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At least 1,315 records · Page 73Linked to original sources
USGS2026

Alternative future vegetation pathways reveal potential transformations of western US ecosystems

Managing ecosystems in an era of rapid change is inherently challenging not only because of uncertainty in future climate but also due to diverse responses of ecosystems to climate. Projections of ecological transformation alongside information about plausible vegetation trajectories can help land managers explore divergent scenarios and consider how modeled outcomes match their observations. Climate-analog impact models (AIMs) compare environmental information (e.g., vegetation types) between sets of climatically similar locations to infer change and can be used to identify multiple outcomes. We used AIMs to project changes in vegetation across the western United States under a mid-21st century climate scenario, characterize ecological transformation vulnerability based on projection divergence, and demonstrate how AIMs can inform decision-making. We projected high or very high vulnerability to ecological transformation across 29% of the western US, nearly 1 M km 2 . Vulnerability varied among vegetation groups; 75% of alpine vegetation had high or very high vulnerability vs. 6% of desert scrub. We estimate that 9% of the study area faces a high likelihood of transformation based on combined measures of vulnerability and projection agreement. Transformation at the vegetation type ( n = 50) level is projected for 40% (1.4 M km 2 ) of the study area, based on primary projections. As vegetation shifts towards types supported by a more arid climate, forested area is expected to contract by 9% and subalpine forests specifically by 54%. Elsewhere, vulnerability is low or trajectories are uncertain, implying opportunities for managers to intervene. Dry forests, for example, could be stabilized through vegetation management and intentional fire use. Our findings suggest likely ecological transformations with significant downstream consequences for ecosystem services and natural resources. They are best used within decision-making frameworks that draw on multiple lines of evidence including local expertise and complementary knowledge systems.

Arizona, California, Colorado, Idaho, Montana, Nev↗
USGS2009

Science-Based Strategies for Sustaining Coral Ecosystems

Coral ecosystems and their natural capital are at risk. Greenhouse gas emissions, overfishing, and harmful land-use practices are damaging our coral reefs. Overwhelming scientific evidence indicates that the threats are serious, and if they are left unchecked, the ecological and social consequences will be significant and widespread. Although the primary stressors to coral ecosystems are known, science-based strategies are needed to more accurately explain natural processes and forecast human-induced change. Collaborations among managers and scientists and enhanced mapping, monitoring, research, and modeling can lead to effective mitigation plans. U.S. Geological Survey scientists and their partners assess coral ecosystem history, ecology, vulnerability, and resiliency and provide study results to decisionmakers who may devise policies to sustain coral resources and the essential goods and services they provide.

Fact Sheet↗
USGS2009

NASA and USGS invest in invasive species modeling to evaluate habitat for Africanized Honey Bees

Invasive non-native species, such as plants, animals, and pathogens, have long been an interest to the U.S. Geological Survey (USGS) and NASA. Invasive species cause harm to our economy (around $120 B/year), the environment (e.g., replacing native biodiversity, forest pathogens negatively affecting carbon storage), and human health (e.g., plague, West Nile virus). Five years ago, the USGS and NASA formed a partnership to improve ecological forecasting capabilities for the early detection and containment of the highest priority invasive species. Scientists from NASA Goddard Space Flight Center (GSFC) and the Fort Collins Science Center developed a longterm strategy to integrate remote sensing capabilities, high-performance computing capabilities and new spatial modeling techniques to advance the science of ecological invasions [Schnase et al., 2002].

