Mark A. Bradford’s research while affiliated with Yale University and other places

The explanations behind observations of global patterning in species diversity pre-date the field of ecology itself. The generation of new species-area theories, in particular, far outpaces their falsification, resulting in a centuries-old accumulation in species diversity theories. We use historical assessment and new data analysis to argue that one of the earliest recognized and most consistent patterns in species diversity is not strictly an ecological phenomenon and, when ecological mechanism is invoked, the range of potential mechanisms is too numerous for tractable hypothesis falsification. We provide a historical parallel in that the normal distribution once was treated as a pattern assuming a biological mechanism rather than a statistical distribution that can be generated by biological and non-biological forces. Similarly, power law distributions are ubiquitous in aggregated data, such as the species-area relationship. That nearly identical broad-scale aggregation patterns are observed for both ecological and non-ecological data as a function of area suggest that these broad-scale patterns reflect a statistical distribution that, in itself, cannot be used to discern between or among ecological and non-ecological mechanisms. We argue that by seeking processes in such a ubiquitous pattern, ecologists may read ecological mechanism into statistical patterns, and we suggest that falsifying broad-scale diversity distribution hypotheses should be a greater priority than generating or parameterizing new ones.

Anthropogenic disturbance can decrease woodland diversity in the species‐rich herbaceous layer of eastern deciduous forests, and ant‐dispersed (myrmecochorous) plants may be particularly affected due to their limited ability to re‐colonise secondary forests. Consequently, we predicted that myrmecochorous plants and their keystone seed‐dispersing ants would increase with time since the last disturbance, as reflected by young, middle or mature forest successional stage. Specifically, we hypothesized that myrmecochore abundance and richness would be relatively lowest in the youngest forests, moderate in middle‐aged forests and highest in mature forests. We also hypothesized that experimentally introducing ant bait in a regular pattern, as might be expected from intact species‐rich myrmecochore communities, would elicit greater ant foraging interest than intermittent baiting, as might be expected in recently disturbed depauperate myrmecochore communities. We found the highest myrmecochore plant abundance in mature forests, but we found the same for herbaceous plants overall. Moreover, regular and intermittent bait offerings elicited similar overall ant responses. The observational results suggested that myrmecochorous plants respond to forest successional stage as do other woodland plants, and our experimental results suggest that disrupted seed delivery by myrmecochores did not affect Aphaenogaster abundance or foraging behaviour. As such, the myrmecochore communities studied here appeared as resilient as other woodland herbs, and seed‐dispersing ants did not appear dependent on myrmecochore plant communities. Indeed, given the relatively high myrmecochore richness and abundance across our study sites, forest type (e.g., rich cove forest) might better predict myrmecochore success than successional stage.

In contrast to its Assessment Reports, less is known about the social science processes through which the Intergovernmental Panel on Climate Change (IPCC) produces methodologies for greenhouse gas emissions reporting. This limited attention is problematic, as these greenhouse gas inventories are critical components for identifying, justifying, and adjudicating national-level mitigation commitments. We begin to fill this gap by descriptively assessing, drawing on data triangulation that incorporates ecological and political analysis, the historical process for developing emissions guidelines. Our systematic descriptive efforts highlight processes and structures through which inventories might become disconnected from the latest peer-reviewed environmental science. To illustrate this disconnect, we describe the IPCC guideline process, outlining themes that may contribute to discrepancies, such as diverging logics and timeframes, discursive power, procedural lock-in, resource constraints, organizational interests, and complexity. The themes reflect challenges to greenhouse gas inventories themselves, as well as broader challenges to integrating climate change science and policy. • Highlights • This article provides an illustrative analysis of the Intergovernmental Panel on Climate Change’s greenhouse gas inventory guideline process • There is evidence for substantive discrepancies between empirical literature and these guidelines • Particularly for forest soil organic carbon reporting, inventory guidelines are influenced by a multitude of political and scientific actors • Explanations for these discrepancies merit further inquiry, and include institutional lock-in, political influence, discursive power, resource constraints, and world views

Understanding whether soil microbial respiration adapts to the ambient thermal climate with an enhanced or compensatory response, hence potentially stimulating or slowing down soil carbon losses with warming, is key to accurately forecast and model climate change impacts on the global carbon cycle. Despite the interest in this topic and the plethora of recent studies in natural ecosystems, it has been seldom explored in croplands. Using two recently published independent datasets of soil microbial metabolic quotient (MMQ; microbial respiration rate per unit biomass) and carbon use efficiency (CUE; partitioning of C to microbial growth vs. respiration), we find a compensatory thermal adaptive response for MMQ in global croplands. That is, mean annual temperature (MAT) has a negative effect on MMQ. However, this compensatory thermal adaptation is only half or less of that found in previous studies for noncultivated ecosystems. In contrast to the negative MMQ‐MAT pattern, microbial CUE increases with MAT across global croplands. By incorporating this positive CUE‐MAT relationship (greater C partitioning into microbial growth rather than respiration with increasing temperature) into a microbial‐explicit soil organic carbon model, we successfully predict the thermal compensation of MMQ observed in croplands. Our model‐data integration and database cross‐validation suggest that a warmer climate may select for microbial communities with higher CUE, providing a plausible mechanism for their compensatory metabolic response. By helping to identify appropriate representations of microbial physiological processes in soil biogeochemical models, our work will help build confidence in model projections of cropland C dynamics under a changing climate.

