Fine-scale Model Research Articles

Combining spatial ecology and economics to incorporate recreational fishing into marine spatial planning

Abstract Many researchers have pointed to coastal environments and their use for energy production, aquaculture, and conservation as key to solving profound challenges facing humanity. Navigating this transition in ocean space necessitates tools to quantify impacts on displaced marine users, including recreational fishers. In this study, we present a novel method combining spatial ecological models of recreational catch with fine-scale random utility models of site choice to predict the impacts of site closures on recreational fishing experiences (fisher welfare). We demonstrate our approach by modelling data from over 10 000 fishing trips to fine-scale sites across 1500 km of coastline in the south-west of Australia and simulating the effects of no-take marine reserves recently implemented in the Australian Marine Parks. The results show how the combination of random utility modelling with spatial ecological methods provides a powerful tool for investigating recreational fisheries as joint social-ecological systems and incorporating recreational fishing into marine spatial planning.

Practical implementation of a method for global single-phase flow-based transmissibility upscaling using generic flow boundary conditions and its application on models with non-local heterogeneities

Local upscaling techniques fall short in capturing the dynamic effects of laterally continuous non-local geologic heterogeneities, for example, high-permeability channels, vertically thin, yet, horizontally extensive shale layers. Global upscaling techniques have been introduced to remedy the shortcomings of local scale-up approaches. In order to address the upscaling challenges associated with non-local heterogeneities, we have developed a practical and effective flow-based transmissibility scale-up method that computes coarse-scale transmissibilities using flux derived from single-phase flow simulations with generic global boundary conditions, referred to as the global transmissibility upscaling (GTU) method. GTU targets more accurate upscaling of laterally continuous non-local heterogeneities including the ones modeled on distorted cornerpoint grids. A variant of GTU is developed in this context that uses fluxes computed by use of the multi-point flux approximation (MPFA) discretization technique at the fine scale. While various global upscaling methods were described in the literature, they are rarely used in industrial applications due to challenges related to implementation. We describe in detail an effective practical implementation of the GTU method in a comprehensive industrial-grade simulation package and share our learnings with GTU on aggressively upscaled models with non-local heterogeneities. We demonstrate the application of GTU to several challenging reservoir models. Comparisons of GTU-based model responses are made with ones stemming from local upscaling techniques popularly utilized in the industry using fine-scale model predictions as reference. Investigated numerical examples demonstrate that the GTU method leads to notable improvements in the accuracy delivered by coarse-scale models. Especially, the application of GTU is most impactful for high-upscaling-ratio models with complex fine-scale connectivity because GTU preserves the fine-scale behavior more authentically compared to local techniques. We demonstrate that GTU naturally complements an innovative regional multiphase upscaling (RMU) method on aggressively upscaled models with strong multiphase flow associated with displacement-type recovery mechanisms. It has also been observed that using MPFA on cornerpoint grids to obtain fine-scale fluxes for GTU gives rise to more accurate coarse-scale pressure behavior. We have also observed that GTU is especially effective when combined with a regional multiphase upscaling method for ultimate accuracy in simulations of displacement-based recovery mechanisms. • A practical global transmissibility upscaling method using global flows enables fast and accurate flow simulations. • Global transmissibility upscaling preserves the fine-scale information more authentically compared to local techniques. • Global transmissibility upscaling combined with regional multiphase upscaling for fast and accurate flow simulation. • Speed-up values in the order of 10–50 fold are achieved over fine-scale model with a good compromise of accuracy.

Variational based effective models for inelastic materials

AbstractMechanical systems with inelastic materials and given boundary conditions can be generally defined and solved. However, for large systems, this problem becomes non trivial and computationally costly, especially when microstructures occure. Therefore, multi‐scale modeling is used to capture the behavior of the system.In this study, we present a methodology to provide such models for inelastic materials starting from the variational scheme. Using the variational principle we define the material strain energy and dissipation potential at the coarse scale (macro scale) starting from their counterparts on the fine scale, through the homogenization scheme. Then we solve the material model at the macro level instead of solving the fine scale model or the whole combination of scales. This multi‐scale model offers a reduced number of degrees of freedom and can generally provide the behavior of the system. Moreover, the macro scale model preserves the mathematical structure of the micro scale one. Nevertheless, for this effective model, special care needs to be given to the selection of the inelastic variables solved on the macro scale. This model is illustrated by the application to problems in classical plasticity.

