The importance of internal climate variability in climate impact projections.

Uncertainty in climate projections is driven by three components: scenario uncertainty, intermodel uncertainty, and internal variability. Although socioeconomic climate impact studies increasingly take into account the first two components, little attention has been paid to the role of internal variability, although underestimating this uncertainty may lead to underestimating the socioeconomic costs of climate change. Using large ensembles from seven coupled general circulation models with a total of 414 model runs, we partition the climate uncertainty in classic dose-response models relating county-level corn yield, mortality, and per-capita gross domestic product to temperature in the continental United States. The partitioning of uncertainty depends on the time frame of projection, the impact model, and the geographic region. Internal variability represents more than 50% of the total climate uncertainty in certain projections, including mortality projections for the early 21st century, although its relative influence decreases over time. We recommend including uncertainty due to internal variability for many projections of temperature-driven impacts, including early-century and midcentury projections, projections in regions with high internal variability such as the Upper Midwest United States, and impacts driven by nonlinear relationships.

Potential for perceived failure of stratospheric aerosol injection deployment.

As anthropogenic activities warm the Earth, the fundamental solution of reducing greenhouse gas emissions remains elusive. Given this mitigation gap, global warming may lead to intolerable climate changes as adaptive capacity is exceeded. Thus, there is emerging interest in solar radiation modification, which is the process of deliberately increasing Earth's albedo to cool the planet. Stratospheric aerosol injection (SAI)-the theoretical deployment of particles in the stratosphere to enhance reflection of incoming solar radiation-is one strategy to slow, pause, or reverse global warming. If SAI is ever pursued, it will likely be for a specific aim, such as affording time to implement mitigation strategies, lessening extremes, or reducing the odds of reaching a biogeophysical tipping point. Using an ensemble climate model experiment that simulates the deployment of SAI in the context of an intermediate greenhouse gas trajectory, we quantified the probability that internal climate variability masks the effectiveness of SAI deployment on regional temperatures. We found that while global temperature was stabilized, substantial land areas continued to experience warming. For example, in the SAI scenario we explored, up to 55% of the global population experienced rising temperatures over the decade following SAI deployment and large areas exhibited high probability of extremely hot years. These conditions could cause SAI to be perceived as a failure. Countries with the largest economies experienced some of the largest probabilities of this perceived failure. The potential for perceived failure could therefore have major implications for policy decisions in the years immediately following SAI deployment.

Internal variability and forcing influence model-satellite differences in the rate of tropical tropospheric warming.

Climate-model simulations exhibit approximately two times more tropical tropospheric warming than satellite observations since 1979. The causes of this difference are not fully understood and are poorly quantified. Here, we apply machine learning to relate the patterns of surface-temperature change to the forced and unforced components of tropical tropospheric warming. This approach allows us to disentangle the forced and unforced change in the model-simulated temperature of the midtroposphere (TMT). In applying the climate-model-trained machine-learning framework to observations, we estimate that external forcing has produced a tropical TMT trend of 0.25 ± 0.08 K⋅decade-1 between 1979 and 2014, but internal variability has offset this warming by 0.07 ± 0.07 K⋅decade-1. Using the Community Earth System Model version 2 (CESM2) large ensemble, we also find that a discontinuity in the variability of prescribed biomass-burning aerosol emissions artificially enhances simulated tropical TMT change by 0.04 K⋅decade-1. The magnitude of this aerosol-forcing bias will vary across climate models, but since the latest generation of climate models all use the same emissions dataset, the bias may systematically enhance climate-model trends over the satellite era. Our results indicate that internal variability and forcing uncertainties largely explain differences in satellite-versus-model warming and are important considerations when evaluating climate models.

Skin microbiome disturbance linked to drought-associated amphibian disease.

