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At least 973 records · Page 54Linked to original sources
USGS2005

A new approach for predicting drought-related vegetation stress: Integrating satellite, climate, and biophysical data over the U.S. central plains

Droughts are normal climate episodes, yet they are among the most expensive natural disasters in the world. Knowledge about the timing, severity, and pattern of droughts on the landscape can be incorporated into effective planning and decision-making. In this study, we present a data mining approach to modeling vegetation stress due to drought and mapping its spatial extent during the growing season. Rule-based regression tree models were generated that identify relationships between satellite-derived vegetation conditions, climatic drought indices, and biophysical data, including land-cover type, available soil water capacity, percent of irrigated farm land, and ecological type. The data mining method builds numerical rule-based models that find relationships among the input variables. Because the models can be applied iteratively with input data from previous time periods, the method enables to provide predictions of vegetation conditions farther into the growing season based on earlier conditions. Visualizing the model outputs as mapped information (called VegPredict) provides a means to evaluate the model. We present prototype maps for the 2002 drought year for Nebraska and South Dakota and discuss potential uses for these maps.

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2015

Truth, models, model sets, AIC, and multimodel inference: a Bayesian perspective

Statistical inference begins with viewing data as realizations of stochastic processes. Mathematical models provide partial descriptions of these processes; inference is the process of using the data to obtain a more complete description of the stochastic processes. Wildlife and ecological scientists have become increasingly concerned with the conditional nature of model-based inference: what if the model is wrong? Over the last 2 decades, Akaike's Information Criterion (AIC) has been widely and increasingly used in wildlife statistics for 2 related purposes, first for model choice and second to quantify model uncertainty. We argue that for the second of these purposes, the Bayesian paradigm provides the natural framework for describing uncertainty associated with model choice and provides the most easily communicated basis for model weighting. Moreover, Bayesian arguments provide the sole justification for interpreting model weights (including AIC weights) as coherent (mathematically self consistent) model probabilities. This interpretation requires treating the model as an exact description of the data-generating mechanism. We discuss the implications of this assumption, and conclude that more emphasis is needed on model checking to provide confidence in the quality of inference.

Journal of Wildlife Management↗
USGS2011

Postfledging survival of Grasshopper Sparrows in grasslands managed with fire and grazing

More accurate estimates of survival after nestlings fledge are needed for population models to be parameterized and population dynamics to be understood during this vulnerable life stage. The period after fledging is the time when chicks learn to fly, forage, and hide from predators. We monitored postfledging survival, causespecific mortality, and movements of Grasshopper Sparrows ( Ammodramus savannarum ) in grassland managed with fire and grazing. In 2009, we attached radio transmitters to 50 nestlings from 50 different broods and modeled their survival in response to climatic, biological, and ecological variables. There was no effect of treatment on survival. The factor most influencing postfledging survival was age; no other variable was significant. The majority of chicks (74%) died within 3 days of radio-transmitter attachment. We attributed most mortality to mesopredators (48%) and exposure (28%). Fledglings' movements increased rapidly for the first 4 days after they left the nest and were relatively stable for the remaining 10 days we tracked them. On average, fledglings took flight for the first time 4 days after fledging and flew ≥10 m 9 days after fledging. Our data show that the Grasshopper Sparrow's survival rates may be less than most models relying on nest-success estimates predict, and we emphasize the importance of incorporating estimates of survival during the postfledging period in demographic models.

Iowa↗
USGS2013

Ability of matrix models to explain the past and predict the future of plant populations.

Uncertainty associated with ecological forecasts has long been recognized, but forecast accuracy is rarely quantified. We evaluated how well data on 82 populations of 20 species of plants spanning 3 continents explained and predicted plant population dynamics. We parameterized stage-based matrix models with demographic data from individually marked plants and determined how well these models forecast population sizes observed at least 5 years into the future. Simple demographic models forecasted population dynamics poorly; only 40% of observed population sizes fell within our forecasts' 95% confidence limits. However, these models explained population dynamics during the years in which data were collected; observed changes in population size during the data-collection period were strongly positively correlated with population growth rate. Thus, these models are at least a sound way to quantify population status. Poor forecasts were not associated with the number of individual plants or years of data. We tested whether vital rates were density dependent and found both positive and negative density dependence. However, density dependence was not associated with forecast error. Forecast error was significantly associated with environmental differences between the data collection and forecast periods. To forecast population fates, more detailed models, such as those that project how environments are likely to change and how these changes will affect population dynamics, may be needed. Such detailed models are not always feasible. Thus, it may be wiser to make risk-averse decisions than to expect precise forecasts from models.

