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At least 613 records · Page 34Linked to original sources
USGS2013

An integrated multi-criteria scenario evaluation web tool for participatory land-use planning in urbanized areas: The Ecosystem Portfolio Model

Land-use land-cover change is one of the most important and direct drivers of changes in ecosystem functions and services. Given the complexity of the decision-making, there is a need for Internet-based decision support systems with scenario evaluation capabilities to help planners, resource managers and communities visualize, compare and consider trade-offs among the many values at stake in land use planning. This article presents details on an Ecosystem Portfolio Model (EPM) prototype that integrates ecological, socio-economic information and associated values of relevance to decision-makers and stakeholders. The EPM uses a multi-criteria scenario evaluation framework, Geographic Information Systems (GIS) analysis and spatially-explicit land-use/land-cover change-sensitive models to characterize changes in important land-cover related ecosystem values related to ecosystem services and functions, land parcel prices, and community quality-of-life (QoL) metrics. Parameters in the underlying models can be modified through the interface, allowing users in a facilitated group setting to explore simultaneously issues of scientific uncertainty and divergence in the preferences of stakeholders. One application of the South Florida EPM prototype reported in this article shows the modeled changes (which are significant) in aggregate ecological value, landscape patterns and fragmentation, biodiversity potential and ecological restoration potential for current land uses compared to the 2050 land-use scenario. Ongoing refinements to EPM, and future work especially in regard to modifiable sea level rise scenarios are also discussed.

Environmental Modelling and Software↗
USGS2024

Climate change amplifies ongoing declines in sagebrush ecological integrity

Understanding how climate change will contribute to ongoing declines in sagebrush ecological integrity is critical for informing natural resource management, yet complicated by interactions with wildfire and biological invasions. Here, we assessed potential future changes in sagebrush ecological integrity under a range of scenarios using an individual plant-based simulation model, integrated with remotely sensed estimates of current sagebrush ecological integrity. The simulation model allowed us to estimate how climate change, wildfire, and invasive annuals interact to alter the potential abundance of key plant functional types that influence sagebrush ecological integrity: sagebrush, perennial grasses, and annual grasses. Our results suggest that climate driven reductions in sagebrush ecological integrity may occur over broader areas than increases in sagebrush ecological integrity. Declines in sagebrush ecological integrity were most likely in hot and dry regions while increases were more likely in cool and wet regions. Increases in wildfire probability, largely driven by increasing invasive annual grass abundance, resulted in projected declines in sagebrush abundance and sagebrush ecological integrity in some regions, particularly the Great Basin. The most common projected transitions in sagebrush habitat classification were declines from Core Sagebrush Area to Growth Opportunity Area and from Growth Opportunity Area to Other Rangeland Area. Responses varied considerably across projections from different global climate models, highlighting the importance of climate uncertainty. However, our projections tended to be robust in areas that currently have the highest sagebrush ecological integrity. Our results provide a long-term perspective on the vulnerability of sagebrush ecosystems to climate change and may inform geographic prioritization of conservation and restoration investments. These results suggest that ongoing threats, such as the continued invasion by annual grasses and increased wildfire frequency, are likely to be amplified by climate change, and imply that the current imbalance between capacity for conservation to address threats to sagebrush will grow as the climate warms.

Rangeland Ecology and Management↗
USGS2022

Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture–recapture movement model

Animal movement is a fundamental ecological process affecting the survival and reproduction of individuals, the structure of populations, and the dynamics of communities. Methods to quantify animal movement and spatiotemporal abundances, however, are generally separate and therefore omit linkages between individual-level and population-level processes. We describe an integrated spatial capture–recapture (SCR) movement model to jointly estimate (1) the number and distribution of individuals in a defined spatial region and (2) movement of those individuals through time. We applied our model to a study of polar bears ( Ursus maritimus ) in a 28,125 km 2 survey area of the eastern Chukchi Sea, USA in 2015 that incorporated capture–recapture and telemetry data. In simulation studies, the model provided unbiased estimates of movement, abundance, and detection parameters using a bivariate normal random walk and correlated random walk movement process. Our case study provided detailed evidence of directional movement persistence for both male and female bears, where individuals regularly traversed areas larger than the survey area during the 36-day study period. Scaling from individual- to population-level inferences, we found that densities varied from <0.75 bears/625 km 2 grid cell/day in nearshore cells to 1.6–2.5 bears/grid cell/day for cells surrounded by sea ice. Daily abundance estimates ranged from 53 to 69 bears, with no trend across days. The cumulative number of unique bears that used the survey area increased through time due to movements into and out of the area, resulting in an estimated 171 individuals using the survey area during the study (95% credible interval 124–250). Abundance estimates were similar to a previous multiyear integrated population model using capture–recapture and telemetry data (2008–2016; Regehr et al., Scientific Reports 8:16780, 2018). Overall, the SCR–movement model successfully quantified both individual- and population-level space use, including the effects of landscape characteristics on movement, abundance, and detection, while linking the movement and abundance processes to directly estimate density within a prescribed spatial region and temporal period. Integrated SCR–movement models provide a generalizable approach to incorporate greater movement realism into population dynamics and link movement to emergent properties including spatiotemporal densities and abundances.

