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At least 1,207 records · Page 67Linked to original sources

USGS2021

Terrestrial ecological risk analysis via dietary exposure at uranium mine sites in the Grand Canyon watershed (Arizona, USA)

The U.S. Department of the Interior recently included uranium (U) on a list of mineral commodities that are considered critical to economic and national security. The uses of U for commercial and residential energy production, defense applications, medical device technologies, and energy generation for space vehicles and satellites are known, but the environmental impacts of uranium extraction are not always well quantified. We conducted a screening-level ecological risk analysis based on exposure to mining-related elements via diets and incidental soil ingestion for terrestrial biota to provide context to chemical characterization and exposures at breccia pipe U mines in northern Arizona. Relative risks, calculated as hazard quotients (HQs), were generally low for all biological receptor models. Our models screened for risk to omnivores and insectivores (HQs>1) but not herbivores and carnivores. Uranium was not the driver of ecological risk; arsenic, cadmium, copper, and zinc were of concern for biota consuming ground-dwelling invertebrates. Invertebrate species composition should be considered when applying these models to other mining locations or future sampling at the breccia pipe mine sites. Dietary concentration thresholds (DCTs) were also calculated to understand food concentrations that may lead to ecological risk. The DCTs indicated that critical concentrations were not approached in our model scenarios, as evident in the very low HQs for most models. The DCTs may be used by natural resource and land managers as well as mine operators to screen or monitor for potential risk to terrestrial receptors as mine sites are developed and remediated in the future.

Arizona↗

USGS1990

The evolution of sex-change mechanisms in fishes

Five distinct sex-change mechanisms are identified among sequentially hermaphroditic fishes based on socio-ecological characteristics. The primary determinants of the sex-change mechanisms appear to be social organization and mating system, which in turn depend on resource distribution in space and time. The ability of a single individual to control all mating in the social unit, which is related to the size of the social unit, differentiates three suppression mechanisms from two induction mechanisms. Sex-change suppression, which is characteristic of species with small group size and rigid dominance hierarchies, refers to inevitable sex change in the absence of group dominance. Ability to migrate between resource patches differentiates protogynous suppression (e.g. in Labroides dimidiatus ) from protandrous suppression (e.g. in Amphiprion spp.). Early sex change appears to have evolved from protogynous suppression under special conditions involving the loss of mating control by a single dominant individual in certain species (e.g. Centropyge spp. ). Sex-change induction, which is characteristic of species with large social groups lacking rigid dominance hierarchies, refers to the requirement that sex change must be induced by specific characteristics of (or changes in) the social group, regardless of dominance status. Ability to distinguish sex, or its importance, differentiates sex-ratio induction (e.g. Anthias squamipinnis ) from size-ratio induction (e.g. Thalassoma spp.). Alternative models account for the possibility that all cases of sex change require stimulation from smaller conspecifics (universal induction-inhibition model) or that all fish have the genetic capacity to switch mechanisms, depending on changing ecological conditions and resulting changes in mating system (behavioral-scaling model). Neurophysiological models suggest that induction mechanisms, which require at least two categories of environmental stimuli, may have evolved from the simpler suppression mechanisms, which require only one kind of input from the environment.

Environmental Biology of Fishes↗

USGS1979

Mathematics and mallard management

Waterfowl managers can effectively use simple population models to aid in making management decisions. We present a basic model of the change in population size as related to survival and recruitment. A management technique designed to increase survival of mallards (Anas platyrhynchos) by limiting harvest on the Chippewa National Forest, Minnesota, is used to illustrate the application of models in decision making. The analysis suggests that the management technique would be of limited effectiveness. In a 2nd example, the change in mallard population in central North Dakota is related to implementing programs to create dense nesting cover with or without supplementary predator control. The analysis suggests that large tracts of land would be required to achieve a hypothetical management objective of increasing harvest by 50% while maintaining a stable population. Less land would be required if predator reduction were used in combination with cover management, but questions about effectiveness and ecological implications of large scale predator reduction remain unresolved. The use of models as a guide to planning research responsive to the needs of management is illustrated.