Conservation Biology↗
USGS2023

Conservation at the nexus of niches: Multidimensional niche modeling to improve management of Prairie Chub

Objective A central challenge in applied ecology is understanding how organisms are spatially and temporally distributed and how management might be tailored to maintain or restore species distributions. The niche concept is central to understanding species distributions, but the diversity of niche definitions requires that multiple dimensions be considered. For example, the Grinnellian niche concept focuses on environmental conditions that allow species to persist, the Eltonian niche concept stresses the influence of biotic interactions, and the fundamental niche concept considers both abiotic and biotic environmental features to define spaces that organisms could occupy. Methods We combined abiotic ( A ), biotic ( B ), and movement ( M ) information (collectively, BAM model) to map the multidimensional niche of Prairie Chub Macrhybopsis australis , a regionally endemic freshwater fish currently under review for listing under the Endangered Species Act. We estimated A using remotely sensed environmental riverscape variables, B using the spatial distribution of a hybridization zone between Prairie Chub and Shoal Chub M. hyostoma , and M using data from a mark–recapture study. Result The BAM model estimated the spatial extent of multiple niches, including the Grinnellian ( A ; extent = 944 km of river), Eltonian ( B ; 2974 km), and fundamental niche (overlap of A + B ; 645 km) niches. When A , B , and M components were combined, the estimated extent of the Prairie Chub niche was 645 km. Conclusion Our work shows that the realized, multidimensional niche of Prairie Chub includes medium to large rivers with high habitat connectivity in the upper–middle Red River basin upstream of the distribution of Shoal Chub. The current Prairie Chub distribution could be maintained by preventing further habitat fragmentation and maintaining the environmental gradient separating Prairie Chub from Shoal Chub. Expansion of the species distribution may be possible through restoration of longitudinal fluvial connectivity.

North American Journal of Fisheries Management↗
USGS1995

Modeling and predicting intertidal variations of the salinity field in the Bay/Delta

San Francisco Bay and the Sacramento/San Joaquin Delta have challenged estuarine modelers for some years. Accurate, broad-scale models of this estuary have been in demand by those concerned with its ecological health and the development of sound management policies. A description and better understanding of the dynamics that govern the bay/delta are complicated by the system's complexity, requiring models that are sophisticated enough to capture the subtle physical processes involved. One approach to simulating daily to monthly variability in the bay is the development of intertidal model using tidally-averaged equations and a time step on the order of the day. An intertidal numerical model of the bay's physics, capable of portraying seasonal and inter-annual variability, would have several uses. Observations are limited in time and space, so simulation could help fill the gaps. Also, the ability to simulate multi-year episodes (eg, an extended drought) could provide insight into the response of the ecosystem to such events. Finally, such a model could be used in a forecast mode wherein predicted delta flow is used as model input, and predicted salinity distribution is output with estimates days and months in advance. This note briefly introduces such a tidally-averaged model (Uncles and Peterson, in press) and a corresponding predictive scheme for baywide forecasting.

California↗
USGS2025

Impacts of flowering rush (Butomus umbellatus L.) on macrophyte diversity and composition in the Upper Mississippi River

