial scales. We emphasize that even though physiological responses to changing environmental drivers should be interpreted differently depending on the observational scale, there are large uncertainties in each data set which are often underestimated. Assumptions made by the vegetation models about the main processes influencing WUE strongly impact the modelled historical trends. We provide recommendations for improving long‐term observation‐based estimates of WUE that will better inform the representation of WUE in vegetation models.     

Empirical and theoretical challenges in aboveground–belowground ecology

A growing body of evidence shows that aboveground and belowground communities and processes are intrinsically linked, and that feedbacks between these subsystems have important implications for community structure and ecosystem functioning. Almost all studies on this topic have been carried out from an empirical perspective and in specific ecological settings or contexts. Belowground interactions operate at different spatial and temporal scales. Due to the relatively low mobility and high survival of organisms in the soil, plants have longer lasting legacy effects belowground than aboveground. Our current challenge is to understand how aboveground–belowground biotic interactions operate across spatial and temporal scales, and how they depend on, as well as influence, the abiotic environment. Because empirical capacities are too limited to explore all possible combinations of interactions and environmental settings, we explore where and how they can be supported by theoretical approaches to develop testable predictions and to generalise empirical results. We review four key areas where a combined aboveground–belowground approach offers perspectives for enhancing ecological understanding, namely succession, agro-ecosystems, biological invasions and global change impacts on ecosystems. In plant succession, differences in scales between aboveground and belowground biota, as well as between species interactions and ecosystem processes, have important implications for the rate and direction of community change. Aboveground as well as belowground interactions either enhance or reduce rates of plant species replacement. Moreover, the outcomes of the interactions depend on abiotic conditions and plant life history characteristics, which may vary with successional position. We exemplify where translation of the current conceptual succession models into more predictive models can help targeting empirical studies and generalising their results. Then, we discuss how understanding succession may help to enhance managing arable crops, grasslands and invasive plants, as well as provide insights into the effects of global change on community re-organisation and ecosystem processes.     

Scaling environmental change through the community-level: a trait-based response-and-effect framework for plants

Predicting ecosystem responses to global change is a major challenge in ecology. A critical step in that challenge is to understand how changing environmental conditions influence processes across levels of ecological organization. While direct scaling from individual to ecosystem dynamics can lead to robust and mechanistic predictions, new approaches are needed to appropriately translate questions through the community level. Species invasion, loss, and turnover all necessitate this scaling through community processes, but predicting how such changes may influence ecosystem function is notoriously difficult. We suggest that community‐level dynamics can be incorporated into scaling predictions using a trait‐based response–effect framework that differentiates the community response to environmental change (predicted by response traits) and the effect of that change on ecosystem processes (predicted by effect traits). We develop a response‐and‐effect functional framework, concentrating on how the relationships among species' response, effect, and abundance can lead to general predictions concerning the magnitude and direction of the influence of environmental change on function. We then detail several key research directions needed to better scale the effects of environmental change through the community level. These include (1) effect and response trait characterization, (2) linkages between response‐and‐effect traits, (3) the importance of species interactions on trait expression, and (4) incorporation of feedbacks across multiple temporal scales. Increasing rates of extinction and invasion that are modifying communities worldwide make such a research agenda imperative.     

Stoichiometric distribution models: ecological stoichiometry at the landscape extent

Human activities are altering the fundamental geography of biogeochemicals. Yet we lack an understanding of how the spatial patterns in organismal stoichiometry affect biogeochemical processes and the tools to predict the impacts of global changes on biogeochemical processes. In this contribution we develop stoichiometric distribution models (StDMs), which allow us to map spatial structure in resource elemental composition across a landscape and evaluate spatial responses of consumers. We parameterise StDMs for a consumer‐resource (moose‐white birch) system and demonstrate that we can develop predictive models of resource stoichiometry across a landscape and that such models could improve our predictions of consumer space use. With results from our study system application, we argue that explicit consideration of the spatial patterns in organismal elemental composition may uncover emergent individual, population, community and ecosystem properties that are not revealed at the local extents routinely used in ecological stoichiometry. We discuss perspectives for further developments and application of StDMs to advance three emerging frameworks for spatial ecosystem ecology in an era of global change; meta‐ecosystem theory, macroecological stoichiometry and remotely sensed biogeochemistry. Progress on these emerging frameworks will allow for the integration of ecological stoichiometry and individual space use and fitness.     

