Fine Spatiotemporal Scales Research Articles

Hybrid Machine Learning Approach to Zero-Inflated Data Improves Accuracy of Dengue Prediction.

Spatiotemporal dengue forecasting using machine learning (ML) can contribute to the development of prevention and control strategies for impending dengue outbreaks. However, training data for dengue incidence may be inflated with frequent zero values because of the rarity of cases, which lowers the prediction accuracy. This study aimed to understand the influence of spatiotemporal resolutions of data on the accuracy of dengue incidence prediction using ML models, to understand how the influence of spatiotemporal resolution differs between quantitative and qualitative predictions of dengue incidence, and to improve the accuracy of dengue incidence prediction with zero-inflated data. We predicted dengue incidence at six spatiotemporal resolutions and compared their prediction accuracy. Six ML algorithms were compared: generalized additive models, random forests, conditional inference forest, artificial neural networks, support vector machines and regression, and extreme gradient boosting. Data from 2009 to 2012 were used for training, and data from 2013 were used for model validation with quantitative and qualitative dengue variables. To address the inaccuracy in the quantitative prediction of dengue incidence due to zero-inflated data at fine spatiotemporal scales, we developed a hybrid approach in which the second-stage quantitative prediction is performed only when/where the first-stage qualitative model predicts the occurrence of dengue cases. At higher resolutions, the dengue incidence data were zero-inflated, which was insufficient for quantitative pattern extraction of relationships between dengue incidence and environmental variables by ML. Qualitative models, used as binary variables, eased the effect of data distribution. Our novel hybrid approach of combining qualitative and quantitative predictions demonstrated high potential for predicting zero-inflated or rare phenomena, such as dengue. Our research contributes valuable insights to the field of spatiotemporal dengue prediction and provides an alternative solution to enhance prediction accuracy in zero-inflated data where hurdle or zero-inflated models cannot be applied.

Predator-prey space use and landscape features influence movement behaviors in a large-mammal community.

Predator hunting strategies, such as stalking versus coursing behaviors, are hypothesized to influence antipredator behaviors of prey and can describe the movement behaviors of predators themselves. Predators and prey may alter their movement in relation to predator hunting modes, yet few studies have evaluated how these strategies influence movement behaviors of free-ranging animals in a multiple-predator, multiple-prey system. We fit hidden Markov models (HMM) with movement data derived from >400 GPS-collared ungulates and large predators in eastern Washington, USA. We used these models to test our hypotheses that stalking (cougars [Puma concolor]) and coursing (gray wolves [Canis lupus]) predators would exhibit different broad-scale movement behaviors consistent with their respective hunting strategies in areas that increased the likelihood of encountering or capturing ungulate prey (e.g., habitats selected by deer [Odocoileus spp.]). Similarly, we expected that broadscale movement behaviors of prey would change in response to background levels of predation risk associated with each predator's hunting strategy. We found that predators and ungulate prey adjusted their broadscale movements in response to one another's long-term patterns of habitat selection but not based on differences in predator-hunting strategies. Predators changed their movement behaviors based on the type of prey, whereas ungulates generally reduced movement in areas associated with large predators, regardless of the predator's hunting strategy. Both predator and prey movements varied in response to landscape features but not necessarily based on habitat that would facilitate specific hunting behaviors. Our results suggest that predators and prey adjust their movements at broad temporal scales in relation to long-term patterns of risk and resource distributions, potentially influencing their encounter rates with one another at finer spatiotemporal scales. Habitat features further influenced changes in movement, resulting in a complex combination of movement behaviors in multiple-predator, multiple-prey systems.

