Future Conditions Research Articles

Acclimation of Photosynthesis to CO2 Increases Ecosystem Carbon Storage due to Leaf Nitrogen Savings.

Photosynthesis is the largest flux of carbon between the atmosphere and Earth's surface and is driven by enzymes that require nitrogen, namely, ribulose-1,5-bisphosphate (RuBisCO). Thus, photosynthesis is a key link between the terrestrial carbon and nitrogen cycle, and the representation of this link is critical for coupled carbon-nitrogen land surface models. Models and observations suggest that soil nitrogen availability can limit plant productivity increases under elevated CO2. Plants acclimate to elevated CO2 by downregulating RuBisCO and thus nitrogen in leaves, but this acclimation response is not currently included in land surface models. Acclimation of photosynthesis to CO2 can be simulated by the photosynthetic optimality theory in a way that matches observations. Here, we incorporated this theory into the land surface component of the Energy Exascale Earth System Model (ELM). We simulated land surface carbon and nitrogen processes under future elevated CO2 conditions to 2100 using the RCP8.5 high emission scenario. Our simulations showed that when photosynthetic acclimation is considered, photosynthesis increases under future conditions, but maximum RuBisCO carboxylation and thus photosynthetic nitrogen demand decline. We analyzed two simulations that differed as to whether the saved nitrogen could be used in other parts of the plant. The allocation of saved leaf nitrogen to other parts of the plant led to (1) a direct alleviation of plant nitrogen limitation through reduced leaf nitrogen requirements and (2) an indirect reduction in plant nitrogen limitation through an enhancement of root growth that led to increased plant nitrogen uptake. As a result, reallocation of saved leaf nitrogen increased ecosystem carbon stocks by 50.3% in 2100 as compared to a simulation without reallocation of saved leaf nitrogen. These results suggest that land surface models may overestimate future ecosystem nitrogen limitation if they do not incorporate leaf nitrogen savings resulting from photosynthetic acclimation to elevated CO2.

Agricultural innovation for climate change: limited but positive impacts of commercialized drought-tolerant corn

Abstract The extent to which new technologies can countervail the risks posed by climate change is a critical element for designing adaptation strategies. This study uses new experimental data spanning 17 US states from 2008 to 2023 to examine the potential impact of recently commercialized drought tolerant (DT) traits on both yield and yield resilience in US corn production. We find that there is no yield advantage for DT hybrids under average weather conditions, but they improve yield resilience, particularly with respect to precipitation. These effects are spatially heterogeneous, such that DT has a positive yield impact in the droughty, western US, but a small, or even negative, impact in the central and eastern US. In addition, the presence of DT reduces yield variance and kurtosis, and increases skewness, all of which imply a reduction in yield risk. Using the statistical model estimates, we project the impact of DT on corn yields under future climate conditions obtained from 20 general circulation models with two representative concentration pathways. The projected ensemble means of yield gains are 6.34 bu/acre and 5.39 bu/acre under moderate and extreme warming scenarios, respectively, by the mid-twenty-first century. These gains compensate for 23% and 13.5% of total yield loss due to climate change. Our results indicate that current commercial DT hybrids reduce yield risk, improve resilience with respect to precipitation, and have the potential to offer moderate benefits under climate change warming scenarios.

Rubisco activity and activation state dictate photorespiratory plasticity in Betula papyrifera acclimated to future climate conditions

Plant metabolism faces a challenge of investing enough enzymatic capacity to a pathway without overinvestment. As it takes energy and resources to build, operate, and maintain enzymes, there are benefits and drawbacks to accurately matching capacity to the pathway influx. The relationship between functional capacity and physiological load could be explained through symmorphosis, which would quantitatively match enzymatic capacity to pathway influx. Alternatively, plants could maintain excess enzymatic capacity to manage unpredictable pathway influx. In this study, we use photorespiration as a case study to investigate these two hypotheses in Betula papyrifera. This involves altering photorespiratory influx by manipulating the growth environment, via changes in CO2 concentration and temperature, to determine how photorespiratory capacity acclimates to environmental treatments. Surprisingly, the results from these measurements indicate that there is no plasticity in photorespiratory capacity in B. papyrifera, and that a fixed capacity is maintained under each growth condition. The fixed capacity is likely due to the existence of reserve capacity in the pathway that manages unpredictable photorespiratory influx in dynamic environments. Additionally, we found that B. papyrifera had a constant net carbon assimilation under each growth condition due to an adjustment of functional rubisco activity driven by changes in activation state. These results provide insight into the acclimation ability and limitations of B. papyrifera to future climate scenarios currently predicted in the next century.

