Spatially-explicit Model Research Articles

GEOSPATIAL MODELING OF ROADSIDE VEGETATION RISK ON DISTRIBUTION POWER LINES IN CONNECTICUT

Abstract. Roadside trees cause almost 90% of the power outages in the forested Northeastern US. Management of roadside vegetation risk on electrical infrastructure demands timely and accurate information on forest conditions. Tasking conventional ground-based scouting methods along thousands of kilometers of powerlines in a repeated fashion are labor-/cost-/time-intense. Geospatial and earth observation (EO) technologies serve as cost-effective tools in monitoring, inspecting, and managing utility corridors. EO technologies, from drones, aircraft, to satellites can efficiently acquire information over large areas at regular intervals while probing forest physical structure and health conditions. LiDAR is a useful data stream for modeling terrain conditions and estimation of multiple forest inventory variables that explain the physical structure of the forest. Various EO imagery provides information on bio-physical characteristics of trees that affect forest health at finer granularity. The goal of this study is to combine multiple environmental variables to develop a spatially-explicit vegetation risk model using machine learning algorithms. Some of the key inputs used in our analysis include LiDAR-derived tree-related variables (e.g., tree height, proximity pixels, canopy cover), LiDAR-derived terrain data (slope, aspect, topographic index), soil characteristics, vegetation management data (tree trimming methods), infrastructure data (wire type), and power outages reported from 2005 to 2017 in Connecticut. Findings of this research will be vital in informing vegetation management decision-making processes, which eventually reduce power outages and the cost of utility corridor maintenance.

Confirming the broadscale eradication success of nutria (Myocastor coypus) from the Delmarva Peninsula, USA

Nutria (Myocastor coypus) were introduced to the eastern shore of Chesapeake Bay, USA in the 1940s. They reached peak densities in the late 1990s, causing massive wetland loss. Beginning in 2002, a systematic plan to eradicate nutria from the 1.7M ha Delmarva Peninsula was implemented. Since that time the nutria population has been effectively reduced, and no nutria have been detected since May 2015. A lack of detection does not equate with complete absence. We address the following three questions. (1) What is the expected probability of nutria eradication from the Delmarva Peninsula as of the end of 2020? (2) If the probability of eradication is below the management target of 0.95, how much more surveillance is required? (3) How sensitive is the estimated probability of eradication to varying levels of public surveillance and modelled population growth rates? These questions were addressed by employing a stochastic spatially-explicit surveillance model that uses data in which no nutria were detected to quantify the probability of complete absence (PoA) over the entire Delmarva Peninsula. We applied an analytical framework that decomposes the spatial risk of survivors and data into management zones, and took advantage of low-cost public reporting of nutria sightings. Active surveillance by the eradication program included detector dog and tracker surveys, shoreline surveys, detection with ground and water platforms (with hair snares), and camera traps. Results showed that the PoA increased with time and surveillance from a beginning PoA in May 2015 of 0.01 to a mean of 0.75 at the end of 2020. This indicates that the PoA on the Delmarva was well below the target threshold of 0.95 for declaring eradication success. However, given continued surveillance without detection, a PoA of 0.95 would be achieved by June 2022. This analysis provides an objective mechanism to align the expectations of policy makers, managers and the public on when eradication of nutria from the entire Delmarva Peninsula should be declared successful.

Modelling lymphatic filariasis elimination in American Samoa: GEOFIL predicts need for new targets and six rounds of mass drug administration

BackgroundAs part of the global effort to eliminate the debilitating mosquito-borne disease lymphatic filariasis (LF), seven rounds of two-drug (diethylcarbamazine and albendazole) mass drug administration (MDA) were conducted in American Samoa over 2000–2006. However subsequent surveys demonstrated ongoing transmission prompting further rounds of three-drug (diethylcarbamazine, albendazole, and ivermectin) MDA starting in 2018. MethodsWe extend GEOFIL, a spatially-explicit agent-based model of LF transmission to predict the probability and timing of the local elimination or resurgence of LF for different MDA scenarios starting in 2018: two-drug vs. three-drug MDA, two to seven annual rounds, and population coverage rates of 55–75%. We developed an interactive visualisation comparing the effect of MDA strategies on different outcomes. ResultsAt least six annual rounds of three-drug MDA treating 75% of the population were required to achieve LF elimination in American Samoa by 2035 in > 50% of simulations. In scenarios where MDA did not achieve elimination, prevalence doubled approximately every three years, even if MDA reduced antigen prevalence to <1% (the target recommended by the World Health Organisation). Prevalence in six- and seven-year-old children was approximately one quarter of the prevalence in the general population. ConclusionThe three rounds of three-drug MDA conducted in 2018, 2019, and 2021 may have come close to WHO targets but are unlikely to interrupt LF transmission in American Samoa without further interventions. The recommended post-MDA surveillance strategy of testing primarily six and seven-year-old children will delay detection of resurgence compared to population representative surveys. The recommended elimination targets (reducing antigen prevalence below 0.5%, 1%, or 2%) may not be sufficient to interrupt transmission in countries with LF epidemiology like American Samoa. Alternative surveillance strategies and interventions designed to identify and eliminate spatially localized residual transmission may need to be considered. Interactive visualisations may assist decision-makers to choose locally appropriate strategies.

