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Understanding how temperature influences the scaling of physiological rates through levels of biological organization is critical for predicting plant responses to climate. Metabolic theory predicts that many rates increase exponentially with temperature following an activation energy (E) of 0.32 eV for photosynthesis. Here, I evaluate this prediction for net photosynthesis and organ, individual, and ecosystem growth. Observed E for photosynthesis varied widely but was not statistically different from predictions, while E for organs was greater than predicted, and E for individuals and ecosystems only weakly characterized temperature responses. I review several hypotheses that may underlie these results. Understanding how multiple rate-limiting processes coalesce into a single E that characterizes metabolic responses to temperature, and how to best estimate E from unimodal data, remain important challenges.

This article is a Commentary on Bär et al., 217: 1484–1493.

Variation in terrestrial net primary production (NPP) with climate is thought to originate from a direct influence of temperature and precipitation on plant metabolism. However, variation in NPP may also result from an indirect influence of climate by means of plant age, stand biomass, growing season length and local adaptation. To identify the relative importance of direct and indirect climate effects, we extend metabolic scaling theory to link hypothesized climate influences with NPP, and assess hypothesized relationships using a global compilation of ecosystem woody plant biomass and production data. Notably, age and biomass explained most of the variation in production whereas temperature and precipitation explained almost none, suggesting that climate indirectly (not directly) influences production. Furthermore, our theory shows that variation in NPP is characterized by a common scaling relationship, suggesting that global change models can incorporate the mechanisms governing this relationship to improve predictions of future ecosystem function. Net primary production is affected by temperature and precipitation, but whether this is a direct kinetic effect on plant metabolism or an indirect ecological effect mediated by changes in plant age, plant biomass or growing season length is unclear — this study develops metabolic scaling theory to be able to answer this question and applies it to a global data set of plant productivity, concluding that it is indirect effects that explain the influence of climate on productivity, which is characterized by a common scaling relationship across climate gradients. Plant productivity response to climate Net primary production is affected by temperature and precipitation, but is this a direct effect on plant metabolism or an indirect ecological effect mediated by changes in growing season length and plant biomass? Here, Sean Michaletz et al . develop metabolic scaling theory to test the relative importance of direct and indirect climate effects. Applying their model to a global data set of plant productivity, the authors conclude that it is indirect effects that explain the influence of climate on productivity. Temperature and water availability are fundamental drivers of plant physiology and ecosystem metabolism at local scales, but at global scales climate influences net primary production indirectly via plant age and stand biomass, which is largely driven by maximum plant size.

Climate warming is increasingly leading to marked changes in plant and animal biodiversity, but it remains unclear how temperatures affect microbial biodiversity, particularly in terrestrial soils. Here we show that, in accordance with metabolic theory of ecology, taxonomic and phylogenetic diversity of soil bacteria, fungi and nitrogen fixers are all better predicted by variation in environmental temperature than pH. However, the rates of diversity turnover across the global temperature gradients are substantially lower than those recorded for trees and animals, suggesting that the diversity of plant, animal and soil microbial communities show differential responses to climate change. To the best of our knowledge, this is the first study demonstrating that the diversity of different microbial groups has significantly lower rates of turnover across temperature gradients than other major taxa, which has important implications for assessing the effects of human-caused changes in climate, land use and other factors. Climate warming has a wide range of effects on biodiversity. Here, Zhou et al . show that although variation in environmental temperature is a primary driver of soil microbial biodiversity, microbes show much lower rates of turnover across temperature gradients than other major taxa.

Terrestrial net primary production (NPP) varies across global climate gradients, but the mechanisms through which climate drives this variation remain subject to debate. Specifically, it is debatable whether NPP is primarily influenced by 'direct' effects of climate on the kinetics of plant metabolism or 'indirect' effects of climate on plant size, stand biomass, stand age structure and growing season length. We clarify several issues in this debate by presenting multiple lines of evidence that support a primarily indirect influence of climate on global variation in NPP across broad geographical gradients. First, we highlight > 60 years of research that suggests leaf area, growing season length, plant biomass and/or plant age are better predictors of NPP than climate or latitude. Second, we refute recent claims that using biomass and age as predictors of NPP represents circular reasoning. Third, we illustrate why effects of climate on the kinetics of plant production must be evaluated using instantaneous (not annualized) rates of productivity. Fourth, we review recent analyses showing that the effects of biomass and age on NPP are much stronger than the effects of climate. Fifth, we present new analyses of a high-quality NPP dataset that demonstrate further that biomass, age and growing season length are better predictors of global variation in NPP than climate variables. Our results are consistent with the hypothesis that variation in NPP across global climate gradients primarily reflects the influence of climate on growing season length and stand biomass, as well as stand age, rather than the effects of temperature and precipitation on the kinetics of metabolism. However, this hypothesis should be evaluated further using larger, high-quality observational and experimental datasets spanning multiple geographical scales.

In a rapidly changing climate, alpine plants may persist by adapting to new conditions. However, the rate at which the climate is changing might exceed the rate of adaptation through evolutionary processes in long-lived plants. Persistence may depend on phenotypic plasticity in morphology and physiology. Here we investigated patterns of leaf trait variation including leaf area, leaf thickness, specific leaf area, leaf dry matter content, leaf nutrients (C, N, P) and isotopes (δ C and δ N) across an elevation gradient on Gongga Mountain, Sichuan Province, China. We quantified inter- and intra-specific trait variation and the plasticity in leaf traits of selected species to experimental warming and cooling by using a reciprocal transplantation approach. We found substantial phenotypic plasticity in most functional traits where δ N, leaf area, and leaf P showed greatest plasticity. These traits did not correspond with traits with the largest amount of intraspecific variation. Plasticity in leaf functional traits tended to enable plant populations to shift their trait values toward the mean values of a transplanted plants' destination community, but only if that population started with very different trait values. These results suggest that leaf trait plasticity is an important mechanism for enabling plants to persist within communities and to better tolerate changing environmental conditions under climate change.