Autonomous data sampling for high-resolution spatiotemporal fish biomass estimates

Many key ecological dynamics such as biomass distributions are only detectable on a fine spatiotemporal scale. Autonomous data collection with Unmanned Surface Vehicles (USV) creates new possibilities for cost efficient and high-resolution aquatic data sampling. However, the spatial coverage and sampling resolution remain uncertain due to the novelty of the technology. Further, there is no established method for analysing such fine-scale autocorrelated data without aggregation, potentially compromising data resolution. We here used a USV with an echosounder, a conductivity-temperature sensor and a flourometer to collect data from April–July 2019–2023 in a 60x80km area in the central Baltic Sea. The USV covered a total distance of 8000 nmi, over 42–81 days per year, with an average speed of 0.5 m/s. We combined the hydroacoustic data with publicly available oceanographic data from Copernicus Marine Service Information (CMSI) to describe seasonal distribution dynamics of a small pelagic fish community. Key oceanographic variables collected by the USV were correlated with CMSI estimates at daily/monthly resolution, respectively, to test for suitability to scale (Temperature 0.99/0.97; Salinity −0.77/−0.26; Chlorophyll-a 0.12/0.28). We investigated two approaches of Species Distribution Models (SDMs): generalized additive models (GAM) versus spatiotemporal generalized linear mixed effect models (GLMM). The GLMMs explained the observed data better than the GAMs (R2 0.31 and 0.20, respectively). The addition of environmental variables increased the explanatory capability of GAM and GLMM by 25 % and ~ 3 %, respectively. Due to the high data resolution, we found significant amounts of positive autocorrelation (R: 0.05–0.30) across more than 50 lags of observation. However, we found that diel patterns in fish detection strongly affected the abundance estimates due to diel vertically migrating species hiding in the ‘acoustic dead zone’ near the seabed. Such dynamics could only be estimated and corrected for in predictions on the high-resolution data, complicating the trade-off between autocorrelation and high-resolution for SDMs even further. We compared estimates and effect sizes/directions in identical SDMs on 2x2km/month aggregated (i.e non-autocorrelated) observations and non-aggregated (i.e. autocorrelated) observations, and found relatively little difference in spatiotemporal estimates (r = 0.80). For the first time, we predicted the distribution of a small pelagic fish community at a high spatial resolution, in an area essential to breeding top predators, opening up for new applications in ecological studies locally and globally.

Fingerprint localisation for fine‐scale wildlife tracking using automated radio telemetry

Abstract Automated radio telemetry systems are one of the most widely applicable methods for tracking wildlife at fine spatiotemporal scales. A growing trend is for these systems to apply large networks of receivers to detect radio signal strength (RSS) from radio‐tagged individuals. Analytical methods to derive position estimates using RSS localisation, however, are relatively underdeveloped in the field of wildlife tracking. Here, we apply approaches from indoor positioning systems to develop a new method, radio fingerprinting, for localising radio‐tagged animals in structurally complex, outdoor environments. This method characterises the RSS patterns at known locations to generate a radio map of the study area, which is then used to predict the location of new RSS patterns. We conducted field tests to evaluate the localisation accuracy of radio fingerprinting relative to multilateration, a commonly used method for RSS localisation. To do so, we established an experimental receiver network covering multiple habitat types and compared the localisation accuracy of radio fingerprinting to multilateration. Additionally, we evaluated how a variety of features characteristic of typical tracking data sets affected the accuracy of both methods. While both methods had a similar median error under ideal conditions (~30 m), radio fingerprint localisation method offered several advantages over multilateration. Multilateration localisation estimates were highly affected by missed detections from the nearest receiver to the test point, which occurred in 30% of cases. In these cases, the median error was 103 m, over a 3.5‐fold increase. Distance to the nearest receiver also biased multilateration estimates with error increasing as the distance increased. Additionally, errors from multilateration estimates were higher in more densely vegetated areas. In contrast, fingerprinting position estimates were largely robust to each of these scenarios. Automated radio telemetry enables the fine‐scale, continuous tracking of range‐resident animals. We present radio fingerprinting as a localisation method in outdoor environments where standard localisation methods are error‐prone due to habitat heterogeneity and missed detections. This approach can be applied to any automated radio telemetry hardware and to any study system where a radio map can be generated.

