Spatiotemporal Scales Research Articles

Enhancing the Assembly Properties of Bottom-Up Coarse-Grained Phospholipids.

A plethora of key biological events occur at the cellular membrane where the large spatiotemporal scales necessitate dimensionality reduction or coarse-graining approaches over conventional all-atom molecular dynamics simulation. Constructing coarse-grained descriptions of membranes systematically from statistical mechanical principles has largely remained challenging due to the necessity of capturing amphipathic self-assembling behavior in coarse-grained models. We show that bottom-up coarse-grained lipid models can possess metastable morphological behavior and that this potential metastability has ramifications for accurate development and training. We in turn develop a training algorithm which evades metastability issues by linking model training to self-assembling behavior, and demonstrate its robustness via construction of solvent-free coarse-grained models of various phospholipid membranes, including lipid species such as phosphatidylcholines, phosphatidylserines, sphingolipids, and cholesterol. The resulting coarse-grained lipid models are orders of magnitude faster than their atomistic counterparts while retaining structural fidelity and constitute a promising direction for the development of coarse-grained models of realistic cell membranes.

Power Brownian motion: an Ornstein-Uhlenbeck lookout

Abstract The well-known Ornstein-Uhlenbeck process (OUP) is the central go-to Gaussian model for statistical-equilibrium processes. The recently-introduced power Brownian motion (PBM) is a Gaussian model for diffusive motions, regular and anomalous alike. Using the Lamperti transform, this paper establishes PBM as the ‘diffusion counterpart’ of the OUP. Namely, the paper shows that PBM is for diffusive motions what the OUP is for statistical-equilibrium processes. The intimate parallels between the OUP and PBM are explored and illuminated via four main perspectives. 1) Statistical characterizations. 2) Kernel-integration with respect to Gaussian white noise. 3) Spatio-temporal scaling of the Wiener process. 4) Langevin stochastic dynamics driven by Gaussian white noise. To date, the prominent Gaussian models for anomalous diffusion are fractional Brownian motion (FBM), and scaled Brownian Motion (SBM). Due to its intimate OUP parallels, due to the ‘anomalous features’ it displays, due to the fact that it encompasses SBM, and following a detailed comparison to FBM: this paper argues the case for PBM as a central go-to Gaussian model for regular and anomalous diffusion.

Quantifying chemomechanical weakening in muscovite mica with a simple micromechanical model

In response to gradual nanoindentation, the surface of muscovite mica deforms by sudden stochastic nanometer-scale displacement bursts. Here, the statistics of these displacement events are interpreted using a statistical model previously used to model earthquakes to understand how chemically reactive environments alter the surface properties of this material. We show that the statistics of nanoindentation displacement bursts in muscovite mica are tuned by chemomechanical weakening in a manner similar to how the statistics of model events are tuned by a mechanical weakening parameter that describes how easily system-spanning cracks can be nucleated. Because the predictions of this model are independent of any surface defects or structural details, these results suggest this simple model can be universally used to describe chemomechanical weakening in many systems prone to slip avalanches on a wide range of spatio-temporal scales.

Scales and self-sustained mechanism of reattachment unsteadiness in separated shock wave/boundary layer interaction

The spatio-temporal scales, as well as a comprehensive self-sustained mechanism of the reattachment unsteadiness in shock wave/boundary layer interaction, are investigated in this study. Direct numerical simulations reveal that the reattachment unsteadiness of a Mach 7.7 laminar inflow causes over $26\,\%$ variation in wall friction and up to $20\,\%$ fluctuation in heat flux at the reattachment of the separation bubble. A statistical approach, based on the local reattachment upstream movement, is proposed to identify the spanwise and temporal scales of reattachment unsteadiness. It is found that two different types, i.e. self-induced and random processes, dominate different regions of reattachment. A self-sustained mechanism is proposed to comprehend the reattachment unsteadiness in the self-induced region. The intrinsic instability of the separation bubble transports vorticity downstream, resulting in an inhomogeneous reattachment line, which gives rise to baroclinic production of quasi-streamwise vortices. The pairing of these vortices initiates high-speed streaks and shifts the reattachment line upstream. Ultimately, viscosity dissipates the vortices, triggering instability and a new cycle of reattachment unsteadiness. The temporal scale and maximum vorticity are estimated with the self-sustained mechanism via order-of-magnitude analysis of the enstrophy. The advection speed of friction, derived from the assumption of coherent structures advecting with a Blasius-type boundary layer, aligns with the numerical findings.