Bridging Taxonomic and Disciplinary Divides in Infectious Disease

Pathogens traverse disciplinary and taxonomic boundaries, yet infectious disease research occurs in many separate disciplines including plant pathology, veterinary and human medicine, and ecological and evolutionary sciences. These disciplines have different traditions, goals, and terminology, creating gaps in communication. Bridging these disciplinary and taxonomic gaps promises novel insights and important synergistic advances in control of infectious disease. An approach integrated across the plant-animal divide would advance our understanding of disease by quantifying critical processes including transmission, community interactions, pathogen evolution, and complexity at multiple spatial and temporal scales. These advances require more substantial investment in basic disease research.     

Nitrogen limitation on land: how can it occur in Earth system models?

The representation of the nitrogen (N) cycle in Earth system models (ESMs) is strongly motivated by the constraint N poses on the sequestration of anthropogenic carbon (C). Models typically implement a stoichiometric relationship between C and N in which external supply and assimilation by organisms are adjusted to maintain their internal stoichiometry. N limitation of primary productivity thus occurs if the N supply from uptake and fixation cannot keep up with the construction of tissues allowed by C assimilation. This basic approach, however, presents considerable challenges in how to faithfully represent N limitation. Here, we review how N limitation is currently implemented and evaluated in ESMs and highlight challenges and opportunities in their future development. At or near steady state, N limitation is governed by the magnitude of losses from the plant‐unavailable pool vs. N fixation and there are considerable differences in how models treat both processes. In nonsteady‐state systems, the accumulation of N in pools with slow turnover rates reduces N available for plant uptake and can be challenging to represent when initializing ESM simulations. Transactional N limitation occurs when N is incorporated into various vegetation and soil pools and becomes available to plants only after it is mineralized, the dynamics of which depends on how ESMs represent decomposition processes in soils. Other challenges for ESMs emerge when considering seasonal to interannual climatic oscillations as they create asynchronies between C and N demand, leading to transient alternations between N surplus and deficit. Proper evaluation of N dynamics in ESMs requires conceptual understanding of the main levers that trigger N limitation, and we highlight key measurements and observations that can help constrain these levers. Two of the biggest challenges are the mechanistic representation of plant controls on N availability and turnover, including N fixation and organic matter decomposition processes.     

陆地生态系统过程对气候变暖的响应与适应

陆地生态系统包含一系列时空连续、尺度多元且互相联系的生态学过程。由于大部分生态学过程都受到温度调控, 因此气候变暖会对全球陆地生态系统产生深远的影响。近年来, 全球变化生态学的基本科学问题之一是陆地生态系统的关键过程如何响应与适应全球气候变暖。围绕该问题, 该文梳理了近年来的研究进展, 重点关注植物生理生态过程、物候期、群落动态、生产力及其分配、凋落物与土壤有机质分解、养分循环等过程对温度升高的响应与适应机理。通过定量分析近20年来发表于主流期刊的相关论文, 展望了该领域的前沿方向, 包括物种性状对生态系统过程的预测能力, 生物地球化学循环的耦合过程, 极端高温与低温事件的响应与适应机理, 不对称气候变暖的影响机理和基于过程的生态系统模拟预测等。基于这些研究进展, 该文建议进一步研究陆地生态系统如何适应气候变暖, 更多关注我国的特色生态系统类型, 并整合实验、观测或模型等研究手段开展跨尺度的合作研究。     

Continental‐scale syntheses of Australian pyromes – misclassification of south‐western eucalypt woodlands misinforms management

Global and continental‐scale analysis of ecological phenomena can offer important insights through the identification of patterns and associations not detectable at smaller scales. However, using proxies for ecological phenomena, such as vegetation mapping for spatially projecting fire regime niches and post‐fire plant responses, require critical examination of predictions to determine utility. Using local studies in south‐western Australia, we demonstrate that while this approach has been largely successful in mallee woodland and shrubland, it has failed in eucalypt woodland, with the consequence that values for a range of fire‐related parameters from the continent‐wide approaches, if adopted in informing management, would result in undesirable conservation outcomes for the world's largest extant temperate woodland.     

