Climate variability slows evolutionary responses of Colias butterflies to recent climate change

How does recent climate warming and climate variability alter fitness, phenotypic selection and evolution in natural populations? We combine biophysical, demographic and evolutionary models with recent climate data to address this question for the subalpine and alpine butterfly, Colias meadii, in the southern Rocky Mountains. We focus on predicting patterns of selection and evolution for a key thermoregulatory trait, melanin (solar absorptivity) on the posterior ventral hindwings, which affects patterns of body temperature, flight activity, adult and egg survival, and reproductive success in Colias. Both mean annual summer temperatures and thermal variability within summers have increased during the past 60 years at subalpine and alpine sites. At the subalpine site, predicted directional selection on wing absorptivity has shifted from generally positive (favouring increased wing melanin) to generally negative during the past 60 years, but there is substantial variation among years in the predicted magnitude and direction of selection and the optimal absorptivity. The predicted magnitude of directional selection at the alpine site declined during the past 60 years and varies substantially among years, but selection has generally been positive at this site. Predicted evolutionary responses to mean climate warming at the subalpine site since 1980 is small, because of the variability in selection and asymmetry of the fitness function. At both sites, the predicted effects of adaptive evolution on mean population fitness are much smaller than the fluctuations in mean fitness due to climate variability among years. Our analyses suggest that variation in climate within and among years may strongly limit evolutionary responses of ectotherms to mean climate warming in these habitats.     

Assessing the effect of climate change on carbon sequestration in a Mexican dry forest in the Yucatan Peninsula

Assessing the effect of climate change on carbon sequestration in tropical forest ecosystems is important to inform monitoring, reporting, and verification (MRV) for reducing deforestation and forest degradation (REDD), and to effectively assess forest management options under climate change. Two process-based models, Forest-DNDC and Biome-BGC, with different spatial modeling scales were evaluated to estimate the potential effect of climate change on carbon sequestration in a tropical dry semi-deciduous forest in the Yucatan Peninsula of Mexico. The results from the simulations using the two models show that carbon sequestration in this dry forest is highly sensitive to warming. Carbon uptake in this forest may increase or decrease slightly with a corresponding increase or decrease in precipitation; however, with an increase in temperature, carbon uptake may decrease significantly, showing that warming may be the main climate factor that impacts carbon storage in this tropical dry forest. Model performance evaluation indicates that both models may be used to estimate C stocks, but DNDC may be better than BGC for assessing the effect of climate change on C dynamics.     

The world is not flat: defining relevant thermal landscapes in the context of climate change

Although climates are rapidly changing on a global scale, these changes cannot easily be extrapolated to the local scales experienced by organisms. In fact, such generalizations might be quite problematic. For instance, models used to predict shifts in the ranges of species during climate change rarely incorporate data resolved to <1 km(2), although most organisms integrate climatic drivers at much smaller scales. Empirical studies alone suggest that the operative temperatures of many organisms vary by as much as 10-20 °C on a local scale, depending on vegetation, geology, and topography. Furthermore, this variation in abiotic factors ignores thermoregulatory behaviors that many animals use to balance heat loads. Through a set of simulations, we demonstrate how variability in elevational topography can attenuate the effects of warming climates. These simulations suggest that changing climates do not always impact organisms negatively. Importantly, these simulations involve well-known relationships in biophysical ecology that show how no two organisms experience the same climate in the same way. We suggest that, when coupled with thermoregulatory behavior, variation in topographic features can mask the acute effect of climate change in many cases.     

Moving forward in global‐change ecology: capitalizing on natural variability

Natural resources managers are being asked to follow practices that accommodate for the impact of climate change on the ecosystems they manage, while global‐ecosystems modelers aim to forecast future responses under different climate scenarios. However, the lack of scientific knowledge about short‐term ecosystem responses to climate change has made it difficult to define set conservation practices or to realistically inform ecosystem models. Until recently, the main goal for ecologists was to study the composition and structure of communities and their implications for ecosystem function, but due to the probable magnitude and irreversibility of climate‐change effects (species extinctions and loss of ecosystem function), a shorter term focus on responses of ecosystems to climate change is needed. We highlight several underutilized approaches for studying the ecological consequences of climate change that capitalize on the natural variability of the climate system at different temporal and spatial scales. For example, studying organismal responses to extreme climatic events can inform about the resilience of populations to global warming and contribute to the assessment of local extinctions. Translocation experiments and gene expression are particular useful to quantitate a species' acclimation potential to global warming. And studies along environmental gradients can guide habitat restoration and protection programs by identifying vulnerable species and sites. These approaches identify the processes and mechanisms underlying species acclimation to changing conditions, combine different analytical approaches, and can be used to improve forecasts of the short‐term impacts of climate change and thus inform conservation practices and ecosystem models in a meaningful way.     

