Climate Data Research Articles

Exploring the Influencing Factors of Net Ecosystem Productivity (NEP) Based on Random Forest and SHAP

As global climate change intensifies, Net Ecosystem Productivity (NEP) serves as a crucial indicator for measuring the carbon absorption and release of ecosystems, playing a vital role in understanding the carbon cycle and shaping climate policy. The Northwestern region of China, characterized as a typical inland arid and semi-arid area, is particularly sensitive to global changes. Understanding the environmental drivers of NEP in this region is critical for both regional ecological protection and global environmental management. This study utilized environmental data from five provinces in Northwestern China, applying the Random Forest (RF) model and SHapley Additive exPlanation (SHAP) method to analyze the primary environmental factors influencing NEP. The research integrated climatic data (including temperature, precipitation, wind speed, and solar radiation), soil characteristics such as pH, organic carbon content, and soil texture, topographic attributes elevation and slope, and a Human Footprint. The RF model identified significant environmental factors impacting NEP, and SHAP values were used to explain the specific contributions of these factors. Furthermore, multiple linear regression analysis revealed interactions among environmental factors. The results indicate that solar radiation (Srad), precipitation, topsoil reference bulk density (T_REF_BULK), temperature, and the topsoil clay fraction (T_CLAY) significantly influence NEP. Notably, interactions between aspect and T_CLAY, aspect and T_REF_BULK, as well as the human footprint (HFP) and Srad, also show significant impacts on NEP. This study confirms that solar radiation, precipitation, soil characteristics, and human activities are the primary environmental drivers of NEP in the Northwest region of China, with solar radiation playing the most critical promotional role. The findings not only provide a scientific basis for the management of ecosystems in Northwestern China but also offer references for formulating global climate change mitigation strategies and estimating the global carbon budget. Future research should further explore the specific mechanisms and interactions of these drivers across different ecosystems to more comprehensively understand and predict the trends in NEP changes.

Application of wavelet and seasonal-based emotional ANN (EANN) models to predict solar irradiance

This study models solar irradiance at six stations in Iran and the USA on an hourly scale. We explored two seasonal emotional artificial neural networks (EANN): sequence-EANN (SEANN) and wavelet EANN (WEANN). Analyzing ten years of climatic and solar data, we evaluated uncertainty using prediction intervals (PIs) computed via the bootstrap method based on artificial neural networks (ANNs). Unlike standalone EANNs, the proposed seasonal models effectively captured seasonal information and leveraged time series processing advantages. Utilizing Wavelet and Fourier transforms, these models captured long-short autoregressive dependencies in solar irradiance, addressing extended seasonal dependencies. Results showed that the seasonal EANN models outperformed the classic EANN model by approximately 15 % and the classic feed-forward neural network (FFNN) by about 25 % in both training and testing. The WEANN model demonstrated the highest performance in PIs, with an average normalized mean PI width (NMPIW) of 0.8 and an average PI coverage probability (PICP) of 0.96.

Analyzing the impacts of climate change on ecosystem services provided by apple orchards in Southeast France using a process-based model

We know that fruit production, especially in the Mediterranean, will need to adapt to climate change to ensure the sustainability of fruit tree-based agroecosystems. However, there is a lack of evidence on the long-term effects of this change on sustainability indicators. To fill this gap, we used a fruit tree model, QualiTree, to analyze the impacts ofclimate change on the ecosystem services provided by apple orchards in south-eastern France. To do this, a blooming model was parameterized to simulate blooming date on the basis of climate data, and QualiTree was supplemented with a model of nitrogen processes in the tree and a soil module describing resource input (irrigation, mineral and organic fertilization), transfer in the soil (water and nitrogen) and metabolic transformation-immobilization (mineralization, (de)nitrification). This type of extension makes it possible to simulate a wide array of ecosystem services, including C sequestration, nitrate leaching and nitrous oxide emissions. The model was compared with data from an apple orchard in southeastern France. The predicted daily mean and variability over time of fruit growth, composition and soil water content were consistent with observed data. QualiTree was then used to assess the potential impacts of climate change on the ecosystem services supplied by apple orchards. For this purpose, weather variables from 2020 to 2100 were generated for three contrasted greenhouse gas emission scenarios, and simulations were performed under two irrigation schemes (no restriction and restricted use of water). Model outputs indicated that, on average, marketable apple yields would increase until 2050 and then subsequently decrease. The fruit refractometric index, an indicator of fruit quality, was projected to sharply decrease with the intensity of climate change. Ecosystem services such as C sequestration by the orchard will decrease with climate change severity, mainly due to a higher mineralization of soil humus, whereas N2O emissions will increase with larger denitrification rates. Soil water availability, fertility, drainage and leaching were predicted to depend more on the irrigation strategy than on climate change severity. The new functions performed in QualiTree broadened its predictive capabilities and allowed for a better understanding of ecosystem service delivery in fruit orchards under varying climate conditions.