Predicting the future distribution of the Barbary ground squirrel (Atlantoxerus getulus) under climate change using niche overlap analysis and species distribution modeling.

This study combines niche overlap analysis with species distribution modeling (SDM) to examine the niche dynamics of Atlantoxerus getulus, a ground squirrel native to Morocco and Algeria that has been introduced to the Canary Islands. We compiled 1272 records of A. getulus in its native and exotic ranges and five bioclimatic variables for present and future climate conditions for the years 2050 and 2070. We assessed the ecological niche of the species using exploratory and ordination analyses, followed by the prediction of its distribution using the SpatialMaxent model. Our results showed that the niches of A. getulus exhibited equivalence (p > 0.05) and significant similarity (p < 0.05) between the native and exotic ranges. No observed niche expansion in the exotic area is shown to be associated with complete niche stability. However, 90% of the niche in the Canary Islands remains unfilled, suggesting potential for further invasion. Our results highlighted habitat contractions ranging from 41% (SSP245-2050) to 60% (SSP585-2070), associated with a shift in the centroid of suitable habitat towards the Atlantic coast. These contractions are particularly severe in Algeria, where suitable habitats could disappear by 2050, contrasting with stable habitats maintained in the Canary Islands under all scenarios. Urgent habitat restoration in Algeria is crucial, including efforts to combat poaching. In Morocco, targeted in situ conservation is recommended, while in the Canary Islands, the focus should be on invasive species management and public awareness campaigns to prevent further spread.

Optimal investments in private land conservation depend more on landholder preferences than climate change

Abstract Effective private land conservation strategies that consider both landholder preferences and future climatic conditions are critical for preserving biodiversity and ecosystem services. Yet, the interaction and relative importance of these factors for conservation planning performance is unknown. Here, we assess the importance of considering landholder preferences and climate change for prioritising locations for conservation tenders to recruit landholders for conservation covenants. To achieve this we develop a planning framework that accounts for the tender process to optimise investment across regions and apply it to koala-focused tenders in New South Wales (NSW), Australia, exploring four planning approaches that consider or are ignorant to landholder preferences or climate change. We find that optimal investments depend more on landholder preferences than climate change, and when landholder preferences are ignored, there is little benefit in accounting for climate change. Our analysis reveals new insights into this important interaction.&amp;#xD;

Neural network based estimates of the climate impact on mortality in Germany: application to storyline climate simulations

The aim of this work is the prediction of heat-related mortality for Germany under future, i.e. hotter, climate conditions. The prediction is made based on 2m temperature data from climate storyline simulations using machine learning techniques. We use an echo state network for linking the outputs of storyline climate simulations to the target data. The target data are all-cause mortality rates of Germany for all ages. The network is trained with present day climate model outputs. Model outputs of future, i.e. 2K and 4K warmer, storylines are used to predict mortality rates under such climatic conditions. We find that we can train an echo state network with recent temperature data and mortality and make plausible predictions about expected developments of mortality in Germany based on future climate storylines. The trained network can successfully predict mortality rates for future climate conditions. We find increased mortality during the summer months which is attributed to the presence of more severe heat waves. The mortality decrease found during winter can be explained milder winters leading to fewer deaths caused by respiratory diseases. However, mortality in winter is largely influenced by other factors such as influenza waves or vaccination rate and explainability due to temperature is limited.

