High Spatiotemporal Variation Research Articles

Airborne Measurements Reveal High Spatiotemporal Variation and the Heavy‐Tail Characteristic of Nitrous Oxide Emissions in Iowa

AbstractNitrous oxide (N2O) emissions from croplands contribute substantially to the climate impact of agriculture. Emissions of N2O are controlled by both anthropogenic and environmental factors and can vary by orders of magnitude over short times and distances. This nature of emissions is difficult to capture with models and presents an observational challenge. In the 2021 and 2022 growing season, we collected airborne measurements of N2O over Iowa to characterize N2O emissions at the farm to multi‐county spatial scales across days. We link our airborne observations to surface emissions using a Lagrangian particle dispersion model and quantify emissions using a Bayesian inversion framework. We find emissions magnitudes across Iowa, showing greater skew than modeled predictions, with a small fraction of fields contributing disproportionately to the total [25% of the domain contributing to 52%–77% of total emissions]. In addition to the high spatial variation, we find high temporal variability between flight days by a factor of 2 at the 100 km scale, and an order of magnitude at the 2 km scale], with peak emissions occurring at median soil moisture. This work illustrates the importance of transient, intense emissions from concentrated areas in explaining total cropland emissions, and demonstrates how airborne measurements can provide insights into variation that may be missed by other observational systems. Investigation into environmental factors highlights the need for observations, models and their input data to be spatiotemporally resolved to enable direct comparison, facilitating evaluation of predicted and reported emissions and providing guidance and feedback on mitigation strategies.

Spatiotemporal changes and driving factors of ecosystem services between karst and non-karst World Heritage sites in Southwest China

Understanding the spatiotemporal variation and drivers of ecosystem services is fundamental to optimal management and sustainable development of World Heritage (WH) sites. Although WH sites face multiple natural and anthropogenic threats, our understanding of their ecosystem services is still limited, especially for karst WH sites. In this study, we assessed habitat quality (HQ), carbon storage (CS), soil retention (SR), water conservation (WC), and the combined ecosystem service (CES) of karst and non-karst WH sites in Southwest China from 2000 to 2020 using the InVEST model. We also assessed trade-offs/synergies among ecosystem services using the spatial overlay method, and identified driving factors of variation in ecosystem services using geographical detector and structural equation models. The results showed that ecosystem services of the WH sites exhibited high spatiotemporal variation. In particular, there were higher values in the property zone than in the buffer zone, and an increasing trend in SR but a decreasing trend in HQ and CES over time. Compared to non-karst sites, karst WH sites had significantly lower values of HQ, CS, SR, and CES, but higher spatial heterogeneity in CS, WC, and CES. Weak trade-offs among ecosystem services dominated the WH sites, with the proportion of weak synergies increasing over time. Compared to non-karst sites, karst WH sites had a significantly lower proportion of strong synergies and a significantly higher proportion of weak synergies. The provision of ecosystem services was primarily influenced by natural factors (e.g., landscape division index and normalized difference vegetation index), followed by anthropogenic factors (e.g., distance from road and population density). Overall, these findings may have important implications for decision-making aimed at protecting the outstanding universal value, authenticity, and integrity of WH with different attributes.

The varied diet of the golden jackal (Canis aureus): Experiences from stomach analyses

The golden jackal population shows a rapid expansion in Europe during the recent decades, raising several management and conservation issues. Among others, the opportunistic feeding strategies might be a reason which is responsible for the invasive spreading and survival success of the species in various parts of Hungary. Our aim was to analyze the diet composition of the jackal through stomach content analysis to provide an insight about the evolution and behavioural adaptations of this mesopredator. The stomach samples were collected between 2003 and 2014 from different parts of Hungary. The percentage frequency of occurrence (%FO) as well as the biomass (%B) of the stomach content data were analyzed. Statistical analysis tests based on the presence and absence data of the remains of wild ungulates (cervids and wild boar) from seasonal data (winter-spring and summer-autumn) and between gender groups were conducted. Also, comparisons of the presence and absence data for the three main food categories (rodents, big game and vegetative/plant parts) were statistically tested.The results of the comparisons did not show any significant differences between the classes. This can be explained due to the high spatio-temporal variation of the data. The findings of our study shows the presence of a varied composition of food items such as rodents, insects, fruits, plant parts, ungulates (wild boar, cervids), reptiles (such as lizards and pond turtles), bird species (such as pheasants) as well as jaw remains of the red fox. It is noteworthy to mention in this context that majority of the ungulate remains from the stomach contents were associated with maggots, which indicated the presence of carrion consumption. Our study, based on varied diet composition, supports and confirms the opportunistic, scavenging and highly adaptive foraging of the golden jackal.

