Future Scenarios Research Articles

Estimating the dynamics of ecosystem functions under climate change in a temperate forest region

Understanding the dynamics and spatial patterns of forest biodiversity and functioning is vital for effective conservation and management. This study introduces a new framework for estimating the spatio-temporal dynamics of biodiversity and ecosystem functions in northeastern China. Our novel approach evaluates 96 future scenarios, including four points in time, four socio-economic pathways, and six global climate models based on an extensive network of permanent field plots with specific bioclimatic, topographic, and soil data. Our findings indicate a future trend of diminishing biodiversity and forest biomass productivity and increasing biomass density and carbon storage. The relationship between biodiversity and ecosystem functionality indicates a non-linear response to changes in temperature and precipitation, and a stable correlation between biodiversity and ecosystem stability across most of the socio-economic pathways. Unravelling the complex relationship between biodiversity, ecosystem functions, and stability of production, in the face of global change, requires new methods of analysis that were used in this study, including machine learning and other advanced methods.

Effects of climate change and land use on the hydrologic regime using the Hydro-BID tool: A case study of the Andean mountain basin in Colombia

ABSTRACT Changes on the land surface generate changes in land cover, which directly affect the availability of water in watersheds. This article evaluates the case study regarding the effects on the hydrological regime of the Andean mountain basin on the Coello river basin in Colombia due to changes in land use/land cover during the 2000–2019 period by the use of the Hydro-BID tool. The physical analysis of the land surface included the processing of Landsat 7 ETM and Landsat 8 OLI satellite images for the years 2001, 2003, 2015, and 2019. Seven types of coverage were determined based on these data using the Mixed Gaussian Method. The changes between each year were evaluated, after which the land use/land cover change for the year 2050 was predicted using a Markov chain. The multi-temporal analysis showed a decrease in forested areas during the studied period, while low vegetation significantly increased within the watershed. This trend was shown to continue in the future scenario for the year 2050, with an increase in flow on the watershed of 59.6%. Additionally, the climate change scenarios were modeled with the changes in land use. The combined effects established a progressive decrease in the modal flow.

Cost-Minimization Analysis for Subcutaneous Daratumumab in the Treatment of Newly Diagnosed Multiple Myeloma in Three Gulf Countries

Background: The second most common hematologic cancer worldwide is multiple myeloma (MM), with incidence and mortality rates that have more than doubled over the past 30 years. The safety and efficacy of daratumumab regimens in the treatment of newly diagnosed MM (NDMM) is demonstrated in clinical trials. Objective: To assess the financial effects of the adoption of subcutaneous daratumumab (dara-SC) rather than intravenous daratumumab (dara-IV) for the treatment of NDMM in three Gulf countries (Qatar, Oman and the United Arab Emirates; UAE), a cost-minimization model was constructed. Methods: We performed static cost minimization analyses from a societal perspective to evaluate the costs and possible reductions in resource utilization associated with a shift from dara-IV infusion to dara-SC injection for NDMM patients over a 5-year time horizon. The model included 2 scenarios: the current scenario in which 100% of patients with NDMM are treated with dara-IV infusion and a future scenario in which dara-SC injection is gradually adopted over the modeled time horizon. The model differentiated precisely between autologous stem cell transplantation (ASCT)–eligible and ASCT-ineligible NDMM patients in terms of their number in each group and the associated therapeutic regimens. One-way sensitivity analyses were also conducted. Results: The model showed that the use of dara-SC in NDMM patients who were eligible or ineligible for ASCT resulted in lower non-drug costs, including premedication drug costs, adverse-effect costs, administration costs, medical staff costs, and indirect costs. The resulting total savings over the 5-year time horizon of the model for Hamad Medical Corporation, Sultan Qaboos University Hospital/Royal Hospital, Sheikh Shakhbout Medical City (SSMC), and Tawam Hospital were QAR −2 522 686, OMR −143 214, AED −30 010 627, and AED −5 003 471, respectively. Conclusion: The introduction of dara-SC as a front-line treatment for NDMM patients in Qatar (Hamad Medical Corporation), Oman (Sultan Qaboos University Hospital, Royal Hospital-MOH), and the UAE (SSMC and Tawam Hospital) can help save resources and minimize constraints on the healthcare system.

Future climate doubles the risk of hydraulic failure in a wet tropical forest.

