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At least 343 records · Page 19Linked to original sources
USGS2021

Diffusion modeling reveals effects of multiple release sites and human activity on a recolonizing apex predator

Background Reintroducing predators is a promising conservation tool to help remedy human-caused ecosystem changes. However, the growth and spread of a reintroduced population is a spatiotemporal process that is driven by a suite of factors, such as habitat change, human activity, and prey availability. Sea otters ( Enhydra lutris ) are apex predators of nearshore marine ecosystems that had declined nearly to extinction across much of their range by the early 20th century. In Southeast Alaska, which is comprised of a diverse matrix of nearshore habitat and managed areas, reintroduction of 413 individuals in the late 1960s initiated the growth and spread of a population that now exceeds 25,000. Methods Periodic aerial surveys in the region provide a time series of spatially-explicit data to investigate factors influencing this successful and ongoing recovery. We integrated an ecological diffusion model that accounted for spatially-variable motility and density-dependent population growth, as well as multiple population epicenters, into a Bayesian hierarchical framework to help understand the factors influencing the success of this recovery. Results Our results indicated that sea otters exhibited higher residence time as well as greater equilibrium abundance in Glacier Bay, a protected area, and in areas where there is limited or no commercial fishing. Asymptotic spread rates suggested sea otters colonized Southeast Alaska at rates of 1–8 km/yr with lower rates occurring in areas correlated with higher residence time, which primarily included areas near shore and closed to commercial fishing. Further, we found that the intrinsic growth rate of sea otters may be higher than previous estimates suggested. Conclusions This study shows how predator recolonization can occur from multiple population epicenters. Additionally, our results suggest spatial heterogeneity in the physical environment as well as human activity and management can influence recolonization processes, both in terms of movement (or motility) and density dependence.

Alaska↗
USGS2015

Landscape genomics of Sphaeralcea ambigua in the Mojave Desert: a multivariate, spatially-explicit approach to guide ecological restoration

Local adaptation influences plant species’ responses to climate change and their performance in ecological restoration. Fine-scale physiological or phenological adaptations that direct demographic processes may drive intraspecific variability when baseline environmental conditions change. Landscape genomics characterize adaptive differentiation by identifying environmental drivers of adaptive genetic variability and mapping the associated landscape patterns. We applied such an approach to Sphaeralcea ambigua , an important restoration plant in the arid southwestern United States, by analyzing variation at 153 amplified fragment length polymorphism loci in the context of environmental gradients separating 47 Mojave Desert populations. We identified 37 potentially adaptive loci through a combination of genome scan approaches. We then used a generalized dissimilarity model (GDM) to relate variability in potentially adaptive loci with spatial gradients in temperature, precipitation, and topography. We identified non-linear thresholds in loci frequencies driven by summer maximum temperature and water stress, along with continuous variation corresponding to temperature seasonality. Two GDM-based approaches for mapping predicted patterns of local adaptation are compared. Additionally, we assess uncertainty in spatial interpolations through a novel spatial bootstrapping approach. Our study presents robust, accessible methods for deriving spatially-explicit models of adaptive genetic variability in non-model species that will inform climate change modelling and ecological restoration.

California, Nevada↗
USGS2018

Fitting N-mixture models to count data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches

