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Microbes both affect the global carbon cycle that influences climate change, and are in turn influenced by environmental change. Here, we address how this happens using data from a long-term whole-ecosystem warming experiment at a boreal peatland to answer how temperature and CO2jointly influence communities of abundant, diverse, yet poorly understood non-fungi microbial Eukaryotes (protists). These microbes influence ecosystem function directly through photosynthesis and respiration, and indirectly, through predation on decomposers (bacteria, fungi). Using a combination of high-throughput fluid imaging and 18S amplicon sequencing, we report large climateinduced, community-wide shifts in the community functional composition of these microbes (size, shape, metabolism) that could alter overall function in peatlands. Importantly, we demonstrate a taxonomic convergence but a functional divergence in response to warming and elevated CO2but most environmental responses are contingent on organismal size, such that warming effects on functional composition are reversed by elevated CO2and amplified in larger microbes but not smaller ones. These findings show how the interactive effects of warming and rising CO2could alter the structure and function of peatland microbial food webs — a fragile ecosystem that stores 25% of terrestrial carbon and is increasingly threatened by human exploitation.

Climate change is affecting how energy and matter flow through ecosystems, thereby altering global carbon and nutrient cycles. Microorganisms play a fundamental role in carbon and nutrient cycling and are thus an integral link between ecosystems and climate. Here, we highlight a major black box hindering our ability to anticipate ecosystem climate responses: viral infections within complex microbial food webs. We show how understanding and predicting ecosystem responses to warming could be challenging—if not impossible—without accounting for the direct and indirect effects of viral infections on different microbes (bacteria, archaea, fungi, protists) that together perform diverse ecosystem functions. Importantly, understanding how rising temperatures associated with climate change influence viruses and virus-host dynamics is crucial to this task, yet is severely understudied. In this perspective, we (i) synthesize existing knowledge about virus-microbe-temperature interactions and (ii) identify important gaps to guide future investigations regarding how climate change might alter microbial food web effects on ecosystem functioning. To provide real-world context, we consider how these processes may operate in peatlands—globally significant carbon sinks that are threatened by climate change. We stress that understanding how warming affects biogeochemical cycles in any ecosystem hinges on disentangling complex interactions and temperature responses within microbial food webs.

Climate change is affecting how energy and matter flow within ecosystems, altering global carbon and nutrient cycles. Microorganisms play a fundamental role in carbon and nutrient cycling and are thus an integral link between ecosystems and climate. Here, we highlight a major black box hindering our ability to anticipate ecosystem climate responses: viral infections within complex microbial food webs. We show how understanding and predicting ecosystem responses to warming could be challenging—if not impossible—without accounting for the direct and indirect effects of viral infections on different microbes (bacteria, fungi, protists) that together perform diverse ecosystem functions. Importantly, understanding how rising temperatures associated with climate change influence viruses and virus-host dynamics is crucial to this task, yet severely understudied. In this perspective, we 1) synthesize existing knowledge about virus-microbe-temperature interactions and 2) identify important gaps to guide future investigations regarding how climate change might alter microbial food web effects on ecosystem functioning. To provide real-world context, we consider how these processes may operate in peatlands—globally significant carbon sinks that are threatened by climate change. We stress that understanding how warming affects biogeochemical cycles in any ecosystem hinges on disentangling complex interactions and temperature responses within microbial food webs.

As biomass dynamics capture information on population dynamics and ecosystem-level processes (e.g., changes in production over time), understanding how rising temperatures associated with global climate change influence biomass dynamics is a pressing issue in ecology. The total biomass of a species depends on its density and its average mass. Disentangling how biomass dynamics may respond to increasingly warm and variable temperatures may thus ultimately depend on understanding how temperature influences both density and mass dynamics. Here, we address this issue by keeping track of experimental microbial populations growing to carrying capacity for 15 days at two different temperatures in the presence and absence of temperature variability. We show that temperature influences biomass through its effects on density and mass dynamics, which have opposite effects on biomass and can offset each other. We also show that temperature variability influences biomass, but that effect is independent of any effects on density or mass dynamics. Last, we show that reciprocal effects of density and mass shift significantly across temperature regimes, suggesting that rapid and environment-dependent eco-phenotypic dynamics underlie biomass responses. Overall, our results connect temperature effects on population and phenotypic dynamics to explain how biomass responds to temperature regimes, thus shedding light on processes at play in cosmopolitan and massively abundant microbes as the world experiences increasingly hot and variable temperatures.