eastern Chukchi Sea↗
USGS2011

Development of a bioenergetics model for age-0 American shad

Bioenergetics modeling can be used as a tool to investigate the impact of non-native age-0 American shad ( Alosa sapidissima ) on reservoir and estuary food webs. The model can increase our understanding of how these fish influence lower trophic levels as well as predatory fish populations that feed on juvenile salmonids. Bioenergetics modeling can be used to investigate ecological processes, evaluate alternative research hypotheses, provide decision support, and quantitative prediction. Bioenergetics modeling has proven to be extremely useful in fisheries research (Ney et al. 1993,Chips and Wahl 2008, Petersen et al. 2008). If growth and diet parameters are known, the bioenergetics model can be used to quantify the relative amount of zooplankton or insects consumed by age-0 American shad. When linked with spatial and temporal information on fish abundance, model output can guide inferential hypothesis development to demonstrate where the greatest impacts of age-0 American shad might occur. Bioenergetics modeling is particularly useful when research questions involve multiple species and trophic levels (e.g. plankton communities). Bioenergetics models are mass-balance equations where the energy acquired from food is partitioned between maintenance costs, waste products, and growth (Winberg 1956). Specifically, the Wisconsin bioenergetics model (Hanson et al. 1997) is widely used in fisheries science. Researchers have extensively tested, reviewed, and improved on this modeling approach for over 30 years (Petersen et al. 2008). Development of a bioenergetics model for any species requires three key components: 1) determine physiological parameters for the model through laboratory experiments or incorporate data from a closely related species, 2) corroboration of the model with growth and consumption estimates from independent research, and 3) error analysis of model parameters. Wisconsin bioenergetics models have been parameterized for many of the salmonids and predatory fishes encountered in the lower Columbia River (Petersen and Ward 1999). The Wisconsin bioenergetics model has not been developed for American shad, however Limburg (1996) parameterized a simplified bioenergetics growth model for this species. A common application for the Wisconsin bioenergetics model is to estimate the consumption or growth of a fish population under different temperature and feeding scenarios (Ney 1993). One advantage of the bioenergetics approach is that consumption can be estimated without direct field measurements of predation rate (prey·predator -1 · day -1 ; Petersen and Ward 1999). Field estimates of fish consumption are time consuming and costly to determine, and estimates may show wide variance due to environmental and sampling variability. However, the consumption parameters used in a newly developed bioenergetics model must be verified with field and laboratory estimates of consumption (Ney 1993). The objective of this research was to parameterize a Wisconsin bioenergetics model for age-0 American shad using published physiological data on American shad and closely related alosine species. The American shad bioenergetics model will be used as a tool to explore various hypotheses about how age-0 American shad directly and indirectly affect Columbia River salmon through ecological interactions in lower Columbia River food webs. One over-arching focus of the larger research project was to identify potential interactions between age-0 American shad and juvenile salmonids, addressing potential outcomes through bioenergetics modeling scenarios. This report contains two bioenergetics modeling applications to demonstrate how these models can be used to address management questions and direct research effort. The first modeling application uses the American shad bioenergetics model described in this report to explore prey consumption by age-0 American shad (Chapter 1, this report). Dietary data on age-0 American shad and previously published reports on the diet of juvenile fall Chinook salmon (Rondorf et al. 1990, USGS unpublished data) suggested there might be considerable dietary overlap between these species in the lower Columbia River. The U.S. Geological Survey (USGS) was interested in using the American shad bioenergetics model to explore hypotheses concerning dietary overlap between age-0 American shad and emigrating fall Chinook salmon. The second modeling application uses the fall Chinook salmon bioenergetics model (Koehler et al. 2006) to explore the growth potential of juvenile fall Chinook salmon predating on age-0 American shad in the lower Columbia River. This modeling was based dietary information on a small number of age-0 fall Chinook salmon (n = 13) collected in John Day Reservoir in 1994 - 1996 (unpublished USGS data). Analysis of this dietary data found that these salmonids were feeding primarily on age-0 American shad (> 75% by weight).