MIXED EVIDENCE FOR NICHE CONSERVATISM IN MOUNTAIN BEAVER (APLODONTIA RUFA) LINEAGES

Range shifts in response to past climatic changes have been documented in a variety of species and have often resulted in the isolation of relict populations. Understanding how these isolated populations develop local adaptations or maintain their historic climatic niche is crucial to creating effective management plans in the face of current climate change. While Mountain Beavers (Aplodontia rufa) have endured through major climatic shifts in the past, they maintain physiological constraints that limit their distribution to cool, humid climates. Increasing temperatures since the last glacial maximum likely had a strong influence in reducing their range. The species now persists as 5 genetically distinct clades, but it is not clear to what extent climatic differences have driven genetic isolation compared to other factors like topography. We compared species-distribution models (SDMs) for the 5 clades of Mountain Beaver to understand whether this species tends towards niche conservatism or adapts to local climates. Presence points from the Global Biodiversity Information Facility were divided into clades and combined with climatic layers from BioClim to develop SDMs for each clade. Niche overlap was then compared to genetic relatedness between all pairings of clades. High temperatures were a limiting factor in distribution for all clades and, despite a low level of niche overlap at broad scales, Mountain Beavers appeared to display some level of niche conservatism. These landscape level SDMs showed that some clades do exist in a warmer climate than other Mountain Beavers; however, fine-scale models for the Point Arena subspecies suggested they persist by selecting the coolest places within that range. This suggests that niche overlap between clades may be higher than what is detected at the coarser scale. Further research is needed to understand the mechanisms limiting the distribution of these subspecies.

A fine-scale marine mammal movement model for assessing long-term aggregate noise exposure

Understanding the impacts of anthropogenic sound on marine mammals is important for effective mitigation and management. Sound impacts can cause behavioral changes that lead to displacement from preferred habitat and can have negative influence on vital rates. Here, we develop a movement model to better understand and simulate how whales respond to anthropogenic sound over ecologically meaningful space and time scales. The stochastic model is based on a sequential Monte Carlo sampler (a particle filter). The movement model takes account of vertical dive information and is influenced by the underwater soundscape and the historical whale distribution in the region. In the absence of noise disturbance, the simulator is shown to recover the historical whale distribution in the region. When noise disturbance is incorporated, the whale’s behavioral response is determined through a dose–response function dependent on the received level of sound. The aggregate impact is assessed by considering both the duration of foraging loss and the spatial shift to alternate (and potentially less favorable) habitat. Persistence of the behavioral response in time is treated through a ‘disruption’ parameter. We apply the approach to a population of fin whales whose distribution overlaps naval sonar testing activities beside the Southern California range complex. The simulation shows the consequences of one year of naval sonar disturbance are a function of: i) how loud the sound source is, ii) where the disturbed whales are relative to preferred (high density) habitat, and iii) how long a whale takes before returning to a pre-disturbance state. The movement simulator developed here is a generic movement modeling tool that can be adapted for different species, different regions, and any acoustic disturbances with known impacts on animal populations.

Modeling the past, present, and future distributions of endangered white abalone (Haliotis sorenseni) to inform recovery efforts in California.

White abalone (Haliotis sorenseni) was once commonly found in coastal waters of the Southern California Bight (SCB) and south to Punta Abreojos, Baja California, Mexico. During the 1970s, white abalone supported a commercial fishery, which reduced the population and resulted in the closure of the fishery in 1996. When population levels continued to decline, National Marine Fisheries Service (NMFS) listed the species as endangered under the Endangered Species Act. The California Department of Fish and Wildlife and NMFS began surveying the wild populations, propagating specimens in captivity, and protecting its seabed habitat. We modeled coarse-scale (17 x 17 km) historical (using fishery-dependent data [1955–1996]) and contemporary (using fishery-independent data [1996–2017]) distributions of white abalone throughout its historical domain using random forests and maximum entropy (MaxEnt), respectively, and its fine-scale (10 x 10 m) contemporary distribution (fishery-independent data) using MaxEnt. We also investigated potential outplanting habitat farther north under two scenarios of future climate conditions. The coarse-scale models identified potential regions to focus outplanting efforts within SCB while fine-scale models can inform population monitoring and outplanting activities in these particular areas. These models predict that areas north of Point Conception may become candidate outplant sites as seawater temperatures continue to rise in the future due to climate change. Collectively, these results provide guidance on the design and potential locations for experimental outplanting at such locations to ultimately improve methods and success of recovery efforts.