Future Climate Projections and Uncertainty Evaluations for Frost Decay Exposure Index in Norway

To implement the geographical and future climate adaptation of building moisture design for building projects, practitioners need efficient tools, such as precalculated climate indices to assess climate loads. Among them, the Frost Decay Exposure Index (FDEI) describes the risk of freezing damage for clay bricks in facades. Previously, the FDEI has been calculated for 12 locations in Norway using 1961–1990 measurements. The purpose of this study is both updating the FDEI values with new climate data and future scenarios and assessing how such indices may be suitable as a climate adaptation tool in building moisture safety design. The validity of FDEI as an expression of frost decay potential is outside the scope of this study. Historical data from the last normal period as well as future estimated climate data based on 10 different climate models forced by two emission scenarios (representative concentration pathways 4.5 and 8.5) have been analyzed. The results indicate an overall decline in FDEI values on average, due to increased winter temperatures leading to fewer freezing events. Further, the variability between climate models and scenarios necessitates explicit uncertainty evaluations, as single climate model calculations may result in misleading conclusions due to high variability between models.

Scalable spatiotemporal prediction with Bayesian neural fields

Spatiotemporal datasets, which consist of spatially-referenced time series, are ubiquitous in diverse applications, such as air pollution monitoring, disease tracking, and cloud-demand forecasting. As the scale of modern datasets increases, there is a growing need for statistical methods that are flexible enough to capture complex spatiotemporal dynamics and scalable enough to handle many observations. This article introduces the Bayesian Neural Field (BayesNF), a domain-general statistical model that infers rich spatiotemporal probability distributions for data-analysis tasks including forecasting, interpolation, and variography. BayesNF integrates a deep neural network architecture for high-capacity function estimation with hierarchical Bayesian inference for robust predictive uncertainty quantification. Evaluations against prominent baselines show that BayesNF delivers improvements on prediction problems from climate and public health data containing tens to hundreds of thousands of measurements. Accompanying the paper is an open-source software package (https://github.com/google/bayesnf) that runs on GPU and TPU accelerators through the Jax machine learning platform.

Extreme snowfall variations in the Southeastern Tibetan Plateau under warming climate

Snowfall is a critical component of the Earth system and an important indicator and amplifier of climate change. Climate warming is reducing the seasonal snowpack globally, which could have catastrophic consequences for the regions in high dependence on snow for water recharge. However, the climate influences on extreme snowfall events, which significantly impact humans, are still poorly understood. This study uses the Tibetan Plateau snowpack observation dataset combined with NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) climate data to investigate the spatial and temporal characteristics of snowfall, and to explore its response mechanism to climate change and future change trends across the Lower Yarlung Zangbo River (LYZR) in Southeastern Tibetan Plateau (SETP). Annual snowfall decreased at a rate of 1.05 mm per decade (P < 0.05) during the historical period (1960–2014). Relative to 1960–2014, annual snowfall would decline of ∼38 % and ∼ 73 % by the end of the 21st century under the SSP245 and SSP585 scenarios, respectively. At the same time, a general decrease in extreme snowfall averaged the LYZR is expected for historical and future periods, but it showed notable spatial variations. The regional increase in extreme snowfall is mainly distributed along the valley of Brahmaputra-Yarlung Zangbo. In addition, precipitation accounted for 66.5 % of the snowfall variations during the historical period. Meanwhile, according to future warming, temperature would dominate snowfall variations, contributing 56.66 % to 72.92 % during 2015–2100. The projected response of mean and extreme snowfall to climate change indicates that it is a double-edged sword. To scientifically address the risks and challenges posed by snowfall changes in alpine regions, it is imperative to limit future climate warming.

