Paleoclimate Research Articles

Using Earth observation to support the predictive accuracy of species distribution models in ecological restoration: a case study of Poland beavers (Castor fiber)

Abstract. The ability of Earth observation and remote sensing to identify the potential of species to restore ecosystems is a critical element in combating habitat degradation and biodiversity loss. Historical climate data and current species occurrence, coupled with geospatial modelling methods, can significantly aid in decision-making to support ecosystem restoration. We chose Eurasian beavers (spp. Castor fiber) as our case species, which are known ecosystem engineers that create wetland habitats and significantly alter/reinforce the morphology, dynamics, and hydraulics of riverine landscapes. To investigate their ecosystem restoration potential, we applied species distribution modelling (SDM) to determine their current and future distribution scenarios. Our SDMs used current and simulated future environmental data along with different classes of rasters such as elevation, inland swamps, water bodies, natural seasonally or permanently wet grasslands, open mires, riparian mixed forests, riparian swamp broadleaved forests, riparian swamp coniferous forests, watercourses, protected areas, and urban and built-up areas derived from Google Earth Engine to predict areas suitable for beaver habitats. Further, by using the zonation method, we were able to develop a stepping stone model to prioritize habitat conservation efforts. We provide habitat projections according to time periods of "2041–2060", "2061–2080" and "2081–2100", and to different climate change scenarios (SSP126, SSP245, SSP370, and SSP585). Thus, using a potential nature-based solution, we have employed remote sensing data and tools to develop a holistic framework for not only quantifying the ecosystem restoration potential of beavers but will also provide a new direction for understanding species-specific importance in climate change mitigation.

Impact of shifting patterns of the South Asian High on interannual variability of Indian summer monsoon rainfall in homogeneous regions

AbstractThe movement and intensity indices of the South Asian High (SAH), an upper‐level anticyclonic‐circulation over the Tibetan Plateau, play a crucial role in Indian summer monsoon rainfall (ISMR) during June–September. The present study aims to document (i) the association between SAH and ISMR in the periods of June–July–August (JJA) and July–August–September (JAS), and (ii) the ISMR changes at different spatial and temporal scales. The Bayesian change‐point detection method identifies 1981 as change point and the dataset has been split into two groups (past climate:1940–1980 and current climate:1981–2020) to analyze temporal variations in ISMR. Results indicate that the northwest–southeast index (INW–SE), north–south index (INS) and intensity index (IINT) of the SAH are strongly correlated (˜0.67, ˜0.60, and ˜0.51, respectively) with ISMR whereas the east–west index (IEW) is negatively correlated (˜−0.52) during the JAS season. This relation was stronger in the past climate than the current climate, except for the IINT index during the JAS season. The INW–SE and INS indices are closely associated with all‐India, northwest India (NWI), and central India (CI) rainfall, whereas IINT is associated with south peninsular India (SPI) during the JAS season. The increased rainfall over NWI and SPI in the current climate is strongly associated with positive INS and IINT indices, respectively, during the JAS season. The northeast India (NEI) rainfall did not show any association with SAH indices; however, the IEW is strongly associated with increased NEI rainfall during El Niño years. A significant positive (negative) relation between meridional (zonal) wind shear and SAH indices except the IEW index is observed. Further, the positive (negative) SAH indices favor more (moderate) ISMR due to strong positive (negative) moisture anomalies. The study demonstrates the movement and intensity of the SAH in the dynamical shifts of seasonal rainfall patterns across the Indian homogeneous regions.

Climate migration management? Contrasting international policy approaches with evidence from Ghana