Quantifying spatial interaction centrality in urban population mobility: A mobility feature- and network topology-based locational measure

Spatial interaction centrality reflects the relative importance of population mobility within a location in urban population mobility. Population mobility networks visually represent urban population mobility, with mobility features and network topology contributing to the quantification of spatial interaction centrality of locations (i.e., geographical nodes). However, existing centrality measures rarely consider mobility features and network topology simultaneously. Centrality quantification also ignores the differences in distance effects between long- and short-distance trips. These factors have led to the inaccurate quantification of centrality. We propose an algorithm called k-dis-weight-shell that quantifies the spatial interaction centrality of geographical nodes at different spatiotemporal scales. Considering the different effects of distance on long- and short-distance trips, we use a spatial continuous wavelet transformation to estimate the radiation radius of geographical nodes. Then, by combining network topology with mobility features (mobility distance and flow), the algorithm transforms them into a ranked order of spatial interaction centrality. Tested in Wuhan and Chengdu, our algorithm outperforms six existing benchmarks. For cases in urban planning and epidemic management, results show that k-dis-weight-shell effectively distinguishes similarities and differences between the distribution of population mobility's spatial interaction centrality and the urban center hierarchy at a coarse spatiotemporal scale. Additionally, it reveals a double wave phenomenon of spatiotemporal correlation between population mobility and COVID-19 transmission before and after lockdown at a fine spatiotemporal scale.

Impacts of the Chengdu 2021 world university games on NO2 pollution: Implications for urban vehicle electrification promotion

Emissions of nitrogen oxides (NOx) are a dominant contributor to ambient nitrogen dioxide (NO2) concentrations, but the quantitative relationship between them at an intracity scale remains elusive. The Chengdu 2021 FISU World University Games (July 22 to August 10, 2023) was the first world-class multisport event in China after the COVID-19 pandemic which led to a substantial decline in NOx emissions in Chengdu. This study evaluated the impact of variations in NOx emissions on NO2 concentrations at a fine spatiotemporal scale by leveraging this event-driven experiment. Based on ground-based and satellite observations, we developed a data-driven approach to estimate full-coverage hourly NO2 concentrations at 1 km resolution. Then, a random-forest-based meteorological normalization method was applied to decouple the impact of meteorological conditions on NO2 concentrations for every grid cell, the resulting data were then compared with the timely bottom-up NOx emissions. The SHapley-Additive-exPlanation (SHAP) method was employed to delineate the individual contributions of meteorological factors and various emission sources to the changes in NO2 concentrations. According to the full-coverage meteorologically normalized NO2 concentrations, a decrease in NOx emissions and favorable meteorological conditions accounted for 80 % and 20 % of the NO2 reduction, respectively, across Chengdu city during the control period. Within the strict control zone, a 30 % decrease in the meteorologically normalized NO2 concentrations was observed during the control period. The normalized NO2 concentrations demonstrated a strong correlation with NOx emissions (R = 0.96). Based on the SHAP analysis, traffic emissions accounted for 73 % of the reduction in NO2 concentrations, underscoring the significance of traffic control measures in improving air quality in urban areas. This study provides insights into the relationship between NO2 concentrations and NOx emissions using real-world data, which implies the substantial benefits of vehicle electrification for sustainable urban development.

Individual foraging site fidelity persists within and across stopover seasons in a migratory shorebird, Numenius phaeopus (Whimbrel)

Abstract Site fidelity—returning repeatedly to the same site—can help many migratory species reduce uncertainty in their environment, especially when migratory stopover periods leave little time to explore and evaluate new habitat. Avian taxa, though, have shown wide variation in their levels of site fidelity during migration, and few studies have been able to examine individual-level fidelity at fine spatiotemporal scales. We used a high-resolution GPS tracking dataset of Numenius phaeopus (Whimbrel), a long-distance migratory shorebird, to assess levels of fidelity to specific foraging and roosting sites during migration, both within and between stopover seasons. We found that individuals are almost exclusively faithful to one shared roost site at night, but disperse to individual foraging territories during the day that overlap with each other by <20%. Individuals remain faithful to these distinct territories over time, on average shifting the center of their daily home ranges by <1.5 km within a single season, and overlapping with their previous season’s home range by 70% when they return during subsequent stopovers. Our findings reveal for the first time that a shorebird species exhibits fine-scale, individual foraging site fidelity during and between migratory stopovers—an important insight to inform effective conservation and management action.