Monitoring animal populations is central to wildlife and fisheries management, and the use of N-mixture models toward these efforts has markedly increased in recent years. Nevertheless, relatively little work has evaluated estimator performance when basic assumptions are violated. Moreover, diagnostics to identify when bias in parameter estimates from N-mixture models is likely is largely unexplored. We simulated count data sets using 837 combinations of detection probability, number of sample units, number of survey occasions, and type and extent of heterogeneity in abundance or detectability. We fit Poisson N-mixture models to these data, quantified the bias associated with each combination, and evaluated if the parametric bootstrap goodness-of-fit (GOF) test can be used to indicate bias in parameter estimates. We also explored if assumption violations can be diagnosed prior to fitting N-mixture models. In doing so, we propose a new model diagnostic, which we term the quasi-coefficient of variation (QCV). N-mixture models performed well when assumptions were met and detection probabilities were moderate (i.e., ≥0.3), and the performance of the estimator improved with increasing survey occasions and sample units. However, the magnitude of bias in estimated mean abundance with even slight amounts of unmodeled heterogeneity was substantial. The parametric bootstrap GOF test did not perform well as a diagnostic for bias in parameter estimates when detectability and sample sizes were low. The results indicate the QCV is useful to diagnose potential bias and that potential bias associated with unidirectional trends in abundance or detectability can be diagnosed using Poisson regression. This study represents the most thorough assessment to date of assumption violations and diagnostics when fitting N-mixture models using the most commonly implemented error distribution. Unbiased estimates of population state variables are needed to properly inform management decision making. Therefore, we also discuss alternative approaches to yield unbiased estimates of population state variables using similar data types, and we stress that there is no substitute for an effective sample design that is grounded upon well-defined management objectives.

Ecological Modelling↗
USGS2015

Simulating range-wide population and breeding habitat dynamics for an endangered woodland warbler in the face of uncertainty

Population viability analyses provide a quantitative approach that seeks to predict the possible future status of a species of interest under different scenarios and, therefore, can be important components of large-scale species’ conservation programs. We created a model and simulated range-wide population and breeding habitat dynamics for an endangered woodland warbler, the golden-cheeked warbler ( Setophaga chrysoparia ). Habitat-transition probabilities were estimated across the warbler's breeding range by combining National Land Cover Database imagery with multistate modeling. Using these estimates, along with recently published demographic estimates, we examined if the species can remain viable into the future given the current conditions. Lastly, we evaluated if protecting a greater amount of habitat would increase the number of warblers that can be supported in the future by systematically increasing the amount of protected habitat and comparing the estimated terminal carrying capacity at the end of 50 years of simulated habitat change. The estimated habitat-transition probabilities supported the hypothesis that habitat transitions are unidirectional, whereby habitat is more likely to diminish than regenerate. The model results indicated population viability could be achieved under current conditions, depending on dispersal. However, there is considerable uncertainty associated with the population projections due to parametric uncertainty. Model results suggested that increasing the amount of protected lands would have a substantial impact on terminal carrying capacities at the end of a 50-year simulation. Notably, this study identifies the need for collecting the data required to estimate demographic parameters in relation to changes in habitat metrics and population density in multiple regions, and highlights the importance of establishing a common definition of what constitutes protected habitat, what management goals are suitable within those protected areas, and a standard operating procedure to identify areas of priority for habitat conservation efforts. Therefore, we suggest future efforts focus on these aspects of golden-cheeked warbler conservation and ecology.

Ecological Modelling↗
USGS2020

Consequences of ignoring group association in spatial capture-recapture analysis

Many models in population ecology, including spatial capture–recapture (SCR) models, assume that individuals are distributed and detected independently of one another. In reality, this is rarely the case – both antagonistic and gregarious relationships lead to non-independent spatial configurations, with territorial exclusion at one end of the spectrum and group-living at the other. Previous simulation studies suggest that grouping has limited impact on the outcome of SCR analyses. However, group associations entail not only spatial clustering of activity centers but also coordinated space use by group members, potentially impacting both ecological and observation processes underlying SCR analysis. We simulated SCR scenarios with different strengths of aggregation (clustering of individuals into groups with shared activity centers) and cohesion (synchronization of detection patterns of members of a group). We then fit SCR models to the simulated data sets and evaluated the effect of aggregation and cohesion on parameter estimates. Low to moderate aggregation and cohesion did not impact the bias and precision of estimates of density and the scale parameter of the detection function. However, non-independence between individuals led to high levels of overdispersion. Overdispersion strongly decreased the coverage of confidence intervals around parameter estimates, thereby increasing the probability of erroneous predictions. Our results indicate that SCR models are robust to moderate levels of aggregation and cohesion. Nonetheless, spatial dependence between individuals can lead to false inference. We recommend that practitioners 1) test for the presence of overdispersion in SCR data caused by aggregation and cohesion, and, if necessary, 2) correct their variance estimates using the overdispersion factor ĉ . Approaches for doing both are described in this paper. We also urge the development of SCR models that incorporate spatial associations between individuals not only to account for overdispersion but also to obtain quantitative information about social aspects of study populations.