ABSTRACT Donors and international organizations are seeking to manage migration out of climate-affected areas through local climate change adaptation and resilience-building. This article assesses the prominence of such approaches, considers their possible efficacy through an empirical case, identifies weaknesses, and offers alternatives. First, we identify and discuss international policy approaches seeking to shape climate-related mobility, drawing on document review of bilateral donor and international organizations’ policy and strategy documents regarding adaptation and climate-related migration. Then, we consider the efficacy of in place, or ‘in situ’, approaches that emphasize adaptation to limit mobility in practice. This is through a case-based analysis of dams and irrigation in the dry savannah zone of Northern Ghana, where poverty and outmigration are the highest in the country, and climate change severely undermines rural livelihoods. Donor-supported national efforts in this region explicitly link in situ adaptation and resilience-building with aims to limit outmigration. In addition to extensive background research including historical analysis, satellite and climate data analysis and a household survey (n = 403), this particular case analysis is based on individual and key informant interviews (n = 16 and 8, respectively), focus group discussions (n = 6) and document and policy review from Ghana, with focus on the Upper West Region. We document significant limitations broadly relevant to in situ approaches: they are unlikely to address diverse climate impacts as well as non-climatic factors shaping migration, and they will likely struggle to accommodate highly differentiated needs, preferences and mobility patterns within populations. Conversely, we find that in situ approaches seeking to reduce climate-related outmigration dominate among donor policies – often over focus on poverty reduction or development outcomes. These findings call for rethinking of donor and domestic policies that seek to limit outmigration through adaptation and resilience building. Rather, there is a need for development – and choice-oriented approaches that constructively integrate local adaptation and migration options for improved development outcomes.

Late Glacial and Holocene history of climate, vegetation landscapes and fires in South Taiga of Western Siberia based on radiocarbon dating and multi-proxy palaeoecological research of sediments from Shchuchye Lake

Abstract To investigate long-term relationships between climate, vegetation, landscape geochemistry and fires in the boreal forest zone of Western Siberia, a sediment core of 345 cm was collected from Shchuchye Lake (located in south taiga zone of southeast part of West Siberian plain) and investigated by spore-pollen, radiocarbon, LOI and charcoal analyses. Quantitative palaeoclimate was reconstructed based on pollen data. Investigation revealed 13.2 cal ka history of vegetation, climate, landscapes and fires. In the dry climate of Late Glacial, the landscape was treeless. Continuous permafrost existed in the soil. In the middle of the YD cooling 12.4–12.2 cal ka BP, our data showed warming that caused degradation of permafrost in soils and settlement of spruce in moist places. Later, thawing and accumulation of moisture in a local lowering in relief increased and a lake was formed. With the beginning of the Holocene, the climate sharply changed to warmer and wetter. Intensified surface flow caused accumulation of mineral and carbonate fraction in the lake. Dense birch forests spread on drylands. As a result, the leaching regime initiated the formation of podzols in the soil. At about 10.0 cal ka BP, Scots pine (Pinus sylvestris) quickly spread in the area of investigation. Fires became more frequent and more intense during the dry Late Glacial time, sharply decreasing with increased precipitation in the Early Holocene, and again moderately increasing with spread of pine forests in the mid Holocene. With the transition to Late Holocene (after 6.0 cal ka BP), the intensity of regional background fires and number of local fires decreased.

Barley vulnerability to climate change: perspectives for cultivation in South America.

Barley (Hordeum vulgare) is a globally significant cereal crop, widely used in both food production and brewing. However, it is particularly vulnerable to climate change, especially extreme temperature fluctuations, which can severely reduce yields. To address this challenge, a detailed climate zoning study was conducted to assess the suitability of barley production areas across South America, considering both current conditions and future climate scenarios from the Intergovernmental Panel on Climate Change (IPCC). The study utilized historical climate data along with projections from the CMIP6 IPSL-CM6A-LR model for the period 2021-2100. Several indices, such as evapotranspiration, were calculated, and factors like soil composition and topography were integrated into the classification of regions based on their agricultural potential. Critical variables in this assessment included temperature, precipitation, and water or thermal excess. The results showed that 6.59% of South America's territory is currently suitable for barley cultivation without additional irrigation, with these regions concentrated primarily in temperate southern areas. In contrast, 18.62% of the region is already unsuitable due to excessive heat. Projections under future climate scenarios indicate a shrinking of suitable areas, alongside an expansion of unsuitable regions. In the worst-case scenario, only 1.48% of the territory would remain viable for barley farming. These findings emphasize the crop's vulnerability to climate change, underscoring the urgency of developing agricultural adaptation strategies. The predicted contraction in suitable barley cultivation areas demonstrates the profound impact of climate change on agriculture and highlights the need for proactive measures to ensure sustainable barley production in South America.