Storm resilience of subtidal soft‐bottom mussel beds: Mechanistic insights, threshold quantification and management implications

AbstractWith the projected escalation of extreme storm events, coastal ecosystems risk undergoing catastrophic shifts and losing essential ecosystem services. Subtidal soft‐bottom mussel beds, vital components of these ecosystems, are particularly vulnerable to hydrodynamically‐induced dislodgement (i.e., detachment of mussel clumps from the bed), especially during storms. However, the mechanisms underlying the resilience—comprising both resistance and recovery—of these beds to storms remain unclear, despite being essential for informed management.This study addresses this knowledge gap regarding subtidal soft‐bottom mussel beds by: (i) quantifying their dislodgement threshold (i.e., the hydrodynamics causing widespread dislodgement of mussel clumps) using novel in situ monitoring methodologies in a representative region, namely the Dutch Wadden Sea; (ii) unveiling the influence of prior life history (here, wave exposure extent) and storm durations on their dislodgement thresholds through a flume study; and (iii) assessing the impacts of repeated storms and prior life histories (here, wave exposure extent and substrate types) on their recovery (i.e. mussel re‐aggregation) through mesocosm experiments.Integrated experimental evidence indicates that: (i) hydrodynamic‐induced dislodgement is a sudden process characterized by distinct near‐bed orbital velocity thresholds, which were identified at our study site to be between 0.45 and 0.50 m s−1; (ii) peak storm intensity, rather than storm duration, primarily drives the dislodgement of subtidal soft‐bottom mussel beds, and prior wave exposure extent regulates the dislodgement threshold; (iii) repeated storms do not seem to affect the recovery of these beds following storm‐related disturbances when the conditions between storms are conducive to mussel re‐aggregation, whereas substrate type significantly impacts recovery.Synthesis and applications. Overall, concerns regarding subtidal soft‐bottom mussel beds degradation primarily stem from increasing storm intensity and their limited resistance to such events. The methodology we developed enables low‐cost quantification of mussel resistance thresholds across broad spatiotemporal scales, facilitating the pinpointing of vulnerable areas. Our findings inform strategic management by highlighting the influential role of prior life histories in shaping mussel bed resistance and the potential to accelerate mussel bed recovery through substrate modification (e.g., shell additions). Both our methodology and findings hold promise for application in comparable ecosystems, such as oyster and coral reefs.

Ecological and social drivers of Mexican Wolf home range size across spatiotemporal scales

Ecological and social drivers of Mexican Wolf home range size across spatiotemporal scales

Texas Water Observatory: A Distributed Network for Monitoring Water, Energy, and Carbon Cycles under Variable Climate and Land Use on Gulf Coast Plains