Flowering rush ( Butomus umbellatus L.), a perennial plant native to Eurasia, made a widespread appearance in the Upper Mississippi River in the United States in 2020, following extremely high river discharge during the previous year. Flowering rush expanded rapidly and was found at 1–10% of sites (n = 6,630 total sites) across a 400 km river reach within the first 4 years of invasion. Flowering rush invaded at least 12 of 31 wetland vegetation classes, including submersed aquatic, rooted-floating, deep marsh, and shallow marsh. Analysis of long-term macrophyte data and our targeted field study revealed that plant diversity declined with greater abundance of flowering rush over a 4-year early invasion period, suggesting that native species were displaced. Furthermore, species correlation plots showed a significant negative correlation (r < -0.1) between flowering rush and several native species, including wild celery, water stargrass, and wild rice. Non-metric multi-dimensional scaling (NMDS) ordination placed flowering rush near the center of the plot, which may indicate tolerance to a wide range of environmental conditions such as water depth, flow, and substrate. Centering on the NMDS plot also shows that flowering rush invades many types of vegetated aquatic land cover classes, which was also supported by our geographic information systems analysis of land cover invasion. These habitat associations and ecological impacts of the recent, widespread invasion of flowering rush in the Upper Mississippi River can help inform restoration and management actions during early invasion. Continuing long-term data collection can break limitations on modeling cause-effect relationships and provide insights to the future ecological trajectory of the macrophyte community to this non-native invasive species.

Illinois, Iowa, Minnesota, Wisconsin↗
USGS2021

Socio-technical scales in socio-environmental modeling: Managing a system-of-systems modeling approach

System-of-systems approaches for integrated assessments have become prevalent in recent years. Such approaches integrate a variety of models from different disciplines and modeling paradigms to represent a socio-environmental (or social-ecological) system aiming to holistically inform policy and decision-making processes. Central to the system-of-systems approaches is the representation of systems in a multi-tier framework with nested scales. Current modeling paradigms, however, have disciplinary-specific lineage, leading to inconsistencies in the conceptualization and integration of socio-environmental systems. In this paper, a multidisciplinary team of researchers, from engineering, natural and social sciences, have come together to detail socio-technical practices and challenges that arise in the consideration of scale throughout the socio-environmental modeling process. We identify key paths forward, focused on explicit consideration of scale and uncertainty, strengthening interdisciplinary communication, and improvement of the documentation process. We call for a grand vision (and commensurate funding) for holistic system-of-systems research that engages researchers, stakeholders, and policy makers in a multi-tiered process for the co-creation of knowledge and solutions to major socio-environmental problems.

Environmental Modelling and Software↗
USGS2011

Velocity-based movement modeling for individual and population level inference

Understanding animal movement and resource selection provides important information about the ecology of the animal, but an animal's movement and behavior are not typically constant in time. We present a velocity-based approach for modeling animal movement in space and time that allows for temporal heterogeneity in an animal's response to the environment, allows for temporal irregularity in telemetry data, and accounts for the uncertainty in the location information. Population-level inference on movement patterns and resource selection can then be made through cluster analysis of the parameters related to movement and behavior. We illustrate this approach through a study of northern fur seal ( Callorhinus ursinus ) movement in the Bering Sea, Alaska, USA. Results show sex differentiation, with female northern fur seals exhibiting stronger response to environmental variables.

Alaska↗
USGS2016

Flexible characterization of animal movement pattern using net squared displacement and a latent state model

Background Characterizing the movement patterns of animals is an important step in understanding their ecology. Various methods have been developed for classifying animal movement at both coarse (e.g., migratory vs. sedentary behavior) and fine (e.g., resting vs. foraging) scales. A popular approach for classifying movements at coarse resolutions involves fitting time series of net-squared displacement (NSD) to models representing different conceptualizations of coarse movement strategies (i.e., migration, nomadism, sedentarism, etc.). However, the performance of this method in classifying actual (as opposed to simulated) animal movements has been mixed. Here, we develop a more flexible method that uses the same NSD input, but relies on an underlying discrete latent state model. Using simulated data, we first assess how well patterns in the number of transitions between modes of movement and the duration of time spent in a mode classify movement strategies. We then apply our approach to elucidate variability in the movement strategies of eight giant tortoises ( Chelonoidis sp.) using a multi-year (2009&ndash;2014) GPS dataset from three different Galapagos Islands. Results With respect to patterns of time spent and the number of transitions between modes, our approach out-performed previous efforts to distinguish among migration, dispersal, and sedentary behavior. We documented marked inter-individual variation in giant tortoise movement strategies, with behaviors indicating migration, dispersal, nomadism and sedentarism, as well as hybrid behaviors such as &ldquo;exploratory residence&rdquo;. Conclusions Distilling complex animal movement into discrete modes remains a fundamental challenge in movement ecology, a problem made more complex by the ever-longer duration, ever-finer resolution, and gap-ridden trajectories recorded by GPS devices. By clustering into modes, we derived information on the time spent within one mode and the number of transitions between modes which enabled finer differentiation of movement strategies over previous methods. Ultimately, the techniques developed here address limitations of previous approaches and provide greater insights with respect to characterization of movement strategies across scales by more fully utilizing long-term GPS telemetry datasets.