Microbial communities regulate ecosystem responses to climate change. However, predicting these responses is challenging because of complex interactions among processes at multiple levels of organization. Organismal traits that determine individual performance and ecological interactions are essential for scaling up environmental responses from individuals to ecosystems. We combine protist microcosm experiments and mathematical models to show that key traits—cell size, shape, and contents—each explain different aspects of species’ demographic responses to changes in temperature. These differences in species’ temperature responses have complex cascading effects across levels of organization—causing nonlinear shifts in total community respiration rates across temperatures via coordinated changes in community composition, equilibrium densities, and community–mean species mass in experimental protist communities that tightly match theoretical predictions. Our results suggest that traits explain variation in population growth, and together, these two factors scale up to influence community- and ecosystem-level processes across temperatures. Connecting the multilevel microbial processes that ultimately influence climate in this way will help refine predictions about complex ecosystem–climate feedbacks and the pace of climate change itself.

Climate warming will likely disrupt the flow of matter and energy within ecosystems, threatening the global carbon balance. Microorganisms are fundamental components of carbon cycling and are thus integral to ecosystem climate responses. However, ecosystem responses to warming are uncertain due to the functional and trophic complexity of microbial food webs. Here, we expose two major black boxes hindering our ability to anticipate ecosystem climate responses: viral infection and predation by microbial predators. We review current knowledge and uncover critical gaps in knowledge about how warming will impact these important top-down controls on the global carbon cycle. Understanding and predicting ecosystem responses to climate change will require disentangling complex direct and indirect responses within microbial food webs.

Anthropogenic increases in temperature and nutrient loads will likely impact food web structure and stability. Although their independent effects have been reasonably well studied, their joint effects—particularly on coupled ecological and phenotypic dynamics—remain poorly understood. Here we experimentally manipulated temperature and nutrient levels in microbial food webs and used time-series analysis to quantify the strength of reciprocal effects between ecological and phenotypic dynamics across trophic levels. We found that i) joint –often interactive– effects of temperature and nutrients on ecological dynamics are more common at higher trophic levels, ii) temperature and nutrients interact to shift the relative strength of top-down vs. bottom-up control, and iii) rapid phenotypic change mediates observed ecological responses to changes in temperature and nutrients. Our results uncover how feedbacks between ecological and phenotypic dynamics mediate food web responses to environmental change. This suggests important but previously unknown ways that temperature and nutrients might jointly control the rapid eco-phenotypic feedbacks that determine food web dynamics in a changing world.

Body size is a fundamental trait linked to many ecological processes—from individuals to ecosystems. Although the effects of body size on metabolism are well-known, the potential reciprocal effects of body size and density are less clear. Specifically, 1) whether changes in body size or density more strongly influence the other and 2) whether coupled rapid changes in body size and density are due to plasticity, rapid evolutionary change, or a combination of both.Here, we address these two issues by experimentally tracking population density and mean body size in the protist Tetrahymena pyriformis as it grows from low density to carrying capacity. We then use Convergent Cross Mapping time series analyses to infer the direction, magnitude, and causality of the link between body size and ecological dynamics. We confirm the results of our analysis by experimentally manipulating body size and density while keeping the other constant. Last, we fit mathematical models to our experimental time series that account for purely plastic change in body size, rapid evolution in size, or a combination of both, to gain insight into the processes that most likely explain the observed dynamics.Our results indicate that changes in body size more strongly influence changes in density than the other way around, but also show that there is reciprocity in this effect (i.e., a feedback). We show that a model that only accounts for purely plastic change in size most parsimoniously explains observed, coupled phenotypic and ecological dynamics.Together, these results suggest 1) that body size can shift dramatically through plasticity, well within ecological timescales, 2) that rapid changes in body size may have a larger effect on ecological dynamics than the reverse, but 3) phenotypic and ecological dynamics influence each as populations grow. Overall, we show that rapid plastic changes in functional traits like body size may play a fundamental –but currently unrecognized– role in familiar ecological processes such as logistic population growth.

