Global responses of wetland methane emissions to extreme temperature and precipitation.

As global warming continues, events of extreme heat or heavy precipitation will become more frequent, while events of extreme cold will become less so. How wetlands around the globe will react to these extreme events is unclear yet critical, because they are among the greatest natural sources of methane(CH4). Here we use seven indices of extreme climate and the rate of methane emission from global wetlands(WME) during 2000-2019 simulated by 12 published models as input data. Our analyses suggest that extreme cold (particularly extreme low temperatures) inhibits WME, whereas extreme heat (particularly extreme high temperatures) accelerates WME. Our results also suggest that daily precipitation >10 mm accelerates WME, while much higher daily precipitation levels can slow WME. The correlation of extreme high temperature and precipitation with rate of WME became stronger during the study period, while the correlation between extreme low temperature and WME rate became weaker.

Investigating the long-term response of plateau vegetation productivity to extreme climate: insights from a case study in Qinghai Province, China.

Over the past three decades, there has been a significant global climate change characterized by an increase in the intensity and frequency of extreme climate events. The vegetation status in Qinghai Province has undergone substantial changes, which are more pronounced than other regions in the Qinghai-Tibet Plateau. However, a clear understanding of the response characteristics of plateau vegetation to extreme climate events is currently lacking. In this study, we investigated the response of net primary productivity (NPP) to different forms of extreme climate events across regions characterized by varying levels of aridity and elevation gradients. Specifically, we observed a significant increase in NPP in relatively arid regions. Our findings indicate that, in relatively arid regions, single episodes of high-intensity precipitation have a pronounced positive effect (higher correlation) on NPP. Furthermore, in high-elevation regions (4000-6000 m), both the intensity and frequency of precipitation events are crucial factors for the increase in regional NPP. However, continuous precipitation can have significant negative impacts on certain areas within relatively wet regions. Regarding temperature, a reduction in the number of frost days within a year has been shown to lead to a significant increase in NPP in arid regions. This reduction allows vegetation growth rate to increase in regions where it was limited by low temperatures. Vegetation conditions in drought-poor regions are expected to continue to improve as extreme precipitation intensifies and extreme low-temperature events decrease.

Climate change effects and their implications for the financial markets: Evidence from the United Kingdom.

This study aims to examine how the climate affects the behaviour of the stock market. To achieve this, we have drawn on daily data from Jan 2005 to Jan 31, 2023 and several environmental factors (e.g., temperature, humidity, cloud cover and visibility) to account for extreme weather conditions using the 21-day moving average and its standard deviation. The empirical analysis has revealed three key findings regarding the impact of weather on the stock market's behaviour. First, various forms of extreme weather conditions consistently lead to influence stock behaviour. Second, results provide valuable insights into market behaviour and help investors to make more informed investment decisions. Third, the weather conditions have new information about the climate risk and investors should react to it swiftly in light of our findings. The saliency theory can help reconcile the theoretical conflicts between the real options and risk-shifting theories when it comes to investing in uncertain and extreme climate conditions.

Evaluation of best management practices for mitigating harmful algal blooms risk in an agricultural lake basin using a watershed model integrated with Bayesian Network approach.

Lake eutrophication caused by nitrogen and phosphorus has led to frequent harmful algal blooms (HABs), especially under the unknown challenges of climate change, which have seriously damaged human life and property. In this study, a coupled SWAT-Bayesian Network (SWAT-BN) model framework was constructed to elucidate the mechanisms between non-point source nitrogen pollution in agricultural lake watersheds and algal activities. A typical agricultural shallow lake basin, the Taihu Basin (TB), China, was chosen in this study, aiming to investigate the effectiveness of best management practices (BMPs) in controlling HABs risks in TB. By modeling total nitrogen concentration of Taihu Lake from 2007 to 2022 with four BMPs (filter strips, grassed waterway, fertilizer application reduction and no-till agriculture), the results indicated that fertilizer application reduction proved to be the most effective BMP with 0.130 of Harmful Algal Blooms Probability Reduction (HABs-PR) when reducing 40% of fertilizer, followed by filter strips with 0.01 of HABs-PR when 4815ha of filter strips were conducted, while grassed waterway and no-till agriculture showed no significant effect on preventing HABs. Furthermore, the combined practice between 40% fertilizer application reduction and 4815ha filter strips construction showed synergistic effects with HABs-PR increasing to 0.171. Precipitation and temperature data were distorted to model scenarios of extreme events. As a result, the combined approach outperformed any single BMP in terms of robustness under extreme climates. This research provides a watershed-level perspective on HABs risks mitigation and highlights the strategies to address HABs under the influence of climate change.

