Roles of Reactive Carbonyl Species (RCS) in Plant Response to Abiotic Stress.

Abiotic and biotic stress conditions lead to production of reactive carbonyl species (RCS) which are lipid peroxide derivatives and have detrimental effects on plant cells especially at high concentrations. There are several molecules that can be classified in RCS; among them, 4-hydroxy-(E)-2-nonenal (HNE) and acrolein are widely recognized and studied because of their toxicity. The toxicity mechanisms of RCS are well known in animals but their roles in plant systems especially signaling aspects in metabolism need to be addressed. This chapter focuses on the production mechanisms of RCS in plants as well as how plants scavenge and modify them to prevent irreversible damage in the cell. We aimed to get a comprehensive look at the literature to summarize the signaling roles of RCS in plant metabolism and their interaction with other signaling mechanisms such as highly recognized reactive oxygen species (ROS) signaling. Changing climate promotes more severe abiotic stress effects on plants which also decrease yield on the field. The effects of abiotic stress conditions on RCS metabolism are also gathered in this chapter including their signaling roles during abiotic stresses. Different methods of measuring RCS in plants are also presented in this chapter to draw more attention to the study of RCS metabolism in plants.

Can Mercury Influence Carbon Dioxide Levels? Implications for the Implementation of the Minamata Convention on Mercury.

The Paris Agreement and the Minamata Convention on Mercury are two of the most important environmental conventions being implemented concurrently, with a focus on reducing carbon and mercury emissions, respectively. The relation between mercury and carbon influences the interactions and outcomes of these two conventions. This perspective investigates the link between mercury and CO2, assessing the consequences and exploring the policy implications of this link. We present scientific evidence showing that mercury and CO2 levels are negatively correlated under natural conditions. As a result of this negative correlation, the CO2 level under the current mercury reduction scenario is predicted to be 2.4-10.1 ppm higher than the no action scenario by 2050, equivalent to 1.0-4.8 years of CO2 increase due to human activity. The underlying causations of this negative correlation are complex and need further research. Economic analysis indicates that there is a trade-off between the benefits and costs of mercury reduction actions. As reducing mercury emission may inadvertently undermine efforts to achieve climate goals, we advocate for devising a coordinated implementation strategy for carbon and mercury conventions to maximize synergies and reduce trade-offs.

Spatial versus spatio-temporal approaches for studying metacommunities: a multi-taxon analysis in Mediterranean and tropical temporary ponds.

Prior research on metacommunities has largely focused on snapshot surveys, often overlooking temporal dynamics. In this study, our aim was to compare the insights obtained from metacommunity analyses based on a spatial approach repeated over time, with a spatio-temporal approach that consolidates all data into a single model. We empirically assessed the influence of temporal variation in the environment and spatial connectivity on the structure of metacommunities in tropical and Mediterranean temporary ponds. Employing a standardized methodology across both regions, we surveyed multiple freshwater taxa in three time periods within the same hydrological year from multiple temporary ponds in each region. To evaluate how environmental, spatial and temporal influences vary between the two approaches, we used nonlinear variation partitioning analyses based on generalized additive models. Overall, this study underscores the importance of adopting spatio-temporal analytics to better understand the processes shaping metacommunities. While the spatial approach suggested that environmental factors had a greater influence, our spatio-temporal analysis revealed that spatial connectivity was the primary driver influencing metacommunity structure in both regions. Temporal effects were equally important as environmental effects, suggesting a significant role of ecological succession in metacommunity structure.

Significant shifts in latitudinal optima of North American birds.