The Earth Observer↗
USGS2021

A roadmap for sampling and scaling biological nitrogen fixation in terrestrial ecosystems

Accurately quantifying rates and patterns of biological nitrogen fixation (BNF) in terrestrial ecosystems is essential to characterize ecological and biogeochemical interactions, identify mechanistic controls, improve BNF representation in conceptual and numerical modelling, and forecast nitrogen limitation constraints on future carbon (C) cycling. While many resources address the technical advantages and limitations of different methods for measuring BNF, less systematic consideration has been given to the broader decisions involved in planning studies, interpreting data, and extrapolating results. Here, we present a conceptual and practical road map to study design, study execution, data analysis and scaling, outlining key considerations at each step. We address issues including defining N‐fixing niches of interest, identifying important sources of temporal and spatial heterogeneity, designing a sampling scheme (including method selection, measurement conditions, replication, and consideration of hotspots and hot moments), and approaches to analysing, scaling and reporting BNF. We also review the comparability of estimates derived using different approaches in the literature, and provide sample R code for simulating symbiotic BNF data frames and upscaling. Improving and standardizing study design at each of these stages will improve the accuracy and interpretability of data, define limits of extrapolation, and facilitate broader use of BNF data for downstream applications. We highlight aspects—such as quantifying scales of heterogeneity, statistical approaches for dealing with non‐normality, and consideration of rates versus ecological significance—that are ripe for further development.

Methods in Ecology and Evolution↗
USGS2024

Cytology in cnidaria using Exaiptasia as a model

A need exists for additional methods to examine cnidaria at the cellular level to aid our understanding of health, anatomy, and physiology of this important group of organisms. This need is particularly acute given that disease is emerging as a major factor in declines of ecologically important functional groups such as corals. Here we describe a simple method to process cnidarian cells for microscopic examination using the model organism Exaiptasia . We show that this organism has at least 18 cell types or structures that can be readily distinguished based on defined morphological features. Some of these cells can be related back to anatomic features of the animal both at the light microscope and ultrastructural level. The cnidome of Exaiptasia may be more complex than what is currently understood. Moreover, cnidarian cells, including some types of cnidocytes, phagocytize cells other than endosymbionts. Finally, our findings shed light on morphologic complexity of cell-associated microbial aggregates and their intimate intracellular associations. The tools described here could be useful for other cnidaria.

Diseases of Aquatic Organisms↗
USGS2014

From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments

Ecosystem services mapping and modeling has focused more on supply than demand, until recently. Whereas the potential provision of economic benefits from ecosystems to people is often quantified through ecological production functions, the use of and demand for ecosystem services has received less attention, as have the spatial flows of services from ecosystems to people. However, new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems. Our analysis includes five ecosystem services: carbon sequestration and storage, riverine flood regulation, sediment regulation for reservoirs, open space proximity, and scenic viewsheds. Each ecosystem service is characterized by different beneficiary groups and means of service flow. Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use. With the exception of the carbon sequestration service, regions that actually provided services to people, i.e., connected to beneficiaries via flow paths, amounted to 16-66% of those theoretically capable of supplying services, i.e., all ecosystems across the landscape. These results offer a more complete understanding of the spatial dynamics of ecosystem services and their effects, and may provide a sounder basis for economic valuation and policy applications than studies that consider only theoretical service provision and/or use.

Washington↗
USGS2009

Using time-dependent models to investigate body condition and growth rate of the giant gartersnake

Identifying links between phenotypic attributes and fitness is a primary goal of reproductive ecology. Differences in within-year patterns of body condition between sexes of gartersnakes in relation to reproduction and growth are not fully understood. We conducted an 11-year field study of body condition and growth rate of the giant gartersnake Thamnophis gigas across 13 study areas in the Central Valley of California, USA. We developed a priori mixed effects models of body condition index (BCI), which included covariates of time, sex and snout-vent length and reported the best-approximating models using an information theoretic approach. Also, we developed models of growth rate index (GRI) using covariates of sex and periods based on reproductive behavior. The largest difference in BCI between sexes, as predicted by a non-linear (cubic) time model, occurred during the mating period when female body condition (0.014??0.001 se) was substantially greater than males (-0.027??0.002 se). Males likely allocated energy to search for mates, while females likely stored energy for embryonic development. We also provided evidence that males use more body energy reserves than females during hibernation, perhaps because of different body temperatures between sexes. We found GRI of male snakes was substantially lower during the mating period than during a non-mating period, which indicated that a trade-off existed between searching for mates and growth. These findings contribute to our understanding of snake ecology in a Mediterranean climate. ?? 2009 The Zoological Society of London.

