The mean annual soil temperature (SBIO1, 1 x 1 km resolution) modelled here is consistently cooler than ERA5L (9 x 9 km) soil temperature in forested areas. (a) Spatial representation of the difference between SBIO1 based on our model and based on ERA5L soil temperature data. Negative values (blue colours) indicate areas where our model predicts cooler soil temperature. Dark grey areas (Greenland and Antarctica) are excluded from our models. Asterisk in Scandinavia indicates the highlighted area in panels d to f (see below). (b) Distribution of the difference between SBIO1 and ERA5L along the macroclimatic gradient (represented by SBIO1 itself) based on a random subsample of 50,000 points from the map in a). Red line from a Generalized Additive Model (GAM) with k = 4. (c-e) High-resolution zoomed panels of an area of high elevational contrast in Norway (from 66.0-66.4°N, 15.0-16.0°E) visualizing SBIO1 (c), ERA5L (d) and their difference (e), to highlight the higher spatial resolution as obtained with SBIO1

The mean annual soil temperature (SBIO1, 1 x 1 km resolution) modelled here is consistently cooler than ERA5L (9 x 9 km) soil temperature in forested areas. (a) Spatial representation of the difference between SBIO1 based on our model and based on ERA5L soil temperature data. Negative values (blue colours) indicate areas where our model predicts cooler soil temperature. Dark grey areas (Greenland and Antarctica) are excluded from our models. Asterisk in Scandinavia indicates the highlighted area in panels d to f (see below). (b) Distribution of the difference between SBIO1 and ERA5L along the macroclimatic gradient (represented by SBIO1 itself) based on a random subsample of 50,000 points from the map in a). Red line from a Generalized Additive Model (GAM) with k = 4. (c-e) High-resolution zoomed panels of an area of high elevational contrast in Norway (from 66.0-66.4°N, 15.0-16.0°E) visualizing SBIO1 (c), ERA5L (d) and their difference (e), to highlight the higher spatial resolution as obtained with SBIO1

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Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we...

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... we trained RF models (with the same hyperparameters as selected during the grid-search procedure) using each of 100 bootstrap iterations. Each of these trained RF models was then used to classify the predictor layer stack, to generate per-pixel 95% confidence intervals and standard deviation for the modelled monthly offsets (Figure 5a, Figure S6a). The mean R² value of the RF models for the monthly mean temperature offset was 0.70 (from 0.64 to 0.78) at 0-5 cm and 0.76 (0.63-0.85) at 5 to 15 cm across all 12 monthly models. ...
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... bootstrap approach to validate modelled monthly offsets indicated high consistency among the outcomes of 100 bootstrapped models ( Figure 5, Figure S6a), with standard deviations in most months and across most parts of the globe around or below ±1°C. One exception to this was the temperature offset at high latitudes of the Northern ...
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... highlight that the current availability of in situ soil temperature measurements is significantly lower in the tropics (Table S5), where our model had to extrapolate temperatures beyond the range used to calibrate the model (Figure 5b, Figure S6b). ...
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... our comparison with a mean annual soil temperature product derived from the coarse-resolution ERA5L topsoil temperature showed that spatial variability, for example, driven by topographic heterogeneity, is much better captured here than in the coarser resolution of the ER A5L-based product (Figure 6c-e). Nevertheless, our predictions at the coarse scale showed to be condensed within a 5°C range of values from the ERA5L-predictions, for more than 95% of pixels globally. ...
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... our predictions at the coarse scale showed to be condensed within a 5°C range of values from the ERA5L-predictions, for more than 95% of pixels globally. Noteworthy, our predictions resulted in consistently cooler soil temperature predictions than topsoil conditions provided by ERA5L across large areas, such as the boreal and tropical forest biomes (Figure 6a,b). Additionally, our models predicted lower values for SBIO1 than ERA5L in all regions with mean annual soil temperature below 0°C, except for a few locations around Greenland and Svalbard (Figure 6a,b). ...
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... our predictions resulted in consistently cooler soil temperature predictions than topsoil conditions provided by ERA5L across large areas, such as the boreal and tropical forest biomes (Figure 6a,b). Additionally, our models predicted lower values for SBIO1 than ERA5L in all regions with mean annual soil temperature below 0°C, except for a few locations around Greenland and Svalbard (Figure 6a,b). ...