Changes in climate can alter environmental conditions faster than most species can adapt. A prediction under a warming climate is that species will shift their distributions poleward through time. While many studies focus on range shifts, latitudinal shifts in species' optima can occur without detectable changes in their range. We quantified shifts in latitudinal optima for 209 North American bird species over the last 55 y. The latitudinal optimum (m) for each species in each year was estimated using a bespoke flexible non-linear zero-inflated model of abundance vs. latitude, and the annual shift in m through time was quantified. One-third (70) of the bird species showed a significant shift in their optimum. Overall, mean peak abundances of North American birds have shifted northward, on average, at a rate of 1.5 km per year (±0.58 SE), corresponding to a total distance moved of 82.5 km (±31.9 SE) over the last 55 y. Stronger poleward shifts at the continental scale were linked to key species' traits, including thermal optimum, habitat specialization, and territoriality. Shifts in the western region were larger and less variable than in the eastern region, and they were linked to species' thermal optimum, habitat density preference, and habitat specialization. Individual species' latitudinal shifts were most strongly linked to their estimated thermal optimum, clearly indicating a climate-driven response. Displacement of species from their historically optimal realized niches can have dramatic ecological consequences. Effective conservation must consider within-range abundance shifts. Areas currently deemed "optimal" are unlikely to remain so.

Clumped isotopes reveal relationship between mussel growth and river discharge.

Freshwater mussels preserve valuable information about hydrology, climate, and population dynamics, but developing seasonal chronologies can be problematic. Using clumped isotope thermometry, we produced high-resolution reconstructions of modern and historic (~ 1900) temperatures and δ18Owater from mussel shells collected from an impounded river, the Brazos in Texas, before and after damming. We also performed high-resolution growth band analyses to investigate relationships between mussel growth rate, rainfall, and seasonal temperature. Reconstructed δ18Owater and temperature vary little between the modern (3R5) and historic shell (H3R). However, a positive relationship between reconstructed δ18Owater and growth rate in H3R indicates that aside from diminished growth in winter, precipitation and flow rate are the strongest controls on mussel growth in both modern and pre-dam times. Overall, our results demonstrate (1) the impact, both positive and negative, of environmental factors such as flow alteration and temperature on mussel growth and (2) the potential for clumped isotopes in freshwater mussels as a paleohydrology and paleoclimate proxies in terrestrial environments.

Microbial evolution-An under-appreciated driver of soil carbon cycling.

Although substantial advances in predicting the ecological impacts of global change have been made, predictions of the evolutionary impacts have lagged behind. In soil ecosystems, microbes act as the primary energetic drivers of carbon cycling; however, microbes are also capable of evolving on timescales comparable to rates of global change. Given the importance of soil ecosystems in global carbon cycling, we assess the potential impact of microbial evolution on carbon-climate feedbacks in this system. We begin by reviewing the current state of knowledge concerning microbial evolution in response to global change and its specific effect on soil carbon dynamics. Through this integration, we synthesize a roadmap detailing how to integrate microbial evolution into ecosystem biogeochemical models. Specifically, we highlight the importance of microscale mechanistic soil carbon models, including choosing an appropriate evolutionary model (e.g., adaptive dynamics, quantitative genetics), validating model predictions with 'omics' and experimental data, scaling microbial adaptations to ecosystem level processes, and validating with ecosystem-scale measurements. The proposed steps will require significant investment of scientific resources and might require 10-20 years to be fully implemented. However, through the application of multi-scale integrated approaches, we will advance the integration of microbial evolution into predictive understanding of ecosystems, providing clarity on its role and impact within the broader context of environmental change.

Study on the outdoor thermal comfort of college students under different activity intensities in a high-altitude climate zone.

Introduction: Research on the outdoor thermal comfort (OTC) of a university campus is beneficial to the physical and mental health of college students. Methods: In this study, the OTC of students attending Tibet University in Lhasa, which experiences high-altitude cold climate conditions, under different activity intensities was studied using field measurements and a questionnaire survey. Results: With the increase in activity intensity, the comfort physiologically equivalent temperature (PET) value gradually increased in summer, while the comfortable PET value gradually decreased in winter. The most comfortable PET value is 17.6°C in summer and 11.5°C in winter. The neutral PET of Tibetan college students during outdoor activities in summer was 16.3°C, and the neutral PET of outdoor activities in winter was 12.1°C. Gender and ethnicity had different effects on thermal sensation under different activity intensities. Under vigorous-intensity activities, PET in winter and summer had the greatest influence on thermal sensation. The situation was different under moderate-intensity activity. PET had the greatest influence on thermal sensation in summer, and Tmrt had the greatest influence on thermal sensation in winter. Discussion: These findings provide a basis for an improved design of the outdoor environment under different outdoor activity intensities in high-altitude areas.

