Models of the temperature offset between soil and air temperature have low standard deviations and good global coverage. Analyses for the temperature offset between in situ measured topsoil (0-5 cm depth) temperature and gridded air temperature. (a) Standard deviation (in °C) over the predictions from a cross-validation analysis that iteratively varied the set of covariates (explanatory data layers) and model hyperparameters across 100 models and evaluated model strength using 10-fold cross-validation, for January (left) and July (right), as examples of the two most contrasting months. (b) The fraction of axes in the multidimensional environmental space for which the pixel lies inside the range of data covered by the sensors in the database. Low values indicate increased extrapolation

Models of the temperature offset between soil and air temperature have low standard deviations and good global coverage. Analyses for the temperature offset between in situ measured topsoil (0-5 cm depth) temperature and gridded air temperature. (a) Standard deviation (in °C) over the predictions from a cross-validation analysis that iteratively varied the set of covariates (explanatory data layers) and model hyperparameters across 100 models and evaluated model strength using 10-fold cross-validation, for January (left) and July (right), as examples of the two most contrasting months. (b) The fraction of axes in the multidimensional environmental space for which the pixel lies inside the range of data covered by the sensors in the database. Low values indicate increased extrapolation

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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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... model uncertainty as reported in Figure 5a and and ERA5, as well as differences across the macroclimatic gradient, to identify mismatches between both data sets. ...
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... found positive and negative temperature offsets of up to 10°C between in situ measured mean annual topsoil temperature and gridded air temperature (mean = 3.0 ± 2.1°C standard deviation, Figure 1, 0-5 cm, depth; 5-15 cm is available in Figures S2, S5). The magnitude and direction of these temperature offsets varied considerably within and across biomes. ...
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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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... At the same time, it is absolutely clear that a strong relationship was manifested in ST with AT. The largescale study by Lembrechts et al. [37] led to the creation of global soil temperature maps. This paper showed that the mean annual soil temperature was differed markedly from the corresponding air temperature in the grid, up to 10 °C (average = 3.0 ± 2.1 °C), with significant fluctuations depending on biomes and seasons. ...
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Changes in key climate index affect ecosystems and biodiversity. In this regard, the assessment of the climatic conditions is of particular relevance. This study presents the results of an analysis of climate change and digital mapping of soil temperature in the Yangan-Tau UNESCO Global Geopark (Russia). The steady increase in air temperature was revealed, which causes an increase in the sum of active temperatures and the duration of the warm period of the year. Annual total precipitation during 1966-2020 changed the sign of the trend: positive trend (increase in precipitation) in 1966-1990, while in 1991-2020 is negative. The hydrothermal indicators determined the increase in aridity during the warm period. Based on the analysis of changes in the climatic conditions of the geopark, a comparison was made of the temperature regimes of air and soil, and the relationship between these indicators in the snow-free period was revealed (R2 = 0.62). An approach is presented for digital mapping of the temperature regime of the surface layer of soils based on ground-based research data and the results of interpretation of the Landsat thermal bands. A significant relationship between soil temperature and Earth surface temperature for the snow-free period was revealed (R2 = 0.83). Based on the obtained regression model and Landsat 8-9 data for the snow-free period of 2013-2022 the map of the distribution of average temperatures of the surface layer of soil from May to October was produced, which clearly demonstrated the relationship between soil temperature and biomes.
... Bioclimatic data are particularly suitable for ordination approaches such as the COUE scheme, still they do not necessarily describe the environmental features truly affecting organism occurrences. Therefore, it is pivotal to select variables that better represent the environment experienced by organisms, such as instream conditions, soil temperature or other micro-habitat features (Bramer et al. 2018, Lembrechts et al. 2021. Unfortunately, an accurate representation of microhabitat features over broad spatial scales can be extremely challenging because of the limitations of remote sensing when trying to derive extremely fine-grained information (Lembrechts et al. 2021). ...
... Therefore, it is pivotal to select variables that better represent the environment experienced by organisms, such as instream conditions, soil temperature or other micro-habitat features (Bramer et al. 2018, Lembrechts et al. 2021. Unfortunately, an accurate representation of microhabitat features over broad spatial scales can be extremely challenging because of the limitations of remote sensing when trying to derive extremely fine-grained information (Lembrechts et al. 2021). Datasets aiming to represent instream conditions at a global level are broad-scale estimates, mostly based on soil chemistry and geological features of nearby environments, and hence in some cases they may not accurately represent the actual conditions experienced by organisms. ...
... Finally, even the best global datasets of ecological variables currently available have some limitations, and they can provide an imperfect representation of conditions experienced by organisms (Domisch et al. 2015). The development of high-quality datasets that well represent microhabitat conditions is one of the major issues for modelling studies, and recent broad-scale efforts promise improvements that will be extremely useful for future studies (Potter et al. 2013, Bennie et al. 2014, Lembrechts et al. 2021, Marta et al. 2022. The increased resolution of recently available waterbody maps (Allen and Pavelsky 2018) might soon allow the realistic accounting for dispersal limitation while retaining high statistical power. ...