Large-scale overlap and fine-scale avoidance: assessing interactions between coyotes, bobcats, and cougars at multiple scales

Abstract In multi-carnivore systems individuals must forage and reproduce while also competing with other carnivores and avoiding intraguild predation. These interactions may vary by strength and scales across different ecosystems. We used occupancy analyses and attraction–avoidance indices to assess large- and fine-scale interactions, respectively, between Cougars (Puma concolor), Bobcats (Lynx rufus), and Coyotes (Canis latrans) in northeast Oregon based on data from camera traps set during the summer and fall of 2016 and 2017. To determine the importance of habitat preferences and spatial overlap, we compared occupancy (probability of use) models that included habitat covariates with and without species co-occurrence terms. Detection was different for all species and was influenced by roads, game trails, and canopy cover. Terrain ruggedness was important for all species; Bobcats showed the strongest preference for ruggedness, Cougars showed a weak preference, and Coyotes significantly but weakly avoided rugged areas. The top probability of use model showed that Bobcats and Coyotes were 1.88 times more likely to spatially overlap on roads. We also found evidence that interactions among carnivores were scale-dependent. At larger scales, habitat preference was more important than interactions with other carnivores, whereas at finer scales, most carnivores avoided each other. At fine spatiotemporal scales, our attraction–avoidance analyses showed that Bobcats avoided Coyotes and Coyotes avoided both Bobcats and Cougars. These findings suggest that carnivores in our study system were adept at minimizing conflict through fine-scale avoidance in space and time, that the human footprint can influence carnivore interactions, and that studying carnivore interactions at multiple scales is important for understanding the effects of spatial overlap and potential competition.

Mapping dynamic human sentiments of heat exposure with location-based social media data

Understanding urban heat exposure dynamics is critical for public health, urban management, and climate change resilience. Near real-time analysis of urban heat enables quick decision-making and timely resource allocation, thereby enhancing the well-being of urban residents, especially during heatwaves or electricity shortages. To serve this purpose, we develop a cyberGIS framework to analyze and visualize human sentiments of heat exposure dynamically based on near real-time location-based social media (LBSM) data. Large volumes and low-cost LBSM data, together with a content analysis algorithm based on natural language processing are used effectively to generate near real-time heat exposure maps from human sentiments on social media at both city and national scales with km spatial resolution and census tract spatial unit. We conducted a case study to visualize and analyze human sentiments of heat exposure in Chicago and the United States in September 2021. Enabled with high-performance computing, dynamic visualization of heat exposure is achieved with fine spatiotemporal scales while heat exposure detected from social media data can be used to understand heat exposure from a human perspective and allow timely responses to extreme heat. HIGHLIGHTS Near real-time and high spatial resolution mapping of human sentiments of heat exposure with Twitter data An integrated cyberGIS and machine learning framework for visualizing heat exposure with Twitter data Human sentiment of heat exposure mapping in the City of Chicago and the United States

Evolution of rates, patterns, and driving forces of green eco-spaces in a subtropical hilly region