Future drought overestimations due to no constraints of CO2 physiological effect and land-atmosphere coupling on potential evapotranspiration

Abstract Various offline drought indices have been widely used to project dryness/wetness and drought changes. However, the results derived from these indices often differ from or even contradict observations and direct projections made by coupled climate models. Therefore, it is crucial to investigate this scientific debate thoroughly and identify the potential causes. This study adopts a water demand-side perspective, focusing on potential evapotranspiration (PET), to address such controversy. Employing the Standardized Precipitation-Evapotranspiration Index (SPEI), three PET models including the Food and Agriculture Organization of the United Nations’ report 56 (FAO-56) Penman-Monteith (PM) model, a corrected FAO-56 PM model incorporating CO2 physiological effect (PMCO2), and a land-atmosphere coupled PET model (PET-LAC) are further compared. Despite projected increases in PET across most land areas, the PM shows the most pronounced increases among these PET models. Compared to PMCO2 and PET-LAC, the PM model predicts the most significant drying, with the 3-month SPEI decreasing by 0.50±0.23 /yr. Additionally, it projects the most substantial drought intensification, with increases in areas, intensity, and duration of 28±6.9%, 0.70±0.20/yr, and 2.90±0.83 month/yr, respectively. Meanwhile, these projections correspond to the most extensive area percentages, with 78.5±10% for drying, 94.8±7.2% for drought intensity, and 93.6±7.9% for drought duration. These findings imply that the commonly used PM model overestimates future drought conditions. Differences and contradictions between the drought projections from PM-based offline indices and direct climate model outputs can be partly attributed to the omission of CO2 physiological effect and land-atmosphere coupling constraints in the PM model. This study highlights the importance of improving PET models by incorporating these constraints, thereby providing valuable insights for enhancing the accuracy of future drought assessments.

Experimental coral reef communities transform yet persist under mitigated future ocean warming and acidification

Coral reefs are among the most sensitive ecosystems affected by ocean warming and acidification, and are predicted to collapse over the next few decades. Reefs are predicted to shift from net accreting calcifier-dominated systems with exceptionally high biodiversity to net eroding algal-dominated systems with dramatically reduced biodiversity. Here, we present a two-year experimental study examining the responses of entire mesocosm coral reef communities to warming (+2 °C), acidification (-0.2 pH units), and combined future ocean (+2 °C, -0.2 pH) treatments. Contrary to modeled projections, we show that under future ocean conditions, these communities shift structure and composition yet persist as novel calcifying ecosystems with high biodiversity. Our results suggest that if climate change is limited to Paris Climate Agreement targets, coral reefs could persist in an altered state rather than collapse.

Potential Distribution and Carbon Sequestration of Rhizophora mangle L. in El Vizcaíno Biosphere Reserve, Baja California Sur, Mexico

Mangroves are a type of vegetation distributed in warm areas of the planet. Despite their importance, this flora is seriously threatened by both human activities and climate change. One of the main benefits provided by mangroves is carbon capture and storage, which is key for climate change mitigation. The main objective of this study was to identify the potential distribution and carbon sequestration potential of Rhizophora mangle L. in El Vizcaíno Biosphere Reserve. Potential distribution models were obtained for Baja California Sur, Mexico, using the MaxLike algorithm. For each projection, we used bioclimatic variables from the WorldClim project for current and future conditions (2050 and 2070), two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) and three General Circulation Models (ACCESS-CM2, EC-Earth3-Veg and MPI-ESM1-2-HR). The potential distribution models were developed within the perimeter of El Vizcaíno Biosphere Reserve as a case study to establish the potential for carbon sequestration under different climate change scenarios. Our results show a possible future carbon sequestration from 10,177,174 Mg of CO2 and up to 14,022,367 Mg of CO2 for the ACCESS-CM2 SSP5-8.5 to 2070 and MPI-ESM1-2-HR SSP2-4.5 to 2070 projections, respectively. Mangrove species such as Rhizophora mangle can be an important part of climate change mitigation and adaptation.