Paleoclimate data assimilation with CLIMBER-X: An ensemble Kalman filter for the last deglaciation.

Using the climate model CLIMBER-X, we present an efficient method for assimilating the temporal evolution of surface temperatures for the last deglaciation covering the period 22000 to 6500 years before the present. The data assimilation methodology combines the data and the underlying dynamical principles governing the climate system to provide a state estimate of the system, which is better than that which could be obtained using just the data or the model alone. In applying an ensemble Kalman filter approach, we make use of the advances in the parallel data assimilation framework (PDAF), which provides parallel data assimilation functionality with a relatively small increase in computation time. We find that the data assimilation solution depends strongly on the background evolution of the decaying ice sheets rather than the assimilated temperatures. Two different ice sheet reconstructions result in a different deglacial meltwater history, affecting the large-scale ocean circulation and, consequently, the surface temperature. We find that the influence of data assimilation is more pronounced on regional scales than on the global mean. In particular, data assimilation has a stronger effect during millennial warming and cooling phases, such as the Bølling-Allerød and Younger Dryas, especially at high latitudes with heterogeneous temperature patterns. Our approach is a step toward a comprehensive paleo-reanalysis on multi-millennial time scales, including incorporating available paleoclimate data and accounting for their uncertainties in representing regional climates.

Density-dependent species interactions modulate alpine treeline shifts.

Species interactions such as facilitation and competition play a crucial role in driving species range shifts. However, density dependence as a key feature of these processes has received little attention in both empirical and modelling studies. Herein, we used a novel, individual-based treeline model informed by rich in situ observations to quantify the contribution of density-dependent species interactions to alpine treeline dynamics, an iconic biome boundary recognized as an indicator of global warming. We found that competition and facilitation dominate in dense versus sparse vegetation scenarios respectively. The optimal balance between these two effects was identified at an intermediate vegetation thickness where the treeline elevation was the highest. Furthermore, treeline shift rates decreased sharply with vegetation thickness and the associated transition from positive to negative species interactions. We thus postulate that vegetation density must be considered when modelling species range dynamics to avoid inadequate predictions of its responses to climate warming.

Vertical structural complexity of plant communities represents the combined effects of resource acquisition and environmental stress on the Tibetan Plateau.

The vertical structural complexity (VSC) of plant communities reflects the occupancy of spatial niches and is closely related to resource utilization and environmental adaptation. However, understanding the large-scale spatial pattern of VSC and its underlying mechanisms remains limited. Here, we systematically investigate 2013 plant communities through grid sampling on the Tibetan Plateau. VSC is quantified as the maximum plant height within a plot (Height-max), coefficient of variation of plant height (Height-var), and Shannon evenness of plant height (Height-even). Precipitation dominates the spatial variation in VSC in forests and shrublands, supporting the classic physiological tolerance hypothesis. In contrast, for alpine meadows, steppes, and desert grasslands in extreme environments, non-resource limiting factors (e.g., wide diurnal temperature ranges and strong winds) dominate VSC variation. Generally, with the shifting of climate from favorable to extreme, the effect of resource availability gradually decreases, but the effect of non-resource limiting factors gradually increases, and that the physiological tolerance hypothesis only applicable in favorable conditions. With the help of machine learning models, maps of VSC at 1-km resolution are produced for the Tibetan Plateau. Our findings and maps of VSC provide insights into macroecological studies, especially for adaptation mechanisms and model optimization.

Uppermost global tree elevations are primarily limited by low temperature or insufficient moisture.