Future climate presents conflicting implications for forest biomass. We evaluate how plant hydraulic traits, elevated CO2 levels, warming, and changes in precipitation affect forest primary productivity, evapotranspiration, and the risk of hydraulic failure. We used a dynamic vegetation model with plant hydrodynamics (FATES-HYDRO) to simulate the stand-level responses to future climate changes in a wet tropical forest in Barro Colorado Island, Panama. We calibrated the model by selecting plant trait assemblages that performed well against observations. These assemblages were run with temperature and precipitation changes for two greenhouse gas emission scenarios (2086-2100: SSP2-45, SSP5-85) and two CO2 levels (contemporary, anticipated). The risk of hydraulic failure is projected to increase from a contemporary rate of 5.7% to 10.1-11.3% under future climate scenarios, and, crucially, elevated CO2 provided only slight amelioration. By contrast, elevated CO2 mitigated GPP reductions. We attribute a greater variation in hydraulic failure risk to trait assemblages than to either CO2 or climate. Our results project forests with both faster growth (through productivity increases) and higher mortality rates (through increasing rates of hydraulic failure) in the neo-tropics accompanied by certain trait plant assemblages becoming nonviable.

Dynamic population modeling of bacterioplankton community response to gelatinous marine zooplankton bloom collapse and its impact on marine nutrient balance

The diverse microbial community in the ocean, encompassing various metabolic types, interacts with the wide array of compounds in the dissolved organic matter (DOM) pool, thereby influencing the ocean’s biogeochemical state and, consequently, the global climate. Our understanding of the interactions between specific DOM constituents and microbial consortia remains limited, necessitating further refinement to achieve a mechanistic comprehension of the relationship between the DOM field and the microbial metabolic network. Attaining this level of understanding is crucial for accurately predicting the marine ecosystem’s response to natural and anthropogenic perturbations. To address this gap, we developed a bacterial population model based on the von Foerster equation. This model aims to describe the complex microbial-mediated degradation of gelatinous zooplankton (hereinafter ‘jellyfish’) detritus, as an important, but largely overlooked source of DOM in the ocean. By considering microbial growth and decay, as well as DOM uptake, and nutrient release, the model is able to describe the microbial community’s life cycle, and the biochemical transformations of the jellyfish-derived organic matter. We fitted the model to results of laboratory microcosm experiments conducted to simulate scenarios experienced by ambient microbiomes during decay of two different jellyfish species in the northern Adriatic Sea. By interpreting the fitted parameters, we highlight the differences in the microbial response to different jellyfish species, namely how these affect the microbial community composition and the release of nutrients. This model has been specifically designed for integration with ocean circulation models to create a comprehensive physical-biogeochemical ocean model. Such an extended model can be utilized for multi-scale simulations to assess the system’s response to jellyfish and jellyfish-derived organic matter. Given that jellyfish blooms may become more prevalent under future ocean scenarios, this modeling approach is essential for understanding their potential impact on marine ecosystems.

Analysis of the Distribution Pattern of Phenacoccus manihoti in China under Climate Change Based on the Biomod2 Model.

The changing global climate has significantly impacted the spread of plant pests. The cassava mealybug (Phenacoccus manihoti) is among the most dangerous quarantine pests affecting cassavas worldwide, causing substantial losses in agricultural production and food security across several regions. Although China is currently free of the cassava mealybug, its proximity to affected countries and extensive trade with these regions necessitate a detailed understanding of the pest's distribution pattern and dynamic ecological niche changes. Using the Biomod2 model, we selected two historical climate scenarios and two future climate scenarios (SSP1-2.6 and SSP5-8.5) to investigate the distribution patterns, potential habitats, distribution centers, and dynamic ecological niches of cassava mealybugs in China. Key environmental variables influencing the distribution were identified, including bio4, bio8, bio12, bio18, and bio19. The potential habitat of cassava mealybugs is mainly located in several provinces in southern China. In the future, the suitable habitat is projected to expand slightly under the influence of climate change, maintaining the overall trend, but the distribution center of suitable areas will shift northward. Dynamic ecological niche prediction results indicate the potential for further expansion; however, the ecological niches may be unequal and dissimilar in the invaded areas. The predictions could serve as a valuable reference for early warning systems and management strategies to control the introduction of cassava mealybugs.