Regionally Coupled Climate Model ROM Projects More Plausible Precipitation Change Over Central Equatorial Africa

AbstractUnraveling plausible future rainfall change (ΔPr) patterns is crucial for tailoring societies' responses to climate change‐induced hazards. This study compares rainfall projections from the regionally coupled ocean model (ROM) and its atmospheric component, the regional atmospheric model REMO, over Central Equatorial Africa (CEA). Both models are forced by the Earth system model MPI‐ESM‐LR following the Representative Concentration Pathway 8.5. Results reveal increased rainfall across most of CEA, with ROM projecting more widespread and intensified wetting than REMO, although REMO produces more precipitation under future conditions, underscoring the influence of historical biases on REMO's projection. Examining processes underpinning changes unveils strong controls of sea and land surface temperature changes in ΔPr differences between the two models. Specifically, ROM mitigates warming more over the Atlantic than over CEA landmass compared to REMO, inducing enhancement of the Congo Basin cell and increased precipitable water content through specific humidity, affecting deep convection. Both models project enhanced Sahel and Kalahari thermal lows, with ROM better depicting the Kalahari low's warmer nature than the Sahel low. The resulting temperature gradients strengthen the northern and southern shallow meridional Hadley overturning circulation. ROM simulates the wetter conditions than REMO, attributed to its weaker northern Hadley Cell, which restricts the likelihood of northward moisture divergence toward the Sahel. Additionally, differences in mid‐tropospheric moisture convergence differentiate between ROM and REMO's wetness relative to the historical period and under future conditions. ROM projections are more plausible, in association with the reliability of its added value under the historical climate and mechanisms underlying Δpr.

Climate change alters the future of natural floristic regions of deep evolutionary origins

Biogeographic regions reflect the organization of biotas over long evolutionary timescales but face alterations from recent anthropogenic climate change. Here, we model species distributions for 189,269 vascular plant species of the world under present and future climates and use this data to generate biogeographic regions based on phylogenetic dissimilarity. Our analysis reveals declines in phylogenetic beta diversity for years 2040 to 2100, leading to a future homogenization of biogeographic regions. While some biogeographic boundaries will persist, climate change will alter boundaries separating biogeographic realms. Such boundary alterations will be determined by altitude variation, heterogeneity of temperature seasonality, and past climate velocity. Our findings suggest that human activities may now surpass the geological forces that shaped floristic regions over millions of years, calling for the mitigation of climate impacts to meet international biodiversity targets.

Shrub cover declined as Indigenous populations expanded across southeast Australia.

Wildfires in forests globally have become more frequent and intense because of changes in climate and human management. Shrub layer fuels allow fire to spread vertically to forest canopy, creating high-intensity fires. Our research provides a deep-time perspective on shrub fuel loads in fire-prone southeastern Australia. Comparing 2833 records for vegetation cover, past climate, biomass burning, and human population size across different phases of human occupation, we demonstrated that Indigenous population expansion and cultural fire use resulted in a 50% reduction in shrub cover, from approximately 30% from the early to mid-Holocene (12 to 6 thousand years ago) to 15% during the late to mid-Holocene (6 to 1 thousand years ago). Since the start of British colonization to the present, shrub cover has increased to the highest ever recorded (mean of 35% land cover), increasing the risk of high-intensity fires.

Nature vs. nurture: Drivers of site productivity in loblolly pine (Pinus taeda L.) forests in the southeastern US

Forest productivity is one of the most important aspects of forest management, landscape planning, and climate change assessment. However, although there are multiple elements known to affect productivity, most of them rely on the “nature” of the edaphic, climatic, and geographic conditions, and only some specific aspects can be modified through forest management or “nurture”. Through evaluation of site resource availability and an understanding of the main drivers of productivity, management can present solutions to overcome site resource limitations to productivity. Therefore, understanding the implications of a specific management regime requires understanding what drives productivity across large spatial extents and among different management regimes. In this study, we used data from over 1 million hectares of industrial forestland, covering over 6000 different soils and several management regimes of Pinus taeda L. plantations, as well as plot-based data from the Forest Inventory and Analysis (FIA) program, facilitating a comparison of planted and natural Pinus taeda stands. Combined with US Geological Survey LiDAR data, we computed site index and generated wall-to-wall productivity maps for planted Pinus taeda stands in the southeastern US, as well as point-based site index estimates for the FIA dataset. We modeled site index using a random forest algorithm considering edaphic, geologic, and physiographic province information based on the Forest Productivity Cooperative “SPOT” system, and also included climate and management history data. Our model predicted site index with an R2 of 0.701 and RMSE of 1.41 m on the industrial data and R2 of 0.417 and RMSE of 1.84 m for the FIA data. We found that year of establishment of the forest, physiographic province, and geology, were the most important drivers of site index. The soil classification modifier indicating root restrictions were the most important soil-specific variable. Additionally, we found an average increase in site index of 3.05 m since the 1950s for all FIA data, and an average increase of 4.73 m for all industrial data since the 1970s. For the latest period analyzed (2000–2012), average site index in planted FIA plots was 1.2 m higher than naturally regenerated FIA plots, and site index in all industrial forestland had a site index almost 3 m greater than planted FIA plots. Overall, we believe this work sets the foundation for better understanding of forest productivity and highlights the importance of intensive silviculture to improve productivity, and as an additional tool to achieve the economic, environmental, and social objectives.

