Advancing global change biology through experimental manipulations: Where have we been and where might we go?

Meta-analysis reveals that the effects of precipitation change on soil and litter fauna in forests depend on body size

Anthropogenic climate change is altering precipitation regimes at a global scale. While precipitation changes have been linked to changes in the abundance and diversity of soil and litter invertebrate fauna in forests, general trends have remained elusive due to mixed results from primary studies. We used a meta-analysis based on 430 comparisons from 38 primary studies to address associated knowledge gaps, (i) quantifying impacts of precipitation change on forest soil and litter fauna abundance and diversity, (ii) exploring reasons for variation in impacts and (iii) examining biases affecting the realism and accuracy of experimental studies. Precipitation reductions led to a decrease of 39% in soil and litter fauna abundance, with a 35% increase in abundance under precipitation increases, while diversity impacts were smaller. A statistical model containing an interaction between body size and the magnitude of precipitation change showed that mesofauna (e.g. mites, collembola) responded most to changes in precipitation. Changes in taxonomic richness were related solely to the magnitude of precipitation change. Our results suggest that body size is related to the ability of a taxon to survive under drought conditions, or to benefit from high precipitation. We also found that most experiments manipulated precipitation in a way that aligns better with predicted extreme climatic events than with predicted average annual changes in precipitation and that the experimental plots used in experiments were likely too small to accurately capture changes for mobile taxa. The relationship between body size and response to precipitation found here has far-reaching implications for our ability to predict future responses of soil biodiversity to climate change and will help to produce more realistic mechanistic soil models which aim to simulate the responses of soils to global change.

Designing More Informative Multiple-Driver Experiments

For decades, multiple-driver/stressor research has examined interactions among drivers that will undergo large changes in the future: temperature, pH, nutrients, oxygen, pathogens, and more. However, the most commonly used experimental designs-present-versus-future and ANOVA-fail to contribute to general understanding or predictive power. Linking experimental design to process-based mathematical models would help us predict how ecosystems will behave in novel environmental conditions. We review a range of experimental designs and assess the best experimental path toward a predictive ecology. Full factorial response surface, fractional factorial, quadratic response surface, custom, space-filling, and especially optimal and sequential/adaptive designs can help us achieve more valuable scientific goals. Experiments using these designs are challenging to perform with long-lived organisms or at the community and ecosystem levels. But they remain our most promising path toward linking experiments and theory in multiple-driver research and making accurate, useful predictions.

Attaining freshwater and estuarine-water soil saturation in an ecosystem-scale coastal flooding experiment

Coastal upland forests are facing widespread mortality as sea-level rise accelerates and precipitation and storm regimes change. The loss of coastal forests has significant implications for the coastal carbon cycle; yet, predicting mortality likelihood is difficult due to our limited understanding of disturbance impacts on coastal forests. The manipulative, ecosystem-scale Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m²), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. Transient saturation (5 h) of the entire soil rooting zone (0-30 cm) across a 2000 m² coastal forest was attained by delivering 300 m³ of water through a spatially distributed irrigation network at a rate just above the soil infiltration rate. Our water delivery approach also elevated the water table (typically ~ 2 m belowground) and achieved extensive, low-level inundation (~ 8 cm standing water). A TEMPEST simulation approximated a 15-cm rainfall event and based on historic records, was of comparable intensity to a 10-year storm for the area. This characterization was supported by showing that Hurricane Ida's (~ 5 cm rainfall) hydrologic impacts were shorter (40% lower duration) and less expansive (80% less coverage) than those generated through experimental manipulation. Future work will apply TEMPEST treatments to evaluate coastal forest resilience to changing hydrologic disturbance regimes and identify conditions that initiate ecosystem state transitions.

Are experiment sample sizes adequate to detect biologically important interactions between multiple stressors?