Oregon↗
USGS2026

Severity and certainty metric for kelp stressors: A tool for research, management, and conservation

Foundation species face multiple threats, and many are in decline worldwide. Kelps are no exception, with declines documented throughout their range. Stressors such as warming, eutrophication, and reduced water quality—often linked to human activities—are well recognized, yet their relative severity and certainty have not been systematically evaluated. Such a framework may aid scientific, regulatory, and conservation organizations in prioritizing actions and decision making. Here we build on a conceptual model of stressors to kelp in the Salish Sea by rating stressor severity and scientific certainty. We convened a panel of regional kelp experts to perform the rating exercise for two canopy taxa, Nereocystis luetkeana and Macrocystis tenuifolia , as well as a grouping for understory species, including both the sporophyte (adult) and gametophyte (juvenile) life stages for these algae. Our process produced a rich dataset which can be separated by stressor, taxa, and life stage allowing for broad applications. The panel identified temperature, low nutrients, and substrate change/benthic sedimentation as the most severe stressors to kelp, with relatively high certainty for most taxa. Certainty scores were relatively high for Nereocystis and Macrocystis sporophytes but were considerably lower for understory species and the gametophyte life stage, for most stressors, highlighting research gaps. Our data set could be applied for management prioritization, setting ecological baselines, and spatial modeling exercises. We encourage scientific, regulatory, and conservation organizations to consider both our data and our process when evaluating their needs. As foundation species continue to face threats, exercises such as this can provide scientists and managers with actionable indices that support strategies to stem degradation.

Ecological Indicators↗
USGS2015

Performance metrics and variance partitioning reveal sources of uncertainty in species distribution models

Species distribution models (SDMs) are widely used in basic and applied ecology, making it important to understand sources and magnitudes of uncertainty in SDM performance and predictions. We analyzed SDM performance and partitioned variance among prediction maps for 15 rare vertebrate species in the southeastern USA using all possible combinations of seven potential sources of uncertainty in SDMs: algorithms, climate datasets, model domain, species presences, variable collinearity, CO 2 emissions scenarios, and general circulation models. The choice of modeling algorithm was the greatest source of uncertainty in SDM performance and prediction maps, with some additional variation in performance associated with the comprehensiveness of the species presences used for modeling. Other sources of uncertainty that have received attention in the SDM literature such as variable collinearity and model domain contributed little to differences in SDM performance or predictions in this study. Predictions from different algorithms tended to be more variable at northern range margins for species with more northern distributions, which may complicate conservation planning at the leading edge of species' geographic ranges. The clear message emerging from this work is that researchers should use multiple algorithms for modeling rather than relying on predictions from a single algorithm, invest resources in compiling a comprehensive set of species presences, and explicitly evaluate uncertainty in SDM predictions at leading range margins.

