Budget Model Research Articles

Marine aquaculture spatial planning on market orientation for Pacific oyster in Shandong, China

Accessing the production value is essential to the selection of suitable sites for aquaculture farms. This study employed a Dynamic Energy Budget (DEB) model and used market-oriented indicators, including shell length, flesh weight, condition index and minimum farming duration, to analyze the suitability for Pacific oyster aquaculture in offshore Shandong, China. Chlorophyll a (Chl-a) concentration, Total Suspended Sediment (TSS) concentration, and Sea Surface Temperature (SST) were extracted by remote sensing and used as forcing variables in the bioenergetic model. Four market-oriented indicators and five zones in the study area were chosen to develop a Suitable Market-Aquaculture site-selection model (SMASM) and estimate the oyster farming suitability. The results demonstrated that Sanggou Bay exhibits high potential for Pacific oyster year-round aquaculture. Eastern Laizhou Bay is suitable for the autumn-spring fattening culture of adult oysters. Laoshan Bay and Changshan Islands are suitable for promoting rapid growth in juvenile oysters. The results of our model were consistent with the oyster production value in Shandong, showing an overall increasing trend except for the anomalous value observed in 2019. Sudden drops in water temperature, red tides, and significant increases in turbidity can exert detrimental effects on oyster production, which has been evidenced to be influenced by Super Typhoon Lekima in 2019. Although this study concentrated on a specific geographical region, the model could be applied to other regions to develop market-oriented suitability management for species and environments worldwide.

Slow biological microplastics removal under ocean pollution phase-out trajectories

Microplastics are ubiquitous in marine environments and can be incorporated into biological aggregates including marine snows and faecal pellets. These aggregates are suspected to be a major removal mechanism for microplastics from the surface ocean, transporting them to deeper levels and the seafloor as they sink and remineralise. However, simple budget calculations, observations, and model parameter testing suggest that aggregation might also lead to retention of microplastics in the upper ocean, sustaining contamination in biologically-productive environments. The ability of the biological microplastic sink to reduce water column contamination has relevance to the setting of ocean plastics pollution reduction targets, as are currently under negotiation by the International Negotiating Committee of the United Nations Environment Assembly (UNEA). Here we apply eight idealised global pollution reduction trajectories, from 1%–100% per year, starting from the year 2026 and ending in the year 2100 to an Earth System Climate Model with a representation of ocean microplastics and their aggregation in biological particles. We find that the global ocean microplastic inventory and surface concentrations stabilize within this century for reduction rates exceeding 5% per year but the inventory does not substantially decrease under any trajectory. Furthermore, microplastics are retained by marine biology in the surface ocean, where concentrations stabilise to a non-zero value over decades. Lastly we find that irrespective of scenario, contamination of deeper ocean layers continues to increase for the duration of our simulations via the export of microplastics by biological aggregates. These results suggest that ambitious targets for pollution reduction exceeding 5% per year will be required to progress the resolution of the UNEA to ‘end plastic pollution’ in this century, and that ongoing microplastic contamination of the marine food web may be unavoidable.

Generic Carbon Budget Model for Assessing National Carbon Dynamics toward Carbon Neutrality: A Case Study of Republic of Korea

Forests play a crucial role in South Korea’s carbon neutrality goal and require sustainable management strategies to overcome age-class imbalances. The Generic Carbon Budget Model (GCBM) offers a spatially explicit approach to simulate carbon dynamics at a regional scale. In this study, we utilized the GCBM to analyze the carbon budget of forests in South Korea and produce spatiotemporal maps for distribution of the forest biomass. The growth parameters of five representative tree species (Pinus densiflora Siebold & Zucc., Larix kaempferi Carr., Pinus koraiensis Siebold & Zucc., Quercus mongolica Fisch. ex Ledeb., Quercus variabilis Blume), which are the main species in South Korea, were used to operate the model. In addition, spatial data for harvest and thinning management activities were used to analyze the effects of anthropogenic activities. In 2020, the aboveground and belowground biomass were 112.98 and 22.84 tC ha−1, and the net primary productivity was 8.30 tC ha−1 year−1. These results were verified using comparison with statistics, a literature review, and MODIS NPP. In particular, broadleaf is higher than conifer forest in net primary production. The Canadian GCBM with Korean forest inventory data and yield curves successfully estimated the aboveground and belowground biomass of forests in South Korea. Our study demonstrates that these estimates can be mapped in detail, thereby supporting decision-makers and stakeholders in analyzing the carbon budget of the forests in South Korea and developing novel schemes that can serve regional and national aims related to forest management, wood utilization, and ecological preservation. Further studies are needed to improve the initialization of dead organic matter pools, given the large-scale afforestation efforts in recent decades that have established South Korea’s forests on predominantly non-forest sites.

