Percent Agriculture Research Articles

Illuminating patterns of firefly abundance using citizen science data and machine learning models

As insect populations decline in many regions, conservation biologists are increasingly tasked with identifying factors that threaten insect species and developing effective strategies for their conservation. One insect group of global conservation concern are fireflies (Coleoptera: Lampyridae). Although quantitative data on firefly populations are lacking for most species, anecdotal reports suggest that some firefly populations have declined in recent decades. Researchers have hypothesized that North American firefly populations are most threatened by habitat loss, pesticide use, and light pollution, but the importance of these factors in shaping firefly populations has not been rigorously examined at broad spatial scales. Using data from >24,000 surveys (spanning 2008–16) from the citizen science program Firefly Watch, we trained machine learning models to evaluate the relative importance of a variety of factors on bioluminescent firefly populations: pesticides, artificial lights at night, land cover, soil/topography, short-term weather, and long-term climate. Our analyses revealed that firefly abundance was driven by complex interactions among soil conditions (e.g., percent sand composition), climate/weather (e.g., growing degree days), and land cover characteristics (e.g., percent agriculture and impervious cover). Given the significant impact that climactic and weather conditions have on firefly abundance, there is a strong likelihood that firefly populations will be influenced by climate change, with some regions becoming higher quality and supporting larger firefly populations, and others potentially losing populations altogether. Collectively, our results support hypotheses related to factors threatening firefly populations, especially habitat loss, and suggest that climate change may pose a greater threat than appreciated in previous assessments. Thus, future conservation of North American firefly populations will depend upon 1) consistent and continued monitoring of populations via programs like Firefly Watch, 2) efforts to mitigate the impacts of climate change, and 3) insect-friendly conservation practices.

The context dependency of fish-habitat associations in separated karst ecoregions.

Fish populations may be isolated via natural conditions in geographically separated ecoregions. Although reconnecting these populations is not a management goal, we need to understand how these populations persist across landscapes to develop meaningful conservation actions, particularly for species occupying sensitive karst ecosystems. Our study objective was to determine the physicochemical factors related to the occurrence of four spring-associated fishes. Arbuckle Uplift and Ozark Highlands ecoregions, USA. We used a hierarchical approach to identify habitat relationships at multiple spatial scales. We collected detection data using snorkeling and seining. We examined the physicochemical relationships related to the detection and occurrence of four spring-associated fishes using occupancy modeling in a Bayesian framework. We found physicochemical relationships that differed and were similar between ecoregions for several fishes. For three species, we found different water temperature relationships between ecoregions. Smallmouth bass were ubiquitous in their use of drainage areas in the Ozark Highlands but only associated with the lower network of the Arbuckle Uplift. There were several mirrored relationships between ecoregions, including an interaction between residual pool depth and water temperature, where sites with deeper pools were more likely to be occupied during warmer water temperatures. There were single-species occurrence relationships with percent vegetation and percent agriculture. Lastly, snorkeling was a more efficient sampling method compared to seining for all fishes. Our results indicate stream temperature mitigation may be possible via the maintenance of key channel morphologies, and we identify shared stressors between ecoregions. Channel mitigation to maintain reaches with deeper pools may be an important strategy for maintaining thermal refugia, particularly when considering climate change. Identifying the mechanistic underpinning of other multiscale ecological relationships would be helpful to discern if some of the different ecoregion relationships represent warning signals or interactions with unmeasured biotic or abiotic factors.