Journal of Wildlife Management↗

USGS2016

Climate change implications for tropical islands: Interpolating and interpreting statistically downscaled GCM projections for management and planning

The potential ecological and economic effects of climate change for tropical islands were studied using output from 12 statistically downscaled general circulation models (GCMs) taking Puerto Rico as a test case. Two model selection/model averaging strategies were used: the average of all available GCMs and the average of the models that are able to reproduce the observed large-scale dynamics that control precipitation over the Caribbean. Five island-wide and multidecadal averages of daily precipitation and temperature were estimated by way of a climatology-informed interpolation of the site-specific downscaled climate model output. Annual cooling degree-days (CDD) were calculated as a proxy index for air-conditioning energy demand, and two measures of annual no-rainfall days were used as drought indices. Holdridge life zone classification was used to map the possible ecological effects of climate change. Precipitation is predicted to decline in both model ensembles, but the decrease was more severe in the “regionally consistent” models. The precipitation declines cause gradual and linear increases in drought intensity and extremes. The warming from the 1960–90 period to the 2071–99 period was 4.6°–9°C depending on the global emission scenarios and location. This warming may cause increases in CDD, and consequently increasing energy demands. Life zones may shift from wetter to drier zones with the possibility of losing most, if not all, of the subtropical rain forests and extinction risks to rain forest specialists or obligates.

Puerto Rico↗

USGS2014

Predicting foundation bunchgrass species abundances: Model-assisted decision-making in protected-area sagebrush steppe

Foundation species are structurally dominant members of ecological communities that can stabilize ecological processes and influence resilience to disturbance and resistance to invasion. Being common, they are often overlooked for conservation but are increasingly threatened from land use change, biological invasions, and over-exploitation. The pattern of foundation species abundances over space and time may be used to guide decision-making, particularly in protected areas for which they are iconic. We used ordinal logistic regression to identify the important environmental influences on the abundance patterns of bluebunch wheatgrass ( Pseudoroegneria spicata ), Thurber's needlegrass ( Achnatherum thurberianum ), and Sandberg bluegrass ( Poa secunda ) in protected-area sagebrush steppe. We then predicted bunchgrass abundances along gradients of topography, disturbance, and invasive annual grass abundance. We used model predictions to prioritize the landscape for implementation of a management and restoration decision-support tool. Models were fit to categorical estimates of grass cover obtained from an extensive ground-based monitoring dataset. We found that remnant stands of abundant wheatgrass and bluegrass were associated with steep north-facing slopes in higher and more remote portions of the landscape outside of recently burned areas where invasive annual grasses were less abundant. These areas represented only 25% of the landscape and were prioritized for protection efforts. Needlegrass was associated with south-facing slopes, but in low abundance and in association with invasive cheatgrass ( Bromus tectorum ). Abundances of all three species were strongly negatively correlated with occurrence of another invasive annual grass, medusahead ( Taeniatherum caput-medusae ). The rarity of priority bunchgrass stands underscored the extent of degradation and the need for prioritization. We found no evidence that insularity reduced invasibility; annual grass invasion represents a serious threat to protected-area bunchgrass communities. Our study area was entirely within the Wyoming big sagebrush ecological zone, understood to have inherently low resilience to disturbance and resistance to weed invasion. However, our study revealed important variation in abundance of the foundation species associated with resilience and resistance along the topographic-soil moisture gradient within this zone, providing an important foothold for conservation decision-making in these steppe ecosystems. We found the foundation species focus a parsimonious strategy linking monitoring to decision-making via biogeographic modeling.

Ecosphere↗

USGS2002

Modeling fish community dynamics in Florida Everglades: Role of temperature variation

Temperature variation is an important factor in Everglade wetlands ecology. A temperature fluctuation from 17°C to 32°C recorded in the Everglades may have significant impact on fish dynamics. The short life cycles of some of Everglade fishes has rendered this temperature variation to have even more impacts on the ecosystem. Fish population dynamic models, which do not explicitly consider seasonal oscillations in temperature, may fail to describe the details of such a population. Hence, a model for fish in freshwater marshes of the Florida Everglades that explicitly incorporates seasonal temperature variations is developed. The model's main objective is to assess the temporal pattern of fish population and densities through time subject to temperature variations. Fish population is divided into 2 functional groups (FGs) consisting of small fishes; each group is subdivided into 5-day age classes during their life cycles. Many governing sub-modules are set directly or indirectly to be temperature dependent. Growth, fecundity, prey availability, consumption rates and mortality are examples. Several mortality sub-modules are introduced in the model, of which starvation mortality is set to be proportional to the ratio of prey needed to prey available at that particular time step. As part of the calibration process, the model is run for 50 years to ensure that fish densities do not go to extinction, while the simulation period is about 8 years. The model shows that the temperature dependent starvation mortality is an important factor that influences fish population densities. It also shows high fish population densities at some temperature ranges when this consumption need is minimum. Several sensitivity analyses involving variations in temperature terms, food resources and water levels are conducted to ascertain the relative importance of temperature dependence terms.