As most ecosystems are being challenged by multiple, co-occurring stressors, an important challenge is to understand and predict how stressors interact to affect biological responses. A popular approach is to design factorial experiments that measure biological responses to pairs of stressors and compare the observed response to a null model expectation. Unfortunately, we believe experiment sample sizes are inadequate to detect most non-null stressor interaction responses, greatly hindering progress. Using both real and simulated data, we show sample sizes typical of many experiments (<6) can (i) only detect very large deviations from the additive null model, implying many important non-null stressor-pair interactions are being missed, and (ii) potentially lead to mostly statistical outliers being reported. Computer code that simulates data under either additive or multiplicative null models is provided to estimate statistical power for user-defined responses and sample sizes, and we recommend this is used to aid experimental design and interpretation of results. We suspect that most experiments may require 20 or more replicates per treatment to have adequate power to detect nonadditive. However, estimates of power need to be made while considering the smallest interaction of interest, i.e., the lower limit for a biologically important interaction, which is likely to be system-specific, meaning a general guide is unavailable. We discuss ways in which the smallest interaction of interest can be chosen, and how sample sizes can be increased. Our main analyses relate to the additive null model, but we show similar problems occur for the multiplicative null model, and we encourage similar investigations into the statistical power of other null models and inference methods. Without knowledge of the detection abilities of the statistical tools at hand or the definition of the smallest meaningful interaction, we will undoubtedly continue to miss important ecosystem stressor interactions.

Are experiment sample sizes adequate to detect biologically important interactions between multiple stressors?

As most ecosystems are being challenged by multiple, co‐occurring stressors, an important challenge is to understand and predict how stressors interact to affect biological responses. A popular approach is to design factorial experiments that measure biological responses to pairs of stressors and compare the observed response to a null model expectation. Unfortunately, we believe experiment sample sizes are inadequate to detect most non‐null stressor interaction responses, greatly hindering progress. Using both real and simulated data, we show sample sizes typical of many experiments (<6) can (i) only detect very large deviations from the additive null model, implying many important non‐null stressor‐pair interactions are being missed, and (ii) potentially lead to mostly statistical outliers being reported. Computer code that simulates data under either additive or multiplicative null models is provided to estimate statistical power for user‐defined responses and sample sizes, and we recommend this is used to aid experimental design and interpretation of results. We suspect that most experiments may require 20 or more replicates per treatment to have adequate power to detect nonadditive. However, estimates of power need to be made while considering the smallest interaction of interest, i.e., the lower limit for a biologically important interaction, which is likely to be system‐specific, meaning a general guide is unavailable. We discuss ways in which the smallest interaction of interest can be chosen, and how sample sizes can be increased. Our main analyses relate to the additive null model, but we show similar problems occur for the multiplicative null model, and we encourage similar investigations into the statistical power of other null models and inference methods. Without knowledge of the detection abilities of the statistical tools at hand or the definition of the smallest meaningful interaction, we will undoubtedly continue to miss important ecosystem stressor interactions.

Dryland mechanisms could widely control ecosystem functioning in a drier and warmer world

Responses of terrestrial ecosystems to climate change have been explored in many regions worldwide. While continued drying and warming may alter process rates and deteriorate the state and performance of ecosystems, it could also lead to more fundamental changes in the mechanisms governing ecosystem functioning. Here we argue that climate change will induce unprecedented shifts in these mechanisms in historically wetter climatic zones, towards mechanisms currently prevalent in dry regions, which we refer to as 'dryland mechanisms'. We discuss 12 dryland mechanisms affecting multiple processes of ecosystem functioning, including vegetation development, water flow, energy budget, carbon and nutrient cycling, plant production and organic matter decomposition. We then examine mostly rare examples of the operation of these mechanisms in non-dryland regions where they have been considered irrelevant at present. Current and future climate trends could force microclimatic conditions across thresholds and lead to the emergence of dryland mechanisms and their increasing control over ecosystem functioning in many biomes on Earth.

Pastures and Climate Extremes: Impacts of Cool Season Warming and Drought on the Productivity of Key Pasture Species in a Field Experiment

Shifts in the timing, intensity and/or frequency of climate extremes, such as severe drought and heatwaves, can generate sustained shifts in ecosystem function with important ecological and economic impacts for rangelands and managed pastures. The Pastures and Climate Extremes experiment (PACE) in Southeast Australia was designed to investigate the impacts of a severe winter/spring drought (60% rainfall reduction) and, for a subset of species, a factorial combination of drought and elevated temperature (ambient +3°C) on pasture productivity. The experiment included nine common pasture and Australian rangeland species from three plant functional groups (C3 grasses, C4 grasses and legumes) planted in monoculture. Winter/spring drought resulted in productivity declines of 45% on average and up to 74% for the most affected species (Digitaria eriantha) during the 6-month treatment period, with eight of the nine species exhibiting significant yield reductions. Despite considerable variation in species' sensitivity to drought, C4 grasses were more strongly affected by this treatment than C3 grasses or legumes. Warming also had negative effects on cool-season productivity, associated at least partially with exceedance of optimum growth temperatures in spring and indirect effects on soil water content. The combination of winter/spring drought and year-round warming resulted in the greatest yield reductions. We identified responses that were either additive (Festuca), or less-than-additive (Medicago), where warming reduced the magnitude of drought effects. Results from this study highlight the sensitivity of diverse pasture species to increases in winter and spring drought severity similar to those predicted for this region, and that anticipated benefits of cool-season warming are unlikely to be realized. Overall, the substantial negative impacts on productivity suggest that future, warmer, drier climates will result in shortfalls in cool-season forage availability, with profound implications for the livestock industry and natural grazer communities.