Change of soil carbon storage in monoculture tree plantations across wide environmental gradients: Argentina as a case study

Forest plantations (through afforestation or reforestation) have been proposed as a valuable option to mitigate carbon (C) emissions. However, high spatiotemporal variation has been observed in soil organic carbon (SOC) pools associated to these systems. It has been proposed that SOC stock (Mg/ha) changes under forest plantations (ΔSOC) are related to the SOCstock in the natural system being replaced with positive ΔSOC in the sites with lowest initial SOCstock and negative ΔSOC in sites with initially high SOCstock. Although there is some debate related with statistical artifacts, the slope of this relationship may depend on environmental, biological and anthropic variables. In this context, we took advantage of a recent effort to quantify soil organic C stocks in forest plantations and contiguous land uses along 136 sites across a wide climatic and edaphic gradient in Argentina, South-America, to explore the patterns and drivers of SOC change due to this land-use change. We also added 183 data from a systematic bibliographic survey. Average ΔSOC of all the studied paired sites was low (average of −3.85 ± 29.97 Mg ha−1), with 57 % of the paired sites showing a negative change. After applying a diagnosis method to detect statistical regression-to-the-mean bias and considering the pooled data and the different forest plantation groups (Pinusspp.,Eucalyptusspp.,Salicaceaespp, and native species), only Eucalyptusplantations showed a negative relationship betweenΔSOC and SOCstock in the control situation (i.e. a significant and negative impact on SOC in high-C soils). In general, most of the sites where forest plantations were introduced in Argentina have a relatively low baseline SOC (SOCstock < 200 Mg ha−1), and 98 % of the studied sites showed SOC deficit (Csat-def) estimated as the difference between the observed vs. the theoretical maximal C storage potential based on silt and clay content. SOCstock and the Csat-def variation in forest plantations across regions were explained mainly by the SOCstock of the natural situation where the plantation was installed, followed by edaphic and some climatic variables. Within the silvicultural variables evaluated, only the forest species cultivated had a significant effect at the scale evaluated and within the range of stand densities (medium to high) and ages (close to rotation period in each region) considered. Our results indicate that -along a broad range of environmental situations but within a limited set of silvicultural conditions- in average forests plantations decrease soil carbon stock. However, there are situations -that could be coarsely predicted with a model with multiple variables and their interactions- where the change is positive bringing opportunities to increase the C sequestration service of planted forests.

Runoff, sediment, organic carbon, and nutrient loads from a Canadian prairie micro-watershed under climate variability and land management practices

This study conducted a spatio-temporal analysis of runoff, total suspended sediment, suspended particulate carbon, nitrogen, and phosphorus loadings within the 2.06 km2 Steppler subwatershed in southern Manitoba of Canada based on 11 years of field monitoring data collected at nine stations. Results showed that the nutrient losses were very small because of the implementation of multiple BMPs in the study area. However, a high spatio-temporal variation of runoff and water quality parameters was found for the nine fields within the subwatershed. The average runoff coefficient was 0.19 at the subwatershed outlet with sediment, suspended particulate carbon, total nitrogen, and total phosphorus losses of 73.8, 6.10, 4.54, and 0.76 kg/ha respectively. Spring snowmelt runoff was about 74.5% of the annual runoff at the subwatershed outlet, while for sediment, suspended particulate carbon, total nitrogen, and total phosphorus, the proportions were 61.1%, 63.6%, 74.9%, and 81.2% respectively during the monitoring period, which suggests that BMPs designed for reducing nutrient loadings from snowmelt runoff would be more effective than BMPs designed for reducing pollutant loading from rainfall storms in the study area. Research findings from this study will benefit the enhancement of current BMPs and the development of new BMPs in the region to minimize soil and nutrient losses from agricultural fields and improve water quality in receiving water bodies.

