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At least 307 records · Page 17Linked to original sources
USGS2008

A simulation model for projecting changes in salinity concentrations and species dominance in the coastal margin habitats of the Everglades

Sharp boundaries typically separate the salinity tolerant mangroves from the salinity intolerant hardwood hammock species, which occupy the similar geographical areas of southern Florida. Evidence of strong feedback between tree community-type and the salinity of the unsaturated (vadose) zone of the soil suggests that a severe disturbance that significantly tilts the salinity in the vadose zone might cause a shift from one vegetation type to the other. In this study, a model based upon the feedback dynamics between vegetation and salinity of the vadose zone of the soil was used to take account of storm surge events to investigate the mechanisms that by which this large-scale disturbance could affect the spatial pattern of hardwood hammocks and mangroves. Model simulation results indicated that a heavy storm surge that completely saturated the vadose zone at 30 ppt for 1 day could lead to a regime shift in which there is domination by mangroves of areas previously dominated by hardwood hammocks. Lighter storm surges that saturated the vadose zone at less than 7 ppt did not cause vegetation shifts. Investigations of model sensitivity analysis indicated that the thickness of the vadose zone, coupled with precipitation, influenced the residence time of high salinity in the vadose zone and therefore determined the rate of mangrove domination. The model was developed for a southern Florida coastal ecosystem, but its applicability may be much broader. ?? 2008 Elsevier B.V. All rights reserved.

Ecological Modelling↗
USGS2012

A modeling framework for integrated harvest and habitat management of North American waterfowl: Case-study of northern pintail metapopulation dynamics

We developed and evaluated the performance of a metapopulation model enabling managers to examine, for the first time, the consequences of alternative management strategies involving habitat conditions and hunting on both harvest opportunity and carrying capacity (i.e., equilibrium population size in the absence of harvest) for migratory waterfowl at a continental scale. Our focus is on the northern pintail ( Anas acuta ; hereafter, pintail), which serves as a useful model species to examine the potential for integrating waterfowl harvest and habitat management in North America. We developed submodel structure capturing important processes for pintail populations during breeding, fall migration, winter, and spring migration while encompassing spatial structure representing three core breeding areas and two core nonbreeding areas. A number of continental-scale predictions from our baseline parameterization (e.g., carrying capacity of 5.5 million, equilibrium population size of 2.9 million and harvest rate of 12% at maximum sustained yield [MSY]) were within 10% of those from the pintail harvest strategy under current use by the U.S. Fish and Wildlife Service. To begin investigating the interaction of harvest and habitat management, we examined equilibrium population conditions for pintail at the continental scale across a range of harvest rates while perturbing model parameters to represent: (1) a 10% increase in breeding habitat quality in the Prairie Pothole population (PR); and (2) a 10% increase in nonbreeding habitat quantity along in the Gulf Coast (GC). Based on our model and analysis, a greater increase in carrying capacity and sustainable harvest was seen when increasing a proxy for habitat quality in the Prairie Pothole population. This finding and underlying assumptions must be critically evaluated, however, before specific management recommendations can be made. To make such recommendations, we require (1) extended, refined submodels with additional parameters linking influences of habitat management and environmental conditions to key life-history parameters; (2) a formal sensitivity analysis of the revised model; (3) an integrated population model that incorporates empirical data for estimating key vital rates; and (4) cost estimates for changing these additional parameters through habitat management efforts. We foresee great utility in using an integrated modeling approach to predict habitat and harvest management influences on continental-scale population responses while explicitly considering putative effects of climate change. Such a model could be readily adapted for management of many habitat-limited species.