Monitoring ecological resource change in mountainous and hilly areas (MHAs) is vital for theoretical and practical advancements of ecological resource utilization and management in complex ecosystems. The factors driving structural and functional changes in green eco-spaces (GES) in these areas are complex and uncertain, with notable spatial scale effects. However, analyzing the multi-scale driving mechanisms of ecological and socioeconomic factors at a fine spatiotemporal scale presents significant challenges. To address these challenges, we analyzed dynamic changes in GES and eco-socio-economic development in Shanghang County, a typical mountainous region in southern China. We used multiple linear regression and multi-scale geographically weighted regression model to identify key factors driving GES changes and their multi-scale effects at both global and local levels. Over the past two decades, the GES area in the study area has exhibited a consistent pattern of decline, characterized by phases of gradual decline (2000–2005), sharp decline (2005–2009), slow decline (2009–2019). Key global factors driving GES changes included elevation (ELE), slope (SLOPE), population density (PD), distance to settlements (SETTLE), and distance to administrative centers (ADMIN). These factors exhibited significant spatial heterogeneity and multi-scale effects on GES changes. Specifically, SETTLE, PD, SLOPE, and ELE consistently drove GES changes at the local level, while ADMIN only showed significant localized effects during 2005–2009. The synergy between SETTLE and SLOPE had a considerable impact on GES changes, increasing over time, whereas ELE and PD demonstrated a consistent trade-off effect. These findings provide detailed spatiotemporal insights into the driving mechanisms of natural ecological resources, offering crucial guidance for environmental management, land source management, regional economic development, and biodiversity conservation in Shanghang and analogous subtropical hilly regions worldwide.

Fine-Scale Quantification of Absorbed Photosynthetically Active Radiation (APAR) in Plantation Forests with 3D Radiative Transfer Modeling and LiDAR Data.

Quantifying the relationship between light and stands or individual trees is of great significance in understanding tree competition, improving forest productivity, and comprehending ecological processes. However, accurately depicting the spatiotemporal variability of light under complex forest structural conditions poses a challenge, especially for precise forest management decisions that require a quantitative study of the relationship between fine-scale individual tree structure and light. 3D RTMs (3-dimensional radiative transfer models), which accurately characterize the interaction between solar radiation and detailed forest scenes, provide a reliable means for depicting such relationships. This study employs a 3D RTM and LiDAR (light detection and ranging) data to characterize the light environment of larch plantations at a fine spatiotemporal scale, further investigating the relationship between absorbed photosynthetically active radiation (APAR) and forest structures. The impact of specific tree structural parameters, such as crown diameter, crown area, crown length, crown ratio, crown volume, tree height, leaf area index, and a distance parameter assessing tree competition, on the daily-scale cumulative APAR per tree was investigated using a partial least squares regression (PLSR) model. Furthermore, variable importance in projection (VIP) was also calculated from the PLSR. The results indicate that among the individual tree structure parameters, crown volume is the most important one in explaining individual tree APAR (VIP = 4.19), while the competition from surrounding trees still plays a role in explaining individual tree APAR to some extent (VIP = 0.15), and crown ratio contributes the least (VIP = 0.03). Regarding the spatial distribution of trees, the average cumulative APAR per tree of larch plots does not increase with an increase in canopy gap fraction. Tree density and average cumulative APAR per tree were fitted using a natural exponential equation, with a coefficient of determination (R2 = 0.89), and a small mean absolute percentage error (MAPE = 0.03). This study demonstrates the potential of combining 3D RTM with LiDAR data to quantify fine-scale APAR in plantations, providing insights for optimizing forest structure, enhancing forest quality, and implementing precise forest management practices, such as selective breeding for superior tree species.

Performance of Vehicle Add-on Mobile Monitoring System PM2.5 measurements during wildland fire episodes.