Effects of landscape attributes and climate variables on catchment hydrology

Catchments characteristics, such as geomorphology, geology, soil, land use, and climatic variables, play an important role in total stream flow responses, a critical resource for people and the environment. Most of the previous literatures were applied a conventional statistical regression model to assess the relationship between landscape-climate descriptors, and streamflow and PET. However, a conventional statistical regression model didn’t consider dependence of explanatory variables that were collected or extracted across both space and time. This paper investigated the impacts of landscape attributes and climate variables on catchment scale temporal variation of total streamflow and spatio-temporal variation of potential evapotranspiration (PET) in the Mille catchment using multiple linear regression techniques, and the importance of this study was to test spatial autocorrelation in the spatial regression model which is required to properly assess and quantify the relationship between hydrological regime response components and Landscape-climate descriptors in a catchment with topographically complex, and high spatio-temporal climatic variation like in our case study area, the Mille catchment. Statistical regression analysis revealed significant relationships between streamflow and climate variables, especially with rainfall. Mean maximum temperature is the most dominant factor controlling temporal variation of potential evapotranspiration at a monthly scale, whereas NDVI is the most significant factor that controls the spatial variability of PET. The multiple regression model shows that 91.1% of temporal variation in streamflow was accounted for rainfall, whereas, 96.6% and 78.4% of temporal and spatial variation in potential evapotranspiration was accounted for in maximum temperature and NDVI, respectively. Methods also can be applied to catchments with similar landscape attributes and climate variables.

Methane and carbon dioxide fluxes at high spatiotemporal resolution from a small temperate lake

Lakes are hotspots for CH4 and CO2 effluxes, but their magnitude and underlying drivers are still uncertain due to high spatiotemporal variation within and between lakes. We measured CH4 and CO2 fluxes at high temporal (hourly) and spatial resolution (approx. 13 m) using 24 automatic floating chambers equipped with continuously recording sensors that enabled the determination of diffusive and ebullitive gas fluxes. Additionally, we measured potential drivers such as weather patterns, water temperature, and O2 above the sediment. During five days in autumn 2021, we conducted measurements at 88 sites in a small, shallow eutrophic Danish Lake. CH4 ebullition was intense (mean 54.8 μmol m−2 h−1) and showed pronounced spatiotemporal variation. Ebullition rates were highest in deeper, hypoxic water (5–7 m). Diffusive CH4 fluxes were 4-fold lower (mean 15.0 μmol m−2 h−1) and spatially less variable than ebullitive fluxes, and significantly lower above hard sediments and submerged macrophyte stands. CO2 concentration in surface waters was permanently supersaturated at the mid-lake station, and diffusive fluxes (mean 919 μmol m−2 h−1) tended to be higher from deeper waters and increased with wind speed. To obtain mean whole-lake fluxes within an uncertainty of 20 %, we estimated that 72 sites for CH4 ebullition, 39 sites for diffusive CH4 fluxes and 27 sites for diffusive CO2 fluxes would be required. Thus, accurate whole-lake quantification of the dominant ebullitive CH4 flux requires simultaneous operation of many automated floating chambers. High spatiotemporal variability challenges the identification of essential drivers and current methods for upscaling lake CH4 and CO2 fluxes. We successfully overcame this challenge by using automatic floating chambers, which offer continuous CH4 and CO2 flux measurements at high temporal resolution and, thus, are an improvement over existing approaches.

Species traits and observer behaviors that bias data assimilation and how to accommodate them.

