Mississippi Alluvial Plain Research Articles

Phosphorus distributions in alluvial soils of the Lower Mississippi River Basin: A case of dual legacies.

Legacies can become intertwined, none more so than the body of work of Dr. Andrew Sharpley examining agricultural nutrient delivery to waterbodies and the phosphorus (P) accumulation in agricultural soils, or "legacy P." Although Sharpley's work focused on the anthropogenic influence on soil P, our study suggests soils of the Lower Mississippi Alluvial Plain (MAP) represent a natural legacy with moderate levels of available P resulting from minimal anthropogenic input. In 2019, we collected surface (0-5cm) soil samples from four regionally dominant soil series in either cropland or forested land uses, spanning 76 locations within the MAP. Soil chemical and physical properties were measured utilizing a suite of extractions and texture analysis to correlate properties with soil P values. Total soil P did not vary between land uses. Mehlich-3 extractable P was slightly higher in cropland soils due to higher concentrations in Forestdale and Sharkey soils. Dundee, Forestdale, and Sharkey cropland soils showed significant associations between Mehlich-3-extractable iron (Fe) and P. Ratios of total carbon (TC) to total nitrogen (C:N) and TC to P (C:P) were consistent across all sampled soil series but differed between forest and cropland soils. These ratios are critical for establishing baseline soil nutrient values in simulation models and can be used to improve water quality model simulations that help guide P management in the MAP. As Sharpley routinely demonstrated, understanding sources of P is critical for developing an appropriate management strategy. This study provides critical knowledge on soil P dynamics in the MAP region.

Cropland abandonment between 1986 and 2018 across the United States: spatiotemporal patterns and current land uses

Knowing where and when croplands have been abandoned or otherwise removed from cultivation is fundamental to evaluating future uses of these areas, e.g. as sites for ecological restoration, recultivation, bioenergy production, or other uses. However, large uncertainties remain about the location and time of cropland abandonment and how this process and the availability of associated lands vary spatially and temporally across the United States. Here, we present a nationwide, 30 m resolution map of croplands abandoned throughout the period of 1986–2018 for the conterminous United States (CONUS). We mapped the location and time of abandonment from annual cropland layers we created in Google Earth Engine from 30 m resolution Landsat imagery using an automated classification method and training data from the U.S. Department of Agriculture Cropland Data Layer. Our abandonment map has overall accuracies of 0.91 and 0.65 for the location and time of abandonment, respectively. From 1986 to 2018, 12.3 (±2.87) million hectares (Mha) of croplands were abandoned across CONUS, with areas of greatest change over the Ogallala Aquifer, the southern Mississippi Alluvial Plain, the Atlantic Coast, North Dakota, northern Montana, and eastern Washington state. The average annual nationwide abandoned area across our study period was 0.51 Mha per year. Annual abandonment peaked between 1997 and 1999 at a rate of 0.63 Mha year−1, followed by a continuous decrease to 0.41 Mha year−1 in 2009–2011. Among the abandoned croplands, 53% (6.5 Mha) changed to grassland and pasture, 18.6% (2.28 Mha) to shrubland and forest, 8.4% (1.03 Mha) to wetlands, and 4.6% (0.56 Mha) to non-vegetated lands. Of the areas that we mapped as abandoned, 19.6% (2.41 Mha) were enrolled in the Conservation Reserve Program as of 2020. Our new map highlights the long-term dynamic nature of agricultural land use and its relation to various competitive pressures and land use policies in the United States.

Water quality, habitat, and fish assemblage relationships in middle-order agriculture and forest streams of the Mississippi Alluvial Plain