Wildlife Biology↗
USGS2008

Evaluating intercepts from demographic models to understand resource limitation and resource thresholds

Understanding resource limitation is critical to effective management and conservation of wild populations, however resource limitation is difficult to quantify partly because resource limitation is a dynamic process. Specifically, a resource that is limiting at one time may become non-limiting at another time, depending upon changes in its availability and changes in the availability of other resources. Methods for understanding resource limitation, therefore, must consider the dynamic effects of resources on demography. We present approaches for interpreting results of demographic modeling beyond analyzing model rankings, model weights, slope estimates, and model averaging. We demonstrate how interpretation of y-intercepts, odds ratios, and rates of change can yield insights into resource limitation as a dynamic process, assuming logistic regression is used to link estimates of resources with estimates of demography. In addition, we show how x-intercepts can be evaluated with respect to odds ratios to understand resource thresholds. ?? 2007 Elsevier B.V. All rights reserved.

Ecological Modelling↗
USGS2006

Assessing vaccination as a control strategy in an ongoing epidemic: Bovine tuberculosis in African buffalo

Bovine tuberculosis (BTB) is an exotic disease invading the buffalo population ( Syncerus caffer ) of the Kruger National Park (KNP), South Africa. We used a sex and age-structured epidemiological model to assess the effectiveness of a vaccination program and define important research directions. The model allows for dispersal between a focal herd and background population and was parameterized with a combination of published data and analyses of over 130 radio-collared buffalo in the central region of the KNP. Radio-tracking data indicated that all sex and age categories move between mixed herds, and males over 8 years old had higher mortality and dispersal rates than any other sex or age category. In part due to the high dispersal rates of buffalo, sensitivity analyses indicate that disease prevalence in the background population accounts for the most variability in the BTB prevalence and quasi-eradication within the focal herd. Vaccination rate and the transmission coefficient were the second and third most important parameters of the sensitivity analyses. Further analyses of the model without dispersal suggest that the amount of vaccination necessary for quasi-eradication (i.e. prevalence < 5%) depends upon the duration that a vaccine grants protection. Vaccination programs are more efficient (i.e. fewer wasted doses) when they focus on younger individuals. However, even with a lifelong vaccine and a closed population, the model suggests that >70% of the calf population would have to be vaccinated every year to reduce the prevalence to less than 1%. If the half-life of the vaccine is less than 5 years, even vaccinating every calf for 50 years may not eradicate BTB. Thus, although vaccination provides a means of controlling BTB prevalence it should be combined with other control measures if eradication is the objective.

Kruger National Park↗
USGS2022

Stochastic agent-based model for predicting turbine-scale raptor movements during updraft-subsidized directional flights