Modelling soil organic carbon dynamics under extreme climate and land use and land cover changes in Western Oromia Regional state, Ethiopia.

The combined effects of changes in climate and land use and land cover can lead to a decrease in soil organic carbon, potentially affecting soil fertility and agricultural output. The study aimed to evaluate the dynamics of soil organic carbon under various extreme climate and land use and land cover scenarios. The data on land use types and extreme climate indices between 2015 and 2070 were, respectively, sourced from the IPCC and the European Copernicus Climate Change Service webpages. The 2015 baseline data for soil organic carbon was obtained from the African Soil Information Service's website. Data quality control and model validation were conducted to ensure the reliability of the collected data and the predictive model. A generalized regression model was chosen for its accuracy and reliability in predicting soil organic carbon dynamics under different shared socio-economic pathways such as SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. The study revealed that variations in extreme climate and land use patterns significantly influenced the organic carbon content of the soil. Increased dry days and the conversion of forest and grassland into farmland resulted in a drop in soil organic carbon, while increased wet days and warming temperatures significantly increase it under each scenario. The soil organic carbon content increased by 5.82 and 2.8 g/kg for the SSP1-2.6 and SSP2-4.5 scenarios, respectively, but decreased by 6.90 g/kg under the SSP5-8.5 scenario. Overall, the higher emission scenarios had a significant negative impact on soil organic carbon levels, while the low emission scenarios had a positive impact. Sustainable land management practices are crucial for preserving and managing soil organic carbon levels.

Does Trust Lead to the Adoption of a Productive Climate Attitude? Relationship Between Trust, Corruption, and Climate Attitude in Developing Regions.

This study examines the relationship between institutional trust from an individual and societal perspective and perceived corruption and climate attitudes of individuals in Latin America. To this end, multilevel modeling was used to test whether the attitudes of individuals from 285 regions of Latin America are influenced by these constructs. Based on the results, it was found that in contrast to studies in developed countries, where institutional trust is positively associated with pro-climate attitudes, in Latin America institutional trust acts as an inhibiting factor and is inversely related to climate attitudes. Furthermore, the perception of corruption in public institutions was also identified as a factor inhibiting collective action to combat climate change. Moderation analysis revealed that individuals' level of education significantly influences this relationship, with a notable difference in climate attitudes between individuals with low and high levels of trust, especially among those with less education. These findings highlight the importance of taking regional specificities into account when examining the relationship between institutional trust, perceptions of corruption, and climate attitudes, and underscore the need for public policies that promote transparency and accountability of institutions to foster effective collective action on climate change.

Can extreme climatic events induce shifts in adaptive potential? A conceptual framework and empirical test with Anolis lizards.

Multivariate adaptation to climatic shifts may be limited by trait integration that causes genetic variation to be low in the direction of selection. However, strong episodes of selection induced by extreme climatic pressures may facilitate future population-wide responses if selection reduces trait integration and increases adaptive potential (i.e., evolvability). We explain this counter-intuitive framework for extreme climatic events in which directional selection leads to increased evolvability and exemplify its use in a case study. We tested this hypothesis in two populations of the lizard Anolis scriptus that experienced hurricane-induced selection on limb traits. We surveyed populations immediately before and after the hurricane as well as the offspring of post-hurricane survivors, allowing us to estimate both selection and response to selection on key functional traits: forelimb length, hindlimb length, and toepad area. The direct selection was parallel in both islands and strong in several limb traits. Even though overall limb integration did not change after the hurricane, both populations showed a non-significant tendency toward increased evolvability after the hurricane despite the direction of selection not being aligned with the axis of most variance (i.e., body size). The population with comparably lower between-limb integration showed a less constrained response to selection. Hurricane-induced selection, not aligned with the pattern of high trait correlations, likely conflicts with selection occurring during normal ecological conditions that favours functional coordination between limb traits, and would likely need to be very strong and more persistent to elicit a greater change in trait integration and evolvability. Future tests of this hypothesis should use G-matrices in a variety of wild organisms experiencing selection due to extreme climatic events.

