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As thermal regimes change worldwide, projections of future population and species persistence often require estimates of how population growth rates depend on temperature. These projections rarely account for how temporal variation in temperature can systematically modify growth rates relative to projections based on constant temperatures. Here, we tested the hypothesis that time-averaged population growth rates in fluctuating thermal environments differ from growth rates in constant conditions as a consequence of Jensen's inequality, and that the thermal performance curves (TPCs) describing population growth in fluctuating environments can be predicted quantitatively based on TPCs generated in constant laboratory conditions. With experimental populations of the green alga Tetraselmis tetrahele, we show that nonlinear averaging techniques accurately predicted increased as well as decreased population growth rates in fluctuating thermal regimes relative to constant thermal regimes. We extrapolate from these results to project critical temperatures for population growth and persistence of 89 phytoplankton species in naturally variable thermal environments. These results advance our ability to predict population dynamics in the context of global change.

Soil temperature (ST) has a key role in Arctic ecosystem functioning and global environmental change. However, soil thermal conditions do not necessarily follow synoptic temperature variations. This is because local biogeophysical processes can lead to a pronounced soil-atmosphere thermal offset (∆T) while altering the coupling (βT) between ST and ambient air temperature (AAT). Here, we aim to uncover the spatiotemporal variation in these parameters and identify their main environmental drivers. By deploying a unique network of 322 temperature loggers and surveying biogeophysical processes across an Arctic landscape, we found that the spatial variation in ∆T during the AAT≤0 period (mean ∆T = 6.0 °C, standard deviation ± 1.2 °C) was directly and indirectly constrained by local topography controlling snow depth. By contrast, during the AAT>0 period, ∆T was controlled by soil characteristics, vegetation and solar radiation (∆T = −0.6 °C ± 1.0 °C). Importantly, ∆T was not constant throughout the seasons reflecting the influence of βT on the rate of local soil warming being stronger after (mean βT = 0.8 ± 0.1) than before (βT = 0.2 ± 0.2) snowmelt. Our results highlight the need for continuous microclimatic and local environmental monitoring, and suggest a potential for large buffering and non-uniform warming of snow-dominated Arctic ecosystems under projected temperature increase.

Phenotypic plasticity may increase the performance and fitness and allow organisms to cope with variable environmental conditions. We studied within‐generation plasticity and transgenerational effects of thermal conditions on temperature tolerance and demographic parameters in Drosophila melanogaster. We employed a fully factorial design, in which both parental (P) and offspring generations (F1) were reared in a constant or a variable thermal environment. Thermal variability during ontogeny increased heat tolerance in P, but with demographic cost as this treatment resulted in substantially lower survival, fecundity, and net reproductive rate. The adverse effects of thermal variability (V) on demographic parameters were less drastic in flies from the F1, which exhibited higher net reproductive rates than their parents. These compensatory responses could not totally overcome the challenges of the thermally variable regime, contrasting with the offspring of flies raised in a constant temperature (C) that showed no reduction in fitness with thermal variation. Thus, the parental thermal environment had effects on thermal tolerance and demographic parameters in fruit fly. These results demonstrate how transgenerational effects of environmental conditions on heat tolerance, as well as their potential costs on other fitness components, can have a major impact on populations’ resilience to warming temperatures and more frequent thermal extremes. Within‐generation plasticity and transgenerational effects can ameliorate the impact of stressful thermal conditions on physiological and fitness‐related traits.

As marine heatwaves and mass coral bleaching events rise in frequency and severity, there is an increasing need for high-resolution satellite products that accurately predict reef thermal environments over large spatio-temporal scales. Deciding which global sea surface temperature (SST) dataset to use for research or management depends in part on the desired spatial resolution. Here, we evaluate two SST datasets – the lower-resolution CoralTemp v3.1 (0.05° ~ 5 km grid) and the Multiscale Ultra-high Resolution MUR v4.1 (0.01° ~ 1 km grid) – in their ability to predict in situ reef thermal environments (nightly mean and daily maximum) and the severity of past bleaching in Palau, western Pacific Ocean. We expected higher-resolution data to improve prediction accuracy, yet CoralTemp data explained 10% additional variability of in situ temperatures and 70% additional variance in past bleaching. This likely relates to differential data protocols; despite MUR using a higher spatial resolution grid, CoralTemp achieves higher raw satellite observation density in coastal areas by utilising geostationary satellites. MUR SSTs were also consistently more variable. These results reinforce calls to develop more accurate high-resolution SST products for coral reefs. Our paper demonstrates that higher spatial resolution SST data is not necessarily better at predicting in situ thermal environments of coral reefs and local marine heatwave impacts.