Fine particulate matter (PM2.5) resulting from wildland fire is a significant public health risk in the United States (U.S.). The existing stationary monitoring network and the tools used to alert the public of smoke conditions, such as the Air Quality Index or NowCast, are not optimized to capture actual exposure concentrations in impacted communities given that wildland fire smoke plumes have characteristically steep exposure concentration gradients that can vary over fine spatiotemporal scales. In response, we developed and evaluated a lightweight, universally attachable mobile PM2.5 monitoring system to provide supplemental, real-time air quality information during wildfire incidents and prescribed burning activities. We retroactively assessed the performance of the mobile monitor compared to nearby (100-1500 m) stationary low-cost sensors and regulatory monitors using 1 minute averaged data collected during two large wildfires in the western U.S. and during one small, prescribed burn in the Midwest. The mobile measurements were highly correlated (R 2 > 0.85) with the stationary network during the large wildfires. Further, 1 minute averaged mobile measurements differed from three collocated stationary instruments by <25% on average for fourteen out of fifteen total passages. For the small, prescribed burn, rapidly changing conditions near the fire border complicated the comparison of mobile and stationary measurements but the spatial maximum concentrations measured by both instruments were consistent. In general, this work highlights the value of using portable sensor technologies to address the monitoring challenges presented by dynamic wildland fire conditions and demonstrates the value in combining mobile monitoring with stationary data where possible.

A Review of Unmanned Aerial Vehicles Usage as an Environmental Survey Tool within Tidal Stream Environments

Tidal energy is a rapidly developing area of the marine renewable energy sector that requires converters to be placed within areas of fast current speeds to be commercially viable. Tidal environments are also utilised by marine fauna (marine mammals, seabirds and fish) for foraging purposes, with usage patterns observed at fine spatiotemporal scales (seconds and metres). An overlap between tidal developments and fauna creates uncertainty regarding the environmental impact of converters. Due to the limited number of tidal energy converters in operation, there is inadequate knowledge of marine megafaunal usage of tidal stream environments, especially the collection of fine-scale empirical evidence required to inform on and predict potential environmental effects. This review details the suitability of using multirotor unmanned aerial vehicles within tidal stream environments as a tool for capturing fine-scale biophysical interactions. This includes presenting the advantages and disadvantages of use, highlighting complementary image processing and automation techniques, and showcasing the limited current examples of usage within tidal stream environments. These considerations help to demonstrate the appropriateness of unmanned aerial vehicles, alongside applicable image processing, for use as a survey tool to further quantify the potential environmental impacts of marine renewable energy developments.

SIF-based GPP modeling for evergreen forests considering the seasonal variation in maximum photochemical efficiency

Solar-induced chlorophyll fluorescence (SIF) has shown great potential in estimating gross primary production (GPP). However, their quantitative relationship is not invariant, which undermines the reliability of empirical SIF-based GPP estimation at fine spatiotemporal scales, especially under extreme conditions. In this study, we developed a parsimonious mechanistic model for SIF-based GPP estimation in evergreen needle forests (ENF) by employing the Mechanistic Light Response framework and Eco-Evolutionary theory to describe the light and dark reactions during photosynthesis, respectively. Specifically, we found that considering the seasonal variation in a key parameter of the MLR framework, the maximum photochemical efficiency of photosystem II (ΦPSIImax), can avoid the GPP overestimation in winter and early spring due to the relatively low environmental sensitivity of SIF. Compared to the estimates from other benchmark models, our GPP estimates were closer to the 1: 1 line and had higher accuracy (average R2 = 0.86, RMSE=1.99 μmol m−2 s−1) across sites. Furthermore, the changes in the relationship between SIF and J (refers to the electron transport rate) contribute a lot to the dynamic SIF–GPP relationship in this study, while the J–GPP relationship is less variant when the temperature drops. The seasonal variation in the SIF–J relationship, especially the reduction in its slope at low temperatures, is found largely explained by the ΦPSIImax. These results indicate the importance of the uncertainty caused by the variation in the SIF–J relationship for SIF-based GPP estimation, and the consideration of changes in ΦPSIImax under extreme conditions (such as severe winter in this study) is crucial for the improvement of GPP estimation via SIF.