Multiple porosity model of a heterogeneous layered gas hydrate deposit in Ulleung Basin, East Sea, Korea: A study on depressurization strategies, reservoir geomechanical response, and wellbore stability

Precise simulation of the geomechanical response is crucial for the gas hydrate production such as from the deposits in Ulleung Basin, East Sea, Korea, which exhibit severe vertical heterogeneity of the hydrate-bearing sand and mud layers. Physically, depressurization causing hydrate dissociation can induce critical subsidence. Meanwhile, it is numerically cumbersome to apply the fine-scale single porosity model to the heterogeneous layered system particularly for geomechanics. In this study, to investigate geomechanical responses of the intercalated layer system based on various depressurization scenarios, we employ a multiple porosity (MP) model with upscaled geomechanical properties while flow variables kept in the fine-scale. For the rigorous two-way coupling between the fluid flow and geomechanics, the fixed-stress sequential method is employed with two individual simulators, the TOUGH+HYDRATE for the flow and an in-house code (called the ROCMECH) for the geomechanics. Specifically for the ROCMECH, we applied axisymmetric geomechanics for more precise calculation of deformation near the wellbore. Numerical experiments of field-wide simulations are about two different comparison studies for depressurization: a constant bottom hole pressure (BHP) and several different BHPs cases. For a constant BHP case, gas production periods, i.e., 14- and 100-days productions representing short- and long-term, respectively, are compared, while several different BHPs case including continuous and periodic depressurizations is focused on the comparison of the productivity and geomechanical stability. From the case of constant BHP, we confirm significant vertical displacement due to the weaker stiffness of the formation after hydrate dissociation, which can aggravate as the production continues. Among the cases with varying BHP, the periodic depressurization scenario exhibits superiority with significantly small subsidence despite similar productivity. The MP model employed in this study can help devise depressurization strategies for gas hydrate reservoirs, especially for the precise and efficient calculation of the geomechanical responses of the intercalated layer system.

Discontinuous Galerkin methods through the lens of variational multiscale analysis

In this article, we present a theoretical framework for integrating discontinuous Galerkin methods in the variational multiscale paradigm. Our starting point is a projector-based multiscale decomposition of a generic variational formulation that uses broken Sobolev spaces and Lagrange multipliers to accommodate the non-conforming nature at the boundaries of discontinuous Galerkin elements. We show that existing discontinuous Galerkin formulations, including their penalty terms, follow immediately from a specific choice of multiscale projector. We proceed by defining the “fine-scale closure function”, which captures the closure relation between the remaining fine-scale term in the discontinuous Galerkin formulation and the coarse-scale solution via a single integral expression for each basis function in the coarse-scale test space. We show that the projectors that correspond to discontinuous Galerkin methods lead to fine-scale closure functions with more compact support and smaller amplitudes compared to the fine-scale closure function of the classical (conforming) finite element method. This observation provides a new perspective on the natural stability of discontinuous Galerkin methods for hyperbolic problems, and may open the door to rigorously designed variational multiscale based fine-scale models that are suitable for DG methods.

A learning-based multiscale method and its application to inelastic impact problems

The macroscopic properties of materials that we observe and exploit in engineering application result from complex interactions between physics at multiple length and time scales: electronic, atomistic, defects, domains etc. Multiscale modeling seeks to understand these interactions by exploiting the inherent hierarchy where the behavior at a coarser scale regulates and averages the behavior at a finer scale. This requires the repeated solution of computationally expensive finer-scale models, and often a priori knowledge of those aspects of the finer-scale behavior that affect the coarser scale (order parameters, state variables, descriptors, etc.). We address this challenge in a two-scale setting where we learn the fine-scale behavior from off-line calculations and then use the learnt behavior directly in coarse scale calculations. The approach builds on the recent success of deep neural networks by combining their approximation power in high dimensions with ideas from model reduction. It results in a neural network approximation that has high fidelity, is computationally inexpensive, is independent of the need for a priori knowledge, and can be used directly in the coarse scale calculations. We demonstrate the approach on problems involving the impact of magnesium, a promising light-weight structural and protective material.