Water Science and Technology↗

USGS2011

Occupancy and abundance of wintering birds in a dynamic agricultural landscape

Assessing wildlife management action requires monitoring populations, and abundance often is the parameter monitored. Recent methodological advances have enabled estimation of mean abundance within a habitat using presence–absence or count data obtained via repeated visits to a sample of sites. These methods assume populations are closed and intuitively assume habitats within sites change little during a field season. However, many habitats are highly variable over short periods. We developed a variation of existing occupancy and abundance models that allows for extreme spatio‐temporal differences in habitat, and resulting changes in wildlife abundance, among sites and among visits to a site within a field season. We conducted our study in sugarcane habitat within the Everglades Agricultural Area southeast of Lake Okeechobee in south Florida. We counted wintering birds, primarily passerines, within 245 sites usually 5 times at each site during December 2006–March 2007. We estimated occupancy and mean abundance of birds in 6 vegetation states during the sugarcane harvest and allowed these parameters to vary temporally or spatially within a vegetation state. Occupancy and mean abundance of the common yellowthroat ( Geothlypis trichas ) was affected by structure of sugarcane and uncultivated edge vegetation (occupancy = 1.00 [ = 0.96–1.00] and mean abundance = 7.9 [ = 3.2–19.5] in tall sugarcane with tall edge vegetation versus 0.20 [ = 0.04–0.71] and 0.22 [ = 0.04–1.2], respectively, in short sugarcane with short edge vegetation in one half of the study area). Occupancy and mean abundance of palm warblers ( Dendroica palmarum ) were constant (occupancy = 1.00, = 0.69–1.00; mean abundance = 18, = 1–270). Our model may enable wildlife managers to assess rigorously effects of future edge habitat management on avian distribution and abundance within agricultural landscapes during winter or the breeding season. The model may also help wildlife managers make similar management decisions involving other dynamic habitats such as wetlands, prairies, and even forested areas if forest management or fires occur during the field season.

Florida↗

USGS2023

Sediment sources and connectivity linked to hydrologic pathways and geomorphic processes: A conceptual model to specify sediment sources and pathways through space and time

Sediment connectivity is a conceptualization for the transfer and storage of sediment among different geomorphic compartments across upland landscapes and channel networks. Sediment connectivity and dysconnectivity are linked to the water cycle and hydrologic systems with the associated multiscale interactions with climate, soil, topography, ecology, and landuse/landcover under natural variability and human intervention. We review current sediment connectivity and modeling approaches evaluating and quantifying water and sediment transfer in catchment systems. Many studies highlight the interaction between sediment and water in defining landscape connectivity, but many efforts to quantify and/or simulate sediment connectivity rely on the topographic/structural controls on sediment erosion and delivery. More recent modeling efforts integrate functional and structural connectivity to capture hydrologic properties influencing sediment delivery. Though the recent modeling development is encouraging, a comprehensive sediment connectivity framework, which integrates geomorphic and hydrologic processes across spatiotemporal scales, has not yet been accomplished. Such an effort requires understanding the hydrologic and geomorphic processes that control sediment source, storage, and transport at different spatiotemporal scales and across various geophysical conditions. We propose a path for developing this new understanding through an integrated hydrologic and sediment connectivity conceptual model that broadly categorizes dominant processes and patterns relevant to understanding sediment flux dynamics. The conceptual model describes hydrologic–sediment connectivity regimes through spatial-temporal feedback between hydrologic processes and geomorphic drivers. We propose that in combining hydrologic and sediment connectivity into a single conceptual model, patterns emerge such that catchments will exist in a single characteristic behavior at a particular instance, which would shift with space and time, and with landscape disturbances. Using the conceptual model as a “thinking” tool, we extract case studies from a multidisciplinary literature review—from hydrology, geomorphology, biogeochemistry, and watershed modeling to remote-sensing technology—that correspond to each of the dominant hydrologic–sediment connectivity regimes. Sediment and water interactions in real-world examples through various observational and modeling techniques illustrate the advancements in the spatial and temporal scales of landscape connectivity observations and simulations. The conceptual model and case studies provide a foundation for advancing the understanding and predictive capability of watershed sediment processes at multiple spatiotemporal scales. Plain language summary: Soil erosion and movement across the landscape are closely linked to rain events and flow pathways. Landscape connectivity is a way to consider how soil erosion from different parts of the landscape is connected to the streams. We explore where soil erosion occurs and how eroded soil moves across the landscape through the interaction with rainfall and drainage. The comprehensive understanding of sediment connectivity and its dependence on rainfall characteristics and watershed hydrology may help to inform the effective distribution of conservation funds and management actions to address water pollution from excess sediment.