Macroeconomic Determinants of Income Inequality in Sri Lanka

Even though several different income redistribution plans have been put in place by succeeding governments, income disparity has persisted in Sri Lanka over the past 50 years. Using secondary data from 1978 to 2021, this study aims to pinpoint the macroeconomic factors that contribute to income inequality in Sri Lanka. The basic estimating method is the autoregressive distributed lag model (ARDL), a rarely used analytical tool in Sri Lanka. The results show that the primary macroeconomic predictors of income inequality in Sri Lanka among the selected variables are government spending, trade openness, average prices, the share of agriculture in the GDP, and per capita GDP. Additionally, the findings demonstrate that trade openness and government spending cause income inequality, supporting Barro's theory. Additionally, the average price level and the percentage of agriculture in the GDP have helped to lessen income disparity in the nation. Per capita GDP also shows a marginal significance, supporting Kuznets’ view, which stresses that increasing a country’s GDP increases inequality. However, financial deepening and secondary school enrolment ratios do not show significant impacts emphasizing that financial and education factors do not contribute to inequality in the Sri Lankan setting. The study suggested that policy priority should be given to developing the agricultural sector and catching the spillover effect of international trade to reduce income inequality. That would ultimately lead to lowering trade-induced inequality. Furthermore, a crucial policy choice to lower the expense of daily life for the poor is to maintain a constant overall price level. Government transfer programs should primarily target the poor and maintain a proper monitoring mechanism to capture the outcome of those transfers also important. Currently, such a follow-up system is not in place; therefore, the government’s targets of reducing inequality couldn’t be realized to the fullest.

Landscape factors and allochthonous congeneric species influence Callithrix aurita occurrence in Brazilian Atlantic Forest remnants.

The buffy-tufted-ear marmoset (Callithrix aurita) is a small primate endemic to the Brazilian Atlantic Forest biome, and one of the 25 most endangered primates in the world, due to fragmentation, loss of habitat, and invasion by allochthonous Callithrix species. Using occurrence data for C. aurita from published data papers, we employed model selection using Akaike Information Criterion corrected for small samples and cumulative AICc weight (w +) to evaluate whether fragment size, distance to fragments with allochthonous species, altitude, connectivity, and surrounding matrices influence the occurrence of C. aurita within its distributional range. Distance to fragments with C. jacchus (w + = 0.94) and nonvegetated areas (w + = 0.59) correlated negatively with C. aurita occurrence. Conversely, the percentage of agriculture and pasture mosaic (w + = 0.61) and the percentage of savanna formation (w + = 0.59) in the surrounding matrix correlated positively with C. aurita occurrence. The findings indicate that C. aurita is isolated in forest fragments surrounded by potentially inhospitable matrices, along with proximity of a more generalist and invasive species, thereby increasing the possibility of introgressive hybridization. The findings also highlighted the importance of landscape elements and allochthonous congeneric species for C. aurita conservation, besides indicating urgency for allochthonous species management. Finally, the approach used here can be applied to improve conservation studies of other endangered species, such as C. flaviceps, which is also endemic to the Brazilian Atlantic Forest and faces the same challenges.

Qualidade da água superficial em comunidades rurais do estado de Goiás durante a estação seca e sua relação com o uso e a ocupação do solo

Abstract Different land uses and occupations can influence water quality and affect the lives of the population, especially of people who live in rural areas. Because rural populations live under conditions of socio-environmental vulnerability, it is necessary to monitor the quality of water resources to prevent diseases. This work aimed to analyze surface water quality in rural and traditional communities in the state of Goiás through the Bascarán Water Quality Index (WQIB) to evaluate the effects of the predominant land use and occupation of each location. Raw water samples were collected from specific points during the dry season. The presence of pesticides was verified through chromatographic analysis, without quantification, and 11 physical-chemical and microbiological parameters were assessed according to standard methods. The results showed that the WQIB ranged in quality from “good” to “pleasant”. Classification as “pleasant” was statistically related to a high incidence of pasture area (> 80%) and classification as “good” with a percentage of agriculture below 30%. The main land uses and occupations were forest, pasture and agriculture. The apparent parameters color, thermotolerant coliforms and dissolved oxygen also indicated contamination of water courses. The detection of pesticides with a high degree of toxicity in the analyzed water resources, mainly in two rivers (the Facada and Sucuapara creeks), put human health at risk in rural areas, even under conditions of small exposure. Carbofuran, banned since 2017, was detected near the Itacaiú community, making it necessary to alert the local government, residents and tourists who use the Araguaia River for different purposes.

Combined effects of landscape composition and agrochemicals on frog communities amid sugarcane-dominated agroecosystems.