Aim: To test the latitudinal gradient in plant species diversity for self-similarity across taxonomic scales and amongst taxa. Location: North America. Methods: We used species richness data from 245 local vascular plant floras to quantify the slope and shape of the latitudinal gradients in species diversity (LGSD) across all plant species as well as within each family and order. We calculated the contribution of each family and order to the empirical LGSD. Results: We observed the canonical LGSD when all plants were considered with floras at the lowest latitudes having, on average, 451 more species than floras at the highest latitudes. When considering slope alone, most orders and families showed the expected negative slope, but 31.7% of families and 27.7% of orders showed either no significant relationship between latitude and diversity or a reverse LGSD. Latitudinal patterns of family diversity account for at least 14% of this LGSD. Most orders and families did not show the negative slope and concave-down quadratic shape expected by the pattern for all plant species. A majority of families did not make a significant contribution in species to the LGSD with 53% of plant families contributing little to nothing to the overall gradient. Ten families accounted for more than 70% of the gradient. Two families, the Asteraceae and Fabaceae, contributed a third of the LGSD. Main Conclusions: The empirical LGSD we describe here is a consequence of a gradient in the number of families and diversification within relative few plant families. Macroecological studies typically aim to generate models that are general across taxa with the implicit assumption that the models are general within taxa. Our results strongly suggest that models of the latitudinal gradient in plant species richness that rely on environmental covariates (e.g. temperature, energy) are likely not general across plant taxa.

Leaf thermoregulation has been documented in a handful of studies, but the generality and origins of this pattern are unclear. We suggest that leaf thermoregulation is widespread in both space and time, and originates from the optimization of leaf traits to maximize leaf carbon gain across and within variable environments. Here we use global data for leaf temperatures, traits and photosynthesis to evaluate predictions from a novel theory of thermoregulation that synthesizes energy budget and carbon economics theories. Our results reveal that variation in leaf temperatures and physiological performance are tightly linked to leaf traits and carbon economics. The theory, parameterized with global averaged leaf traits and microclimate, predicts a moderate level of leaf thermoregulation across a broad air temperature gradient. These predictions are supported by independent data for diverse taxa spanning a global air temperature range of ∼60 °C. Moreover, our theory predicts that net carbon assimilation can be maximized by means of a trade-off between leaf thermal stability and photosynthetic stability. This prediction is supported by globally distributed data for leaf thermal and photosynthetic traits. Our results demonstrate that the temperatures of plant tissues, and not just air, are vital to developing more accurate Earth system models. Leaves vary their temperatures to improve their physiological performance. Theoretical considerations predict, and data for diverse taxa show, a moderate level of leaf thermoregulation maximizing net carbon assimilation.

The Arctic is warming twice as fast as the rest of the planet, leading to rapid changes in species composition and plant functional trait variation. Landscape-level maps of vegetation composition and trait distributions are required to expand spatially-limited plot studies, overcome sampling biases associated with the most accessible research areas, and create baselines from which to monitor environmental change. Unmanned aerial vehicles (UAVs) have emerged as a low-cost method to generate high-resolution imagery and bridge the gap between fine-scale field studies and lower resolution satellite analyses. Here we used field spectroscopy data (400–2500 nm) and UAV multispectral imagery to test spectral methods of species identification and plant water and chemistry retrieval near Longyearbyen, Svalbard. Using the field spectroscopy data and Random Forest analysis, we were able to distinguish eight common High Arctic plant tundra species with 74% accuracy. Using partial least squares regression (PLSR), we were able to predict corresponding water, nitrogen, phosphorus and C:N values ( r 2 = 0.61–0.88, RMSEmean = 12%–64%). We developed analogous models using UAV imagery (five bands: Blue, Green, Red, Red Edge and Near-Infrared) and scaled up the results across a 450 m long nutrient gradient located underneath a seabird colony. At the UAV level, we were able to map three plant functional groups (mosses, graminoids and dwarf shrubs) at 72% accuracy and generate maps of plant chemistry. Our maps show a clear marine-derived fertility gradient, mediated by geomorphology. We used the UAV results to explore two methods of upscaling plant water content to the wider landscape using Sentinel-2A imagery. Our results are pertinent for high resolution, low-cost mapping of the Arctic.

Heat injuries sustained in a fire can initiate a cascade of complex mechanisms that affect the physiology of trees after fires. Uncovering the exact physiological mechanisms and relating specific injuries to whole-plant and ecosystem functioning is the focus of intense current research. Recent studies have made critical steps forward in our understanding of tree physiological processes after fires, and have suggested mechanisms by which fire injuries may interact with disturbances such as drought, insects and pathogens. We outline a conceptual framework that unifies the involved processes, their interconnections, and possible feedbacks, and contextualizes these responses with existing hypotheses for disturbance effects on plants and ecosystems. By focusing on carbon and water as currencies of plant functioning, we demonstrate fire-induced cambium/phloem necrosis and xylem damage to be main disturbance effects. The resulting carbon starvation and hydraulic dysfunction are linked with drought and insect impacts. Evaluating the precise process relationships will be crucial for fully understanding how fires can affect tree functionality, and will help improve fire risk assessment and mortality model predictions. Especially considering future climate-driven increases in fire frequency and intensity, knowledge of the physiological tree responses is important to better estimate postfire ecosystem dynamics and interactions with climate disturbances.