Pathwalker: A New Individual-Based Movement Model for Conservation Science and Connectivity Modelling

Understanding organism movement is at the heart of many ecological disciplines. The study of landscape connectivity—the extent to which a landscape facilitates organism movement—has grown to become a central focus of spatial ecology and conservation science. Several computational algorithms have been developed to model connectivity; however, the major models in use today are limited by their lack of flexibility and simplistic assumptions of movement behaviour. In this paper, we introduce a new spatially-explicit, individual- and process-based model called Pathwalker, which simulates organism movement and connectivity through heterogeneous landscapes as a function of landscape resistance, the energetic cost of movement, mortality risk, autocorrelation, and directional bias towards a destination, all at multiple spatial scales. We describe the model’s structure and parameters and present statistical evaluations to demonstrate the influence of these parameters on the resulting movement patterns. Written in Python 3, Pathwalker works for any version of Python 3 and is freely available to download online. Pathwalker models movement and connectivity with greater flexibility compared with the dominant connectivity algorithms currently available in conservation science, thereby, enabling more detailed predictions for conservation practice and management. Moreover, Pathwalker provides a highly capable simulation framework for exploring theoretical and methodological questions that cannot be addressed with empirical data alone.

Spatially-explicit population modeling to predict large carnivore recovery and expansion

Despite extensive range contractions, several large carnivore species have recently recolonized portions of their former range. Since the 1990s, American black bear reports have increased in Missouri, USA, corresponding with increasing abundance and distribution. As effective management benefits from sound information on wildlife demographics and spatial distribution, we used a spatially-explicit metapopulation model built from local data to inform a stage matrix, which was used in conjunction with a previously created habitat suitability model to quantify and predict the growth and expansion of the black bear population. We collected demographic data during 2011–2021 from 162 female bears with 159 young from 70 litters. Average litter size was 1.95, 42% of cubs were female, and cub survival to yearling was, on average, 95% for females and 70% for males. The habitat suitability model identified 53 core areas most suitable for bears in Missouri. Increasing from previous local estimates of bear population size (∼280 bears in 2012), the model estimated the total bear population inhabiting core habitat patches in year 10 as 732 individuals (598–873, 5th–95th percentiles), with an average annual growth rate of 1.11. The estimated carrying capacity of the core habitat patches (excluding cubs-of-the-year) was ∼2700 bears, but adding marginal and highly marginal habitat patches increased the carrying capacity to ∼5500 bears. Population estimates were most sensitive to variations in stage matrix parameters though overall patch colonization estimates were relatively stable. Our work is an important step toward understanding the recolonization potential of black bears in Missouri and can aid future studies projecting large carnivore density in other areas as well as values and attitudes toward these wide-ranging species. Developing holistic conservation frameworks will be critical if human acceptance toward large carnivores continues increasing and carnivore recolonization of historical range continues.

Using spatially-explicit plant competition models to optimise crop productivity in intercropped systems