Response and adaptation of terrestrial ecosystem processes to climate warming

陆地生态系统包含一系列时空连续、尺度多元且互相联系的生态学过程。由于大部分生态学过程都受到温度调控, 因此气候变暖会对全球陆地生态系统产生深远的影响。近年来, 全球变化生态学的基本科学问题之一是陆地生态系统的关键过程如何响应与适应全球气候变暖。围绕该问题, 该文梳理了近年来的研究进展, 重点关注植物生理生态过程、物候期、群落动态、生产力及其分配、凋落物与土壤有机质分解、养分循环等过程对温度升高的响应与适应机理。通过定量分析近20年来发表于主流期刊的相关论文, 展望了该领域的前沿方向, 包括物种性状对生态系统过程的预测能力, 生物地球化学循环的耦合过程, 极端高温与低温事件的响应与适应机理, 不对称气候变暖的影响机理和基于过程的生态系统模拟预测等。基于这些研究进展, 该文建议进一步研究陆地生态系统如何适应气候变暖, 更多关注我国的特色生态系统类型, 并整合实验、观测或模型等研究手段开展跨尺度的合作研究。     

Interactions between temperature and nutrients across levels of ecological organization

Temperature and nutrient availability play key roles in controlling the pathways and rates at which energy and materials move through ecosystems. These factors have also changed dramatically on Earth over the past century as human activities have intensified. Although significant effort has been devoted to understanding the role of temperature and nutrients in isolation, less is known about how these two factors interact to influence ecological processes. Recent advances in ecological stoichiometry and metabolic ecology provide a useful framework for making progress in this area, but conceptual synthesis and review are needed to help catalyze additional research. Here, we examine known and potential interactions between temperature and nutrients from a variety of physiological, community, and ecosystem perspectives. We first review patterns at the level of the individual, focusing on four traits – growth, respiration, body size, and elemental content – that should theoretically govern how temperature and nutrients interact to influence higher levels of biological organization. We next explore the interactive effects of temperature and nutrients on populations, communities, and food webs by synthesizing information related to community size spectra, biomass distributions, and elemental composition. We use metabolic theory to make predictions about how population‐level secondary production should respond to interactions between temperature and resource supply, setting up qualitative predictions about the flows of energy and materials through metazoan food webs. Last, we examine how temperature–nutrient interactions influence processes at the whole‐ecosystem level, focusing on apparent vs. intrinsic activation energies of ecosystem processes, how to represent temperature–nutrient interactions in ecosystem models, and patterns with respect to nutrient uptake and organic matter decomposition. We conclude that a better understanding of interactions between temperature and nutrients will be critical for developing realistic predictions about ecological responses to multiple, simultaneous drivers of global change, including climate warming and elevated nutrient supply.     

Modelling nutritional mutualisms: challenges and opportunities for data integration

Nutritional mutualisms are ancient, widespread, and profoundly influential in biological communities and ecosystems. Although much is known about these interactions, comprehensive answers to fundamental questions, such as how resource availability and structured interactions influence mutualism persistence, are still lacking. Mathematical modelling of nutritional mutualisms has great potential to facilitate the search for comprehensive answers to these and other fundamental questions by connecting the physiological and genomic underpinnings of mutualisms with ecological and evolutionary processes. In particular, when integrated with empirical data, models enable understanding of underlying mechanisms and generalisation of principles beyond the particulars of a given system. Here, we demonstrate how mathematical models can be integrated with data to address questions of mutualism persistence at four biological scales: cell, individual, population, and community. We highlight select studies where data has been or could be integrated with models to either inform model structure or test model predictions. We also point out opportunities to increase model rigour through tighter integration with data, and describe areas in which data is urgently needed. We focus on plant‐microbe systems, for which a wealth of empirical data is available, but the principles and approaches can be generally applied to any nutritional mutualism.     