Strengthening Understanding of Customer Services and Practical Instructions for Hospital Financial Management

Increasing competition in the health industry at the national level makes the hospital management always tidy up in improving the quality of service to patients and improving the quality of medical infrastructure facilities according to the standard HNAC (Hospital National Accreditation Committee). Armed with the success of the value chain strategy concept training agenda at Elizabeth Semarang Hospital on May 16, 2018, I submitted the same training proposal to the Miriam Pratama Clinic, Kudus. The underlying factor is the urgency of the MIRIAM Pratama Clinic to raise the level of its business to become an C accredited hospital so that it can continue to survive and from the results of their communication with one of my relatives in Kudus who has heard me conduct training on the topic of value chain strategy at Elizabeth Hospital Semarang. So then I made a communication with the MIRIAM Pratama Clinic to get approval for the implementation of the training activities on March 18, 2019. Seeing the urgency of increasing the status of this business level, then I added aspects of financial management with the basis of considerations to raise the level of business and build a system of value chain strategies from Swayne, et.al. (2006) a large cash flow is needed. And this will be fulfilled with the right budgeting model. Finally, the activity was successfully carried out on March 18, 2019 with a very good response from participants who asked about how to regulate social and business motives in managing clinical businesses in the midst of a lack of cash flow and a budgeting model that does not have to be a month to month approach from Berger (2008). So the short answer is to raise the level of business, business motives should be a priority because improving the quality of services will require large costs and to fulfill them is not necessary with a month to month budgeting approach from Berger (2008) but can be from budget flexibility according to the clinical work calendar. In the mentoring activities on April 18, 2019 there were more positive results from the training activities on March 18, 2019 namely the more enthusiastic doctors, nurses and non-nursing staff working and more positive testimonials from key patients, mothers giving birth and their families. This has had a positive impact on status of the clinic as a C accreditation.

Statistical downscaling of GRACE terrestrial water storage changes based on the Australian Water Outlook model

The coarse spatial resolution of the Gravity Recovery and Climate Experiment (GRACE) dataset has limited its application in local water resource management and accounting. Despite efforts to improve GRACE spatial resolution, achieving high resolution downscaled grids that correspond to local hydrological behaviour and patterns is still limited. To overcome this issue, we propose a novel statistical downscaling approach to improve the spatial resolution of GRACE-terrestrial water storage changes (ΔTWS) using precipitation, evapotranspiration (ET), and runoff data from the Australian Water Outlook. These water budget components drive changes in the GRACE water column in much of the global land area. Here, the GRACE dataset is downscaled from the original resolution of 1.0° × 1.0° to 0.05° × 0.05° over a large hydro-geologic basin in northern Australia (the Cambrian Limestone Aquifer—CLA), capturing sub- grid heterogeneity in ΔTWS of the region. The downscaled results are validated using data from 12 in-situ groundwater monitoring stations and water budget estimates of the CLA’s land water storage changes from April 2002 to June 2017. The change in water storage over time (ds/dt) estimated from the water budget model was weakly correlated (r = 0.34) with the downscaled GRACE ΔTWS. The weak relationship was attributed to the possible uncertainties inherent in the ET datasets used in the water budget, particularly during the summer months. Our proposed methodology provides an opportunity to improve freshwater reporting using GRACE and enhances the feasibility of downscaling efforts for other hydrological data to strengthen local-scale applications.