Estimating future streamflow under climate and land use change conditions in northeastern Hokkaido, Japan

Study regionAbashiri River, northeastern Hokkaido, Japan. Study focusIn response to the growing concern over likely future climate and land use land cover (LULC) changes, a better understanding of the consequent streamflow changes is needed in northeastern Hokkaido, Japan. The present study focuses on the Abashiri River Basin (ARB), northeastern Hokkaido, and estimates the future local streamflow and its change in quantity, seasonality, flow regime, and spatial distribution under various possible impacts of climate and LULC conditions. The estimation is produced using the Soil and Water Assessment Tool (SWAT), a hydrological model suitable for managemental analysis. Future conditions are applied as alterations to the initial model inputs. New hydrological insights for the regionPossible impact of climate change leads to significant changes in streamflow quantity and flow pattern around the snowmelt season, while negative and insignificant change in streamflow is expected in response to possible LULC change. The results highlight a concerning future for local agricultural water use. Consequently, practical recommendations for local water resources management are advised, focusing on precautionary measures and development insights. Compared to existing research in this region, the present study provides a comprehensive estimation of the possible future streamflow changes on a finer spatiotemporal scale. Further application in the future to help the local water resources management is expected.

A predominantly diurnal tropical mammal increases nocturnality in response to high temperatures

AbstractThe extent to which animals may compensate for rising temperatures through behavioral thermoregulation is an important unknown in predicting responses to climate change. Flexibility in temporal activity in particular may be an important factor in determining species' vulnerability. In this study, we examine whether the white‐lipped peccary, a predominantly diurnal Neotropical mammal, shifts its temporal activity and microclimate selection in response to daily temperature variation in the Pantanal biome of Brazil. From November 2021 to October 2022, we deployed camera traps and microclimate data loggers to record white‐lipped peccary activity and air temperature on a fine spatiotemporal scale. We found that the percentage of peccary activity that is nocturnal increased with maximum daily temperature at a rate of 3.7% per 1°C past a threshold of 31.2°C, and increased with maximum daily wet‐bulb temperature at a rate of 4.7% per 1°C past a threshold of 24.9°C. The afternoon period (i.e., solar noon to sunset) went from having the highest frequency of peccary activity at the lower end of our observed temperature range to having the lowest frequency of activity at the higher end. We did not find evidence of microclimate selection. Our findings indicate that white‐lipped peccaries in the Pantanal demonstrate substantial behavioral flexibility in their response to high temperatures, which may help to buffer them against the impact of rising temperatures caused by climate change.Abstract in Portuguese is available with online material.

An Integrative Framework for Assessment of Urban Flood Response to Changing Climate

AbstractIncreasing frequency of extreme rainfall induced catastrophic urban flood events in recent decades demands proactive efforts to assess flood risk and vulnerability. Here, we develop an integrative framework for fine spatiotemporal scale assessment of urban flood response to historical and future projected changes in extreme precipitation. The framework includes three main components—nonstationary modeling of historical extreme precipitation, modeling of future precipitation, and urban flood simulations. It also provides robust estimates of uncertainty in design precipitation from statistical modeling, multiple climate models and stochastic uncertainty, estimated using machine learning techniques. We demonstrate the proposed framework for White Oak Bayou watershed in Houston, Texas, US. Two‐dimensional hydrologic‐hydraulic Interconnected Channel and Pond Routing model is used to simulate flood response from design precipitation for historical (1986–2020) and two future (2021–2050 and 2071–2100) periods in three (SSP1‐2.5, SSP2‐4.5, and SSP5‐8.5) future climate scenarios. Results show that nonstationary design estimates for historical precipitation are 14%–25% higher than the stationary estimates for 100‐year event of 1‐ to 24‐hr duration. Contrary to general global trends, we found a significant reduction in future design precipitation in all three emission scenarios. Additionally, stochastic uncertainty in future design precipitations is found to be larger than the modeling uncertainty in historical estimates and climate model uncertainty in future estimates. Flood response in terms of peak flood and total flood volume suggests that the difference in stationary versus nonstationary historical and future design precipitation are substantial to cause considerable change in simulated flood.