Journal of Zoology↗
USGS2011

Spatial pattern formation of coastal vegetation in response to external gradients and positive feedbacks affecting soil porewater salinity: A model study

Coastal vegetation of South Florida typically comprises salinity-tolerant mangroves bordering salinity-intolerant hardwood hammocks and fresh water marshes. Two primary ecological factors appear to influence the maintenance of mangrove/hammock ecotones against changes that might occur due to disturbances. One of these is a gradient in one or more environmental factors. The other is the action of positive feedback mechanisms, in which each vegetation community influences its local environment to favor itself, reinforcing the boundary between communities. The relative contributions of these two factors, however, can be hard to discern. A spatially explicit individual-based model of vegetation, coupled with a model of soil hydrology and salinity dynamics is presented here to simulate mangrove/hammock ecotones in the coastal margin habitats of South Florida. The model simulation results indicate that an environmental gradient of salinity, caused by tidal flux, is the key factor separating vegetation communities, while positive feedback involving the different interaction of each vegetation type with the vadose zone salinity increases the sharpness of boundaries, and maintains the ecological resilience of mangrove/hammock ecotones against small disturbances. Investigation of effects of precipitation on positive feedback indicates that the dry season, with its low precipitation, is the period of strongest positive feedback.

Florida↗
USGS2005

Why is metal bioaccumulation so variable? Biodynamics as a unifying concept

Ecological risks from metal contaminants are difficult to document because responses differ among species, threats differ among metals, and environmental influences are complex. Unifying concepts are needed to better tie together such complexities. Here we suggest that a biologically based conceptualization, the biodynamic model, provides the necessary unification for a key aspect in risk: metal bioaccumulation (internal exposure). The model is mechanistically based, but empirically considers geochemical influences, biological differences, and differences among metals. Forecasts from the model agree closely with observations from nature, validating its basic assumptions. The biodynamic metal bioaccumulation model combines targeted, high-quality geochemical analyses from a site of interest with parametrization of key physiological constants for a species from that site. The physiological parameters include metal influx rates from water, influx rates from food, rate constants of loss, and growth rates (when high). We compiled results from 15 publications that forecast species-specific bioaccumulation, and compare the forecasts to bioaccumulation data from the field. These data consider concentrations that cover 7 orders of magnitude. They include 7 metals and 14 species of animals from 3 phyla and 11 marine, estuarine, and freshwater environments. The coefficient of determination ( R 2 ) between forecasts and independently observed bioaccumulation from the field was 0.98. Most forecasts agreed with observations within 2-fold. The agreement suggests that the basic assumptions of the biodynamic model are tenable. A unified explanation of metal bioaccumulation sets the stage for a realistic understanding of toxicity and ecological effects of metals in nature.

Environmental Science & Technology↗
USGS2012

Uncertainty in assessing the impacts of global change with coupled dynamic species distribution and population models

Concern over rapid global changes and the potential for interactions among multiple threats are prompting scientists to combine multiple modelling approaches to understand impacts on biodiversity. A relatively recent development is the combination of species distribution models, land‐use change predictions, and dynamic population models to predict the relative and combined impacts of climate change, land‐use change, and altered disturbance regimes on species' extinction risk. Each modelling component introduces its own source of uncertainty through different parameters and assumptions, which, when combined, can result in compounded uncertainty that can have major implications for management. Although some uncertainty analyses have been conducted separately on various model components – such as climate predictions, species distribution models, land‐use change predictions, and population models – a unified sensitivity analysis comparing various sources of uncertainty in combined modelling approaches is needed to identify the most influential and problematic assumptions. We estimated the sensitivities of long‐run population predictions to different ecological assumptions and parameter settings for a rare and endangered annual plant species ( Acanthomintha ilicifolia , or San Diego thornmint). Uncertainty about habitat suitability predictions, due to the choice of species distribution model, contributed most to variation in predictions about long‐run populations.