Cost-effectiveness of erosion mitigation to meet water clarity targets in the Manawatū-Whanganui Region of New Zealand

Soil erosion is a significant environmental issue worldwide. It affects water quality, biodiversity, and land productivity. New Zealand government agencies and regional councils work to mitigate soil erosion through policies, management programmes, and funding for soil conservation projects. Information about cost-effectiveness is crucial for planning, targeting, and implementing erosion mitigation to achieve improvements in sediment-related water quality. While there is a good understanding of the costs of erosion mitigation measures, there is a dearth of literature on their cost-effectiveness in reducing sediment loads and improving water quality at the catchment level. In this study, we estimate the cost-effectiveness of erosion mitigation measures in meeting visual water clarity targets. The analysis utilizes the spatially explicit SedNetNZ erosion process and sediment budget modelling in the Manawatū-Whanganui Region and region-specific mitigation costs. The erosion mitigation measures considered in the analysis include afforestation, bush retirement, riparian retirement, space-planted trees, and gully tree planting. We modelled two scenarios with on-farm erosion mitigation implemented across the region from 2021 to 2100, resulting in a 48% and 60% reduction of total sediment load. We estimate the marginal costs to achieve the visual national bottom line for water clarity, as assessed by the length of waterways that meet the clarity targets. We also estimate the marginal costs of improving average water clarity, which can be linked with non-market valuation studies when conducting a cost-benefit analysis. We find that gully tree planting and space-planted trees are the most cost-effective mitigation measures and that riparian retirement is the least cost-effective. Moreover, cost-effectiveness is highly dependent on current land use and the biophysical features of the landscape. Our estimates can be used in cost-benefit analysis to plan and prioritize soil erosion mitigation at the catchment and regional levels.

Optimizing Treatment of Schizophrenia: Clinical and Economical Potential for Patient Switching to Long‑Acting Injectables

INTRODUCTION: Long-acting injectable antipsychotics (LAIs), due to a lower frequency of administration, could address the well-established challenge of non-adherence to oral ones. We conducted a Network Meta-Analysis (NMA) to assess the relationship between administration frequency and effectiveness. The recent introduction of a semi-annual paliperidone palmitate formulation, along with the arrival of monthly paliperidone generics, could emphasize LAI’s advantages. Aim of this paper is to present the results derived from an updated version of our previous NMA. These results are used to fuel a budget impact model built to evaluate the economic implications of optimizing dosing intervals.METHODS: We compare the current distribution of patients among available LAI active substances and dosing frequencies with an optimized scenario. In this scenario, 20% of patients are switched to the next permitted regimen with a longer inter-dosing interval. Drug acquisition costs and relapse management costs are taken into account over a one-year simulation period; these last items are estimated by means of the event rates obtained from the updated meta-analysis. The optimized scenario incorporates the reduced cost resulting from the expiration of patents.RESULTS: Throughout the analysis, a total of 11,600 patients were able to switch from shorter to longer dosing intervals, leading to an overall optimization of quality of care. The greater expenditure incurred by the Italian National Health Service (NHS) in the acquisition of newer and longer-lasting drugs is offset by savings associated with the arrival of generics of monthly paliperidone palmitate and the shift toward less-relapsing regimens. The net impact on the NHS budget is a saving of more than 19 million Euros.CONCLUSION: This economic saving has the potential to initiate a virtuous process: it could be reinvested to fund a further shift from oral daily therapies, which are less expensive but marked by poor compliance, to LAIs. According to our simulation, nearly 40 thousand patients could undergo this transition, without additional expenses for the NHS.

