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Results for “Ecological Modelling”

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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 ( < "> < 50 m) and wind conditions are known at facility scale ( < "> < 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 > "> > 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↗

USGS2011

Increasing accuracy of dispersal kernels in grid-based population models

Dispersal kernels in grid-based population models specify the proportion, distance and direction of movements within the model landscape. Spatial errors in dispersal kernels can have large compounding effects on model accuracy. Circular Gaussian and Laplacian dispersal kernels at a range of spatial resolutions were investigated, and methods for minimizing errors caused by the discretizing process were explored. Kernels of progressively smaller sizes relative to the landscape grid size were calculated using cell-integration and cell-center methods. These kernels were convolved repeatedly, and the final distribution was compared with a reference analytical solution. For large Gaussian kernels ( σ > 10 cells), the total kernel error was <10 −11 compared to analytical results. Using an invasion model that tracked the time a population took to reach a defined goal, the discrete model results were comparable to the analytical reference. With Gaussian kernels that had σ ≤ 0.12 using the cell integration method, or σ ≤ 0.22 using the cell center method, the kernel error was greater than 10%, which resulted in invasion times that were orders of magnitude different than theoretical results. A goal-seeking routine was developed to adjust the kernels to minimize overall error. With this, corrections for small kernels were found that decreased overall kernel error to <10 −11 and invasion time error to <5%.

Ecological Modelling↗

USGS2015

A conceptual model for site-level ecology of the giant gartersnake ( Thamnophis gigas ) in the Sacramento Valley, California

Giant gartersnakes (Thamnophis gigas) comprise a species of semi-aquatic snakes precinctive to marshes in the Central Valley of California (Hansen and Brode, 1980; Rossman and others, 1996). Because more than 90 percent of their historical wetland habitat has been converted to other uses (Frayer and others, 1989; Garone, 2007), giant gartersnakes have been listed as threatened by the State of California (California Department of Fish and Game Commission , 1971) and the United States (U.S. Fish and Wildlife Service, 1993). Giant gartersnakes currently occur in a highly modified landscape, with most extant populations occurring in the rice - growing regions of the Sacramento Valley, especially near areas that historically were tule marsh habitat (Halstead and others, 2010, 2014). In ricelands and managed marshes, many operational decisions likely affect the health and viability of giant gartersnake populations. Land-use decisions, including the management of water, aquatic vegetation, terrestrial vegetation, and co-occurring species, have the potential to affect giant gartersnakes. Little is known, however, about the effects of these types of decisions on the viability of giant gartersnake populations. Bayesian network models are a useful tool to help guide decisions with uncertain outcomes. These models require the articulation of what experts think they know about a system, and facilitate learning about the hypothesized relations (Marcot and others, 2001; Uusitalo , 2007). Bayesian networks further provide a clear visual display of the model that facilitates understanding among various stakeholders (Marcot and others, 2001; Uusitalo , 2007). Empirical data and expert judgment can be combined, as continuous or categorical variables, to update knowledge about the system (Marcot and others, 2001; Uusitalo , 2007). Importantly, Bayesian network models allow inference from causes to consequences, but also from consequences to causes, so that data can inform the states of nodes (values of different random variables) in either direction (Marcot and others, 2001; Uusitalo , 2007). Because they can incorporate both decision nodes that represent management actions and utility nodes that quantify the costs and benefits of outcomes, Bayesian networks are ideally suited to risk analysis and adaptive management (Nyberg and others, 2006; Howes and others, 2010). Thus, Bayesian network models are useful in situations where empirical data are not available, such as questions concerning the responses of giant gartersnakes to management.

California↗

USGS2015

Estimating the short-term recovery potential of little brown bats in the eastern United States in the face of White-nose syndrome

White-nose syndrome (WNS) was first detected in North American bats in New York in 2006. Since that time WNS has spread throughout the northeastern United States, southeastern Canada, and southwest across Pennsylvania and as far west as Missouri. Suspect WNS cases have been identified in Minnesota and Iowa, and the causative agent of WNS ( Pseudogymnoascus destructans ) has recently been detected in Mississippi. The impact of WNS is devastating for little brown bats ( Myotis lucifugus ), causing up to 100% mortality in some overwintering populations, and previous research has forecast the extirpation of the species due to the disease. Recent evidence indicates that remnant populations may persist in areas where WNS is endemic. We developed a spatially explicit model of little brown bat population dynamics to investigate the potential for populations to recover under alternative scenarios. We used these models to investigate how starting population sizes, potential changes in the number of bats overwintering successfully in hibernacula, and potential changes in demographic rates of the population post WNS may influence the ability of the bats to recover to former levels of abundance. We found that populations of the little brown bat and other species that are highly susceptible to WNS are unlikely to return to pre-WNS levels in the near future under any of the scenarios we examined.

