Can redoing classic measurements and experiments reveal why species respond so differently to environmental change and improve our forecasts?

Abstract. Model intercomparisons are emerging as a powerful approach to improve our understanding of biodiversity and ecosystem responses to global changes, thereby supporting policy design and decision-making. However, efforts to date have focused on climate change impacts on species richness at a global scale, while regional impacts remain comparatively understudied. To address this gap, we propose a protocol for a biodiversity model intercomparison project focused on regional-to-continental scale patterns of species abundance and occurrence across space and time. This protocol covers a continuum of modeling approaches, ranging from correlative to process-explicit models. We detail the standardized input data used for model calibration –climate, land-use, and biodiversity data– and outline a common framework for model validation and performance assessment. Our protocol is structured in two phases. The first phase involves model specification, calibration, and validation using historical data, together with counterfactual experiments designed to attribute observed changes in population abundance and occurrence to different environmental drivers. In the second phase, model projections are compared under future climate and land-use scenarios. This two-phase approach enables direct comparison of model predictive performance on common benchmark data, providing an empirical basis for assessing confidence in future projections. By combining regional-scale biodiversity time series, common calibration and out-of-sample validation, and explicit representation of ecological processes, this protocol advances regional biodiversity model intercomparisons and sets the stage to reveal fundamental insights into which models, processes, and drivers determine historical and future biodiversity dynamics.

Body size declines are a common response to warming via both plasticity and evolution, but variable size responses have been observed for terrestrial ectotherms. We investigate how temperature-dependent development and growth rates in ectothermic organisms induce variation in size responses. Leveraging long-term data for six montane grasshopper species spanning 1,768-3 901 m, we detect size shifts since ~1960 that depend on elevation and species' seasonal timing. Size shifts have been concentrated at low elevations, with the early emerging species (those that overwinter as juveniles) increasing in size, while later season species are becoming smaller. Interannual temperature variation accounts for the size shifts. The earliest season species may be able to take advantage of warmer conditions accelerating growth during early spring development, whereas warm temperatures may adversely impact later season species via mechanisms such as increased rates of energy use or thermal stress. Grasshoppers tend to capitalize on warm conditions by both getting bigger and reaching adulthood earlier. Our analysis further reinforces the need to move beyond expectations of universal responses to climate change to consider how environmental exposure and sensitivity vary across elevations and life histories.

The fitness implications of climate variability and change are often estimated by integrating an organism's thermal sensitivity of performance across a time series of experienced body temperatures. Although this approach is an important first step in evaluating an organism's sensitivity to climate or climate change, it ignores potential influences of recent exposure to thermal stress on current thermal sensitivity. Here, we account for recent thermal stress by estimating rates of damage, repair, and other carryover effects; and we illustrate the approach with fecundity and development rate data from experiments that exposed aphids to various stressful and fluctuating temperatures. Our analyses indicate that heat stress for these aphids starts near the upper thermal limit for performance; that heat stress intensifies with both the exposure duration and with temperature; and that there is considerable capacity for repair at temperatures near the thermal optimum for performance. Results from experiments with aphids indicate that incorporating time series of damage, recovery, and repair will be necessary to anticipate fitness outcomes of climate change and variability.

The concurrent pressures of rising global temperatures, rates and incidence of species decline, and emergence of infectious diseases represent an unprecedented planetary crisis. Intergovernmental reports have drawn focus to the escalating climate and biodiversity crises and the connections between them, but interactions among all three pressures have been largely overlooked. Non-linearities and dampening and reinforcing interactions among pressures make considering interconnections essential to anticipating planetary challenges. In this Review, we define and exemplify the causal pathways that link the three global pressures of climate change, biodiversity loss, and infectious disease. A literature assessment and case studies show that the mechanisms between certain pairs of pressures are better understood than others and that the full triad of interactions is rarely considered. Although challenges to evaluating these interactions—including a mismatch in scales, data availability, and methods—are substantial, current approaches would benefit from expanding scientific cultures to embrace interdisciplinarity and from integrating animal, human, and environmental perspectives. Considering the full suite of connections would be transformative for planetary health by identifying potential for co-benefits and mutually beneficial scenarios, and highlighting where a narrow focus on solutions to one pressure might aggravate another.