Tarımsal ürün bazlı su bütçesi modeli: Seyhan Havzası örneği

Konumsal bilgi teknolojileri; tarımsal üretimi etkileyen toprak, iklim, bitki türü, su isteği vb. faktörlerin doğru, güvenilir, ekonomik ve hızlı değerlendirilmesine imkan veren bir teknoloji haline gelmiştir. Bu teknoloji konuma bağlı ürün tercihinin çok daha doğru belirlenmesine imkan tanıyarak; tarımsal alanların sürdürülebilirliğinin desteklenmesini olası kılmıştır. Bu çalışma ile Seyhan Havzası için konumsal bilgi teknolojisi yardımıyla gerçekleştirilen Ürün Su Bütçesi Modeli (Agrometshell) ile su yeterlilik indeksi, toplam su ihtiyacı, fazla su ve eksik su değerleri tahmin edilerek 23 ürün grubu içinden konum bazlı en uygun ürün önerilmiştir. Ürün grupları için elde edilen su yeterlilik indeksi 0 ile 100 arasında değerler almıştır. Su yeterlilik haritalarının istatistiklerine bakıldığında Seyhan Havzası genelinde ortalama su yeterlilik yüzdesi en yüksek baklagil yem bitkileri, turpgiller ve bağcılık ürün gruplarında olduğu tespit edilmiştir. Ortalama su yeterlilik yüzdesinin en düşük olduğu ürün grupları ise patlıcangiller, sert kabuklu meyveler, sıcak iklim tahılları olarak tespit edilmiştir. Ürün su bütçesi modeli ile bitkilerin fenolojik gelişiminin takibi yapılmış ve aynı zamanda iklimsel koşulların bitkilerin gelişimlerini nasıl etkileyeceği tahmin edilmiştir. Böylelikle risk analizleri yapılarak bu risklere karşı önlem alınmasında yol gösterici olacak çalışmalar yapılmasına katkı sağlayacağı düşünülmektedir. Bu çalışma kapsamında çıktıların görselleştirilmesi ve değerlendirilmesi sürecinde de CBS’den yararlanılmış ve böylelikle model sonuçlarının daha iyi ifade edilmesi sağlanmıştır.

Bio-QSARs 2.0: Unlocking a new level of predictive power for machine learning-based ecotoxicity predictions by exploiting chemical and biological information

Practical, legal, and ethical reasons necessitate the development of methods to replace animal experiments. Computational techniques to acquire information that traditionally relied on animal testing are considered a crucial pillar among these so-called new approach methodologies. In this light, we recently introduced the Bio-QSAR concept for multispecies aquatic toxicity regression tasks. These machine learning models, trained on both chemical and biological information, are capable of both cross-chemical and cross-species predictions. Here, we significantly extend these models’ applicability. This was realized by increasing the quantity of training data by a factor of approximately 20, accomplished by considering both additional chemicals and aquatic organisms. Additionally, variable test durations and associated random effects were accommodated by employing a machine learning algorithm that combines tree-boosting with mixed-effects modeling (i.e., Gaussian Process Boosting). We also explored various biological descriptors including Dynamic Energy Budget model parameters, taxonomic distances, as well as genus-specific traits and investigated the inclusion of mode-of-action information. Through these efforts, we developed Bio-QSARs for fish and aquatic invertebrates with exceptional predictive power (R squared of up to 0.92 on independent test sets). Moreover, we made considerable strides to make models applicable for a range of use cases in environmental risk assessment as well as research and development of chemicals. Models were made fully explainable by implementing an algorithmic multicollinearity correction combined with SHapley Additive exPlanations. Furthermore, we devised novel approaches for applicability domain construction that take feature importance into account. We are hence confident these models, which are available via open access, will make a significant contribution towards the implementation of new approach methodologies and ultimately have the potential to support “Green Chemistry” and “Green Toxicology”.

Use of a Blood Biomarker Test Improves Economic Utility in the Evaluation of Older Patients Presenting with Cognitive Impairment.