The Vulnerability of Malagasy Protected Areas in the Face of Climate Change

This study examines the vulnerability of Madagascar’s protected areas (PAs) to climate change, focusing on climate change velocity, and its impact on biodiversity. We analyzed current and near future climate data using principal component analysis (PCA) and climate change velocity metrics to predict shifts in climatic conditions from the present to the near future, while under the mild and extreme emission scenarios (SSP 126, SSP 585). Forward velocities, which are characterized by the minimum distances that must be overcome by species to keep in track with their appropriate comparative climate, are most pronounced in western and southern Madagascar. In contrast, the backward velocity, which uses future climatic conditions in grid cells in comparison to current conditions, is more common in the eastern regions of the island, and hints at the minimum distance that organisms would have to overcome in colonizing a new habitat. Even though the correlations between PA size and climate change velocity are weak, there is a tendency for larger PAs to exhibit more stable climatic conditions. Conservation strategies must prioritize enhancing the resilience of PAs through adaptive management to mitigate climate impacts. Our findings provide crucial insights for policymakers and conservation planners to develop climate-smart strategies that ensure the long-term efficacy of Madagascar’s PA network.

Satellite‐Based Solar Irradiance Forecasting: Replacing Cloud Motion Vectors by Deep Learning

Satellite‐based (SAT) methods are widely used to forecast surface solar irradiance up to several hours ahead. Herein, a cloud index‐based version of the Heliosat method is applied to infer irradiance from Meteosat Second Generation images. The cloud index (CI) is derived from images in the visible range and quantifies the impact of clouds on surface solar irradiance. Conventional SAT methods utilize cloud motion vectors (CMVs) from consecutive CI images to predict future cloud conditions and subsequently retrieve irradiance. In this study, HelioNet is introduced—a convolutional neural network (CNN) with UNet architecture designed to predict future CI situations from sequences of preceding CI images. Forecasts of two HelioNet configurations are benchmarked against CMV and persistence over a full year (2023), with lead times (LT) up to 4 h. HelioNet15 min recursively generates forecasts at 15 min resolution. HelioNethybrid begins with forecasts at 15 min resolution for , then uses a 45 min resolved model to forecast all remaining LT steps. HelioNet15 min achieves root mean square error (RMSE) improvements of &gt;15% over the CMV model within the first hour on image level. HelioNethybrid shows superior performance for all LT across all metrics considered, with an average RMSE improvement of &gt;11% on image and 8% at irradiance level.

Assessing the impacts of land use and climate change on the distribution patterns of Ulex europaeus L. (Fabaceae) in the Canary Islands

AbstractBiological invasions are one of the major threats to biodiversity, but their impact is particularly detrimental on oceanic islands like the Canary Archipelago. The common gorse (Ulex europaeus L. (Fabaceae)) is a highly invasive shrub with established populations in Tenerife, the sole island of the archipelago where it is present. Understanding the habitat preferences of U. europaeus is essential for predicting its current and potential future distribution across Tenerife and other Canary Islands, guiding effective local management practices. In 2019 and 2020, we surveyed different populations of U. europaeus in Tenerife, retrieving information on its abundance, presence of other invasive species and most frequent natural and anthropogenic characteristics of the landscape. We used this information to build explanatory and predictive models to identify the key natural and anthropogenic drivers of U. europaeus abundance in Tenerife and estimate the potential distribution of the species across the whole archipelago under current and future climatic conditions. Our findings showed that U. europaeus thrives in humid areas impacted by human activities where other invasive species persist. Both current and future climatic conditions do not support the presence of the species in the more arid islands of the archipelago (i.e., Fuerteventura and Lanzarote), rather highlight that the windwards, and thus more humid, areas of the other islands might support the species also under future warmer climatic scenarios. These findings deepen our understanding of U. europaeus local dynamics and are crucial to inform targeted management strategies to mitigate its impact across the Canary Archipelago and, ultimately, oceanic islands.

Optimizing Strategy for Property Development Plan Changes: A Multi-Criteria Decision-Making Approach Using AHP Methods (Case Study: Project Bumi Cipta 3 Jakarta)

This study examines the optimization strategy of property development plan changes for Bumi Cipta 3 Jakarta, a project by PT Cipta Bangun Property, one of the largest property companies in Indonesia. The analysis uses the Analytic Hierarchy Process (AHP), a multi-criteria decision-making (MCDM) method. The project is motivated by significant post-pandemic shifts in office space demand within Jakarta’s Central Business District. The study identifies four alternative development options: continuing with the office tower plan, or pivoting to an apartment, data center, or hospital. By interviewing key decision-makers and using the AHP method, the research evaluates financial and non-financial criteria, such as market attractiveness and site analysis. The findings suggest that the hospital alternative offers the highest potential in both financial returns and market alignment, followed by the data center and apartment projects. Strategic recommendations are provided to prioritize the hospital project while keeping the other alternatives in consideration for phased development, thus ensuring flexibility in response to future market conditions.