Exploration of a model driven by climate to simulate pond water temperature in aquaculture systems

IntroductionInterannual climate variability in the Asian mega deltas has been posing a wide range of climate risks in the aquaculture systems of the region. Water temperature variation is one of the key risks related to disease outbreak, fish health, and loss and damage in fish production. However, Climate information can improve the ability to predict changes in pond water quality parameters at the farm level using publicly available weather and climate data. Little research has been done to translate weather data into water temperature forecasts using mechanistic models in order to provide farmers with relevant forecasting information in the context of climate services.MethodsThe advantage of mechanistic models over statistical models is that they are based on physical processes and can therefore be used in a wider range of environmental conditions. In this study, we used an energy balance model to investigate its ability to simulate pond water temperature at daily and seasonal timescales in the southwest and northeast regions of Bangladesh. The model was able to adequately simulate pond water temperature at a daily timescale using publicly available weather data, and the accuracy of the model was lower at the study site with very heavy rainfall events.ResultsSensitivity analyses showed that the model was also able to simulate the impact of air temperature cold and hot spells on the pond water temperature. Connecting the model with seasonal air temperature forecasts resulted in very small variations in the forecasted seasonal pond water temperature, in large part due to the low variability observed in water temperature at seasonal scale in the study sites.DiscussionClimate information can improve the ability to predict changes in pond water quality parameters at the farm level using publicly available weather and climate data. Hence, these improved predictions are important to help fish-farmers make informed decisions for managing associated climate risks.

Quantifying urban climate response to large-scale forcing modified by local boundary layer effects

Over the past two decades, the joint manifestation of global warming and rapid urbanization has significantly increased the occurrence of heatwaves and the formation of urban heat islands in temperate cities. Consequently, this synergy has amplified the frequency and duration of periods with tropical nights (TNs) in these urban areas. While the occurrences of such extreme events demonstrate irregular and nonlinear annual patterns, they consistently manifest a discernible rising decadal trend in local or regional climatic data. In urban regions situated amidst hilly or mountainous landscapes, changing wind directions—often associated with uphill or downhill thermal flows—profoundly impact the spread and dispersion of heat-related pollution, creating unique natural ventilation patterns. Using the Lausanne/Pully urban area in Switzerland as examples of hilly and lakeshore temperate cities, this study explores the influence of wind patterns and natural urban ventilation on the nonlinearity of recorded climatic data within an urban environment. This study integrates a mesoscale numerical weather prediction model (COSMO-1), a microscale Computational Fluid Dynamics (CFD) model, field observations, variational mode decomposition technique, and statistical analysis to investigate how wind speed and direction critically influence the nonlinearity of recorded long-term trends of extreme events, specifically focusing on the frequency and duration of TNs in lakeshore and hilly cities. The results strongly indicate a direct correlation between the frequency of TNs and the occurrence of specific moderate wind patterns. These wind patterns are exclusively captured by the microscale CFD model, unlike the mesoscale model, which neglects both urban morphology and complex hilly terrains. The impact of temporal and spatial variability of the wind field on long-term observations at fixed measurement stations suggests that caution should be exercised when relying on limited spatial measurement points to monitor and quantify long-term urban climate trends, particularly in cities located in complex terrains.