Datasets that monitor biodiversity capture information differently depending on their design, which influences observer behavior and can lead to biases across observations and species. Combining different datasets can improve our ability to identify and understand threats to biodiversity, but this requires an understanding of the observation bias in each. Two datasets widely used to monitor bird populations exemplify these general concerns: eBird is a citizen science project with high spatiotemporal resolution but variation in distribution, effort, and observers, whereas the Breeding Bird Survey (BBS) is a structured survey of specific locations over time. Analyses using these two datasets can identify contradictory population trends. To understand these discrepancies and facilitate data fusion, we quantify species-level reporting differences across eBird and the BBS in three regions across the United States by jointly modeling bird abundances using data from both datasets. First, we fit a joint Species Distribution Model that accounts for environmental conditions and effort to identify reporting differences across the datasets. We then examine how these differences in reporting are related to species traits. Finally, we analyze species reported to one dataset but not the other and determine whether traits differ between reported and unreported species. We find that most species are reported more in theBBS than eBird. Specifically, we find that compared to eBird, BBS observers tend to report higher counts of common species and species that are usually detected by sound. We also find that species associated with water are reported less in the BBS. Species typically identified by sound are reported more at sunrise than later in the morning. Our results quantify reporting differences in eBird and theBBS to enhance our understanding of how each captures information and how they should be used. The reporting rates we identify can also be incorporated into observation models through detectability or effort to improve analyses across species and datasets. The method demonstrated here can be used to compare reporting rates across any two or more datasets to examine biases.

Rentabilidade do uso da irrigação no consórcio palma-sorgo para agricultores em ambiente semiárido

ABSTRACT Irrigation plays a fundamental role in advancing agricultural frontiers and increasing crop productivity, especially in semi-arid environments, because they exhibit high spatiotemporal rainfall variation. This practice is suggested even for adapted crops, such as forage cactus (Opuntia sp.), sorghum (Sorghum bicolor), and single or intercropping systems. In this study, aimed to evaluate the profitability of using irrigation in a single and intercropping forage cactus-sorghum system in a semi-arid region. The experimental design was in randomized blocks in a 5 × 3 factorial arrangement: five drip irrigation depths (0, 25, 50, 75, and 100% of the reference evapotranspiration - ET0) and three cropping systems (single forage cactus, single sorghum, and intercropping forage cactus-sorghum), with four replicates. The 0% ET0 treatment corresponded to rainfed conditions at a depth of 355 mm. A cycle of forage cactus and two sorghum cultivars (plant and regrowth) was conducted between November 2014 and November 2015. It was verified that the highest costs for implantation of the crop systems in irrigated conditions are due to the acquisition of irrigation and input systems, representing 85.3% of the effective operational cost. The single sorghum and forage cactus-sorghum systems provided the highest fresh matter productivity (62,013.05 and 60,075.36 kg ha-1, respectively). Based on economic indicators, the adoption of irrigation depths (25, 50, 75, and 100% ET0) promoted profits for the systems (single and intercropping) from the second year of implementation.

Evaluation of an open forecasting challenge to assess skill of West Nile virus neuroinvasive disease prediction

BackgroundWest Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement.MethodsWe performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill.ResultsSimple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill.ConclusionsHistorical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases).Graphical

Disease and weather induce rapid shifts in a rangeland ecosystem mediated by a keystone species (Cynomys ludovicianus).