Weathering the storm: Decreased activity and glucocorticoid levels in response to inclement weather in breeding Columbian ground squirrels.

Inclement weather can rapidly modify the thermal conditions experienced by animals, inducing changes in their behavior, body condition, and stress physiology, and affecting their survival and breeding success. For animals living in variable environments, the extent to which they have adapted to cope with inclement weather is not established, especially for hibernating species with a short active season that are constrained temporally to breed and store energy for subsequent hibernation. We examined behavioral (foraging activity) and physiological (body mass and fecal cortisol metabolites) responses of Columbian ground squirrels (Urocitellus columbianus), small hibernating rodents inhabiting open meadows in Rocky Mountains, to 3 events of inclement weather (two snow storms in May 2021 and May 2022, one heavy rainfall in June 2022). We found that individuals adapted to inclement weather conditions by (1) reducing above-ground activity, including foraging, (2) decreasing the mobilization of stored resources as indicated by a decrease in the activity of the hypothalamo-pituitary-adrenal (HPA) axis and lower fecal cortisol metabolites in the hours/days following periods of inclement weather; and (3) compensating through increased foraging and more local activity when favorable conditions resumed. As a result, body mass and growth did not decrease following short periods of inclement weather. Columbian ground squirrels were well-adapted to short periods of inclement weather, coping via modifications of their behavior and the activity of the HPA axis.

Satellite monitoring reveals short-term cumulative and time-lag effect of drought and heat on autumn photosynthetic phenology in subtropical vegetation.

Comparing with the effect of the average climate change on vegetation phenology, the impacts of extreme climate events remain unclear, especially considering their characteristic cumulative and time-lag effects. Using solar-induced chlorophyll fluorescence (SIF) satellite records, we investigated the cumulative and time-lag effects of drought and heat events on photosynthesis, particularly for the end date of autumn photosynthesis (EOP), in subtropical vegetation in China. Our results showed a negative effect of drought on the delay of EOP, with the cumulative effect on 30.12% (maximum continuous dry days, CDD), 34.82% (dry days, DRD), and 26.14% (dry period, DSDI) of the study area and the general time-lag effect on 50.73% (maximum continuous dry days), 56.61% (dry days), and 47.55% (dry period) of the study area. The cumulative and lagged time were 1-3 months and 2-3 months, respectively. In contrast, the cumulative effect of heat on EOP was observed in 16.27% (warm nights, TN90P), 23.66% (moderate heat days, TX50P), and 19.19% (heavy heat days, TX90P) of the study area, with cumulative time of 1-3 months. The lagged time was 3-4 months, detected in 31.02% (warm nights), 45.86% (moderate heat days), and 36.52% (heavy heat days) of the study area. At the vegetation community level, drought and heat had relatively rapid impacts on EOP in the deciduous broadleaved forest, whereas evergreen forests and bushes responded to heat slowly and took a longer time. Our results revealed that drought and heat have short-term cumulative and time-lag effects on the EOP of subtropical vegetation in China, with varying effects among different vegetation types. These findings provide new insights into the effect of drought and heat on subtropical vegetation and confirm the need to consider these effects in the development of prediction models of autumn phenology for subtropical vegetation.

Hurricane effects on Neotropical lizards span geographic and phylogenetic scales.