Mosquito dynamics and their drivers in peri-urban Antananarivo, Madagascar: insights from a longitudinal multi-host single-site survey

BackgroundAntananarivo, the capital city of Madagascar, is experiencing a steady increase in population growth. Due to the abundance of mosquito vectors in this locality, the population exposed to mosquito-borne diseases is therefore also increasing, as is the risk of epidemic episodes. The aim of the present study was to assess, in a resource-limited setting, the information on mosquito population dynamics and disease transmission risk that can be provided through a longitudinal entomological study carried out in a multi-host single site.MethodsMosquitoes were collected every 15 days over 16 months (from January 2017 to April 2018) using six CDC-light traps in a peri-urban area of Antananarivo. Multivariable generalised linear models were developed using indoor and outdoor densities of the predominant mosquito species as response variables and moon illumination, environmental data and climatic data as the explanatory variables.ResultsOverall, 46,737 mosquitoes belonging to at least 20 species were collected, of which Culex antennatus (68.9%), Culex quinquefasciatus (19.8%), Culex poicilipes (3.7%) and Anopheles gambiae sensu lato (2.3%) were the most abundant species. Mosquito densities were observed to be driven by moon illumination and climatic factors interacting at different lag periods. The outdoor models demonstrated biweekly and seasonal patterns of mosquito densities, while the indoor models demonstrated only a seasonal pattern.ConclusionsAn important diversity of mosquitoes exists in the peri-urban area of Antananarivo. Some well-known vector species, such as Cx. antennatus, a major vector of West Nile virus (WNV) and Rift-Valley fever virus (RVFV), Cx. quinquefasciatus, a major vector of WNV, Cx. poicilipes, a candidate vector of RVFV and An. gambiae sensu lato, a major vector of Plasmodium spp., are abundant. Importantly, these four mosquito species are present all year round, even though their abundance declines during the cold dry season, with the exception of Cx. quinquefasciatus. The main drivers of their abundance were found to be temperature, relative humidity and precipitation, as well as—for outdoor abundance only—moon illumination. Identifying these drivers is a first step towards the development of pathogen transmission models (R0 models), which are key to inform public health stakeholders on the periods of most risk for vector-borne diseases.Graphical

Save A Leg – Exploring Disparities in Community Characteristics Between Regions of High and Low Leg Amputation Rates

ObjectiveTo explore the disparities in community characteristics between the regions with the highest and lowest leg amputation rates. ApproachThis study used an exploratory method to describe the disparities between community characteristics of these two regions. The study team used several data sources: 1) We used the Dartmouth Atlas Healthcare Map to identify the highest and lowest leg amputation areas. This data was based on leg amputations per 1,000 Medicare enrollees by Hospital referral regions (HRRs) in 2015. 2) We used population estimates and income data from The Census Bureau. 3) We gathered climate, crime index, walk score, bike lanes, and park data from numerous websites. ResultsFlorence, SC has the highest leg amputation rate and Bridgeport, CT has the lowest across the nation. Florence also shows higher cardiovascular disease and diabetes discharge rates. Florence’s population has a slightly higher percentage of persons 65 years and over, a higher percentage of bachelor’s degrees, a higher per capita income, and a lower poverty rate. Florence also has fewer parks, is less walkable or bikeable, and has worse crime. It is worth noting that Bridgeport is close to Yale New Haven Hospital, which has a Limb Preservation Program that could contribute to the region’s extremely low leg amputation rate. ConclusionsThere are disparities in community characteristics between the regions with the highest and lowest leg amputation rates. ImplicationsFuture work needs to investigate the association between community characteristics and leg amputation rates and assess the impact of the Limb Preservation Program.

Maternal And preGnancy hEalth duriNg elevaTed heAt – MAGENTA: Novel data linkages and methods to understand the impacts of climate change on deprived communities in Wales and London.

Background and ObjectivesThe health impacts from changing climate, including increasing frequency, severity, and duration of heat are projected to worsen. The MAGENTA study will explore the impact of heat on pregnancy-related outcomes for deprived communities in Wales and London. ApproachMAGENTA will create linked data cohorts capturing some of the most deprived and diverse communities in Europe to analyse the impact deprivation, ethnicity, heatwaves, and urban heat islands have on pregnancy-related health. A consented data-linked cohort of pregnant women will provide biological samples and undergo direct measurement of themselves and their home environments to understand the heat stress response of pregnant women not acclimatised to heat during their pregnancy. Core to the study is our public involvement strategy which includes pregnant women and women who have recently delivered a baby, co-producing the research and how results are communicated. ResultsOur electronic cohorts cover &gt;10 years of climate and health data and capture all births in Wales &gt;300,000 and &gt;1.1 million in London. We will present how the novel data linkages can work in multiple trusted research environments, approaches for sharing methods in geospatial modelling and statistics across the project and preliminary findings from the study. Findings and ImplicationsThe MAGENTA study will help inform expectant parents, families and policy makers how to prepare for climate change in temperate regions. The comparison between different populations in Wales and London will give insight on where policies and interventions can be generally applied and where they need to be contextually adapted.