Spatiotemporal Dynamics and Scenario Simulation of Regional Green Spaces in a Rapidly Urbanizing Type I Large City: A Case Study of Changzhou, China

The rapid urbanization observed in major Chinese cities has resulted in the degradation of both urban and rural environments. In response to this challenge, the concept of regional green spaces has emerged as an innovative approach to coordinate and manage green space resources across urban and rural areas. This study focuses on conducting a comprehensive analysis of the evolution, driving factors, and future scenarios of regional green spaces in Changzhou, which serves as a representative Type I large city in China. To accomplish this analysis, Landsat satellite images from 1992, 2002, 2012, and 2022 were utilized. Various methodologies, including landscape pattern indices for quantitative evaluation, the CLUE-S model, logistic regression for qualitative evaluation, and the Markov–FLUS model, were employed. The findings indicate a continuous decline in the area of regional green spaces in Changzhou, decreasing from 248.23 km2 in 1992 to 204.46 km2 in 2022. Landscape pattern analysis reveals an increase in fragmentation, complexity, irregularity, and human interference within these green spaces. Logistic regression analysis identifies key driving factors influencing regional green spaces, including elevation, urban population, and proximity to water bodies and transportation. The scenario simulations provide valuable insights into potential future trends of regional green spaces. According to the economic priority scenario, a modest increase in regional green spaces is anticipated, while the ecological priority scenario indicates substantial growth. Conversely, the inertial development scenario predicts a continued decline in regional green spaces. This research emphasizes the significance of achieving a harmonious coexistence between economic progress and environmental preservation. It emphasizes the necessity of optimizing the arrangement of green areas within a region while fostering public engagement in the conservation of these spaces. The findings contribute to the protection and sustainable development of the urban environment in the Yangtze River Delta region.

Water-energy-food-greenhouse gases nexus management in urban environments: A robust multi-agent decision-support system

Conceptual urban water metabolism (CUWM) models provide a holistic view of the efficiency of urban water systems. These models can be linked with multi-agent reinforcement learning (MARL) models to mimic stakeholders' reactions to various strategies. However, the outcomes derived from CUWM-MARL models come with uncertainty. As a result, this paper introduces a decision support system (DSS) that is aware of these uncertainties and uses this information to select robust management strategies based on the output of CUWM-MARL models. First, future scenarios are generated by exploring all combinations of critical values of deeply uncertain variables and values of uncertain variables sampled from an appropriate multivariate copula distribution. Then, a computationally efficient surrogate model is developed to alleviate the computational load of the MARL sub-model. The surrogate model is run through all future scenarios to calculate the system's key performance indicators (KPIs) for each management strategy. Urban water managers can use these KPIs and social choice methods to find consensus management strategies. The proposed methodology has been implemented in the western part of the Tehran metropolitan area, Iran. This study evaluates 26 management strategies consisting of various projects, including reducing water distribution network leakage, implementing urban demand control projects, and utilizing semi-centralized or decentralized wastewater treatment plants. The strategy chosen by the uncertainty-aware DSS enhances the total utility and fairness indices by 125% and 4%, respectively. Moreover, it effectively improves groundwater quality, reduces energy consumption and greenhouse gas emissions, and enhances water supply reliability, ultimately contributing to farmers' job security.

Assessing uncertainty in bioclimatic modelling: a comparison of two high-resolution climate datasets in northern Patagonia

Climate change is reshaping forest ecosystems, presenting urgent and complex challenges that demand attention. In this context, research that quantifies interactions between climate and forests is substantial. However, modelling at a spatial resolution relevant for ecological processes presents a significant challenge, especially given the diverse geographical contexts in which it is applied. In our study, we aimed to assess the effects of applying CHELSA v.2.1 and WorldClim v2.1 data on bioclimatic analysis within the Río Puelo catchment area in northern Patagonia. To achieve this, we inter-compared and evaluated present and future bioclimates, drawing on data from both climate datasets. Our findings underscore substantial consistency between both datasets for temperature variables, confirming the reliability of both for temperature analysis. However, a strong contrast emerges in precipitation predictions, with significant discrepancies highlighted by minimal overlap in bioclimatic classes, particularly in steep and elevated terrains. Thus, while CHELSA and WorldClim provide valuable temperature data for northern Patagonia, their use for precipitation analysis requires careful consideration of their limitations and potential inaccuracies. Nevertheless, our bioclimatic analyses of both datasets under different scenarios reveal a uniform decline in mountain climates currently occupied by N. pumilio, with projections suggesting a sharp decrease in their coverage under future climate scenarios.