Ecological Modelling↗

USGS2025

Foraging of wading birds on a patchy landscape: Simulating effects of social information, interference competition, and patch selection on prey intake and individual distribution

Foragers on patchy landscapes must acquire sufficient resources despite uncertainty in the location and amount of the resources. Optimal Foraging Theory posits that foragers deal with this uncertainty by using strategies that optimize resource intake within foraging periods. For species such as wading birds, this optimization is closely linked to their survival and reproductive success. Understanding the influence of patch selection on individual resource intake and foraging distribution is therefore crucial. In this study, we simulated how resource distribution, interference competition, and social cues—such as aggregation behaviors—influence resource intake and foraging spatial distribution. We employed an individual-based model simulating wading bird foraging behaviors, with 900 individuals simultaneously foraging across a landscape with unknown resource distribution. Birds employed one of three patch-finding strategies: random, cue-searching, or hybrid, which uses both searching strategies. Each bird decided whether to remain in a patch based on a prey density threshold. We compared the daily resource intake and foraging distribution of birds across different modeled patch-finding strategies, resource distribution patterns, and the presence or absence of interference competition. Wading birds exhibiting aggregation behavior displayed increased intake rates when resources were concentrated and interference minimal. Aggregation behavior led to a closer match with the ideal free distribution when the prey density threshold was optimal. These findings provide theoretical support that aggregation behavior is effective in scenarios where resources are concentrated in a few patches, social cues are used by relatively few individuals, and interference competition is limited.

Ecological Modelling↗

USGS2017

Intraspecific variability and reaction norms of forest understory plant species traits

Trait-based models of ecological communities typically assume intraspecific variation in functional traits is not important, though such variation can change species trait rankings along gradients in resources and environmental conditions, and thus influence community structure and function. We examined the degree of intraspecific relative to interspecific variation, and reaction norms of 11 functional traits for 57 forest understory plant species, including: intrinsic water-use efficiency (iWUE), Δ 15 N, 5 leaf traits, 2 stem traits and 2 root traits along gradients in light, nitrogen, moisture and understory cover. Our results indicate that interspecific trait variation exceeded intraspecific variation by at least 50% for most, but not all traits. Intraspecific variation in Δ 15 N, iWUE, leaf nitrogen content and root traits was high (47-70%) compared with most leaf traits and stem traits (13-38%). Δ 15 N varied primarily along gradients in abiotic conditions, while light and understory cover were relatively less important. iWUE was related primarily to light transmission, reflecting increases in photosynthesis relative to stomatal conductance. Leaf traits varied mainly as a function of light availability, with some reaction norms depending on understory cover. Plant height increased with understory cover, while stem specific density was related primarily to light. Resources, environmental conditions and understory cover did not contribute strongly to the observed variation in root traits. Gradients in resources, environmental conditions and competition all appear to control intraspecific variability in most traits to some extent. However, our results suggest that species cross-over (i.e., trait rank reversals) along the gradients measured here are generally not a concern. Intraspecific variability in understory plant species traits can be considerable. However, trait data collected under a narrow range of environmental conditions appears sufficient to establish species rankings and scale between community and ecosystem levels using trait-based models. Investigators may therefore focus on obtaining a sufficient sample size within a single set of conditions rather than characterizing trait variation across entire gradients in order to optimize sampling efforts.

Oregon↗

USGS2021

The Chesapeake Bay program modeling system: Overview and recommendations for future development

The Chesapeake Bay is the largest, most productive, and most biologically diverse estuary in the continental United States providing crucial habitat and natural resources for culturally and economically important species. Pressures from human population growth and associated development and agricultural intensification have led to excessive nutrient and sediment inputs entering the Bay, negatively affecting the health of the Bay ecosystem and the economic services it provides. The Chesapeake Bay Program (CBP) is a unique program formally created in 1983 as a multi-stakeholder partnership to guide and foster restoration of the Chesapeake Bay and its watershed. Since its inception, the CBP Partnership has been developing, updating, and applying a complex linked modeling system of watershed, airshed, and estuary models as a planning tool to inform strategic management decisions and Bay restoration efforts. This paper provides a description of the 2017 CBP Modeling System and the higher trophic level models developed by the NOAA Chesapeake Bay Office, along with specific recommendations that emerged from a 2018 workshop designed to inform future model development. Recommendations highlight the need for simulation of watershed inputs, conditions, processes, and practices at higher resolution to provide improved information to guide local nutrient and sediment management plans. More explicit and extensive modeling of connectivity between watershed landforms and estuary sub-areas, estuarine hydrodynamics, watershed and estuarine water quality, the estuarine-watershed socioecological system, and living resources will be important to broaden and improve characterization of responses to targeted nutrient and sediment load reductions. Finally, the value and importance of maintaining effective collaborations among jurisdictional managers, scientists, modelers, support staff, and stakeholder communities is emphasized. An open collaborative and transparent process has been a key element of successes to date and is vitally important as the CBP Partnership moves forward with modeling system improvements that help stakeholders evolve new knowledge, improve management strategies, and better communicate outcomes.

Chesapeake Bay estuary and watershed↗

USGS2007

Optimal management of non-Markovian biological populations

Wildlife populations typically are described by Markovian models, with population dynamics influenced at each point in time by current but not previous population levels. Considerable work has been done on identifying optimal management strategies under the Markovian assumption. In this paper we generalize this work to non-Markovian systems, for which population responses to management are influenced by lagged as well as current status and/or controls. We use the maximum principle of optimal control theory to derive conditions for the optimal management such a system, and illustrate the effects of lags on the structure of optimal habitat strategies for a predator-prey system.

Ecological Modelling↗