Frontiers in Water↗

USGS2015

Encapsulating model complexity and landscape-scale analyses of state-and-transition simulation models: an application of ecoinformatics and juniper encroachment in sagebrush steppe ecosystems

State-and-transition simulation modeling relies on knowledge of vegetation composition and structure (states) that describe community conditions, mechanistic feedbacks such as fire that can affect vegetation establishment, and ecological processes that drive community conditions as well as the transitions between these states. However, as the need for modeling larger and more complex landscapes increase, a more advanced awareness of computing resources becomes essential. The objectives of this study include identifying challenges of executing state-and-transition simulation models, identifying common bottlenecks of computing resources, developing a workflow and software that enable parallel processing of Monte Carlo simulations, and identifying the advantages and disadvantages of different computing resources. To address these objectives, this study used the ApexRMS® SyncroSim software and embarrassingly parallel tasks of Monte Carlo simulations on a single multicore computer and on distributed computing systems. The results demonstrated that state-and-transition simulation models scale best in distributed computing environments, such as high-throughput and high-performance computing, because these environments disseminate the workloads across many compute nodes, thereby supporting analysis of larger landscapes, higher spatial resolution vegetation products, and more complex models. Using a case study and five different computing environments, the top result (high-throughput computing versus serial computations) indicated an approximate 96.6% decrease of computing time. With a single, multicore compute node (bottom result), the computing time indicated an 81.8% decrease relative to using serial computations. These results provide insight into the tradeoffs of using different computing resources when research necessitates advanced integration of ecoinformatics incorporating large and complicated data inputs and models. - See more at: http://aimspress.com/aimses/ch/reader/view_abstract.aspx?file_no=Environ2015030&flag=1#sthash.p1XKDtF8.dpuf

AIMS Environmental Science↗

USGS2013

Inferring the relative resilience of alternative states

Ecological systems may occur in alternative states that differ in ecological structures, functions and processes. Resilience is the measure of disturbance an ecological system can absorb before changing states. However, how the intrinsic structures and processes of systems that characterize their states affects their resilience remains unclear. We analyzed time series of phytoplankton communities at three sites in a floodplain in central Spain to assess the dominant frequencies or “temporal scales” in community dynamics and compared the patterns between a wet and a dry alternative state. The identified frequencies and cross-scale structures are expected to arise from positive feedbacks that are thought to reinforce processes in alternative states of ecological systems and regulate emergent phenomena such as resilience. Our analyses show a higher species richness and diversity but lower evenness in the dry state. Time series modeling revealed a decrease in the importance of short-term variability in the communities, suggesting that community dynamics slowed down in the dry relative to the wet state. The number of temporal scales at which community dynamics manifested, and the explanatory power of time series models, was lower in the dry state. The higher diversity, reduced number of temporal scales and the lower explanatory power of time series models suggest that species dynamics tended to be more stochastic in the dry state. From a resilience perspective our results highlight a paradox: increasing species richness may not necessarily enhance resilience. The loss of cross-scale structure (i.e. the lower number of temporal scales) in community dynamics across sites suggests that resilience erodes during drought. Phytoplankton communities in the dry state are therefore likely less resilient than in the wet state. Our case study demonstrates the potential of time series modeling to assess attributes that mediate resilience. The approach is useful for assessing resilience of alternative states across ecological and other complex systems.