BackgroundAgriculture has greatly influenced water quality, habitats, and fish assemblages in streams of the Mississippi Alluvial Plain (MAP) ecoregion. However, MAP streams have historically been understudied compared to streams in other agricultural regions of the USA. In this study, water quality, habitat, and fish assemblage composition were assessed seasonally (spring, summer, and fall) in eight representative MAP streams located across three U.S. states. The study design included four streams containing highly agricultural watersheds (herein termed “agriculture” streams) and four streams containing mostly forested watersheds (herein termed “forest” streams), which were intended to represent reference conditions for MAP streams.ResultsIn general, forest streams contained significantly better instream and riparian habitats than agriculture streams (P = 0.010–0.040) whereas agriculture streams contained significantly greater levels of primary nutrients (P < 0.001–0.010). Differences between agriculture and forest streams with respect to other physical and chemical variables were intermittent and season dependent. Fish assemblages in agriculture and forest streams were structured primarily along an environmental gradient reflecting instream habitat conditions, water nutrient concentrations, and benthic chlorophyll-a production. Structurally, fish assemblages in both stream types contained many regionally common species, though some species appeared to exhibit affinities for a particular stream type. Functionally, fish assemblages in agriculture streams contained more tolerant species, more omnivores, and fewer insectivores compared to forest stream assemblages, which were nearly all insectivores. Overall, one-third of the fish specimens collected in forest streams classified as intolerant species.ConclusionsOur results suggested that stream water quality, habitat, and fish assemblages differed between agriculture and forest streams in the MAP, with fish assemblages exhibiting both structural and functional differences. Results were consistent with a larger body of literature from smaller, headwater streams whereby land-use changes (e.g., row-crop agriculture) impacted the physical, chemical, and biological characteristics of stream ecosystems. Results further highlight the importance of land use management and its effects on habitat diversity in stream ecosystems, and that protecting the few remaining undisturbed or less-disturbed streams should be a priority.

Human risk to tick encounters in the southeastern United States estimated with spatial distribution modeling.

Expanding geographic distribution and increased populations of ticks has resulted in an upsurge of human-tick encounters in the United States (US), leading to an increase in tickborne disease reporting. Limited knowledge of the broadscale spatial range of tick species is heightened by a rapidly changing environment. Therefore, we partnered with the Forest Inventory and Analysis (FIA) program of the Forest Service, U.S. Department of Agriculture and used passive tick surveillance to better understand spatiotemporal variables associated with foresters encountering three tick species (Amblyomma americanum L., Dermacentor variabilis Say, and Ixodes scapularis L.) in the southeastern US. Eight years (2014-2021) of tick encounter data were used to fit environmental niche and generalized linear models to predict where and when ticks are likely to be encountered. Our results indicate temporal and environmental partitioning of the three species. Ixodes scapularis were more likely to be encountered in the autumn and winter seasons and associated with soil organic matter, vegetation indices, evapotranspiration, temperature, and gross primary productivity. By contrast, A. americanum and D. variabilis were more likely to be encountered in spring and summer seasons and associated with elevation, landcover, temperature, dead belowground biomass, vapor pressure, and precipitation. Regions in the southeast least suitable for encountering ticks included the Blue Ridge, Mississippi Alluvial Plain, and the Southern Florida Coastal Plain, whereas suitable regions included the Interior Plateau, Central Appalachians, Ozark Highlands, Boston Mountains, and the Ouachita Mountains. Spatial and temporal patterns of different tick species can inform outdoorsmen and the public on tick avoidance measures, reduce tick populations by managing suitable tick habitats, and monitoring areas with unsuitable tick habitat for potential missed encounters.

Automated Detection of On-Farm Irrigation Reservoirs in Two Critical Groundwater Regions of Arkansas

In eastern Arkansas, the use of surface water for crop irrigation is steadily increasing in response to declining aquifers. Effective conjunctive water management requires accurate and timely information on the locations, sizes, and numbers of on-farm irrigation reservoirs. A method for remotely locating and characterizing on-farm reservoirs was developed using relative elevation and near-infrared imagery. With 62% accuracy, the method automatically identified 429 irrigation reservoirs within a 1.9-Mha area in less than an hour using an off-the-shelf laptop. Reservoirs not accurately identified (i.e., false negatives) were caused by the presence of vegetation or turbidity within the reservoirs. There were no false positive detections. This approach for identifying elevated reservoirs is applicable across the Mississippi Alluvial Plain (MAP) that encompasses over 4-Mha of irrigated cropland and other agricultural areas having low-relief.