Ecological Modelling↗
USGS2019

Grounding simulation models with qualitative case studies: Toward a holistic framework to make climate science usable for US public land management

Policies directing agencies and public land managers to incorporate climate change into management face several barriers. These stem, in part, from a disconnect between the information that is produced and the information needs of local resource managers. A disproportionate focus on the natural and physical sciences in climate vulnerability and adaptation assessment obscure understandings of complex social systems and the interactions and feedbacks in social-ecological systems. We use a qualitative case study of bison management on Department of the Interior-managed and tribal lands to explore how a social-science driven Determinants and Analogue Vulnerability Assessment (DAVA) can inform ecological response models, specifically simulation models that account for multiple drivers of change. First, we illustrate how a DAVA approach can help to: 1) identify key processes, entities, and interactions across scales; 2) document local impacts, indicators, and monitoring efforts of drought and climate; and 3) identify major tradeoffs and uncertainties. We then demonstrate how qualitative narratives can inform simulation models by: 1) prioritizing model components included in modeling efforts; 2) framing joint management and climate scenarios; and 3) parameterizing and evaluating model performance. We do this by presenting a conceptual joint agent-based/state-and-transition simulation modeling framework. Simulation models can represent multiple interacting variables and can identify surprising, emergent outcomes that might not be evident from qualitative analysis alone, and we argue that qualitative case studies can ground simulation models in local contexts and help make them more structurally realistic and useful. Together, these can provide a step toward developing actionable climate change adaptation strategies.

Climate Risk Management↗
USGS2019

Development and implementation of an empirical habitat change model and decision support tool for estuarine ecosystems

Widespread land use change in coastal ecosystems has led to a decline in the amount of habitat available for fish and wildlife, lower production of ecosystem goods and services, and loss of recreational and aesthetic value. This has prompted global efforts to restore the natural hydrologic regimes of developed shorelines, especially resource-rich estuaries, but the resilience of these restored ecosystems in the face of accelerated sea-level rise (SLR) remains uncertain. We implemented a Mo nitoring-based S imulation of A ccretion i n C oastal E s tuaries (MOSAICS) in R statistical software to address uncertainty in the resilience of modified estuarine habitats, using the Nisqually River Delta in the Pacific Northwest USA as a case study. MOSAICS is a spatially explicit model with a numerical foundation that uses empirical monitoring datasets to forecast habitat change in response to rising tidal levels. Because it accounts for the crucial ecomorphodynamic feedbacks between tidal inundation, vegetative growth, and sediment accretion, MOSAICS can be used to determine whether alternative management scenarios, such as enhanced sediment inputs, will bolster estuarine resilience to SLR. Under moderate SLR (0.62 m), the model predicted that a two-fold increase in mean daily suspended sediment during the rainy season was sufficient to maintain Nisqually’s emergent marshes through 2100, but under high SLR (1.35 m) MOSAICS indicated that greater sediment additions would be necessary to prevent submergence. A comparison between a restored marsh with subsided and high-elevation areas and a relict marsh demonstrated that the subsided restoration area was highly susceptible to SLR. Findings from the MOSAICS model highlight the importance of a site’s initial elevation, capacity for producing above and belowground biomass, and suspended sediment availability when considering management actions in estuaries and other coastal ecosystems.

Washington↗
USGS2024

Modeling nearshore total phosphorus in Lake Michigan using linked hydrodynamic and water quality models

Although the offshore water of Lake Michigan has been below the Great Lakes Water Quality Agreement (GLWQA) total phosphorus (TP) spring target concentration of 7 µg L −1 for several decades, higher TP concentrations occur in the nearshore, contributing to the resurgence of Cladophora and other nutrient related issues. The most recent update of the GLWQA specifically calls for the assessment of current nearshore conditions. Such assessment would require an intensive monitoring program supported by water quality models. Here we applied multiple versions of a phosphorus-based model linked to an unstructured-grid hydrodynamic model. We focus on the nearshore regions of Lake Michigan around the Grand and Muskegon rivers – a region with strong riverine TP influence and intensive monitoring. Results from a TP model were compared to observational data and to a previously published Phosphorus-based Nutrient–Phytoplankton–Zooplankton–Detrital–Mussel (NPZDM) model. Model results and observational data show that parts of the nearshore can be well above the target TP concentrations but, due to the dynamic nature of this region, the TP concentrations can change rapidly. The models’ skill statistics in predicting individual observations were variable, but it was able to simulate temporal and spatial trends and captured the distribution of observations in our study area. The similarity between the results of the TP and NPZDM models demonstrated the TP concentrations in this nearshore area are driven by hydrodynamics and river TP loads, which are likely the reasons for the higher observed TP concentrations. Simplicity, transparency, and ease of use of the TP model make it a useful tool for supporting nearshore assessments and estimating existing and future nearshore TP concentrations.