Ecological Modelling↗
USGS2021

Simulation of dissolved organic carbon flux in the Penobscot Watershed, Maine

Dissolved organic carbon (DOC) is an important component of the carbon cycle as a measure of the hydrological transport of carbon between terrestrial carbon pools into soil pools and eventually into streams. As a result, changes in DOC in rivers and streams may indicate alterations in the storage of terrestrial carbon. Exploring the complex interactions between biogeochemical cycling and hydrologic processes, as well as the micro-climate variabilities that impact the rate of DOC fluxes, are challenging because the information is not readily available from in-situ measurements or from empirical models alone. This is particularly true of large-scale watersheds. The Penobscot Watershed is the largest watershed of the Gulf of Maine and the second largest in New England. Its typical soils, with high organic matter and a large forested and wetland landscape, result in higher DOC fluxes than what has been observed previously for most rivers in the northern temperate or boreal zones (Hope et al., 1994; Mulholland, 1997; Aitkenhead and McDowell, 2000). In this study, we emphasized the simulation of streamflow and DOC fluxes from the Penobscot Watershed (and several tributaries within the Penobscot Watershed) using the spatially distributed process-based Regional Hydro-Ecological Simulation System (RHESSys) model. Simulated results were evaluated using field measurements (streamflow, DOC fluxes) and remotely sensed products (Net Primary Production (NPP) and Leaf Area Index (LAI) from Moderate Resolution Imaging Spectroradiometer (MODIS). The average DOC flux for the Penobscot Watershed during 2004-2012 using the RHESSys model was 69 kg C/ha/year. The RHESSys simulated DOC flux is shown to correlate well with observed values, as well as with results previously reported from the empirical Load Estimator (LOADEST) model (71 kg C/ha/year) for 2004-2007 ( Huntington and Aiken, 2013). Our simulated results also show a temporal variation in the amount of DOC flux, indicating that the antecedent DOC concentration from one year can impact the DOC export in following years. Thus, DOC concentration is positively correlated with streamflow and antecedent precipitation, in agreement with previous studies (Ågren et al., 2010; Huntington and Aiken, 2013; Tian et al., 2013 ). The successful application of the rigorous RHESSys model in the Penobscot Watershed makes it a reasonable platform to test future scenarios impacting the hydrology and biogeochemistry within similar large complex watersheds.

Maine↗
USGS2023

Developing fluvial fish species distribution models across the conterminous United States—A framework for management and conservation

This report explains the steps and specific methods used to predict fluvial fish occurrences in their native ranges for the conterminous United States. In this study, boosted regression tree models predict distributions of 271 ecologically important fluvial fish species using relations between fish presence/absence and 22 natural and anthropogenic landscape variables. Models developed for the freshwater portions of the ranges for species represented 28 families. Cyprinidae was the family with the most species (87 of 271) modeled for this study, followed by Percidae (34) and Ictaluridae (17). Model predictive performance was evaluated using four metrics: area under the receiver operating characteristic curve, sensitivity, specificity, and True Skill Statistic, which are all from tenfold cross-validation results. The relative importance of the predictor variables in the boosted regression tree models was calculated and ranked for each species. The three strongest natural predictors of fish distributions were network catchment area, the mean annual air temperature of the local catchment, and the maximum elevation of the local catchment, while the three strongest anthropogenic predictors were downstream main stem dam density, distance to downstream main stem dam, and the percentage of pasture/hay land use area within network catchment boundaries. Study results showed 61 fish species were sensitive to climate variables, and 40 fish species were sensitive to anthropogenic stressors. The models developed in this study can be used to derive critical information regarding habitat protection priorities, anthropogenic threats, and potential effects of climate change on habitat suitability, aiding in efforts to conserve fluvial fishes now and into the future.

Conterminous United States↗
USGS2011

Natural and human dimensions of a quasi-wild species: The case of kudzu

The human dimensions of biotic invasion are generally poorly understood, even among the most familiar invasive species. Kudzu ( Pueraria montana (Lour.) Merr.) is a prominent invasive plant and an example of quasi-wild species, which has experienced repeated introduction, cultivation, and escape back to the wild. Here, we review a large body of primary scientific and historic records spanning thousands of years to characterize the complex relationships among kudzu, its natural enemies, and humans, and provide a synthesis and conceptual model relevant to the ecology and management of quasi-wild invasive species. We documented over 350, mostly insect, natural enemy species and their impacts on kudzu in its native East Asian range. These natural enemies play a minor role in limiting kudzu in its native range, rarely generating severe impacts on populations of wild kudzu. We identified a number of significant influences of humans including dispersal, diverse cultural selection, and facilitation through disturbances, which catalyzed the expansion and exuberance of kudzu. On the other hand, harvest by humans appears to be the major control mechanism in its native areas. Humans thus have a complex relationship with kudzu. They have acted as both friend and foe, affecting the distribution and abundance of kudzu in ways that vary across its range and over time. Our conceptual model of kudzu emphasizes the importance of multiple human dimensions in shaping the biogeography of a species and illustrates how kudzu and other quasi-wild species are more likely to be successful invaders.