Extending irrigation reservoir histories for improved groundwater modeling and conjunctive water management in two Arkansas critical groundwater areas

Agricultural land managers in the Mississippi Alluvial Plain rely on the Mississippi River Valley Alluvial Aquifer for the majority of the water used for irrigation in the region. Pumping that outpaces recharge has resulted in significant cones of depression in the region, increasing pumping costs and forcing producers to use more surface water, often in the form of constructing on-farm storage reservoirs. At the same time, efforts are underway to improve groundwater models to run scenarios of conjunctive water management in the region. The improved models require accurate depiction of how the system is being managed including the use of on-farm storage reservoirs. This study extends our knowledge of on-farm storage reservoir construction from those established by a previous study that tracked reservoir construction between 1996 and 2015 backward an additional 20 years to 1976. This was accomplished by using older multi-band satellite and aerial imagery. The study area covers portions of two Critical Groundwater Regions in eastern Arkansas. Reservoir construction was classified into five-year bins starting in 1976 and ending in 2015. Details of how older imagery was processed and analyzed are provided. Previously, nearly 50% of the on-farm storage reservoirs had unknown construction dates, but this research study resolved construction dates for 85% and 72% of the reservoirs in the two study areas. Relationships between time and depth to groundwater, reservoir size, previous land use, and saturated aquifer thickness are described and contrasted in the two study areas across the 40-year study period. Use of the information provided will help guide policy and resource allocation for future reservoir construction activities and help improve conjunctive water management of the region. Specifically, the addition of this historic information will allow modelers to further refine groundwater-surface water models and should improve water resources scenarios that include reservoirs as a tool to reduce groundwater decline.

Winter-time cover crop identification: A remote sensing-based methodological framework for new and rapid data generation

Accurately identifying and systematically mapping winter-time cover crops and their phenological characteristics offer significant benefits to agricultural producers and policymakers, as cover crops are one of several potential solutions to climate change mitigation. We present a methodological framework for identifying and mapping the presence of winter-time cover crops at the field level and aggregated to county scales from 2013 to 2019 by using the Google Earth Engine (GEE), a random forest classifier with time series data from Landsat 8, and yearly cover crop training data from the United States Department of Agriculture (USDA)-Natural Resources Conservation Service (NRCS). The methodology was tested with data from the Mississippi Alluvial Plain (MAP) region. Despite the inter-annual agronomic and climatic variations across space, results demonstrated an overall mean classification accuracy of 97.7%, with a kappa coefficient of 0.94. Results also revealed a 34% increase in model-predicted cover crop adoption in the study region from 2013 to 2019. Based on GEE, this study created, for the first time, a 30-m spatial and temporal resolution binary annual datasets and then aggregated them at the county level within the MAP study region. This multi-year novel dataset may improve our ability to anticipate and quantify the impact of summer crop production gains owing to cover crop adoption for extended periods and evaluate the adoption of cover crops on local soil ecosystems, biogeochemical cycles, and services. The methodology developed and tested broadly applies to other regions where cover crops have been promoted for climate-change mitigation and improving soil health for long-term sustainability. Agricultural producers, policymakers, and cost-share providers may use this information to develop agricultural conservation methods and land-use policies that minimize soil erosion and help mitigate climate change effects in the long run.

Salinization and sedimentation drive contrasting assembly mechanisms of planktonic and sediment-bound bacterial communities in agricultural streams.

Agriculture is the most dominant land use globally and is projected to increase in the future to support a growing human population but also threatens ecosystem structure and services. Bacteria mediate numerous biogeochemical pathways within ecosystems. Therefore, identifying linkages between stressors associated with agricultural land use and responses of bacterial diversity is an important step in understanding and improving resource management. Here, we use the Mississippi Alluvial Plain (MAP) ecoregion, a highly modified agroecosystem, as a case study to better understand agriculturally associated drivers of stream bacterial diversity and assembly mechanisms. In the MAP, we found that planktonic bacterial communities were strongly influenced by salinity. Tolerant taxa increased with increasing ion concentrations, likely driving homogenous selection which accounted for ~90% of assembly processes. Sediment bacterial phylogenetic diversity increased with increasing agricultural land use and was influenced by sediment particle size, with assembly mechanisms shifting from homogenous to variable selection as differences in median particle size increased. Within individual streams, sediment heterogeneity was correlated with bacterial diversity and a subsidy-stress relationship along the particle size gradient was observed. Planktonic and sediment communities within the same stream also diverged as sediment particle size decreased. Nutrients including carbon, nitrogen, and phosphorus, which tend to be elevated in agroecosystems, were also associated with detectable shifts in bacterial community structure. Collectively, our results establish that two understudied variables, salinity and sediment texture, are the primary drivers of bacterial diversity within the studied agroecosystem, whereas nutrients are secondary drivers. Although numerous macrobiological communities respond negatively, we observed increasing bacterial diversity in response to agricultural stressors including salinization and sedimentation. Elevated taxonomic and phylogenetic bacterial diversity likely increases the probability of detecting community responses to stressors. Thus, bacteria community responses may be more reliable for establishing water quality goals within highly modified agroecosystems that have experienced shifting baselines.

