Summer Temperatures Research Articles

Different climate conditions drive variations in gross primary productivity and woody biomass accumulation in a temperate and a boreal conifer forest in Canada

Understanding the complex relationships between climate, forest carbon (C) uptake and biomass growth has become a research priority, crucial for assessing the climate change impacts on forest C sequestration. Such associations are expected to vary across biomes, due to different climate constraints on tree physiology. However, our understanding of the seasonal dynamics of long-term C sequestration and how climate influences them in different forest biomes is still limited.We investigated a boreal Pinus banksiana forest (Old Jack Pine, OJP) and a temperate Pinus strobus plantation (Turkey Point, TP39) in Canada. We aimed to assess the link between C uptake and above-ground woody biomass growth, and the effects of climate inter-annual variability on them. We used daily records of climate and gross primary productivity (GPP, period 1999–2019 at OJP and 2003–2018 at TP39), and investigated xylem biomass proxies at cell (cell wall area) and tree-ring level (ring wall area) from 1970 to 2019 at OJP, and from 1970 to 2018 at TP39.In both forests, the direct link between C sink and C source was revealed by the common inter-annual variability of GPP and xylem biomass. GPP and xylem biomass were mostly influenced by spring and late summer temperature at OJP. However, at both sites, summer drought negatively influenced GPP, and biomass growth especially in recent decades. Analysis of dry and cold years evidenced short-term negative effects of low temperature on GPP and xylem biomass in OJP, and of drought in TP39.Long-term intra-annual analyses are crucial to assess the variable effects of climate on forest C cycle. Warm spring and autumn can benefit both GPP and biomass growth, but even in the boreal forest, summer drought has negative impacts. Increasing dry spells, especially in the temperate site, could reduce future forest capacity to uptake C and fix it in wood biomass.

Seasonal Phytoplankton Characteristics Related with Region-Specific Coastal Environments in the Korean Peninsula

The seasonal dynamics of phytoplankton communities in Korean coastal waters (KCWs) are influenced by complex interactions between ocean currents and nearshore human activities. Despite these influences, the understanding of seasonal phytoplankton changes and their environmental relationships in KCWs remains limited. We investigate the influence of the distinct characteristics of the three seas surrounding the KCWs (the Yellow Sea, the South Sea, and the East Sea) on seasonal phytoplankton communities based on field surveys conducted at 23 stations between 2020 and 2021. The East Sea exhibited higher winter temperatures due to the Jeju and Tsushima warm currents, while summer temperatures were lower compared to the other regions, highlighting the role of currents and deeper oceanic waters. The Yellow Sea showed significant freshwater influence with low salinity levels from major rivers, contrasting with the higher salinity in the East Sea. These differences led to a disparity in the productivity of the two regions: the highest value of Chl. a was observed to be 6.05 µg L–1 in the Yellow Sea in summer. Diatoms dominated in nutrient-rich conditions, particularly in the Yellow Sea, where they comprised up to 80–100% of the phytoplankton community in summer, winter, and spring. PCA analysis revealed positive correlations between diatoms and Chl. a, while cryptophytes, which thrive in the absence of diatom proliferation, showed no such correlation, indicating their opportunistic growth in nutrient-limited conditions. This study highlights the significant impact of region-specific hydrographic factors on phytoplankton communities in KCWs, with diatoms dominating in summer and cryptophytes and dinoflagellates showing seasonal and regional variations. Understanding these dynamics is crucial for predicting phytoplankton bloom dynamics and their ecological implications in coastal ecosystems.

Temperature variation and urban electricity consumption in China: Implications for demand management and planning

With the pressing challenges of climate change and rapid urbanization, understanding the impact of temperature variation on electricity consumption in urban areas is crucial for effective demand management and planning. This study investigates the relationship between temperature and electricity usage in Chinese urban centers, shedding light on critical patterns that can inform sustainable energy policies. Using panel data analysis reveals a significant impact of temperature change on household electricity consumption. The findings indicate an optimal temperature range of 18–24 °C and a non-linear association between temperature and power usage. Notably, the rise in summer temperatures has a greater impact on electricity consumption than the decrease in colder winters. The study also highlights variations in the influence of temperature on electricity consumption based on regional economic development and north-south disparities. The study suggests implications for optimizing power supply planning.