Extreme climate events such as droughts, cold snaps, and hurricanes can be powerful agents of natural selection, producing acute selective pressures very different from the everyday pressures acting on organisms. However, it remains unknown whether these infrequent but severe disruptions are quickly erased by quotidian selective forces, or whether they have the potential to durably shape biodiversity patterns across regions and clades. Here, we show that hurricanes have enduring evolutionary impacts on the morphology of anoles, a diverse Neotropical lizard clade. We first demonstrate a transgenerational effect of extreme selection on toepad area for two populations struck by hurricanes in 2017. Given this short-term effect of hurricanes, we then asked whether populations and species that more frequently experienced hurricanes have larger toepads. Using 70 y of historical hurricane data, we demonstrate that, indeed, toepad area positively correlates with hurricane activity for both 12 island populations of Anolis sagrei and 188 Anolis species throughout the Neotropics. Extreme climate events are intensifying due to climate change and may represent overlooked drivers of biogeographic and large-scale biodiversity patterns.

Extreme climate index estimation and projection in association with enviro-meteorological parameters using random forest-ARIMA hybrid model over the Vidarbha region, India.

This study aims to estimate and analyse extreme climate indices such as standardised precipitation index (SPI) coupled with enviro-met (air pollutants and meteorological) parameters over the Vidarbha region from 1980 to 2019. Seasonal SPI, also known as the draught index, is derived from rainfall data using the R language. An attempt is made to determine the best combination of enviro-met on SPI using the random forest (RF) models. The study region is divided into four zones to assess the microclimatic impact on the forecast model. Three sets of data combinations, viz., meteorological and air pollution parameters, are applied for SPI prediction using RF. The autoregressive integrated moving average (ARIMA) model is also used for a future scenario projection. It is observed from the projection results that the drought severity is enhancing with time. The drought severity scale from 1980 to 1989 is found to be between - 1 and 1, but the scale increases from 1990 to 2019 (- 3). From 1990 to 2019, SPI's negative (-) scale has become more prominent in all Vidarbha regions. These trends are indicative of drought severity and will have a significant impact on both life and property.

Long-Term Ecological Research on Ecosystem Responses to Climate Change.

In this article marking the 40th anniversary of the US National Science Foundation's Long Term Ecological Research (LTER) Network, we describe how a long-term ecological research perspective facilitates insights into an ecosystem's response to climate change. At all 28 LTER sites, from the Arctic to Antarctica, air temperature and moisture variability have increased since 1930, with increased disturbance frequency and severity and unprecedented disturbance types. LTER research documents the responses to these changes, including altered primary production, enhanced cycling of organic and inorganic matter, and changes in populations and communities. Although some responses are shared among diverse ecosystems, most are unique, involving region-specific drivers of change, interactions among multiple climate change drivers, and interactions with other human activities. Ecosystem responses to climate change are just beginning to emerge, and as climate change accelerates, long-term ecological research is crucial to understand, mitigate, and adapt to ecosystem responses to climate change.

Survive a Warming Climate: Insect Responses to Extreme High Temperatures.

Global change includes a substantial increase in the frequency and intensity of extreme high temperatures (EHTs), which influence insects at almost all levels. The number of studies showing the ecological importance of EHTs has risen in recent years, but the knowledge is rather dispersed in the contemporary literature. In this article, we review the biological and ecological effects of EHTs actually experienced in the field, i.e., when coupled to fluctuating thermal regimes. First, we characterize EHTs in the field. Then, we summarize the impacts of EHTs on insects at various levels and the processes allowing insects to buffer EHTs. Finally, we argue that the mechanisms leading to positive or negative impacts of EHTs on insects can only be resolved from integrative approaches considering natural thermal regimes. Thermal extremes, perhaps more than the gradual increase in mean temperature, drive insect responses to climate change, with crucial impacts on pest management and biodiversity conservation.

Satellite data and machine learning reveal the incidence of late frost defoliations on Iberian beech forests.