Much ecological research aims to explain how climate impacts biodiversity and ecosystem-level processes through functional traits that link environment with individual performance. However, the specific climatic drivers of functional diversity across space and time remain unclear due largely to limitations in the availability of paired trait and climate data. We compile and analyze a global forest dataset using a method based on abundance-weighted trait moments to assess how climate influences the shapes of whole-community trait distributions. Our approach combines abundance-weighted metrics with diverse climate factors to produce a comprehensive catalog of trait–climate relationships that differ dramatically—27% of significant results change in sign and 71% disagree on sign, significance, or both—from traditional species-weighted methods. We find that ( i ) functional diversity generally declines with increasing latitude and elevation, ( ii ) temperature variability and vapor pressure are the strongest drivers of geographic shifts in functional composition and ecological strategies, and ( iii ) functional composition may currently be shifting over time due to rapid climate warming. Our analysis demonstrates that climate strongly governs functional diversity and provides essential information needed to predict how biodiversity and ecosystem function will respond to climate change.

Predicting food web structure in future climates is a pressing goal of ecology. These predictions may be impossible without a solid understanding of the factors that structure current food webs. The most fundamental aspect of food web structure—the relationship between the number of links and species—is still poorly understood. Some species interactions may be physically or physiologically ‘forbidden'—like consumption by non-consumer species—with possible consequences for food web structure. We show that accounting for these ‘forbidden interactions' constrains the feasible link-species space, in tight agreement with empirical data. Rather than following one particular scaling relationship, food webs are distributed throughout this space according to shared biotic and abiotic features. Our study provides new insights into the long-standing question of which factors determine this fundamental aspect of food web structure.

Microbial communities regulate ecosystem responses to climate change. But predicting these responses is challenging due to complex interactions among processes at multiple ecological scales. Organismal traits that determine individual performance and ecological interactions are essential for scaling up predictions of environmental responses from individuals to ecosystems. We combine experiments and mathematical models to show that key microbial traits—cell size, shape, and cell contents—independently drive shifts in demographic rates across temperatures, having cascading effects on community structure, dynamics, and ecosystem function. Moreover, intra- and interspecific trait variation play distinct, trait-specific roles in temperature responses. These species-level responses scale up to cause predictable, nonlinear shifts in microbial community composition and respiration rates, with direct implications for the effects of warming on the global carbon cycle. Mechanistically linking microbes with climate using traits will help refine predictions about complex ecosystem-climate feedbacks and the pace of climate change.

Forests are integral to global carbon cycling but are threatened by anthropogenic degradation and climate change. Assessing this global threat has been hindered by a lack of clear, flexible, and easy-to-use productivity models along with a lack of functional trait and productivity data for parameterizing and testing those models. Current productivity models are either extremely complex requiring up to hundreds of parameters, many sub-models, and considerable computational expense or rely on statistical trait-growth relationships that can be hard to extrapolate to new systems or climates. Here we provide a simple alternative: a remote sensing canopy functional model (RS-CFM) that uses remotely-sensed foliar traits and canopy structure data to efficiently map productivity at high-resolution and large spatial scales. We test this model by quantifying net primary productivity (NPP) at 0.01-ha resolution in 30,040 hectares of Peruvian tropical rainforest along a 3,322-m Amazon-to-Andes elevation gradient. Our model predicts local NPP and elevational shifts in NPP much more accurately and in greater detail than a prominent alternative method—NASA’s MODIS NPP product. Furthermore, we show how NPP estimates depend on light competition and identify the appropriate spatial resolution for remote productivity estimation. Our framework opens up possibilities to fully harness remote sensing data and reliably scale up from traits to map regional or global productivity in a more direct, efficient, and cost-effective manner.

Understanding which foodwebs thrive or collapse is a major challenge that has been mostly studied in terms of topology and interaction strength. Yet the relative importance of these properties is hotly debated due to limited research on how they interact and which forces generate them. Here, we construct a foodweb model that incorporates mass-based constraints on density dependence, maximum consumption rate, and the likelihood and strength of interactions, which in turn control overall topologies and interaction strength distributions. Our model predicts both stability and connectivity that closely match real foodwebs ranging widely in size (29-163 species) and connectivity (113-1086 interactions). Despite their absence in most research, density dependence and maximum consumption rate are not only required to accurately predict stability but have stronger impacts than the more-frequently-studied interaction strength. Our results demonstrate that predicting foodweb stability requires simultaneously considering multiple foodweb properties—all of which naturally emerge from species masses.