A seasonal algorithm of the snow-covered area fraction for mountainous terrain

Abstract. The snow cover spatial variability in mountainous terrain changes considerably over the course of a snow season. In this context, fractional snow-covered area (fSCA) is an essential model parameter characterizing how much ground surface in a grid cell is currently covered by snow. We present a seasonal fSCA algorithm using a recent scale-independent fSCA parameterization. For the seasonal implementation, we track snow depth (HS) and snow water equivalent (SWE) and account for several alternating accumulation–ablation phases. Besides tracking HS and SWE, the seasonal fSCA algorithm only requires subgrid terrain parameters from a fine-scale summer digital elevation model. We implemented the new algorithm in a multilayer energy balance snow cover model. To evaluate the spatiotemporal changes in modeled fSCA, we compiled three independent fSCA data sets derived from airborne-acquired fine-scale HS data and from satellite and terrestrial imagery. Overall, modeled daily 1 km fSCA values had normalized root mean square errors of 7 %, 12 % and 21 % for the three data sets, and some seasonal trends were identified. Comparing our algorithm performances to the performances of the CLM5.0 fSCA algorithm implemented in the multilayer snow cover model demonstrated that our full seasonal fSCA algorithm better represented seasonal trends. Overall, the results suggest that our seasonal fSCA algorithm can be applied in other geographic regions by any snow model application.

Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan

In the context of rapid urbanization, the refined management of cities is facing higher requirements. In improving urban population management levels and the scientific allocation of resources, fine-scale population data plays an increasingly important role. The current population estimation studies mainly focus on low spatial resolution, such as city-scale and county scale, without considering differences in population distributions within cities. This paper mines and defines the spatial correlations of multi-source data, including urban building data, point of interest (POI) data, census data, and administrative division data. With populations mainly distributed in residential buildings, a population estimation model at a subdistrict scale is established based on building classifications. Composed of spatial information and attribute information, POI data are spaced irregularly. Based on this characteristic, the text classification method, frequency-inverse document frequency (TF-IDF), is applied to obtain functional classifications of buildings. Then we screen out residential buildings, and quantify characteristic variables in subdistricts, including perimeter, area, and total number of floors in residential buildings. To assess the validity of the variables, the random forest method is selected for variable screening and correlation analysis, because this method has clear advantages when dealing with unbalanced data. Under the assumption of linearity, multiple regression analysis is conducted, to obtain a linear model of the number of buildings, their geometric characteristics, and the population in each administrative division. Experiments showed that the urban fine-scale population estimation model established in this study can estimate the population at a subdistrict scale with high accuracy. This method improves the precision and automation of urban population estimation. It allows the accurate estimation of the population at a subdistrict scale, thereby providing important data to support the overall planning of regional energy resource allocation, economic development, social governance, and environmental protection.

Mapping out-of-school adolescents and youths in low- and middle-income countries

Education is a human right and a driver of development, but, is still not accessible for a vast number of adolescents and school-age-youths. Out-of-school adolescents and youth rates (SDG 4.3.1) in lower and middle-income countries have been at a virtual halt for almost a decade. Thus, there is an increasing need to understand geographic variation on accessibility and school attendance to aid in reducing inequalities in education. Here, the aim was to estimate physical accessibility and secondary school non-attendance amongst adolescents and school-age youths in Tanzania, Cambodia, and the Dominican Republic. Community cluster survey data were triangulated with the spatial location of secondary schools, non-proprietary geospatial data and fine-scale population maps to estimate accessibility to all levels of secondary school education and the number of out-of-school. School attendance rates for the three countries were derived from nationally representative household survey data, and a Bayesian model-based geostatistical framework was used to estimate school attendance at high resolution. Results show a sub-national variation in accessibility and secondary school attendance rates for the three countries considered. Attendance was associated with distance to the nearest school (R2 > 70%). These findings suggest increasing the number of secondary schools could reduce the long-distance commuted to school in low-income and middle-income countries. Future work could extend these findings to fine-scale optimisation models for school location, intervention planning, and understanding barriers associated with secondary school non-attendance at the household level.