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... Especially at high latitudes and altitudes, heat conditions in the form of ST, snow variations, and thawing and freezing cycles are closely linked to SM dynamics (Zhang et al., 2005). For the interdependence on land processes, on the one hand, the mean annual ST shows up to a 10 K difference from the corresponding air temperature, with marked variations in space and time (Lembrechts et al., 2022). On the other hand, ST can largely increase surface air temperature variability and persistence (Zhang et al., 2005). ...
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A coupled soil temperature (ST) and moisture (SM) balance reflects a synthetic climate regime, having huge ecological impacts. This paper used ST and SM data from the European Center for Medium‐Range Weather Forecasts climate reanalysis‐Land and the Coupled Model Intercomparison Project Phase 6 and leaf area index (LAI) data from the Global Land Surface Satellite Product Suite. The focus was on understanding joint ST‐SM changes and the resulting ecological response across China. The results show that during 2000–2020, 24.5% of the land area in China experienced a warming‐drying trend resulting in a 9.7% LAI decrease, while 6.4% of the area experienced a warming‐wetting trend leading to an 8.6% LAI increase. During 2015–2100, 30.6% of the land area in China will be warmer and drier, while 55.2% of the area will be warmer but wetter across three shared socioeconomic pathways (SSP126, 245, and 585). Superimposed on the long‐term trends, there are also significant spatiotemporal variabilities in ST and SM on annual to decadal timescales. The LAI also showed substantial short‐term fluctuations in both typical regions and ecosystems despite consistent long‐term increases. Our findings suggest that ecosystems could be impaired on annual to decadal scales by adverse soil conditions in the twenty‐first century, but in terms of long‐term trends, ecosystems may be resilient partly because of the compensating effects of global warming and regional hydrological changes. Impact studies should thus focus more on annual to decadal soil‐ecosystem anomalous events.
... year variation may be required to accurately predict these interactions in future climate contexts. This call to account for finer-temporal scale weather variation is echoed by calls for a greater appreciation of spatial variation in microclimate (Lembrechts et al., 2022), which is likely important to performance and behaviors (e.g., microclimate refugia) of both herbivores and pathogens (e.g., Pincebourde & Casas, 2015;Stewart et al., 2021;Warren & Mordecai, 2010). Future studies could track microclimate within experimental warming treatments to evaluate whether warming alters fine-scale spatial variation in the microclimate or how temporal variation in weather translates into microclimates within plots. ...
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Both theory and prior studies predict that climate warming should increase attack rates by herbivores and pathogens on plants. However, past work has often assumed that variation in abiotic conditions other than temperature (e.g., precipitation) do not alter warming responses of plant damage by natural enemies. Studies over short time periods span low variation in weather, and studies over long‐time scales often neglect to account for fine‐scale weather conditions. Here, we used a 20+ year warming experiment to investigate if warming affects on herbivory and pathogen disease are dependent on variation in ambient weather observed over three years. We studied three common grass species in a subalpine meadow in the Colorado Rocky Mountains, USA. We visually estimated herbivory and disease every two‐weeks during the growing season and evaluated weather conditions during the previous two‐ or four‐week time interval (two‐week average air temperature, two‐ and four‐week cumulative precipitation) as predictors of the probability and amount of damage. Herbivore attack was 13% more likely and damage amount was 29% greater in warmed plots than controls across the focal species but warming treatment had little affect on plant disease. Herbivory presence and damage increased the most with experimental warming when preceded by wetter, rather than drier, fine‐scale weather, but preceding ambient temperature did not strongly interact with elevated warming to influence herbivory. Disease presence and amount increased, on average, with warmer weather and more precipitation regardless of warming. Synthesis: The effect of warming over reference climate on herbivore damage is dependent on and amplified by fine scale weather variation, suggesting more boom‐and‐bust damage dynamics with increasing climate variability. However, the mean effect of regional climate change is likely reduced monsoon rainfall, for which we predict a reduction in insect herbivore damage. Plant disease was generally unresponsive to warming, which may be a consequence of our coarse disease estimates that did not track specific pathogen species or guilds. The results point towards temperature as an important but not sufficient determinant and regulator of species interactions, where precipitation and other constraints may determine the affect of warming.