An increase in river water temperatures is being detected worldwide, with some predictions of an up to 4°C rise by 2050. Such stream temperature increase is likely to affect aquatic communities, with predicted declines and range shifts of cold stenothermic species and a facilitated dispersal and population development in temperature-tolerant species, including invasive ones. This study analyzed how thermal changes affect macroinvertebrate community compositions in three differentiated thermal regions within a single stream system. In each thermal region, we used standard surber sampling in combination with in-stream cross-exposure experiments, comparing the thermal response of native vs. non-native gammarids ( Gammarus roeselii and Dikerogammarus villosus ). Macroinvertebrate species composition was highly dependent on temperature, with indigenous gammarids preferring colder sites and invasive ones such as D. villosus being dominant at higher temperatures. Species composition was also strongly affected by season, substratum and the presence of macrophytes. In light of climate change, consideration of shifts in community composition highly driven by temperature needs to become integrated with biological response patterns related to morphological and flow degradation, chemical pollution and fine sediment. Such an approach is crucial for the effective conservation and restoration of native biodiversity and for a realistic prediction of the ability to reach policy targets for aquatic ecosystems.

Tropical organisms colonizing temperate environments face reduced average temperatures and dramatic thermal fluctuations. Theoretical models postulate that thermal specialization should be favored either when little environmental variation is experienced within generations or when among-generation variation is small relative to within-generation variation. To test these predictions, we studied six temperate species of damselflies differing in latitudinal distribution. We developed a computer model simulating how organisms experience environmental variation (accounting for diapause and voltinism) and performed a laboratory experiment assaying thermal sensitivities of growth rates. The computer model showed opposing latitudinal trends in among- and within-generation thermal variability: within-generation thermal variability decreased toward higher latitudes, whereas relative levels of among-generation thermal variability peaked at midlatitudes (where a shift in voltinism occurred). The growth experiment showed that low-latitude species were more thermally generalized than mid- and high-latitude species, supporting the prediction that generalists are favored under high levels of within-generation variation. Northern species had steeper, near-exponential reaction norms suggestive of thermal specialization. However, they had strikingly high thermal optima and grew very slowly over most of the thermal range they are expected to experience in the field. This observation is at present difficult to explain. These results highlight the importance of considering interactions between life history and environmental variation when deriving expectations of thermal adaptation.

We show here that wild Atlantic salmon parr and smolt from thermally distinct river systems were impacted differently by cooler and warmer thermal variability. Growth, thermal tolerance and swimming performance were all impacted by river of origin, thermal acclimation temperature and life stage, highlighting the potential need for more river specific conservation guidelines. Abstract Temperature in many natural aquatic environments follows a diel cycle, but to date, we know little on how diel thermal cycles affect fish biology. The current study investigates the growth, development and physiological performance of wild Atlantic salmon collected from the Miramichi and Restigouche rivers (NB, Canada). Fish were collected as parr and acclimated to either 16–21 or 19–24°C diel thermal cycles throughout the parr and smolt life stages. Both Miramichi and Restigouche Atlantic salmon parr grew at similar rates during 16–21 or 19–24°C acclimations. However, as smolts, the growth rates of the Miramichi (−8% body mass day−1) and Restigouche (−38% body mass day−1) fish were significantly slower at 19–24°C, and were in fact negative, indicating loss of mass in this group. Acclimation to 19–24°C also increased Atlantic salmon CTmax. Our findings suggest that both life stage and river origin impact Atlantic salmon growth and performance in the thermal range used herein. These findings provide evidence for local adaptation of Atlantic salmon, increased vulnerability to warming temperatures, and highlight the differential impacts of these ecologically relevant diel thermal cycles on the juvenile life stages in this species.