Abstract In the Gulf Coastal Plains of Texas, a state-of-the-art distributed network of field observatories, known as the Texas Water Observatory (TWO), is developed to better understand the water, energy, and carbon cycles across the critical zone (encompassing aquifers, soils, plants, and atmosphere) at different spatiotemporal scales. Using more than 300 advanced real-time/near-real-time sensors, this observatory monitors high-frequency water, energy, and carbon storage and fluxes in the Brazos River corridor, which are critical for coupled hydrologic, biogeochemical, and land–atmosphere process understanding in the region. TWO provides a regional resource for better understanding and/or managing agriculture, water resources, ecosystems, biodiversity, disasters, health, energy, and weather/climate. TWO infrastructure spans common land uses in this region, including traditional/aspirational cultivated agriculture, rangelands, native prairie, bottomland hardwood forest, and coastal wetlands. Sites represent landforms from low-relief erosional uplands to depositional lowlands across climatic and geologic gradients of central Texas. We present the overarching vision of TWO and describe site design, instrumentation specifications, data collection, and quality control protocols. We also provide a comparison of water, energy, and carbon budget across sites, including evapotranspiration, carbon fluxes, radiation budget, weather, profile soil moisture and soil temperature, soil hydraulic properties, hydrogeophysical surveys, groundwater levels, and groundwater quality reported at TWO primary sites for 2018–20 (with certain data gaps). In conjunction with various Earth-observing remote sensing and legacy databases, TWO provides a master testbed to evaluate process-driven or data-driven critical zone science, leading to improved natural resource management and decision support at different spatiotemporal scales. Significance Statement We provide the vision, design setup, and data acquisition of a state-of-the-art network of field observatories across the Gulf Coastal Plains of Texas. This observatory provides a wealth of measurements of the water, energy, and carbon fluxes, thereby providing a critical testbed for improving the understanding of terrestrial hydrological, biogeochemical, and atmospheric processes across diverse land-use and climate conditions.

Provision of land use and forest density maps in semi-arid areas of Iran using Sentinel-2 satellite images and vegetation indices

Zagros forests of Iran have several environmental, ecological, and socioeconomic values which provide the unique habitats for many endemic species. However, these ecosystems have severely been destroyed by many anthropogenic and natural factors in recent years. Knowledge of area, distribution, and density of these forests and current natural and human-made land uses inside these ecosystems is necessary for protective management of Zagros forests. This research was performed to obtain the land use and forest density maps in Zagros vegetative area of Fars province in southwestern Iran using Sentinel-2A, vegetation indices, Google Earth, and field data. First the boundary of Zagros forests in Fars province was digitized on Google Earth images. Then, Sentinel-2A satellite images covering the forest region in Fars province were obtained from Copernicus website. A pilot area (16000 ha) in the province was considered to investigate the precision of classification methods. Then, Sentinel-2A satellite image of pilot region was classified by some supervised classification methods (ML: maximum likelihood, MD: minimum distance, MaD: mahalanobis distance, SAM: spectral angle mapper, NN: neural network, SVM: support vector machine, and RF: random forest). In addition, the efficiency of different vegetation indices (NDVI, TNDVI, SAVI, and RVI) was evaluated for classifying the forest density in the pilot region. Furthermore, pilot area was considered to test the precision of Google Earth satellite images in this study. For this purpose, 270 field samples were taken as square plots in nine land uses. Selection of the plots inside each land use was randomly. After initial analysis, all satellite images covering the forest area in Fars province were classified by the most accurate algorithm (SVM) to obtain the land use map. On the other hand, the most accurate vegetation index (SAVI) was used to classify the forest density in the study area. Validation of final maps was performed using several random plots on Google Earth images. The initial results of this research indicated that Google Earth images have an overall accuracy (OA) of 96% compared to ground truth in Fars province. Results of land use map showed that Zagros forest has covered an area of 794651.79 ha in Fars province. The findings of this research demonstrated that SVM algorithm (OA: 92.94%, and k: 0.85) has efficiently classified the land uses in Fars province. In addition, SAVI (r2=0.719, p<0.001) has highly been correlated to the forest density in Zagros vegetative area of Fars province. Therefore, land use mapping using SVM algorithm and Sentinel-2A images, and forest density mapping using SAVI is highly recommended in Zagros forests of Fars province in time series which is essential for protective management of these forests in spatio-temporal scale.