Habitat loss and changing climate have direct impacts on native species but can also interact with disease pathogens to influence wildlife communities. In the North American Great Plains, black-tailed prairie dogs (Cynomys ludovicianus) are a keystone species that create important grassland habitat for numerous species and serve as prey for predators, but lethal control driven by agricultural conflict has severely reduced their abundance. Novel disease dynamics caused by epizootic plague (Yersinia pestis) within prairie dog colonies have further reduced prairie dog abundances, in turn destabilizing associated wildlife communities. We capitalized on a natural experiment, collecting data on prairie dog distributions, vegetation structure, avian abundance, and mesocarnivore and ungulate occupancy before (2015-2017) and after (2018-2019) a plague event in northeastern Wyoming, USA. Plague decimated black-tailed prairie dog populations in what was then the largest extant colony complex, reducing colony cover in the focal area from more than 10,000 ha to less than 50 ha. We documented dramatic declines in mesocarnivore occupancy and raptor abundance post-plague, with probability of occupancy or abundance approaching zero in species that rely on prairie dogs for a high proportion of their diet (e.g., ferruginous hawk [Buteo regalis], American badger [Taxidea taxus], and swift fox [Vulpes velox]). Following the plague outbreak, abnormally high precipitation in 2018 hastened vegetation recovery from prairie dog disturbance on colonies in which constant herbivory had formerly maintained shortgrass structure necessary for certain colony-associates. As a result, we observed large shifts in avian communities on former prairie dog colonies, including near-disappearance of mountain plovers (Charadrius montanus) and increases in mid-grass associated songbirds (e.g., lark bunting [Calamospiza melanocorys]). Our research highlights how precipitation can interact with disease-induced loss of a keystone species to induce drastic and rapid shifts in wildlife communities. Although grassland taxa have co-evolved with high spatiotemporal variation, fragmentation of the remaining North American rangelands paired with higher-than-historical variability in climate and disease dynamics are likely to destabilize these systems in the future.

Spatiotemporal variability and diffusive emissions of greenhouse gas in a shallow eutrophic lake in Inner Mongolia, China

Aquatic ecosystems are globally significant sources of greenhouse gases (GHG) to the atmosphere, offsetting the terrestrial sinks. A one-year field study was carried out in a shallow eutrophic Lake Wuliangsuhai, Inner Mongolia (draining waters from one of the largest irrigation areas in China), to estimate diffusive GHG fluxes and their relative importance in global warming potential (GWP). Our results showed high spatiotemporal variation in dissolved CO2, CH4 and N2O concentrations, while they did not differ significantly between the bottom and surface layers of the shallow waterbody. In general, GHG concentrations and diffusive fluxes were higher in the north part of the lake than in the south. GHG concentrations in the water under the ice were significantly higher than those during the open-water period. Spatial variability of GHG concentrations varied with the bathymetry of the lake. The location of study sites relative to the main inflow and abundance of submerged macrophytes were the main controlling factors of GHG concentrations, as indicated by the consistency of GHG concentrations at the sampling sites, particularly for N2O. The total diffusive GHG emission from Lake Wuliangsuhai was 76.9 ± 5.4 Gg CO2 equivalents yr−1, with CO2, CH4 and N2O contributing 16 %, 83 %, and 1 %, respectively. Overall, the results suggest that shallow lakes in mid-latitude arid areas with cold winters can be potentially important GHG sources. However, those lakes are insufficiently represented in the scientific literature, and therefore they deserve more research attention.

Assessing the Performance of Irrigation Systems in Large Scale Urban Parks: Application to the Case of Valdebebas, Madrid (Spain)

This paper presents a novel approach to assess spatial and temporal irrigation performance in urban parks and can assist park manager/operator decisions in irrigation management. First, irrigation needs are estimated by traditional irrigation scheduling and the irrigation zones with similar water needs that share the same electric valve (hydrozones) are identified. Then, irrigation performance is calculated using the relative water supply (RWS) indicator and mapped (GIS software). This approach can be adapted to various spatial and temporal scales. In this study, it was applied to the Valdebebas urban development VBB (Madrid) between the 2017 and 2019 irrigation seasons. The results for the VBB parks showed high spatio-temporal variation in irrigation performance among plant typologies within an irrigation season, which can be explained by the landscape coefficient KL variation across the parks. Likewise, this variation was also observed among the three evaluated seasons; explained among other factors by differences in irrigation management. For each hydrozone, the estimation of the NDVI index by Sentinel-2A satellite images in 2019 showed a threshold on irrigation performance. Thus, the remote sensing data together with the proposed approach can be a valuable tool for helping park managers/technicians adopt better decisions on irrigation practices.