Michigan↗
USGS2015

Linking resource selection and mortality modeling for population estimation of mountain lions in Montana

To be most effective, the scale of wildlife management practices should match the range of a particular species’ movements. For this reason, combined with our inability to rigorously or regularly census mountain lion populations, several authors have suggested that mountain lions be managed in a source-sink or metapopulation framework. We used a combination of resource selection functions, mortality estimation, and dispersal modeling to estimate cougar population levels in Montana statewide and potential population level effects of planned harvest levels. Between 1980 and 2012, 236 independent mountain lions were collared and monitored for research in Montana. From these data we used 18,695 GPS locations collected during winter from 85 animals to develop a resource selection function (RSF), and 11,726 VHF and GPS locations from 142 animals along with the locations of 6343 mountain lions harvested from 1988–2011 to validate the RSF model. Our RSF model validated well in all portions of the State, although it appeared to perform better in Montana Fish, Wildlife and Parks (MFWP) Regions 1, 2, 4 and 6, than in Regions 3, 5, and 7. Our mean RSF based population estimate for the total population (kittens, juveniles, and adults) of mountain lions in Montana in 2005 was 3926, with almost 25% of the entire population in MFWP Region 1. Estimates based on a high and low reference population estimates produce a possible range of 2784 to 5156 mountain lions statewide. Based on a range of possible survival rates we estimated the mountain lion population in Montana to be stable to slightly increasing between 2005 and 2010 with lambda ranging from 0.999 (SD = 0.05) to 1.02 (SD = 0.03). We believe these population growth rates to be a conservative estimate of true population growth. Our model suggests that proposed changes to female harvest quotas for 2013–2015 will result in an annual statewide population decline of 3% and shows that, due to reduced dispersal, changes to harvest in one management unit may affect population growth in neighboring units where smaller or even no changes were made. Uncertainty regarding dispersal levels and initial population density may have a significant effect on predictions at a management unit scale (i.e. 2000 km 2 ), while at a regional scale (i.e. 50,000 km 2 ) large differences in initial population density result in relatively small changes in population growth rate, and uncertainty about dispersal may not be as influential. Doubling the presumed initial density from a low estimation of 2.19 total animals per 100 km 2 resulted in a difference in annual population growth rate of only 2.6% statewide when compared to high density of 4.04 total animals per 100 km 2 (low initial population estimate λ = 0.99, while high initial population estimate λ = 1.03). We suggest modeling tools such as this may be useful in harvest planning at a regional and statewide level.

Ecological Modelling↗
USGS2019

A spatial model of streaked horned lark breeding habitat in the Columbia River, USA

The streaked horned lark (hereafter “lark”; Eremophila alpestris strigata ) is a federally listed bird subspecies of the Pacific Northwest that occupies open landscapes with short vegetation and abundant bare ground. Across its breeding range, which has contracted dramatically, the lark relies primarily on human-modified habitats maintained in early successional states (e.g., agricultural fields, airfields, deposition islands). The focus of our study is the lower Columbia River where larks primarily occupy deposition islands created as a byproduct of shipping channel deepening (dredging) projects. The U.S. Army Corps of Engineers has proactively adopted a shifting-habitat-mosaic management approach when placing dredge (sand) materials to maintain lark habitat throughout the project area. To help achieve their goal, we created a geospatial tool capable of quantifying and tracking habitat suitability for larks throughout the project area with satellite imagery. We created spatial models of lark breeding habitat by incorporating lark survey data, Sentinel-2 satellite imagery, dredge-deposition maps, and surface-elevation models. We built candidate predictive models of lark breeding habitat after combining survey data (years 2016 and 2017), predictor variables, and logistic regression. In addition, we created a novel seral-stage predictor variable to identify young, mature, old, and unsuitable locations; critical information for lark conservation planning and dredge-disposal operational planning. We challenged probability models the year they were constructed (2016) and the following year (2017) with set-aside survey data. The best model contained terrain ruggedness, deposition age, vegetation greenness (e.g., lushness and density of vegetation) inside a 10-m cell, and heterogeneity in greenness within a 100-m radius. Verification accuracy ranged from 75 to 83% depending on the model tested and year. Importantly, the seral-stage variable allowed us to quantify and locate early, optimal, and late habitats. Model results will facilitate conservation planning decisions by informing real-time management decisions. Furthermore, they provide a foundation for application in other human-modified habitats across the species’ range.