Dynamic evolution trend and driving mechanisms of water conservation in the Yellow River Basin, China

Water conservation (WC) is a critical ecological service function in the Yellow River Basin (YRB). There is currently a lack of detailed exploration of WC development processes and the impact mechanisms of driving factors at spatiotemporal scales in the YRB. By collecting data on DEM, land use, soil, meteorology, reservoirs, and observed discharge, this study established a large-scale WC model using the soil and water assessment tool (SWAT). The abrupt change test, empirical orthogonal function (EOF), wavelet analysis, hierarchical partitioning analysis (HPA), geodetectors, and aridity index were employed to analyze the multi-spatiotemporal characteristics and driving forces of WC calculated using the water balance method. The results are as follows: (1) The average WC among the YRB was 9.11 mm (74.68 × 108 m3) from 1960 to 2020. Pasture and forests contributed to 48.65% and 22.05% of the average annual WC, respectively. (2) WC exhibited four forms: less/more in the YRB, more in the southeast (northwest), and less in the northwest (southeast). (3) Forests and pastures in land use had higher average WC capacity, while Gansu, Shaanxi, and Qinghai ranked in the top three for average WC among the nine provinces. (4) Precipitation was the major driving force affecting WC variations, with the interaction between precipitation and actual evapotranspiration being the most significant. (5) Drought was a significant cause of negative WC. Protecting and managing crucial WC areas was essential for improving the ecological environment. This research elucidates the driving forces of WC in the YRB, providing scientific support for improving regional WC and promoting sustainable development.

Evaluating the effects of meteorology and emission changes on ozone in different regions over China based on machine learning

Systematically understanding the impact of meteorological conditions on regional ozone pollution helps to retrieve ozone dataset and evaluate emission changes on ozone variation. Here, more air-quality observation sites were collected, and Random Forest algorithm was applied to retrieve daily maximum 8 h average (MDA8) O3 concentrations at 1 km resolution in 2019-2020 in China. The region-season model and whole-retrieved model were established to compare the contributions of meteorological variables to ozone variations on spatiotemporal scale. The former model outperformed the latter for retrieval capability, but the predictive ability of the latter was slight stronger. This may be associated with the spatiotemporal heterogeneity of meteorological influence. Daily ozone variability in China was mainly influenced by meteorology, especially in North China Plain in autumn. The key factors were temperature, ultraviolet radiation and relative humidity over the whole country, but varied significantly in different regions and seasons. The effects of meteorology and emission sources on ozone were separated by weather normalization technique. Meteorological conditions were particularly favorable for increasing ozone concentrations in spring, but particularly unfavorable in winter. During the COVID-19 lockdown, the increases in ozone in spring and winter of 2020 were the contribution of the combination of meteorology and emissions; while the decreases in ozone in summer and autumn of 2020 were mainly due to the changes of emission sources, although meteorological conditions were unfavorable to ozone mitigation in heavily polluted areas. Our findings provide scientific basis for the prevention and control of ozone pollution for the regional scale in China.

Precipitation Extremes and Their Modulation by Convective Organization in RCEMIP

AbstractWe examine the influence of convective organization on extreme tropical precipitation events using model simulation data from the Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP). At a given SST, simulations with convective organization have more intense precipitation extremes than those without it at all scales, including instantaneous precipitation at the grid resolution (3 km). Across large‐domain simulations with convective organization, models with explicit convection exhibit better agreement in the response of extreme precipitation rates to warming than those with parameterized convection. Among models with explicit convection, deviations from the Clausius‐Clapeyron scaling of precipitation extremes with warming are correlated with changes in organization, especially on large spatiotemporal scales. Though the RCEMIP ensemble is nearly evenly split between CRMs which become more and less organized with warming, most of the models which show increased organization with warming also allow super‐CC scaling of precipitation extremes. We also apply an established precipitation extremes scaling to understand changes in the extreme condensation events leading to extreme precipitation. Increased organization leads to greater increases in precipitation extremes by enhancing both the dynamic and implied efficiency contributions. We link these contributions to environmental variables modified by the presence of organization and suggest that increases in moisture in the aggregated region may be responsible for enhancing both convective updraft area fraction and precipitation efficiency. By leveraging a controlled intercomparison of models with both explicit and parameterized convection, this work provides strong evidence for the amplification of tropical precipitation extremes and their response to warming by convective organization.

Elucidating the spatiotemporal dynamics of glucose metabolism with genetically encoded fluorescent biosensors.