Las Tablas de Daimiel floodplain wetland↗

USGS2023

Models of underlying autotrophic biomass dynamics fit to daily river ecosystem productivity estimates improve understanding of ecosystem disturbance and resilience

Directly observing autotrophic biomass at ecologically relevant frequencies is difficult in many ecosystems, hampering our ability to predict productivity through time. Since disturbances can impart distinct reductions in river productivity through time by modifying underlying standing stocks of biomass, mechanistic models fit to productivity time series can infer underlying biomass dynamics. We incorporated biomass dynamics into a river ecosystem productivity model for six rivers to identify disturbance flow thresholds and understand the resilience of primary producers. The magnitude of flood necessary to disturb biomass and thereby reduce ecosystem productivity was consistently lower than the more commonly used disturbance flow threshold of the flood magnitude necessary to mobilize river bed sediment. The estimated daily maximum percent increase in biomass (a proxy for resilience) ranged from 5% to 42% across rivers. Our latent biomass model improves understanding of disturbance thresholds and recovery patterns of autotrophic biomass within river ecosystems.

Ecology Letters↗

USGS2016

The automated reference toolset: A soil-geomorphic ecological potential matching algorithm

Ecological inventory and monitoring data need referential context for interpretation. Identification of appropriate reference areas of similar ecological potential for site comparison is demonstrated using a newly developed automated reference toolset (ART). Foundational to identification of reference areas was a soil map of particle size in the control section (PSCS), a theme in US Soil Taxonomy. A 30-m resolution PSCS map of the Colorado Plateau (366,000 km 2 ) was created by interpolating ∼5000 field soil observations using a random forest model and a suite of raster environmental spatial layers representing topography, climate, general ecological community, and satellite imagery ratios. The PSCS map had overall out of bag accuracy of 61.8% (Kappa of 0.54, p < 0.0001), and an independent validation accuracy of 93.2% at a set of 356 field plots along the southern edge of Canyonlands National Park, Utah. The ART process was also tested at these plots, and matched plots with the same ecological sites (ESs) 67% of the time where sites fell within 2-km buffers of each other. These results show that the PSCS and ART have strong application for ecological monitoring and sampling design, as well as assessing impacts of disturbance and land management action using an ecological potential framework. Results also demonstrate that PSCS could be a key mapping layer for the USDA-NRCS provisional ES development initiative.

Soil Science Society of America Journal↗

USGS2011

Estimating occupancy dynamics in an anuran assemblage from Louisiana, USA

Effective monitoring programs are designed to track changes in the distribution, occurrence, and abundance of species. We developed an extension of Royle and Kéry's (2007) single species model to estimate simultaneously temporal changes in probabilities of detection, occupancy, colonization, extinction, and species turnover using data on calling anuran amphibians, collected from 2002 to 2006 in the Lower Mississippi Alluvial Valley of Louisiana, USA. During our 5-year study, estimates of occurrence probabilities declined for all 12 species detected. These declines occurred primarily in conjunction with variation in estimates of local extinction probabilities (cajun chorus frog [ Pseudacris fouquettei ], spring peeper [ P. crucifer ], northern cricket frog [ Acris crepitans ], Cope's gray treefrog [ Hyla chrysoscelis ], green treefrog [ H. cinerea ], squirrel treefrog [ H. squirella ], southern leopard frog [ Lithobates sphenocephalus ], bronze frog [ L. clamitans ], American bullfrog [ L. catesbeianus ], and Fowler's toad [ Anaxyrus fowleri ]). For 2 species (eastern narrowmouthed toad [ Gastrophryne carolinensis ] and Gulf Coast toad [ Incilius nebulifer ]), declines in occupancy appeared to be a consequence of both increased local extinction and decreased colonization events. The eastern narrow-mouthed toad experienced a 2.5-fold increase in estimates of occupancy in 2004, possibly because of the high amount of rainfall received during that year, along with a decrease in extinction and increase in colonization of new sites between 2003 and 2004. Our model can be incorporated into monitoring programs to estimate simultaneously the occupancy dynamics for multiple species that show similar responses to ecological conditions. It will likely be an important asset for those monitoring programs that employ the same methods to sample assemblages of ecologically similar species, including those that are rare. By combining information from multiple species to decrease the variance on estimates of individual species, our results are advantageous compared to single-species models. This feature enables managers and researchers to use an entire community, rather than just one species, as an ecological indicator in monitoring programs.