Rapid expansion of wind energy development across the world has highlighted the need to better understand turbine-caused avian mortality. The risk to golden eagles ( Aquila chrysaetos ) is of particular concern due to their small population size and conservation status. Golden eagles subsidize their flight in part by soaring in orographic updrafts, which can place them in conflict with wind turbines utilizing the same low-altitude wind resource. Understanding the behavior of soaring raptors in varying atmospheric conditions can therefore be relevant to predicting and mitigating their risk of collision. We present a predictive movement model that simulates individual paths of golden eagles during directional flight (such as migration) that is subsidized by orographic updraft. We modeled eagles in a 50 km by 50 km study area in Wyoming containing three wind power plants with documented golden eagle collisions with turbines. The movement model is applicable to any region where ground elevation is known at turbine scale ( &lt; "> < 50 m) and wind conditions are known at facility scale ( &lt; "> < 3 km). For a given set of atmospheric conditions, the model simulates movements of thousands of orographic soaring eagles to produce a density map quantifying the relative probability of eagle presence. We validated the simulated tracks with GPS telemetry data showing four directional tracks made by golden eagles transiting through the area in 2019 and 2020. For each eagle track, validation was performed using the ratio of the model-simulated eagle presence likelihood with uniform eagle presence and the presence computed using directed random-walk movements. We found that the predictive performance of the model was significantly better (likelihood ratio &gt; "> > 1) for low-altitude movements than high-altitude movements that can involve thermal-soaring. We employed the model to produce seasonal presence maps for migrating golden eagles. We found significant turbine-level variations in eagle presence between northerly and southerly migration routes through the study area. Overall, the proposed model offers a generalizable, probabilistic, and predictive tool to assist wind energy developers, ecologists, wildlife managers, and industry consultants in estimating the potential for conflict between soaring birds and wind turbines, thereby reducing the need for site-specific data on golden eagle movements.

Wyoming↗
USGS2026

Mississippi’s resiliency and changing environmental conditions

Mississippi is experiencing shifts in environmental conditions, many of which are expected to intensify in the coming decades, and will have important implications for ecosystems, species, and natural resource management across the state. Historical climate analyses and downscaled projections summarized for the state’s Environmental Protection Agency (EPA) level III Ecoregions show that Mississippi will likely face increases in extremely hot days, more intense heatwaves, greater evapotranspiration, and reductions in cold-season extremes. Temperature trends indicate that while Mississippi has warmed relatively little over the last century, recent years have been among the warmest on record. Projections suggest substantial increases in the number of days with maximum temperatures at or above 95°F across all Ecoregions, particularly under higher warming scenarios. Correspondingly, the number of freezing nights is expected to decline, with statewide decreases of 7–21 days depending on the global warming level and timeframe. These combined patterns are likely to reshape habitat conditions, influence species’ thermal stress, and alter ecological processes such as soil moisture retention and stream temperature regimes. Mississippi’s hydrological systems are also expected to be significantly affected. Extreme rainfall events, particularly the most intense 1% of precipitation days, are projected to increase across the state, raising risks of flooding, erosion, and altered stream flow. Coastal Mississippi faces additional stress from sea‑level rise, which is already contributing to more frequent high‑tide flooding; projections show continued increases in inundation risk throughout the century. These flooding patterns, combined with stronger hurricane rainfall rates and the potential for landfalling storms to stall, pose ongoing threats to coastal ecosystems and communities. The state is also vulnerable to extreme weather hazards such as tornadoes. Recent research shows a geographic shift in tornado activity toward the Southeast, with Mississippi experiencing some of the nation’s greatest increases in favorable tornado days and nighttime tornado frequency—hazards that have direct implications for wildlife, habitat stability, and human safety. Drought conditions may intensify as well: higher temperatures and increased evapotranspiration during dry spells could worsen water stress regardless of uncertainty in average annual precipitation. These changing environmental conditions have important consequences for Mississippi’s Species of Greatest Conservation Need (SGCN). Vulnerability assessments and ecological niche models indicate that several fish, mussel, crayfish, plant, amphibian, bird, and mammal species may face heightened stress due to warming temperatures, shifting hydrology, habitat loss, and increases in extreme events. For example, many cold‑water and moisture‑dependent species in the state’s rivers and wetlands are already near their thermal limits, and further warming could reduce suitable habitat. Birds breeding in bottomland hardwood forests, such as the Prothonotary Warbler, are increasingly susceptible to nest loss from flooding. Similarly, species like the Louisiana Black Bear may encounter higher risks from habitat inundation and shifting food availability. The “A Closer Look” section underscores the vulnerability of the Mississippi Alluvial Plain—one of the state’s most ecologically rich yet heavily altered Ecoregions. Its bottomland hardwood forests, wetlands, and floodplain habitats support diverse wildlife but are increasingly threatened by rising temperatures, hydrological changes, development pressures, and compounded disturbances. These habitats provide critical ecosystem services such as water filtration, flood attenuation, and biodiversity support, making their conservation particularly urgent under future conditions. Collectively, these findings indicate that Mississippi’s ongoing and projected environmental changes present both challenges and opportunities for natural resource management. Incorporating adaptation strategies into the State Wildlife Action Plan will be essential for sustaining habitats, mitigating risks, and supporting the long‑term resilience of Mississippi’s wildlife and ecosystems.