Global demand for crops will continue increasing over the next few decades to cover both food and biofuel needs. This demand will put further pressure to expand arable land and replace natural habitats. However, we are only beginning to understand the combined effects of agrochemicals and land-use change on tropical freshwater biodiversity. In this study, we analyzed how pond-dwelling anuran larvae responded to pond characteristics, landscape composition, and agrochemical contamination in a sugarcane-dominated agroecosystem in Brazil. Then we used an information theoretical approach with generalized linear models to relate species richness and abundance to predictor variables. The variation in tadpole abundance was associated with both agrochemical concentration (e.g., ametryn, diuron, and malathion) and landscape variables (e.g., percentage of forest, percentage of agriculture, and distance to closest forest). The relationship between species abundance and agrochemicals was species-specific. For example, the abundances of Scinax fuscovarius and Physalaemus nattereri were negatively associated with ametryn, and Dendropsophus nanus was negatively associated with tebuthiuron, whereas that of Leptodactylus fuscus was positively associated with malathion. Conversely, species richness was associated with distance to forest fragments and aquatic vegetation heterogeneity, but not agrochemicals. Although we were unable to assign a specific mechanism to the variation in tadpole abundance based on field observations, the lower abundance of three species in ponds with high concentrations of agrochemicals suggest they negatively impact some frog species inhabiting agroecosystems. We recommend conserving ponds near forest fragments, with abundant stratified vegetation, and far from agrochemical runoffs to safeguard more sensitive pond-breeding species.

Land Inequality by Mode of Irrigation in Pakistan, 1990-2000

In Pakistan agriculture land accounts for a large portion of total wealth and the distribution of this land effects household welfare and agriculture efficiency. Evidence shows that in developing countries, land inequality is detrimental to overall efficiency due to incomplete markets and therefore results in welfare losses to those with little or no land as there are several benefits associated with access to land [Vollrath (2007)]. Agriculture plays a pivotal role in the economy of Pakistan which contribute 22.4 percent to Gross Domestic Product, 43.05 percent of labour force engaged in agriculture sector and 67 percent of population reside in rural areas out of which 30 percent of people living below the official poverty line. As the climate of Pakistan is arid to semi arid, its 80 percent agriculture is irrigated. Pakistan has one of the largest irrigation system in the world based on Indus basin irrigation system which plays an important role in the development of agriculture and the nature of distribution of irrigation water across farm size groups determines to a significant extent the nature of distribution of agriculture income.

Landscape structure shapes the diversity of plant reproductive traits in agricultural landscapes in the Brazilian Cerrado

The replacement of native vegetation by crops and pasturelands compromises the functional diversity of plants and negatively influence ecosystem functions such as pollination and seed dispersal. Here, we investigate the effects of landscape structure on the diversity of functional traits in agricultural landscapes in the Brazilian Cerrado. We sampled the woody plant community in 49 sites in forests and savannas, and analyzed the density and richness of pollination and seed dispersal syndromes. We performed model selection using Akaike Information Criterion to test whether landscape variables such as habitat amount, percentage of agriculture and pasture, patch and edge densities, and compositional heterogeneity at different spatial scales influence the density and richness of woody plant functional traits related to seed dispersal and pollination syndromes. The amount of forest and patch density were the landscape predictors that explained most of the diversity of plant functional traits. The amount of agricultural and savanna as well as edge density and landscape heterogeneity also affected functional traits. Our results suggest that habitat amount and configuration in the landscapes drive the diversity of functional traits, pointing that maintaining habitats within Cerrado agricultural landscapes is determinant for the plant’s functional traits diversity. Furthermore, a heterogeneous mosaic may favor the density or richness of plants pollinated by insects and hummingbirds. The continuous loss and fragmentation of habitats may lead to loss of ecological functions compromising the provision of ecosystems services in agricultural landscapes in the Cerrado ecoregion.

PATHOGEN SURVEY AND PREDICTORS OF LYMPHOPROLIFERATIVE DISEASE VIRUS INFECTION IN WILD TURKEYS (MELEAGRIS GALLOPAVO).