The challenges that spatial context present for synthesizing community ecology across scales

Accurately characterizing spatial patterns on landscapes is necessary to understand the processes that generate biodiversity, a problem that has applications in ecological theory, conservation planning, ecosystem restoration, and ecosystem management. However, the measurement of biodiversity patterns and the ecological and evolutionary processes that underlie those patterns is highly dependent on the study unit size, boundary placement, and number of observations. These issues, together known as the modifiable areal unit problem, are well known in geography. These factors limit the degree to which results from different metacommunity and macro‐ecological studies can be compared to draw new inferences, and yet these types of comparisons are widespread in community ecology. Using aquatic community datasets, we demonstrate that spatial context drives analytical results when landscapes are sub‐divided. Next, we present a framework for using resampling and neighborhood smoothing to standardize datasets to allow for inferential comparisons. We then provide examples for how addressing these issues enhances our ability to understand the processes shaping ecological communities at landscape scales and allows for informative meta‐analytical synthesis. We conclude by calling for greater recognition of issues derived from the modifiable areal unit problem in community ecology, discuss implications of the problem for interpreting the existing literature, and identify tools and approaches for future research.     

Interannual variation of terrestrial carbon cycle: Issues and perspectives

With accumulation of carbon cycle observations and model developments over the past decades, exploring interannual variation (IAV) of terrestrial carbon cycle offers the opportunity to better understand climate–carbon cycle relationships. However, despite growing research interest, uncertainties remain on some fundamental issues, such as the contributions of different regions, constituent fluxes and climatic factors to carbon cycle IAV. Here we overviewed the literature on carbon cycle IAV about current understanding of these issues. Observations and models of the carbon cycle unanimously show the dominance of tropical land ecosystems to the signal of global carbon cycle IAV, where tropical semiarid ecosystems contribute as much as the combination of all other tropical ecosystems. Vegetation photosynthesis contributes more than ecosystem respiration to IAV of the global net land carbon flux, but large uncertainties remain on the contribution of fires and other disturbance fluxes. Climatic variations are the major drivers to the IAV of net land carbon flux. Although debate remains on whether the dominant driver is temperature or moisture variability, their interaction,that is, the dependence of carbon cycle sensitivity to temperature on moisture conditions, is emerging as key regulators of the carbon cycle IAV. On timescales from the interannual to the centennial, global carbon cycle variability will be increasingly contributed by northern land ecosystems and oceans. Therefore, both improving Earth system models (ESMs) with the progressive understanding on the fast processes manifested at interannual timescale and expanding carbon cycle observations at broader spatial and longer temporal scales are critical to better prediction on evolution of the carbon–climate system.     

Making predictive ecology more relevant to policy makers and practitioners

One of the aims of ecology is to aid policy makers and practitioners through the development of testable predictions of relevance to society. Here, we argue that this capacity can be improved in three ways. Firstly, by thinking more clearly about the priority issues using a range of methods including horizon scanning, identifying policy gaps, identifying priority questions and using evidence-based conservation to identify knowledge gaps. Secondly, by linking ecological models with models of other systems, such as economic and social models. Thirdly, by considering alternative approaches to generate and model data that use, for example, discrete or categorical states to model ecological systems. We particularly highlight that models are essential for making predictions. However, a key to the limitation in their use is the degree to which ecologists are able to communicate results to policy makers in a clear, useful and timely fashion.     

Ecological potentials of biodiversity modelled from information entropies: Plant species diversity of North-Central European forests as an example

The paper deals with the hypothesis that ecosystems have well-defined potentials of biodiversity. These potentials can be quantified as information entropy of the corresponding ecosystem type. The hypothesis is verified for the diversity of plant species.