Glucose metabolism has been well understood for many years, but some intriguing questions remain regarding the subcellular distribution, transport, and functions of glycolytic metabolites. To address these issues, a living cell metabolic monitoring technology with high spatiotemporal resolution is needed. Genetically encoded fluorescent sensors can achieve specific, sensitive, and spatiotemporally resolved metabolic monitoring in living cells and invivo, and dozens of glucose metabolite sensors have been developed recently. Here, we highlight the importance of tracking specific intermediate metabolites of glycolysis and glycolytic flux measurements, monitoring the spatiotemporal dynamics, and quantifying metabolite abundance. We then describe the working principles of fluorescent protein sensors and summarize the existing biosensors and their application in understanding glucose metabolism. Finally, we analyze the remaining challenges in developing high-quality biosensors and the huge potential of biosensor-based metabolic monitoring at multiple spatiotemporal scales.

From text to insights: leveraging NLP to assess how landscape features shape tourist perceptions and emotions toward traditional villages

Abstract Understanding tourist perceptions and the relationship between landscape features and emotional attitudes in traditional village is crucial for sustainable development. However, quantifying these perceptions over vast spatiotemporal scales is challenging. Additionally, there is a paucity of knowledge on which landscape features influence tourist perceptions within varied samples on a spatial scale. In this study, we analyzed 39,130 online reviews of 57 traditional Chinese villages from 2018 to 2023. Utilizing Word2Vec and machine learning techniques, we identified 14 key landscape feature indicators. These were quantitatively scored using expert evaluations, and their relationship to tourists’ emotional attitudes was determined through linear regression analysis. The analysis revealed that architectural elements, service facilities, convenience, and sanitation are positively associated with tourists’ emotional attitudes towards traditional villages. Contrary to expectations, the frequency of landscape feature mentions did not correlate significantly with emotional attitudes, challenging traditional assumptions about the visibility of landscape features and their impact.

Temporal gravimetry, campaign and permanent GNSS, and interferometric radar techniques: A comparative approach to quantifying land deformation rates in coastal Texas

Land subsidence and local sea-level rise are well-known on-going hazards that negatively impacting coastal regions, exacerbating coastal flooding, threatening infrastructure stability, accelerating erosion, and amplifying the risks of inundation and property damage. This study used four techniques to quantify land deformation rates in the Texas Coastal Bend region and to investigate the controlling factors on two different spatiotemporal scales: (1) local scale, where biweekly temporal gravity and campaign Global Navigation Satellite System (GNSS) measurements were acquired at six locations over 2 years (October 2020–September 2022); and (2) regional scale, where vertical displacement time series were extracted from the Interferometric Synthetic Aperture Radar (InSAR) and 15 permanent GNSS stations over 5 years (January 2017–November 2021). The observed inconsistency between land deformation rates derived from gravity (range: −33.32 ± 149.30 to +338.68 ± 107.93 mm/yr) and campaign GNSS (range: −17.91 ± 14.90 to +5.69 ± 9.93 mm/yr) surveys could be attributed to the short campaign period and the infrequency of data acquisition. The InSAR-derived deformation rates (range: −12.16 ± 0.12 to +11.70 ± 0.00 mm/yr) were consistent with the permanent GNSS-derived rates (range: −14.0 ± 0.6 to +9.0 ± 3.0 mm/yr); both indicated spatiotemporal variability in land deformation rates across the Texas Coastal Bend. A total of 4 coastal towns (−2.3 ± 3.3 mm/yr), 3 cities (−1.4 ± 2.6 mm/yr), 3 inland towns (−3.6 ± 4.2 mm/yr), and 7 industrial plants (−3.8 ± 5.8 mm/yr) were found to have significant land subsidence rates due to sediment compaction, growth faulting, oil/gas extraction, and groundwater extraction. Results of this study emphasize the advantages and limitations of four different techniques in quantifying land deformation rates; can enhance estimates of local sea-level rise; and offer valuable insights to support coastal communities in mitigating the impacts of land subsidence and improving their resilience.