Vulnerability, Resilience and Adaptation of Lagos Coastal Communities to Flooding

Lagos has been identified as one of the 50 cities most vulnerable to extreme sea levels. The state also ranked 30th among 136 port cities in terms of population exposure to flooding under a past climate scenario (2005) and 15th under a future climate scenario (2070s). The state faces significant environmental strains as a result of its geo-economic status. Some of the problems are wetland loss, pollution, population pressure, restricted access to drinkable water, and flooding. All these factors have contributed to the instability of Lagos ecosystems, but the impact of flooding is particularly significant because Lagos is surrounded by aquatic ecosystems and its low elevation and topography increase its susceptibility to flooding. The method adopted in this review involved the use of PICO (Population, Intervention, Control, and Outcomes) criteria to synthesize the research questions and objectives. Thereafter, PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines was employed for the study selection criteria, search strategies and data extraction methods. A broad search strategy involving databases (Google scholar, Science Direct), pertinent keywords and search filters was used to identify relevant articles and minimize selection bias. After the search, PICO criteria was again used to select the studies to be considered in the review. The selected text were downloaded and the essential scientific information were extracted and analyzed in the study. The impacts of flooding are numerous. Flooding is a key factors that has prevented Africa’s growing urban population from escaping poverty, and it also impedes the achievement of some SDGs. This is because many African cities lack the resources and infrastructures needed to withstand extreme weather conditions. Surviving in flood-prone cities like Lagos is a daunting task because flood affects livelihood, human health, and can even cause death. Specifically, the 2020 flood incidence in Nigeria affected about 40% of the local government areas, and 97% of the states, displacing over 120,000 persons and killing 68 persons aside from properties and farmlands destroyed. Women and children are the most vulnerable. The adoption of advanced flood risk management strategies could help in flood containment and management in the state.

Modelling Multi-Scenario Ecological Network Patterns and Dynamic Spatial Conservation Priorities in Mining Areas

Mining activities have significantly altered the land use patterns of mining areas, exacerbated the degree of landscape fragmentation, and thereby led to the loss of biodiversity. Ecological networks have been recognized as an essential component for enhancing habitat connectivity and protecting biodiversity. However, existing studies lack dynamic analysis at the landscape scale under multiple future scenarios for mining areas, which is adverse to the identification of ecological conservation regions. This study used the MOP-PLUS (multi-objective optimization problem and patch-level land use simulation) model to simulate the land use patterns in the balance of ecology and economy (EEB) scenario and ecological development priority (EDP) scenario for the Shendong coal base. Then, climate change and land use patterns were integrated into ecosystem models to analyze the dynamic changes in the ecological networks. Finally, the conservation priorities were constructed, and dynamic conservation hotspots were identified using landscape mapping methods. The following results were obtained: (1) From 2000 to 2020, large grassland areas were replaced by mining areas, while cultivated land was replenished. By 2030, the forest and grassland areas (967.00 km2, 8989.70 km2) will reach their peaks and the coal mine area (356.15 km2) will reach its nadir in the EDP scenario. (2) The fragmentation of ecological sources intensified (MPS decreased from 19.81 km2 to 18.68 km2) and ecological connectivity declined (in particular, α decreased by 6.58%) from 2000 to 2020. In 2030, the connectivity in the EDP scenario will increase, while the connectivity in the EEB scenario will be close to that of 2020. (3) The central and southeastern parts of the Shendong coal base have higher conservation priorities, which urgently need to be strengthened. This study offers guidance on addressing the challenges of habitat and biodiversity conservation in mining areas.