More than 16 million Americans living with cognitive impairment warrant a diagnostic evaluation to determine the cause of this disorder. The recent availability of disease-modifying therapies for Alzheimer's disease (AD) is expected to significantly drive demand for such diagnostic testing. Accurate, accessible, and affordable methods are needed. Blood biomarkers (BBMs) offer advantages over usual care amyloid positron emission tomography (PET) and cerebrospinal fluid (CSF) biomarkers in these regards. This study used a budget impact model to assess the economic utility of the PrecivityAD® blood test, a clinically validated BBM test for the evaluation of brain amyloid, a pathological hallmark of AD. The model compared 2 scenarios: (1) baseline testing involving usual care practice, and (2) early use of a BBM test before usual care CSF and PET biomarker use. At a modest 40% adoption rate, the BBM test scenario had comparable sensitivity and specificity to the usual care scenario and showed net savings in the diagnostic work-up of $3.57 million or $0.30 per member per month in a 1 million member population, translating to over $1B when extrapolated to the US population as a whole and representing a 11.4% cost reduction. Savings were driven by reductions in the frequency and need for CSF and PET testing. Additionally, BBM testing was associated with a cost savings of $643 per AD case identified. Use of the PrecivityAD blood test in the clinical care pathway may prevent unnecessary testing, provide cost savings, and reduce the burden on both patients and health plans.

The effects of local variations in conditions on carbon storage and release in the continental mantle.

Recent advances indicate that the amount of carbon released by gradual degassing from the mantle needs to be revised upwards, whereas the carbon supplied by plumes may have been overestimated in the past. Variations in rock types and oxidation state may be very local and exert strong influences on carbon storage and release mechanisms. Deep subduction may be prevented by diapirism in thick sedimentary packages, whereas carbonates in thinner sequences may be subducted. Carbonates stored in the mantle transition zone will melt when they heat up, recognized by coupled stable isotope systems (e.g. Mg, Zn, Ca). There is no single 'mantle oxygen fugacity', particularly in the thermal boundary layer (TBL) and lowermost lithosphere, where very local mixtures of rock types coexist. Carbonate-rich melts from either subduction or melting of the uppermost asthenosphere trap carbon by redox freezing or as carbonate-rich dykes in this zone. Deeply derived, reduced melts may form further diamond reservoirs, recognized as polycrystalline diamonds associated with websteritic silicate minerals. Carbon is released by either edge-driven convection, which tears sections of the TBL and lower lithosphere down so that they melt by a mixture of heating and oxidation, or by lateral advection of solids beneath rifts. Both mechanisms operate at steps in lithosphere thickness and result in carbonate-rich melts, explaining the spatial association of craton edges and carbonate-rich magmatism. High-pressure experiments on individual rock types, and increasingly on reactions between rocks and melts, are fine-tuning our understanding of processes and turning up unexpected results that are not seen in studies of single rocks. Future research should concentrate on elucidating local variations and integrating these with the interpretation of geophysical signals. Global concepts such as average sediment compositions and a uniform mantle oxidation state are not appropriate for small-scale processes; an increased focus on local variations will help to refine carbon budget models.

Land as a common resource: fostering rural social sustainability in the face of fragmentation and rural social sustainability in Bade, Yobe State, Nigeria

This study examines the impact of land fragmentation on rural sustainability in Bade Local Government Area, Yobe State, Nigeria. The goal of the research is to comprehend the extent and nature of land fragmentation in the area, assess its effects on rural sustainability indicators, and propose sustainable land use strategies. A cross-sectional research design was employed for this study, utilizing a questionnaire survey to gather qualitative and quantitative data. Descriptive statistics and a farm budget model were used to analyze farmers’ socio-economic characteristics, land fragmentation patterns, and indicators of rural sustainability. The study discover that a significant number of farmers possess land sizes between 2 and 4 hectares, indicating a high level of land fragmentation. The majority of farmers acquired land through either inheritance or purchase even though limited access to land is also observed due to rental and government allocation practices. This study highlights the importance of sustainable land use practices for rural sustainability. Addressing land fragmentation requires policy interventions that promote land consolidation and agricultural mechanization. Enhancing access to land and secure land tenure rights can attract investment and improve productivity. The study recommends policy interventions and support mechanisms to mitigate land fragmentation and foster sustainable rural development in the area. In conclusion, this study underscores the significance of sustainable land use practices in enhancing agricultural productivity, reducing poverty, and improving the well-being of rural communities.