Growing populations of Wild Turkeys (Meleagris gallopavo) may result in increased disease transmission among wildlife and spillover to poultry. Lymphoproliferative disease virus (LPDV) is an avian retrovirus that is widespread in Wild Turkeys of eastern North America, and infections may influence mortality and parasite co-infections. We aimed to identify individual and spatial risk factors of LPDV in Maine's Wild Turkeys. We also surveyed for co-infections between LPDV and reticuloendotheliosis virus (REV), Mycoplasma gallisepticum, and Salmonella pullorum to estimate trends in prevalence and examine covariance with LPDV. From 2017 to 2020, we sampled tissues from hunter-harvested (n=72) and live-captured (n=627) Wild Turkeys, in spring and winter, respectively, for molecular detection of LPDV and REV. In a subset of captured individuals (n=235), we estimated seroprevalence of the bacteria M. gallisepticum and S. pullorum using a plate agglutination test. Infection rates for LPDV and REV were 59% and 16% respectively, with a co-infection rate of 10%. Seroprevalence for M. gallisepticum and S. pullorum were 74% and 3.4%, with LPDV co-infection rates of 51% and 2.6%, respectively. Infection with LPDV and seroprevalence of M. gallisepticum and S. pullorum decreased, whereas REV infection increased, between 2018 and 2020. Females (64%), adults (72%), and individuals sampled in spring (76%) had higher risks of LPDV infection than males (47%), juveniles (39%), and individuals sampled in winter (57%). Furthermore, LPDV infection increased with percent forested cover (β=0.014±0.007) and decreased with percent agriculture cover for juveniles (β=-0.061±0.018) sampled in winter. These data enhance our understanding of individual and spatial predictors of LPDV infection in Wild Turkeys and aid in assessing the associated risk to Wild Turkey populations and poultry operations.

Identifying Farming Strategies Associated With Achieving Global Agricultural Sustainability

Sustainable agroecosystems provide adequate food while supporting environmental and human wellbeing and are a key part of the United Nations Sustainable Development Goals (SDGs). Some strategies to promote sustainability include reducing inputs, substituting conventional crops with genetically modified (GM) alternatives, and using organic production. Here, we leveraged global databases covering 121 countries to determine which farming strategies—the amount of inputs per area (fertilizers, pesticides, and irrigation), GM crops, and percent agriculture in organic production—are most correlated with 12 sustainability metrics recognized by the United Nations Food and Agriculture Organization. Using quantile regression, we found that countries with higher Human Development Indices (HDI) (including education, income, and lifespan), higher-income equality, lower food insecurity, and higher cereal yields had the most organic production and inputs. However, input-intensive strategies were associated with greater agricultural greenhouse gas emissions. In contrast, countries with more GM crops were last on track to meeting the SDG of reduced inequalities. Using a longitudinal analysis spanning 2004–2018, we found that countries were generally decreasing inputs and increasing their share of agriculture in organic production. Also, in disentangling correlation vs. causation, we hypothesize that a country's development is more likely to drive changes in agricultural strategies than vice versa. Altogether, our correlative analyses suggest that countries with greater progress toward the SDGs of no poverty, zero hunger, good health and wellbeing, quality education, decent work, economic growth, and reduced inequalities had the highest production of organic agriculture and, to a lesser extent, intensive use of inputs.

Characterizing temporal variability in streams supports nutrient indicator development using diatom and bacterial DNA metabarcoding

Interest in developing periphytic diatom and bacterial indicators of nutrient effects continues to grow in support of the assessment and management of stream ecosystems and their watersheds. However, temporal variability could confound relationships between indicators and nutrients, subsequently affecting assessment outcomes. To document how temporal variability affects measures of diatom and bacterial assemblages obtained from DNA metabarcoding, we conducted weekly periphyton and nutrient sampling from July to October 2016 in 25 streams in a 1293 km2 mixed land use watershed. Measures of both diatom and bacterial assemblages were strongly associated with the percent agriculture in upstream watersheds and total phosphorus (TP) and total nitrogen (TN) concentrations. Temporal variability in TP and TN concentrations increased with greater amounts of agriculture in watersheds, but overall diatom and bacterial assemblage variability within sites—measured as mean distance among samples to corresponding site centroids in ordination space—remained consistent. This consistency was due in part to offsets between decreasing variability in relative abundances of taxa typical of low nutrient conditions and increasing variability in those typical of high nutrient conditions as mean concentrations of TP and TN increased within sites. Weekly low and high nutrient diatom and bacterial metrics were more strongly correlated with site mean nutrient concentrations over the sampling period than with same day measurements and more strongly correlated with TP than with TN. Correlations with TP concentrations were consistently strong throughout the study except briefly following two major precipitation events. Following these events, biotic relationships with TP reestablished within one to three weeks. Collectively, these results can strengthen interpretations of survey results and inform monitoring strategies and decision making. These findings have direct applications for improving the use of diatoms and bacteria, and the use of DNA metabarcoding, in monitoring programs and stream site assessments.