A vegetation database of North-Central European forests containing more than 12000 relevés is analyzed computationally. The samples are classified into ecosystem types that are homogeneous with respect to vegetation patterns, ecological site factors, and, implicitly, with respect to ecosystem processes. Growing numbers of relevés are selected randomly from the representatives of different ecosystem types and investigated mathematically.

Shannon information (product of logarithmic species number and evenness) obeys a hyperbolic saturation equation approaching a finite value on infinite area. This asymptotic limit defines the ecological potential of species diversity. Within a given plant-geographical region, it is determined by ecological site factors like climate and soil controlling interrelations between plants. Competition relationships and hence potentials of phytodiversity are altered by management significantly. The curve of evenness versus area size is hump-shaped. Maximum evenness is proportional to the ecological potential of species diversity. The area size where evenness attains its maximum can be interpreted as the minimum area of the respective forest type. The ecological potentials of plant species diversity modelled from information entropies correspond to vegetation patterns consisting of a limited number of plant species. These vegetation patterns are closely related to ecosystem processes like nutrient cycling, plant nutrition, evapotranspiration, microbial processes, or net-primary production. Revealing the relationships between vegetation patterns and ecosystem processes allows scaling functional information from local measurement scales up to regional scales.

It is suggested to explore genetic, proteomic, and species data in order to derive comprehensive ecological potentials of biodiversity on various levels from population to landscape. The expected results could improve the understanding of the relationship between biodiversity and ecosystem functioning as well as the sustainability of ecosystem management.     

Soil biota diversity and plant diversity both contributed to ecosystem stability in grasslands

Understanding the effects of diversity on ecosystem stability in the context of global change has become an important goal of recent ecological research. However, the effects of diversity at multiple scales and trophic levels on ecosystem stability across environmental gradients remain unclear. Here, we conducted a field survey of α-, β-, and γ-diversity of plants and soil biota (bacteria, fungi, and nematodes) and estimated the temporal ecosystem stability of normalized difference vegetation index (NDVI) in 132 plots on the Mongolian Plateau. After climate and soil environmental variables were controlled for, both the α- and β-diversity of plants and soil biota (mainly via nematodes) together with precipitation explained most variation in ecosystem stability. These findings evidence that the diversity of both soil biota and plants contributes to ecosystem stability. Model predictions of the future effects of global changes on terrestrial ecosystem stability will require field observations of diversity of both plants and soil biota.     

Satellite Ecology (SATECO)—linking ecology,remote sensing and micrometeorology,from plot to regional scale,for the study of ecosystem structure and function

There is a growing requirement for ecosystem science to help inform a deeper understanding of the effects of global climate change and land use change on terrestrial ecosystem structure and function, from small area (plot) to landscape, regional and global scales. To meet these requirements, ecologists have investigated plant growth and carbon cycling processes at plot scale, using biometric methods to measure plant carbon accumulation, and gas exchange (chamber) methods to measure soil respiration. Also at the plot scale, micrometeorologists have attempted to measure canopy- or ecosystem-scale CO₂ flux by the eddy covariance technique, which reveals diurnal, seasonal and annual cycles. Mathematical models play an important role in integrating ecological and micrometeorological processes into ecosystem scales, which are further useful in interpreting time-accumulated information derived from biometric methods by comparing with CO₂ flux measurements. For a spatial scaling of such plot-level understanding, remote sensing via satellite is used to measure land use/vegetation type distribution and temporal changes in ecosystem structures such as leaf area index. However, to better utilise such data, there is still a need for investigations that consider the structure and function of ecosystems and their processes, especially in mountainous areas characterized by complex terrain and a mosaic distribution of vegetation. For this purpose, we have established a new interdisciplinary approach named ‘Satellite Ecology’, which aims to link ecology, remote sensing and micrometeorology to facilitate the study of ecosystem function, at the plot, landscape, and regional scale. This article was contributed at the invitation of the Editorial Committee.