The onset of global climate change has led to abnormal rainfall patterns, disrupting associations between wildlife and their symbiotic microorganisms. We monitored a population of pumpkin toadlets and their skin bacteria in the Brazilian Atlantic Forest during a drought. Given the recognized ability of some amphibian skin bacteria to inhibit the widespread fungal pathogen Batrachochytrium dendrobatidis (Bd), we investigated links between skin microbiome health, susceptibility to Bd and host mortality during a die-off event. We found that rainfall deficit was an indirect predictor of Bd loads through microbiome disruption, while its direct effect on Bd was weak. The microbiome was characterized by fewer putative Bd-inhibitory bacteria following the drought, which points to a one-month lagged effect of drought on the microbiome that may have increased toadlet susceptibility to Bd. Our study underscores the capacity of rainfall variability to disturb complex host-microbiome interactions and alter wildlife disease dynamics.

Declined terrestrial ecosystem resilience.

Terrestrial ecosystem resilience is crucial for maintaining the structural and functional stability of ecosystems following disturbances. However, changes in resilience over the past few decades and the risk of future resilience loss under ongoing climate change are unclear. Here, we identified resilience trends using two remotely sensed vegetation indices, analyzed the relative importance of potential driving factors to resilience changes, and finally assessed the risk of future resilience loss based on the output data of eight models from CMIP6. The results revealed that more than 60% of the ecosystems experienced a conversion from an increased trend to a declined trend in resilience. Attribution analysis showed that the most important driving factors of declined resilience varied regionally. The declined trends in resilience were associated with increased precipitation variability in the tropics, decreased vegetation cover in arid region, increased temperature variability in temperate regions, and increased average temperature in cold regions. CMIP6 reveals that terrestrial ecosystems under SPP585 are expected to experience more intense declines in resilience than those under SSP126 and SSP245, particularly in cold regions. These results highlight the risk of continued degradation of ecosystem resilience in the future and the urgency of climate mitigation actions.

Acclimation capacity to global warming of amphibians and freshwater fishes: Drivers, patterns, and data limitations.

Amphibians and fishes play a central role in shaping the structure and function of freshwater environments. These organisms have a limited capacity to disperse across different habitats and the thermal buffer offered by freshwater systems is small. Understanding determinants and patterns of their physiological sensitivity across life history is, therefore, imperative to predicting the impacts of climate change in freshwater systems. Based on a systematic literature review including 345 experiments with 998 estimates on 96 amphibian (Anura/Caudata) and 93 freshwater fish species (Teleostei), we conducted a quantitative synthesis to explore phylogenetic, ontogenetic, and biogeographic (thermal adaptation) patterns in upper thermal tolerance (CTmax) and thermal acclimation capacity (acclimation response ratio, ARR) as well as the influence of the methodology used to assess these thermal traits using a conditional inference tree analysis. We found globally consistent patterns in CTmax and ARR, with phylogeny (taxa/order), experimental methodology, climatic origin, and life stage as significant determinants of thermal traits. The analysis demonstrated that CTmax does not primarily depend on the climatic origin but on experimental acclimation temperature and duration, and life stage. Higher acclimation temperatures and longer acclimation times led to higher CTmax values, whereby Anuran larvae revealed a higher CTmax than older life stages. The ARR of freshwater fishes was more than twice that of amphibians. Differences in ARR between life stages were not significant. In addition to phylogenetic differences, we found that ARR also depended on acclimation duration, ramping rate, and adaptation to local temperature variability. However, the amount of data on early life stages is too small, methodologically inconsistent, and phylogenetically unbalanced to identify potential life cycle bottlenecks in thermal traits. We, therefore, propose methods to improve the robustness and comparability of CTmax/ARR data across species and life stages, which is crucial for the conservation of freshwater biodiversity under climate change.

Marine reserves can buffer against environmental fluctuations for overexploited but not sustainably harvested fisheries.