Intercropping, by capitalizing on positive biodiversity–productivity relationships, represents a promising option to increase agricultural sustainability. However, the complexity and context-dependency of plant–plant interactions can make it challenging for farmers to find suitable crop combinations. Furthermore, intercropping is usually implemented with standard inter-row spacing and plant densities based on monoculture practices, which might not be the ideal configuration to maximize yield.Here we present a spatially-explicit yield analysis method based on plant ecological interaction models that allowed to optimize crop species combinations and spatial configurations for maximal yield in intercropped systems. We tested this method with three crop species, namely oat, lupine, and camelina. In a first step, field experiments in which crop density and adjacent crop type were varied provided us with indications on which species would compete more with each other. The results showed us that oat and camelina strongly competed with each other. In addition, the distance experiments allowed us to understand how the changes in yield associated with the presence of neighbors vary with distance. This allowed us to find the sets of parameters (identity of neighbors, sowing density, distances between individuals) that optimise intercrop yield (measured as Land Equivalent Ratio [LER]) for the three considered species. Specifically, we show that alternating rows of species led to higher LERs than a homogeneous species mixing, and that 3-species combinations are not necessarily more performant than the best 2-species combinations. In addition, we show that increasing the density of oat is generally beneficial for LER, while increasing the density of lupine is not.By modelling crop yield from simple and reproducible density and distance experiments, our results allow to optimize crop mixtures in terms of species combinations and spatial configurations, for maximal crop yield.

Wind Speed Controls Forest Structure in a Subtropical Forest Exposed to Cyclones: A Case Study Using an Individual-Based Model

Extreme wind blowdown events can significantly modify the structure and composition of forests, and the predicted shift in tropical cyclone regimes due to climate change could strongly impact forests across the tropics. In this study, we coupled an individual-based and spatially-explicit forest dynamics model (TROLL) with a mechanistic model estimating wind damage as a function of tree size, traits, and allometry (ForestGALES). We assimilated floristic trait data and climate data from a subtropical forest site in Taiwan to explore the effect of wind regimes on forest properties. We found that the average canopy height and biomass stocks decreased as wind disturbance strength increased, but biomass stocks showed a nonlinear response. Above a wind intensity threshold, both canopy height and biomass drastically decreased to near-zero, exhibiting a transition to a non-forest state. Wind intensity strongly regulated wind impact, but varying wind frequency did not cause discernible effects. The implementation of within-stand topographic heterogeneity led to weak effects on within-stand forest structure heterogeneity at the study site. In conclusion, the intensity of wind disturbances can potentially greatly impact forest structure by modifying mortality. Individual-based modeling provides a framework in which to investigate the impact of wind regimes on mortality, other factors influencing wind-induced tree mortality, as well as interaction between wind and other forms of forest disturbance and human land use legacy.

Towards an integrative, spatially-explicit modeling for flash floods susceptibility mapping based on remote sensing and flood inventory data in Southern Caspian Sea Littoral, Iran

The goal of this study is mapping flood risks over Golestan province in Iran using one of the artificial intelligence methods called multivariate adaptive regression splines (MARS). In this sense, 14 flood conditioning factors were considered and the maps were made in ArcGIS. Additionally, two novel metaheuristic algorithms namely cat swarm optimization (CSO) and water cycle algorithm (WCA) were applied to optimized the MARS parameters. According to the results, the area under the curve provided by receiver operating characteristic curve illustrated the accuracy of 94.5% for the integrated MARS-WCA. As regards the MARS-WCA model, a total area of 44.74% was identified as highly susceptible for flooding. In addition, to determine the maximum influence of input variables on mapping flood risks, the sensitivity analysis was performed. By performing sensitivity analysis, altitude and slope with NDVI were the three important variables, respectively, for spatially flash flood prediction.

New land-use change scenarios for Brazil: Refining global SSPs with a regional spatially-explicit allocation model.

The future of land use and cover change in Brazil, particularly due to deforestation and forest restoration processes, is critical for the future of global climate and biodiversity, given the richness of its five biomes. These changes in Brazil depend on the interlink between global factors due to its role as one of the main exporters of commodities globally and the national to local institutional, socioeconomic, and biophysical contexts. Aiming to develop scenarios that consider the balance between global (e.g., GDP growth, population growth, per capita consumption of agricultural products, international trade policies, and climatic conditions) and local factors (e.g., land use, agrarian structure, agricultural suitability, protected areas, distance to roads, and other infrastructure projects), a new set of land-use change scenarios for Brazil were developed that aligned with the global structure Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathway (RCPs) developed by the global change research community. The narratives of the new scenarios align with SSP1/RCP 1.9 (Sustainable development scenario), SSP2/RCP 4.5 (Middle of the road scenario), and SSP3/RCP 7.0 (Strong inequality scenario). The scenarios were developed by combining the LuccME spatially explicit land change allocation modeling framework and the INLAND surface model to incorporate the climatic variables in water deficit. Based on detailed biophysical, socioeconomic, and institutional factors for each biome in Brazil, we have created spatially explicit scenarios until 2050, considering the following classes: forest vegetation, grassland vegetation, planted pasture, agriculture, a mosaic of small land uses, and forestry. The results aim to detail global models regionally. They could be used regionally to support decision-making and enrich the global analysis.