Mississippi↗
USGS2013

Simulating mechanisms for dispersal, production and stranding of small forage fish in temporary wetland habitats

Movement strategies of small forage fish (<8 cm total length) between temporary and permanent wetland habitats affect their overall population growth and biomass concentrations, i.e., availability to predators. These fish are often the key energy link between primary producers and top predators, such as wading birds, which require high concentrations of stranded fish in accessible depths. Expansion and contraction of seasonal wetlands induce a sequential alternation between rapid biomass growth and concentration, creating the conditions for local stranding of small fish as they move in response to varying water levels. To better understand how landscape topography, hydrology, and fish behavior interact to create high densities of stranded fish, we first simulated population dynamics of small fish, within a dynamic food web, with different traits for movement strategy and growth rate, across an artificial, spatially explicit, heterogeneous, two-dimensional marsh slough landscape, using hydrologic variability as the driver for movement. Model output showed that fish with the highest tendency to invade newly flooded marsh areas built up the largest populations over long time periods with stable hydrologic patterns. A higher probability to become stranded had negative effects on long-term population size, and offset the contribution of that species to stranded biomass. The model was next applied to the topography of a 10 km × 10 km area of Everglades landscape. The details of the topography were highly important in channeling fish movements and creating spatiotemporal patterns of fish movement and stranding. This output provides data that can be compared in the future with observed locations of fish biomass concentrations, or such surrogates as phosphorus ‘hotspots’ in the marsh.

Ecological Modelling↗
USGS2014

Uncertainty, robustness, and the value of information in managing an expanding Arctic goose population

We explored the application of dynamic-optimization methods to the problem of pink-footed goose (Anser brachyrhynchus) management in western Europe. We were especially concerned with the extent to which uncertainty in population dynamics influenced an optimal management strategy, the gain in management performance that could be expected if uncertainty could be eliminated or reduced, and whether an adaptive or robust management strategy might be most appropriate in the face of uncertainty. We combined three alternative survival models with three alternative reproductive models to form a set of nine annual-cycle models for pink-footed geese. These models represent a wide range of possibilities concerning the extent to which demographic rates are density dependent or independent, and the extent to which they are influenced by spring temperatures. We calculated state-dependent harvest strategies for these models using stochastic dynamic programming and an objective function that maximized sustainable harvest, subject to a constraint on desired population size. As expected, attaining the largest mean objective value (i.e., the relative measure of management performance) depended on the ability to match a model-dependent optimal strategy with its generating model of population dynamics. The nine models suggested widely varying objective values regardless of the harvest strategy, with the density-independent models generally producing higher objective values than models with density-dependent survival. In the face of uncertainty as to which of the nine models is most appropriate, the optimal strategy assuming that both survival and reproduction were a function of goose abundance and spring temperatures maximized the expected minimum objective value (i.e., maxi–min). In contrast, the optimal strategy assuming equal model weights minimized the expected maximum loss in objective value. The expected value of eliminating model uncertainty was an increase in objective value of only 3.0%. This value represents the difference between the best that could be expected if the most appropriate model were known and the best that could be expected in the face of model uncertainty. The value of eliminating uncertainty about the survival process was substantially higher than that associated with the reproductive process, which is consistent with evidence that variation in survival is more important than variation in reproduction in relatively long-lived avian species. Comparing the expected objective value if the most appropriate model were known with that of the maxi–min robust strategy, we found the value of eliminating uncertainty to be an expected increase of 6.2% in objective value. This result underscores the conservatism of the maxi–min rule and suggests that risk-neutral managers would prefer the optimal strategy that maximizes expected value, which is also the strategy that is expected to minimize the maximum loss (i.e., a strategy based on equal model weights). The low value of information calculated for pink-footed geese suggests that a robust strategy (i.e., one in which no learning is anticipated) could be as nearly effective as an adaptive one (i.e., a strategy in which the relative credibility of models is assessed through time). Of course, an alternative explanation for the low value of information is that the set of population models we considered was too narrow to represent key uncertainties in population dynamics. Yet we know that questions about the presence of density dependence must be central to the development of a sustainable harvest strategy. And while there are potentially many environmental covariates that could help explain variation in survival or reproduction, our admission of models in which vital rates are drawn randomly from reasonable distributions represents a worst-case scenario for management. We suspect that much of the value of the various harvest strategies we calculated is derived from the fact that they are state dependent, such that appropriate harvest rates depend on population abundance and weather conditions, as well as our focus on an infinite time horizon for sustainability.