Application of Arc-SWAT Model for Water Budgeting and Water Resource Planning at the Yeralwadi Catchment of Khatav, India

Every facet of life, including human habitation, economic development, food security, etc., depends on water as a valuable resource. Due to the burgeoning population and rapid urbanization, water availability needs to be simulated and measured using hydrologic models and trustworthy data. To fulfill this aim, the SWAT model was processed in this work. The SWAT model was formulated to estimate the hydrological parameters of Yeralwadi using meteorological data from IMD (India Meteorological Department) for the period 1995-2020. The observed discharge data was collected from the HDUG Nasik group and used in the calibration and validation of the Model. The SWAT model was corrected & validated through the SUFI-II algorithm in SWAT-CUP to get a better result. The model’s sensitivity is checked by using statistical parameters like Nash-Sutcliffe Efficiency (NSE) and a coefficient of determination (R2). NSE values were 0.72 and 0.80 in calibration and validation, and R2 were 0.80 & 0.76 in calibration and validation, respectively, indicating the acceptance of the model. Results show that 40.6% of the total yearly precipitation was lost by evapotranspiration. The estimated total discharge from the Yeralwadi catchment was 55.6%, out of which 41.2% was surface runoff and 14.4% was baseflow. The other 17.8% was made up of percolation into confined and unconfined aquifers, which served as soil and groundwater storages. The surface runoff is influenced by Curve number (CnII), SOL_AWC, ESCO, and base flow was influenced by ALPHA-BF and GW_REVAP. This study will be useful to water managers and researchers to develop sustainable water resource management and to alleviate the water scarcity issues in the study basin.

Remote sensing and spectroscopy of lichens.

Lichens are combinations of two symbiotic organisms, a green alga or cyanobacterium and a fungus. They grow in nearly all terrestrial ecosystems and survive in habitats, which are very dry or cold, or too poor in nutrients to maintain vegetation growth. Because lichens grow on visible surfaces and exhibit spectral properties, which are clearly different from, for example, vegetation, it is possible to distinguish them in remote sensing data. In this first systematic review article on remote sensing of lichens, we analyze and summarize which lichen species or genera, and in which habitats and geographical regions, have been remotely sensed, and which remote sensing or spectroscopic technologies have been used. We found that laboratory or insitu measured spectra of over 70 lichen species have been reported to date. We show that studies on remote sensing of lichens fall under seven broad themes: (1) collection of lichen spectra for quantification of lichen species or characteristics, (2) pollution monitoring with lichens as ecological indicators, (3) geological and lithological mapping, (4) desert and dryland monitoring, (5) animal habitat monitoring, (6) land cover or vegetation mapping, and (7) surface energy budget modeling.

Integrated budget impact model to estimate the impact of introducing selpercatinib as a tumor-agnostic treatment option for patients with RET-altered solid tumors in the US