How Maize Farmers in the US Southwest Grew and Prospered Under El Niño but Suffered Under La Niña

Variability in temperature and soil moisture influenced the spread and productive potential of maize. A new low-frequency reconstruction of US Southwest summer temperatures from 3000 BC to present shows generally warmer temperatures until the first century AD. From then until AD 1000, temperatures were generally cooler, though a warming trend continuing to present begins ca. AD 400, interrupted by cooling ca. AD 900 and 1400. Proposing a “ENSO Index” for the US Southwest, we argue that El Niño-like conditions dominated much of the second millennium BC and the first millennium AD, depressing summer temperatures relative to northern hemisphere norms but providing favorable winter moisture for maize. La Niña-like conditions (much less favorable to maize) dominated most of the period from 800 BC – AD 100 and AD 1200 to ca. 1900. We connect these low-frequency trends with tendencies in how and where people lived, emphasizing developments in the ancestral Pueblo area.

Estimating the energy consumption for residential buildings in semiarid and arid desert climate using artificial intelligence

This research aims to develop predictive models to estimate building energy accurately. Three commonly used artificial intelligence techniques were chosen to develop a new building energy estimation model. The chosen techniques are Genetic Programming (GP), Artificial Neural Network (ANN), and Evolutionary Polynomial Regression (EPR). Sixteen energy efficiency measures were collected and used in designing and evaluating the proposed models, which include building dimensions, orientation, envelope construction materials properties, window-to-wall ratio, heating and cooling set points, and glass properties. The performance of the developed models was evaluated in terms of the RMS, R2, and MAPE. The results showed that the EPR model is the most accurate and practical model with an error percent of 2%. Additionally, the energy consumption was found to be mainly governed by three factors which dominate 87% of the impact; which are building size, Solar Heating Glass Coefficient (SHGC), and the target inside temperature in summer.

Key determinants of soil labile nitrogen changes under climate change in the Arctic: A meta-analysis of the responses of soil labile nitrogen pools to experimental warming and snow addition

The Arctic terrestrial ecosystems are undergoing rapid climate change, causing shifts in the dynamics of soil nitrogen (N), a pivotal but relatively underexplored component. To understand the impacts of climate change on soil labile N pools, we performed meta- and decision-tree analyses of 391 observations from 38 peer-reviewed publications across the Arctic, focusing on experimental warming and snow addition. Soil dissolved organic nitrogen (DON), ammonium (NH4+), and nitrate (NO3-) pools under experimental warming exhibited overall standard mean differences (SMDs) ranging from −0.08 to 0.02, with no significance (P > 0.05); however, specific conditions led to significant changes. The key determinants of soil labile N responses to warming were experimental duration and mean annual summer temperature for DON; annual precipitation, soil moisture, and sampling timing for NH4+; and soil layer for NO3-. Snow addition significantly increased all labile N pools (overall SMD = 0.23–0.36; P < 0.05), influenced by factors such as sampling timing and vegetation type for DON; experimental duration and soil moisture for NH4+; and soil pH for NO3-. By consolidating and reprocessing datasets, we not only showed the overall responses of soil labile N pools to climate manipulation experiments in Arctic tundra ecosystems but also identified key determinants for changes in soil N pools among environmental and experimental variables. Our findings demonstrate that warming and snow-cover changes significantly affect soil labile N pools, highlighting how the unique environmental characteristics of different sites influence terrestrial N cycling and underscoring the complexity of Arctic N dynamics under climate change.