Widespread plastic and evolutionary responses to temperature have led to body size declines being proposed as a universal response to warming, but the high degree of variation in terrestrial ectotherms has challenged this view. We investigate whether temperature dependent development and growth rates in ectothermic organisms drive departures from universal size responses. Leveraging a long-term study of montane grasshoppers, we detect size shifts over recent decades that depend on elevation and species' seasonal timing. Size shifts have been focused at low elevations with the earliest emerging species (those that overwinter as juveniles) getting bigger, and later season species getting smaller. The shifts correspond to warmer temperatures at low elevation increasing the sizes of the earliest season species but decreasing the sizes of the later season species. This is consistent with our hypothesis that the earliest season species may be able to take advantage of warmer conditions accelerating growth during early spring development, whereas warm temperatures may adversely impact later season species via mechanisms such as increased rates of energy use or thermal stress. Grasshoppers tend to capitalize on warm conditions by both getting bigger and reaching adulthood earlier. Our analysis further reinforces the need to move beyond expectations of universal responses to climate change to consider how environmental exposure and sensitivity varies across elevations and life histories. ### Competing Interest Statement The authors have declared no competing interest.

Accurately predicting species' range shifts in response to environmental change is paramount for understanding ecological processes and global change. In synthetic analyses, traits emerge as significant but weak predictors of species' range shifts across recent climate change. These studies assume linear responses to traits, while detailed empirical work often reveals trait responses that are unimodal and contain thresholds or other nonlinearities. We hypothesize that the use of linear modeling approaches fails to capture these nonlinearities and, therefore, may be under-powering traits to predict range shifts. We evaluate the predictive performance of approaches that can capture nonlinear relationships (ridge-regularized linear regression, support vector regression with linear and nonlinear kernels, and random forests). We apply our models using six multidecadal range shift datasets for plants, moths, marine fish, birds, and small mammals. We show that nonlinear approaches can perform better than least-squares linear modeling in reproducing historical range shifts. Consistent with expectations, we identify dispersal and climatic niche traits as primary determinants of distribution shifts. Traits identified as important predictors and the direction of trait effects are generally consistent across models, but there are notable exceptions. Among important predictors, there are more consistent responses to climatic niches than dispersal ability. Modest improvements in predictability when accounting for nonlinearities and interactions, and the overall low amount of variance accounted for by trait predictors suggest limits to trait-based statistical predictive frameworks.

Brief introduction: What are microclimates and why are they important?Microclimate science has developed into a global discipline. Microclimate science is increasingly used to understand and mitigate climate and biodiversity shifts. Here, we provide an overview of the current status of microclimate ecology and biogeography in terrestrial ecosystems, and where this field is heading next. Microclimate investigations in ecology and biogeography: We highlight the latest research on interactions between microclimates and organisms, including how microclimates influence individuals, and through them populations, communities and entire ecosystems and their processes. We also briefly discuss recent research on how organisms shape microclimates from the tropics to the poles. Microclimate applications in ecosystem management: Microclimates are also important in ecosystem management under climate change. We showcase new research in microclimate management with examples from biodiversity conservation, forestry and urban ecology. We discuss the importance of microrefugia in conservation and how to promote microclimate heterogeneity. Methods for microclimate science: We showcase the recent advances in data acquisition, such as novel field sensors and remote sensing methods. We discuss microclimate modelling, mapping and data processing, including accessibility of modelling tools, advantages of mechanistic and statistical modelling and solutions for computational challenges that have pushed the state-of-the-art of the field. What's next?We identify major knowledge gaps that need to be filled for further advancing microclimate investigations, applications and methods. These gaps include spatiotemporal scaling of microclimate data, mismatches between macroclimate and microclimate in predicting responses of organisms to climate change, and the need for more evidence on the outcomes of microclimate management.

Thermal performance curves (TPCs) are increasingly used as a convenient approach to predict climate change impacts on ectotherms that accounts for organismal thermal sensitivity; however, directly applying TPCs to temperature data to estimate fitness has yielded contrasting predictions depending on assumptions regarding climate variability. We compare direct application of TPCs to an approach integrating TPCs for different fitness components (e.g., per capita birth rate, adult life span) across ectotherm life cycles into a population dynamic model, which we independently validated with census data and applied to hemipteran insect populations across latitude. The population model predicted that climate change will reduce insect fitness more at higher latitudes due to its effects on survival but will reduce net reproductive rate more at lower latitudes due to its effects on fecundity. Directly applying TPCs underestimated climate change impacts on fitness relative to incorporating the TPCs into the population model due to simplifying survival dynamics across the life cycle. The population model predicted that climate change will reduce mean insect density and increase population variability at higher latitudes via reduced survival, despite faster development and a longer activity period. Our study highlights the importance of considering how multiple fitness components respond to climate variability across the life cycle to better understand and anticipate the ecological consequence of climate change.