Plant phenology and global climate change: Current progresses and challenges

Plant phenology, the annually recurring sequence of plant developmental stages, is important for plant functioning and ecosystem services and their biophysical and biogeochemical feedbacks to the climate system. Plant phenology depends on temperature, and the current rapid climate change has revived interest in understanding and modeling the responses of plant phenology to the warming trend and the consequences thereof for ecosystems. Here, we review recent progresses in plant phenology and its interactions with climate change. Focusing on the start (leaf unfolding) and end (leaf coloring) of plant growing seasons, we show that the recent rapid expansion in ground‐ and remote sensing‐ based phenology data acquisition has been highly beneficial and has supported major advances in plant phenology research. Studies using multiple data sources and methods generally agree on the trends of advanced leaf unfolding and delayed leaf coloring due to climate change, yet these trends appear to have decelerated or even reversed in recent years. Our understanding of the mechanisms underlying the plant phenology responses to climate warming is still limited. The interactions between multiple drivers complicate the modeling and prediction of plant phenology changes. Furthermore, changes in plant phenology have important implications for ecosystem carbon cycles and ecosystem feedbacks to climate, yet the quantification of such impacts remains challenging. We suggest that future studies should primarily focus on using new observation tools to improve the understanding of tropical plant phenology, on improving process‐based phenology modeling, and on the scaling of phenology from species to landscape‐level.     

Comparison of climate change impacts on the growth of C3 and C4 crops in China

Global agricultural production has been significantly affected by climate change. As a large but also weak agricultural country, China must take corresponding adaptation measures in regard to climate change. As C3 and C4 crops have different carbon sequestration pathways, the responses of their growth to climate change are different. This study comprehensively compared the impacts of climate change on the growth of C3 and C4 crops in China by considering several key variables, such as solar radiation, temperature, precipitation, CO₂ concentration, and agro–climatic constraints. The WOFOST (WOrld FOod STudies) model was used to quantitatively simulate and analyze the impacts of these variables on crop yield under four different scenarios. The results show that 1) during the growth period, solar radiation had the most significant change, followed by temperature difference between day and night, daily minimum temperature, daily maximum temperature, and precipitation; 2) the growth indicators of both C3 and C4 crops were more strongly correlated with solar radiation and temperature; and 3) under the four scenarios, changes in temperature and solar radiation had negative effects on both C3 and C4 crops in most regions, and changes in CO₂ concentration had greater impacts on crop yields than other factors. This study revealed the temporal and spatial patterns of crop growth indicators under different climate change scenarios in the past 30 years, which provides a scientific basis for exploring how to adapt to climate change and provide higher levels of crop productivity in China.     

Melanin‐based colour polymorphism responding to climate change

Climate warming leads to a decrease in biodiversity. Organisms can deal with the new prevailing environmental conditions by one of two main routes, namely evolving new genetic adaptations or through phenotypic plasticity to modify behaviour and physiology. Melanin‐based colouration has important functions in animals including a role in camouflage and thermoregulation, protection against UV‐radiation and pathogens and, furthermore, genes involved in melanogenesis can pleiotropically regulate behaviour and physiology. In this article, I review the current evidence that differently coloured individuals are differentially sensitive to climate change. Predicting which of dark or pale colour variants (or morphs) will be more penalized by climate change will depend on the adaptive function of melanism in each species as well as how the degree of colouration covaries with behaviour and physiology. For instance, because climate change leads to a rise in temperature and UV‐radiation and dark colouration plays a role in UV‐protection, dark individuals may be less affected from global warming, if this phenomenon implies more solar radiation particularly in habitats of pale individuals. In contrast, as desertification increases, pale colouration may expand in those regions, whereas dark colourations may expand in regions where humidity is predicted to increase. Dark colouration may be also indirectly selected by climate warming because genes involved in the production of melanin pigments confer resistance to a number of stressful factors including those associated with climate warming. Furthermore, darker melanic individuals are commonly more aggressive than paler conspecifics, and hence they may better cope with competitive interactions due to invading species that expand their range in northern latitudes and at higher altitudes. To conclude, melanin may be a major component involved in adaptation to climate warming, and hence in animal populations melanin‐based colouration is likely to change as an evolutionary or plastic response to climate warming.     