Biogeosciences Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

This article is composed of three independent commentaries about the state of Integrated, Coordinated, Open, Networked (ICON) principles in the American Geophysical Union Biogeosciences section, and discussion on the opportunities and challenges of adopting them. Each commentary focuses on a different topic: (a) Global collaboration, technology transfer, and application (Section 2), (b) Community engagement, community science, education, and stakeholder involvement (Section 3), and (c) Field, experimental, remote sensing, and real-time data research and application (Section 4). We discuss needs and strategies for implementing ICON and outline short- and long-term goals. The inclusion of global data and international community engagement are key to tackling grand challenges in biogeosciences. Although recent technological advances and growing open-access information across the world have enabled global collaborations to some extent, several barriers, ranging from technical to organizational to cultural, have remained in advancing interoperability and tangible scientific progress in biogeosciences. Overcoming these hurdles is necessary to address pressing large-scale research questions and applications in the biogeosciences, where ICON principles are essential. Here, we list several opportunities for ICON, including coordinated experimentation and field observations across global sites, that are ripe for implementation in biogeosciences as a means to scientific advancements and social progress.

Low statistical power and overestimated anthropogenic impacts, exacerbated by publication bias, dominate field studies in global change biology

Field studies are essential to reliably quantify ecological responses to global change because they are exposed to realistic climate manipulations. Yet such studies are limited in replicates, resulting in less power and, therefore, potentially unreliable effect estimates. Furthermore, while manipulative field experiments are assumed to be more powerful than non-manipulative observations, it has rarely been scrutinized using extensive data. Here, using 3847 field experiments that were designed to estimate the effect of environmental stressors on ecosystems, we systematically quantified their statistical power and magnitude (Type M) and sign (Type S) errors. Our investigations focused upon the reliability of field experiments to assess the effect of stressors on both ecosystem's response magnitude and variability. When controlling for publication bias, single experiments were underpowered to detect response magnitude (median power: 18%-38% depending on effect sizes). Single experiments also had much lower power to detect response variability (6%-12% depending on effect sizes) than response magnitude. Such underpowered studies could exaggerate estimates of response magnitude by 2-3 times (Type M errors) and variability by 4-10 times. Type S errors were comparatively rare. These observations indicate that low power, coupled with publication bias, inflates the estimates of anthropogenic impacts. Importantly, we found that meta-analyses largely mitigated the issues of low power and exaggerated effect size estimates. Rather surprisingly, manipulative experiments and non-manipulative observations had very similar results in terms of their power, Type M and S errors. Therefore, the previous assumption about the superiority of manipulative experiments in terms of power is overstated. These results call for highly powered field studies to reliably inform theory building and policymaking, via more collaboration and team science, and large-scale ecosystem facilities. Future studies also require transparent reporting and open science practices to approach reproducible and reliable empirical work and evidence synthesis.

Regionalized dynamic climate series for ecological climate impact research in modern controlled environment facilities