Local summer temperature changes over the past 440 ka revealed by the total air content in the Antarctic EPICA Dome C ice core

Abstract. Seasonal temperature reconstructions from ice cores are missing over glacial–interglacial timescales, preventing a good understanding of the driving factors of Antarctic past climate changes. Here the total air content (TAC) record from the Antarctic EPICA Dome C (EDC) ice core is analyzed over the last 440 ka (thousand years). While the water isotopic record, a tracer for annual mean surface temperature, exhibits a dominant ∼100 kyr cyclicity, the TAC record is associated with a dominant ∼40 kyr cyclicity. Our results show that the TAC record is anti-correlated with the mean insolation over the local astronomical summer half-year. They also show for the first time that it is highly anti-correlated with local summer temperature simulated with an Earth system model of intermediate complexity. We propose that (1) the local summer insolation controls the local summer temperature; (2) the latter, through the development of temperature gradients at the near-surface of the ice sheet (&lt;2 m), is affecting the surface snow structure; and (3) those snow structure changes propagating down to the bottom of the firn through densification are eventually controlling the pore volume at the bubble close-off and consequently the TAC. Hence, our results suggest that the EDC TAC record could be used as a proxy for local summer temperature changes. Also, our new simulations show that the mean insolation over the local astronomical summer half-year is the primary driver of Antarctic summer surface temperature variations, while changes in atmospheric greenhouse gas (GHG) concentrations and Northern Hemisphere (NH) ice sheet configurations play a more important role in Antarctic annual surface temperature changes.

A supervised learning tool for heatwave predictions using daily high summer temperatures

AbstractGlobal temperature is increasing at an alarming rate, which increases the number of heatwaves. Heatwaves have significant impacts, both directly and indirectly, on human and natural systems and can create considerable risk to public health. Predicting the occurrence of a heatwave can save lives, increase the production of crops, improve water quality, and reduce transportation restrictions. Because of its geographical location, Bangladesh is particularly vulnerable to cyclones, droughts, earthquakes, floods, and heatwaves. The Bangladesh Meteorological Department collects temperature data at multiple weather stations, and we use data from 10 weather stations in this research. Data show that most heatwaves occur in the summer months, namely, April, May, and June. In this research, we develop Classification and Regression Tree (CART) models that use daily temperature data for the months of March, April, May, and June to predict the likelihood of a heatwave within the next 7 days, the next 28 days, and on any particular day based on daily high temperatures from the previous 14 days. We also use different model parameters to evaluate the accuracy of the models. Finally, we develop treed Stepwise Logistic Regression models to predict the probability of heatwaves occurring. Even though this research uses data from Bangladesh Meteorological Department, the developed modeling approach can be used in other geographic regions.

Among‐river pattern in relative abundance of two salmonid fishes reflects temperature‐dependent competition

Abstract Temperature is a key driver in determining species distribution and abundance across climatic and geographical gradients. This occurs through direct physiological effects of temperature on performance, but may also result from temperature effects on species interactions. In the current study we experimentally test for temperature‐dependent competition between juvenile anadromous Atlantic salmon and brown trout and evaluate the role of temperature in shaping the relative abundance of these species across 88 Norwegian rivers. When the two species were reared in allopatry their somatic growth rates were similar across a range of temperatures (4–16°C). When reared in sympatry, the growth of salmon was greatly reduced at the coldest temperature relative to when reared in allopatry, whereas this was not the case for trout. The effect of interspecific competition on growth was more similar for the two species at warmer temperatures. Thus, interspecific competition effects were strongly asymmetric only at the lowest temperature, with trout outcompeting salmon. The results from the experiment were reflected in qualitative patterns of relative abundance of salmon and trout in natural populations, which shifted from being trout dominated in rivers with low summer temperatures to being salmon dominated in warmer rivers. These results provide an empirical example of how temperature effects on the relative abundance of species can only be understood in light of information about temperature‐dependent species interactions.

Plant Leaf Functional Adaptions along Urban-Rural Gradients of Jinhua City.