Using Downscaled GRACE Mascon Data to Assess Total Water Storage in Mississippi Alluvial Plain Aquifer.

The importance of high-resolution and continuous hydrologic data for monitoring and predicting water levels is crucial for sustainable water management. Monitoring Total Water Storage (TWS) over large areas by using satellite images such as Gravity Recovery and Climate Experiment (GRACE) data with coarse resolution (1°) is acceptable. However, using coarse satellite images for monitoring TWS and changes over a small area is challenging. In this study, we used the Random Forest model (RFM) to spatially downscale the GRACE mascon image of April 2020 from 0.5° to ~5 km. We initially used eight different physical and hydrological parameters in the model and finally used the four most significant of them for the final output. We executed the RFM for Mississippi Alluvial Plain. The validating data R2 for each model was 0.88. Large R2 and small RMSE and MAE are indicative of a good fit and accurate predictions by RFM. The result of this research aligns with the reported water depletion in the central Mississippi Delta area. Therefore, by using the Random Forest model and appropriate parameters as input of the model, we can downscale the GRACE mascon image to provide a more beneficial result that can be used for activities such as groundwater management at a sub-county-level scale in the Mississippi Delta.

Using taxa-based approaches to delineate stream macroinvertebrate assemblage responses to stressor gradients in modified alluvial agroecosystems

Alluvial plain landscapes are some of the most agriculturally productive lands in the world but often have modified stream ecosystems due to cultivation history. This context requires consideration when establishing water quality management goals. We analyzed state water quality databases to demonstrate that Mississippi Alluvial Plain (MAP) ecoregion streams have elevated specific conductivity (SC) and nutrients and lower macroinvertebrate local and regional taxa pools compared to streams in other ecoregions, potentially reducing the efficacy of traditional biomonitoring approaches within the region. To overcome these challenges, we used threshold indicator taxa analysis (TITAN) to compare macroinvertebrate assemblage responses to water quality gradients among ecoregions in Mississippi. We identified individual taxa and assemblage-level responses to increasing water quality degradation in MAP streams. Observed responses occurred at higher concentrations for SC, total organic carbon (TOC) and total phosphorus (TP), but not total nitrogen (TN) relative to other ecoregions. These responses appeared to be driven by a large proportion of indicator taxa considered tolerant or unresponsive in other ecoregions, responding negatively to increasing water quality stressors in MAP streams. Our observed assemblage-level stressor responses to WQ gradients in MAP streams demonstrate shifting tolerance in highly altered ecosystems may require adjustments to recovery expectations but also provide useful measures for monitoring improvements in regional water quality. For example, our observed macroinvertebrate assemblage response to increasing TP identified a management goal similar to guidance based on distributional analysis of water quality data within the MAP ecoregion (0.11 vs 0.128 mg L−1) and thus provide some biological basis for previously identified nutrient goals for the region. Our approach can guide and monitor success of nutrient reduction efforts in MAP watersheds and other alluvial plain agroecosystems where reference conditions do not exist, and local and regional taxa pools are less diverse and may not support full recovery of ecological assemblages. While our results are promising, they should also be compared with more sensitive and less habitat-limited biological assemblages (e.g., algae or bacteria) to better understand complex ecological responses to best management practices designed to increase sustainability of high production agricultural regions.

A model of transmissivity and hydraulic conductivity from electrical resistivity distribution derived from airborne electromagnetic surveys of the Mississippi River Valley Alluvial Aquifer, Midwest USA