Morphophysiological, biochemical, and nutrient response of spinach (Spinacia oleracea L.) by foliar CeO2 nanoparticles under elevated CO2

Nanomaterials offer considerable benefits in improving plant growth and nutritional status owing to their inherent stability, and efficiency in essential nutrient absorption and delivery. Cerium oxide nanoparticles (CeO2 NPs) at optimum concentration could significantly influence plant morpho-physiology and nutritional status. However, it remains unclear how elevated CO2 and CeO2 NPs interactively affect plant growth and quality. Accordingly, the ultimate goal was to reveal whether CeO2 NPs could alter the impact of elevated CO2 on the nutrient composition of spinach. For this purpose, spinach plant morpho-physiological, biochemical traits, and nutritional contents were evaluated. Spinach was exposed to different foliar concentrations of CeO2 NPs (0, 25, 50, 100 mg/L) in open-top chambers (400 and 600 CO2 μmol/mol). Results showed that elevated CO2 enhanced spinach growth by increasing photosynthetic pigments, as evidenced by a higher photosynthetic rate (Pn). However, the maximum growth and photosynthetic pigments were observed at the highest concentration of CeO2 NPs (100 mg/L) under elevated CO2. Elevated CO2 resulted in a decreased stomatal conductance (gs) and transpiration rate (Tr), whereas CeO2 NPs enhanced these parameters. No significant changes were observed in any of the measured biochemical parameters due to increased levels of CO2. However, an increase in antioxidant enzymes, particularly in catalase (CAT; 14.37%) and ascorbate peroxidase (APX; 10.66%) activities, was observed in high CeO2 NPs (100 mg/L) treatment under elevated CO2 levels. Regarding plant nutrient content, elevated CO2 significantly decreases spinach roots and leaves macro and micronutrients as compared to ambient CO2 levels. CeO2 NPs, in a dose-dependent manner, with the highest increase observed in 100 mg/L CeO2 NPs treatment and increased roots and shoots magnesium (211.62–215.49%), iron (256.68–322.77%), zinc (225.89–181.49%), copper (21.99–138.09%), potassium (121.46–138.89%), calcium (118.22–91.32%), manganese (133.15–195.02%) under elevated CO2. Overall, CeO2 NPs improved spinach growth and biomass and reverted the adverse effects of elevated CO2 on its nutritional quality. These findings indicated that CeO2 NPs could be used as an effective approach to increase vegetable growth and nutritional values to ensure food security under future climatic conditions.

Multifactorial effects of warming, low irradiance, and low salinity on Arctic kelps

Abstract. The Arctic is projected to warm by 2 to 5 °C by the end of the century. Warming causes melting of glaciers, shrinking of the areas covered by sea ice, and increased terrestrial runoff from snowfields and permafrost thawing. Warming, decreasing coastal underwater irradiance, and lower salinity are potentially threatening polar marine organisms, including kelps, that are key species of hard-bottom shallow communities. The present study investigates the physiological responses of four kelp species (Alaria esculenta, Laminaria digitata, Saccharina latissima, and Hedophyllum nigripes) to these environmental changes through a perturbation experiment in ex situ mesocosms. Kelps were exposed for 6 weeks to four experimental treatments: an unmanipulated control; a warming condition under the CO2 emission scenario SSP5-8.5; and two multifactorial conditions combining warming, low salinity, and low irradiance reproducing the future coastal Arctic exposed to terrestrial runoff under two CO2 emission scenarios (SSP2-4.5 and SSP5-8.5). The physiological effects on A. esculenta, L. digitata, and S. latissima were investigated, and gene expression patterns of S. latissima and H. nigripes were analyzed. Across all species and experimental treatments, growth rates were similar, underlying the acclimation potential of these species to future Arctic conditions. Specimens of A. esculenta increased their chlorophyll a content when exposed to low irradiance conditions, suggesting that they may be resilient to an increase in glacier and river runoff with the potential to become more dominant at greater depths. S. latissima showed a lower carbon : nitrogen (C : N) ratio under the SSP5-8.5 multifactorial conditions' treatment, suggesting tolerance to coastal erosion and permafrost thawing. In contrast, L. digitata showed no response to the conditions tested on any of the investigated physiological parameters. The down-regulation of genes coding for heat-shock proteins in H. nigripes and S. latissima underscores their ability to acclimate to heat stress, which portrays temperature as a key influencing factor. Based on these results, it is expected that kelp communities will undergo changes in species composition that will vary at local scale as a function of the changes in environmental drivers.