Movement Ecology↗
USGS2009

Temporal and maternal effects on reproductive ecology of the giant gartersnake ( Thamnophis gigas )

We used mixed-effects models to examine relationships of reproductive characteristics of the giant gartersnake ( Thamnophis gigas ) to improve population modeling and conservation planning for this species. Neonates from larger litters had lower mass, and mass of neonates also was affected by random variation among mothers. Length of mother did not affect relative mass of litters; however, our data suggest that longer mothers expended less reproductive effort per offspring than shorter mothers. We detected random variation in length of neonates among mothers, but these lengths were not related to length of mother or size of litter. Mean size of litter varied among years, but little evidence existed for a relationship between size of litter or mass of litter and length of mother. Sex ratios of neonates did not differ from 1:1.

Southwestern Naturalist↗
USGS2011

Development of a high-resolution binational vegetation map of the Santa Cruz River riparian corridor and surrounding watershed, southern Arizona and northern Sonora, Mexico

This report summarizes the development of a binational vegetation map developed for the Santa Cruz Watershed, which straddles the southern border of Arizona and the northern border of Sonora, Mexico. The map was created as an environmental input to the Santa Cruz Watershed Ecosystem Portfolio Model (SCWEPM) that is being created by the U.S. Geological Survey for the watershed. The SCWEPM is a map-based multicriteria evaluation tool that allows stakeholders to explore tradeoffs between valued ecosystem services at multiple scales within a participatory decision-making process. Maps related to vegetation type and are needed for use in modeling wildlife habitat and other ecosystem services. Although detailed vegetation maps existed for the U.S. side of the border, there was a lack of consistent data for the Santa Cruz Watershed in Mexico. We produced a binational vegetation classification of the Santa Cruz River riparian habitat and watershed vegetation based on NatureServe Terrestrial Ecological Systems (TES) units using Classification And Regression Tree (CART) modeling. Environmental layers used as predictor data were derived from a seasonal set of Landsat Thematic Mapper (TM) images (spring, summer, and fall) and from a 30-meter digital-elevation-model (DEM) grid. Because both sources of environmental data are seamless across the international border, they are particularly suited to this binational modeling effort. Training data were compiled from existing field data for the riparian corridor and data collected by the NM-GAP (New Mexico Gap Analysis Project) team for the original Southwest Regional Gap Analysis Project (SWReGAP) modeling effort. Additional training data were collected from core areas of the SWReGAP classification itself, allowing the extrapolation of the SWReGAP mapping into the Mexican portion of the watershed without collecting additional training data.

Arizona↗
USGS2010

Accuracy of gap analysis habitat models in predicting physical features for wildlife-habitat associations in the southwest U.S.

Despite widespread and long-standing efforts to model wildlife-habitat associations using remotely sensed and other spatially explicit data, there are relatively few evaluations of the performance of variables included in predictive models relative to actual features on the landscape. As part of the National Gap Analysis Program, we specifically examined physical site features at randomly selected sample locations in the Southwestern U.S. to assess degree of concordance with predicted features used in modeling vertebrate habitat distribution. Our analysis considered hypotheses about relative accuracy with respect to 30 vertebrate species selected to represent the spectrum of habitat generalist to specialist and categorization of site by relative degree of conservation emphasis accorded to the site. Overall comparison of 19 variables observed at 382 sample sites indicated ???60% concordance for 12 variables. Directly measured or observed variables (slope, soil composition, rock outcrop) generally displayed high concordance, while variables that required judgments regarding descriptive categories (aspect, ecological system, landform) were less concordant. There were no differences detected in concordance among taxa groups, degree of specialization or generalization of selected taxa, or land conservation categorization of sample sites with respect to all sites. We found no support for the hypothesis that accuracy of habitat models is inversely related to degree of taxa specialization when model features for a habitat specialist could be more difficult to represent spatially. Likewise, we did not find support for the hypothesis that physical features will be predicted with higher accuracy on lands with greater dedication to biodiversity conservation than on other lands because of relative differences regarding available information. Accuracy generally was similar (>60%) to that observed for land cover mapping at the ecological system level. These patterns demonstrate resilience of gap analysis deductive model processes to the type of remotely sensed or interpreted data used in habitat feature predictions. ?? 2010 Elsevier B.V.