Groundwater-flow models require the spatial distribution of the hydraulic conductivity parameter. One approach to defining this spatial distribution in groundwater-flow model grids is to map the electrical resistivity distribution by airborne electromagnetic (AEM) survey and establish a petrophysical relation between mean resistivity calculated as a nonlinear function of the resistivity layering and thicknesses of the layers and aquifer transmissivity compiled from historical aquifer tests completed within the AEM survey area. The petrophysical relation is used to transform AEM resistivity to transmissivity and to hydraulic conductivity over areas where the saturated thickness of the aquifer is known. The US Geological Survey applied this approach to a gain better understanding of the aquifer properties of the Mississippi River Valley alluvial aquifer. Alluvial-aquifer transmissivity data, compiled from 160 historical aquifer tests in the Mississippi Alluvial Plain (MAP), were correlated to mean resistivity calculated from 16,816 line-kilometers (km) of inverted resistivity soundings produced from a frequency-domain AEM survey of 95,000 km2 of the MAP. Correlated data were used to define petrophysical relations between transmissivity and mean resistivity by omitting from the correlations the aquifer-test and AEM sounding data that were separated by distances greater than 1 km and manually calibrating the relation coefficients to slug-test data. The petrophysical relation yielding the minimum residual error between simulated and slug-test data was applied to 2,364 line-km of AEM soundings in the 1,000-km2 Shellmound (Mississippi) study area to calculate hydraulic property distributions of the alluvial aquifer for use in future groundwater-flow models.

An exploration of the vascular flora of Pine City Natural Area, Monroe County, Arkansas, U.S.A., in comparison to the Mississippi Alluvial Plain in eastern Arkansas (U.S.A.)

The Mississippi Alluvial Plain (MAP) ecoregion in eastern Arkansas has had &gt;90% of its land area converted to agriculture and has historically been under-collected floristically, including the ecologically unique site, Pine City Natural Area (PCNA). Actively managed by the Arkansas Natural Heritage Commission, PCNA contains diverse loblolly pine-post oak flatwoods and some of the last known saline barrens in the MAP. Our inventory of the vascular flora in and around PCNA resulted in the documentation of 113 families, 308 genera, and 576 taxa (including infraspecific taxa); 482 (83.7%) are native, 9 (1.6%) are of state conservation concern, and 184 (31.9%) represent county records. The total taxa known for Monroe County, Arkansas, increased from 583 to 767. The most taxa-rich families included Poaceae (92 taxa), Asteraceae (65), Cyperaceae (48), and Fabaceae (42). In the surrounding ten-county region, 27 taxa (4.7%) documented at PCNA had not been vouchered, and 196 taxa (34.0%) are known from half or fewer surrounding MAP counties. We examined families in need of additional exploration in the MAP to better understand county-level floristic richness. The richest graminoid families, the Poaceae, Cyperaceae, and Juncaceae, are apparently the most understudied in the MAP, based on low relative numbers of herbarium specimens. The richness in the PCNA flora, in comparison to the broader MAP, demonstrates the need for continued floristic inventory in eastern Arkansas to locate areas of high conservation value in need of protection.

Ecological Niche Model of Bacillus cereus Group Isolates Containing a Homologue of the pXO1 Anthrax Toxin Genes Infecting Metalworkers in the United States.

While Bacillus cereus typically causes opportunistic infections in humans, within the last three decades, severe and fatal infections caused by isolates of the B. cereus group harboring anthrax toxin genes have been reported in the United States. From 1994 to 2020, seven cases of anthrax-like illness resulting from these isolates have been identified. With one exception, the cases have occurred in the Gulf States region of the United States among metalworkers. We aimed to develop an ecological niche model (ENM) to estimate a spatial area conducive to the survival of these organisms based on the presence of known human infections and environmental variables. The estimated ecological niche for B. cereus was modeled with the maximum entropy algorithm (Maxent). Environmental variables contributing most to the model were soil characteristics (cation exchange capacity, carbon content, soil pH), temperature, enhanced vegetation index (EVI), and land surface temperature (LST). Much of the suitable environments were located throughout the Gulf Coast Plain, Texas Backland Prairies, East Central Texas Plains, Edwards Plateau, Cross Timbers, Mississippi Alluvial Plain, and Central Great Plains. These findings may provide additional guidance to narrow potential risk areas to efficiently communicate messages to metalworkers and potentially identify individuals who may benefit from the anthrax vaccine.

Heavy Precipitation Impacts on Nitrogen Loading to the Gulf of Mexico in the 21st Century: Model Projections Under Future Climate Scenarios