Modern controlled environment facilities (CEFs) enable the simulation of dynamic microclimates in controlled ecological experiments through their technical ability to precisely control multiple environmental parameters. However, few CEF studies exploit the technical possibilities of their facilities, as climate change treatments are frequently applied by static manipulation of an inadequate number of climate change drivers, ignoring intra-annual variability and covariation of multiple meteorological variables. We present a method for generating regionalized climate series in high temporal resolution that was developed to force the TUMmesa Model EcoSystem Analyzer with dynamic climate simulations. The climate series represent annual cycles for a reference period (1987-2016) and the climate change scenarios RCP2.6 and RCP8.5 (2071-2100) regionalized for a climate station situated in a forested region of the German Spessart mountains. Based on the EURO-CORDEX and ReKliEs-DE model ensembles, typical annual courses of daily resolved climatologies for the reference period and the RCP scenarios were calculated from multimodel means of temperature (ta), relative humidity (rh), global radiation (Rg), air pressure (P), and ground-level ozone and complemented by CO2. To account for intra-annual variation and the covariability of multiple climate variables, daily values were substituted by hourly resolved data resampled from the historical record. The resulting present climate Test Reference Year (TRY) well represented a possible annual cycle within the reference period, and expected shifts in future mean values (e.g., higher ta) were reproduced within the RCP TRYs. The TRYs were executed in eight climate chambers of the TUMmesa facility and-accounting for the technical boundaries of the facility-reproduced with high precision. Especially, as an alternative to CEF simulations that reproduce mere day/night cycles and static manipulations of climate change drivers, the method presented here proved well suited for simulating regionalized and highly dynamic annual cycles for ecological CEF studies.

Sea level rise can severely reduce biodiversity and community net production on rocky shores

Sea level rise (SLR), driven by anthropogenic climate change, can be a major threat to coastal ecosystems. Among the most biologically diverse but SLR-threatened coastal ecosystems are rocky shores, especially in regions with a small tidal range. Nonetheless, the impacts of SLR on rocky shore biodiversity, community structure and ecosystem functions have rarely been studied. Here, we use the biogenic intertidal ecosystem, Mediterranean vermetid reefs on the Israeli coast, as case study for testing the potential impact of SLR on reef communities, with surveys, 3D topographic mapping plus SLR simulations, and a manipulative community translocation experiment. We show that: (1) biodiversity is much lower on very shallow, permanently submerged, horizontal rocky surfaces compared to that on intertidal reef platforms, (2) the extensive intertidal platforms will permanently drown under even modest SLR scenarios, (3) the rich intertidal community will transform, when permanently submerged, either to a very different but still rich community when protected from grazing by highly abundant invasive fish (rabbitfish), or to a much poorer turf community when exposed to such fish grazing, and (4) the reef community net production will drastically drop under permanent submersion. Because the main ecosystem engineer of the vermetid reefs, Dendropoma anguliferum (Monterosato, 1878), is nearly extinct in the southeast Levant, it is unlikely that new reefs will be formed higher on the shore in the future, presumably resulting in extensive coastal ecological shifts. Considerable coastal community shifts are forecasted for many regions globally due to SLR, as many shorelines are predicted to suffer from "coastal squeeze". Hence, similar manipulative experiments are encouraged in other regions to test for generality vs. context dependency in SLR ecological impacts. We suggest that in cases where essential/unique intertidal habitats like vermetid reefs are expected to vanish by SLR, constructing carefully-planned, ecologically friendly, artificial alternatives should be considered.

Ambient climate determines the directional trend of community stability under warming and grazing

Changes in ecological processes over time in ambient treatments are often larger than the responses to manipulative treatments in climate change experiments. However, the impacts of human-driven environmental changes on the stability of natural grasslands have been typically assessed by comparing differences between manipulative plots and reference plots. Little is known about whether or how ambient climate regulates the effects of manipulative treatments and their underlying mechanisms. We collected two datasets, one a 36-year long-term observational dataset from 1983 to 2018, and the other a 10-year manipulative asymmetric warming and grazing experiment using infrared heaters with moderate grazing from 2006 to 2015 in an alpine meadow on the Tibetan Plateau. The 36-year observational dataset shows that there was a nonlinear response of community stability to ambient temperature with a positive relationship between them due to an increase in ambient temperature in the first 25 years and then a decrease in ambient temperature thereafter. Warming and grazing decreased community stability with experiment duration through an increase in legume cover and a decrease in species asynchrony, which was due to the decreasing background temperature through time during the 10-year experiment period. Moreover, the temperature sensitivity of community stability was higher under the ambient treatment than under the manipulative treatments. Therefore, our results suggested that ambient climate may control the directional trend of community stability while manipulative treatments may determine the temperature sensitivity of the response of community stability to climate relative to the ambient treatment. Our study emphasizes the importance of the context dependency of the response of community stability to human-driven environmental changes.