Land-Use Feedback under Global Warming—A Transition from Radiative to Hydrological Feedback Regime

Abstract This study examines the effects of land-use (LU) change on regional climate, comparing historical and future scenarios using seven climate models from phase 6 of Coupled Model Intercomparison Project–Land Use Model Intercomparison Project experiments. LU changes are evaluated relative to land-use conditions during the preindustrial climate. Using the Community Earth System Model, version 2–Large Ensemble (CESM2-LE) experiment, we distinguish LU impacts from natural climate variability. We assess LU impact locally by comparing the impacts of climate change in neighboring areas with and without LU changes. Further, we conduct CESM2 experiments with and without LU changes to investigate LU-related climate processes. A multimodel analysis reveals a shift in LU-induced climate impacts, from cooling in the past to warming in the future climate across midlatitude regions. For instance, in North America, LU’s effect on air temperature changes from −0.24° ± 0.18°C historically to 0.62° ± 0.27°C in the future during the boreal summer. The CESM2-LE shows a decrease in LU-driven cooling from −0.92° ± 0.09°C in the past to −0.09° ± 0.09°C in future boreal summers in North America. A hydroclimatic perspective linking LU and climate feedback indicates LU changes causing soil moisture drying in the midlatitude regions. This contrasts with hydrology-only views showing wetter soil conditions due to LU changes. Furthermore, global warming causes widespread drying of soil moisture across various regions. Midlatitude regions shift from a historically wet regime to a water-limited transitional regime in the future climate. This results in reduced evapotranspiration, weakening LU-driven cooling in future climate projections. A strong linear relationship exists between soil moisture and evaporative fraction in midlatitudes. Significance Statement Land–atmosphere feedback involving soil moisture can increase local temperature and affect how land-use (LU) change impacts manifest in a warming climate. Conversely, an increased surface reflectance due to LU change can decrease local temperature in the midlatitude regions. Further, the LU change signal is often mixed with the internal climate variability, making it harder to separate. This study uses a novel technique to separate LU change impact from other climate forcing in the latest generation of climate and Earth system models. In the future climate, soil moisture drying lessens the cooling impact. A large-ensemble climate experiment analysis confirms a significant weakening of the LU-driven cooling impact in the midlatitudes. Both LU and climate changes exacerbate soil moisture drying, leading to a shift toward a water-limited system where hydrological feedback becomes more influential than radiative feedback.

A cradle-to-grave assessment of carbon and energy footprints of ships using synthetic fuel for net-zeros operations

PurposeAchieving net-zero emissions in the maritime sector requires adopting carbon-neutral practices in terms of shipbuilding, operation and recycling. However, research integrating life cycle and energy analysis is missing to inform decision-makers. MethodConsequently, the paper presents models combining a model-based life cycle assessment (MBLCA) and ship energy simulations to examine the footprints of a generic New Panamax Containership (DTC). Analyses were conducted to assess the energy and carbon effects of using synthetic methanol as marine fuel. ResultsThe life-cycle analysis confirms the overwhelming prevalence of operations in the total greenhouse gases emission during the ship's lifetime. The analysis highlights the potential of carbon savings if the vessel consumes methanol produced from neutral energy and renewable carbon sources. However, the magnitude of shipping fuel requirements may profoundly impact energy infrastructures while still requiring large volumes of carbon offset credits. ConclusionThe paper confirms the usefulness of integrating life-cycle analysis and energy modelling to discuss future energy scenarios. The completeness of the models also allows for future research analysing other energy solutions.

Associating reservoir operations with 2D inundation risk and climate uncertainty

ABSTRACT Bridging the research gap between reservoir operations and inundation risks under the future climate, this study integrates a hydrologic reservoir management model with a 2D hydrodynamic model, comparing the conventional regulations and the optimized reservoir operations based on the particle swarm optimization (PSO) algorithm. Results reveal that optimized operations using the PSO algorithm consistently outperform conventional strategies by better-managing peak discharges and controlling downstream inundation. The study further differentiates between PSO-optimized plans: PSO1, which focuses on minimizing inundation areas, and PSO2, which prioritizes peak reduction at the flood control point. Interestingly, PSO2 proves superior for single-point peak reduction, typically the primary objective in current practices, whereas PSO1, despite lesser peak reduction, achieves a smaller inundation area, enhancing basin-scale flood resilience. This discrepancy reveals the need to consider downstream inundation risks as critical evaluation metrics in reservoir optimization, a factor often overlooked in existing studies. The research underscores the importance of updating operational frameworks to incorporate 2D inundation risks and adapt to increased flood risks under changing climate conditions. Despite optimization, future climate scenarios predict increased flood exposure, indicating that the current safety discharge rates and flow regulations at control points are outdated and require revision.