Deep learning nonlinear multiscale dynamic problems using Koopman operator

In this paper, a deep learning method using Koopman operator is presented for modeling nonlinear multiscale dynamical problems. Koopman operator is able to transform a non- linear dynamical system into a linear system in a Koopman invariant subspace. However, it is usually very challenging to choose a set of suitable observation functions spanning the Koopman invariant subspace when only data is available for the model. It is practically important for us to predict the evolution of the state of the dynamical system from the Koopman invariant subspace. To this end, we introduce a reconstruction operator that maps the observation function space to the model's state space. Incorporating measurement data, a set of neural networks are constructed to learn the Koopman invariant subspace and the reconstruction operator. The loss function not only considers the properties of Koopman invariant subspace, but also reflects the prediction of future state, which makes the proposed method can realize the prediction of future state for a relatively long time. It may be experimentally expensive to collect the fine-scale data. It will be challenging to use limited computational resources to generate sufficient fine-scale data for neural network training. To overcome this difficulty, we use the data in a coarse-scale and learn effective coarse models for the nonlinear multiscale dynamical problems. In order to make the learned coarse model effectively capture fine-scale information, the loss functions for the neural networks are constructed using a set of multiscale basis functions, which are assumed to be given as a prior. In this case, an accurate fine-scale model can be derived by downscaling the learned coarse model. The deep learning multiscale models using Koopman operator can achieve a relatively long-time prediction for the evolution of the state of the nonlinear multiscale dynamical problems. A few numerical examples are presented to show that the effectiveness of learning multiscale models and the long-time prediction. The numerical results also demonstrate the advantage of the proposed learning method over some other similar learning methods.

Long-Term Air Pollution Exposures and Major Depression in Older U.S. Adults: The Health and Retirement Study

BACKGROUND AND AIM: Major depression is a leading cause of morbidity worldwide, especially during later life. Since heritability is modest (40-50%), there is interest in understanding risk from environmental factors like air pollution. However, research examining this issue is scarce, and often does not use clinical diagnostic criteria. Here, we aimed to quantify associations of air pollution with prevalent major depression in a cohort that deploys diagnostic assessments. METHODS: We conducted a repeated measure analysis of 2008-2016 interviews from the Health and Retirement Study, a nationally representative cohort of older adults in the United States. Major depression status was determined biennially using the Composite International Diagnostic Interview Short Form, with a cutoff of 5. We estimated 1-year average PM2.5, PM10-2.5, NO2, and O3 concentrations at participant residences using fine-scale spatiotemporal models. To estimate prevalence ratios, we fit generalized linear models that accounted for sample weights and clustering by participant. All models included demographic characteristics, individual and neighborhood socioeconomic status, calendar time, and geographic area. RESULTS:Among 25,305 participants with complete data, there was a mean age of 63 years, 53% were female, and most were Non-Hispanic White (73%), with some Black (11%) and Hispanic participants. Eight percent reported major depression. In fully adjusted models, we observed that higher PM2.5 and NO2 concentrations were associated with a greater prevalence of major depression (PR: 1.13 per 3 µg/m³ PM2.5, 95% CI: 1.03, 1.23; PR: 1.23 per 5 ppb NO2, 95% CI: 1.06, 1.20). Only the association with NO2 was robust to adjustment for other pollutants, however, and no associations were observed with PM10-2.5 or O3. CONCLUSIONS:Overall, we observed a robust association of major depression prevalence with long-term exposure to NO2 but not with other air pollutants. This suggests that traffic may be a modifiable risk factor that could be targeted to reduce socioeconomic disparities in mental health. KEYWORDS: Traffic-Related Air Pollution, Major Depression, Psychiatric Epidemiology, Health Disparities