AbstractWhile spatial heterogeneity of riverine nitrogen (N) loading is predominantly driven by the magnitude of basin‐wide anthropogenic N input, the temporal dynamics of N loading are closely related to the amount and timing of precipitation. However, existing studies do not disentangle the contributions of heavy precipitation versus non‐heavy precipitation predicted by future climate scenarios. Here, we explore the potential responses of N loading from the Mississippi Atchafalaya River Basin to precipitation changes using a well‐calibrated hydro‐ecological model and Coupled Model Intercomparison Project Phase 5 climate projections under two representative concentration pathway (RCP) scenarios. With present agricultural production and management practices, N loading could increase up to 30% by the end of the 21st century under future climate scenarios, half of which would be driven by heavy precipitation. Particularly, the RCP8.5 scenario, in which heavy precipitation and drought events become more frequent, would increase N loading disproportionately to projected increases in river discharge. N loading in spring would contribute 41% and 51% of annual N loading increase under the RCP4.5 and RCP8.5 scenarios, respectively, most of which is related to higher N yield due to increases in heavy precipitation. Anthropogenic N inputs would be increasingly susceptible to leaching loss in the Midwest and the Mississippi Alluvial Plain regions. Our results imply that future climate change alone, including more frequent and intense precipitation extremes, would increase N loading and intensify the eutrophication of the Gulf of Mexico over this coming century. More effective nutrient management interventions are needed to reverse this trend.

Airborne geophysical surveys of the lower Mississippi Valley demonstrate system-scale mapping of subsurface architecture

The Mississippi Alluvial Plain hosts one of the most prolific shallow aquifer systems in the United States but is experiencing chronic groundwater decline. The Reelfoot rift and New Madrid seismic zone underlie the region and represent an important and poorly understood seismic hazard. Despite its societal and economic importance, the shallow subsurface architecture has not been mapped with the spatial resolution needed for effective management. Here, we present airborne electromagnetic, magnetic, and radiometric observations, measured over more than 43,000 flight-line-kilometers, which collectively provide a system-scale snapshot of the entire region. We develop detailed maps of aquifer connectivity and shallow geologic structure, infer relationships between structure and groundwater age, and identify previously unseen paleochannels and shallow fault structures. This dataset demonstrates how regional-scale airborne geophysics can close a scale gap in Earth observation by providing observational data at suitable scales and resolutions to improve our understanding of subsurface structures.

Evaluating Lower Computational Burden Approaches for Calibration of Large Environmental Models.

Realistic environmental models used for decision making typically require a highly parameterized approach. Calibration of such models is computationally intensive because widely used parameter estimation approaches require individual forward runs for each parameter adjusted. These runs construct a parameter‐to‐observation sensitivity, or Jacobian, matrix used to develop candidate parameter upgrades. Parameter estimation algorithms are also commonly adversely affected by numerical noise in the calculated sensitivities within the Jacobian matrix, which can result in unnecessary parameter estimation iterations and less model‐to‐measurement fit. Ideally, approaches to reduce the computational burden of parameter estimation will also increase the signal‐to‐noise ratio related to observations influential to the parameter estimation even as the number of forward runs decrease. In this work a simultaneous increments, an iterative ensemble smoother (IES), and a randomized Jacobian approach were compared to a traditional approach that uses a full Jacobian matrix. All approaches were applied to the same model developed for decision making in the Mississippi Alluvial Plain, USA. Both the IES and randomized Jacobian approach achieved a desirable fit and similar parameter fields in many fewer forward runs than the traditional approach; in both cases the fit was obtained in fewer runs than the number of adjustable parameters. The simultaneous increments approach did not perform as well as the other methods due to inability to overcome suboptimal dropping of parameter sensitivities. This work indicates that use of highly efficient algorithms can greatly speed parameter estimation, which in turn increases calibration vetting and utility of realistic models used for decision making.

Freshwater Mussel Assemblages in the Black River, Missouri and Arkansas

Freshwater mussel assemblages show predictable variation according to large-scale biogeographic factors and stream-size gradients, but smaller-scale assemblage patterns are less well known. The goal of this study was to classify and delineate mussel assemblages of the Black River, Missouri and Arkansas, USA, along an upstream–downstream gradient and with regard to physiography and biogeographical regions. We analyzed mussel assemblages using nonmetric multidimensional scaling and indicator-species analysis. Our results yielded three assemblage groupings distributed along the upstream–downstream gradient and thereby considered aquatic ecological systems (100–1,000 km2) in a hierarchical spatial classification scheme. These groupings also support previously proposed biogeographical differences for mussels and fishes between the Ozark Highlands and Mississippi Alluvial Plain physiographic regions. Each group was characterized by 2–13 indicator species. Our demonstration of small-scale patterns of mussel assemblage change will be useful for conservation planning and for a better understanding of mussel assemblage dynamics.