Exploiting high frequency monitoring and satellite imagery for assessing chlorophyll-a dynamics in a shallow eutrophic lake

Freshwater ecosystems are challenged by cultural eutrophication across the globe, and it is a priority for water managers to implement water quality monitoring at different spatio-temporal scales to control and mitigate the eutrophication process. Phytoplankton abundance is a key indicator of the trophic and water quality status of lakes. Phytoplankton dynamics are characterized by high spatio-temporal variation, driven by physical, chemical and biological factors, that challenge the capacity of routine monitoring with conventional sampling techniques (i.e., boat based sampling) to characterise these complex relationships. In this study, high frequency in situ measurements and multispectral satellite data were used in a synergistic way to explore temporal (diurnal and seasonal) dynamics and spatial distribution of Chlorophyll-a (Chl-a) concentration, a proxy of phytoplankton abundance, together with physico-chemical water parameters in a shallow fluvial-lake system (Mantua Lakes). A good agreement was found between Chl-a retrieved by remote sensing data and Chl-a fluorescence data recorded by multi-parameters probes (R2 = 0.94). The Chl-a maps allowed a seasonal classification of the Mantua Lakes system as eutrophic or hypertrophic. Along the Mantua lakes system an increasing gradient in Chl-a concentration was recorded following the transition from a fluvial to lacustrine system. There was significant seasonal heterogeneity among the sub-basins, probably due to different hydrodynamics, influenced also by macrophyte stands. High-frequency data revealed the importance of rainfall events in the timing and growth dynamics of phytoplankton, particularly for spring and late summer blooms. Combining temporal and spatial data at high resolution improves the understanding of complex fluvial-lake systems. This technique can allow managers to target blooms in near-real time as they move through a system and guide them to localized hot spots enabling timely management action in ecosystems of high conservation and recreational value.

Pesticide Exposure of Residents Living Close to Agricultural Fields in the Netherlands: Protocol for an Observational Study

BackgroundApplication of pesticides in the vicinity of homes has caused concern regarding possible health effects in residents living nearby. However, the high spatiotemporal variation of pesticide levels and lack of knowledge regarding the contribution of exposure routes greatly complicates exposure assessment approaches.ObjectiveThe objective of this paper was to describe the study protocol of a large exposure survey in the Netherlands assessing pesticide exposure of residents living close (<250 m) to agricultural fields; to better understand possible routes of exposure; to develop an integrative exposure model for residential exposure; and to describe lessons learned.MethodsWe performed an observational study involving residents living in the vicinity of agricultural fields and residents living more than 500 m away from any agricultural fields (control subjects). Residential exposures were measured both during a pesticide use period after a specific application and during the nonuse period for 7 and 2 days, respectively. We collected environmental samples (outdoor and indoor air, dust, and garden and field soils) and personal samples (urine and hand wipes). We also collected data on spraying applications as well as on home characteristics, participants' demographics, and food habits via questionnaires and diaries. Environmental samples were analyzed for 46 prioritized pesticides. Urine samples were analyzed for biomarkers of a subset of 5 pesticides. Alongside the field study, and by taking spray events and environmental data into account, we developed a modeling framework to estimate environmental exposure of residents to pesticides.ResultsOur study was conducted between 2016 and 2019. We assessed 96 homes and 192 participants, including 7 growers and 28 control subjects. We followed 14 pesticide applications, applying 20 active ingredients. We collected 4416 samples: 1018 air, 445 dust (224 vacuumed floor, 221 doormat), 265 soil (238 garden, 27 fields), 2485 urine, 112 hand wipes, and 91 tank mixtures.ConclusionsTo our knowledge, this is the first study on residents’ exposure to pesticides addressing all major nondietary exposure sources and routes (air, soil, dust). Our protocol provides insights on used sampling techniques, the wealth of data collected, developed methods, modeling framework, and lessons learned. Resources and data are open for future collaborations on this important topic.International Registered Report Identifier (IRRID)RR1-10.2196/27883

Past eight-year malaria data in Gedeo zone, southern Ethiopia: trend, reporting-quality, spatiotemporal distribution, and association with socio-demographic and meteorological variables