Chronic exposure to air pollution and cognitive decline in older adults

BACKGROUND AND AIM: Growing epidemiologic evidence suggests chronic exposure to fine particulate matter (PM2.5) increases risk of dementia in older adults. Despite this, studies of cognitive impairment, a prodromal sign of dementia, report inconsistent results. Studying repeated measures of cognitive function, rather than measures at a single time point, may be a more robust metric of cognitive outcomes because the study design adjusts for time-fixed confounders that affect air pollution exposure and performance on cognitive tests. Therefore, we examined the effect of air pollution on cognitive decline in a U.S. cohort of older adults. METHODS: This analysis draws from the Ginkgo Evaluation of Memory Study, a clinical trial aimed at understanding dementia prevention. Participants aged 75 years and older were enrolled between 2000 and 2002 and completed a neuropsychological battery of 10 tests repeated annually for up to eight years. Tests examined domains of memory, visuospatial abilities, language, attention, and executive function. Chronic exposure (20 years before baseline) to PM2.5 was estimated using annual averages from fine-scale spatiotemporal models and accounted for residential mobility. Trajectories of cognitive function across levels of PM2.5 were compared using generalized estimating equation regression models to account for correlations among repeated cognitive scores. Models included demographic and socioeconomic characteristics as covariates. RESULTS:This study included 2,587 individuals (mean age=78 years) free of mild cognitive impairment at baseline. Chronic exposure to PM2.5 was associated with lower overall scores of memory and visuospatial abilities. Increased exposure to PM2.5 was not associated with declines in either domain. Associations with language, attention, and executive function will be assessed in future analyses. CONCLUSIONS:This study provides support for associations between increased exposure to particulate matter and worse memory and visuospatial abilities. However, preliminary findings show no association between increased exposure and cognitive decline. KEYWORDS: Air pollution, epidemiology, neurodegenerative outcomes

Numerical Investigation into Coarse-Scale Models of Diffusion in Complex Heterogeneous Media

Computational modelling of diffusion in heterogeneous media is prohibitively expensive for problems with fine-scale heterogeneities. A common strategy for resolving this issue is to decompose the domain into a number of non-overlapping sub-domains and homogenize the spatially-dependent diffusivity within each sub-domain (homogenization cell). This process yields a coarse-scale model for approximating the solution behaviour of the original fine-scale model at a reduced computational cost. In this paper, we study coarse-scale diffusion models in block heterogeneous media and investigate, for the first time, the effect that various factors have on the accuracy of resulting coarse-scale solutions. We present new findings on the error associated with homogenization as well as confirm via numerical experimentation that periodic boundary conditions are the best choice for the homogenization cell and demonstrate that the smallest homogenization cell that is computationally feasible should be used in numerical simulations.

Residual-based closure model for density-stratified incompressible turbulent flows

This paper presents a locally and dynamically adaptive residual-based closure model for density stratified incompressible flows. The method is based on the three-level form of the Variational Multiscale (VMS) modeling paradigm applied to the system of incompressible Navier–Stokes equations and an energy conservation equation for the relative temperature field. The velocity, pressure, and relative temperature fields are additively decomposed into overlapping scales which leads to a set of coupled mixed-field sub-problems for the coarse- and the fine-scales. In the hierarchical application of the VMS method, the fine-scale velocity and relative temperature fields are further decomposed, leading to a nested system of two-way coupled fine-scale level-I and level-II variational subproblems. A direct application of bubble functions approach to the fine-scale variational equations helps derive fine-scale models that are nonlinear and time dependent. Embedding the derived model from the level-II variational equation in the level-I variational equation helps stabilize the convection-dominated mixed-field thermodynamic subproblem. Locally resolving the unconstrained level-I variational equation yields the residual-based turbulence model which is a function of the residual of the Euler–Lagrange equations of the conservation of momentum, mass, and energy. The derived model accommodates forward- and back-scatter of energy and entropy and embeds sub-grid scale physics in the computable scales of the problem. The steps of the derivation show that it is essential to apply the concept of scale separation systematically to the coupled system of equations and it is critical to preserve the coupling between flow and thermal phases in the fine-scale variational equations. The method has been implemented with hexahedral and tetrahedral elements with equal order interpolations for the velocity, pressure, and temperature fields. Several canonical flow cases are presented that include Rayleigh–Bénard instability, Rayleigh–Taylor instability, and turbulent plane Couette flow with stable stratification.

Revisiting the cooling effects of urban greening: Planning implications of vegetation types and spatial configuration