Coastal Sea Ice Concentration Derived from Marine Radar Images: A Case Study from Utqiaġvik, Alaska

We apply the Canny edge algorithm to imagery from the Utqiaġvik coastal sea ice radar system (CSIRS) to identify regions of open water and sea ice and quantify ice concentration. The radar-derived sea ice concentration (SIC) is compared against the (closest to the radar field of view) 25 km resolution NSIDC Climate Data Record (CDR) and the 1 km merged MODIS-AMSR2 sea ice concentrations within the ∼11 km field of view for the year 2022–2023, when improved image contrast was first implemented. The algorithm was first optimized using sea ice concentration from 14 different images and 10 ice analysts (140 analyses in total) covering a range of ice conditions with landfast ice, drifting ice, and open water. The algorithm is also validated quantitatively against high-resolution MODIS-Terra in the visible range. Results show a correlation coefficient and mean bias error between the optimized algorithm, the CDR and MODIS-AMSR2 daily SIC of 0.18 and 0.54, and ∼−1.0 and 0.7%, respectively, with an averaged inter-analyst error of ±3%. In general, the CDR captures the melt period correctly and overestimates the SIC during the winter and freeze-up period, while the merged MODIS-AMSR2 better captures the punctual break-out events in winter, including those during the freeze-up events (reduction in SIC). Remnant issues with the detection algorithm include the false detection of sea ice in the presence of fog or precipitation (up to 20%), quantified from the summer reconstruction with known open water conditions. The proposed technique allows for the derivation of the SIC from CSIRS data at spatial and temporal scales that coincide with those at which coastal communities members interact with sea ice. Moreover, by measuring the SIC in nearshore waters adjacent to the shoreline, we can quantify the effect of land contamination that detracts from the usefulness of satellite-derived SIC for coastal communities.

Development of Statistical Downscaling Model Based on Volterra Series Realization, Principal Components, Climate Classification, and Ridge Regression

This paper applied the fuzzy function approach, combined with the ridge regression model, to produce daily rainfall projections from large-scale climate variables. This study developed a statistical downscaling model based on principal components, c-means fuzzy clustering, Volterra series, and ridge regression. The model is known, hereafter as SDC2R2. In the developed downscaling model, the use of ridge regression, instead of multiple linear regression, is proposed to downscale daily rainfall with wide range (WR) predictors. The WR predictors were applied to sufficiently incorporate climate change signals. The developed model also captured the non-linear interactions of the climate variables by applying the transformation of Volterra series realization over WR predictors. This transformation was performed by applying principal components as orthogonal filters. Further, these principal components were clustered by using c-means clustering and non-linear transformations were applied on these membership functions, to improve the prediction ability of the model. The reanalysis of climate data from the National Centres for Environmental Prediction (NCEP) was used to develop the model and was validated by using the Global Climate Model (GCM) for four locations in the Manawatu River basin. The developed model was used to obtain future daily rainfall projections from three Representative Concentrative Pathways (RCP 2.6, RCP 4.5, and RCP 8.5) scenarios from the Canadian Earth System Model (CanESM2) GCM. The performance of the model was compared with a widely used statistical downscaling model (SDSM). It was observed that the model performed better than SDSM in downscaling rainfall on a daily basis. Every scenario indicated that there is a probability of obtaining high future rainfall frequency. The results of this study provide valuable information for decision-makers since climate change may potentially impact the Manawatu basin.

The impact of climate variation on the spread of Shiga toxin-producing E. coli.