A Minimalist Model of Salt-Marsh Vegetation Dynamics Driven by Species Competition and Dispersal

We present a new bidimensional, spatially-explicit ecological model describing the dynamics of halophytic vegetation in tidal saline wetlands. Existing vegetation models employ relatively simple deterministic or stochastic mechanisms, and are driven by local environmental conditions. In the proposed model, in contrast, vegetation dynamics depend not only on the marsh local habitat, but also on spatially-explicit mechanisms of dispersal and competition among multiple interacting species. The role of habitat quality, here determined by the local elevation relative to the mean sea level as a proxy for environmental conditions, is mathematically modeled by a logistic function that represents the fundamental (theoretical) niche of each halophytic species. Hence, the model does not artificially impose any constraints to the ability of a species to colonize elevated areas where it is usually not observed: such limitations naturally arise through competition with fitter species across marsh topographic gradients. We qualitatively test our model against field data based on a suitable assemblage of focus species, and perform a sensitivity analysis aimed at determining how dynamic equilibria in vegetation distributions are affected by changes in model input parameters. Results indicate that the model is robust and can predict realistic vegetation distributions and species-richness patterns. More importantly, the model is also able to effectively reproduce the outcomes of classical ecological experiments, wherein a species is transplanted to an area outside its realized niche. A direct comparison shows that previous models not accounting for dispersal and interspecific competitions are unable to reproduce such dynamics. Our model can be easily integrated into virtually any existing morphodynamic model, thereby strengthening our ability to simulate the coupled biotic and abiotic evolution of salt marshes under changing climate forcings.

On the prediction of landslide occurrences and sizes via Hierarchical Neural Networks

For more than three decades, the part of the geoscientific community studying landslides through data-driven models has focused on estimating where landslides may occur across a given landscape. This concept is widely known as landslide susceptibility. And, it has seen a vast improvement from old bivariate statistical techniques to modern deep learning routines. Despite all these advancements, no spatially-explicit data-driven model is currently capable of also predicting how large landslides may be once they trigger in a specific study area. In this work, we exploit a model architecture that has already found a number of applications in landslide susceptibility. Specifically, we opt for the use of Neural Networks. But, instead of focusing exclusively on where landslides may occur, we extend this paradigm to also spatially predict classes of landslide sizes. As a result, we keep the traditional binary classification paradigm but we make use of it to complement the susceptibility estimates with a crucial information for landslide hazard assessment. We will refer to this model as Hierarchical Neural Network (HNN) throughout the manuscript. To test this analytical protocol, we use the Nepalese area where the Gorkha earthquake induced tens of thousands of landslides on the 25th of April 2015. The results we obtain are quite promising. The component of our HNN that estimates the susceptibility outperforms a binomial Generalized Linear Model (GLM) baseline we used as benchmark. We did this for a GLM represents the most common classifier in the landslide literature. Most importantly, our HNN also suitably performed across the entire procedure. As a result, the landslide-area-class prediction returned not just a single susceptibility map, as per tradition. But, it also produced several informative maps on the expected landslide size classes. Our vision is for administrations to consult these suite of model outputs and maps to better assess the risk to local communities and infrastructure. And, to promote the diffusion of our HNN, we are sharing the data and codes in a githubsec repository in the hope that we would stimulate others to replicate similar analyses.

Evaluation of Areawide Forecasts of Wind-borne Crop Pests: Sugarcane Aphid (Hemiptera: Aphididae) Infestations of Sorghum in the Great Plains of North America.