Experimental warming differentially affects vegetative and reproductive phenology of tundra plants

Rapid climate warming is altering Arctic and alpine tundra ecosystem structure and function, including shifts in plant phenology. While the advancement of green up and flowering are well-documented, it remains unclear whether all phenophases, particularly those later in the season, will shift in unison or respond divergently to warming. Here, we present the largest synthesis to our knowledge of experimental warming effects on tundra plant phenology from the International Tundra Experiment. We examine the effect of warming on a suite of season-wide plant phenophases. Results challenge the expectation that all phenophases will advance in unison to warming. Instead, we find that experimental warming caused: (1) larger phenological shifts in reproductive versus vegetative phenophases and (2) advanced reproductive phenophases and green up but delayed leaf senescence which translated to a lengthening of the growing season by approximately 3%. Patterns were consistent across sites, plant species and over time. The advancement of reproductive seasons and lengthening of growing seasons may have significant consequences for trophic interactions and ecosystem function across the tundra.

Fine-root functional trait responses to experimental warming: a global meta-analysis

Whether and how warming alters functional traits of absorptive plant roots remains to be answered across the globe. Tackling this question is crucial to better understanding terrestrial responses to climate change as fine-root traits drive many ecosystem processes. We carried out a detailed synthesis of fine-root trait responses to experimental warming by performing a meta-analysis of 964 paired observations from 177 publications. Warming increased fine-root biomass, production, respiration and nitrogen concentration as well as decreased root carbon : nitrogen ratio and nonstructural carbohydrates. Warming effects on fine-root biomass decreased with greater warming magnitude, especially in short-term experiments. Furthermore, the positive effect of warming on fine-root biomass was strongest in deeper soil horizons and in colder and drier regions. Total fine-root length, morphology, mortality, life span and turnover were unresponsive to warming. Our results highlight the significant changes in fine-root traits in response to warming as well as the importance of warming magnitude and duration in understanding fine-root responses. These changes have strong implications for global soil carbon stocks in a warmer world associated with increased root-derived carbon inputs into deeper soil horizons and increases in fine-root respiration.

Ecotrons: Powerful and versatile ecosystem analysers for ecology, agronomy and environmental science

Ecosystems integrity and services are threatened by anthropogenic global changes. Mitigating and adapting to these changes require knowledge of ecosystem functioning in the expected novel environments, informed in large part through experimentation and modelling. This paper describes 13 advanced controlled environment facilities for experimental ecosystem studies, herein termed ecotrons, open to the international community. Ecotrons enable simulation of a wide range of natural environmental conditions in replicated and independent experimental units while measuring various ecosystem processes. This capacity to realistically control ecosystem environments is used to emulate a variety of climatic scenarios and soil conditions, in natural sunlight or through broad-spectrum lighting. The use of large ecosystem samples, intact or reconstructed, minimizes border effects and increases biological and physical complexity. Measurements of concentrations of greenhouse trace gases as well as their net exchange between the ecosystem and the atmosphere are performed in most ecotrons, often quasi continuously. The flow of matter is often tracked with the use of stable isotope tracers of carbon and other elements. Equipment is available for measurements of soil water status as well as root and canopy growth. The experiments ran so far emphasize the diversity of the hosted research. Half of them concern global changes, often with a manipulation of more than one driver. About a quarter deal with the impact of biodiversity loss on ecosystem functioning and one quarter with ecosystem or plant physiology. We discuss how the methodology for environmental simulation and process measurements, especially in soil, can be improved and stress the need to establish stronger links with modelling in future projects. These developments will enable further improvements in mechanistic understanding and predictive capacity of ecotron research which will play, in complementarity with field experimentation and monitoring, a crucial role in exploring the ecosystem consequences of environmental changes.

Modeling Ambitions Outpace Observations of Forest Carbon Allocation

There have been vociferous calls for 'tree-centered' vegetation models to refine predictions of forest carbon (C) cycling. Unfortunately, our global survey at flux-tower sites indicates insufficient empirical data support for this much-needed model development. We urge for a new generation of studies across large environmental gradients that strategically pair long-term ecosystem monitoring with manipulative experiments on mature trees. For this, we outline a versatile experimental framework to build cross-scale data archives of C uptake and allocation to structural, non-structural, and respiratory sinks. Community-wide efforts and discussions are needed to implement this framework, especially in hitherto underrepresented tropical forests. Global coordination and realistic priorities for data collection will thereby be key to achieve and maintain adequate empirical support for tree-centered vegetation modeling.