Can terrestrial ectotherms escape the heat of climate change by moving?

Whether movement will enable organisms to alleviate thermal stress is central to the biodiversity implications of climate change. We use the temperature-dependence of ectotherm performance to investigate the fitness consequences of movement. Movement to an optimal location within a 50 km radius will only offset the fitness impacts of climate change by 2100 in 5 per cent of locations globally. Random movement carries an 87 per cent risk of further fitness detriment. Mountainous regions with high temperature seasonality (i.e. temperate areas) not only offer the greatest benefit from optimal movement but also the most severe fitness consequences if an organism moves to the wrong location. Doubling dispersal capacity would provide modest benefit exclusively to directed dispersers in topographically diverse areas. The benefits of movement for escaping climate change are particularly limited in the tropics, where fitness impacts will be most severe. The potential of movement to lessen climate change impacts may have been overestimated.     

High community turnover and dispersal limitation relative to rapid climate change

Aim Many competing hypotheses seek to identify the mechanisms behind species richness gradients. Yet, the determinants of species turnover over broad scales are uncertain. We test whether environmental dissimilarity predicts biotic turnover spatially and temporally across an array of environmental variables and spatial scales using recently observed climate changes as a pseudo‐experimental opportunity. Location Canada. Methods We used an extensive database of observation records of 282 Canadian butterfly species collected between 1900 and 2010 to characterize spatial and temporal turnover based on Jaccard indices. We compare relationships between spatial turnover and differences in an array of relevant environmental conditions, including aspects of temperature, precipitation, elevation, primary productivity and land cover. Measurements were taken within 100‐, 200‐ and 400‐km grid cells, respectively. We tested the relative importance of each variable in predicting spatial turnover using bootstrap analysis. Finally, we tested for effects of temperature and precipitation change on temporal turnover, including distinctly accounting for turnover under individual species’ potential dispersal limitations. Results Temperature differences between areas correlate with spatial turnover in butterfly assemblages, independently of distance, sampling differences or the spatial resolution of the analysis. Increasing temperatures are positively related to biotic turnover within quadrats through time. Limitations on species dispersal may cause observed biotic turnover to be lower than expected given the magnitude of temperature changes through time. Main conclusions Temperature differences can account for spatial trends in biotic dissimilarity and turnover through time in areas where climate is changing. Butterfly communities are changing quickly in some areas, probably reflecting the dispersal capacities of individual species. However, turnover is lower through time than expected in many areas, suggesting that further work is needed to understand the factors that limit dispersal across broad regions. Our results illustrate the large‐scale effects of climate change on biodiversity in areas with strong environmental gradients.     

An indicator highlights seasonal variation in the response of Lepidoptera communities to warming

The impacts of climate change on species and ecosystems are increasingly evident. While these tend to be clearest with respect to changes in phenology and distribution ranges, there are also important consequences for population sizes and community structure. There is an urgent need to develop ecological indicators that can be used to detect climate-driven changes in ecological communities, and identify how those impacts may vary spatially. Here we describe the development of a new community-based seasonal climate change indicator that uses national population and weather indices. We test this indicator using Lepidopteran and co-located weather data collected across a range of UK Environmental Change Network (ECN) sites. We compare our butterfly indicator with estimates derived from an alternative, previously published metric, the Community Temperature Index (CTI).First, we quantified the effect of temperature on population growth rates of moths and butterflies (Species Temperature Response, STR) by modelling annual variation in national population indices as a function of nationally averaged seasonal variation in temperature, using species and weather data independent of the ECN data. Then, we calculated average STRs for annually summarised species data from each ECN site, weighted by species’ abundance, to produce the Community Temperature Response (CTR). Finally, we tested the extent to which CTR correlated with spatial variation in temperature between sites and the extent to which temporal variation in CTR tracked both annual and seasonal warming trends.Mean site CTR was positively correlated with mean site temperature for moths but not butterflies. However, spatial variation in moth communities was well explained by mean site summer temperature and butterfly communities by winter temperature, respectively accounting for 74% and 63% of variation. Temporal variation in moth and butterfly CTR within sites did not vary with the mean annual temperature but responded to variation in the mean temperature of specific seasons. There were positive correlations between moth seasonal CTRs and seasonal temperatures in winter, spring and summer; and butterfly seasonal CTRs and seasonal temperatures in winter and summer. Butterfly CTR and CTI both correlated spatially and temporally with winter temperature.Our results highlight the need for seasonality to be considered when examining the impact of climate change on communities. Seasonal CTRs may be used to track the impact of changing temperatures on biodiversity and help identify potential mechanisms by which climate change is affecting communities. In the case of Lepidoptera, our results suggest that future warming may reassemble Lepidoptera communities.     