Airborne pests pose a major challenge in agriculture. Integrated pest management programs have been considered a viable response to this challenge, and pest forecasting can aid in strategic management decisions. Annually recurrent areawide sugarcane aphid [Melanaphis sacchari (Zehntner) (Hemiptera: Aphididae)] infestations of sorghum [Sorghum bicolor (L.) Moench (Poales: Poaceae)] in the Great Plains of North America is one of such challenges. As part of the response, a spatially-explicit individual-based model was developed that simulates sugarcane aphid infestations over the southern-to-central part of the region. In this work, we evaluated model forecasts using 2015-2018 field data. The ranges of forecasted days of first infestation significantly overlapped with those observed in the field. The average days of first infestation observed in the field were approximated by the model with differences of less than 28 days in Texas and southern Oklahoma (2015-2018), and in northern Oklahoma (2016-2017). In half of these cases the difference was less than 14 days. In general, the modeled average day of first infestation was earlier than the observed one. As conceptual modeling decisions may impact model forecasts and as various socio-environmental factors may impact spatio-temporal patterns of field data collection, agreement between the forecasts and the observed estimates may vary between locations and seasons. Predictive modeling has the potential to occupy a central position within areawide integrated pest management programs. More detailed consideration of local agricultural practices and local environmental conditions could improve forecasting accuracy, as could broader participation of producers in field monitoring efforts.

Carbon fluxes from contemporary forest disturbances in North Carolina evaluated using a grid-based carbon accounting model and fine resolution remote sensing products

Land use/land cover change is a key component in terrestrial carbon cycle, yet there are still large uncertainties in the terrestrial carbon budget. To reduce such uncertainties and refine the spatial distribution of carbon flux, a 30-m Grid-based Carbon Accounting (GCA) model was proposed. We adapted a well-established bookkeeping model into a spatial-explicit model to utilize Landsat time series stacks and to calculate the carbon fluxes resulting from three types of forest disturbances including forest harvesting, forest-to-urban conversion, and fire. Our model results provide spatial details at sub-ha scale that are crucial for carbon management at individual landowner levels. Sensitivity analysis revealed that both pre-disturbance forest carbon and disturbance intensity had large impact on carbon flux estimates arising from forest disturbances that occurred between 1986 and 2010 in North Carolina. At the state level, forest harvesting and fire from 1986 to 2010 released 88.5 MT and 1.6 MT carbon respectively. During the same period, regrowing trees over the logged area absorbed 142.7 MT carbon while those over burned area absorbed 1.6 MT more. The net flux from harvesting, fire, and post-disturbance growth was −52.5 MT. Conversion of forest to urban resulted in a net source of 5.3 MT. Overall, the areas subject to the three types of disturbances and post-disturbance growth was a net sink of 47.2 MT carbon over the entire study period. While our modeling framework was tested at the 30 m spatial resolution in this study, it can be adapted for use with finer spatial and/or temporal resolution remote sensing products that will become more readily available in the coming years, thus further improve the carbon flux estimates.

Assessing Hybridization Risk Between ESA-Listed Native Bull Trout (Salvelinus confluentus) and Introduced Brook Trout (S. fontinalis) Using Habitat Modeling

The introduction of non-native species can negatively impact native species through reduced genetic fitness resulting from hybridization. The lack of spatiotemporal data on hybrid occurrences makes hybridization risk assessment difficult. Here, we developed a spatially-explicit Hybridization Risk Model (HRM) between native Oregon bull trout, an Endangered Species Act-listed Oregon species, and introduced brook trout by combining an intrinsic potential model (IPM) of brook trout spawning habitat and existing bull trout distribution and habitat use datasets in Oregon, United States. We created an expert-based brook trout IPM classification score (0–1) of streams based on the potential of geophysical attributes (i.e., temperature, discharge, gradient, and valley confinement) to sustain spawning habitats. The HRM included a risk matrix based on the presence/absence of both species as well as the type of habitat (spawning versus other) at 100-m stream segment resolution. We defined the hybridization risk as “extreme” when stream reaches contained bull trout spawning habitat and brook trout were present with IPM moderate or greater scores (IPM &amp;gt;0.5). Conversely, “low” risk reaches contained historic or non-spawning bull trout habitat, brook trout were absent, and IPM scores were low (IPM &amp;lt;0.25). Our HRM classified 34 km of streams with extreme risk of hybridization, 115 km with high risk, 178 km with moderate risk, and 6,023 km with low risk. Our HRM can identify a differential risk of hybridization at multiple spatial scales when either both species coexist in bull trout spawning habitat or are absent. The model can also identify stream reaches that would have higher risk of hybridization, but where brook trout are not currently present. Our modeling approach can be applied to other species, such as cutthroat trout and rainbow trout, Chinook and coho salmon, or similar species occurring elsewhere that potentially hybridize in freshwaters.