Ecological Modelling↗
USGS2020

Double exposure and dynamic vulnerability: Assessing economic well-being, ecological change and the development of the oil and gas industry in coastal Louisiana

The oil and gas industry has been a powerful driver of economic change in coastal Louisiana for the latter half of the 20th century and into the 21st. Yet, the overall impact of the industry on the economic well-being of host communities is varied, both spatially and temporally. While the majority of Louisiana’s oil and gas production now occurs offshore, processing the extracted product is an energy-intensive undertaking requiring an expansive network of land-based infrastructure. Despite the positive economic aspects of this development, there are also potential negatives posed to coastal ecosystems and to communities located adjacent to oil and gas infrastructure. This research utilizes a double exposure framework to explore the relationship between oil and gas infrastructure development, fish and shellfish habitat, and economic well-being in Louisiana’s coastal zone from 1950 to 2010. The approach followed four main steps: (1) Developing a hazardousness of place model to identify areas of magnified risk due to the combined hazards of multiple potential exposure sites related to the extraction and processing of crude oil and natural gas; (2) developing a model of ecological functioning to measure the ability of aquatic habitat to support key fish and shellfish species; (3) utilizing an integrated community economic well-being index to assess change on a decadal timescale; and (4) analyzing selected oil-dependent communities to illustrate how change processes occurring in different energy sectors result in differential outcomes. The results suggest that, for many communities, the dependence on the oil and gas industry has increased economic well-being but also increased sensitivity to natural and human-induced changes, including fluctuating economic conditions, environmental stress, coastal habitat destruction, and increasing social and economic pressures.

Louisiana↗
USGS2016

Interactions of landscape disturbances and climate change dictate ecological pattern and process: spatial modeling of wildfire, insect, and disease dynamics under future climates

Context Interactions among disturbances, climate, and vegetation influence landscape patterns and ecosystem processes. Climate changes, exotic invasions, beetle outbreaks, altered fire regimes, and human activities may interact to produce landscapes that appear and function beyond historical analogs. Objectives We used the mechanistic ecosystem-fire process model FireBGCv2 to model interactions of wildland fire, mountain pine beetle ( Dendroctonus ponderosae ), and white pine blister rust ( Cronartium ribicola ) under current and future climates, across three diverse study areas. Methods We assessed changes in tree basal area as a measure of landscape response over a 300-year simulation period for the Crown of the Continent in north-central Montana, East Fork of the Bitterroot River in western Montana, and Yellowstone Central Plateau in western Wyoming, USA. Results Interacting disturbances reduced overall basal area via increased tree mortality of host species. Wildfire decreased basal area more than beetles or rust, and disturbance interactions modeled under future climate significantly altered landscape basal area as compared with no-disturbance and current climate scenarios. Responses varied among landscapes depending on species composition, sensitivity to fire, and pathogen and beetle suitability and susceptibility. Conclusions Understanding disturbance interactions is critical for managing landscapes because forest responses to wildfires, pathogens, and beetle attacks may offset or exacerbate climate influences, with consequences for wildlife, carbon, and biodiversity.