Oregon↗
USGS2022

A three-dimensional Lagrangian particle tracking model for predicting transport of eggs of rheophilic-spawning carps in turbulent rivers

Grass carp, bighead carp, and silver carp spawn in flowing water. Their eggs, and then larvae, develop while drifting. Hydraulic conditions and water temperature control spawning locations, egg survival, and the downstream distance traveled before the hatched larvae can swim for low velocity nursery habitats. Existing egg drift models simulate the fluvial transport of carp eggs but have limitations in capturing the effect of localized turbulence on egg transport due to inadequate dimensions of hydrodynamics and/or empirical parameterization of river dispersion. We present a three-dimensional Lagrangian particle tracking model that uses fully resolved river hydrodynamics and a continuous random walk algorithm driven by local turbulent kinetic energy and its dissipation rate. We incorporate a new set of equations to compute evolving egg characteristics with fully resolved 3-D hydrodynamics. To demonstrate the performance of the model, we conducted a case study in an eight-kilometer reach of the Missouri River at the discharge of approximately 25% daily flow exceedance. Three-dimensional river hydrodynamics was modeled, calibrated, and evaluated with measurement data. Egg drift was modeled and compared using fully three-dimensional, depth-averaged two-dimensional, and zone-averaged one-dimensional hydrodynamics. The comparison shows a generally good agreement among models of downstream egg transport due to advection but a different dispersion pattern of eggs in the river, as a result of turbulent diffusion and shear induced dispersion.

Ecological Modelling↗
USGS2008

Dispersal leads to spatial autocorrelation in species distributions: A simulation model

Compared to population growth regulated by local conditions, dispersal has been underappreciated as a central process shaping the spatial distribution of populations. This paper asks: (a) which conditions increase the importance of dispersers relative to local recruits in determining population sizes? and (b) how does dispersal influence the spatial distribution patterns of abundances among connected populations? We approached these questions with a simulation model of populations on a coupled lattice with cells of continuously varying habitat quality expressed as carrying capacities. Each cell contained a population with the basic dynamics of density-regulated growth, and was connected to other populations by immigration and emigration. The degree to which dispersal influenced the distribution of population sizes depended most strongly on the absolute amount of dispersal, and then on the potential population growth rate. Dispersal decaying in intensity with distance left close neighbours more alike in population size than distant populations, leading to an increase in spatial autocorrelation. The spatial distribution of species with low potential growth rates is more dependent on dispersal than that of species with high growth rates; therefore, distribution modelling for species with low growth rates requires particular attention to autocorrelation, and conservation management of these species requires attention to factors curtailing dispersal, such as fragmentation and dispersal barriers. ?? 2007 Elsevier B.V. All rights reserved.

Ecological Modelling↗
USGS2009

Modelling predation by transient leopard seals for an ecosystem-based management of Southern Ocean fisheries