Biological Invasions↗
USGS2003

Resource allocation in offspring provisioning: An evaluation of the conditions favoring the evolution of matrotrophy

We used analytic and simulation models to determine the ecological conditions favoring evolution of a matrotrophic fish from a lecithotrophic ancestor given a complex set of trade‐offs. Matrotrophy is the nourishment of viviparous embryos by resources provided between fertilization and parturition, while lecithotrophy describes embryo nourishment provided before fertilization. In fishes and reptiles, embryo nourishment encompasses a continuum from solely lecithotrophic to primarily matrotrophic. Matrotrophy has evolved independently from lecithotrophic ancestors many times in many groups. We assumed matrotrophy increased the number of offspring a viviparous female could gestate and evaluated conditions of food availability favoring lecithotrophy or matrotrophy. The matrotrophic strategy was superior when food resources exceeded demand during gestation but at a risk of overproduction and reproductive failure if food intake was limited. Matrotrophic females were leaner during gestation than lecithotrophic females, yielding shorter life spans. Our models suggest that matrotrophic embryo nourishment evolved in environments with high food availability, consistently exceeding energy requirements for maintaining relatively large broods. Embryo abortion with some resorption of invested energy is a necessary preadaptation to the evolution of matrotrophy. Future work should explore trade‐offs of age‐specific mortality and reproductive output for females maintaining different levels of fat storage during gestation.

American Naturalist↗
USGS2019

Sampling and analysis frameworks for inference in ecology

1. Reliable statistical inference is central to ecological research, much of which seeks to estimate population attributes and their interactions. The issue of sampling design and its relationship to inference has become increasingly important due to rapid proliferation of modeling methodology (line transect modeling, capture-recapture, estimation of occurrence, model selection procedures, hierarchical modeling) and new sampling approaches (adaptive sampling, other specialized designs). It is important for ecologists using these advanced methods to be aware of how the linkages between sample selection and data analysis can potentially affect inference. 2. We examine design-based and model-based inference frameworks for ecological data collected randomly, purposively, or opportunistically. We elucidate differences in the probability structures for data arising from these frameworks, clarify the assumptions that underlie them, and demonstrate their differences. 3. Design-based inference builds on a probability structure inherited from randomized data collection, whereas model-based inference relies on an assumed stochastic model of the data. By itself, a design-based approach is of limited value for inferences about causal hypotheses. In contrast, model-based inference is dependent on a conditionality principle that can seldom be shown to be met for an ecological system. We describe the conditions under which one can safely ignore sampling design in model-based analysis, along with inferential implications if these conditions are not met. The special case of opportunistic sampling is discussed. 4. We present a combined framework that takes advantage of both approaches to inference, and provides a robust methodology that can deal with the modeling of sampling problems such as nondetection and misclassification, as well as the exploration of causal hypotheses. The combined framework can be useful for identifying optimal sampling strategies. 5. Each approach to inference has its strengths and weaknesses, and practitioners should be aware of these in order to tailor designs and analyses to specific questions. We use the approaches and their underlying rationales to provide guidelines for choosing designs and estimators for reliable inference.

Methods in Ecology and Evolution↗
USGS2005

Performance of four turbulence closure models implemented using a generic length scale method

A two-equation turbulence model (one equation for turbulence kinetic energy and a second for a generic turbulence length-scale quantity) proposed by Umlauf and Burchard [J. Marine Research 61 (2003) 235] is implemented in a three-dimensional oceanographic model (Regional Oceanographic Modeling System; ROMS v2.0). These two equations, along with several stability functions, can represent many popular turbulence closures, including the k-kl (Mellor-Yamada Level 2.5), k-??, and k-?? schemes. The implementation adds flexibility to the model by providing an unprecedented range of turbulence closure selections in a single 3D oceanographic model and allows comparison and evaluation of turbulence models in an otherwise identical numerical environment. This also allows evaluation of the effect of turbulence models on other processes such as suspended-sediment distribution or ecological processes. Performance of the turbulence models and sediment-transport schemes is investigated with three test cases for (1) steady barotropic flow in a rectangular channel, (2) wind-induced surface mixed-layer deepening in a stratified fluid, and (3) oscillatory stratified pressure-gradient driven flow (estuarine circulation) in a rectangular channel. Results from k-??, k-??, and gen (a new closure proposed by Umlauf and Burchard [J. Marine Research 61 (2003) 235]) are very similar for these cases, but the k-kl closure results depend on a wall-proximity function that must be chosen to suit the flow. Greater variations appear in simulations of suspended-sediment concentrations than in salinity simulations because the transport of suspended-sediment amplifies minor variations in the methods. The amplification is caused by the added physics of a vertical settling rate, bottom stress dependent resuspension, and diffusive transport of sediment in regions of well mixed salt and temperature. Despite the amplified sensitivity of sediment to turbulence models in the estuary test case, the four closures investigated here all generated estuarine turbidity maxima that were similar in their shape, location, and concentrations.