Louisiana↗

USGS2014

Sea Level Affecting Marshes Model (SLAMM) ‐ New functionality for predicting changes in distribution of submerged aquatic vegetation in response to sea level rise

Introduction Submerged aquatic vegetation (SAV) is an ecologically important habitat world‐wide. In Pacific Northwest (PNW) estuaries, SAV in the lower intertidal and shallow subtidal habitats are dominated by the native seagrass, Zostera marina Linnaeus, 1753. Within this report, SAV and seagrass refer to Z. marina seagrass beds in PNW estuaries. Z. marina provides important habitat for juvenile salmon, dungeness crabs, migratory shore birds, and benthic assemblages (e.g., Philips, 1984; Williamson, 2006; Ferraro and Cole, 2007; Shaughnessy et al., 2012). Z. marina typically occurs in a narrow depth range. For example, in Oregon estuaries Zostera marina primarily occurs within the depth range of ‐1 to +1 m relative to Mean Lower Low Water (MLLW) (Young et al. 2012). Because of their narrow depth range, the distribution of these seagrass beds are potentially vulnerable to sea level rise (SLR) through increased water depths and associated reductions in underwater light levels, alterations in tidal variations, altered water movement and wave action, and increased seawater intrusion (Short and Neckles, 1999). The “Sea‐Level Affecting Marshes Model” (SLAMM) is a moderate resolution model used to predict the effects of sea level rise on marsh habitats (Craft et al. 2009). SLAMM has been used extensively on both the west coast (e.g., Glick et al., 2007) and east coast (e.g., Geselbracht et al., 2011) of the United States to evaluate potential changes in the distribution and extent of tidal marsh habitats. However, a limitation of the current version of SLAMM, (Version 6.2) is that it lacks the ability to model distribution changes in seagrass habitat resulting from sea level rise. Because of the ecological importance of SAV habitats, U.S. EPA, USGS, and USDA partnered with Warren Pinnacle Consulting to enhance the SLAMM modeling software to include new functionality in order to predict changes in Zostera marina distribution within Pacific Northwest estuaries in response to sea level rise. Specifically, the objective was to develop a SAV model that used generally available GIS data and parameters that were predictive and that could be customized for other estuaries that have GIS layers of existing SAV distribution. This report describes the procedure used to develop the SAV model for the Yaquina Bay Estuary, Oregon, appends a statistical script based on the open source R software to generate a similar SAV model for other estuaries that have data layers of existing SAV, and describes how to incorporate the model coefficients from the site‐specific SAV model into SLAMM to predict the effects of sea level rise on Zostera marina distributions. To demonstrate the applicability of the R tools, we utilize them to develop model coefficients for Willapa Bay, Washington using site‐specific SAV data.

Oregon↗

USGS2023

Effect of straying, reproductive strategies, and ocean distribution on the structure of American shad populations

The use of species distribution models has proliferated, providing insights for sustainable management of migratory species in a globally changing environment. However, many of these models are based on statistical relationships developed from historical conditions that may not perform well under changing or even analogous conditions caused by climate change. In this paper, we used a mechanistic species distribution model called GR3D (Global Repositioning Dynamics for Diadromous Fish Distribution) to examine the integrated dynamics of American shad ( Alosa sapidissima ) populations across their native range along the Eastern U.S. coast, where the species demonstrates latitudinal variations in life histories and reproductive strategies. The initial design of the model was adapted to incorporate region-specific parameterization to fit the species ecology. Then, a sensitivity analysis was performed to test the influences of uncertain processes regarding American shad distribution at sea, straying and reproduction on key characteristics of the species distribution. The sensitivity analysis showed the influence of the Allee effect (i.e., “depensatory” process) and the homing rate (i.e., fidelity to the breeding sites) on the probability of presence and abundances among catchments and metapopulations estimated by the model. Contrary to the homing rate, the distance of straying did not change the estimated number of metapopulations or abundances. Homing strength, however, was quite influential. The integration of complex migration patterns during the marine phase (i.e., wintering and summering offshore areas) provided more likely estimates of the species' overall distribution. Overall, our study illustrated the utility of incorporating factors governing the large-scale distribution of migratory species to improve local management.