Ecological Modelling↗
USGS1991

Bank accretion and the development of vegetated depositional surfaces along modified alluvial channels

This paper describes the recovery of stable bank form and development of vegetated depositional surfaces along the banks of channelized West Tennessee streams. Most perennial streams in West Tennessee were straightened and dredged since the turn of the century. Patterns of fluvial ecological responses to channelization have previously been described by a six-stage model. Dendrogeomorphic (tree-ring) techniques allowed the determination of location, timing, amount, and rate of bank-sediment deposition. Channel cross sections and ecological analyses made at 101 locations along 12 streams, encompassing bends and straight reaches, show that channel and bank processes initially react vertically to channelization through downcutting. A depositional surface forms on banks once bed-degradation and heightened bank mass wasting processes have eased or slowed. The formation of this depositional surface marks the beginning of bank recovery from channelization. Dominating lateral processes, characteristic of stable or natural channels, return during the formation and expansion of the depositional surface, suggesting a relation with thalweg deflection, point-bar development, and meanderloop extension. Characteristic woody riparian vegetation begins to grow as this depositional surface develops and becomes part of the process and form of restabilizing banks. The depositional surface initially forms low on the bank and tends to maintain a slope of about 24°. Mean accretion rates ranges from 5.9 cm/yr on inside bends to 0 cm/yr on most outside bends; straight reaches have a mean-accretion rate of 4.2 cm/yr. The relatively stable, convex upward, depositional surface expands and ultimately attaches to the flood plain. The time required for the recovery process to reach equilibrium averaged about 50 years. Indicative pioneer speccies of woody riparian vegetation include black willow, river birch, silver maple, and boxelder. Stem densities generally decrease with time after and initial flush of about 160 stems per 100 m 2 . Together bank accretion and vegetative regrowth appear to be the most important environmental processes involved in channel bank recovery from channelization or rejuvenation.

Geomorphology↗
USGS2018

Reproductive success and contaminant associations in tree swallows (Tachycineta bicolor) used to assess a Beneficial Use Impairment in U.S. and Binational Great Lakes’ Areas of Concern

During 2010-2014, tree swallow (Tachycineta bicolor) reproductive success was monitored at 68 sites across all 5 Great Lakes, including 58 sites located within Great Lakes Areas of Concern (AOCs) and 10 non-AOCs. Sample eggs were collected from tree swallow clutches and analyzed for contaminants including polychlorinated biphenyls (PCBs), dioxins and furans, polybrominated diphenyl ethers, and 34 other organic compounds. Contaminant data were available for 360 of the clutches monitored. Markov chain multistate modeling was used to assess the importance of 5 ecological variables and 11 of the dominant contaminants in explaining the pattern of egg and nestling failure rates. Four of 5 ecological variables (Female Age, Date within season, Year, and Site) were important explanatory variables. Of the 11 contaminants, only total dioxin and furan toxic equivalents (TEQs) explained a significant amount of the egg failure probabilities. Neither total PCBs nor PCB TEQs explained the variation in egg failure rates. In a separate analysis, polycyclic aromatic hydrocarbon exposure in nestling diet, used as a proxy for female diet during egg laying, was significantly correlated with the daily probability of egg failure. The 8 sites within AOCs which had poorer reproduction when compared to 10 non-AOC sites, the measure of impaired reproduction as defined by the Great Lakes Restoration Initiative, were associated with exposure to dioxins and furan TEQs, PAHs, or depredation. Only 2 sites had poorer reproduction than the poorest performing non-AOC. Using a classic (non-modeling) approach to estimating reproductive success, 82% of nests hatched at least 1 egg, and 75% of eggs laid, excluding those collected for contaminant analyses, hatched.