While it is well recognised that increasing vegetation cover reduces the Urban Heat Island (UHI) effect in cities, less is understood about the spatial pattern of vegetation type required to maximise cooling benefits. This study examines how different urban vegetation spatial configuration and composition impact on the UHI phenomenon. We investigated this on a set of sites in Metropolitan Melbourne, Australia. Urban vegetation raster data at 20 cm resolution was used to define five height-based vegetation cover types (grass, shrub, small, medium and large trees); and to calculate eight landscape metrics: percentage of landscape (PLAND), mean patch area (AREA_MN), patch density (PD), edge density (ED), mean patch shape index (SHAPE_MN), mean Euclidean nearest-neighbour distance (ENN_MN), Shannon’s diversity index (SHDI) and Landscape shape index (LSI). These vegetation landscape metrics of vegetation types were established as independent variables and statistically analysed with the UHI intensity as the dependent variable using Classification and Regression Tree analysis (CRT). The CRT model was developed based on 6469 records and including depth of 5, 41 nodes and 21 terminal nodes. From the total 43 independent variables, 11 were identified as high impact factors on UHI intensity. Our findings revealed a consistent negative statistical relationship between UHI intensity and PLAND-landscape, PLAND-large tree and PLAND-medium tree across the study sites. The PLAND, ED and AREA_MN were the most prevalent metrics to explain UHI effect, which has also principally demonstrated the joint impact of the three metrics on UHI effect. This study presents a new framework of a fine-scale assessment and modelling for the impact of urban vegetation on UHI; and elaborates a practical approach of using CRT technique to design local-based UHI mitigating strategies taking advantage of different vegetation structure.

Addressing the global snakebite crisis with geo-spatial analyses – Recent advances and future direction

Venomous snakebite is a neglected tropical disease that annually leads to hundreds of thousands of deaths or long-term physical and mental ailments across the developing world. Insufficient data on spatial variation in snakebite risk, incidence, human vulnerability, and accessibility of medical treatment contribute substantially to ineffective on-ground management. There is an urgent need to collect data, fill knowledge gaps and address on-ground management problems. The use of novel, and transdisciplinary approaches that take advantage of recent advances in spatio-temporal models, ‘big data’, high performance computing, and fine-scale spatial information can add value to snakebite management by strategically improving our understanding and mitigation capacity of snakebite. We review the background and recent advances on the topic of snakebite related geospatial analyses and suggest avenues for priority research that will have practical on-ground applications for snakebite management and mitigation. These include streamlined, targeted data collection on snake distributions, snakebites, envenomings, venom composition, health infrastructure, and antivenom accessibility along with fine-scale models of spatio-temporal variation in snakebite risk and incidence, intraspecific venom variation, and environmental change modifying human exposure. These measures could improve and ‘future-proof’ antivenom production methods, antivenom distribution and stockpiling systems, and human-wildlife conflict management practices, while simultaneously feeding into research on venom evolution, snake taxonomy, ecology, biogeography, and conservation.

Examining Scale Dependent Environmental Effects on American Lobster (Homarus americanus) Spatial Distribution in a Changing Gulf of Maine

The Gulf of Maine (GOM) is a highly complex environment and previous studies have suggested the need to account for spatial nonstationarity in species distribution models (SDMs) for the American lobster (Homarus americanus). To explore impacts of spatial nonstationarity on species distribution, we compared models with the following three assumptions : (1) large-scale and stationary relationships between species distributions and environmental variables; (2) meso-scale models where estimated relationships differ between eastern and western GOM, and (3) finer-scale models where estimated relationships vary across eastern, central, and western regions of the GOM. The spatial scales used in these models were largely determined by the GOM coastal currents. Lobster data were sourced from the Maine-New Hampshire Inshore Bottom Trawl Survey from years 2000–2019. We considered spatial and environmental variables including latitude and longitude, bottom temperature, bottom salinity, distance from shore, and sediment grain size in the study. We forecasted distributions for the period 2028–2055 using each of these models under the Representative Concentration Pathway (RCP) 8.5 “business as usual” climate warming scenario. We found that the model with the third assumption (i.e., finest scale) performed best. This suggests that accounting for spatial nonstationarity in the GOM leads to improved distribution estimates. Large-scale models revealed a tendency to estimate global relationships that better represented a specific location within the study area, rather than estimating relationships appropriate across all spatial areas. Forecasted distributions revealed that the largest scale models tended to comparatively overestimate most season × sex × size group lobster abundances in western GOM, underestimate in the western portion of central GOM, and overestimate in the eastern portion of central GOM, with slightly less consistent and patchy trends amongst groups in eastern GOM. The differences between model estimates were greatest between the largest and finest scale models, suggesting that fine-scale models may be useful for capturing effects of unique dependencies that may operate at localized scales. We demonstrate how estimates of season-, sex-, and size- specific American lobster spatial distribution would vary based on the spatial scale assumption of nonstationarity in the GOM. This information may help develop appropriate local adaptation measures in a region that is susceptible to climate change.