Crop yield response to climate change varies with cropping intensity

Projections of the response of crop yield to climate change at different spatial scales are known to vary. However, understanding of the causes of systematic differences across scale is limited. Here, we hypothesize that heterogeneous cropping intensity is one source of scale dependency. Analysis of observed global data and regional crop modelling demonstrate that areas of high vs. low cropping intensity can have systematically different yields, in both observations and simulations. Analysis of global crop data suggests that heterogeneity in cropping intensity is a likely source of scale dependency for a number of crops across the globe. Further crop modelling and a meta‐analysis of projected tropical maize yields are used to assess the implications for climate change assessments. The results show that scale dependency is a potential source of systematic bias. We conclude that spatially comprehensive assessments of climate impacts based on yield alone, without accounting for cropping intensity, are prone to systematic overestimation of climate impacts. The findings therefore suggest a need for greater attention to crop suitability and land use change when assessing the impacts of climate change.     

Mechanistic forecasts of species responses to climate change: The promise of biophysical ecology

A core challenge in global change biology is to predict how species will respond to future environmental change and to manage these responses. To make such predictions and management actions robust to novel futures, we need to accurately characterize how organisms experience their environments and the biological mechanisms by which they respond. All organisms are thermodynamically connected to their environments through the exchange of heat and water at fine spatial and temporal scales and this exchange can be captured with biophysical models. Although mechanistic models based on biophysical ecology have a long history of development and application, their use in global change biology remains limited despite their enormous promise and increasingly accessible software. We contend that greater understanding and training in the theory and methods of biophysical ecology is vital to expand their application. Our review shows how biophysical models can be implemented to understand and predict climate change impacts on species' behavior, phenology, survival, distribution, and abundance. It also illustrates the types of outputs that can be generated, and the data inputs required for different implementations. Examples range from simple calculations of body temperature at a particular site and time, to more complex analyses of species' distribution limits based on projected energy and water balances, accounting for behavior and phenology. We outline challenges that currently limit the widespread application of biophysical models relating to data availability, training, and the lack of common software ecosystems. We also discuss progress and future developments that could allow these models to be applied to many species across large spatial extents and timeframes. Finally, we highlight how biophysical models are uniquely suited to solve global change biology problems that involve predicting and interpreting responses to environmental variability and extremes, multiple or shifting constraints, and novel abiotic or biotic environments.     

Climate change in Australian tropical rainforests: an impending environmental catastrophe

It is now widely accepted that global climate change is affecting many ecosystems around the globe and that its impact is increasing rapidly. Many studies predict that impacts will consist largely of shifts in latitudinal and altitudinal distributions. However, we demonstrate that the impacts of global climate change in the tropical rainforests of northeastern Australia have the potential to result in many extinctions. We develop bioclimatic models of spatial distribution for the regionally endemic rainforest vertebrates and use these models to predict the effects of climate warming on species distributions. Increasing temperature is predicted to result in significant reduction or complete loss of the core environment of all regionally endemic vertebrates. Extinction rates caused by the complete loss of core environments are likely to be severe, nonlinear, with losses increasing rapidly beyond an increase of 2 degrees C, and compounded by other climate-related impacts. Mountain ecosystems around the world, such as the Australian Wet Tropics bioregion, are very diverse, often with high levels of restricted endemism, and are therefore important areas of biodiversity. The results presented here suggest that these systems are severely threatened by climate change.     

Contrasting spatial and temporal global change impacts on butterfly species richness during the 20th century

Regional patterns of species richness are often explained by models using temperature or measures habitat suitability. Generally, species richness is positively associated with temperature, and negatively associated with habitat degradation. While these models have been well tested across spatial scales, they have rarely been tested on a temporal scale – in part due to the difficulty in ascertaining accurate historical data at an appropriate resolution. In this study, we compared the results of temporal and spatial models, each incorporating two predictors of species richness: temperature, and human population density (as a surrogate of human-related habitat impacts). We found that the change in species richness from the early to late part of the 20th century was positively correlated with temperature change, and negatively correlated with human population density change. When we compared these results to two spatial models using contemporary and historic data, the spatial effects of temperature on butterfly richness were similar to its temporal effects, while the effect of human population density through time is the opposite of its spatial effect. More generally, the assumption that spatial patterns are equivalent to temporal ones when applying macroecological data to global change is clearly unreliable.     