BackgroundInformed decision making is underlined by all tiers in the health system. Poor data record system coupled with under- (over)-reporting of malaria cases affects the country’s malaria elimination activities. Thus, malaria data at health facilities and health offices are important particularly to monitor and evaluate the elimination progresses. This study was intended to assess overall reported malaria cases, reporting quality, spatiotemporal trends and factors associated in Gedeo zone, South Ethiopia.MethodsPast 8 years retrospective data stored in 17 health centers and 5 district health offices in Gedeo Zone, South Ethiopia were extracted. Malaria cases data at each health center with sociodemographic information, between January 2012 and December 2019, were included. Meteorological data were obtained from the national meteorology agency of Ethiopia. The data were analyzed using Stata 13.ResultsA total of 485,414 suspected cases were examined for malaria during the previous 8 years at health centers. Of these suspects, 57,228 (11.79%) were confirmed malaria cases with an overall decline during the 8-year period. We noted that 3758 suspected cases and 467 confirmed malaria cases were not captured at the health offices. Based on the health centers records, the proportions of Plasmodium falciparum (49.74%) and P. vivax (47.59%) infection were nearly equivalent (p = 0.795). The former was higher at low altitudes while the latter was higher at higher altitudes. The over 15 years of age group accounted for 11.47% of confirmed malaria cases (p < 0.001). There was high spatiotemporal variation: the highest case record was during Belg (12.52%) and in Dilla town (18,150, 13.17%, p < 0.001) which is located at low altitude. Monthly rainfall and minimum temperature exhibited strong associations with confirmed malaria cases.ConclusionA notable overall decline in malaria cases was observed during the eight-year period. Both P. falciparum and P. vivax were found at equivalent endemicity level; hence control measures should continue targeting both species. The noticed under reporting, the high malaria burden in urban settings, low altitudes and Belg season need spatiotemporal consideration by the elimination program.

Correction to: Assessment of Groundwater Potential in the Kalahandi District of Odisha (India) Using Remote Sensing, Geographic Information System and Analytical Hierarchy Process

Groundwater has become the ultimate source for domestic consumption of many urban and rural populations in the world due to unavailability of sufficient good-quality surface water. Because of high spatiotemporal variation in occurrence and distribution of this diminishing resource, understanding its nature is important for sustainable development and use. Geospatial technology has made this tedious task simpler by offering a new avenue for groundwater potential zoning. The present study was attempted to identify groundwater potential zones in the Kalahandi district of Odisha with the application of remote sensing, geographic information system and analytical hierarchy process (AHP). Various features such as geomorphology, land use/land cover, soil, land slope, drainage and lineament density and dynamic groundwater were assigned weights according to the Saaty’s scale by considering their influences on groundwater potential. The weights were then normalized using AHP and eigenvector technique. ArcGIS 10.6.1 was used to integrate all the features for developing the groundwater potential map of the district. The area was demarcated into five groundwater prospect zones, namely ‘very good’, ‘good’, ‘moderate’, ‘poor’ and ‘very poor’ covering 19, 6, 34, 28 and 13% of the district, respectively. Thus, it can be inferred that there is ample scope of groundwater development as 59% of the area comes under moderate to very good groundwater zones. On the other hand, suitable strategies can be devised for groundwater augmentation in poor to very poor zones. This study will be useful in formulating suitable strategies for the management of groundwater resources of the district.

Spatiotemporal variation of streambed quality and fine sediment deposition in five freshwater pearl mussel streams, in relation to extreme drought, strong rain and snow melt