Environmental changes induced by urbanization may significantly alter plant survival strategies, thereby introducing uncertainties in their ability to withstand extreme heat. This study, centered on Jinhua City, distinguished urban, suburban, and rural areas to represent the various intensities of urbanization. It examined the leaf function properties of evergreen and deciduous trees common in these regions, focusing on leaf and branch characteristics. Employing an analysis of variance (ANOVA), principal component analysis (PCA), and path analysis (PA) of the plant functional traits and the climatic factors of each region, this study assessed the impact of urbanization on plant survival strategies. By tracking changes in plant functional traits from June to August, it explored the capacity of plants to acclimate to urban-warming-related heat stress across different urbanization gradients. The findings revealed that leaf thickness (LT) and stomatal size (SS) initially decreased and then increased, whereas specific leaf area (SLA) and leaf tissue density (LTD) first rose and then declined, from rural to urban regions. From June to August, branch wood density (WD), chlorophyll (Chl) content, LTD, and leaf dry matter content (LDMC) increased, whereas SLA and leaf water content (LWC) diminished, in all regions. PCA suggested that there was no significant change in the resource allocation strategy of plants (p > 0.05), with drought tolerance significantly reduced in the suburbs on the gradient of urbanization (p < 0.05). During the summer, with high temperature, plants were predominantly biased towards slow-return, conservative strategies, particularly among evergreen species. Compared to precipitation, PA revealed a significant urban warming effect. During summer, temperature was the main factor influencing resource investment strategy and drought resistance, with a notably stronger impact on the former. The high temperature in summer promoted a conservative survival strategy in plants, and the urbanization effect increased their tolerance to high temperatures.

Assessing the Link between Wildfires, Vulnerability, and Climate Change: Insights from the Regions of Greece

Forests provide a wide range of ecosystem services which are important for achieving sustainable development. Anthropogenic climate change has led to the increased frequency and severity of forest fires, which imply losses of valuable ecosystem services. This paper provides a methodological framework based on Multiple Criteria Decision Aid methods for determining relative regional vulnerabilities associated with forest fires. Different notions of ex-post vulnerability are defined based on the forest area burned and the frequency of forest fires, and their relation to the regions’ area and certain socioeconomic characteristics. The climatic drivers of forest fire occurrence are explored by linking forest fires with summer and spring temperatures and precipitation, using econometric count data analysis. The methodology is applied to Greece and its administrative regions for the period 2000–2022. Ex-post vulnerability of regions to forest fires based on physical and socioeconomic characteristics is calculated, and expected changes in the frequency of fires of specific size classes conditional on the evolution of mean seasonal regional temperature and precipitation according to IPCC scenarios are predicted. Relative vulnerability estimates and the impact of specific climatic drivers on forest fires will be useful in designing policies for preserving forests as natural capital and promoting sustainability.

Dinoflagellate cysts as proxies of environmental, ocean and climate changes in the Atlantic realm during the quaternary

Over the last four decades, organic-walled dinoflagellate cysts have shown high potential as tracers of past sea-surface conditions during the Quaternary. These microfossils relate to the pelagic productivity of both phototrophic and heterotrophic protist organisms and are recovered in high numbers in almost all marine environment settings from the nearshore and estuarine systems to the distal continental margin. In polar environments, where other conventional proxies are rare or absent, dinoflagellate cysts showed a relatively high diversity of species and a close relationship with sea-ice cover duration, winter and summer temperature, and salinity, enabling quantitative reconstructions of several oceanic variables over time. From the temperate to the tropical latitudes of the Atlantic Ocean, their modern distribution highlights a response to primary productivity and seasonal contrasts in surface temperature. They also have proven that they could be used as tracers of eutrophication in stratified systems and can also highlight human impact on their distribution. In this paper, we present an overview of dinoflagellate cysts as ecological tracers in recent and past sediments of the Atlantic Ocean, from the Arctic to the Antarctic. We provide examples of their use as proxies in paleoclimatic-palaeoceanographic studies at glacial to interglacial time scales, with emphasis on the last ice age to recent (last 25 kyr), the northern North Atlantic and western-eastern tropic North Atlantic. We also discuss their potential as tracers of anthropogenic stress in coastal environments.

Assessment of the Effect of Meteorological Conditions on the Concentration of Suspended PM2.5 Particulate Matter in Central Europe

The purpose of this study was to use principal component analysis to determine the effect of meteorological elements on the concentration of PM2.5 particulate matter in Krakow, the capital of the Lesser Poland Voivodeship in southern Poland. Daily values for selected meteorological elements measured in spring, summer, autumn, and winter over a 10-year period, obtained from the Institute of Meteorology and Water Management—National Research Institute, were adopted as variables explaining PM2.5 concentrations. Data on particulate air pollution were obtained from the air monitoring station in Krakow. In spring, autumn, and winter, the first factor significantly influencing the PM2.5 concentration was the maximum, minimum, and average temperature. In summer, the average and maximum temperatures were significant. The second factor in spring was precipitation and wind speed, and the third was relative humidity. In summer, the second factor was atmospheric pressure, and the third was relative humidity. The second factor in autumn was atmospheric pressure and precipitation, and the third was relative humidity. In winter, the second factor was wind speed, and the third was precipitation and relative humidity. Throughout the study, the annual mean PM2.5 concentrations exceeded acceptable and target levels defined by the Regulation of the Minister of the Environment, and even further exceeded the level recommended by the WHO.