Ocean Modelling↗
USGS2024

Treed Gaussian processes for animal movement modeling

Wildlife telemetry data may be used to answer a diverse range of questions relevant to wildlife ecology and management. One challenge to modeling telemetry data is that animal movement often varies greatly in pattern over time, and current continuous-time modeling approaches to handle such nonstationarity require bespoke and often complex models that may pose barriers to practitioner implementation. We demonstrate a novel application of treed Gaussian process (TGP) modeling, a Bayesian machine learning approach that automatically captures the nonstationarity and abrupt transitions present in animal movement. The machine learning formulation of TGPs enables modeling to be nearly automated, while their Bayesian formulation allows for the derivation of movement descriptors with associated uncertainty measures. We demonstrate the use of an existing R package to implement TGPs using the familiar Markov chain Monte Carlo algorithm. We then use estimated movement trajectories to derive movement descriptors that can be compared across individuals and populations. We applied the TGP model to a case study of lesser prairie-chickens ( Tympanuchus pallidicinctus ) to demonstrate the benefits of TGP modeling and compared distance traveled and residence times across lesser prairie-chicken individuals and populations. For broad usability, we outline all steps necessary for practitioners to specify relevant movement descriptors (e.g., turn angles, speed, contact points) and apply TGP modeling and trajectory comparison to their own telemetry datasets. Combining the predictive power of machine learning and the statistical inference of Bayesian methods to model movement trajectories allows for the estimation of statistically comparable movement descriptors from telemetry studies. Our use of an accessible R package allows practitioners to model trajectories and estimate movement descriptors, facilitating the use of telemetry data to answer applied management questions.

Ecology and Evolution↗
USGS2013

Social-ecological predictors of global invasions and extinctions

Most assessments of resilience have been focused on local conditions. Studies focused on the relationship between humanity and environmental degradation are rare, and are rarely comprehensive. We investigated multiple social-ecological factors for 100 countries around the globe in relation to the percentage of invasions and extinctions within each country. These 100 countries contain approximately 87% of the world’s population, produce 43% of the world’s per capita gross domestic product (GDP), and take up 74% of the earth’s total land area. We used an information theoretic approach to determine which models were most supported by our data, utilizing an a priori set of plausible models that included a combination of 15 social-ecological variables, each social-ecological factor by itself, and selected social-ecological factors grouped into three broad classes. These variables were per capita GDP, export-import ratio, tourism, undernourishment, energy efficiency, agricultural intensity, rainfall, water stress, wilderness protection, total biodiversity, life expectancy, adult literacy, pesticide regulation, political stability, and female participation in government. Our results indicate that as total biodiversity and total land area increase, the percentage of endangered birds also increases. As the independent variables (agricultural intensity, rainfall, water stress, and total biodiversity) in the ecological class model increase, the percentage of endangered mammals in a country increases. The percentage of invasive birds and mammals in a country increases as per capita GDP increases. As life expectancy increases, the percentage of invasive and endangered birds and mammals increases. Although our analysis does not determine mechanisms, the patterns observed in this study provide insight into the dynamics of a complex, global, social-ecological system.

Ecology and Society↗
USGS2025

Preprint: Simulated seasonal loads of total nitrogen and total phosphorus by major source from watersheds draining to Washington waters of the Salish Sea, 2005 through 2020