Anopheles sampling collections in the health districts of Korhogo (Côte d'Ivoire) and Diébougou (Burkina Faso) between 2016 and2018.

Characterizing the entomological profile of malaria transmission at fine spatiotemporal scales is essential for developing and implementing effective vector control strategies. Here, we present a fine-grained dataset of Anopheles mosquitoes (Diptera: Culicidae) collected in 55 villages of the rural districts of Korhogo (Northern Côte d'Ivoire) and Diébougou (South-West Burkina Faso) between 2016 and 2018. In the framework of a randomized controlled trial, Anopheles mosquitoes were periodically collected by Human Landing Catches experts inside and outside households, and analyzed individually to identify the genus and, for a subsample, species, insecticide resistance genetic mutations, Plasmodium falciparum infection, and parity status. More than 3,000 collection sessions were carried out, achieving about 45,000h of sampling efforts. Over 60,000 Anopheles were collected (mainly A.gambiae s.s., A.coluzzii, and A.funestus). The dataset is published as a Darwin Core archive in the Global Biodiversity Information Facility, comprising four files: events, occurrences, mosquito characterizations, and environmental data.

Assessing bike accessibility to metro systems by integrating crowdedness

Bike-metro integration is regarded as an effective means of improving the access to metro systems. This study aims to assess the metro accessibility by biking at a finer spatiotemporal scale using a real bike trajectory dataset generated by cyclists. To achieve this goal, we propose a metro accessibility level (MAL) indicator that explicitly integrates metro crowdedness into the accessibility measurement. We then introduce a method to examine the possibility of avoiding metro crowdedness by using the bike as the access mode. The proposed indicator and method are applied to Shanghai, China as a case study. Results show that bike-metro integration increases the accessibility to metro systems in terms of larger population coverage and a higher accessibility level. Omitting the metro crowdedness leads to an overestimation of the accessibility to metro systems, and the overestimation for the morning peak is larger than that of the afternoon peak. Only 19% of the population in walking catchment areas of crowded stations can shift from crowded stations to non-crowded ones. These results provide a good reference for transportation planning, modeling, and policymaking to improve bike-metro integration.

Spatiotemporal overlap with invasive wild pigs (Sus scrofa) varies by species and season in a temperate ecosystem

AbstractWild pigs (Sus scrofa), which are invasive in many regions globally, can alter ecosystems and compete with native species through interference competition and resource exploitation. Wild pig impacts on other species may increase with greater niche overlap, which could vary over time based on environmental conditions, resource availability, or biological traits like diet, especially as seasonal variation in wild pig diet has been widely documented. A limited number of studies have assessed spatial or temporal overlap between native species and invasive wild pigs, with only a handful simultaneously assessing overlap in these niche dimensions. We investigated the potential for interspecific interactions involving invasive wild pigs in the Piedmont region of South Carolina, USA, by examining seasonal spatiotemporal overlap with other wildlife using N‐mixture models and diel activity overlap analyses. Site use by white‐tailed deer (Odocoileus virginianus) and coyote (Canis latrans) was negatively associated with wild pig activity in the fall, when the species had high diel activity overlap, indicating spatial partitioning could reduce interference competition with wild pigs in this season. Conversely, white‐tailed deer site use was positively associated with wild pig activity in the winter, suggesting higher spatial overlap may be necessary if resources are limited. Site use by bobcat (Lynx rufus) and nine‐banded armadillo (Dasypus novemcinctus) in the spring, along with raccoon (Procyon lotor) and wild turkey (Meleagris gallopavo) site use in the summer, was positively associated with wild pig activity. With the exception of diurnal wild turkey, diel activity overlap between these species and wild pigs was high, although temporal partitioning could have occurred at finer spatiotemporal scales than we examined. Our results collectively emphasize the importance of accounting for seasonal spatial and temporal responses by individual species to invasive wild pigs, with special consideration given to species in seasons where high niche overlap with wild pigs is anticipated.