The impact of anthropogenic global warming has induced significant upward dispersal of trees to higher elevations at alpine treelines. Assessing vertical deviation from current uppermost tree distributions to potential treeline positions is crucial for understanding ecosystem responses to evolving global climate. However, due to data resolution constraints and research scale limitation, comprehending the global pattern of alpine treeline elevations and driving factors remains challenging. This study constructed a comprehensive quasi-observational dataset of uppermost tree distribution across global mountains using Google Earth imagery. Validating the isotherm of mean growing-season air temperature at 6.6 ± 0.3°C as the global indicator of thermal treeline, we found that around two-thirds of uppermost tree distribution records significantly deviated from it. Drought conditions constitute the primary driver in 51% of cases, followed by mountain elevation effect which indicates surface heat (27%). Our analyses underscore the multifaceted determinants of global patterns of alpine treeline, explaining divergent treeline responses to climate warming. Moisture, along with temperature and disturbance, plays the most fundamental roles in understanding global variation of alpine treeline elevation and forecasting alpine treeline response to ongoing global warming.

Weather or not-Global climate databases: Reliable on tropical mountains?

Global, spatially interpolated climate datasets such as WorldClim and CHELSA, widely used in research, are based on station data, which are rare in tropical mountains. However, such biodiversity hotspots are of high ecological interest and require accurate data. Therefore, the quality of such gridded datasets needs to be assessed. This poses a kind of dilemma, as proving the reliability of these potentially weakly modelled data is usually not possible due to the lack of stations. Using a unique climate dataset with 170 stations, mainly from the montane and alpine zones of sixteen mountains in Tanzania including Kilimanjaro, we show that the accuracy of such datasets is very poor. Not only is the maximum amount of mean annual precipitation drastically underestimated (partly more than 50%), but also the elevation of the precipitation maximum deviates up to 850m. Our results show that, at least in tropical regions, they should be used with greater caution than before.

Rainfall as a driver for near-surface turbulence and air-water gas exchange in freshwater aquatic systems.

Gas fluxes from aquatic ecosystems are a significant component of the carbon cycle. Gas exchange across the air-water interface is regulated by near-surface turbulence and can be controlled by different atmospheric forcing conditions, with wind speed and surface buoyancy flux being the most recognized drivers in empirical studies and modeling approaches. The effect of rainfall on near-surface turbulence has rarely been studied and a consistent relationship between rain rate and near-surface turbulence has not yet been established. In this study, we addressed some limitations still present in the quantitative understanding of the effect of rain rate on near-surface turbulence and on the resulting gas transfer velocity in freshwater. We performed controlled laboratory experiments over a wide range of rain rates (7 to 90 mm h-1) and estimated gas transfer velocities from high-resolution measurements of O2 concentration, while rain-induced turbulence was characterized based on particle image velocimetry. We found that the rain-induced dissipation rates of turbulent kinetic energy declined with depth following a consistent power-law relationship. Both energy dissipation rates and gas transfer velocity increased systematically with the rain rate. The results confirm a causal relationship between rainfall, turbulence, and gas exchange. We propose a power-law relationship between near-surface turbulent dissipation rates and rain rate. In combination with surface renewal theory, we derived a direct relationship between gas transfer velocity and rain rate, which can be used to assess the importance of short-term drivers, such as rain events, on gas dynamics and biogeochemical cycling in aquatic ecosystems.

Mediterranean fruit fly population phenological patterns are strongly affected by elevation and host presence.

The Mediterranean fruit fly (medfly) (Ceratitis capitata, Diptera: Tephritidae), is an extremely polyphagous pest that threatens the fruit production and trading industry worldwide. Monitoring C. capitata populations and analysing its dynamics and phenology is considered of outmost importance for designing and implementing sound management approaches. The aim of this study was to investigate the factors regulating the population dynamics of the C. capitata in a coastal and semi-mountainous area. We focused on effects of topography (e.g. elevation), host presence and seasonal patterns of ripening on the phenological patterns considering data collected in 2008. The experimental area is characterized by mixed fruit orchards, and Mediterranean climate with mild winters. Two trap types were used for population monitoring. The female targeted McPhail type and the male targeted Jackson type. Traps were placed in farms located at different elevations and landscape morphology (coastal and semi-mountainous areas). The main crops included citrus, apples, peaches, plums, pears, figs, quinces and apricots. Adult captures were first recorded in May, peaked in mid-summer and mid-autumn and almost ceased at the end of the season (January 2008). Captures in the coastal areas preceded that of highlands by 15 days. Most of the adults detected during the fruit ripening of late stone fruit cultivars (first peak) and citrus (second peak). The probability of capturing the first adults preceded almost three weeks the peak of adult captures either considering the elevation or host focus analyses. The results provide valuable information on the seasonal population trend of C. capitata in mixed fruit Mediterranean orchards and can support the set-up of IPM systems in areas with various landscapes and different hosts throughout the fruit growing season.