Globally, decision-makers are seeking management levers that can mitigate the negative effects of climate change on ecosystems that have already been transformed from their natural state by the effects of fishing. An important question is whether marine reserves can provide buffering (i.e., population-level resilience) against climate disturbances to fished populations. Here, we examine one aspect of this question, by asking whether marine reserves can reduce the variability in either overall biomass or in fishery yield, in the face of environmental variability. This could happen because greater reproduction of longer-lived, larger fish inside reserves could supplement recruitment to the fished portion of the population. We addressed this question using age-structured population models, assuming a system where some proportion of the coastline is protected in marine reserves (0%-30%), and the remainder is fished (at a range of possible harvest rates). We modeled populations with sedentary adults and dispersal via a larval pool. Since recent extreme climate events (e.g., marine heatwaves) have reduced juvenile survival for some fish species, we assumed that environmental variability affected the survival of the first age class in our model. We viewed population variability as a question of buffering, measured as the proportion of time a simulated population spent below a target reference point, with the idea that marine reserves could prevent the population from reaching low levels in the face of fishing and environmental variability. We found that fisheries with more area in marine reserves always had less variability in biomass. However, adding marine reserves only reduced variability in fisheries yield when the fished part of the population was being harvested at a rate exceeding the maximum sustainable yield. This new result on reducing variability is in line with previous findings that the "spillover" effects of marine reserve benefits to fishery yields only accrue when the fishery outside reserve boundaries is being overharvested.

Anthropogenic Fingerprint Detectable in Upper Tropospheric Ozone Trends Retrieved from Satellite.

Tropospheric ozone (O3) is a strong greenhouse gas, particularly in the upper troposphere (UT). Limited observations point to a continuous increase in UT O3 in recent decades, but the attribution of UT O3 changes is complicated by large internal climate variability. We show that the anthropogenic signal ("fingerprint") in the patterns of UT O3 increases is distinguishable from the background noise of internal variability. The time-invariant fingerprint of human-caused UT O3 changes is derived from a 16-member initial-condition ensemble performed with a chemistry-climate model (CESM2-WACCM6). The fingerprint is largest between 30°S and 40°N, especially near 30°N. In contrast, the noise pattern in UT O3 is mainly associated with the El Niño-Southern Oscillation (ENSO). The UT O3 fingerprint pattern can be discerned with high confidence within only 13 years of the 2005 start of the OMI/MLS satellite record. Unlike the UT O3 fingerprint, the lower tropospheric (LT) O3 fingerprint varies significantly over time and space in response to large-scale changes in anthropogenic precursor emissions, with the highest signal-to-noise ratios near 40°N in Asia and Europe. Our analysis reveals a significant human effect on Earth's atmospheric chemistry in the UT and indicates promise for identifying fingerprints of specific sources of ozone precursors.

Timing outweighs magnitude of rainfall in shaping population dynamics of a small mammal species in steppe grassland.

Climate change-induced shifts in species phenology differ widely across trophic levels, which may lead to consumer-resource mismatches with cascading population and ecosystem consequences. Here, we examined the effects of different rainfall patterns (i.e., timing and amount) on the phenological asynchrony of population of a generalist herbivore and their food sources in semiarid steppe grassland in Inner Mongolia. We conducted a 10-y (2010 to 2019) rainfall manipulation experiment in 12 0.48-ha field enclosures and found that moderate rainfall increases during the early rather than late growing season advanced the timing of peak reproduction and drove marked increases in population size through increasing the biomass of preferred plant species. By contrast, greatly increased rainfall produced no further increases in vole population growth due to the potential negative effect of the flooding of burrows. The increases in vole population size were more coupled with increased reproduction of overwintered voles and increased body mass of young-of-year than with better survival. Our results provide experimental evidence for the fitness consequences of phenological mismatches at the population level and highlight the importance of rainfall timing on the population dynamics of small herbivores in the steppe grassland environment.

Long-term decrease in Asian monsoon rainfall and abrupt climate change events over the past 6,700 years.

Asian summer monsoon (ASM) variability and its long-term ecological and societal impacts extending back to Neolithic times are poorly understood due to a lack of high-resolution climate proxy data. Here, we present a precisely dated and well-calibrated tree-ring stable isotope chronology from the Tibetan Plateau with 1- to 5-y resolution that reflects high- to low-frequency ASM variability from 4680 BCE to 2011 CE. Superimposed on a persistent drying trend since the mid-Holocene, a rapid decrease in moisture availability between ∼2000 and ∼1500 BCE caused a dry hydroclimatic regime from ∼1675 to ∼1185 BCE, with mean precipitation estimated at 42 ± 4% and 5 ± 2% lower than during the mid-Holocene and the instrumental period, respectively. This second-millennium-BCE megadrought marks the mid-to late Holocene transition, during which regional forests declined and enhanced aeolian activity affected northern Chinese ecosystems. We argue that this abrupt aridification starting ∼2000 BCE contributed to the shift of Neolithic cultures in northern China and likely triggered human migration and societal transformation.