The importance of greenhouse gas inventories cannot be overstated: the process of producing inventories informs strategies that governments will use to meet emissions reduction targets. The Intergovernmental Panel on Climate Change (IPCC) leads an effort to develop and refine internationally agreed upon methodologies for calculating and reporting greenhouse gas emissions and removals. We argue that these guidelines are not equipped to handle the task of developing national greenhouse gas inventories for most countries. Inventory guidelines are vital to implementing climate action, and we highlight opportunities to improve their timeliness and accuracy. Such reforms should provide the means to better understand and advance the progress countries are making toward their Paris commitments. Now is the time to consider challenges posed by the current process to develop the guidelines, and to avail the policy community of recent major advances in quantitative and expert synthesis to overhaul the process and thereby better equip multi-national efforts to limit climate change.

Scientists devote substantive time and resources to research to help solve environmental problems. Managers and policy makers must decide which actions will lead to desired environmental outcomes, based on the best-available research. Yet decision-makers frequently do not use much of this evidence. They may be unaware of it, lack access to it, not understand it, or view it as irrelevant. This means a valuable resource (research) is often wasted. To improve the impact of science on decision making, we outline a set of practical steps: (1) Identify and understand your audience (or partners); (2) Clarify the need for evidence; (3) Gather "just enough" evidence; and (4) Share and discuss the evidence. These are guidelines, not a strict recipe for success, and can be challenging to implement. But we believe that these recommendations should translate into science being used more often when informing environmental and conservation decisions.

The degree to which climate warming will stimulate soil organic carbon (SOC) losses via heterotrophic respiration remains uncertain, in part because different or even opposite microbial physiology and temperature relationships have been proposed in SOC models. We incorporated competing microbial carbon use efficiency (CUE)−mean annual temperature (MAT) and enzyme kinetic−MAT relationships into SOC models, and compared the simulated mass‐specific soil heterotrophic respiration rates with multiple published datasets of measured respiration. The measured data included 110 dryland soils globally distributed, and two continental to global‐scale cross‐biome datasets. Model−data comparisons suggested that a positive CUE−MAT relationship best predicts the measured mass‐specific soil heterotrophic respiration rates in soils distributed globally. These results are robust when considering models of increasing complexity and competing mechanisms driving soil heterotrophic respiration−MAT relationships (e.g., carbon substrate availability). Our findings suggest that a warmer climate selects for microbial communities with higher CUE, as opposed to the often hypothesized reductions in CUE by warming based on soil laboratory assays. Our results help to build the impetus for, and confidence in, including microbial mechanisms in soil biogeochemical models used to forecast changes in global soil carbon stocks in response to warming. This article is protected by copyright. All rights reserved.

Heterotrophic soil microbial respiration—one of the main processes of carbon loss from the soil to the atmosphere—is sensitive to temperature in the short term. However, how this sensitivity is affected by long-term thermal regimes is uncertain. There is an expectation that soil microbial respiration rates adapt to the ambient thermal regime, but whether this adaptation magnifies or reduces respiration sensitivities to temperature fluctuations remains unresolved. This gap in understanding is particularly pronounced for drylands because most studies conducted so far have focused on mesic systems. Here, we conduct an incubation study using soil samples from 110 global drylands encompassing a wide gradient in mean annual temperature. We test how mean annual temperature affects soil respiration rates at three assay temperatures while controlling for substrate depletion and microbial biomass. Estimated soil respiration rates at the mean microbial biomass were lower in sites with higher mean annual temperatures across the three assayed temperatures. The patterns observed are consistent with expected evolutionary trade-offs in the structure and function of enzymes under different thermal regimes. Therefore, our results suggest that soil microbial respiration adapts to the ambient thermal regime in global drylands. © 2019, The Author(s), under exclusive licence to Springer Nature Limited.

Tree stems from wetland, floodplain and upland forests can produce and emit methane (CH4). Tree CH4 stem emissions have high spatial and temporal variability, but there is no consensus on the biophysical mechanisms that drive stem CH4 production and emissions. Here, we summarize up to 30 opportunities and challenges for stem CH4 emissions research, which, when addressed, will improve estimates of the magnitudes, patterns and drivers of CH4 emissions and trace their potential origin. We identified the need: (1) for both long‐term, high‐frequency measurements of stem CH4 emissions to understand the fine‐scale processes, alongside rapid large‐scale measurements designed to understand the variability across individuals, species and ecosystems; (2) to identify microorganisms and biogeochemical pathways associated with CH4 production; and (3) to develop a mechanistic model including passive and active transport of CH4 from the soil–tree–atmosphere continuum. Addressing these challenges will help to constrain the magnitudes and patterns of CH4 emissions, and allow for the integration of pathways and mechanisms of CH4 production and emissions into process‐based models. These advances will facilitate the upscaling of stem CH4 emissions to the ecosystem level and quantify the role of stem CH4 emissions for the local to global CH4 budget.

Translating the ever-increasing wealth of information on microbiomes (environment, host or built environment) to advance our understanding of system-level processes is proving to be an exceptional research challenge. One reason for this challenge is that relationships between characteristics of microbiomes and the system-level processes that they influence are often evaluated in the absence of a robust conceptual framework and reported without elucidating the underlying causal mechanisms. The reliance on correlative approaches limits the potential to expand the inference of a single relationship to additional systems and advance the field. We propose that research focused on how microbiomes influence the systems they inhabit should work within a common framework and target known microbial processes that contribute to the system-level processes of interest. Here, we identify three distinct categories of microbiome characteristics (microbial processes, microbial community properties and microbial membership) and propose a framework to empirically link each of these categories to each other and the broader system-level processes that they affect. We posit that it is particularly important to distinguish microbial community properties that can be predicted using constituent taxa (community-aggregated traits) from those properties that cannot currently be predicted using constituent taxa (emergent properties). Existing methods in microbial ecology can be applied to more explicitly elucidate properties within each of these three categories of microbial characteristics and connect them with each other. We view this proposed framework, gleaned from a breadth of research on environmental microbiomes and ecosystem processes, as a promising pathway with the potential to advance discovery and understanding across a broad range of microbiome science.