The quantum vortices dynamics: spatio-temporal scale hierarchy and origin of turbulence

Abstract This study investigates the evolution and interaction of quantum vortex loops with a small but non-zero radius of core ${\sf a}$. The quantization scheme of the classical vortex system is based on the approach proposed by the author \cite{Tal,Tal_PhRF}. &amp;#xD; We consider small perturbations in the ring-shaped loops, which include both helical-type shape variations and small excitations of the flow in the vortex core. &amp;#xD;The quantization of the circulation $\Gamma$ is deduced from the first principles of quantum theory. As a result of our approach, the set of quantized circulation values is wider than the standard one.&amp;#xD;The developed theory introduces a hierarchical spatio-temporal scale in the quantum evolution of vortices. We also explore the applicability of this model for describing the origins of turbulence in quantum fluid flows. To achieve this specific objective, we employ the method of random Hamiltonians to describe the interaction of quantum vortex loops.

The Micropaleoecology Framework: Evaluating Biotic Responses to Global Change Through Paleoproxy, Microfossil, and Ecological Data Integration.

The microfossil record contains abundant, diverse, and well-preserved fossils spanning multiple trophic levels from primary producers to apex predators. In addition, microfossils often constitute and are preserved in high abundances alongside continuous high-resolution geochemical proxy records. These characteristics mean that microfossils can provide valuable context for understanding the modern climate and biodiversity crises by allowing for the interrogation of spatiotemporal scales well beyond what is available in neo-ecological research. Here, we formalize a research framework of "micropaleoecology," which builds on a holistic understanding of global change from the environment to ecosystem level. Location: Global. Time period: Neoproterozoic-Phanerozoic. Taxa studied: Fossilizing organisms/molecules. Our framework seeks to integrate geochemical proxy records with microfossil records and metrics, and draws on mechanistic models and systems-level statistical analyses to integrate disparate records. Using multiple proxies and mechanistic mathematical frameworks extends analysis beyond traditional correlation-based studies of paleoecological associations and builds a greater understanding of past ecosystem dynamics. The goal of micropaleoecology is to investigate how environmental changes impact the component and emergent properties of ecosystems through the integration of multi-trophic level body fossil records (primarily using microfossils, and incorporating additional macrofossil data where possible) with contemporaneous environmental (biogeochemical, geochemical, and sedimentological) records. Micropaleoecology, with its focus on integrating ecological metrics within the context of paleontological records, facilitates a deeper understanding of the response of ecosystems across time and space to better prepare for a future Earth under threat from anthropogenic climate change.

Shedding light on pollination deficits: Cueing into plant spectral reflectance signatures to monitor pollination delivery across landscapes

AbstractPollination underlies plant yield, health and reproductive success in agricultural and natural systems worldwide. It is therefore concerning that declining animal pollinator populations compounded by growing demands for food are leading to rising pollination deficits, with globally significant economic and environmental implications.Despite this urgent issue, accurate and scalable tools to quantify and track pollination across useful spatiotemporal scales are lacking. Here, we propose to shed new light on pollination deficits, looking to remote sensing platforms as a transformative mapping and monitoring tool and a solution for pollinator conservation and crop management.Providing a synthesis of our current understanding of pollination‐triggered floral senescence and underlying ultrastructural and metabolic changes, we propose how spectral reflectance technologies could be applied to accurately detect pollination events in real‐time and at the landscape scale.Synthesis and applications: We highlight where research efforts can be targeted to produce scalable methods for identifying field‐relevant bioindicators of pollination. We provide guidance on how spectral imaging accompanied by machine learning and coupled with autonomous operation technologies will enable applications to detect pollination delivery across complex landscapes. Ultimately, such an ecological application will transform our quantitative understanding of pollination services and, by directly linking plant yields and health, will reveal pollination deficits at high resolution to help mitigate risks to food security and ecosystem functioning.