... And intra-annual variation in potential enzyme activity has also been observed(Wallenstein, McMahon and Schimel 2009;Weedon et al. 2014), suggesting seasonal variation in the nutritional requirements of soil microbes. On one level, such seasonal dynamics of biogeochemical process rates are to be expected: intra-annual variation in soil moisture(Mintz and Serafini 1992) and temperature(Lembrechts et al. 2022) are universal, and these factors, along with soil chemical properties, all exert strong controls over soil biogeochemical process rates(Booth, Stark and Rastetter 2005;Davidson and Janssens 2006). If these physical controls (temperature and moisture) are dominant relative to biotic factors, then the observational bias towards growing season measurements may not be problematic. ...
... Duration of winter conditions in the pan-Arctic. Pixels are coloured according to the number of months per year with mean monthly soil temperatures < 0 ℃ (0 -5 cm depth) as modelled in the SoilTemp model(Lembrechts et al. 2020(Lembrechts et al. , 2022. Overview of sampling points for studies that investigated microbial community composition at multiple times of the year including the snow-covered period. ...
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The microbial ecology of arctic and sub-arctic soils is an important aspect of the global carbon cycle, due to the sensitivity of the large soil carbon stocks to ongoing climate warming. These regions are characterised by strong climatic seasonality, but the emphasis of most studies on the short vegetation growing season could potentially limit our ability to predict year-round ecosystem functions. We compiled a database of studies from arctic, subarctic, and boreal environments that include sampling of microbial community and functions outside the growing season. We found that for studies comparing across seasons, in most environments, microbial biomass and community composition vary intra-annually, with the spring thaw period often identified by researchers as the most dynamic time of year. This seasonality of microbial communities will have consequences for predictions of ecosystem function under climate change if it results in: seasonality in process kinetics of microbe-mediated functions; intra-annual variation in the importance of different (a)biotic drivers; and/or potential temporal asynchrony between climate change-related perturbations and their corresponding effects. Future research should focus on 1) sampling throughout the entire year; 2) linking these multi-season measures of microbial community composition with corresponding functional or physiological measurements to elucidate the temporal dynamics of the links between them; and 3) identifying dominant biotic and abiotic drivers of intra-annual variation in different ecological contexts.
... S1 and S2). Therefore, we next performed in planta experiments with tomato plantlets grown at 22°C [48] in order to test metabolite stimulation in GA1 upon root co-colonization in presence of CMR12a. Both isolates efficiently colonized roots when inoculated individually but upon competitive root invasion, CMR12a overgrows GA1, which forms significantly lower populations compared to mono-inoculated plantlets (Fig. 3a). ...
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Bacillus velezensis is considered as model species for plant-associated bacilli providing benefits to its host such as protection against phytopathogens. This is mainly due to the potential to secrete a wide range of secondary metabolites with specific and complementary bioactivities. This metabolite arsenal has been quite well defined genetically and chemically but much remains to be explored regarding how it is expressed under natural conditions and notably how it can be modulated upon interspecies interactions in the competitive rhizosphere niche. Here, we show that B. velezensis can mobilize a substantial part of its metabolome upon the perception of Pseudomonas , as a soil-dwelling competitor. This metabolite response reflects a multimodal defensive strategy as it includes polyketides and the bacteriocin amylocyclicin, with broad antibiotic activity, as well as surfactin lipopeptides, contributing to biofilm formation and enhanced motility. Furthermore, we identified the secondary Pseudomonas siderophore pyochelin as an info-chemical, which triggers this response via a mechanism independent of iron stress. We hypothesize that B. velezensis relies on such chelator sensing to accurately identify competitors, illustrating a new facet of siderophore-mediated interactions beyond the concept of competition for iron and siderophore piracy. This phenomenon may thus represent a new component of the microbial conversations driving the behavior of members of the rhizosphere community.