Fine‐scale climate change: modelling spatial variation in biologically meaningful rates of warming

The existence of fine‐grain climate heterogeneity has prompted suggestions that species may be able to survive future climate change in pockets of suitable microclimate, termed ‘microrefugia’. However, evidence for microrefugia is hindered by lack of understanding of how rates of warming vary across a landscape. Here, we present a model that is applied to provide fine‐grained, multidecadal estimates of temperature change based on the underlying physical processes that influence microclimate. Weather station and remotely derived environmental data were used to construct physical variables that capture the effects of terrain, sea surface temperatures, altitude and surface albedo on local temperatures, which were then calibrated statistically to derive gridded estimates of temperature. We apply the model to the Lizard Peninsula, United Kingdom, to provide accurate (mean error = 1.21 °C; RMS error = 1.63 °C) hourly estimates of temperature at a resolution of 100 m for the period 1977–2014. We show that rates of warming vary across a landscape primarily due to long‐term trends in weather conditions. Total warming varied from 0.87 to 1.16 °C, with the slowest rates of warming evident on north‐east‐facing slopes. This variation contributed to substantial spatial heterogeneity in trends in bioclimatic variables: for example, the change in the length of the frost‐free season varied from +11 to ?54 days and the increase in annual growing degree‐days from 51 to 267 °C days. Spatial variation in warming was caused primarily by a decrease in daytime cloud cover with a resulting increase in received solar radiation, and secondarily by a decrease in the strength of westerly winds, which has amplified the effects on temperature of solar radiation on west‐facing slopes. We emphasize the importance of multidecadal trends in weather conditions in determining spatial variation in rates of warming, suggesting that locations experiencing least warming may not remain consistent under future climate change.     

Climate change causes critical transitions and irreversible alterations of mountain forests

Mountain forests are at particular risk of climate change impacts due to their temperature limitation and high exposure to warming. At the same time, their complex topography may help to buffer the effects of climate change and create climate refugia. Whether climate change can lead to critical transitions of mountain forest ecosystems and whether such transitions are reversible remain incompletely understood. We investigated the resilience of forest composition and size structure to climate change, focusing on a mountain forest landscape in the Eastern Alps. Using the individual‐based forest landscape model iLand, we simulated ecosystem responses to a wide range of climatic changes (up to a 6°C increase in mean annual temperature and a 30% reduction in mean annual precipitation), testing for tipping points in vegetation size structure and composition under different topography scenarios. We found that at warming levels above +2°C a threshold was crossed, with the system tipping into an alternative state. The system shifted from a conifer‐dominated landscape characterized by large trees to a landscape dominated by smaller, predominantly broadleaved trees. Topographic complexity moderated climate change impacts, smoothing and delaying the transitions between alternative vegetation states. We subsequently reversed the simulated climate forcing to assess the ability of the landscape to recover from climate change impacts. The forest landscape showed hysteresis, particularly in scenarios with lower precipitation. At the same mean annual temperature, equilibrium vegetation size structure and species composition differed between warming and cooling trajectories. Here we show that even moderate warming corresponding to current policy targets could result in critical transitions of forest ecosystems and highlight the importance of topographic complexity as a buffering agent. Furthermore, our results show that overshooting ambitious climate mitigation targets could be dangerous, as ecological impacts can be irreversible at millennial time scales once a tipping point has been crossed.     