The U.S. Geological Survey (USGS) and the Washington State Department of Ecology (Ecology) have developed watershed models of seasonal load estimates of total nitrogen (TN) and total phosphorus (TP) discharging into the Washington waters of the Salish Sea from 2005 through 2020. The modeling approach used was dynamic SPARROW (SPAtially Referenced Regressions On Watershed attributes), a statistical-physical watershed modeling technique, initially applied at large spatial scales to represent long-term average stream loads throughout a stream network, refined here to estimate seasonal TN and TP loads across watersheds to clarify upstream contributions from discernable point and nonpoint sources delivered to marine waters at surface water confluences along the shoreline and quantify when, where, and why they were high or low. Upstream contributing sources included permitted treated wastewater facilities, crop fertilizer, animal feeding operations, septic systems, urban land and stormwater, atmospheric deposition (TN only), nitrogen fixation by Red Alder Alnus rubra trees (TN only), and background geologic material (TP only). Instream load magnitudes and their source compositions varied widely across watersheds, and even within each watershed, yet the largest loads typically occurred in the large rivers during winter and fall when streamflow was highest. Likewise, instream loads were typically lowest in summer during low streamflow, yet the relative instream aquatic decay was highest. The seasonal storage lag component of those nonpoint sources was estimated to contribute a quarter of the seasonal instream load during winter and fall high streamflow and sometimes half of the instream load during summer low streamflow. A key aspect of Ecology’s current Puget Sound Nutrient Source Reduction Project is consideration of upstream watershed contributions of nutrients to their marine-water discharge points. Simulated seasonal loads carried by streams to 63 river mouth marine discharge points 9 ranged by several orders-of-magnitude for both TN and TP due to the spatial and seasonal differences in hydrologic flows, magnitude and timing of contributing sources, and in-stream decay. The Snohomish and Skagit Rivers discharged the largest TN and TP loads, yet the Samish River was shown to have some of the highest TN and TP yields and concentrations. Additionally, a reference scenario was developed to provide an estimate of the pre-industrial local and regional loads.

ESS Open Archive↗
USGS2024

2023-2024 Coastal sage scrub and chaparral community monitoring for western San Diego County

Western San Diego County is dominated by shrublands supporting biologically diverse native plant and animal communities. Widespread urbanization has led to regional habitat loss and fragmentation, and many species in these shrubland communities are rare, threatened, or endangered. Large-scale, multiple-species conservation planning has resulted in a regional preserve system that focuses on these shrubland communities. Several large-scale threats are leading to type conversion from shrub-dominated to non-native invasive annual grass-dominated vegetation. To understand the changes that are occurring to native shrublands, we have developed a vegetation monitoring program with several components at multiple spatial scales, focused on quantifying coastal sage scrub (CSS) and chaparral vegetation community characteristics. Several drivers of change associated with type conversion of native shrubland to non-native annual grassland have been identified by previous research including increasing fire frequency, nitrogen deposition from air pollution, and prolonged and intense drought associated with changing climate. Loss of ecological integrity indices have been proposed as useful measures of the threat of degradation and type conversion of shrublands in San Diego County. For this study, ecological integrity is defined as a system’s ability to maintain species’ relationships and functions comparable to natural habitat in the region. Previous studies have identified the percent cover of invasive non-native annual grasses as a proxy for overall ecological degradation (loss of integrity) that is consistent across native taxonomic groups. Increased cover of non-native grass is associated with lower integrity of the shrubland vegetation community as shrub-associated plant and animal species are replaced by species preferring grassy and disturbed habitats. The objectives of this CSS and chaparral vegetation community monitoring plan are to: 1) Determine the distribution, composition, structure, and integrity of CSS and chaparral vegetation communities on conserved lands in western San Diego County, 2) Identify whether these attributes of the vegetation communities are changing over time, and 3) Evaluate relationships of known drivers of change (threats) and environmental factors in association with changes in vegetation community attributes. The CSS and chaparral vegetation community monitoring program is divided into four components: 1) vegetation mapping, 2) GIS/remote sensing office analysis of landscape-scale data, 3) permanent field plots using Unmanned Aerial Systems (UAS) and field data collections, and 4) animal and target species surveys and rapid assessment protocols. The first component, which is not detailed in this vegetation monitoring plan, aims to map vegetation communities every 10-15 years based on a classification developed for western San Diego County. High resolution aerial imagery will be used to update the 2012 vegetation map and expand it across the entire study area. The second component uses remote sensing models to annually track ecological integrity of shrublands across the study area and will include a map of areas of change and areas of stability over several decades. These landscape-scale integrity classifications will be used to analyze ecological processes, threats, and abiotic factors relative to changes to shrubland ecological integrity over time and space. The third component includes field surveys of 100 permanent plots across areas historically mapped as shrublands. Surveys in the 1930s mapped vegetation types using plot data. By using this historical classification map, we included areas that have already type-converted from shrubland to non-native annual grassland. The plots were split between CSS (55 plots) and chaparral (45 plots) and stratified into four geographical eco-subregions to guarantee coverage over small patches of habitat along the heavily developed coast. Surveys will include the collection of UAS imagery at a very high resolution. Species-level identifications will be made from the imagery based on a plant list compiled of all species detected in the plot during a thorough field survey by botanists, combined with geo-referenced samples of plant species locations. In addition, herbaceous cover will be estimated in the field using nine 1-m diameter circles (one per subplot) to obtain ocular estimates of cover for each plant species within the circle frame. Soil samples will be collected and analyzed for important element compositions. These data will be analyzed to evaluate plot-level ecological integrity based on species composition and cover. Repeated monitoring will allow evaluation of changes in vegetation attributes over time with known drivers or threats and other environmental factors. In addition, analyses will focus on indicator species and various measures of biological diversity for the vegetation communities. Finally, animal species and rare plants will be assessed using either taxa-based rapid assessment protocols or specialized species-specific protocols for rarer species. The purpose of these assessments is to document the status, habitat, and threat covariates of specific species and confirm the species composition and diversity of animal taxonomic groups (e.g., pollinators). Diversity and abundance of animal species at vegetation plots will be used to refine measures and thresholds of ecological integrity. Rapid assessment protocols for animal taxonomic groups can include multiple detection methods such as, camera traps, cover boards, and bird point counts. Pollinators will be monitored at plots using a protocol currently being developed in conjunction with but separately from this plan. Target rare plant species will be monitored using the regional Inspect and Manage (IMG) protocol that measures the status of rare plant occurrences and habitat and threat covariates over time. Species-specific animal survey methods will be refined as these species are prioritized for future monitoring. The goal of this monitoring program is to classify CSS and chaparral vegetation community integrity, identify areas of degradation across western San Diego County, and characterize drivers, and environmental factors associated with loss of ecological integrity. A combination of vegetation mapping, landscape-scale remote sensing, and field plots will be used to address all the aspects of our research questions. Data compiled and collected will be available to conservation partners to help inform future management decisions.