Advances in giant clam (Tridacnidae spp.) sclerochronology and sclerochemistry

Understanding past environmental change through high-resolution palaeoenvironmental proxy reconstructions provides crucial baselines for understanding global environmental changes, particularly during the Quaternary Period. Such data are crucial for testing and refining climate models to enable better adaptations to current and future climate changes. Giant clams (Tridacnidae spp.), the largest bivalve mollusc in the Indo-Pacific region, possess distinctive features such as long lifespan, rapid growth, high-resolution growth patterns, wide distribution habitat, and stable shell geochemical composition. These attributes make giant clams a promising and dependable novel palaeoenvironmental archive, offering numerous available geochemical proxies. In this review, we examined preview studies on giant clams conducted for sclerochronology research, spanning publications from 1986 to 2023. The previous studies have proven that isotopic and trace elements proxies of the giant clam, including δ18O, δ13C, Sr/Ca, Mg/Ca, Ba/Ca, and Fe/Ca, can faithfully reconstruct palaeoclimate records including sea surface temperature, dissolved inorganic carbon, insolation, extreme weather events and primary productivity at ultra-high resolution. These proxies have been successfully employed for palaeoclimatic and palaeoenvironmental reconstructions from the Miocene to the present across a range of locations from South Japan to Northern Australia to the Red Sea. These studies have illuminated changes in interannual climate cycles, climate variation, and human migrations over thousands to millions of years. They have generated quantitative records that are valuable for testing and refining numerical climate models, understanding the relationship between past climate cycles and future climate variability, and providing insights into human-environment interactions over time. In the end, the review also addresses the challenges and provided recommendations for future research, highlighting persisting gaps in understanding the influence of microstructure on the shell, age dating of fossil samples, uncertainty in proxies, the need for tank experiments, and access to ultra-high resolution palaeoenvironmental records.

Predicting the Potential Distribution of the Endangered Pyrethrum tatsienense in China Using an Optimized Maxent Model Under Climate Change Scenarios.

Climate change can significantly impact the ecological suitability and diversity of species. Pyrethrum tatsienense, a critically endangered species in China, requires a thorough understanding of its habitat distribution and the environmental factors that affect it in the context of climate change. The Maxent algorithm was used to examine the key factors influencing the distribution of P. tatsienense in China, using data from 127 species occurrences and environmental variables from the Last Interglacial (LIG), Last Glacial Maximum (LGM), Mid-Holocene (MH), current, and future scenarios. The Maxent model was optimized utilizing the R package ENMeval, providing the most accurate predictions for suitable habitats across various scenarios. Results show that suitable regions for P. tatsienense encompass approximately 15.02% (14.42 × 105 km2) of China, predominantly on the Qinghai-Tibetan Plateau. The mean UV-B of the highest month (UVB3: 39.7%), elevation (elev: 28.7%), and the warmest season of precipitation (Bio18: 17.4%) are the major limiting factors for suitable habitat. The optimal species distribution ranges are identified as > 7500 J m-2 day-1 for UVB3, 2700-5600 m for elev, and 150-480 mm for Bio18. Predictions for the historical climate indicate the presence of refugia at the junction of Sichuan, Tibet, and Qinghai. The MH predictions show an increase in climatic suitability for P. tatsienense compared to the LIG and LGM, with an expansion of suitable areas westward. Future climate change scenarios indicate that the potential suitable habitat for P. tatsienense is expected to increase with increasing radiative forcing, with higher latitude regions becoming new marginally suitable habitats. However, predicted environmental changes in western Tibet may drive the loss of highly suitable habitats in the future. These findings enhance our understanding of how environmental factors impact the habitat suitability of P. tatsienense and provide valuable insights for developing effective management and conservation strategies for this important species.