Environmental variability is an inherent feature of natural systems which complicates predictions of species interactions. Primarily, the complexity in predicting the response of organisms to environmental fluctuations is in part because species' responses to abiotic factors are non‐linear, even in stable conditions. Temperature exerts a major control over phytoplankton growth and physiology, yet the influence of thermal fluctuations on growth and competition dynamics is largely unknown. To investigate the limits of coexistence in variable environments, stable mixed cultures with constant species abundance ratios of the marine diatoms, Phaeodactylum tricornutum and Thalassiosira pseudonana, were exposed to different temperature fluctuation regimes (n = 17) under high and low nitrogen (N) conditions. Here we demonstrate that phytoplankton exhibit substantial resilience to temperature variability. The time required to observe a shift in the species abundance ratio decreased with increasing fluctuations, but coexistence of the two model species under high N conditions was disrupted only when amplitudes of temperature fluctuation were high (±8.2°C). N limitation caused the thermal amplitude for disruption of species coexistence to become lower (±5.9°C). Furthermore, once stable conditions were reinstated, the two species differed in their ability to recover from temperature fluctuations. Our findings suggest that despite the expectation of unequal effect of fluctuations on different competitors, cycles in environmental conditions may reduce the rate of species replacement when amplitudes remain below a certain threshold. Beyond these thresholds, competitive exclusion could, however, be accelerated, suggesting that aquatic heatwaves and N availability status are likely to lead to abrupt and unpredictable restructuring of phytoplankton community composition. This figure is a generic representation of Jensen's inequality and scale transition theory, which are the cornerstone theoretical ideas explored in our manuscript.

This study investigates the spatiotemporal dynamics of land surface temperature (LST) across five distinct land use/land cover (LULC) classes through high-resolution unmanned aerial vehicle (UAV) thermal remote sensing. Thermal orthomosaics were systematically captured at four diurnal periods (morning, afternoon, evening, and midnight) over an urban university campus environment. Using stratified random sampling in each class with spatial controls to minimize autocorrelation, we quantified thermal signatures across bare soil, buildings, grassland, paved roads, and water bodies. Statistical analyses incorporating outlier management via the Interquartile Range (IQR) method, spatial autocorrelation assessment using Moran’s I, correlation testing, and Geographically Weighted Regression (GWR) revealed substantial thermal variability across LULC classes, with temperature differentials of up to 17.7 °C between grassland (20.57 ± 5.13 °C) and water bodies (7.10 ± 1.25 °C) during afternoon periods. The Moran’s I analysis indicated notable spatial dependence in land surface temperature, justifying the use of GWR to model these spatial patterns. Impervious surfaces demonstrated pronounced heat retention capabilities, with paved roads maintaining elevated temperatures into evening (13.18 ± 3.49 °C) and midnight (2.25 ± 1.51 °C) periods despite ambient cooling. Water bodies exhibited exceptional thermal stability (SD range: 0.79–2.85 °C across all periods), while grasslands showed efficient nocturnal cooling (ΔT = 23.02 °C from afternoon to midnight). GWR models identified spatially heterogeneous relationships between LST patterns and LULC distribution, with water bodies exerting the strongest localized cooling influence (R2≈ 0.62–0.68 during morning/evening periods). The findings demonstrate that surface material properties significantly modulate diurnal heat flux dynamics, with human-made surfaces contributing to prolonged thermal loading. This research advances urban microclimate monitoring methodologies by integrating high-resolution UAV thermal imagery with robust statistical frameworks, providing empirically-grounded insights for climate-adaptive urban planning and heat mitigation strategies. Future work should incorporate multi-seasonal observations, in situ validation instrumentation, and integration with human thermal comfort indices.

Building performance simulations often rely on standardised meteorological datasets, which may not accurately reflect urban microclimates. This study highlights the complexity of temperature dynamics near buildings influenced by multiple factors. A static correction factor is unfeasible for adjusting meteorological data from suburban stations to city centres. To address this, air temperature variability in different urban locations was analysed using data from a city-centre weather station, 32 facade-mounted sensors and thermographic imaging. The selected locations for analysis are representative of approximately 25% of the housing stock in Wrocław, Poland. This was compared with standard meteorological data from Wrocław II, located at an airport. These analyses formed the basis for developing a predictive machine-learning model to account for thermal variability based on building type, location, facade orientation and facade materials. Focusing on the summer period to assess nocturnal cooling potential, the model achieved high accuracy, with R2 > 0.93 in training and 0.92 in validation. These findings underscore the need for microclimate-informed meteorological adjustments in building simulations. Practice relevance Implementing corrections to outdoor temperature in urban environments can significantly enhance the accuracy of energy modelling, particularly for natural ventilation strategies addressing urban overheating. However, due to the complexity of temperature dynamics near buildings, defining a single static correction factor for climate parameters such as temperature is unlikely to be effective. Analysis indicates that even multiple regression approaches result in correction factors that are difficult to apply in practice. Adjustments must be determined separately for facade orientation, building height, external wall structure and urban location. The proposed method enables the generation of dynamic, high-spatial-resolution temperature data, incorporating facade orientation and specific building floor heights. These refined datasets can be used to adjust meteorological station measurements, improving the accuracy of simulations. When applied to a typical meteorological year dataset, this approach may offer a way to better represent thermal variability in urban environments, supporting more context-sensitive building-energy modelling.