Evaluating the tradeoffs between water conservation, aesthetic value, evaporative cooling and CO2 emissions in St. augustinegrass and buffalograss

Urban lawns comprise a significant portion of urban greenery and provide several ecosystem services. Nevertheless, maintaining lawns comes with significant water costs in semi-arid inland southern California, as they require consistent irrigation to stay healthy and productive. The main objective of this study was to evaluate the effect of a wide range of irrigation rates and frequencies applied autonomously by a smart ET-based controller on the aesthetic value, cooling potential, and CO2 efflux of two warm-season turfgrass species. For three years, we studied the responses of Buffalograss and St. Augustinegrass to six irrigation rates and two irrigation frequencies in Riverside, California. Under historical average climate conditions, the minimum irrigation rate for Buffalograss was 93 % ETo, and for St. Augustinegrass, it was 74 % ETo. Under projected future climate conditions, the estimated minimum irrigation rate for Buffalograss did not change, but for St. Augustinegrass, it increased by 4 % and 7 % in 2100 under low emission (RCP 4.5) and high emission (RCP 8.5) scenarios, respectively. On average, canopy minus air temperature in Buffalograss was 6.2 ℃, and in St. Augustinegrass, it was 1.1 ℃. The average CO2 efflux in Buffalograss was 122.3 µg CO2-C m−2 s−1, and in St. Augustinegrass, it was 182.8 µg CO2-C m−2 s−1. Our results showed that turfgrass aesthetic values, cooling potential, and CO2 efflux diminished as the irrigation rate decreased, but at different rates for each turfgrass species.

Integrated Systematic Framework for Forecasting China’s Consumer Confidence: A Machine Learning Approach

This study aims to introduce a novel approach for predicting China’s consumer confidence index (CCI), a key economic indicator that reflects consumers’ confidence in current and future economic conditions. While traditional statistical models and economic indicators are the primary tools for forecasting CCI, their reliance on linear assumptions limits their ability to capture the complex, dynamic relationships inherent in economic systems. In response, this study proposes a two-step method that integrates social network analysis (SNA) and machine learning (ML) to enhance prediction accuracy by accounting for the nonlinear interactions and systemic interdependencies that drive consumer confidence. The use of SNA enables the identification of critical variables and their interconnected roles in shaping consumer sentiment, while ML models, specifically the gradient boosting decision tree (GBDT), leverage these relationships to provide more precise predictions. Utilizing monthly data from 1999 to 2023, the combined SNA and GBDT approach significantly improves the accuracy of CCI forecasts, particularly during periods of high volatility. The results of this study hold substantial value for policymakers, market analysts, and economists, as they offer a systems-oriented framework for economic forecasting. By demonstrating the effectiveness of combining SNA with ML technologies, this research not only advances the methodological toolkit for economic forecasting, but also provides a new lens through which the complex, adaptive nature of economic systems can be better understood and managed. This integrated approach paves the way for future developments in forecasting models that more accurately reflect the evolving dynamics of consumer confidence in a rapidly changing economic environment.