Climate warming is driving an advance of leaf unfolding date in temperate deciduous forests, promoting longer growing seasons and higher carbon gains. However, an earlier leaf phenology also increases the risk of late frost defoliation (LFD) events. Compiling the spatiotemporal patterns of defoliations caused by spring frost events is critical to unveil whether the balance between the current advance in leaf unfolding dates and the frequency of LFD occurrence is changing and represents a threaten for the future viability and persistence of deciduous forests. We combined satellite imagery with machine learning techniques to reconstruct the spatiotemporal patterns of LFD events for the 2003-2018 period in the Iberian range of European beech (Fagus sylvatica), at the drier distribution edge of the species. We used MODIS Vegetation Index Products to generate a Normalized Difference Vegetation Index (NDVI) time series for each 250 × 250 m pixel in a total area of 1,013 km2 (16,218 pixels). A semi-supervised approach was used to train a machine learning model, in which a binary classifier called Support Vector Machine with Global Alignment Kernel was used to differentiate between late frost and non-late frost pixels. We verified the obtained estimates with photointerpretation and existing beech tree-ring chronologies to iteratively improve the model. Then, we used the model output to identify topographical and climatic factors that determined the spatial incidence of LFD. During the study period, LFD was a low recurrence phenomenon that occurred every 15.2 yr on average and showed high spatiotemporal heterogeneity. Most LFD events were condensed in 5 yr and clustered in western forests (86.5% in one-fifth of the pixels) located at high elevation with lower than average precipitation. Elevation and longitude were the major LFD risk factors, followed by annual precipitation. The synergistic effects of increasing drought intensity and rising temperature combined with more frequent late frost events may determine the future performance and distribution of beech forests. This interaction might be critical at the beech drier range edge, where the concentration of LFD at high elevations could constrain beech altitudinal shifts and/or favor species with higher resistance to late frosts.

Ecosystem carbon losses following a climate-induced mangrove mortality in Brazil.

Drought events may induce mangrove mortality and dieback events worldwide as a result of climate extremes. As mangroves sequester large quantities of carbon, quantifying the losses of these stocks following climate disturbances may guide wetland governance strategies globally. In Southeast Brazil, we determined the total ecosystem carbon stocks (TECS) of pristine mangroves that were up to 1851 Mg of carbon per hectare (Mg C ha-1), which are the highest stocks measured from South American and raising estimates of Brazil's mangrove TECS to 0.52 Pg C. A mangrove mortality event in the same estuary resulted in a 14.6 % decrease in TECS (270.5 Mg C ha-1) and loss of 20 % of mangrove soil carbon within less than 2-years. Carbon dioxide emissions from this impact were 992.8 Mg CO2e ha-1, which are slightly lower than emissions from land use disturbances on mangroves worldwide. Our results suggest that climate effects on mangroves can become significant sources of greenhouse gases globally.

Extreme climate historical variation based on tree-ring width record in the Tianshan Mountains of northwestern China.

The increasing frequency and intensity of extreme climate events have caused serious impacts on the service functions of terrestrial ecosystems and the production and life of human society in recent years. The warm nights (TN90p) variable of the 26 extreme climate indicators was the main factor controlling the tree radial growth of Schrenk spruce (Picea schrenkiana) in the Tianshan Mountains region based on the responses of tree-ring width in the 5 sample sites. Therefore, TN90p in the growth season from May to September (TN90p5-9) during 1735-2016 was reconstructed on the basis of the time stability of the growth-climate relationships. The interpretation rate of variance of the reconstructed equation was 45.4% (R2adj = 44.4%, F = 45.7). The reconstruction showed four relatively high TN90p5-9 historic intervals (1747-1798, 1856-1872, 1906-1951, and 2002-2016) and four low intervals (1735-1747, 1798-1856, 1872-1900, and 1951-2002). The occurrence frequency of extreme high values was higher than that of extreme low values during the reconstruction period of 1735-2016. The extreme values of reconstruction were consistent with historical droughts and large-scale volcanic eruptions, indicating that the reconstruction series had high accuracy. Multi-window spectral periodic analysis and spatial correlation analysis revealed that TN90p5-9 variation in the study area was affected by large-scale sea-air stress factors. In particular, the TN90p5-9 obtained by using R/S analysis (rescaled range analysis) will continue to show an upward trend in the relative period of time in the future. This trend will lead to a further decrease in the radial growth of trees and even trigger forest death events.

The adaptive potential of plant populations in response to extreme climate events.