Objective To estimate the potential budget impact on US third party payers (commercial or Medicare) associated with addition of selpercatinib as a tumor-agnostic treatment for patients with Rearranged during Transfection (RET)-altered solid tumors. Methods An integrated budget impact model (iBIM) with 3-year (Y) time horizon was developed for 19 RET-altered tumors. It is referred to as an integrated model because it is a single model that integrated results across multiple tumor types (as opposed to tumor-specific models developed traditionally). The model estimated eligible patient populations and included tumor-specific comparator treatments for each tumor type. Estimated annual total costs (2022USD, $) included costs of drug, administration, supportive care, and toxicity. For a one-million-member plan, the number of patients with RET-altered tumors eligible for treatment, incremental total costs, and incremental per-member per-month (PMPM) costs associated with introduction of selpercatinib treatment were estimated. Uncertainty associated with model parameters was assessed using various sensitivity analyses. Results Commercial perspective estimated 11.68 patients/million with RET-altered tumors as treatment-eligible annually, of which 7.59 (Y1), 8.17 (Y2), and 8.76 (Y3) patients would be selpercatinib-treated (based on forecasted market share). The associated incremental total and PMPM costs (commercial) were estimated to be: $873,099 and $0.073 (Y1), $2,160,525 and $0.180 (Y2), and $2,561,281 and $0.213 (Y3), respectively. The Medicare perspective estimated 55.82 patients/million with RET-altered tumors as treatment-eligible annually, of which 36.29 (Y1), 39.08 (Y2), and 41.87 (Y3) patients would be selpercatinib-treated. The associated incremental total and PMPM costs (Medicare) were estimated to be: $4,447,832 and $0.371 (Y1), $11,076,422 and $0.923 (Y2), and $12,637,458 and $1.053 (Y3), respectively. One-way sensitivity analyses across both perspectives identified drug costs, selpercatinib market share, incidence of RET, and treatment duration as significant drivers of incremental costs. Conclusions Three-year incremental PMPM cost estimates suggest a modest impact on payer-budgets associated with introduction of tumor-agnostic selpercatinib treatment.

Predictions of rhizosphere microbiome dynamics with a genome-informed and trait-based energy budget model

Soil microbiomes are highly diverse, and to improve their representation in biogeochemical models, microbial genome data can be leveraged to infer key functional traits. By integrating genome-inferred traits into a theory-based hierarchical framework, emergent behaviour arising from interactions of individual traits can be predicted. Here we combine theory-driven predictions of substrate uptake kinetics with a genome-informed trait-based dynamic energy budget model to predict emergent life-history traits and trade-offs in soil bacteria. When applied to a plant microbiome system, the model accurately predicted distinct substrate-acquisition strategies that aligned with observations, uncovering resource-dependent trade-offs between microbial growth rate and efficiency. For instance, inherently slower-growing microorganisms, favoured by organic acid exudation at later plant growth stages, exhibited enhanced carbon use efficiency (yield) without sacrificing growth rate (power). This insight has implications for retaining plant root-derived carbon in soils and highlights the power of data-driven, trait-based approaches for improving microbial representation in biogeochemical models.

Long-term exposure to high perceived temperature and risk of mortality among patients with chronic kidney disease

Health risks due to climate change are emerging, particularly from high-temperature exposure. The perceived temperature is an equivalent temperature based on the complete heat budget model of the human body. Therefore, we aimed to analyze the effect of perceived temperature on overall mortality among patients with chronic kidney disease. In total, 32,870 patients with chronic kidney disease in Seoul participated in this retrospective study (2001–2018) at three medical centers. The perceived temperature during the summer season was calculated using meteorological factors, including the air temperature near the automated weather station, dew point temperature, wind velocity, and total cloud amount. We assessed the association between perceived temperature using Kriging spatial interpolation and mortality in patients with CKD in the time-varying Cox proportional hazards model that was adjusted for sex, age, body mass index, hypertension, diabetes mellitus, estimated glomerular filtration rate, smoking, alcohol consumption, and educational level. During the 6.14 ± 3.96 years of follow-up, 3863 deaths were recorded. In multivariable analysis, the average level of perceived temperature and maximum level of perceived temperature demonstrated an increased risk of overall mortality among patients with chronic kidney disease. The concordance index for mortality of perceived temperature was higher than temperature, discomfort index, and heat index. When stratified by age, diabetes mellitus, and estimated glomerular filtration rate, patients with chronic kidney disease with young age (age <65 years) showed higher hazard ratio for mortality (interaction P = 0.049). Moreover, the risk of death in the winter and spring seasons was more significant compared to that of the summer and autumn seasons. Therefore, long-term exposure to high perceived temperature during summer increases the risk of mortality among patients with chronic kidney disease.

