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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.

 

Abstract Organisms living in seasonal environments often adjust physiological capacities and sensitivities in response to (or in anticipation of) environment shifts. Such physiological and morphological adjustments (“acclimation” and related terms) inspire opportunities to explore the mechanistic bases underlying these adjustments, to detect cues inducing adjustments, and to elucidate their ecological and evolutionary consequences. Seasonal adjustments (“seasonal acclimation”) can be detected either by measuring physiological capacities and sensitivities of organisms retrieved directly from nature (or outdoor enclosures) in different seasons or less directly by rearing and measuring organisms maintained in the laboratory under conditions that attempt to mimic or track natural ones. But mimicking natural conditions in the laboratory is challenging – doing so requires prior natural-history knowledge of ecologically relevant body temperature cycles, photoperiods, food rations, social environments, among other variables. We argue that traditional laboratory-based conditions usually fail to approximate natural seasonal conditions (temperature, photoperiod, food, ‘lockdown’). Consequently, whether the resulting acclimation shifts correctly approximate those in nature is uncertain, and sometimes is dubious. We argue that background natural history information provides opportunities to design acclimation protocols that are not only more ecologically relevant, but also serve as templates for testing the validity of traditional protocols. Finally, we suggest several best practices to help enhance ecological realism. 

Evolutionary adaptation to temperature and climate depends on both the extent to which organisms experience spatial and temporal environmental variation (exposure) and how responsive they are to the environmental variation (sensitivity). Theoretical models and experiments suggesting substantial potential for thermal adaptation have largely omitted realistic environmental variation. Environmental variation can drive fluctuations in selection that slow adaptive evolution. We review how carefully filtering environmental conditions based on how organisms experience their environment and further considering organismal sensitivity can improve predictions of thermal adaptation. We contrast taxa differing in exposure and sensitivity. Plasticity can increase the rate of evolutionary adaptation in taxa exposed to pronounced environmental variation. However, forms of plasticity that severely limit exposure, such as behavioral thermoregulation and phenological shifts, can hinder thermal adaptation. Despite examples of rapid thermal adaptation, experimental studies often reveal evolutionary constraints. Further investigating these constraints and issues of timescale and thermal history are needed to predict evolutionary adaptation and, consequently, population persistence in changing and variable environments. 

Populations of insects can differ in how sensitive their development, growth, and performance are to environmental conditions such as temperature and daylength. The environmental sensitivity of development can alter phenology (seasonal timing) and ecology. Warming accelerates development of most populations. However, high-elevation and season-limited populations can exhibit developmental plasticity to either advance or prolong development depending on conditions. We examine how diurnal temperature variation and daylength interact to shape growth, development, and performance of several populations of the montane grasshopper, Melanoplus boulderensis , along an elevation gradient. We then compare these experimental results to observed patterns of development in the field. Although populations exhibited similar thermal sensitivities of development under long-day conditions, development of high-elevation populations was more sensitive to temperature under short-day conditions. This developmental plasticity resulted in rapid development of high elevation populations in short-day conditions with high temperature variability, consistent with their observed capacity for rapid development in the field when conditions are permissive early in the season. Notably, accelerated development generally did not decrease body size or alter body shape. Developmental conditions did not strongly influence thermal tolerance but altered the temperature dependence of performance in difficult-to-predict ways. In sum, the high-elevation and season-limited populations exhibited developmental plasticity that enables advancing or prolonging development consistent with field phenology. Our results suggest these patterns are driven by the thermal sensitivity of development increasing when days are short early in the season compared to when days are long later in the season. Developmental plasticity will shape phenological responses to climate change with potential implications for community and ecosystem structure. 

Understanding and predicting the biological consequences of climate change requires considering the thermal sensitivity of organisms relative to environmental temperatures. One common approach involves ‘thermal safety margins’ (TSMs), which are generally estimated as the temperature differential between the highest temperature an organism can tolerate (critical thermal maximum, CT_max) and the mean or maximum environmental temperature it experiences. Yet, organisms face thermal stress and performance loss at body temperatures below their CT_max,and the steepness of that loss increases with the asymmetry of the thermal performance curve (TPC).

Global.

2015–2019.

Ants, fish, insects, lizards and phytoplankton.

We examine variability in TPC asymmetry and the implications for thermal stress for 384 populations from 289 species across taxa and for metrics including ant and lizard locomotion, fish growth, and insect and phytoplankton fitness.

We find that the thermal optimum (T_opt, beyond which performance declines) is more labile than CT_max, inducing interspecific variation in asymmetry. Importantly, the degree of TPC asymmetry increases with T_opt. Thus, even though populations with higher T_opts in a hot environment might experience above‐optimal body temperatures less often than do populations with lower T_opts, they nonetheless experience steeper declines in performance at high body temperatures. Estimates of the annual cumulative decline in performance for temperatures above T_optsuggest that TPC asymmetry alters the onset, rate and severity of performance decrement at high body temperatures.

Species with the same TSMs can experience different thermal risk due to differences in TPC asymmetry. Metrics that incorporate additional aspects of TPC shape better capture the thermal risk of climate change than do TSMs.

 

ABSTRACT Organisms respond to shifts in climate means and variability via distinct mechanisms. Accounting for these differential responses and appropriately aggregating them is central to understanding and predicting responses to climate variability and change. Separately considering fitness components can clarify organismal responses: fecundity is primarily an integrated, additive response to chronic environmental conditions over time via mechanisms such as energy use and acquisition, whereas survival can be strongly influenced by short-term, extreme environmental conditions. In many systems, the relative importance of fecundity and survival constraints changes systematically along climate gradients, with fecundity constraints dominating at high latitudes or altitudes (i.e. leading range edges as climate warms), and survival constraints dominating at trailing range edges. Incorporating these systematic differences in models may improve predictions of responses to recent climate change over models that assume similar processes along environmental gradients. We explore how detecting and predicting shifts in fitness constraints can improve our ability to forecast responses to climate gradients and change. 

Species with different life histories and communities that vary in their seasonal constraints tend to shift their phenology (seasonal timing) differentially in response to climate warming.

We investigate how these variable phenological shifts aggregate to influence phenological overlap within communities. Phenological advancements of later season species and extended durations of early season species may increase phenological overlap, with implications for species' interactions such as resource competition.

We leverage extensive historic (1958–1960) and recent (2006–2015) weekly survey data for communities of grasshoppers along a montane elevation gradient to assess the impact of climate on shifts in the phenology and abundance distributions of species. We then examine how these responses are influenced by the seasonal timing of species and elevation, and how in aggregate they influence degrees of phenological overlap within communities.

In warmer years, abundance distributions shift earlier in the season and become broader. Total abundance responds variably among species and we do not detect a significant response across species. Shifts in abundance distributions are not strongly shaped by species' seasonal timing or sites of variable elevations. The area of phenological overlap increases in warmer years due to shifts in the relative seasonal timing of compared species. Species that overwinter as nymphs increasingly overlap with later season species that advance their phenology. The days of phenological overlap also increase in warm years but the response varies across sites of variable elevation. Our phenological overlap metric based on comparing single events—the dates of peak abundance—does not shift significantly with warming.

Phenological shifts are more complex than shifts in single dates such as first occurrence. As abundance distributions shift earlier and become broader in warm years, phenological overlap increases. Our analysis suggests that overall grasshopper abundance is relatively robust to climate and associated phenological shifts but we find that increased overlap can decrease abundance, potentially by strengthening species interactions such as resource competition.