The frequency and magnitude of extreme climate events are increasing with global change, yet we lack predictions and empirical evidence for the ability of wild populations to persist and adapt in response to these events. Here, we used Fisher's Fundamental Theorem of Natural Selection to evaluate the adaptive potential of Lasthenia fremontii, a herbaceous winter annual that is endemic to seasonally flooded wetlands in California, to alternative flooding regimes that occur during El Niño Southern Oscillation (ENSO) events. The results indicate that populations may exhibit greater adaptive potential in response to dry years than wet years, and that the relative performance of populations will change across climate scenarios. More generally, our findings show that extreme climate events can substantially change the potential for populations to adapt to climate change by modulating the expression of standing genetic variation and mean fitness.

Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States.

Increasing drought and extreme rainfall are major threats to maize production in the United States. However, compared to drought impact, the impact of excessive rainfall on crop yield remains unresolved. Here, we present observational evidence from crop yield and insurance data that excessive rainfall can reduce maize yield up to -34% (-17 ± 3% on average) in the United States relative to the expected yield from the long-term trend, comparable to the up to -37% loss by extreme drought (-32 ± 2% on average) from 1981 to 2016. Drought consistently decreases maize yield due to water deficiency and concurrent heat, with greater yield loss for rainfed maize in wetter areas. Excessive rainfall can have either negative or positive impact on crop yield, and its sign varies regionally. Excessive rainfall decreases maize yield significantly in cooler areas in conjunction with poorly drained soils, and such yield loss gets exacerbated under the condition of high preseason soil water storage. Current process-based crop models cannot capture the yield loss from excessive rainfall and overestimate yield under wet conditions. Our results highlight the need for improved understanding and modeling of the excessive rainfall impact on crop yield.

A reality check for climate change experiments: Do they reflect the real world?

Experiments are widely used in ecology, particularly for assessing global change impacts on ecosystem function. However, results from experiments often are inconsistent with observations made under natural conditions, suggesting the need for rigorous comparisons of experimental and observational studies. We conducted such a "reality check" for a grassland ecosystem by compiling results from nine independently conducted climate change experiments. Each experiment manipulated growing season precipitation (GSP) and measured responses in aboveground net primary production (ANPP). We compared results from experiments with long-term (33-yr) annual precipitation and ANPP records to ask if collectively (n = 44 experiment-years) experiments yielded estimates of ANPP, rain-use efficiency (RUE, grams per square meter ANPP per mm precipitation), and the relationship between GSP and ANPP comparable to observations. We found that mean ANPP and RUE from experiments did not deviate from observations. Experiments and observational data also yielded similar functional relationships between ANPP and GSP, but only within the range of historically observed GSP. Fewer experiments imposed extreme levels of GSP (outside the observed 33-yr record), but when these were included, they altered the GSP-ANPP relationship. This result underscores the need for more experiments imposing extreme precipitation levels to resolve how forecast changes in climate regimes will affect ecosystem function in the future.

Seed banks of native forbs, but not exotic grasses, increase during extreme drought.

Extreme droughts such as the one that affected California in 2012-2015 have been linked to severe ecological consequences in perennial-dominated communities such as forests. In annual communities, drought impacts are difficult to assess because many species persist through facultative multiyear seed dormancy, which leads to the development of seed banks. Impacts of extreme drought on the abundance and composition of the seed banks of whole communities are little known. In 80 heterogeneous grassland plots where cover is dominated by ~15 species of exotic annual grasses and diversity is dominated by ~70 species of native annual forbs, we grew out seeds from soil cores collected early in the California drought (2012) and later in the multiyear drought (2014), and analyzed drought-associated changes in the seed bank. Over the course of the study we identified more than 22,000 seedlings to species. We found that seeds of exotic annual grasses declined sharply in abundance during the drought while seeds of native annual forbs increased, a pattern that resembled but was even stronger than the changes in aboveground cover of these groups. Consistent with the expectation that low specific leaf area (SLA) is an indicator of drought tolerance, we found that the community-weighted mean SLA of annual forbs declined both in the seed bank and in the aboveground community, as low-SLA forbs increased disproportionately. In this system, seed dormancy reinforces the indirect benefits of extreme drought to the native forb community.