Forecasting ecological water demand of an arid oasis under a drying climate scenario based on deep learning methods

Ecological water diversion projects (EWDP) are an effective management tool for restoring oasis ecosystems in arid regions. Given the potential for drier climatic conditions in arid regions in the future, it is essential to develop water diversion strategies that can adapt to climate change and reduce the risk of oasis ecosystem degradation. Here, this study used a Bayesian optimization-based long- and short-term memory (BO-LSTM) model to determine the optimal amount of water diversion needed to maintain healthy growth of oasis vegetation under future climate change scenarios in the Qingtu Oasis, which is a typical downstream oasis of inland rivers restored by EWDP in China. The results showed that the BO-LSTM model effectively captured the response of oasis vegetation to changes in water inundation areas and drought stress with low computational cost and high accuracy. The study revealed that regional vegetation became more vulnerable than previously thought when extreme drought and a drying trend were taken into account. It was found that if the amount of water entering the oasis ranges from 10 to 15 million m3, there will be a decline in the growth of oasis vegetation as indicated by the normalized difference vegetation index (NDVI). Even if current levels of water diversion (20 million m3) are maintained, oasis vegetation may still face growth decline due to meteorological drought. The optimal amount of water diversion was determined to be 25 million m3, resulting in a 21.7% increase in NDVI regardless of drought events. This study represents an innovative approach as it couples EWDP, climate change, and oasis vegetation dynamics based on deep learning models, which unveils divergent responses of oasis vegetation to climate change and EWDP and verifies a non-linear relationship between water diversion amounts and ecological benefits generated.

RESILIENCE AND LESSONS LEARNED IN COLOMBIAN FAMILY FARMING DURING THE COVID-19 PANDEMIC

<p><strong>Background: </strong>A health crisis exposes people to food vulnerability by restricting the mobilization and commercial exchange of food. During the COVID-19 pandemic, an ample food supply was available to ensure the basic consumption of the population, prompting two questions: What resilient practices did farmers adopt to produce under conditions of high sanitary restriction, and what lessons did they derive to confront future crises? <strong>Objective:</strong> The article presents and collects the experiences of 59 small family farmers in Colombia in implementing good practices for economic and financial resilience and the lessons learned from the Covid-19 pandemic, constituting a new source of knowledge to understand human behaviors and actions in the face of adversity and the experiential learnings gained to act in future crisis scenarios. <strong>Methodology</strong>: To characterize the resilient practices of agricultural producers in the department of Norte de Santander, Colombia, amid the COVID-19 pandemic crisis, researchers conducted interviews with 59 smallholder farmers from 12 municipalities. The study encompassed 11 agricultural products and 6 animal products, ultimately identifying three overarching categories: economic resilience, financial resilience, and lessons learned.<strong> Main Results: </strong>The results indicate strategies for diversification of production, use of local resources, solidarity-based family work, short marketing chains and an economy of scarcity in family spending. <strong>Implications: </strong>These lessons can inform future policies and practices to better prepare for and respond to similar challenges. <strong>Conclusion: </strong>The study's findings provide valuable insights for policymakers and small farmers, emphasizing the importance of local resource utilization, community cooperation, and adaptive strategies to enhance resilience and sustain livelihoods during crises. </p>

Level Up Your Residency: Can a Novel Two-Week Simulation Rotation Really Make a Difference?

This study describes a unique two-week simulation-based medical education (SBME) rotation for transitional year (TY) residents. During the rotation, residents are fully integrated into the simulation team, actively participating in clinically based interprofessional scenarios, procedural techniques, and mixed reality experiences. Residents also created and ran their own simulations while receiving content expert feedback. We evaluated the rotation's effectiveness in preparing TY graduates for their specific advanced residency track. A retrospective survey evaluated the experiences of 11 TY residents who participated in a unique two-week simulation rotation. The survey assessed residents' perceptions of the program's value and skill development, course design, scenario relevance to future practice, and preparedness to develop future scenarios. Residents (11 out of 12 residents, 92% response rate) overwhelmingly endorsed the simulation rotation (100% positive, 45.45% extremely valuable). The program demonstrably improved core clinical skills (100% reported improvement) and fostered self-efficacy for future practice. Scenario relevance was high (81.82% highly relevant). Collaboration and communication skills showed promise (72.73% positive) while highlighting a potential area for future refinement. Residents unanimously agreed on effective time allocation and the program's value for debriefing skills. Notably, 91% strongly supported residency-specific simulation training. The two-week simulation was perceived by prior TY residents as valuable, with a majority finding the experience highly valuable across multiple survey questions. Residents overwhelmingly expressed a preference for residency-specific training, suggesting future development of specialty-tailored modules and enhanced debriefing sessions. The findings highlight the program's effectiveness and successful implementation into a TY residency curriculum.