Climatic and environmental changes since last deglaciation in the steppe region of northern China and their impact on early population survival patterns

The northern Chinese steppe is situated in the periphery of the East Asian Summer Monsoon and exhibits high sensitivity to variations in monsoon intensity and global climate change. Therefore, comprehending the climatic evolution history of this region is pivotal for elucidating early human civilization development. The research focuses on a fully enclosed crater lake located in the northern Chinese steppe by utilizing n-alkanes, total organic carbon (TOC), total nitrogen (TN) in lake sediments, based on AMS-14C dating analysis, the climatic and environmental evolution history of this region from the last deglaciation to the middle Holocene has been reconstructed, and the impact of climate change on the evolution of early human civilization is elucidated. The findings indicate that the EASM driven by summer solar radiation, exerts control over the climate and hydrology in the steppe region of northern China. Since the last deglaciation, the gradual warming and humidification of the climate environment have significantly enhanced the animal and plant resources, thereby establishing a solid foundation for the transition of early human populations in northern Chinese steppe from high-mobility hunter-gatherers to settled communities. Additionally, this climatic shift has provided an optimal backdrop for the emergence of primitive agriculture. The changes of stone tool assemblages unearthed at related sites also confirm the above views. However, the climate model underwent rapid changes during the period of 7.2–6.4 cal kyr BP, leading to the occurrence of extreme arid climate events. Consequently, regional precipitation decreased, lake ecology became imbalanced, woody plants retreated in the basin, and vegetation coverage was greatly reduced. The aforementioned factor may have played a pivotal role in the disappearance of early populations within the region. Consequently, climate and environmental change not only serve as the primary driving force behind human civilization’s development but also act as an inexorable catalyst for its decline.

Multidisciplinarity in Geoscientific Research

The multidisciplinary approach of geoscientific research in the academic setting underscores the increasing necessity of integrating diverse fields of knowledge to tackle complex interconnected issues. This multidisciplinary field seeks to uncover the complex operations of the Earth, encompassing the analysis of its past movements of tectonic plates, historical climates, and valuable natural resources like minerals and hydrocarbons. In this context, geospatial intelligence plays a crucial role by enabling the effective analysis and interpretation of extensive geographic datasets, thus enhancing our understanding of environmental dynamics. Nevertheless, advancements in technology, globalization, and the difficulties posed by environmental and climatic transformations demand an even more profound collaboration among various disciplines, facilitating an expansive viewpoint and yielding more holistic solutions to contemporary predicaments.

Cryptic degassing and protracted greenhouse climates after flood basalt events

AbstractLarge igneous provinces erupt highly reactive, predominantly basaltic lavas onto Earth’s surface, which should boost the weathering flux leading to long-term CO2 drawdown and cooling following cessation of volcanism. However, throughout Earth’s geological history, the aftermaths of multiple Phanerozoic large igneous provinces are marked by unexpectedly protracted climatic warming and delayed biotic recovery lasting millions of years beyond the most voluminous phases of extrusive volcanism. Here we conduct geodynamic modelling of mantle melting and thermomechanical modelling of magma transport to show that rheologic feedbacks in the crust can throttle eruption rates despite continued melt generation and CO2 supply. Our results demonstrate how the mantle-derived flux of CO2 to the atmosphere during large igneous provinces can decouple from rates of surface volcanism, representing an important flux driving long-term climate. Climate–biogeochemical modelling spanning intervals with temporally calibrated palaeoclimate data further shows how accounting for this non-eruptive cryptic CO2 can help reconcile the life cycle of large igneous provinces with climate disruption and recovery during the Permian–Triassic, Mid-Miocene and other critical moments in Earth’s climate history. These findings underscore the key role that outgassing from intrusive magmas plays in modulating our planet’s surface environment.

Climate change is expected to reduce the potential distribution of Ceiba glaziovii in Caatinga, the largest area of dry tropical forest in South America