Evaluation of satellite-derived precipitation datasets: a case study in the Ten Tributaries region of the Yellow River Basin

ABSTRACT Satellite-derived precipitation datasets are essential components of hydrological simulations, particularly in data-scarce regions of western China. However, a comprehensive assessment of their accuracy and reliability is required. Here, the accuracy of two high-resolution satellite-derived precipitation datasets, Integrated Multi-satellite Retrievals for GPM – Final (IMERG-F) and Gauge-Adjusted Global Satellite Mapping of Precipitation (GSMaP-Gauge), was evaluated across the Ten Tributaries region of the Yellow River Basin in western China using four quantitative metrics and three categorical scoring indicators. This evaluation sought to ascertain the retrieval accuracy of these products on both the daily scale and hourly scale of heavy precipitation events, and investigated their inversion error characteristics across various spatiotemporal scales. Both datasets effectively captured the spatiotemporal patterns of annual average precipitation within the study area. Notably, the daily-scale accuracy of these satellite-derived precipitation products surpassed their hourly and half-hourly counterparts. Both GPM-IMERG and GSMaP-Gauge adeptly reproduced most precipitation events in the Ten Tributaries region, with peak detection performance observed in the central and southern zones, providing a reliable data source for drought monitoring and hydrological modeling. Overall, compared with GPM-IMERG, GSMaP-Gauge displayed superior inversion accuracy across diverse spatiotemporal scales.

3.E. Scientific session: Environmental Health Tracking Systems: a tool for planetary health

Abstract Planetary health offers a good framework for better understanding the interconnection between human health and environmental changes, promoting effective cross-sector actions and partnerships, and ensuring policy coherence. Effective implementation of this conceptual framework requires monitoring and reporting on indicators relevant to planetary health that capture the scope, spatial and temporal scales of changes in natural systems that affect human health and well-being. An approach in that sense are the environmental public health tracking systems (EPHT), which imply the ongoing collection, integration, analysis, and interpretation of data at the same spatiotemporal scale about environmental hazards, and related human exposure and health effects, maintaining appropriate data protection measures. It aims to provide public-health decision makers with timely, accurate and systematic data for quantifying health impacts related to environmental factors. EPHT system are proposed as an essential tool for designing and monitoring policies and actions that prevent and reduce environmental health burdens (EHB) efficiently and cost-effectively, and as an alerting system against present and future climatic and health crisis. Pursuing such goals requires the use of health information systems that enable the monitoring and assessing of environmental health in multiple dimensions, considering specificities across different geographical and cultural settings. EPHT system requires defining a rationale on how to select the dimensions and indicators to be included. Common challenges for defining those indicators are related to the availability of data recording, data standardization, and the linkage, integration and sharing of databases. In parallel, it is also important to define a decision aiding tools in which evidence and data are understandable and inform about the extent to which specific policy goals are attained, tailoring such tools to the specific settings. Present session attends to answers to those challenges by sharing experiences from different contexts and countries Key messages • EPHT systems represent an effective tool for progressing in achieving planetary health goals and as a surveillance alerting system for future health crisis. • Adjusting EH data quality recording and analysis in EPHT systems is essential for improving resources assignment, and the effectiveness of healthy policies in reducing EHB.

Regional Factors Affecting Smallmouth Bass and Largemouth Bass Recruitment in Midwestern USA Reservoirs

ABSTRACTRegional factors correlated with recruitment of black bass (largemouth bass; Micropterus nigricans and smallmouth bass; Micropterus dolomieu), two important fishes, are rarely studied, despite the importance of recruitment variation in influencing fish populations. We sought to identify factors that drove variation in age‐0 and age‐1 black bass abundance. Age‐0 or age‐1 black bass catch per unit effort (CPUE) in reservoirs in Kansas and Illinois, USA, was positively correlated with local short‐term (i.e., mean April precipitation) environmental variables. In contrast, age‐0 or age‐1 black bass CPUE was generally negatively correlated with long‐term environmental variables and river regulation metrics (i.e., reservoir elevation) in the Mississippi and Missouri river reservoirs. Our findings highlight that consideration of spatiotemporal scale is important when managing black bass populations.