... These chemical and physical soil factors are usually released with cross-validation scores and uncertainty maps, which help users to perceive the level of confidence and, thereby, the number of observations that support the target geographical areas (e.g., https://soilgrids.org). Recently, Lembrechts et al. [26] highlighted the difference between in situ soil temperature measurements and atmospheric air temperature (up to 10°C in some areas, mean 3.0 ± 2.1°C) at a global scale, in particular in cold and dry biomes. These authors advocated the need to collect soil data in yet unsampled geographical areas to improve the quality and density of environmental data, essential for spatial interpolation. ...
Article
Detecting the extrinsic selective pressures shaping genomic variation is critical for a better understanding of adaptation and for forecasting evolutionary responses of natural populations to changing environmental conditions. With increasing availability of geo-referenced environmental data, landscape genomics provides unprecedented insights into how genomic variation and underlying gene functions affect traits potentially under selection. Yet, the robustness of genotype–environment associations used in landscape genomics remains tempered due to various limitations, including the characteristics of environmental data used, sampling designs employed, and statistical frameworks applied. Here, we argue that using complementary or new environmental data sources and well-informed sampling designs may help improve the detection of selective pressures underlying patterns of local adaptation in various organisms and environments.
... We used the georeferences of the communities' location and study year unit to extract soil annual mean temperature at a 1-km 2 resolution for 0-5 cm soil depth (Lembrechts et al. 2022). Additionally, other edaphic variables were used from the respective projects for each community: soil pH was measured using a digital pH meter, in CaCl 2 ; water content in the soil was measured by comparing masses of dry and wet soil samples and expressed in % fresh weight; and carbon content was measured in the soil dry weight; Indonesia, USA and Canada: elemental analyser; Germany: automated CHNSO analyser. ...
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The relationship between species' body masses and densities is strongly conserved around a three‐quarter power law when pooling data across communities. However, studies of local within‐community relationships have revealed major deviations from this general pattern, which has profound implications for their stability and functioning. Despite multiple contributions of soil communities to people, there is limited knowledge on the drivers of body mass–abundance relationships in these communities. We compiled a dataset comprising 155 soil–animal communities across four countries (Canada, Germany, Indonesia, USA), all sampled using the same methodology. We tested if variation in local climatic and edaphic conditions drives differences in local body mass–abundance scaling relationships. We found substantial variation in the slopes of this power‐law relationship across local communities. Structural equation modeling showed that soil temperature and water content have a positive and negative net effect, respectively, on soil communities. These effects are mediated by changes in local edaphic conditions (soil pH and carbon content) and the body‐mass range of the communities. These results highlight ways in which alterations of soil climatic and edaphic conditions interactively impact the distribution of abundance between populations of small and large animals. These quantitative mechanistic relationships facilitate our understanding of how global changes in environmental conditions, such as temperature and precipitation, will affect community–abundance distributions and thus the stability and functioning of soil–animal communities.
... Second, we compared the use of TDT parameters (sCT max and coefficients of thermal sensitivity z) and dCT max to examine the potential for plasticity of thermal tolerance through adult acclimation in workers. Finally, we used the recently released global map of soil temperatures (Lembrechts & al. 2022), that likely offers enhanced resolution to track environmental variables for ground-dwelling insects (Pincebourde & Salle 2020), to revisit the potential association between species' heat tolerance and generalized climatic variables or latitude. ...
... Only georeferenced records were used and were restricted from longitude 30° W to 145° E and latitude 30° N to 72° N, since the species considered in this study are endemic to the palearctic ecozone. To match each species' distribution with climatic and distribution variables, soil temperature bioclimatic variables were used from recent maps of global soil temperature at 0 -5 cm depth (Lembrechts & al. 2022), precipitation variables from the WorldClim v2.1 database (Fick & Hijmans 2017), and limits of distribution based on data from AntMaps (Janicki & al. 2016) (Tab. 2). ...