Are northern bobwhites an umbrella species for open‐land birds in Ohio?

AbstractBirds that inhabit open lands such as grasslands and shrublands are rapidly declining across North America. A common practice for multi‐species management is to focus on umbrella species whose habitat requirements overlap with several other species. We evaluated whether the northern bobwhite (Colinus virginianus; bobwhite) could serve as an umbrella species for open‐land birds in Ohio, USA. We related landscape metrics to abundance patterns and assessed whether bobwhite occupancy positively predicts presence of open‐land birds. We combined bird survey data from the second Ohio Breeding Bird Atlas (2006–2011) with land cover data from the 2011 National Land Cover Database (Homer et al. 2015) to construct single‐season N‐mixture models to identify landscape metrics that influence bobwhite abundance. Bobwhite abundance was positively predicted by forest cohesion, percent agriculture, percent barren, and percent grassland. Of the 34 focal species, bobwhites were a significant positive predictor for 12, and a significant negative predictor for 10. The model with only bobwhite occupancy probability as a predictor was the best supported model for only willow flycatcher (Empidonax traillii). These results suggest that bobwhite land cover type requirements are too specialized to meet the needs of broader species guilds, instead affording protection for a narrower range of individual species that share specific habitat requirements with bobwhites. Management for bobwhites may still be able to promote co‐occurrence for declining species across multiple guilds by identifying locations where focused management can create more suitable conditions for species with positive co‐occurrence.

Emergent Freshwater Insects Serve as Subsidies of Methylmercury and Beneficial Fatty Acids for Riparian Predators Across an Agricultural Gradient.

Aquatic-to-terrestrial subsidies have the potential to provide riparian consumers with benefits in terms of physiologically important organic compounds like omega-3 long-chain polyunsaturated fatty acids (n-3 LCPUFAs). However, they also have a "dark side" in the form of exposure to toxicants such as mercury. Human land use intensity may also determine whether subsidies provide benefits or come at a cost for riparian predators. We sampled insects as well as Eastern Phoebe (Sayornis phoebe) chicks in 2015-2016 within the southern Finger Lakes region to understand how food quality, in terms of n-3 LCPUFAs and methylmercury (MeHg), of emergent freshwater insects compared with that of terrestrial insects and how land use affected the quality of prey, predator diet composition, and MeHg exposure. Across the landscape, freshwater insects had a significantly higher percentage of the n-3 LCPUFA eicosapentaenoic acid (EPA) compared to terrestrial insects and contained significantly more MeHg than terrestrial insects did. In spite of differences in MeHg concentrations between aquatic and terrestrial insects, chick MeHg concentrations were not related to diet composition. Instead, chick MeHg concentrations increased with several metrics of human land use intensity, including percent agriculture. Our findings suggest that freshwater subsidies provide predators with both risks and benefits, but that predator MeHg exposure can vary with human land use intensity.

Use of occupancy modelling to identify sample sizes and waterbodies with fish exceeding mercury consumption advisory thresholds.