Shiga toxin-producing Escherichia coli (STEC) is influenced by seasonality, but there is limited understanding of how specific climatic variables contribute to disease spread. This information aids in understanding disease transmission dynamics and could potentially inform public health modeling. This retrospective cohort study analyzed public health data from Ontario, Canada, between 2012 and 2021, along with historical climate data from Environment Canada. We employed Seasonal Autoregressive Integrated Moving Average (S-ARIMA) models to assess how temperature and precipitation impact the incidence of STEC infections, measured per 10,000,000 population. The study included 1658 confirmed STEC cases. A significant correlation was found between STEC incidence and climatic variables. Each degree Celsius increase in maximum temperature was associated with a rise of 3 STEC cases per 10,000,000 population (Centers for Disease Control and Prevention (2024)). Additionally, each millimeter of increased precipitation correlated with an increase of 1.1 cases per 10,000,000 population. The findings demonstrate a significant impact of temperature and precipitation on STEC transmission, highlighting the importance of integrating meteorological data into public health surveillance. This integration may help inform public health responses and support healthcare systems in planning for future outbreaks. Further studies are needed to refine predictive models and develop effective early warning systems for clinical settings.

To Enact or to Implement?

Abstract This study analyses Ghana’s climate-change legal and policy frameworks, pinpointing significant gaps and offering targeted recommendations to bolster the nation’s resilience against climate change. Despite Ghana’s commitments under international agreements and the formulation of national policies, challenges persist in policy implementation, sectoral integration, research, stakeholder engagement, funding and adaptation strategies. The paper argues for a robust, integrated approach to enhance Ghana’s climate governance, highlighting the necessity for improved implementation capacity, cross-sectoral policy harmonisation, enhanced climate data collection, active stakeholder participation, innovative financing and a stronger focus on adaptation and resilience. Key recommendations advocate for political commitment, collaborative efforts and resource mobilisation to ensure effective climate action. This condensed analysis contributes to the broader dialogue on climate-change management in emerging economies, offering insights for policy-makers and stakeholders on advancing climate resilience and sustainable development.

Invasive-Weed-Optimization-Based Extreme Learning Machine for Prediction of Lake Water Level Using Major Atmospheric–Oceanic Climate Scenarios

Fresh water lakes are vulnerable assets that need to be protected against manmade/natural challenges like climate change and anthropogenesis activities. This study addresses the predictability of the lake water level changes based on the knowledge acquired directly from the climate data. Two fresh water lakes named Lake Iznik and Uluabat, located in Turkey, are addressed. Time series of the lake water levels during October 1990–September 2019 at a monthly scale, along with the corresponding anomalies of 24 Large-Scale Atmospheric–Oceanic Oscillations (LSAOOs) from around the globe, are used in the analysis. The relationship between variables and the structure of the models are initially acquired based on the significance of the dependence between climate indices and lake water levels with consideration of the significance of the Spearman rank-order coefficient. Then, the time series are divided into training (80%) and testing (20%) sets. The Extreme Learning Method (ELM), enhanced with the genetic algorithm (ELM-GA) and Invasive Weed Optimization (ELM-IWO), is then used in the predictive models. Based on the results, Lake Uluabat showed a stronger teleconnection with LSAOOs, while the ELM-GA for Lake Iznik and ELM-IWA for Lake Uluabat depicted the best performance in the prediction of lake water levels. Comparison of the enhanced ELM-IWO to the corresponding ELM-GA illustrates that the ELM-IWO reveals more acceptable results owing to its flexible nature.

A Multi‐Modelling Approach for Informing the Conservation of a Cold‐Adapted Terrestrial Amphibian in the Face of Climate Change