Natural variability contributes to model-satellite differences in tropical tropospheric warming.

A long-standing discrepancy exists between general circulation models (GCMs) and satellite observations: The multimodel mean temperature of the midtroposphere (TMT) in the tropics warms at approximately twice the rate of observations. Using a large ensemble of simulations from a single climate model, we find that tropical TMT trends (1979-2018) vary widely and that a subset of realizations are within the range of satellite observations. Realizations with relatively small tropical TMT trends are accompanied by subdued sea-surface warming in the tropical central and eastern Pacific. Observed changes in sea-surface temperature have a similar pattern, implying that the observed tropical TMT trend has been reduced by multidecadal variability. We also assess the latest generation of GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). CMIP6 simulations with muted warming over the central and eastern Pacific also show reduced tropical tropospheric warming. We find that 13% of the model realizations have tropical TMT trends within the observed trend range. These simulations are from models with both small and large climate sensitivity values, illustrating that the magnitude of tropical tropospheric warming is not solely a function of climate sensitivity. For global averages, one-quarter of model simulations exhibit TMT trends in accord with observations. Our results indicate that even on 40-y timescales, natural climate variability is important to consider when comparing observed and simulated tropospheric warming and is sufficiently large to explain TMT trend differences between models and satellite data.

Assessment of climatic and anthropogenic influences on vegetation dynamics in China: a consideration of climate time-lag and cumulative effects.

Determining the factors that drive vegetation variation is complicated by the intricate interactions between climatic and anthropogenic influences. Neglecting the short-term time-lag and cumulative effects of climate on vegetation growth (i.e., temporal effects) exacerbates the uncertainty in attributing long-term vegetation dynamics. This study evaluated the climatic and anthropogenic influences on vegetation dynamics in China from 2000 to 2019 by analyzing normalized difference vegetation index (NDVI), temperature, precipitation, solar radiation, and ten anthropogenic indicators through linear regression, correlation, multiple linear regression (MLR), residual, and principal component analyses. Across most regions, growing season NDVI (G-NDVI) exhibited heightened sensitivity to climatic variables from earlier periods or from both earlier and current periods, signaling extensive temporal climatic effects. Constructing new time series for temperature, precipitation, and solar radiation from 2000 to 2019, based on the optimal vegetation response timing to each climatic variable, revealed significant correlations with G-NDVI across 27.9%, 26.7%, and 23.3% of the study area, respectively. Climate variability and anthropogenic activities contributed 45% and 55% to the G-NDVI increase in China, respectively. Afforestation significantly promoted vegetation greening, while agricultural development had a marginally positive influence. In contrast, urbanization negatively impacted vegetation, particularly in eastern China, where farmland conversion to constructed land has been prevalent over the past two decades. Neglecting temporal effects would significantly reduce the areas with robust MLR models linking G-NDVI to climatic variables, thereby increasing uncertainty in attributing vegetation changes. The findings highlight the necessity of integrating multiple anthropogenic factors and climatic temporal effects in evaluating vegetation dynamics and ecological restoration.

A review of recent advances and future prospects in calculation of reference evapotranspiration in Bangladesh using soft computing models.