Oxygenated streambeds are considered a key requirement for the successful recruitment of stream fauna, including highly endangered freshwater pearl mussel Margaritifera margaritifera. Excessive amounts of fines impede exchange between open water and interstitial, leading to colmation and low oxygen levels in the juvenile habitat. Understanding the dynamic relationship between sediment delivery, transport, deposition and remobilization in relation to anthropogenic drivers is still poorly understood, yet is essential for conservation and restoration.This study analysed spatiotemporal sediment dynamics and interstitial habitat quality in five pearl mussel streams at the border region between Bavaria, Saxony and the Czech Republic during 2018 and 2019, comparing extremely dry periods with higher discharge events caused by snow melt and rainfall. Physicochemical habitat conditions within the streambed and sediment deposition were recorded in high spatial resolution along the stream courses, with a particular focus on the effects of tributaries and outflows of man-made fishponds.Habitat conditions were unsuitable for juvenile pearl mussels at the majority of sites, indicated by pronounced differences in physicochemical parameters between open water and the substrate, independent of discharge conditions. Sediment deposition varied markedly between discharge events, in terms of both the quality and quantity of deposits. Snow melt resulted in the highest sedimentation rates, but the smallest proportion of fine particles. During low flow conditions, fine sediment deposition was highly variable, ranging from 0.048 to 4.170 kg/week/m², mostly independent of flow velocity. High spatiotemporal variation was observed within and amongst stream systems, revealing different longitudinal patterns of fine sediment deposition, with catchment land use as the main driver. Temporal variability in sediment deposition was mainly associated with the discharge condition while abiotic parameters varied mainly with season.The high site-specificity of sedimentation rates and substrate conditions in response to different discharge events highlights the importance of an adapted conservation management which considers anthropogenic effects at the local scale.

Ecological correlates of space use patterns in wild western lowland gorillas.

The distribution of resources is a crucial determinant of animals' space use (e.g., daily travel distance, monthly home range size, and revisitation patterns). We examined how variation in ecological parameters affected variability in space use patterns of western lowland gorillas, Gorilla gorilla gorilla. They are an interesting species for investigating this topic because key components of their diet are nonfruit items (herbaceous vegetation and tree leaves) that occur at low density and are sparsely distributed, and fruits, which show high spatiotemporal variation in availability. We estimated how availability of nonfruit foods and fruit, frugivory (proportion of feeding time consuming fruit), and swamps in areas used by the gorillas influenced daily travel distance, monthly home range size, and revisit frequency to grid cells in the home range of one habituated gorilla group in Loango National Park, Gabon. Using location data from 2015 to 2018, we found that the gorillas decreased their daily travel distance as both the density of nonfruit foods and the proportion of swamps in areas used increased. Daily travel distances were shorter when both frugivory and availability of fruit were higher, yet, daily travel distances were longer when availability of fruit was low but frugivory was still high. Furthermore, monthly home range size increased as frugivory increased and monthly revisit frequencies to an area increased as fruit availability of an area increased. In conclusion, the availability of both nonfruit and fruit influenced the gorillas' space use patterns. Gorillas decreased foraging effort when food availability was high but were willing to incur increasing foraging costs to feed on fruit when availability was low. This study highlights how animals have to adjust their space use with changing resource availability and it emphasizes the value of examining multiple parameters of space use.

Mechanisms of Cold Region Hydrologic Change to Recent Wetting in a Northern Glaciated Landscape

AbstractThe Northern Great Plains (NGP) experienced a transition from a long drought cycle to a deluge cycle in the late 1900s. Embedded within this long‐term transition were shorter‐term decadal‐scale dry and wet periods. A recent NGP drought (1999–2004) was followed in 2005 by a wet period that continues to present day. However, how surface hydrology in a northern glaciated landscape responds to increased precipitation is poorly understood and a physically based modeling framework is needed to clarify the underlying processes. This study utilizes a physically based hydrologic model to investigate the impacts of increased precipitation on the water budget by examining its partition into intermediate processes from 2004–2017 in the Mauvais Coulee Basin (MCB). The MCB model evaluations against snow observations (2016–2017) and outlet streamflow observations (2004–2017) imply strong model performance. Simulated snow exhibits high spatiotemporal variation due to variability in precipitation, snow redistribution from stubble fields to wooded areas, and snow accumulation in small depressions. Our modeling results identify two major phases (before and after 2011) exhibiting different hydrologic responses. The period before 2011 is dominated by streamflow and evapotranspiration (ET), while extreme ET dominance is observed after 2011. This switch is due to climatic conditions involving changes in the vapor density difference and depressional and subsurface storage by intermediate processes. A clear contrast in the prewinter soil moisture‐streamflow relationship between before and after 2011 is reported. Our findings help illuminate the important influences of increased precipitation upon the intermediate processes in controlling streamflow in a northern glaciated prairie landscape.