Landscape Ecology↗
USGS2023

Using state-and-transition simulation models to scope post-fire success in restoring greater sage-grouse habitat

Wildfires are increasingly modifying wildlife habitat in the western United States and managers need ways to scope the pace and degree to which post-fire restoration actions can re-create habitat in dynamic landscapes. We developed a spatially explicit state-transition simulation model (STSM) to project post-fire revegetation and the potential for sage-grouse habitat restoration in sagebrush ecosystems. The model included annual fires, annual grass invasion, conifer encroachment, and projected annual vegetation growth caused by natural regeneration as well as sagebrush seeding and planting. We cross-referenced resulting vegetation maps with greater sage-grouse ( Centrocercus urophasianus ) habitat needs and evaluated trajectories of potential habitat at three Priority Areas for Conservation in the Great Basin. We compared outcomes among different types of revegetation actions (natural regeneration, seeding, planting), treatment durations, and treatment area sizes. In all scenarios, sagebrush cover was generally insufficient to meet sage-grouse needs for at least a decade post-fire, and the best habitat classes declined or remained at low proportions of landscapes for >50 years post-fire. Under current fire patterns, the pace of habitat restoration is likely to lag behind losses from wildfires. Our results indicate additional efforts beyond sagebrush revegetation actions (e.g., fire suppression, invasive grass treatment) will likely be necessary to maintain and restore areas to meet sage-grouse habitat needs in burned landscapes. Our results also underscore the need for broad-scale habitat restoration strategies that expand the ability to reestablish sagebrush in large, burned areas, as well as strategies for defining which areas should be prioritized for revegetation within the biome. Our landscape models and resulting vegetation maps can be integrated with other restoration prioritization or wildlife monitoring tools that support land manager decision-making. By gauging potential benefits of restoration decisions, our approach can provide information to aid choices on where to invest time, money, and effort and how best to mitigate losses and plan long-term restoration and recovery for landscapes across the sagebrush biome.

Great Basin↗
USGS2018

Temperature-influenced energetics model for migrating waterfowl

Climate and weather affect avian migration by influencing when and where birds fly, the energy costs and risks of flight, and the ability to sense cues necessary for proper navigation. We review the literature of the physiology of avian migration and the influence of climate, specifically temperature, on avian migration dynamics. We use waterfowl as a model guild because of the ready availability of empirical physiological data and their enormous economic value, but our discussion and expectations are broadly generalizable to migratory birds in general. We detail potential consequences of an increasingly warm climate on avian migration, including the possibility of the cessation of migration by some populations and species. Our intent is to lay the groundwork for including temperature effects on energetic gains and losses of migratory birds with the expected consequences of increasing temperatures into a predictive modeling framework. To this end, we provide a simulation of migration progression exclusively focused on the influence of temperature on the physiological determinants of migration. This simulation produced comparable results to empirically derived and observed values for different migratory factors (e.g., body fat content, flight range, departure date). By merging knowledge from the arenas of avian physiology and migratory theory we have identified a clear need for research and have developed hypotheses for a path forward.

Ecological Modelling↗
USGS2015

Arctic biodiversity: Increasing richness accompanies shrinking refugia for a cold-associated tundra fauna