Remote sensing of trait-based ecology can improve our understanding of the effect of trait diversity on ecosystem functioning., Spatially continuous data on functional diversity will improve our ability to predict global change impacts on ecosystem properties. We applied methods that combine imaging spectroscopy and foliar traits to estimate remotely sensed functional diversity in tropical forests across an Amazon-to-Andes elevation gradient (215 to 3537 m). We evaluated the scale dependency of community assembly processes and examined whether tropical forest productivity could be predicted by remotely sensed functional diversity. Functional richness of the community decreased with increasing elevation. Scale-dependent signals of trait convergence, consistent with environmental filtering, play an important role in explaining the range of trait variation within each site and along elevation. Single- and multitrait remotely sensed measures of functional diversity were important predictors of variation in rates of net and gross primary productivity. Our findings highlight the potential of remotely sensed functional diversity to inform trait-based ecology and trait diversity-ecosystem function linkages in hyperdiverse tropical forests.

As global environmental conditions continue to change at an unprecedented rate, many species will experience increases in natural and anthropogenic stress. Generally speaking, selection is expected to favor adaptations that reduce the negative impact of environmental stress (i.e., stress tolerance). However, natural environmental variables typically fluctuate, exhibiting various degrees of temporal autocorrelation, known as environmental colors, which may complicate evolutionary responses to stress. Here we combine experiments and theory to show that temporal environmental autocorrelation can determine long-term evolutionary responses to stress without affecting the total amount of stress experienced over time. Experimental evolution of RNA virus lineages in differing environmental autocorrelation treatments agreed closely with predictions from our theoretical models that stress tolerance is favored in less autocorrelated (whiter) environments but disfavored in more autocorrelated (redder) environments. This is explained by an interaction between environmental autocorrelation and a phenotypic trade-off between stress tolerance and reproductive ability. The degree to which environmental autocorrelation influences evolutionary trajectories depends on the shape of this trade-off as well as the relative level of tolerance exhibited by novel mutants. These results suggest that long-term evolutionary dynamics depend not only on the overall strength of selection but also on the way that selection is distributed over time.

Much ecological research aims to explain how climate impacts biodiversity and ecosystem-level processes through functional traits that link environment with individual performance. However, the specific climatic drivers of functional diversity across space and time remain unclear due largely to limitations in the availability of paired trait and climate data. We compile and analyze a global forest dataset using a method based on abundance-weighted trait moments to assess how climate influences the shapes of whole-community trait distributions. Our approach combines abundance-weighted metrics with diverse climate factors to produce a comprehensive catalog of trait-climate relationships that differ dramatically-27% of significant results change in sign and 71% disagree on sign, significance, or both-from traditional species-weighted methods. We find that (

Understanding the processes that influence range expansions during climate warming is paramount for predicting population extirpations and preparing for the arrival of non-native species. While climate warming occurs over a background of variation due to cyclical processes and irregular events, the temporal structure of the thermal environment is largely ignored when forecasting the dynamics of non-native species. Ecological theory predicts that high levels of temporal autocorrelation in the environment– relatedness between conditions occurring in close temporal proximity– will favor populations that would otherwise have an average negative growth rate by increasing the duration of favorable environmental periods. Here, we invoke such theory to explain the success of biological invasions and evaluate the hypothesis that sustained periods of high environmental temperature can act synergistically with increases in mean temperature to favor the establishment of non-native species. We conduct a 60-day field mesocosm experiment to measure the population dynamics of the non-native cladoceran zooplankter Daphnia lumholtzi and a native congener Daphnia pulex in ambient temperature environments (control), warmed with recurrent periods of high environmental temperatures (uncorrelated-warmed), or warmed with sustained periods of high environmental temperatures (autocorrelated-warmed), such that both warmed treatments exhibited the same mean temperature but exhibited different temporal structures of their thermal environments. Maximum D. lumholtzi densities in the warmed-autocorrelated treatment were three- and eight-fold higher relative to warmed-uncorrelated and control treatments, respectively. Yet, D. lumholtzi performed poorly across all experimental treatment relative to D. pulex and were undetectable the end of the experiment. Using mathematical models we show that this increase in performance can occur alongside increasing temporal autocorrelation, and should occur over a broad range of warming scenarios. These results provide both empirical and theoretical evidence that the temporal structure of the environment can influence the performance of species undergoing range expansions due to climate warming. This article is protected by copyright. All rights reserved.