Ecosphere↗

USGS2014

Modeling future scenarios of light attenuation and potential seagrass success in a eutrophic estuary

Estuarine eutrophication has led to numerous ecological changes, including loss of seagrass beds. One potential cause of these losses is a reduction in light availability due to increased attenuation by phytoplankton. Future sea level rise will also tend to reduce light penetration and modify seagrass habitat. In the present study, we integrate a spectral irradiance model into a biogeochemical model coupled to the Regional Ocean Model System (ROMS). It is linked to a bio-optical seagrass model to assess potential seagrass habitat in a eutrophic estuary under future nitrate loading and sea-level rise scenarios. The model was applied to West Falmouth Harbor, a shallow estuary located on Cape Cod (Massachusetts) where nitrate from groundwater has led to eutrophication and seagrass loss in landward portions of the estuary. Measurements of chlorophyll, turbidity, light attenuation, and seagrass coverage were used to assess the model accuracy. Mean chlorophyll based on uncalibrated in-situ fluorometry varied from 28 μg L −1 at the landward-most site to 6.5 μg L −1 at the seaward site, while light attenuation ranged from 0.86 to 0.45 m -1 . The model reproduced the spatial variability in chlorophyll and light attenuation with RMS errors of 3.72 μg L −1 and 0.07 m -1 respectively. Scenarios of future nitrate reduction and sea-level rise suggest an improvement in light climate in the landward basin with a 75% reduction in nitrate loading. This coupled model may be useful to assess habitat availability changes due to eutrophication and sediment resuspension and fully considers spatial variability on the tidal timescale.

Massachusetts↗

USGS2023

Monkeypox virus in animals: Current knowledge of viral transmission and pathogenesis in wild animal reservoirs and captive animal models

Mpox, formerly called monkeypox, is now the most serious orthopoxvirus (OPXV) infection in humans. This zoonotic disease has been gradually re-emerging in humans with an increasing frequency of cases found in endemic areas, as well as an escalating frequency and size of epidemics outside of endemic areas in Africa. Currently, the largest known mpox epidemic is spreading throughout the world, with over 85,650 cases to date, mostly in Europe and North America. These increased endemic cases and epidemics are likely driven primarily by decreasing global immunity to OPXVs, along with other possible causes. The current unprecedented global outbreak of mpox has demonstrated higher numbers of human cases and greater human-to-human transmission than previously documented, necessitating an urgent need to better understand this disease in humans and animals. Monkeypox virus (MPXV) infections in animals, both naturally occurring and experimental, have provided critical information about the routes of transmission; the viral pathogenicity factors; the methods of control, such as vaccination and antivirals; the disease ecology in reservoir host species; and the conservation impacts on wildlife species. This review briefly described the epidemiology and transmission of MPXV between animals and humans and summarizes past studies on the ecology of MPXV in wild animals and experimental studies in captive animal models, with a focus on how animal infections have informed knowledge concerning various aspects of this pathogen. Knowledge gaps were highlighted in areas where future research, both in captive and free-ranging animals, could inform efforts to understand and control this disease in both humans and animals.

Viruses↗

USGS2022

Potential cheatgrass abundance within lightly invaded areas of the Great Basin

Context Anticipating where an invasive species could become abundant can help guide prevention and control efforts aimed at reducing invasion impacts. Information on potential abundance can be combined with information on the current status of an invasion to guide management towards currently uninvaded locations where the threat of invasion is high. Objectives We aimed to support management by developing predictive maps of potential cover for cheatgrass ( Bromus tectorum ), a problematic invader that can transform plant communities. We integrated our predictions of potential abundance with mapped estimates of current cover to quantify invasion potential within lightly invaded areas. Methods We used quantile regression to model cheatgrass abundance as a function of climate, weather, and disturbance, treating outputs as low to high invasion scenarios. We developed a species-specific set of covariates and validated model performance using spatially and temporally independent data. Results Potential cheatgrass abundance was higher in areas that had burned, at low elevations, and when fall germination conditions were more favorable. Our results highlight the extensive areas across the Great Basin where cheatgrass abundance could increase to levels that can alter fire behavior and cause other ecological impacts. Conclusions We predict potential cheatgrass abundance to quantify relative invasion risk. Our model results provide high and low scenarios of cheatgrass abundance to guide resource allocation and planning efforts across shrubland ecosystems of the Great Basin that remain relatively uninvaded. Combining information on an invasive species’ current and potential abundance can yield spatial predictions to guide resource allocation and management action.

California, Idaho, Nevada, Oregon, Utah↗