Habitat suitability and niche interaction between the invasive snail Achatina fulica and its biocontrol flatworm Platydemus manokwari in Southeast Asia

Bioinvasions are increasingly altering community structures around the world, especially with the changing climate. In invaded areas, biocontrol agents are introduced to regulate the invasive species. However, without thorough evaluation, these agents can proliferate and threaten other organisms. We assessed the niche dynamics between invasive Achatina fulica (Giant African Snail) and its biocontrol agent, the Platydemus manokwari (New Guinea Flatworm), in Southeast Asia. Species occurrence and environmental data were used to model the habitat suitability of both species in the present and future climate scenarios using ecological niche modeling with the MaxEnt algorithm. These models predicted 25.9% and 42.0% of the current conditions to be suitable and 73.8% and 57.8% to be unsuitable for A. fulica and P. manokwari, respectively. There was a predicted steady increase in suitable areas and a gradual decrease in A. fulica’s unsuitable areas as the carbon emissions are predicted to increase. Moderate to high niche overlap of 61.2% to 83.4% was expected between the species under different climate scenarios. Predicting the suitable areas for invasive species and their niche interaction with other species in future scenarios will aid in identifying vulnerable areas for conservation.

Fish and tips: Historical and projected changes in commercial fish species' habitat suitability in the Southern Hemisphere

Global warming has significantly altered fish distribution patterns in the ocean, shifting towards higher latitudes and deeper waters. This is particularly relevant in high-latitude marine ecosystems, where climate-driven environmental changes are occurring at higher rates than the global average. Species Distribution Models (SDMs) are increasingly being used for predicting distributional shifts in habitat suitability for marine species as a response to climate change. Here, we used SDMs to project habitat suitability changes for a range of high-latitude, pelagic and benthopelagic commercial fish species and crustaceans (10 species); from 1850 to two future climate change scenarios (SSP1–2.6: low climate forcing; and SSP5–8.5: high climate forcing). The study includes 11 Large Marine Ecosystems (LME) spanning South America, Southern Africa, Australia, and New Zealand. We identified declining and southward-shifting patterns in suitable habitat areas for most species, particularly under the SSP5–8.5 scenario and for some species such as Argentine hake (Merluccius hubbsi) in South America, or snoek (Thyrsites atun) off Southern Africa. Geographical constraints will likely result in species from Southern Africa, Australia, and New Zealand facing the most pronounced habitat losses due to rising sea surface temperatures (SST). In contrast, South American species might encounter greater opportunities for migrating southward. Additionally, the SSP5–8.5 scenario predicts that South America will be more environmentally stable compared to other regions. Overall, our findings suggest that the Patagonian shelf could serve as a climate refuge, due to higher environmental stability highlighting the importance of proactive management strategies in this area for species conservation. This study significantly contributes to fisheries and conservation management, providing valuable insights for future protection efforts in the Southern Hemisphere.

Quantifying weather-induced unreliable public transportation service in cold regions under future climate model scenarios

Climate change, particularly in cold regions, significantly challenges public transportation systems. This study conducts a comprehensive analysis of weather patterns and public transit reliability in the context of climate change impacts. Leveraging advanced modeling techniques, including a ridge regression model for snow water equivalent data estimation and a long short-term memory (LSTM) based on recurrent neural network, the study aims to assess the reliability trends of the rapid transit system under various climate scenarios. The findings reveal that climate change in general increases weather-related delays in the Toronto transit system. The number of short delays decreased accordingly due to changes in winter temperatures but exacerbated long delays as the number of weather extremes increased. The LSTM model performed effectively in predicting delays, especially for the rapid transit system sensitive to weather variations. This study emphasizes the need for robust planning and interventions to increase the resilience of transit systems against climate change and highlights the importance of the integration of climate and extreme weather considerations into transportation management.