Understanding complex spatial dynamics from mechanistic models through spatio‐temporal point processes

Landscape heterogeneity affects population dynamics, which determine species persistence, diversity and interactions. These relationships can be accurately represented by advanced spatially‐explicit models (SEMs) allowing for high levels of detail and precision. However, such approaches are characterised by high computational complexity, high amount of data and memory requirements and spatio‐temporal outputs may be difficult to analyse. A possibility to deal with this complexity is to aggregate outputs over time or space, but then interesting information may be masked and lost, such as local spatio‐temporal relationships or patterns. An alternative solution is given by meta‐models and meta‐analysis, where simplified mathematical relationships are used to structure and summarise the complex transformations from inputs to outputs. Here, we propose an original approach to analyse SEM outputs. By developing a meta‐modelling approach based on spatio‐temporal point processes (STPPs), we characterise spatio‐temporal population dynamics and landscape heterogeneity relationships in agricultural contexts. A landscape generator and a spatially‐explicit population model simulate hierarchically the pest–predator dynamics of codling moth and ground beetles in apple orchards over heterogeneous agricultural landscapes. Spatio‐temporally explicit outputs are simplified to marked point patterns of key events, such as local proliferation or introduction events. Then, we construct and estimate regression equations for multi‐type STPPs composed of event occurrence intensity and magnitudes. Results provide local insights into spatio‐temporal dynamics of pest–predator systems. We are able to differentiate the contributions of different driver categories (i.e. spatio‐temporal, spatial, population dynamics). We highlight changes in the effects on occurrence intensity and magnitude when considering drivers at global or local scale. This approach leads to novel findings in agroecology where, for example, we show that the organisation of cultivated patches and semi‐natural elements play different roles for pest regulation depending on the scale considered. It aids to formulate guidelines for biological control strategies at global and local scale.

An Optimality-Based Spatial Explicit Ecohydrological Model at Watershed Scale: Model Description and Test in a Semiarid Grassland Ecosystem

Optimality principles have been applied in ecohydrological modeling to derive optimal vegetation properties and describe co-evolution states of vegetation and water cycle. Unfortunately, most existing optimality-based models only consider vertical vegetation-soil-water interactions on plot scale, without considering the lateral hydrological processes. This work aims to extend the field-scale Vegetation Optimality Model (VOM) to the watershed scale. Lateral flow is incorporated to VOM through a hierarchical strategy, establishing the Distributed Vegetation Optimality Model (DisVOM). The model is tested with long-tem flux measurements in the Walnut Gulch watershed, a United States Agricultural Research Service (US-ARS) experimental watershed in southern Arizona. The results indicate the model performance is acceptable for most of years, especially for the growing season. The seasonal dynamic of ET, soil water, and GPP demonstrate good consistency with observations. The model provides reasonable spatial distribution of ET and GPP, suggesting the model can discriminate the effect of lateral flow on water redistribution, and consequently on root water uptake, as well as carbon assimilation. The model could be a useful tool assessing the impact of climate change and human activities on vegetation and water cycle.

Estimating the effect of non-pharmaceutical interventions to mitigate COVID-19 spread in Saudi Arabia

BackgroundThe Kingdom of Saudi Arabia (KSA) quickly controlled the spread of SARS-CoV-2 by implementing several non-pharmaceutical interventions (NPIs), including suspension of international and national travel, local curfews, closing public spaces (i.e., schools and universities, malls and shops), and limiting religious gatherings. The KSA also mandated all citizens to respect physical distancing and to wear face masks. However, after relaxing some restrictions during June 2020, the KSA is now planning a strategy that could allow resuming in-person education and international travel. The aim of our study was to evaluate the effect of NPIs on the spread of the COVID-19 and test strategies to open schools and resume international travel.MethodsWe built a spatial-explicit individual-based model to represent the whole KSA population (IBM-KSA). The IBM-KSA was parameterized using country demographic, remote sensing, and epidemiological data. A social network was created to represent contact heterogeneity and interaction among age groups of the population. The IBM-KSA also simulated the movement of people across the country based on a gravity model. We used the IBM-KSA to evaluate the effect of different NPIs adopted by the KSA (physical distancing, mask-wearing, and contact tracing) and to forecast the impact of strategies to open schools and resume international travels.ResultsThe IBM-KSA results scenarios showed the high effectiveness of mask-wearing, physical distancing, and contact tracing in controlling the spread of the disease. Without NPIs, the KSA could have reported 4,824,065 (95% CI: 3,673,775–6,335,423) cases by June 2021. The IBM-KSA showed that mandatory mask-wearing and physical distancing saved 39,452 lives (95% CI: 26,641–44,494). In-person education without personal protection during teaching would have resulted in a high surge of COVID-19 cases. Compared to scenarios with no personal protection, enforcing mask-wearing and physical distancing in schools reduced cases, hospitalizations, and deaths by 25% and 50%, when adherence to these NPIs was set to 50% and 70%, respectively. The IBM-KSA also showed that a quarantine imposed on international travelers reduced the probability of outbreaks in the country.ConclusionsThis study showed that the interventions adopted by the KSA were able to control the spread of SARS-CoV-2 in the absence of a vaccine. In-person education should be resumed only if NPIs could be applied in schools and universities. International travel can be resumed but with strict quarantine rules. The KSA needs to keep strict NPIs in place until a high fraction of the population is vaccinated in order to reduce hospitalizations and deaths.