Projecting ecological and evolutionary responses to variable and changing environments is central to anticipating and managing impacts to biodiversity and ecosystems. Current modeling approaches are largely phenomenological and often fail to accurately project responses due to numerous biological processes at multiple levels of biological organization responding to environmental variation at varied spatial and temporal scales. Limited mechanistic understanding of organismal responses to environmental variability and extremes also restricts predictive capacity. We outline a strategy for identifying and modeling the key organismal mechanisms across levels of biological organization that mediate ecological and evolutionary responses to environmental variation. A central component of this strategy is quantifying timescales and magnitudes of climatic variability and how organisms experience them. We highlight recent empirical research that builds this information and suggest how to design future experiments that can produce more generalizable principles. We discuss how to create biologically informed projections in a feasible way by combining statistical and mechanistic approaches. Predictions will inform both fundamental and practical questions at the interface of ecology, evolution, and Earth science such as how organisms experience, adapt to, and respond to environmental variation at multiple hierarchical spatial and temporal scales.

Most ecological analyses and forecasts use weather station data or coarse interpolated, gridded air temperature data. Yet, these products often poorly capture the microclimates experienced by organisms that respond to fine-scale spatial and temporal environmental variation near the surface. Sources of historic and projected future data with finer spatial and temporal resolution are proliferating. We qualitatively and quantitatively review and evaluate the available data on three core issues central to microclimate modeling: the quality of the input environmental data, the ability of algorithms to capture microclimatic processes given environmental forcing data, and how best to access microclimatic data. We show how differences between observed environmental conditions and those estimated using environmental forcing data, microclimate algorithms, and precomputed microclimate datasets can be substantial depending on the variable, location, and season. The choice of environmental dataset to parameterize biophysical models has ramifications for biological estimates, such as the duration of potential activity and incidence of thermal stress. New data sources offering high temporal and spatial resolution correspond well to observational data and have the potential to revolutionize understanding of the ecological implications of microclimate variability. We provide resources to help users select and access appropriate environmental data for biological applications, including users' guides and interactive visualization, to better infer how organisms experience climate variability and change.

Synthesis research in ecology and environmental science improves understanding, advances theory, identifies research priorities, and supports management strategies by linking data, ideas, and tools. Accelerating environmental challenges increases the need to focus synthesis science on the most pressing questions. To leverage input from the broader research community, we convened a virtual workshop with participants from many countries and disciplines to examine how and where synthesis can address key questions and themes in ecology and environmental science in the coming decade. Seven priority research topics emerged: (1) diversity, equity, inclusion, and justice (DEIJ), (2) human and natural systems, (3) actionable and use-inspired science, (4) scale, (5) generality, (6) complexity and resilience, and (7) predictability. Additionally, two issues regarding the general practice of synthesis emerged: the need for increased participant diversity and inclusive research practices; and increased and improved data flow, access, and skill-building. These topics and practices provide a strategic vision for future synthesis in ecology and environmental science.

Much understanding of organismal responses to climate change and variability relies on the assumption that body temperatures are equal to temporally averaged air temperatures high above the ground. However, most organisms experience microclimates near the ground and acute exposure to solar and thermal radiation and thermal extremes can substantially elevate or depress their body temperatures. We introduce the TrenchR package, which aids in Translating Environmental Change into organismal responses. The package includes microclimate models to vertically scale weather station data to organismal heights. Additional functions model and temporally partition air and soil temperatures and solar radiation. TrenchR biophysical modeling tools include both general models for heat flows and specific models to predict body temperatures for a variety of ectothermic taxa. We also offer utility functions to aid in estimating the organismal and environmental parameters needed for biophysical ecology. TrenchR focuses on simple and modular functions so users can create transparent and flexible models for biophysical applications. The package aims to introduce and enable microclimate and biophysical modeling to improve ecological and evolutionary forecasting. We further this aim through a series of educational modules that introduce the field of biophysical ecology.