Great Lakes↗
USGS2026

Satellite time series analysis to quantify changing climax ciénegas using a state and transition model approach

Ciénegas are rare wetlands in arid landscapes of the North American Southwest, historically providing critical ecological and hydrological functions but increasingly threatened by changing climate and land use pressures. This study quantifies changes in ciénega condition and floodplain dynamics using a state-and-transition model (STM) informed by expert knowledge and remote sensing. Key factors include woody plant encroachment, water availability, and soil aggradation. We mapped 31 ciénegas with high-resolution imagery and analyzed Landsat data (1985–2023) to assess vegetation health and moisture using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII). Results show substantial interannual variability in phenology, water stress, and soil moisture, with regional drying and elevation strongly influencing ciénega resilience. We classified ciénegas into three functional states—healthy, desiccated, and dormant—and mapped their 2023 condition. Trend analyses indicate most ciénegas exhibit greening despite drought, though localized variability underscores the need for site-specific management. None are in a stable climax (reference) state; rather, they transition among states in response to external drivers. Increasing woody plant cover and surface drying, likely linked to declining regional water tables, favor deep-rooted species over wetland grasses—a pattern mirrored in adjacent control plots. Spatially explicit analysis revealed intra-ciénega variability often masked by aggregated data, highlighting the importance of high-resolution monitoring. Seasonal and long-term trends provide context for understanding ciénega dynamics, including degradation and restoration pathways. This study emphasizes the importance of groundwater conservation and demonstrates how remote sensing supports long-term monitoring. The STM framework offers a practical tool for adaptive management to sustain freshwater resources in arid environments.

Arizona, New Mexico↗
USGS2011

Incorporating parametric uncertainty into population viability analysis models

Uncertainty in parameter estimates from sampling variation or expert judgment can introduce substantial uncertainty into ecological predictions based on those estimates. However, in standard population viability analyses, one of the most widely used tools for managing plant, fish and wildlife populations, parametric uncertainty is often ignored in or discarded from model projections. We present a method for explicitly incorporating this source of uncertainty into population models to fully account for risk in management and decision contexts. Our method involves a two-step simulation process where parametric uncertainty is incorporated into the replication loop of the model and temporal variance is incorporated into the loop for time steps in the model. Using the piping plover, a federally threatened shorebird in the USA and Canada, as an example, we compare abundance projections and extinction probabilities from simulations that exclude and include parametric uncertainty. Although final abundance was very low for all sets of simulations, estimated extinction risk was much greater for the simulation that incorporated parametric uncertainty in the replication loop. Decisions about species conservation (e.g., listing, delisting, and jeopardy) might differ greatly depending on the treatment of parametric uncertainty in population models.

Biological Conservation↗
USGS1998

A model of northern pintail productivity and population growth rate

Our objective was to synthesize individual components of reproductive ecology into a single estimate of productivity and to assess the relative effects of survival and productivity on population dynamics. We used information on nesting ecology, renesting potential, and duckling survival of northern pintails ( Anas acuta ) collected on the Yukon-Kuskokwim Delta (Y-K Delta), Alaska, 1991-95, to model the number of ducklings produced under a range of nest success and duckling survival probabilities. Using average values of 25% nest success, 11% duckling survival, and 56% renesting probability from our study population, we calculated that all young in our population were produced by 13% of the breeding females, and that early-nesting females produced more young than later-nesting females. Further, we calculated, on average, that each female produced only 0.16 young females/nesting season. We combined these results with estimates of first-year and adult survival to examine the growth rate (X) of the population and the relative contributions of these demographic parameters to that growth rate. Contrary to aerial survey data, the population projection model suggests our study population is declining rapidly (X = 0.6969). The relative effects on population growth rate were 0.1175 for reproductive success, 0.1175 for first-year survival, and 0.8825 for adult survival. Adult survival had the greatest influence on X for our population, and this conclusion was robust over a range of survival and productivity estimates. Given published estimates of annual survival for adult females (61%), our model suggested nest success and duckling survival need to increase to approximately 40% to achieve population stability. We discuss reasons for the apparent discrepancy in population trends between our model and aerial surveys in terms of bias in productivity and survival estimates.

Alaska↗