... 2: Correlation coefficients (R) of the regressions between climatic variables averaged over species distribution, dynamic CT max (dCT max (0.1 °C / min) ), static CT max (sCT max (60min) ), and the coefficient of thermal sensitivity (z) for the 13 species included in the dataset. Distribution data were extracted from the GABI database (Guénard & al. 2017), and matched with the global map of soil temperature at a 0 -5 cm depth (for temperature variables, Lembrechts & al. 2022) or the WordClim database (for precipitation variables, Fick & Hijmans 2017). After correction for sampling bias, dCT max (0.1 °C / min) , sCT max (60min) , and z were regressed against extremes of latitudinal distribution as well as average values for soil and precipitation variables for each species considering the phylogenetic non-independence of the data. ...
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The thermal-death-time (TDT) model has proven highly consistent in describing upper thermal limits in ectotherms through space and time. TDT model parameterization could thus yield new insights into the associations between heat tolerance, acclimation strategies, and species distribution in a range of animal models, including ants. In this study, we first demonstrate that TDT parameterization represents a strong conceptual model to describe upper thermal limits in a multispecies comparison of European ant species. In addition to accurately predicting heat tolerance from dynamic and static assays, TDT models further provide species-specific coefficients of thermal sensitivity (z) that are largely uncorrelated with absolute thermal limits. Second, using these validated parameters, we show that neither heat tolerance nor coefficients of heat sensitivity are responsive to adult acclimation and, using a recently released database of soil temperatures, show that soil surface temperatures are poor predictors of species' heat tolerance. These results highlight that TDT models offer strong conceptual advantages to unify heat-tolerance metrics resulting from various methodologies, but also that most of the interspecific information on heat tolerance is already captured in the simpler, more commonly used dynamic assays. In addition, the lack of clear association between thermal limits, thermal sensitivity, and ground temperatures lends further support to the suggestion that the evolution of heat tolerance in ants is driven by temperature variations at the microclimatic scale, behavior, and phylogenetic history.
... However, while most biological processes in shortstatured tundra vegetation take place close to the ground or in the shallow soil layer, temperatures are commonly monitored by climate stations at 2 m height. These temperature measurements provide an important macroclimatic baseline, yet they likely fail to capture conditions relevant for most tundra organisms (sensu Lembrechts et al., 2020Lembrechts et al., , 2022. To accurately assess and predict tundra ecosystem processes including vegetation development for organisms at different heights, it is therefore important to determine how sitespecific factors affect free-air, canopy-level, near-surface, and soil temperature, as well as the temperature difference among these layers (Convey et al., 2018). ...
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Climate warming is inducing widespread vegetation changes in Arctic tundra ecosystems, with the potential to alter carbon and nutrient dynamics between vegetation and soils. Yet, we lack a detailed understanding of how variation in vegetation and topography influences fine‐scale temperatures (‘microclimate’) that mediate these dynamics, and at what resolution vegetation needs to be sampled to capture these effects. We monitored microclimate at 90 plots across a tundra landscape in western Greenland. Our stratified random study design covered gradients of topography and vegetation, while nested plots (0.8 to 100 m2) enabled comparison across different sampling resolutions. We used Bayesian mixed‐effect models to quantify the direct influence of plot‐level topography, moisture and vegetation on soil, near‐surface and canopy‐level temperatures (‐6; 2; and 15 cm). During the growing season, colder soils were predicted by shrub cover (‐0.24 °C per 10% increase), bryophyte cover (‐0.35 °C per 10% increase) and vegetation height (‐0.17 °C per 1 cm increase). The same three factors also predicted the magnitude of differences between soil and above‐ground temperatures, indicating warmer soils at low cover/height, but colder soils under closed/taller canopies. These findings were consistent across plot sizes, suggesting that spatial predictions of microclimate may be possible at the operational scales of satellite products. During winter, snow cover (+0.75 °C per 10 snow‐covered days) was the key predictor of soil microclimate. Topography and moisture explained little variation in the measured temperatures. Our results underline the close connection of vegetation and snow with microclimate in the Arctic tundra, but also point to the need for more studies disentangling their complex interplay across tundra environments and seasons. Future shifts in vegetation cover and height will likely mediate the impact of atmospheric warming on the tundra soil environment, with potential implications for below‐ground organisms and ecosystem functioning.