Prediction of Potential Suitable Distribution Areas for Northeastern China Salamander (Hynobius leechii) in Northeastern China

The Northeastern China Salamander (Hynobius leechii) is classified as a rare, nationally protected Class II wild animal in China. Its population is declining, and its habitat is deteriorating. This study aimed to predict the distribution of suitable habitats for the Northeastern China Salamander under both current and future climate scenarios, utilizing the MaxEnt model optimized through ENMeval parameters. Species distribution data were collected from field surveys, existing literature, amphibian records in China, and the Global Biodiversity Information Network. A total of 97 records were compiled, with duplicate records within the ENMTools grid unit removed, ensuring that only one record existed within every 5 km. Ultimately, 58 distinct distribution points for the Northeastern China Salamander were identified. The R software package ‘ENMeval 2.0’ was employed to optimize the feature complexity (FC) and regularization multiplier (RM), and the optimized model was applied to assess the suitable distribution regions for the Northeastern China Salamander under present and future climate conditions. The findings indicated that rainfall and temperature are the primary environmental factors influencing Hynobius. Currently, the suitable habitat for the Northeastern China Salamander constitutes 6.6% of the total area of Northeastern China. Projections for the periods of 2050 and 2070 suggest that suitable habitats for the Northeastern China Salamander will continue to expand towards higher latitudes across three climate scenarios. While this study focuses solely on climate change factors and acknowledges certain limitations, it serves as a reliable reference and provides essential information for the distribution and conservation of the Northeastern China Salamander.

Cold Tolerance Assay Reveals Evidence of Climate Adaptation Among American Elm (Ulmus americana L.) Genotypes

T he American elm (Ulmus americana L.), once a dominant species in North American floodplain forests, has suffered significant population declines due to Dutch elm disease (DED). Despite this, some elms persist, potentially exhibiting disease resistance and climate-adaptive traits that could facilitate restoration. Understanding these traits is crucial for selecting genotypes suited to current and future climatic conditions, particularly in colder regions. This study evaluated the mid-winter cold tolerance of American elm genotypes across a climatic gradient to ascertain evidence of local climate adaptation. We used relative electrolyte leakage (REL) to assess mid-winter cold tolerance of current-year shoots on eleven survivor genotypes from New England and one susceptible, control genotype from Ohio. The lethal temperature, at which 50% of cellular leakage occurs (LT50), was determined and compared with 30-year climate data to identify potential climate adaptation. Genotypes from colder regions exhibited greater cold hardiness, indicating local adaptation to climate. Observed mid-winter LT50 values (−42.8 °C to −37.7 °C) were in excess of the 30-year minimum air temperature, even at the coldest source location. This calls into question whether mid-winter cold tolerance is the critical period for injury to American elm and more attention should be given to environmental conditions that cause de-acclimation to cold. By understanding the adaptive capacity of American elm, managers can better select mother trees for regional seed orchards, ensuring the long-term success of restoration initiatives.

Phenology of the spruce bark beetle Ips typographus in the UK under past, current and future climate conditions

Summary The European spruce bark‐beetle, Ips typographus, is a major pest of Norway spruce across mainland Europe; however, until 2018, it was considered absent from the United Kingdom (UK). The finding of a breeding population of I. typographus in Kent, England, in 2018 and subsequent findings in pheromone traps across the southeast of England, has led to an urgent need to improve understanding of environmental suitability across the UK. Two distinct published phenological models for I. typographus, developed in Sweden and Austria, were adapted for use in the UK and validated against pheromone trap data collected at the original site of infestation and in the surrounding area from 2019 to 2022. Both models captured some, but not all, of the within‐season variation in trap catches. One, the PHENIPS model, more accurately captured early season flight patterns. The climate in the southeast of England is mild enough to facilitate two generations of the beetle, while further north the pest is expected to be univoltine. At high altitude in Scotland, there is insufficient thermal warming within a season for the completion of one annual generation. Phenological modelling does not explain why the pest failed to establish in England prior to 2018 including when a large amount of infested material was imported to sawmills in the 1940s. Under 2 and 3°C global warming scenarios, we might expect to see an increase in potential voltinism across the UK in the next few decades, increasing the risk of large outbreaks of I. typographus were it allowed to establish. Societal impact statementIn 2018, a breeding population of the European spruce bark‐beetle, Ips typographus, was discovered in woodland in southeast England. Ips typographus is a major forest pest in continental Europe; however, despite previous findings at ports and sawmills, this was the first recorded infestation in the UK. The number of generations per year (voltinism) varies with latitude and altitude. Applying a phenological model, we find that the current climate in southern UK could support two generations, while temperature accumulation in parts of Scotland may be insufficient to support one generation. Global warming will increase voltinism and hence the risk of establishment.