As ancestral biodiversity responded dynamically to late-Quaternary climate changes, so are extant organisms responding to the warming trajectory of the Anthropocene. Ecological predictive modeling, statistical hypothesis tests, and genetic signatures of demographic change can provide a powerful integrated toolset for investigating these biodiversity responses to climate change, and relative resiliency across different communities. Within the biotic province of Beringia, we analyzed specimen localities and DNA sequences from 28 mammal species associated with boreal forest and Arctic tundra biomes to assess both historical distributional and evolutionary responses and then forecasted future changes based on statistical assessments of past and present trajectories, and quantified distributional and demographic changes in relation to major management regions within the study area. We addressed three sets of hypotheses associated with aspects of methodological, biological, and socio-political importance by asking (1) what is the consistency among implications of predicted changes based on the results of both ecological and evolutionary analyses; (2) what are the ecological and evolutionary implications of climate change considering either total regional diversity or distinct communities associated with major biomes; and (3) are there differences in management implications across regions? Our results indicate increasing Arctic richness through time that highlights a potential state shift across the Arctic landscape. However, within distinct ecological communities, we found a predicted decline in the range and effective population size of tundra species into several discrete refugial areas. Consistency in results based on a combination of both ecological and evolutionary approaches demonstrates increased statistical confidence by applying cross-discipline comparative analyses to conservation of biodiversity, particularly considering variable management regimes that seek to balance sustainable ecosystems with other anthropogenic values. Refugial areas for cold-adapted taxa appear to be persistent across both warm and cold climate phases and although fragmented, constitute vital regions for persistence of Arctic mammals.

Ecosphere↗
USGS2013

Development of a Fluvial Egg Drift Simulator to evaluate the transport and dispersion of Asian carp eggs in rivers

Asian carp are migrating towards the Great Lakes and are threatening to invade this ecosystem, hence there is an immediate need to control their population. The transport of Asian carp eggs in potential spawning rivers is an important factor in its life history and recruitment success. An understanding of the transport, development, and fate of Asian carp eggs has the potential to create prevention, management, and control strategies before the eggs hatch and develop the ability to swim. However, there is not a clear understanding of the hydrodynamic conditions at which the eggs are transported and kept in suspension. This knowledge is imperative because of the current assumption that suspension is required for the eggs to survive. Herein, FluEgg (Fluvial Egg Drift Simulator), a three-dimensional Lagrangian model capable of evaluating the influence of flow velocity, shear dispersion and turbulent diffusion on the transport and dispersal patterns of Asian carp eggs is presented. The model's variables include not only biological behavior (growth rate, density changes) but also the physical characteristics of the flow field, such as mean velocities and eddy diffusivities. The performance of the FluEgg model was evaluated using observed data from published flume experiments conducted in China with water-hardened Asian carp eggs as subjects. FluEgg simulations show a good agreement with the experimental data. The model was also run with observed data from the Sandusky River in Ohio to provide a real-world demonstration case. This research will support the identification of critical hydrodynamic conditions (e.g., flow velocity, depth, and shear velocity) to maintain eggs in suspension, assist in the evaluation of suitable spawning rivers for Asian carp populations and facilitate the development of prevention, control and management strategies for Asian carp species in rivers and water bodies.

Ecological Modelling↗
USGS2007

Modeling tradeoffs in avian life history traits and consequences for population growth

Variation in population dynamics is inherently related to life history characteristics of species, which vary markedly even within phylogenetic groups such as passerine birds. We computed the finite rate of population change (??) from a matrix projection model and from mark-recapture observations for 23 bird species breeding in northern Arizona. We used sensitivity analyses and a simulation model to separate contributions of different life history traits to population growth rate. In particular we focused on contrasting effects of components of reproduction (nest success, clutch size, number of clutches, and juvenile survival) versus adult survival on ??. We explored how changes in nest success or adult survival coupled to costs in other life history parameters affected ?? over a life history gradient provided by our 23 Arizona species, as well as a broader sample of 121 North American passerine species. We further examined these effects for more than 200 passeriform and piciform populations breeding across North America. Model simulations indicate nest success and juvenile survival exert the largest effects on population growth in species with moderate to high reproductive output, whereas adult survival contributed more to population growth in long-lived species. Our simulations suggest that monitoring breeding success in populations across a broad geographic area provides an important index for identifying neotropical migratory populations at risk of serious population declines and a potential method for identifying large-scale mechanisms regulating population dynamics. ?? 2007 Elsevier B.V. All rights reserved.

Ecological Modelling↗