Evapotranspiration (ETo) is a complex and non-linear hydrological process with a significant impact on efficient water resource planning and long-term management. The Penman-Monteith (PM) equation method, developed by the Food and Agriculture Organization of the United Nations (FAO), represents an advancement over earlier approaches for estimating ETo. Eto though reliable, faces limitations due to the requirement for climatological data not always available at specific locations. To address this, researchers have explored soft computing (SC) models as alternatives to conventional methods, known for their exceptional accuracy across disciplines. This critical review aims to enhance understanding of cutting-edge SC frameworks for ETo estimation, highlighting advancements in evolutionary models, hybrid and ensemble approaches, and optimization strategies. Recent applications of SC in various climatic zones in Bangladesh are evaluated, with the order of preference being ANFIS > Bi-LSTM > RT > DENFIS > SVR-PSOGWO > PSO-HFS due to their consistently high accuracy (RMSE and R2). This review introduces a benchmark for incorporating evolutionary computation algorithms (EC) into ETo modeling. Each subsection addresses the strengths and weaknesses of known SC models, offering valuable insights. The review serves as a valuable resource for experienced water resource engineers and hydrologists, both domestically and internationally, providing comprehensive SC modeling studies for ETo forecasting. Furthermore, it provides an improved water resources monitoring and management plans.

Emerging trends and spatial shifts of drought potential across global river basins.

Droughts have devastating effects on various sectors and are difficult to quantify and track because of the invisible and slow but prevalent propagation. This dilemma is more significant in the case of the complex interactions between land and atmosphere mechanisms, which are inadequately considered in previous drought metrics. Here, we investigate the spatiotemporal variability of the recently devised metric called 'Drought Potential Index (DPI)', which incorporates the antecedent land water storage and current precipitation. Using the spatial weighted centroid method, we elucidate the emerging spatial movement of the DPI within 168 major global river basins and analyze its influential factors. Improved drought detection and performance disparity of DPI as compared with multi-scale (i.e., 1, 3, 6, 9, 12-month) Standardized Precipitation Index, ensemble soil moisture anomaly, and Total Storage Deficit Index corroborate the robustness and improved insights of DPI. Higher increasing trends in DPI are detected over dryland basins (0.39 ± 0.43 %/a) than in the humid zones (0.15 ± 0.34 %/a). Six hotspot basins, namely, Don, Yellow, Haihe, Rio Grande, Sao Francisco, and Ganges river basins, are identified with increasing (2.1-3.5%/a) DPI during 2003-2021. The interannual occurrence of the highest DPI, spatial shifts, and relative contribution of DPI's constituent variables correspond well to the climatic and anthropogenic changes in humid and dry land basins. The absolute latitudinal/longitudinal shifts of ∼2° (as high as ∼3.2/4.9°) in DPI in 30% (47 out of 168 basins) of the global basins highlight the need for analyzing the water scarcity problems from both the perspectives of long-term trends and spatial shifts. Our findings provide a global assessment of the spatiotemporal shifts of drought potential and will be beneficial to understanding the anthropogenic and climatic influences on water resource management under a changing environment.

Active remote sensing data and dispersal processes improve predictions for an invasive aquatic plant during a climatic extreme in Great Lakes coastal wetlands.

Invasive aquatic plants pose a significant threat to coastal wetlands. Predicting suitable habitat for invasive aquatic plants in uninvaded yet vulnerable wetlands remains a critical task to prevent further harm to these ecosystems. The integration of remote sensing and geospatial data into species distribution models (SDMs) can help predict where new invasions are likely to occur by generating spatial outputs of habitat suitability. The objective of this study was to assess the efficacy of utilizing active remote sensing datasets (synthetic aperture radar (SAR) and light detection and ranging (LiDAR) with multispectral imagery and other geospatial data in predicting the potential distribution of an invasive aquatic plant based on its biophysical habitat requirements and dispersal dynamics. We also considered a climatic extreme (lake water levels) during the study period to investigate how these predictions may change between years. We compiled a time series of 1628 field records on the occurrence of Hydrocharis morsus-ranae (European frogbit; EFB) with nine remote sensing and geospatial layers as predictors to train and assess the predictive capacity of random forest models to generate habitat suitability in Great Lakes coastal wetlands in northern Michigan, USA. We found that SAR and LiDAR data were useful as proxies for key biophysical characteristics of EFB habitat (emergent vegetation and water depth), and that a vegetation index calculated from spectral imagery was one of the most important predictors of EFB occurrence. Our SDM using all predictors yielded the highest mean overall accuracy of 88.3% and a true skill statistic of 75.7%. Two of the most important predictors of EFB occurrence were dispersal-related: 1) distance to the nearest known EFB population (m), and 2) distance to nearest public boat launch (m). The area of highly suitable habitat (pixels assigned ≥0.8 probability) was 74% larger during a climatically extreme high water-level year compared to an average year. Our findings demonstrate that active remote sensing can be integrated into SDM workflows as proxies for important drivers of invasive species expansion that are difficult to measure in other ways. Moreover, the importance of a proxy variable for endogenous dispersal (distance to nearest known population) in these SDMs indicates that EFB is currently spreading, and thereby less influenced by within-site dynamics such as interspecific competition. Lastly, we found that extreme climatic conditions can dramatically change this species' niche, and therefore we recommend that future studies include dynamic climate conditions in SDMs to more accurately forecast the spread during early invasion stages.