Identifying waterbodies where fish methylmercury concentrations are elevated is critical for development of consumption guidelines. However, mercury concentrations vary among waterbodies and fishes due to a suite of environmental conditions and detection of elevated mercury concentrations is imperfect, resulting in inaccurate consumption guidelines. Occupancy models may be a useful approach for addressing these issues but have not been used for these purposes. Our objectives were to use occupancy modeling to (1) estimate number of samples needed to detect mercury levels surpassing >0.30 mg/kg wet weight in fish at a waterbody (2) identify individual fish-level factors associated with detection probability, and (3) identify environmental-level factors linked to elevated mercury levels in fish at a waterbody. Mercury concentrations were estimated from >500 largemouth bass Micropterus salmoides and walleye Sander vitreus from 30 waterbodies throughout Iowa, USA to identify individuals with concentrations > or <0.30 mg/kg. Probability of detecting mercury concentrations >0.30 mg/kg varied between species and increased with fish length; consequently, more samples were needed to detect elevated mercury concentrations in small versus large fish. The probability of a waterbody having fish with elevated mercury levels increased with the percent grassland and declined with percent agriculture in the watershed, providing prioritization metrics for mercury surveillance programs. Our results demonstrate that occupancy models can be a valuable tool for mercury surveillance due to their ability to estimate necessary sample sizes and identify fish sizes and waterbodies with elevated mercury concentrations while accounting for imperfect detection probabilities.

Configurationally analysis of relationships between land-cover characteristics and river water quality in a real scenario

The composition and configuration of land uses/covers (LULC) have a key role in determining the quality of surface waters. This study investigated the relationships between 207 landscape metrics (LMs) at landscape and class levels and water quality parameters (WQPs) by analytically approach in Mazandaran sub-basins, north of Iran. For this purpose, at first the basic WQPs were identified by principal component analysis (PCA). Then, stepwise linear regression analysis was applied to recognize optimal LMs for estimating each of the WQPs individually. Finally, the effect of the spatial configuration of LULC classes on WQPs and their variability was analytically evaluated by real samples. According to the PCA results, SAR, TDS, pH, PO43−, and NO3− were identified as principal WQPs in Mazandaran Rivers. The results also showed that the interspersion and juxtaposition index of bare lands, related circumscribing circle of agriculture, percentage of the forest, connectivity index of residential, and percentage of agriculture were the optimal metrics for estimating SAR, pH, TDS, PO43−, and NO3− levels, respectively. The metrics at the class level also had more ability to describe the WQPs. In this study, a suitable model for future LULC establishment in Mazandaran Province with the aim of improving effects on surface water quality was proposed.

Risks for cyanobacterial harmful algal blooms due to land management and climate interactions

The frequency and severity of cyanobacteria harmful blooms (CyanoHABs) have been increasing with frequent eutrophication and shifting climate paradigms. CyanoHABs produce a spectrum of toxins and can trigger neurological disorder, organ failure, and even death. To promote proactive CyanoHAB management, geospatial risk modeling can act as a predictive mechanism to supplement current mitigation efforts. In this study, iterative AIC analysis was performed on 17 watershed-level biophysical parameters to identify the strongest predictors based on Sentinel-2-derived cyanobacteria cell densities (CCD) for 771 waterbodies in Georgia Piedmont. This study used a streamlined watershed delineation technique, a 1-meter LULC classification with ~88% accuracy, and a technique to predict CyanoHAB risk in small-to-medium sized waterbodies. Landscape characteristics were computed utilizing the Google Earth Engine platform that enabled large spatio-temporal scope and variable inclusion. Watershed maximum winter temperature, percent agriculture, percent forest, percent impervious, and waterbody area were the strongest predictors of CCD with a 0.33 R-squared. Warmer winter temperatures allow cyanobacteria to be photosynthetically active year-round, and trigger CyanoHABs when warmer temperatures and nutrients are introduced in early spring, typically referred to as Spring Bloom in southeast U.S. The risk models revealed an unexpected significant linear relationship between percent forest and CCD. It is due to the fact that land reclamation via reforestation in the piedmont have left legacy sediment and nutrients which are mobilized as surface runoff to the watershed after rain events. A Jenks Natural Break scheme assigned waterbodies to CyanoHAB risk groups, and of the 771 waterbodies, 24.38% were low, 37.35% and 38.26% were medium and high risk respectively. This research supplements existing cyanobacteria risk modeling methods by introducing a novel, scalable, and reproducible method to determine yearly regional risk. Future studies should include factors such as demographic, socioeconomic, labor, and site-specific environmental conditions to create more holistic CyanoHAB risk outputs.