Food webs: Where is the uniqueness?

Conservation planning relies on mapping areas of unique biological diversity and high vulnerability. A new study has found that unique trophic networks are threatened by warming climate in the Arctic and high human footprint in southern Europe.

The environmental conditions of endemism hotspots shape the functional traits of mammalian assemblages.

Endemic (small-ranged) species are distributed non-randomly across the globe. Regions of high topography and stable climates have higher endemism than flat, climatically unstable regions. However, it is unclear how these environmental conditions interact with and filter mammalian traits. Here, we characterize the functional traits of highly endemic mammalian assemblages in multiple ways, testing the hypothesis that these assemblages are trait-filtered (less functionally diverse) and dominated by species with traits associated with small range sizes. Compiling trait data for more than 5000 mammal species, we calculated assemblage means and multidimensional functional metrics to evaluate the distribution of traits across each assemblage. We then related these metrics to the endemism of global World Wildlife Fund ecoregions using linear models and phylogenetic fourth-corner regression. Highly endemic mammalian assemblages had small average body masses, low fecundity, short lifespans and specialized habitats. These traits relate to the stable climate and rough topography of endemism hotspots and to mammals' ability to expand their ranges, suggesting that the environmental conditions of endemism hotspots allowed their survival. Furthermore, species living in endemism hotspots clustered near the edges of their communities' functional spaces, indicating that abiotic trait filtering and biotic interactions act in tandem to shape these communities.

Land potential assessment and trend-analysis using 2000-2021 FAPAR monthly time-series at 250 m spatial resolution.

The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the hypothetical absence of short-term anthropogenic influence, such as intensive agriculture and urbanization. Knowledge on this ecological land potential could support the assessment of levels of land degradation as well as restoration potentials. Monthly aggregated FAPAR time-series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8-day GLASS FAPAR V6 product for 2000-2021 and used to determine long-term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio-physical variables representing climate, terrain, landform, and vegetation cover, as well as several variables representing human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (extremely randomized trees, gradient descended trees and artificial neural network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R2 of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual vs. potential FAPAR. The produced global maps of actual vs. potential FAPAR and long-term trends were each spatially matched with stable and transitional land cover classes. The assessment showed large negative FAPAR gaps (actual lower than potential) for classes: urban, needle-leave deciduous trees, and flooded shrub or herbaceous cover, while strong negative FAPAR trends were found for classes: urban, sparse vegetation and rainfed cropland. On the other hand, classes: irrigated or post-flooded cropland, tree cover mixed leaf type, and broad-leave deciduous showed largely positive trends. The framework allows land managers to assess potential land degradation from two aspects: as an actual declining trend in observed FAPAR and as a difference between actual and potential vegetation FAPAR.

Global annual wetland dataset at 30 m with a fine classification system from 2000 to 2022.

Wetlands play a key role in maintaining ecological balance and climate regulation. However, due to the complex and variable spectral characteristics of wetlands, there are no publicly available global 30-meter time-series wetland dynamic datasets at present. In this study, we present novel global 30 m annual wetland maps (GWL_FCS30D) using time-series Landsat imagery on the Google Earth Engine platform, covering the period of 2000-2022 and containing eight wetland subcategories. Specifically, we make full use of our prior globally distributed wetland training sample pool, and adopt the local adaptive classification and spatiotemporal consistency checking algorithm to generate annual wetland maps. The GWL_FCS30D maps were found to achieve an overall accuracy and Kappa coefficient of 86.95 ± 0.44% and 0.822, respectively, in 2020, and show great temporal variability in the United States and the European Union. We expect the dataset would provide vital support for wetland ecosystems protection and sustainable development.