Experiences of ecosystem changes on food services of mopane woodland communities in Vhembe, South Africa.

Mopane woodlands have been shifting. While it is important to understand the spatial patterns that characterise this phenomenon, it is even more important to understand the impacts of shifting Mopane woodlands on rural communities that rely on them. This study sought to establish the impacts of shifting mopane woodlands on the production of indigenous plant food in Ward 12 of Musina local municipality in the Vhembe District municipality in the Limpopo province of South Africa. To accomplish this, the study utilised a hybrid inductive approach involving thematic-based questionnaire interviews and an exploratory view to gain insight into the narratives of focus group participants. Results revealed that seven (7) out of eleven (11) indigenous plant foods are becoming extinct, thereby limiting food sources of indigenous and local people who used to rely on them. The spatial pattern of the plant foods that are still available has now changed as they no longer grow within the reach of local communities. The community members are struggling to adapt to these changes. From these observations, we recommend that local and regional levels' policies related to natural resource management should consider the unique challenges faced by communities experiencing disruptive ecosystem changes and provide the necessary support for sustainable adaptation.

Patterns of rainfall and temperature and their relationships with potential evapotranspiration rates over the period 1981-2022 in parts of central, western, southern, and southwestern Uganda.

Uganda in East Africa is experiencing highly variable rainfall which is exacerbated by temperatures warming at faster rates. This study analyzed rainfall and temperature patterns in comparison with the potential evaporation transpiration rates (PETs) for parts of Central, Western, Southern, and Southwestern Uganda for varying periods from 1981 to 2022. For rainfall onset date (OD), threshold of 0.85 mm for a rainy day, rainfall of 20 mm accumulated over 5 days with at least 3 rain days, and dry spell not exceeding 9 days in the next 30 days were used. The rainfall cessation dates (RCDs) are determined when water balance (WB) falls below 5 mm in 7 days in the last month of the expected season (May and December) for the first and second season, respectively. Standardized rainfall anomaly was utilized to show seasonal and annual rainfall variability. Pearson's correlation (r) coefficient was used to show the relationship between weather variables (rainfall, temperature) and PET at five rainfall stations. Results showed highly varied onset and cessation dates for March-May (MAM) seasonal rainfall compared to those of September-December (SOND). Results showed highly variable onset and cessation of rainfall over the region and statistically significantly increasing trends in both maximum and minimum temperatures across the region, with the highest rate of increase of maximum and minimum temperature of 0.70 and 0.65 °C per decade respectively. Moreover, the maximum temperature and PET showed strong positive correlation coefficient (r) that ranged from 0.76 to 0.90 across the regions, which likely contribute to excess evaporation from the surfaces, soil moisture deficits that negatively affect plant biomass production, low crop yields and food insecurity. PET and rainfall revealed insignificant statistical negative correlation as indicated by the correlation coefficient ranging from - 0.04 to - 0.22. We recommend water management and conservation practices such as mulching, zero tillage, agroforestry, planting drought-resistant crops, and using affordable irrigation systems during period of water deficit.

Availability of the current and future water resources in Equatorial Central Africa: case of the Nyong forest catchment in Cameroon.