California↗
USGS2019

Quantifying trends and uncertainty in prehistoric forest composition

Forest ecosystems in eastern North America were in flux over the last several thousand years, well before Euro-American land clearance and the 20th-century onset of anthropogenic climate change. However, the magnitude and uncertainty of prehistoric vegetation change have been difficult to quantify because of the multiple ecological, dispersal, and sedimentary processes that govern the relationship between forest composition and fossil pollen assemblages. Here we extend STEPPS, a Bayesian hierarchical spatio-temporal pollen-vegetation model, to estimate changes in forest composition in the upper Midwestern United States from about 2000 to 200 years ago. Using this approach, we identify areas of statistically and ecologically significant change. Between 2000 and 200 years ago, forest composition significantly changed across broad regions of north-central Wisconsin and Minnesota. Rates of compositional change varied spatially, and can be linked to previously reported events. The single largest change is the infilling of Tsuga canadensis in northern Wisconsin over the past 2000 years. Despite this range in-filling, the range limit of T. canadensis was largely stable, with modest expansion westward. The regional ecotone between temperate hardwood forests and northern mixed hardwood/conifer forests shifted southwestward by 15-20 km in Minnesota and Northwestern Wisconsin. Fraxinus, Ulmus, and other mesic hardwoods expanded in the Big Woods region of southern Minnesota. However, some areas showed no significant change, suggesting high complexity in the spatiotemporal patterns of past forest dynamics. The increasing density of paleoecological data networks and advances in statistical modeling approaches now enables the confident detection of subtle but significant changes in forest composition over the last 2000 years.

Minnesota, Wisconsin↗
USGS2011

A promising tool for subsurface permafrost mapping-An application of airborne geophysics from the Yukon River Basin, Alaska

Permafrost is a predominant physical feature of the Earth's Arctic and Subarctic clines and a major consideration encompassing ecosystem structure to infrastructure engineering and placement. Perennially frozen ground is estimated to cover about 85 percent of the state of Alaska where northern reaches are underlain with continuous permafrost and parts of interior Alaska are underlain by areas of discontinuous and (or) sporadic permafrost (fig. 1). The region of Interior Alaska, where permafrost is scattered among unfrozen ground, is a complex mosaic of terrains and habitats. Such diversity creates arrays of lakes and surface-water and groundwater patterns that continental populations of migratory waterfowl and internationally significant fisheries have adapted to over time. A road or pipeline might pass over frozen and unfrozen ground, affecting the types of materials and engineering approaches needed to sustain the infrastructure. Effective mapping of discontinuous permafrost at scales meaningful ecologically and (or) from an engineering perspective has been a long-standing challenge. Using techniques such as borehole logging for site-specific assessments or botanical techniques that can suggest underlying permafrost distributions can be labor intensive and difficult to accomplish at the scale and remoteness of much of Alaska. The climate is changing in the Arctic and Subarctic regions. The warming observed throughout much of Alaska could create widespread changes in permafrost. How the warming of the permafrost affects near-surface processes, ecosystems, and community infrastructure and ecosystems is not clear. A better understanding of the dynamic distribution and physical properties of permafrost, from continuous to discontinuous, will provide knowledge of how the permafrost environment may change in the future and help inform engineering and natural resource response strategies. Here we discuss an application of an airborne remote sensing methodology for mapping and shade imaging permafrost characteristics at various scales. This work provides the first look into three-dimensional distribution of permafrost in the areas around Fort Yukon and is a demonstration of the application of airborne electromagnetic (AEM) to permafrost mapping. Such an approach is attractive, particularly in Arctic and Subarctic studies, where ground access is difficult and ecosystems are fragile. In June 2010, the U.S. Geological Survey (USGS) conducted an AEM survey near Fort Yukon, Alaska. The primary focus of this survey was to map the distribution of permafrost in selected areas in order to supply information for the development of groundwater models of the Yukon River Basin. However, the methodologies have more far-reaching ecological and engineering applications. Approximately 1,800 line kilometers were acquired in a combination of typical block style surveying in the immediate area of Fort Yukon and in long reconnaissance lines over a broader area. The widely spaced lines were flown to cross the modern Yukon River in "X" like patterns with intersections at features that have been previously studied (fig. 2). AEM is used to gather data on the electrical resistivity of materials in the subsurface below the flight path of the helicopter, which are then analyzed to interpret the subsurface lithology and the location and extent of permafrost. For this survey, the electrical resistivity was imaged to depths on the order of 50-100 meters. Images from the survey can be qualitatively compared with known permafrost features and suggest new permafrost features. Electrical properties of earth materials are affected by lithology as well as temperature and the presence of ice; frozen materials become substantially more resistive. This allows for the identification of permafrost from the resistivity image (Abraham and others, 2011). In the area of Fort Yukon, the AEM survey shows elevated resistivities extending to depth, likely indicative of thick permafrost. This depth corresponds well to observations from a borehole drilled in the area in the late 1990s, which detected permafrost to a depth of about 100 meters (Clark and others, 2009). In contrast to the area of Fort Yukon, the Yukon River and its floodplain are not associated with deep resistive sediments, suggesting a lack of deep permafrost, at least within the depth range of the AEM mapping (fig. 3).