Pollen- and Weather-Based Machine Learning Models for Estimating Regional Olive Production

The olive tree is one of the most common type of cultivation in the Mediterranean area, having high economic and social importance. The Alentejo region, Portugal, is an area with a high presence of olive groves, which in 2022 accounted for 201,474 hectares. The aim of this study was to assess the relationship between olive pollen, weather data, and olive tree production, between the years 2002 and 2022. Pollen data were obtained from an urban station located in Évora, in the Alentejo region, and were used to calculate several metrics, such as the Pollen Season Duration (PSD), Seasonal Pollen Index (SPIn), peak value, and weekly pollen accumulation values. Monthly minimum, maximum, and mean temperature and precipitation sums were obtained from the E-OBS observational dataset. Considering the relationship between pollen/weather and olive production, mutual information and correlation analyses were conducted. Subsequently, several machine learning algorithms were trained using pollen and weather datasets, and we obtained suitable forecast models for olive tree production after cross-validation. The results showed high variability in pollen concentrations in Évora over the years. Complex associations were found, with certain weeks of pollen accumulation showing significant mutual information with olive production, particularly during June. The analyzed linear correlation coefficients remained generally low, underscoring the challenge of predicting olive production based on linear relationships. Among the machine learning algorithms employed to predict olive production, Decision Trees, Extreme Gradient Boosting, and Gradient Boosting Regressor were the most robust performers (r2 &gt; 0.70), while linear models displayed a subpar performance (r2 &lt; 0.5), emphasizing the complexity of this approach. These models highlight the roles of maximum and minimum temperatures during March and May and pollen accumulation during the second half of June. The developed models may be used as decision-support tools by growers and stakeholders to further enhance the sustainability of the thriving olive sector in southern Portugal.

Impact of seasonal global land surface temperature (LST) change on gross primary production (GPP) in the early 21st century

Understanding the impact of global land surface temperature (LST) changes on gross primary production (GPP) is crucial for addressing global sustainability challenges effectively. This article explores the effects of winter and summer temperature variations on GPP from 2001 to 2020 on a global scale, employing a range of modeling techniques to investigate the complex relationship between temperature changes and GPP. The study employs various modeling approaches, including linear regression models, artificial neural networks, Random Forest (RF) models, and the XGBoost algorithm to examine both linear and non-linear relationships between global temperature changes and GPP. The results reveal that the RF and XGBoost models effectively capture the non-linear relationship between LST and GPP during both summer and winter, demonstrating a high level of statistical significance (P < 0.01) and achieving R2 values of 0.598. Furthermore, the study applies a Sen+MK trend analysis model to identify six distinct trend patterns in LST and GPP during both summer and winter seasons. In summer, the area exhibiting a non-significant increasing trend (NSI) for GPP covers 65,066,274.77 km2, whereas in winter, a strong significant decreasing trend (SSD) for GPP spans 64,537,108.31 km2. Notably, GPP patterns closely mirror those of LST, suggesting that rising high-temperature conditions during summer lead to reduced GPP, while increased low temperatures during winter promote GPP growth. Robust correlations between LST and GPP are observed under various trend conditions in both summer and winter, displaying strong statistical significance (P < 0.01), with SSD achieving a maximum R2 of 0.594. These findings contribute significantly to our comprehensive understanding of the dynamic relationship between LST and GPP and offer valuable insights for addressing the sustainable development of global climate change challenges.