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Species distribution models are often used to predict the potential distributions of invasive species outside their native ranges and rely on the assumption of realized niche conservatism. Analyses observed that freshwater invasive species often show high degrees of niche expansion, suggesting limited reliability of species distribution models. However, observed niche shifts can arise because of both actual niche shifts, determined by biological factors, and apparent shifts, due to methodological issues. We compared metrics of niche dynamics calculated using different sets of variables to identify factors that could influence the rate of niche shifts. We collected presence‐only data for 40 freshwater invasive animal species, then measured niche shift dynamics using 14 different combinations of environmental variables. Shifts were assessed measuring niche overlap, expansion and unfilling, and testing for niche conservatism. We then built generalized linear mixed models relating niche shifts to methodological choices and biological features. Our results showed that methodological choices strongly affected all the considered niche dynamics metrics, while the effects of biological features were less prominent. Moreover, different niche dynamic measures sometimes provided contradictory assessments of niche conservatism. Niche analyses are powerful tools to predict areas at risk of invasion, but inappropriate methodological choices can lead to apparent niche shifts, questioning niche model reliability and biological interpretation. The high rate of niche expansion observed in freshwater invasive species highlights the importance of delineating objective criteria to determine the set of variables to be used in niche dynamic assessments.
... Despite the fact that there is an increasing number of publications regarding pathogen concentrations in stormwater runoff (Sidhu et al. 2013;Ahmed et al. 2018Ahmed et al. , 2019McGinnis et al. 2018;Steele et al. 2018), there is very little information on this issue in low-income countries, most of them located in tropical zones. Tropical zones often show high precipitation and temperatures (Gilarranz et al. 2022;Lembrechts et al. 2022) thus favoring pathogen presence. To our knowledge, there are only a handful of studies that measure pathogen concentrations in stormwater in low-income countries in tropical regions (Vieira et al. 2002;Cardonha et al. 2004;Soupir et al. 2010;Katukiza et al. 2014), and most of the studies report concentrations that are higher than those reported in high-income countries. ...
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Sustainable Urban Drainage Systems (SUDS) are commonly used to control flooding in urban areas. These structures store and treat stormwater runoff. Several studies in high-income countries have reported the presence of pathogens in runoff water, but it is expected that runoff water in developing countries contains higher pathogen concentrations given their lack of resources to properly manage sewage; this could result in higher risks of infection for people interacting with SUDS. In this study, we investigated pathogen concentrations (i.e., Salmonella spp. and E. Coli O157 ) at the micropool of a SUDS train composed of a grassed swale followed by a dry extended detention basin in Bogotá (Colombia) during a 25-week period. We also estimated the risk of infection with the analyzed pathogens, given the high level of exposure to the detention structure. Additionally, we investigated if any of the physicochemical or meteorological variables were associated with pathogen concentrations at the site. We found that pathogen concentrations greatly exceeded concentrations reported for stormwater runoff in developed countries, namely 1562 CFU/mL, on average, for Salmonella spp. and 9160 CFU/mL, on average, for E. Coli O157. The risk of infection from Salmonella spp. and E. Coli O157 greatly exceeded risks previously reported for recreational waters and SUDS. Pathogen concentrations were associated with precipitation and the concentration of suspended solids in the runoff. Given our findings, it is recommended that SUDS in developing countries should consider potential higher pathogen concentrations in stormwater runoff to reduce exposure.
... accessed on 27 July 2022). The ground temperature was reported to be about 2 • C higher than the air temperature, ranging from −19 • C to 37 • C [158,159]. In addition to seasonal variations, soil and water temperatures depend on depth, with temperatures decreasing with increasing depth [160,161]. The combination of cold weather and low solar radiation will therefore provide better environmental conditions for conducting a tracing experiment using bacteriophages. ...
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Hydrological tracers, commonly used for characterizing water flow paths and sources, do not often meet all the requirements of an ideal tracer. Trans-disciplinary approaches are advocated as the way forward to enlarge the number of tracers available for investigating hydrological processes. Since the 19th century, hydrological tracers have been increasingly used, particularly in underground areas. The tracer toolbox at hand includes a large variety of options, including fluorescent dyes, isotopes, salts or bacteriophages, with each tracer offering specific qualities and complementarities. While their potential for hydrological studies has been studied in karstic environments since the 1960s, bacteriophages remain insufficiently understood. According to the selection methodology used in this review, more than thirty experiments have been listed, involving in total around seventeen different bacteriophages. These have facilitated the investigation of groundwater, surface water (i.e., river, lake and marine water), wetland and wastewater hydrological processes. The tracing experiments have also highlighted the possible interaction between bacteriophages and the surrounding environments. Bacteriophages have successfully helped researchers to understand the water flow within watersheds. Certain advantages, such as the sensitivity of detection, the ease of producing high concentrations of bacteriophages to be injected, their specificity for a host and their non-pathogenicity for human and animal cells, make bacteriophages appreciable tracer candidates for tracing experiments. However, the adsorption process or environmental factors such as temperature, pH and UV light considerably impact the fate of bacteriophages, thereby leading to an attenuation of the phage signal. Considering both the flaws and the qualities of bacteriophages, their use as hydrological tracers requires new insight and further discussions regarding experimental tracing conditions.
... The Worldclim2 dataset interpolates monthly temperature data compiled from globally distributed weather stations to a 30 arc-second spatial resolution across a temporal range from 1970 to 2000 (Fick and Hijmans, 2017). The mean annual soil temperatures were gained from global gridded soil temperature maps according to Lembrechts et al. (2022), which used over 8500 time series of soil temperatures measured in situ across the world and a machine learning approach to model predictor variables of the soil and air temperature offset, and finally interpolate soil temperatures . We validated this soil temperature dataset in China by comparing the gridded soil temperature with mean annual soil temperatures that were monitored using temperature loggers (Thermochron iButton ®DS1922L-F5#) . ...
... 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.