Yukon River Basin↗
USGS2007

Initial Everglades Depth Estimation Network (EDEN) digital elevation model research and development

The Everglades Depth Estimation Network (EDEN) offers a consistent and documented dataset that can be used to guide large-scale field operations, to integrate hydrologic and ecological responses, and to support biological and ecological assessments that measure ecosystem responses to the Comprehensive Everglades Restoration Plan (Telis, 2006). To produce historic and near-real time maps of water depths, the EDEN requires a system-wide digital elevation model (DEM) of the ground surface. Accurate Everglades wetland ground surface elevation data were non-existent before the U.S. Geological Survey (USGS) undertook the collection of highly accurate surface elevations at the regional scale. These form the foundation for EDEN DEM development. This development process is iterative as additional high accuracy elevation data (HAED) are collected, water surfacing algorithms improve, and additional ground-based ancillary data become available. Models are tested using withheld HAED and independently measured water depth data, and by using DEM data in EDEN adaptive management applications. Here the collection of HAED is briefly described before the approach to DEM development and the current EDEN DEM are detailed. Finally future research directions for continued model development, testing, and refinement are provided.

Florida↗
USGS2016

Extending ordinal regression with a latent zero-augmented beta distribution

Ecological abundance data are often recorded on an ordinal scale in which the lowest category represents species absence. One common example is when plant species cover is visually assessedwithin bounded quadrats and then assigned to pre-defined cover class categories.We present an ordinal beta hurdle model that directly models ordinal category probabilitieswith a biologically realistic beta-distributed latent variable.Ahurdle-at-zero model allows ecologists to explore distribution (absence) and abundance processes in an integrated framework. This provides an alternative to cumulative link models when data are inconsistent with the assumption that the odds ofmoving into a higher category are the same for all categories (proportional odds). Graphical tools and a deviance information criterion were developed to assess whether a hurdle-at-zero model should be used for inferences rather than standard ordinal methods. Hurdle-at-zero and non-hurdle ordinal models fit to vegetation cover class data produced substantially different conclusions.The ordinal beta hurdle model yielded more precise parameter estimates than cumulative logit models, although out-of-sample predictions were similar. The ordinal beta hurdle model provides inferences directly on the latent biological variable of interest, percent cover, and supports exploration of more realistic ecological patterns and processes through the hurdle-at-zero or two-part specification.We provide JAGS code as an on-line supplement. Supplementary materials accompanying this paper appear on-line.