ABSTRACTAimWe aimed to assess potential climate change impacts on the distribution of a cold‐adapted terrestrial amphibian that shows strong intraspecific differentiation.LocationAlps and Dinarides.TaxonAlpine salamander (Salamandra atra).MethodsWe built SDMs for each of two major intraspecific lineages (S. a. atra, which is found throughout the Northern Alps, and S. a. prenjensis, which appears sparsely along the Dinarides) and for the entire species, using occurrence points from a carefully curated database, climate data with 1 km2 spatial resolution and eight modelling techniques. We projected climatically suitable areas to 2070 under two climate scenarios (RCP2.6 and RCP8.5), using 24 general circulation models.ResultsFor S. a. atra, under RCP2.6 scenario, we detected a 3% increase in the extent of climatically suitable areas in the Alps (although with low model agreement), yet a 6% decrease under the RCP8.5 scenario. For S. a. prenjensis, we detected a decrease in the extent of climatically suitable areas in the Dinarides between 7% and 45% (RCP2.6 and RCP8.5, respectively). The SDM predictions for the entire species were most in line with those of the S. a. atra model. Although climate variables were not equally strong in predicting the climatic suitability for both lineages, the minimum temperature of the coldest month revealed an important predictor for the entire species, with clear decreases in suitability towards higher temperature.Main ConclusionsAll models reveal a considerable decrease in climate suitability throughout most of the range of S. atra, which is particularly visible in the Dinarides. We highlight the importance of considering intraspecific variation when modelling climate change impacts on geographically differentiated species whose populations vary in their climatic niche. We provide our study results (occurrence data and maps) via a web application that can be useful for guiding conservation efforts.

Projected distribution shifts of resident monarch butterflies and consequences for migratory monarchs

The charismatic migratory populations of monarch butterflies have declined precipitously in North America. A contributing threat might be the expansion of winter breeding populations in the southern portions of their historical eastern and western summer breeding ranges. Recent research suggests individuals from winter breeding populations are prone to high parasite burdens, resulting in lower fitness compared to migratory counterparts. Temporal and spatial overlap between these individuals and migratory monarchs in both fall and spring mean that interbreeding and use of the same host plants could result in transfer of parasites, especially the debilitating neogregarine Ophryocystis elektroscirrha, increasing the parasite load in migrating populations. We aimed to predict how climate change could affect the distribution of winter breeding monarchs in North America. We used ecological niche modeling of monarch larval observations for winter and current climate data to predict the current and future distributions of winter breeding monarchs across North America. Our analyses predict up to a 38% and 160% increase and a 574 and 340 km northward shift in suitable area for winter breeding monarchs in response to climate change by 2100 for eastern and western migratory populations, respectively. Our results support concerns over potential risk of disease spread from resident monarchs to the migratory monarch populations. In both eastern and western migratory populations this is due to an increase in overlap between the resident population and the areas through which the migratory populations travel during fall and spring migrations. Our results support calls for controlling the spread of non-native tropical milkweed, as winter breeding monarchs depend on this plant for reproduction.

Does the subsidiary's climate risk matter? Evidence from the stock price crash

Combining satellite climate data with the subsidiaries’ geographical locations of listed firms in China, we document a significant positive relation between subsidiary-level climate risk exposure and future stock price crash risk of conglomerate. The results are stronger for firms with higher CEO performance pressure, higher subsidiary climate risk management costs and weaker external governance. Our findings shed light on the economic consequences of climate risks at the subsidiary level.

Evolutionary dynamics of the successful expansion of pandemic Vibrio parahaemolyticus ST3 in Latin America

The underlying evolutionary mechanisms driving global expansions of pathogen strains are poorly understood. Vibrio parahaemolyticus is one of only two marine pathogens where variants have emerged in distinct climates globally. The success of a Vibrio parahaemolyticus clone (VpST3) in Latin America- the first spread identified outside its endemic region of tropical Asia- provided an invaluable opportunity to investigate mechanisms of VpST3 expansion into a distinct marine climate. A global collection of VpST3 isolates and novel Latin American isolates were used for evolutionary population genomics, pangenome analysis and combined with oceanic climate data. We found a VpST3 population (LatAm-VpST3) introduced in Latin America well before the emergence of this clone in India, previously considered the onset of the VpST3 epidemic. LatAm-VpST3 underwent successful adaptation to local conditions over its evolutionary divergence from Asian VpST3 isolates, to become dominant in Latin America. Selection signatures were found in genes providing resilience to the distinct marine climate. Core genome mutations and accessory gene presences that promoted survival over long dispersals or increased environmental fitness were associated with environmental conditions. These results provide novel insights into the global expansion of this successful V. parahaemolyticus clone into regions with different climate scenarios.