Deciphering the Biophysical Impact of Permafrost Greening on Summer Surface Offset

AbstractSatellite observations have shown widespread greening during the last few decades over the northern permafrost region, but the impact of vegetation greening on permafrost thermal dynamics remains poorly understood, hindering the understanding of permafrost‐vegetation‐climate feedbacks. Summer surface offset (SSO), defined as the difference between surface soil temperature and near‐surface air temperature in summer (June‐August), is often predicted as a function of surface thermal characteristics for permafrost modeling. Here we examined the impact of leaf area index (LAI), detected by satellite as a proxy to permafrost vegetation dynamics, on SSO variations from 2003 to 2021 across the northern permafrost region. We observed latitude‐ and biome‐dependent patterns of SSO changes, with a pronounced increase in Siberian shrublands and a decrease in Tibetan grasslands. Based on partial correlation and sensitivity analyses, we found a strong LAI signal (∼30% of climatic signal) on SSO with varying elevation‐ and canopy height‐dependent patterns. Positive correlations or sensitivities, that is, increases in LAI lead to higher SSO, were distributed in relatively cold and wet areas. Biophysical effects of permafrost greening on surface albedo, evapotranspiration, and soil moisture (SM) could link the connection between LAI and SSO. Increased LAI substantially reduced surface albedo and enhanced evapotranspiration, influenced energy redistribution, and further controlled interannual variability of SSO. We also found contrasting effects of LAI on surface SM, consequently leading to divergent impacts on SSO. The results offer a fresh perspective on how greening affects the thermal balance and dynamics of permafrost, which is enlightening for improved permafrost projections.

A circumpolar study unveils a positive non‐linear effect of temperature on arctic arthropod availability that may reduce the risk of warming‐induced trophic mismatch for breeding shorebirds

AbstractSeasonally abundant arthropods are a crucial food source for many migratory birds that breed in the Arctic. In cold environments, the growth and emergence of arthropods are particularly tied to temperature. Thus, the phenology of arthropods is anticipated to undergo a rapid change in response to a warming climate, potentially leading to a trophic mismatch between migratory insectivorous birds and their prey. Using data from 19 sites spanning a wide temperature gradient from the Subarctic to the High Arctic, we investigated the effects of temperature on the phenology and biomass of arthropods available to shorebirds during their short breeding season at high latitudes. We hypothesized that prolonged exposure to warmer summer temperatures would generate earlier peaks in arthropod biomass, as well as higher peak and seasonal biomass. Across the temperature gradient encompassed by our study sites (&gt;10°C in average summer temperatures), we found a 3‐day shift in average peak date for every increment of 80 cumulative thawing degree‐days. Interestingly, we found a linear relationship between temperature and arthropod biomass only below temperature thresholds. Higher temperatures were associated with higher peak and seasonal biomass below 106 and 177 cumulative thawing degree‐days, respectively, between June 5 and July 15. Beyond these thresholds, no relationship was observed between temperature and arthropod biomass. Our results suggest that prolonged exposure to elevated temperatures can positively influence prey availability for some arctic birds. This positive effect could, in part, stem from changes in arthropod assemblages and may reduce the risk of trophic mismatch.

The Effect of Foliar Application with Naphthalene Acetic Acid and Potassium Nitrate on the Growth, Sex Ratio, and Productivity of Cucumbers (Cucumis sativas L.) under High Temperatures in Semi-Arid Areas