Inventory and Assemblage Classification of the Freshwater Mussels (Mollusca: Unionidae) of the Strawberry River, Arkansas, USA, with Implications for Conservation Planning

Spatial hierarchical approaches to classify freshwater systems can add to our understanding of biogeographical patterns and can be used for biodiversity conservation planning. The Strawberry River is located primarily in the Ozark Highlands Central Plateau of north central Arkansas, USA, with a small downstream portion in the Mississippi Alluvial Plain and has been designated an Extraordinary Resource Water, an Ecologically Sensitive Water Body, and a Natural Scenic Waterway. The goals of this study were to document Strawberry River, Arkansas freshwater mussels to aid in conservation planning. Our first objective was to inventory freshwater mussel species in the Strawberry River. Our second objective was to use this stream-wide dataset to classify the freshwater mussel assemblages. We used unpublished survey data of 59 sites distributed from the headwaters to the mouth to inventory species occurrence and abundance, classified mussel assemblages using non-metric multi-dimensional scaling (NMS), and conducted indicator species analysis on resulting assemblages. We observed 39 taxa across the 59 survey sites including two S1, five S2, 16 S3, 11 S4, four S5, and one state non-ranked conservation rank species. Furthermore, our assemblage NMS revealed two distinct freshwater mussel assemblages roughly organized by an upstream (Sites 1–31) to downstream (Sites 32–59) gradient. There were five upstream indicator species and 13 downstream indicator species. This study provides a case study on using existing datasets with NMS and indicator species analyses to classify mussel assemblages and adds to our understanding of freshwater mussel fauna classification at smaller spatial scales. Both NMS and indicator species outcomes can aid in conservation planning for freshwater mussels.

Farmer Behavior Under Groundwater Management Scenarios: Implications for Groundwater Conservation in the Mississippi Alluvial Plain

Concern about sustained availability of fresh groundwater for agricultural use in the Mississippi Alluvial Plain (MAP) mounts as groundwater levels decline. We evaluate the elasticities of demand for groundwater and other agricultural inputs, as well as the overall and output-specific economies of scale for four major irrigated commodities (rice, corn, soybeans, and cotton) in the MAP region. Additionally, we investigate the impacts of two groundwater management policy scenarios, including increasing pumping cost and groundwater use restrictions, on irrigation behavior. The results show price elasticity of demand for groundwater to be [Formula: see text], indicating that it is inelastic, and an increased cost of pumping will not significantly decrease the relative demand for groundwater in the region. Even with policy scenarios that either increase the costs of pumping significantly or restrict groundwater use in the region, groundwater demand still appears to be inelastic. We also document significant overall economies of scale in the region. Our findings have implications for potential policy options aimed at reducing groundwater use. Efficient management practices are important to increase aquifer recharge, and considering human behavior via economic analysis will improve projections of groundwater availability in the MAP region.

Estimating Near-Surface Rigidity from Low-Frequency Noise Using Collocated Pressure and Horizontal Seismic Data

ABSTRACTWe propose a single-station approach to estimate near-surface elastic structure using collocated pressure and seismic instruments. Our main result in this study is near-surface rigidity (shear modulus) structure at 784 EarthScope Transportable Array (TA) stations in operation from mid-2011 to the end of 2018 using coherent horizontal seismic and pressure signals at 0.02 Hz. We isolate time periods for which surface pressure change is the dominant excitation source for seismic signals by searching for data windows with large pressure variations and high-seismic-pressure coherence. We emphasize the importance of using horizontal seismic components for two reasons: first, horizontal seismic signals are significantly higher than vertical signals at 0.02 Hz due to ground tilt, and second, we can analytically compute the predicted horizontal signals without an assumption of atmospheric pressure wavespeed (which is required for predicting the vertical excitation). Sensitivity kernels from 0.01 to 0.05 Hz show that this pressure–seismic coupling is mostly dependent on rigidity shallower than 50 or 100 m. Our estimates of shallow elastic structure show good spatial agreement with large-scale surface geological features. For instance, stations in the Appalachian Mountains mostly have high rigidity, whereas low-rigidity sites dominate the Mississippi Alluvial Plain. Because of the lack of measured velocity profiles, we quantitatively validate our approach by comparing with VS30 models that are based on proxies such as topographic slopes and large-scale surface geology. We estimate near-surface rigidity at 784 TA stations, where these locations have no prior structure information. Our method provides independent information for seismic hazard studies.