... More recently, ecologists have become increasingly aware that the variance (not just the average) is essential to explain many of the ecological processes and phenomena occurring in nature (10)(11)(12)(13). Moreover, natural fluctuations in many abiotic properties exceed the mean change predicted by the end of the 21st century due to climate change, such as surface temperature in the ocean (14) and in soils (15), dissolved oxygen concentration in coral reefs (16) and salt marshes (17), or pH in upwelled coastal waters (18), among others. ...
Article
Jensen’s inequality predicts that the response of any given system to average constant conditions is different from its average response to varying ones. Environmental fluctuations in abiotic conditions are pervasive on Earth; yet until recently, most ecological research has addressed the effects of multiple environmental drivers by assuming constant conditions. One could thus expect to find significant deviations in the magnitude of their effects on ecosystems when environmental fluctuations are considered. Drawing on experimental studies published during the last 30 years reporting more than 950 response ratios ( n = 5,700), we present a comprehensive analysis of the role that environmental fluctuations play across the tree of life. In contrast to the predominance of interactive effects of global-change drivers reported in the literature, our results show that their cumulative effects were additive (58%), synergistic (26%), and antagonistic (16%) when environmental fluctuations were present. However, the dominant type of interaction varied by trophic level (autotrophs: interactive; heterotrophs: additive) and phylogenetic group (additive in Animalia; additive and positive antagonism in Chromista; negative antagonism and synergism in Plantae). In addition, we identify the need to tackle how complex communities respond to fluctuating environments, widening the phylogenetic and biogeographic ranges considered, and to consider other drivers beyond warming and acidification as well as longer timescales. Environmental fluctuations must be taken into account in experimental and modeling studies as well as conservation plans to better predict the nature, magnitude, and direction of the impacts of global change on organisms and ecosystems.
... Third, solitary bees that nest above-ground may be more responsive to changing climate than those that nest below-ground, presumably because environmental conditions are less variable below-ground (Forrest, 2015;Lembrechts et al., 2021). In a previous 9-year study, Stemkovski et al. (2020) found that nest location was the strongest trait predicting wild bee emergence times in montane habitats, with above-ground species emerging on average about 10 days before below-ground nesting species. ...
... This difference is likely because climate is warming faster aboveground than below-ground (Lembrechts et al., 2021), a pattern which is supported by findings that below-ground nesting solitary bees in the Rocky Mountains exhibited lagged emergence times compared to bees nesting in less buffered microclimates (Forrest & Thomson, 2011). Similarly, Stemkovski et al. (2020) found that aboveground nesting bees are more sensitive to variation in snowpack and temperature than below-ground nesters. ...
Article
Across taxa, the timing of life history events (phenology) is changing in response to warming temperatures. However, little is known about drivers of variation in phenological trends among species. We analyzed 168 years of museum specimen and sighting data to evaluate patterns of phenological change in 70 species of solitary bees that varied in three ecological traits: diet breadth (generalist or specialist), seasonality (spring, summer, or fall), and nesting location (above‐ or below‐ground). We estimated changes in onset, median, end, and duration of each bee species’ annual activity (flight duration) using quantile regression. To determine whether ecological traits could explain phenological trends, we compared average trends across species groups that differed in a single trait. We expected that specialist bees would be constrained by their host plants’ phenology and would show weaker phenological change than generalist species. We expected phenological advances in spring and delays in summer and fall. Lastly, we expected stronger shifts in above‐ground versus below‐ground nesters. Across all species, solitary bees have advanced their phenology by 0.43 days/decade. Since 1970, this advancement has increased four‐fold to 1.62 days/decade. Solitary bees have also lengthened their flight period by 0.44 days/decade. Seasonality and nesting location explained variation in trends among species. Spring‐ and summer‐active bees tended to advance their phenology, whereas fall‐active bees tended to delay. Above‐ground nesting species experienced stronger advances than below‐ground nesting bees in spring; however, the opposite was true in summer. Diet breadth was not associated with patterns of phenological change. Our study has two key implications. First, an increasing activity period of bees across the flight season means that bee communities will potentially provide pollination services for a longer period of time during the year. And, since phenological trends in solitary bees can be explained by some ecological traits, our study provides insight into mechanisms underpinning population viability of insect pollinators in a changing world.