Assessing regional connectivity patterns of bivalvia in fragmented archipelagos: Insights from biophysical modeling in French Polynesia

Larval dispersal and connectivity are key processes that drive marine metapopulation dynamics, and therefore should be well characterized when designing effective management strategies. While temperature and food availability can structure marine species connectivity patterns, their contribution has not been thoroughly investigated in highly fragmented archipelagos. We used biophysical modeling of larval dispersal to explore the connectivity patterns of species with complex life-cycles across French Polynesia (FP), a territory formed by more than a hundred small, geographically isolated islands covering an area as large as Europe. We first simulated ten years of larval dispersal to investigate the spatial and temporal (seasonal and interannual) variability in larval dispersal pathways for different hypothetical species exhibiting a range of Larval Precompetency Period (LPP) values. Then, using the black-lip pearl oyster (Pinctada margaritifera) as a model species, we accounted for variability in the LPP induced by temperature and food availability, as derived from a Dynamic Energy Budget (DEB) model. The model showed that food availability and mesoscale turbulence (eddies) in the Marquesas jointly constrained larval dispersal, reducing its potential connectivity with other archipelagos in FP. The DEB simulations also revealed seasonal and interannual variability in connectivity driven by environmental conditions. However, accounting for food and temperature effects on larval development, barely changed the connectivity pattern at regional scale due to the remoteness of this archipelago. Our study thus provides appropriate management units definition at regional scale for the species across FP.

Environmental Risk Assessment with Energy Budget Models: A Comparison Between Two Models of Different Complexity.

The extrapolation of effects from controlled standard laboratory tests to real environmental conditions is a major challenge facing ecological risk assessment (ERA) of chemicals. Toxicokinetic-toxicodynamic (TKTD) models, such as those based on dynamic energy budget (DEB) theory, can play an important role in filling this gap. Through the years, different practical TKTD models have been derived from DEB theory, ranging from the full "standard" DEB animal model to simplified "DEBtox" models. It is currently unclear what impact a different level of model complexity can have on the regulatory risk assessment. In the present study, we compare the performance of two DEB-TKTD models with different levels of complexity, focusing on model calibration on standard test data and on forward predictions for untested time-variable exposure profiles. The first model is based on the standard DEB model with primary parameters, whereas the second is a reduced version with compound parameters, based on DEBkiss. After harmonization of the modeling choices, we demonstrate that these two models can achieve very similar performances both in the calibration step and in the forward prediction step. With the data presented in the present study, selection of the most suitable TKTD model for ERA therefore cannot be based alone on goodness-of-fit or on the precision of model predictions (within current ERA procedures for pesticides) but would likely be based on the trade-off between ease of use and model flexibility. We also stress the importance of modeling choices, such as how to fill gaps in the information content of experimental toxicity data and how to accommodate differences in growth and reproduction between different data sets for the same chemical-species combination. Environ Toxicol Chem 2024;43:440-449. © 2023 ibacon GmbH. Bayer AG and The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

The effects of geomorphology, soil and climate on the trajectory of aboveground biomass accumulation of beech (Fagus sylvatica L.) at the southern range margin

Forest ecosystems are among the most important carbon sinks in the terrestrial biomes. Therefore, the estimation of aboveground biomass accumulation is of fundamental importance for understanding the contribution of forest stands to the global carbon budget. In this study, we proposed a modelling approach to estimate aboveground biomass of beech forests in the Central Apennine based on stand age, climatic variables, topographic features and soil parameters. Using forest inventory data from the local forest management plans, international databases, and mixed-effect linear model, we identified stand age as a major driver of beech forests aboveground biomass. Climatic variables had generally higher influence than soil and topographic features, probably as a consequence of the homogeneous calcareous substrate which favoured the development of soils which are highly suitable for the growth of beech. Temperature range and seasonality were the most important climatic variables. Interestingly, we found that the aboveground biomass in Apennine beech forests is strongly site-specific. Different management approaches during the past centuries, i.e. presence or absence of conversion interventions of the analysed stands, are probably responsible for the growing site's significant influence. By highlighting the ideal locations for allocating the functions of forest production or evaluating the value of ecosystem services that regulate the climate, this study will help to improve both the precision of carbon budget modelling and decision-making in nature conservation and forest management.