Journal of Agricultural, Biological, and Environme↗
USGS2016

Twitter predicts citation rates of ecological research

The relationship between traditional metrics of research impact (e.g., number of citations) and alternative metrics ( altmetrics ) such as Twitter activity are of great interest, but remain imprecisely quantified. We used generalized linear mixed modeling to estimate the relative effects of Twitter activity, journal impact factor, and time since publication on Web of Science citation rates of 1,599 primary research articles from 20 ecology journals published from 2012–2014. We found a strong positive relationship between Twitter activity (i.e., the number of unique tweets about an article) and number of citations. Twitter activity was a more important predictor of citation rates than 5-year journal impact factor. Moreover, Twitter activity was not driven by journal impact factor; the ‘highest-impact’ journals were not necessarily the most discussed online. The effect of Twitter activity was only about a fifth as strong as time since publication; accounting for this confounding factor was critical for estimating the true effects of Twitter use. Articles in impactful journals can become heavily cited, but articles in journals with lower impact factors can generate considerable Twitter activity and also become heavily cited. Authors may benefit from establishing a strong social media presence, but should not expect research to become highly cited solely through social media promotion. Our research demonstrates that altmetrics and traditional metrics can be closely related, but not identical. We suggest that both altmetrics and traditional citation rates can be useful metrics of research impact.

PLoS ONE↗
USGS2014

A suggestion for computing objective function in model calibration

A parameter-optimization process (model calibration) is usually required for numerical model applications, which involves the use of an objective function to determine the model cost (model-data errors). The sum of square errors (SSR) has been widely adopted as the objective function in various optimization procedures. However, ‘square error’ calculation was found to be more sensitive to extreme or high values. Thus, we proposed that the sum of absolute errors (SAR) may be a better option than SSR for model calibration. To test this hypothesis, we used two case studies—a hydrological model calibration and a biogeochemical model calibration—to investigate the behavior of a group of potential objective functions: SSR, SAR, sum of squared relative deviation (SSRD), and sum of absolute relative deviation (SARD). Mathematical evaluation of model performance demonstrates that ‘absolute error’ (SAR and SARD) are superior to ‘square error’ (SSR and SSRD) in calculating objective function for model calibration, and SAR behaved the best (with the least error and highest efficiency). This study suggests that SSR might be overly used in real applications, and SAR may be a reasonable choice in common optimization implementations without emphasizing either high or low values (e.g., modeling for supporting resources management).

Ecological Informatics↗
USGS2013

Hierarchical spatial capture-recapture models: Modeling population density from stratified populations

Capture–recapture studies are often conducted on populations that are stratified by space, time or other factors. In this paper, we develop a Bayesian spatial capture–recapture (SCR) modelling framework for stratified populations – when sampling occurs within multiple distinct spatial and temporal strata. We describe a hierarchical model that integrates distinct models for both the spatial encounter history data from capture–recapture sampling, and also for modelling variation in density among strata. We use an implementation of data augmentation to parameterize the model in terms of a latent categorical stratum or group membership variable, which provides a convenient implementation in popular BUGS software packages. We provide an example application to an experimental study involving small-mammal sampling on multiple trapping grids over multiple years, where the main interest is in modelling a treatment effect on population density among the trapping grids. Many capture–recapture studies involve some aspect of spatial or temporal replication that requires some attention to modelling variation among groups or strata. We propose a hierarchical model that allows explicit modelling of group or strata effects. Because the model is formulated for individual encounter histories and is easily implemented in the BUGS language and other free software, it also provides a general framework for modelling individual effects, such as are present in SCR models.

Methods in Ecology and Evolution↗