Anthropogenic and geologic causes of anomalously high uranium concentrations in groundwater used for drinking water supply in the southeastern San Joaquin Valley, CA

Concentrations of uranium (U) >30 µg/L in groundwater are relatively uncommon in drinking water in the United States but can be of concern in those areas where complex interactions of aquifer materials and anthropogenic alterations of the natural flow regime mobilize U. High concentrations (>30 µg/L) of U in the southeastern San Joaquin Valley, California, USA, have been detected in 24 percent of 257 domestic, irrigation, and public-supply wells sampled across an approximately 110,000 km2 area. In this study we evaluated mechanisms for mobilization of U in the San Joaquin Valley proposed in previous studies, confirming mobilization by HCO3 and refuting mobilization by NO3 and we refined our understanding of the geologic sources of U to the scale of individual alluvial fans. The location of high concentrations depends on the interactions of geological U sources from fluvial fans that originate in the Sierra Nevada to the east and seepage of irrigation water that contains high concentrations of HCO3 that leaches U from the sediments. In addition, interactions with PO4 from fertilized irrigated fields may sequester U in the aquifer. Principal component analysis of the data demonstrates that HCO3 and ions associated with high total dissolved solids in the aquifer and the percentage of agriculture near the well sampled are associated with high U concentrations. Nitrate concentrations do not appear to control release of U to the aquifer. Age dating of the groundwater and generally increasing U concentrations of the past 25 years in resampled wells where irrigation is prevalent suggests that high U concentrations are associated with younger water, indicating that irrigation of fields over the past 100 years has significantly contributed to increasing concentrations and mobilizing U. In some places, the groundwater is supersaturated with uranyl-containing minerals, as would be expected in roll front deposits. In general, the interaction of natural geological sources high in U, the anthropogenically driven addition of HCO3 and possibly phosphate fertilizer, control the location and concentration of U in each individual fluvial fan, but the addition of nitrate in fertilizer does not appear control the location of high U. These geochemical interactions are complex but can be used to determine controls on anomalously high U in alluvial aquifers.

Spatial Modeling of Disaster Resilience in the Lower Mississippi River Basin

Abstract. The Lower Mississippi River Basin (LMRB) in southeastern Louisiana is one of the most impacted and vulnerable coasts in the continental USA. This area has been facing recurring threats from coastal hazards in a long run. These hazardous events have negatively impacted the communities in various degrees. Therefore, identifying the places that are resilient to disasters and understanding the dynamic interactions among indicators are critical topics for pre-disaster preparation, post-disaster recovery, and establishment of mitigation plans. Studies on how variables of community resilience to natural hazards interact as a system that affects the final resilience (i.e., their dynamical linkages) have rarely been conducted. Bayesian network (BN), which represents the interdependencies among variables in a graph while expressing the uncertainty in the form of probability distributions, offers an effective way to investigate the interactions among different resilience components and addresses the natural–human system as a whole. This study employed a BN to study the interdependencies of ten resilience variables and population change in the Lower Mississippi River Basin (LMRB) at the census block group scale. A genetic algorithm was used to identify an optimal BN where population change, a cumulative resilience indicator, was the target variable. The results uncovered a set of important resilience variables that could account for the spatial variation of population changes in a region vulnerable to coastal hazards, also identify and quantify the interactions among these variables in a probabilistic form, the results of which can then be used for future scenario modeling and planning for resilience. Specifically, an optimal Bayesian network model was developed to explain the population change dynamics using data at block group scale. The study first extracted 10 variables from a group of 35 to derive the network. Through a genetic algorithm and after 906 generations, the resultant optimized Bayesian network was achieved with a cross-validation accuracy of 67%. The expectation-maximization (EM) method was used to learn the conditional probability tables, and the junction tree (JT) algorithm was applied to compute the posterior probabilities. Six variables were found to have direct impacts on population change, including hazard exposure, hazard damages, distance to coastline, employment rate, percent housing units built before 1970, and percent households with female householder. The remaining four variables are indirect variables, including percent agriculture land, percent flood zone area, percent owner-occupied housing units, and population density. Each variable has a conditional probability table so that its impacts on the probability of population change can be evaluated as it propagates through the network. These probabilities could be used for scenario modeling in the subsequent studies to help inform policies to reduce vulnerability and enhance resilience. We further used the developed BN model to simulate the probability of percent of population change under different scenarios by varying the probability distributions of some input variables. The simulation process was carried out through computing the posterior probability of the target variable. Findings from the scenarios simulation offer useful information on how the factors interact that lead to population change, which can be used to inform decision making on disaster resilience and adaptation strategies. For example, the scenarios of lowering either the hazard exposure or damages (Scenarios 1 and 2) reveal a spatial pattern of which population-change state 4 (−5.5% to 34.4%) becomes dominant, whereas extreme increase and decrease are less probable. Findings from Scenario 3 show that without the hazard exposure and damages the spatial pattern shows that no extreme population increase or decrease would possibly happen under the current socioeconomic and environmental condition. Results also show that reducing the percentage of housing units built before 1970 would be more effective than increasing the employment rate in terms of reducing population loss.