California↗
USGS2020

Trait‐based variation in host contribution to pathogen transmission across species and resource supplies

Two key knowledge gaps currently limit the development of more predictive and general models of pathogen transmission: (1) the physiological basis of heterogeneity in host contribution to pathogen transmission (reservoir potential) remains poorly understood, and (2) a general means of integrating the ecological dynamics of host communities has yet to emerge. If the traits responsible for differences in reservoir potential also modulate host community dynamics, these traits could be used to predict pathogen transmission as host communities change. In two greenhouse experiments, across 23 host species and two levels of resource supply, the reservoir potential of plant hosts increased significantly along the Leaf Economic Spectrum, a global axis of plant physiological trait covariation that features prominently in models of plant community ecology. This indicates that the traits of the Leaf Economic Spectrum underlie broad differences in reservoir potential across host species and resource supplies. Therefore, host traits could be used to integrate epidemiological models of pathogen transmission with ecological models of host community change.

Ecology↗
USGS2017

Streamflow characteristics from modelled runoff time series: Importance of calibration criteria selection

Ecologically relevant streamflow characteristics (SFCs) of ungauged catchments are often estimated from simulated runoff of hydrologic models that were originally calibrated on gauged catchments. However, SFC estimates of the gauged donor catchments and subsequently the ungauged catchments can be substantially uncertain when models are calibrated using traditional approaches based on optimization of statistical performance metrics (e.g., Nash–Sutcliffe model efficiency). An improved calibration strategy for gauged catchments is therefore crucial to help reduce the uncertainties of estimated SFCs for ungauged catchments. The aim of this study was to improve SFC estimates from modeled runoff time series in gauged catchments by explicitly including one or several SFCs in the calibration process. Different types of objective functions were defined consisting of the Nash–Sutcliffe model efficiency, single SFCs, or combinations thereof. We calibrated a bucket-type runoff model (HBV – Hydrologiska Byråns Vattenavdelning – model) for 25 catchments in the Tennessee River basin and evaluated the proposed calibration approach on 13 ecologically relevant SFCs representing major flow regime components and different flow conditions. While the model generally tended to underestimate the tested SFCs related to mean and high-flow conditions, SFCs related to low flow were generally overestimated. The highest estimation accuracies were achieved by a SFC-specific model calibration. Estimates of SFCs not included in the calibration process were of similar quality when comparing a multi-SFC calibration approach to a traditional model efficiency calibration. For practical applications, this implies that SFCs should preferably be estimated from targeted runoff model calibration, and modeled estimates need to be carefully interpreted.

Tennessee↗