Review: Plant eco-evolutionary responses to climate change: Emerging directions

Contemporary climate change is exposing plant populations to novel combinations of temperatures, drought stress, [CO₂] and other abiotic and biotic conditions. These changes are rapidly disrupting the evolutionary dynamics of plants. Despite the multifactorial nature of climate change, most studies typically manipulate only one climatic factor. In this opinion piece, we explore how climate change factors interact with each other and with biotic pressures to alter evolutionary processes. We evaluate the ramifications of climate change across life history stages,and examine how mating system variation influences population persistence under rapid environmental change. Furthermore, we discuss how spatial and temporal mismatches between plants and their mutualists and antagonists could affect adaptive responses to climate change. For example, plant-virus interactions vary from highly pathogenic to mildly facilitative, and are partly mediated by temperature, moisture availability and [CO₂]. Will host plants exposed to novel, stressful abiotic conditions be more susceptible to viral pathogens? Finally, we propose novel experimental approaches that could illuminate how plants will cope with unprecedented global change, such as resurrection studies combined with experimental evolution, genomics or epigenetics.

Comment on Pescott & Jitlal 2020: Failure to account for measurement error undermines their conclusion of a weak impact of nitrogen deposition on plant species richness

Estimation of the impacts of atmospheric nitrogen (N) deposition on ecosystems and biodiversity is a research imperative. Analyses of large-scale spatial gradients, where an observed response is correlated with measured or modelled deposition, have been an important source of evidence. A number of problems beset this approach. For example, if responses are spatially aggregated then treating each location as statistically independent can lead to biased confidence intervals and a greater probably of false positive results. Using methods that account for residual spatial autocorrelation, Pescott & Jitlal (2020) re-analysed two large-scale spatial gradient datasets from Britain where modelled N deposition at 5 × 5 km resolution had been previously correlated with species richness in small quadrats. They found that N deposition effects were weaker than previously demonstrated leading them to conclude that “previous estimates of Ndep impacts on richness from space-for-time substitution studies are likely to have been over-estimated”. We use a simulation study to show that their conclusion is unreliable despite them recognising that an influential fraction of the residual spatially structured variation could itself be attributable to N deposition. This arises because the covariate used was modelled N deposition at 5 × 5 km resolution leaving open the possibility that measured or modelled N deposition at finer resolutions could explain more variance in the response. Explicitly treating this as spatially auto-correlated error ignores this possibility and leads directly to their unreliable conclusion. We further demonstrate the plausibility of this scenario by showing that significant variation in N deposition at the 1 km square resolution is indeed averaged at 5 × 5 km resolution. Further analyses are required to explore whether estimation of the size of the N deposition effect on plant species richness and other measures of biodiversity is indeed dependent on the accuracy and hence measurement error of the N deposition covariate. Until then the conclusions of Pescott & Jitlal (2020) should be considered premature.

Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data-model integration

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data-model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model-data benchmarking; and data assimilation and ecological forecasting. This community-driven approach is a key to meeting the pressing needs of science and society in the 21st century.

What do you mean "functional" in ecology? Patterns versus processes

Use of the term "functional" trait has increased exponentially in ecology. Although accounting for numerous ecological questions, this concept raises several issues. We propose that the term "functional" could be misleading because (1) no rigorous criteria exist to identify "functional" traits and (2) it suggests that only some traits ("functional" ones) can inform our understanding of species functioning, whatever the scale or discipline. Hence, the concept of "functional" trait in ecology is starting to be challenged and it remains unclear why some traits should be considered functional, whereas other traits should not. We argue that the most used "functional" traits are meaningful because they reflect important differences between populations or species, based on synchronic comparisons, that is, irrespective of time (hereafter "pattern" traits). Hence, they are useful for identifying trade-offs and strategies across large numbers of observations, usually at rather coarse scales, and are most often used in analyses of "big data." However, given that many ecological processes occur across short time scales and narrow gradients of climate and resource availability, the efficacy of these traits to inform us about these ecological processes appears questionable. We show that trait measurements that take time explicitly into account (hereafter "process" traits) differ from pattern traits because they quantify the flows of material and energy within a given environment across a defined period of time. Although pattern traits and process traits are both functional, it is important to understand the differences between the approaches. Moreover, better accounting of ontogeny, life form, plasticity, and genetic variability is required to enhance the convergence between pattern and process approaches. This revised framework allows more explicit connections between trait ecology and other biological sciences. It should enhance the study of processes at all scales in order to investigate efficiently the adaptive responses of biological organisms to climate change.