To anticipate disasters (drought, floods, etc.) caused by environmental forcing and reduce their impacts on its fragile economy, sub-Saharan Africa needs a good knowledge of the availability of current water resources and reliable hydroclimatic forecasts. This study has an objective to quantify the availability of water resources in the Nyong basin and predict its future evolution (2024-2050). For this, the SWAT (Soil and Water Assessment Tool) model was used. The performance of this model is satisfactory in calibration (2001-2005) and validation (2006-2010), with R2, NSE, and KGE greater than 0.64. Biases of - 11.8% and - 13.9% in calibration and validation also attest to this good performance. In the investigated basin, infiltration (GW_RCH), evapotranspiration (ETP), surface runoff (SURQ), and water yield (WYLD) are greater in the East, probably due to more abundant rainfall in this part. The flows and sediment load (SED) are greater in the middle zone and in the Southwest of the basin, certainly because of the flat topography of this part, which corresponds to the valley floor. Two climate models (CCCma and REMO) predict a decline in water resources in this basin, and two others (HIRHAM5 and RCA4) are the opposite. However, based on a statistical study carried out over the historical period (2001-2005), the CCCma model seems the most reliable. It forecasts a drop in precipitation and runoff, which do not exceed - 19% and - 18%, respectively, whatever the emission scenario (RCP4.5 or RCP8.5). Climate variability (CV) is the only forcing whose impact is visible in the dynamics of current and future flows, due to the modest current (increase of + 102 km2 in builds and roads) and future (increase of + 114 km2 in builds and roads) changes observed in the evolution of land use and land cover (LULC). The results of this study could contribute to improving water resource management in the basin studied and the region.

Climate Constrains Photosynthetic Strategies in Darwin's Daisies: A Test of the Climatic Variability and Jack-of-All-Trades Hypotheses.

AbstractEmpirical evidence for the climate variability and performance trade-off hypotheses is limited to animals, and it is unclear whether climate constrains the photosynthetic strategies of plants. The plant genus Scalesia Arn. ex Lindl (family Asteraceae), endemic to the Galápagos archipelago, provides an ideal study system to test these hypotheses because of its species with markedly different leaf morphologies that occupy distinct climatic zones. In this study we tested the classic hypotheses that (1) climate constrains leaf size, (2) high climatic temperature variability selects for thermal generalists (i.e., the climate variability hypothesis), and (3) there is a trade-off between the breadth and rate of photosynthetic performance (i.e., jack-of-all-trades but master of none hypothesis). To do this we measured the leaf morphologies and photosynthetic temperature response curves of 11 Scalesia species. In support of a priori predictions, we found that small-leaved Scalesia species were more likely to occupy hotter and drier climates than large-leaved species, there was a positive relationship between climatic temperature variability and the breadth of photosynthetic performance, and photosynthetic performance was negatively correlated with photosynthetic breadth. Our study is among the first to provide evidence for the performance-breadth trade-off hypothesis in photosynthesis, suggesting that climate change may select for photosynthetic thermal generalists.

Stochastic resonance in climate reddening increases the risk of cyclic ecosystem extinction via phase-tipping.

Human activity is leading to changes in the mean and variability of climatic parameters in most locations around the world. The changing mean has received considerable attention from scientists and climate policy makers. However, recent work indicates that the changing variability, that is, the amplitude and the temporal autocorrelation of deviations from the mean, may have greater and more imminent impact on ecosystems. In this paper, we demonstrate that changes in climate variability alone could drive cyclic predator-prey ecosystems to extinction via so-called phase-tipping (P-tipping), a new type of instability that occurs only from certain phases of the predator-prey cycle. We construct a mathematical model of a variable climate and couple it to two self-oscillating paradigmatic predator-prey models. Most importantly, we combine realistic parameter values for the Canada lynx and snowshoe hare with actual climate data from the boreal forest. In this way, we demonstrate that critically important species in the boreal forest have increased likelihood of P-tipping to extinction under predicted changes in climate variability, and are most vulnerable during stages of the cycle when the predator population is near its maximum. Furthermore, our analysis reveals that stochastic resonance is the underlying mechanism for the increased likelihood of P-tipping to extinction.