USA-scale patterns in wetland water quality as determined from the 2011 National Wetland Condition Assessment.

Water quality is a central component of ecological assessments but less well characterized in wetlands than other waterbody types. The 2011 National Wetland Condition Assessment, spanning freshwater and brackish wetlands across the conterminous USA, provided an unprecedented opportunity to examine water quality patterns across broad wetland types and geographic scales. Surface water samples were obtained from 634 (56%) of sites visited. Total nitrogen (TN), total phosphorus (TP), planktonic chlorophyll (CHLA), and specific conductance (SPCOND) ranged 4 orders of magnitude across sites and were inter-correlated. Woody versus herbaceous vegetation type was an important classifier, with herbaceous sites having standing water more often and generally higher pH, nutrients, and CHLA. Nutrient ratios spanned a range from P-limited to N-limited in most biogeographic regions, and increasing TP was associated with decreasing TN:TP ratios. Compared to national-scale data for other waterbody types (lakes, streams, marine nearshore), wetlands had generally higher TN and TP but not higher CHLA. Differences among biogeographic regions in water quality were concordant between inland wetlands and lakes, and between marine-coast wetlands and the marine nearshore. Associations of TN, TP, and CHLA to percent agriculture or natural land were stronger for the watershed scale than for smaller concentric buffer scales, suggesting that wetlands are influenced by landuse some distance away. SPCOND was related to landuse in inland wetlands but reflected seawater influence in marine-coast wetlands. Water quality exhibits the same general patterns and responses across wetlands as across other waterbody types and thus can provide a basis for ecological classification and condition assessment.

Water Chemistry During Baseflow Helps Inform Watershed Management: A Case Study of the Lake Wister Watershed, Oklahoma

AbstractNonpoint source (NPS) pollution from agricultural and urban development is a primary source of nutrients and decreased water quality in aquatic systems. Installation of best management practices (BMPs) within critical source areas of the watershed can be helpful at reducing the transport of nutrients to waterbodies; however, prioritizing these areas may be difficult. The objective of this study was to develop several potential frameworks for prioritizing subwatersheds using baseflow water chemistry data in relation to a simple human development index (HDI; total percent agriculture and urban development). At a monthly interval, samples were collected at 26 sites throughout the Oklahoma portion of the Lake Wister Watershed (LWW) and analyzed for total nitrogen, total phosphorus, total suspended solids, and chlorophyll a. Changepoint analysis for each parameter found significant thresholds for each of the parameters ranging from 20 to 30% HDI. Changepoint analysis summary statistics were used to develop prioritization frameworks for the LWW that could be used to target subwatersheds where BMP installation would have the greatest effect at improving water quality. Additionally, regression models developed from the relationships between water quality parameters and HDI values serve as realistic targets for improving water quality, with the modeled line representing the target concentration for a given HDI value. After BMPs have been implemented, baseflow monitoring should continue at the subwatershed scale to track changes in water quality. Focusing monitoring efforts at the subwatershed scale will provide an earlier indication of the effectiveness of BMPs, as it may take several decades to detect improvements in water quality at the larger watershed scale.