High temperatures in late spring, summer, and autumn are one of the main elements limiting cucumber production. Heat stress in cucumber plants leads to significant changes in sex expression and negative effects at the physiological stage, including decreased absorption of water and nutrients, decreased photosynthetic functions, and increased respiration, which in turn affects growth and production. Therefore, it is very difficult for cucumber plants exposed to heat stress to improve their productivity, especially in arid and semi-arid regions. Cucumber plants bear a variety of flower forms, including staminate, pistillate, and hermaphrodites that occur in different arrangements. In addition, maleness is considered the main problem in cucumber production, as it significantly reduces fruit yield. Recently, the growth, flowering, and productivity of plants have been controlled with the help of growth-regulating substances, and one of these substances is naphthalene acetic acid (NAA), which is of great importance in modifying sex in cucumber and various cucurbit crops. Two experiments were conducted during 2022 and 2023 during the summer season to study the effect of foliar spraying of two levels of potassium nitrate, 500 mg/L and 1000 mg/L, and two levels of NAA, 50 mg/L and 100 mg/L, individually and together, and the comparison treatment in nine treatments on growth traits, sex ratio, fruit traits and yield of cucumber (Cucumis sativas L.) plants in three replicates in a completely randomized block design. The results indicated that applying the treatment containing 1000 mg/L KNO3 + 100 mg/L NAA led to obtaining the highest value in most vegetable growth, flowering, and production parameters, with a significant increase compared to the control and all other treatments. Followed by treatments consisting of potassium nitrate and NAA, which gave high values in the traits of sex ratio %, fruit setting %, fruit yield, and most other traits. This study showed the importance of spraying with both NAA and potassium nitrate, individually or in combination, and the high combined effect of both in the treatments that contained each of them in the vegetative growth traits and floral traits such as the number of female flowers, the sexual ratio, fruit set percentage, weight fruit, length fruit, and the fruit yield in cucumber plants grown in the summer season with high temperatures

Future carbon sequestration potential in a widespread transcontinental boreal tree species: Standing genetic variation matters!

Climate change (CC) necessitates reforestation/afforestation programs to mitigate its impacts and maximize carbon sequestration. But comprehending how tree growth, a proxy for fitness and resilience, responds to CC is critical to maximize these programs' effectiveness. Variability in tree response to CC across populations can notably be influenced by the standing genetic variation encompassing both neutral and adaptive genetic diversity. Here, a framework is proposed to assess tree growth potential at the population scale while accounting for standing genetic variation. We applied this framework to black spruce (BS, Picea mariana [Mill] B.S.P.), with the objectives to (1) determine the key climate variables having impacted BS growth response from 1974 to 2019, (2) examine the relative roles of local adaptation and the phylogeographic structure in this response, and (3) project BS growth under two Shared Socioeconomic Pathways while taking standing genetic variation into account. We modeled growth using a machine learning algorithm trained with dendroecological and genetic data obtained from over 2600 trees (62 populations divided in three genetic clusters) in four 48-year-old common gardens, and simulated growth until year 2100 at the common garden locations. Our study revealed that high summer and autumn temperatures negatively impacted BS growth. As a consequence of warming, this species is projected to experience a decline in growth by the end of the century, suggesting maladaptation to anticipated CC and a potential threat to its carbon sequestration capacity. This being said, we observed a clear difference in response to CC within and among genetic clusters, with the western cluster being more impacted than the central and eastern clusters. Our results show that intraspecific genetic variation, notably associated with the phylogeographic structure, must be considered when estimating the response of widespread species to CC.

Heatwave frequency and disability status: Thermal inequities in the U.S. South

BackgroundAlthough extreme heat has been found to be disproportionately distributed with respect to socially disadvantaged and marginalized groups, persons with disabilities have received limited attention in previous research on heat exposure disparities. ObjectiveThis gap is addressed by analyzing the relationship between local heatwave frequency and the percentages of people with a disability and specific disability types in the U.S. South—a region characterized by extremely high summer temperatures and greater disability prevalence. MethodsCensus tract level values of heatwave annualized frequency from the U.S. Federal Emergency Management Agency's National Risk Index are linked to relevant disability variables from the latest American Community Survey five-year estimates. Statistical analyses are based on bivariate correlations and multivariable generalized estimating equations that consider spatial clustering of tracts based on climate zone and county. ResultsThe overall percentage of civilian noninstitutionalized persons with a disability and more than one disability are significantly greater (p < 0.001) in census tracts with higher heatwave frequency, after controlling for clustering, race/ethnicity, socioeconomic status, older age, population density, and metropolitan status. Heatwave frequency is also positively and significantly associated (p < 0.01) with the percentages of people with hearing, vision, cognitive, ambulatory, self-care, and independent living difficulties. ConclusionsThese heat-related distributive injustices in the U.S. South demonstrate an urgent need to: (1) include disability status in future research on social disparities in heatwave exposure; (2) conduct more detailed investigations in other regions, states, and nations; and (3) develop disability-inclusive policies and interventions that provide equitable protection during extreme weather events.