Ecological niche modeling is a widely used tool to predict species distribution considering current, past, or future climate change scenarios across different geographic areas. Modeling scenarios allow researchers to assess the impacts of climate change on species distribution and identify priority areas for conservation. This study aimed to model the current and future potential distribution of Ceiba glaziovii under different climate change scenarios in Brazil. The MaxEnt algorithm was used to correlate species occurrence points with bioclimatic variables in current and future climate scenarios. Four General Circulation Models (GCMs) from CMIP6 were employed: BCC-CSM2-MR, CNRM-CM6-1, IPSL-CM6A-LR, and MIROC6, considering optimistic and pessimistic projections. The contribution of variables and model accuracy were assessed using the Jackknife statistical test and the Area Under the Curve (AUC) parameter. AUC values for current and future scenarios demonstrated high accuracy. The bioclimatic variables of precipitation and temperature were the main contributors to determining areas with higher habitat suitability. In the future climate scenario, there was a reduction in areas with good climatic suitability for all four GCMs, considering optimistic and pessimistic projections. Among the areas with high habitat suitability, the IPSL-CM6A-1 model in the optimistic projection showed the smallest reduction, while in the pessimistic scenario, all areas with high suitability disappeared. The species' climatic niche is expected to decrease under all tested climate change scenarios. The central areas of the Caatinga and its transition zones exhibit the highest climatic suitability in current and future scenarios and should be prioritized for the species' conservation.

Evaluation of Clustering Techniques for Solar Irradiance Prediction for Optimal Design of Microgrids in Rural Communities

Abstract The electrification of rural communities is crucial from both social and economic perspectives, aligned with Sustainable Development Goal 7: “Affordable and Clean Energy”. This study presents a comprehensive comparison of clustering techniques, including k-means, Gaussian Mixture Models (GMM), Hierarchical Clustering, Density-based Spatial Clustering of Applications with Noise (DBSCAN), and Agglomerative Clustering, aimed at enhancing solar irradiance prediction. Leveraging historical climate data from a rural community in the coastal region of Ecuador, each technique is evaluated using error metrics such as Mean Absolute Error (MAE) and Coefficient of Determination R2. This assessment identifies the most effective clustering technique in this specific context. In order to delve deeper into these comparisons, simulations are conducted in AMPL to validate and refine the selection of techniques. In this process, it is considered the sizing and design of a microgrid within the Barcelona community, Ecuador, which integrates various energy sources, including solar. Additionally, a penalty system is introduced for unmet energy demands during less critical periods, thereby optimizing efficiency and enhancing energy availability within the community. In conclusion, this paper introduces a scalable methodology to improve solar irradiance prediction, emphasizing the significance of comparing clustering techniques as its main contribution. This advancement in prediction accuracy has the potential to enhance the feasibility and efficiency of renewable energy systems for rural communities, thereby fostering sustainable economic growth and bolstering efforts in climate change mitigation and adaptation.

Cause and Characteristics of Changes in Mesoscale Convective Systems within a Convection-Permitting Regional Climate Model

Abstract Changes in the frequency and intensity of mesoscale convective systems (MCS) are assessed using convection-permitting regional climate model simulations. We present a novel classification method that relates MCSs to the magnitude of their large-scale forcing environments to better understand the changing nature of MCSs across different forcing environments in a possible future climate scenario. Overall, the annual frequency, intensity, and amount of precipitation associated with MCSs are projected to increase for broad portions of the eastern conterminous United States (CONUS). Furthermore, changes in the characteristics of MCSs show larger, longer-lived, and faster MCSs particularly over the Midwest and Southeast. Seasonal examination of this response reveals a robust intensification of March–May (MAM) MCSs. The higher frequency of MAM MCSs are found to occur in weakly forced synoptic environments. Increased rainfall amounts are explained by an intensification of MCSs due to changing thermodynamics and enhanced lower-tropospheric moisture transport into the central CONUS by the North Atlantic Subtropical High. Differences in mid-latitude storm tracks are favorable towards the enhanced development of MCSs associated with strong baroclinic forcing within the Midwest and Northern Plains, but are only realized in the presence of strong changes in lower-tropospheric moisture transport. Overall, these results suggest a shift in the behavior of MAM MCSs that more closely corresponds to the behavior of June–August (JJA) MCSs in the historical climate. The increased occurrence of MCSs in weakly forced environments, particularly in MAM, deserves further investigation.