Spatially-explicit modeling framework for predicting the dose-dependent effects on contraction of myotropes

Myotropes, such as omecamtiv mecarbil and mavacamten, are new therapeutics that bind to sarcomeric proteins. Their clinical development has reinforced the need for new quantitative understanding of sarcomere-level function.

Incorporating environmental variability in a spatially-explicit individual-based model of European sea bass✰

The northern stock of European sea bass (Dicentrarchus labrax) is a large, high value, slow growing and late maturing fish that is an important target species for both commercial and recreational fisheries. Around the UK, scientific assessments have shown a rapid eight-year decline in spawning stock biomass since 2010 attributed to poor recruitment; this was likely driven by environmental factors and high fishing mortality. Management of the stock is informed by scientific assessments in which a population model is fitted to the available data and used to forecast the possible consequences of various catch options. However, the model currently used cannot represent the spatial distribution of the stock or any effects of environmental variability. One approach that may be used to represent the effects of spatial and temporal variation in environmental drivers is with Individual based models (IBMs). In IBMs populations are represented by their constituent individuals that interact with their environment and each other. The mechanistic nature of IBMs is often advantageous as a management tool for complex systems including fisheries. Here we add to an existing IBM to produce a spatio-temporally explicit IBM of the northern stock of sea bass in which individual fish respond to local food supply and sea surface temperature. All life stages (i.e., pelagic stages, juvenile and mature fish) are modelled and individual fish have their own realistic energy budgets driven by observed dynamic maps of phytoplankton density and sea surface temperature. The model is calibrated using Approximate Bayesian Computation (ABC). After calibration by ABC the model gives good fits to key population parameters including spawning stock biomass. The model provides a mechanistic link between observed local food supplies and sea surface temperatures and overall population dynamics. Plots of spatial biomass distribution show how the model uses the energy budget to predict spatial and temporal change in sea bass biomass distribution in response to environmental variability. Our results indicate that the IBM is a promising approach that could be used to support stock assessment with scope for testing a range of spatially and temporally explicit management scenarios in addition to testing stock responses to novel environmental change.

In-stream surface water quality in China: A spatially-explicit modelling approach for nutrients

Nutrient pollution is a widespread problem in rivers in China. Managing nutrient pollution requires better knowledge of in-stream processes governing the surface water quality. As current nutrient models for China mainly focus on river export of nutrients to seas, in-stream surface water quality and their contributing sources and processes are, therefore not well understood. This requires accounting for combined effects of nutrient inputs to rivers from produced waste, biochemistry of different forms of nutrients and their transport by river network. Moreover, improvements can be made in evaluating the model performance of large-scale nutrient models based on water quality measurements in China (using the surface water quality classes from 1 to 6). The objective of this study is to quantify the spatial variation in in-stream water quality for nutrients, and associated sources, for water quality classes in China. Our new Model to Assess River Inputs of Nutrients to seAs (MARINA 3.0) for in-stream water quality distinguishes different nutrient forms including dissolved inorganic (DIN, DIP) and organic (DON, DOP) nitrogen and phosphorus and was applied for the year 2012. Our model simulations compare reasonably well with measurements across 155 river sections. Results show that between 12% and 66% of the streams are highly polluted (exceeding water quality class 3) and depending on nutrient form. Diffuse sources dominate in 76% of the streams for DIN. Point sources such as direct discharges of animal manure dominate in 46%–59% of the streams for DON, DIP and DOP. The dominant sources vary considerably between rivers and nutrient forms. This indicates the need account for nutrient forms in models and national monitoring programs. Our model results could support effective management to reduce nutrient pollution in China.