The impacts of the changing climate on the biological world vary across latitudes, habitats and spatial scales. By contrast, the time of day at which these changes are occurring has received relatively little attention. As biologically significant organismal activities often occur at particular times of day, any asymmetry in the rate of change between the daytime and night‐time will skew the climatic pressures placed on them, and this could have profound impacts on the natural world. Here we determine global spatial variation in the difference in the mean annual rate at which near‐surface daytime maximum and night‐time minimum temperatures and mean daytime and mean night‐time cloud cover, specific humidity and precipitation have changed over land. For the years 1983–2017, we derived hourly climate data and assigned each hour as occurring during daylight or darkness. In regions that showed warming asymmetry of >0.5°C (equivalent to mean surface temperature warming during the 20th century) we investigated corresponding changes in cloud cover, specific humidity and precipitation. We then examined the proportional change in leaf area index (LAI) as one potential biological response to diel warming asymmetry. We demonstrate that where night‐time temperatures increased by >0.5°C more than daytime temperatures, cloud cover, specific humidity and precipitation increased. Conversely, where daytime temperatures increased by >0.5°C more than night‐time temperatures, cloud cover, specific humidity and precipitation decreased. Driven primarily by increased cloud cover resulting in a dampening of daytime temperatures, over twice the area of land has experienced night‐time warming by >0.25°C more than daytime warming, and has become wetter, with important consequences for plant phenology and species interactions. Conversely, greater daytime relative to night‐time warming is associated with hotter, drier conditions, increasing species vulnerability to heat stress and water budgets. This was demonstrated by a divergent response of LAI to warming asymmetry.

Aim: Species distribution models (SDMs) have played a pivotal role in predicting how species might respond to climate change. To generate reliable and realistic predictions from these models requires the use of climate variables that adequately capture physiological responses of species to climate and therefore provide a proximal link between climate and their distributions. Here, we examine whether the climate variables used in plant SDMs are different from those known to influence directly plant physiology.Location: Global.Methods: We carry out an extensive, systematic review of the climate variables used to model the distributions of plant species and provide comparison to the climate variables identified as important in the plant physiology literature. We calculate the top 10 SDM and physiology variables at 2.5° spatial resolution for the globe and use principal component analyses and multiple regression to assess similarity between the climatic variation described by both variable sets.Results: We find that the most commonly used SDM variables do not reflect the most important physiological variables and differ in two main ways: (a) SDM variables rely on seasonal or annual rainfall as simple proxies of water available to plants and neglect more direct measures such as soil water content; and (b) SDM variables are typically averaged across seasons or years and overlook the importance of climatic events within the critical growth period of plants. We identify notable differences in their spatial gradients globally and show where distal variables may be less reliable proxies for the variables to which species are known to respond.Main conclusions: There is a growing need for the development of accessible, fine‐resolution global climate surfaces of physiological variables. This would provide a means to improve the reliability of future range predictions from SDMs and support efforts to conserve biodiversity in a changing climate.

Aim: Climate classification systems (CCSs) can be used to predict how species’ distributions might be altered by climate change and to increase the reliability of these estimates is an important goal in biogeographical research. We produce an objective, global climate classification system (CCS) at high temporal resolution based on plant physiology as a robust way to predict how climate change may impact terrestrial biomes.Location: GlobalTaxon: PlantaeMethods: We construct ten climate variables that capture the physiological processes that determine plant distributions and use cluster analysis to present a new global CCS which accounts for variation in these aspects of climate. We use Kappa statistics to compare the distribution of climate zones in a five‐ and six‐cluster CCS constructed using the physiology variables to the popular Köppen‐Geiger and Köppen‐Trewartha CCSs, respectively, and find good correlation in both cases.Results: Our CCS highlights ten climate zones for plants. We show that clustering of the physiologically relevant variables reproduces known, present‐day patterns of vegetation but also indicates important areas where zone assignment in our physiological CCSs is different to that of the Köppen systems.Main conclusions: The existing Köppen CCSs do not entirely reflect the physiological processes that determine plant distributions. Predictions of climate‐driven changes in plant distributions may thus be unreliable in areas where zone assignment by clustering of physiologically relevant variables is different to that of the Köppen systems. Both the physiological relevance and temporal resolution of climate variables used to construct CCSs should be considered in order to predict reliably how climate change may alter plant distributions and to support an appropriate global response to conserve plant biodiversity for the future.

Aim: Species respond to environmental conditions and so reliable assessments of climate suitability are important for predicting how climate change could alter their distributions. Long‐term average climate data are often used to evaluate the climate suitability of an area, but in these aggregated climate datasets, inter‐annual variability is lost. Due to non‐linearity in species’ biological responses to climate, estimates of long‐term climate suitability from average climate data may be biased and so differ from estimates derived from the average annual suitability over the same period (average response). We investigate the extent to which such differences manifest in a regional assessment of climate suitability for 255 plant species across two 17‐year time periods.Location: Cornwall in South‐West England provides a case study.Taxon: Plantae.Methods: We run a simple mechanistic climate suitability model and derive quantitative estimates of climate suitability for 1984–2000 and 2001–2017. For each period, we run the model using climate data representing average monthly values for that period. We then run the model for each year using monthly climate data for that year and average the annual suitability scores across each period (average response). We compare estimates of climate suitability from these two approaches.Results: Average climate data gave higher estimates of suitability than the average response, suggesting bias against years of poor suitability in temporally aggregated climate datasets. Differences between suitability estimates were larger in areas of high climate variability and correlated with species’ environmental requirements, being larger for species with small thermal niches and narrow ranges of precipitation tolerance.Main Conclusions: Incorporating inter‐annual variability into climate suitability assessments or understanding the extent to which average climate data might obscure this variance will be important to predict reliably the impacts of climate change on species distributions and should be considered when using mechanistic species distribution models.

Biodiversity is declining worldwide. When species are physically active (i.e., their diel niche) may influence their risk of becoming functionally extinct. It may also affect how species losses affect ecosystems. For 5033 terrestrial mammals, we predict future changes to diel global and local functional diversity through a gradient of progressive functional extinction scenarios of threatened species. Across scenarios, diurnal species were at greater risk of becoming functionally extinct than nocturnal, crepuscular, and cathemeral species, resulting in deep functional losses in global diurnal trait space. Redundancy (species with similar roles) will buffer global nocturnal functional diversity; however, across the land surface, losses will mostly occur among functionally dispersed species (species with distinct roles). Functional extinctions will constrict boundaries of cathemeral trait space as megaherbivores, and arboreal foragers are lost. Variation in the erosion of functional diversity across the daily cycle will likely profoundly affect the partitioning of ecosystem functioning between night and day.

Ectotherms make up the majority of terrestrial biodiversity, so it is important to understand their potential responses to climate change. Often, models aiming to achieve this understanding correlate species distributions with ambient air temperature. However, this assumes a constant relationship between the air temperature and body temperature, which determines an ectotherm's thermal performance. To test this assumption, we develop and validate a method for retrospective estimation of ectotherm body temperature using heat exchange equations. We apply the model to predict the body temperature of wild field crickets (Gryllus campestris) in Northern Spain for 1985–2019 and compare these values to air temperature. We show that while air temperature impacts ectotherm body temperature, it captures only a fraction of its thermal experience. Solar radiation can increase the body temperature by more than 20°C above air temperature with implications for physiology and behaviour. The effect of solar radiation on body temperature is particularly important given that climate change will alter cloud cover. Our study shows that the impacts of climate change on species cannot be assumed to be proportional only to changing air temperature. More reliable models of future species distributions require mechanistic links between environmental conditions and thermal ecophysiologies of species.