Water quality–fisheries tradeoffs in a changing climate underscore the need for adaptive ecosystem–based management

Changes driven by both unanticipated human activities and management actions are creating wicked management landscapes in freshwater and marine ecosystems that require new approaches to support decision-making. By linking a predictive model of nutrient- and temperature-driven bottom hypoxia with observed commercial fishery harvest data from Lake Erie (United States-Canada) over the past century (1928-2022) and climate projections (2030-2099), we show how simple, yet robust models and routine monitoring data can be used to identify tradeoffs associated with nutrient management and guide decision-making in even the largest of aquatic ecosystems now and in the future. Our approach enabled us to assess planned nutrient load reduction targets designed to mitigate nutrient-driven hypoxia and show why they appear overly restrictive based on current fishery needs, indicating tradeoffs between water quality and fisheries management goals. At the same time, our temperature results show that projected climate change impacts on hypoxic extent will require more stringent nutrient regulations in the future. Beyond providing a rare example of bottom hypoxia driving changes in fishery harvests at an ecosystem scale, our study illustrates the need for adaptive ecosystem-based management, which can be informed by simple predictive models that can be readily applied over long time periods, account for tradeoffs across multiple management sectors (e.g., water quality, fisheries), and address ecosystem nonstationarity (e.g., climate change impacts on management targets). Such approaches will be critical for maintaining valued ecosystem services in the many aquatic systems worldwide that are vulnerable to multiple drivers of environmental change.

Seasonality in cave dripwater and air properties – implications for speleothem palaeoclimatology, Nova Grgosova Cave (Croatia)

Due to the wealth of climate-sensitive properties, speleothems have been propelled into prominence as one of the most powerful continental archives of past climate changes. However, the multitude of processes that operate in the unsaturated karst zone and in the cave atmosphere can make palaeoclimate interpretations of the speleothem proxies challenging and site specific. Hence, to better understand the climate-proxy relationship, cave monitoring studies are usually undertaken. Here, we present the first results of an ongoing cave monitoring study in the Nova Grgosova Cave in Croatia covering an eighteen-month long monitoring period. The driving mechanisms for Mg/Ca and Sr/Ca variability in dripwater samples that feed ten stalagmites are discussed. The results reveal high variability in infiltration among the monitored sites as well as strong seasonal variability in cave air carbon dioxide (CO2) concentrations. A strong positive correlation between dripwater Mg/Ca and Sr/Ca suggests that prior calcite precipitation (PCP) is taking place at this site affecting the chemical composition of dripwater. Principal component analysis furthermore reveals that dripwater Mg/Ca and Sr/Ca are in strong negative correlation with cave air CO2 concentrations, while there is a weak correlation with dripwater quantity. Cave ventilation is a primary process leading to the PCP at the Nova Grgosova Cave. The seasonality revealed in this study suggests the possibility that the Mg/Ca and Sr/Ca ratio in the speleothems from this cave site can be used to aid seasonal reconstructions of past climate conditions in central Croatia and beyond.

Next-generation public health surveillance: extreme heat event prediction and monitoring system

Abstract Background Extreme Heat Events (EHEs) are a growing threat to public health. The increase in the frequency of occurrence of once rarer EHEs and the rise in their average temperatures are dangerously drastic for public health outcomes. Despite this, existing public health surveillance (PHS) systems fail to leverage IoT and AI technologies to facilitate real-time, continuous monitoring of EHE indicators and its associated health risks. This study closes this gap by proposing a comprehensive PHS system that can, in real-time monitor and predict EHE indicators to provide timely alerts to public health authorities. Methods The EHE PHS system collects EHE detection metrics, including indoor temperature and humidity levels, from IoT sensors and thermostats to map them across Canada, primarily focusing on low-income communities. The system also integrates historical climate data (surface temperature, humidity, wind speed and direction, and air quality indicators) from Environment Canada. The system uses the data to train initial prediction models including CNN, LSTM and GNN. The validated model will be integrated into the system, enabling real-time EHE prediction. The output will be displayed in user-friendly visualizations on the EHE PHS system’s dashboard capabilities. Results The system allows for the analysis of real-time data through dashboards and provides alerts when certain indicators (e.g. excessive or prolonged heat) detrimental to public health outcomes are detected. The alerts enable valuable lead time for public health authorities to implement proactive measures, such as issuing heat advisories and deploying resources to vulnerable areas. Conclusions The EHE PHS system will serve as a blueprint for global public health researchers, utilizing IoT and AI technologies for proactive crisis prevention. This knowledge will inform the development of heat-resilient policies and set a precedent for global public health crisis prevention. Key messages • The proposed EHE PHS system uses AI and IoT technologies, to enhance EHE prediction, risk identification, and real-time monitoring for effective public health interventions. • By integrating advanced predictive models and environmental data, the system facilitates early detection of EHE risk, which can significantly mitigate the health impact on vulnerable populations.