Ecophysiological responses to climate change in cicadas

Cicadas are large hemipteran insects characterized by unique life‐history traits, such as extraordinarily long life cycles, a subterranean/terrestrial habitat transition, xylem sap‐feeding and melodious sound production. These fascinating features of cicadas have attracted much attention in the research fields of physiology and ecology, resulting in an accumulation of knowledge about the underlying mechanisms and their adaptive significance. Although community‐level responses to recent climate change have already been documented for cicada fauna, an understanding of their causal relationships is still at the initial stages. In this review, we summarize current knowledge about environmental adaptations of cicadas to facilitate a deeper understanding of the ecophysiological consequences of climate change. We first outline the diverse responses of cicadas to environmental factors, mainly temperature, and their strategies to cope with naturally fluctuating environments. Then, we discuss the consequence of upcoming climate change by consolidating the current findings. This review highlights the fact that fitness‐relevant activities are fine‐tuned to a species‐specific temperature optimum to achieve habitat segregation among coexisting species, implying that cicada diversity is highly susceptible to climate warming. As a result of their conspicuous large bodies and species‐specific calling songs, cicadas are promising candidates for use as bioindicator species to monitor ecological impacts of climate change. We encourage future works that continuously quantify population‐ and community‐level responses to upcoming climate change, as well as unveil mechanistic links between physiological traits and ecological consequences.     

Population responses within a landscape matrix: a macrophysiological approach to understanding climate change impacts

Global environmental change (GEC) is a significant concern. However, forecasting the outcomes of this change for species and ecosystems remains a major challenge. In particular, predicting specific changes in systems where initial conditions, instabilities, and model errors have large impacts on the outcome is problematic. Indeed, predictive community ecology has been deemed unworthy of pursuit or an unreachable goal. However, new developments in large-scale biology provide ways of thinking that might substantially improve forecasts of local and regional impacts of climate change. Most notably, these are the explicit recognition of the regional and landscape contexts within which populations reside, the matrix approach that can be used to investigate the consequences of population variation across space and within assemblages, and the development of macrophysiology, which explicitly seeks to understand the ecological implications of physiological variation across large spatial and temporal scales. Here we explore how a combination of these approaches might promote further understanding and forecasting of the effects of global climate change and perhaps other GEC drivers on biodiversity. We focus on the population level, examining the ways in which environmental variation might be translated through performance and its plasticity to variation in demography.     

Coarse climate change projections for species living in a fine‐scaled world

Accurately predicting biological impacts of climate change is necessary to guide policy. However, the resolution of climate data could be affecting the accuracy of climate change impact assessments. Here, we review the spatial and temporal resolution of climate data used in impact assessments and demonstrate that these resolutions are often too coarse relative to biologically relevant scales. We then develop a framework that partitions climate into three important components: trend, variance, and autocorrelation. We apply this framework to map different global climate regimes and identify where coarse climate data is most and least likely to reduce the accuracy of impact assessments. We show that impact assessments for many large mammals and birds use climate data with a spatial resolution similar to the biologically relevant area encompassing population dynamics. Conversely, impact assessments for many small mammals, herpetofauna, and plants use climate data with a spatial resolution that is orders of magnitude larger than the area encompassing population dynamics. Most impact assessments also use climate data with a coarse temporal resolution. We suggest that climate data with a coarse spatial resolution is likely to reduce the accuracy of impact assessments the most in climates with high spatial trend and variance (e.g., much of western North and South America) and the least in climates with low spatial trend and variance (e.g., the Great Plains of the USA). Climate data with a coarse temporal resolution is likely to reduce the accuracy of impact assessments the most in the northern half of the northern hemisphere where temporal climatic variance is high. Our framework provides one way to identify where improving the resolution of climate data will have the largest impact on the accuracy of biological predictions under climate change.     

Quantitative approaches in climate change ecology

Contemporary impacts of anthropogenic climate change on ecosystems are increasingly being recognized. Documenting the extent of these impacts requires quantitative tools for analyses of ecological observations to distinguish climate impacts in noisy data and to understand interactions between climate variability and other drivers of change. To assist the development of reliable statistical approaches, we review the marine climate change literature and provide suggestions for quantitative approaches in climate change ecology. We compiled 267 peer‐reviewed articles that examined relationships between climate change and marine ecological variables. Of the articles with time series data (n = 186), 75% used statistics to test for a dependency of ecological variables on climate variables. We identified several common weaknesses in statistical approaches, including marginalizing other important non‐climate drivers of change, ignoring temporal and spatial autocorrelation, averaging across spatial patterns and not reporting key metrics. We provide a list of issues that need to be addressed to make inferences more defensible, including the consideration of (i) data limitations and the comparability of data sets; (ii) alternative mechanisms for change; (iii) appropriate response variables; (iv) a suitable model for the process under study; (v) temporal autocorrelation; (vi) spatial autocorrelation and patterns; and (vii) the reporting of rates of change. While the focus of our review was marine studies, these suggestions are equally applicable to terrestrial studies. Consideration of these suggestions will help advance global knowledge of climate impacts and understanding of the processes driving ecological change.