Correctly quantifying the impacts of rare apex marine predators is essential to ecosystem-based approaches to fisheries management, where harvesting must be sustainable for targeted species and their dependent predators. This requires modelling the uncertainty in such processes as predator life history, seasonal abundance and movement, size-based predation, energetic requirements, and prey vulnerability. We combined these uncertainties to evaluate the predatory impact of transient leopard seals on a community of mesopredators (seals and penguins) and their prey at South Georgia, and assess the implications for an ecosystem-based management. The mesopredators are highly dependent on Antarctic krill and icefish, which are targeted by regional fisheries. We used a state-space formulation to combine (1) a mark-recapture open-population model and individual identification data to assess seasonally variable leopard seal arrival and departure dates, numbers, and residency times; (2) a size-based bioenergetic model; and (3) a size-based prey choice model from a diet analysis. Our models indicated that prey choice and consumption reflected seasonal changes in leopard seal population size and structure, size-selective predation and prey vulnerability. A population of 104 (90-125) leopard seals, of which 64% were juveniles, consumed less than 2% of the Antarctic fur seal pup production of the area (50% of total ingested energy, IE), but ca. 12-16% of the local gentoo penguin population (20% IE). Antarctic krill (28% IE) were the only observed food of leopard seal pups and supplemented the diet of older individuals. Direct impacts on krill and fish were negligible, but the "escapement" due to leopard seal predation on fur seal pups and penguins could be significant for the mackerel icefish fishery at South Georgia. These results suggest that: (1) rare apex predators like leopard seals may control, and may depend on, populations of mesopredators dependent on prey species targeted by fisheries; and (2) predatory impacts and community control may vary throughout the predator's geographic range, and differ across ecosystems and management areas, depending on the seasonal abundance of the prey and the predator's dispersal movements. This understanding is important to integrate the predator needs as natural mortality of its prey in models to set prey catch limits for fisheries. Reliable estimates of the variability of these needs are essential for a precautionary interpretation in the context of an ecosystem-based management. ?? 2009 Elsevier B.V.

Ecological Modelling↗
USGS2017

Integrating the effects of salinity on the physiology of the eastern oyster, Crassostrea virginica, in the northern Gulf of Mexico through a Dynamic Energy Budget model

We present a Dynamic Energy Budget (DEB) model for the eastern oyster, Crassostrea virginica , which enables the inclusion of salinity as a third environmental variable, on top of the standard foodr and temperature variables. Salinity changes have various effects on the physiology of oysters, potentially altering filtration and respiration rates, and ultimately impacting growth, reproduction and mortality. We tested different hypotheses as to how to include these effects in a DEB model for C. virginica . Specifically, we tested two potential mechanisms to explain changes in oyster shell growth (cm), tissue dry weight (g) and gonad dry weight (g) when salinity moves away from the ideal range: 1) a negative effect on filtration rate and 2) an additional somatic maintenance cost. Comparative simulations of shell growth, dry tissue biomass and dry gonad weight in two monitored sites in coastal Louisiana experiencing salinity from 0 to 28 were statistically analyzed to determine the best hypothesis. Model parameters were estimated through the covariation method, using literature data and a set of specifically designed ecophysiological experiments. The model was validated through independent field studies in estuaries along the northern Gulf of Mexico. Our results suggest that salinity impacts C. virginica ’s energy budget predominantly through effects on filtration rate. With an overwhelming number of environmental factors impacting organisms, and increasing exposure to novel and extreme conditions, the mechanistic nature of the DEB model with its ability to incorporate more than the standard food and temperature variables provides a powerful tool to verify hypotheses and predict individual organism performance across a range of conditions.

Ecological Modelling↗
USGS2017

Simulating ungulate herbivory across forest landscapes: A browsing extension for LANDIS-II

Browsing ungulates alter forest productivity and vegetation succession through selective foraging on species that often dominate early succession. However, the long-term and large-scale effects of browsing on forest succession are not possible to project without the use of simulation models. To explore the effects of ungulates on succession in a spatially explicit manner, we developed a Browse Extension that simulates the effects of browsing ungulates on the growth and survival of plant species cohorts within the LANDIS-II spatially dynamic forest landscape simulation model framework. We demonstrate the capabilities of the new extension and explore the spatial effects of ungulates on forest composition and dynamics using two case studies. The first case study examined the long-term effects of persistently high white-tailed deer browsing rates in the northern hardwood forests of the Allegheny National Forest, USA. In the second case study, we incorporated a dynamic ungulate population model to simulate interactions between the moose population and boreal forest landscape of Isle Royale National Park, USA. In both model applications, browsing reduced total aboveground live biomass and caused shifts in forest composition. Simulations that included effects of browsing resulted in successional patterns that were more similar to those observed in the study regions compared to simulations that did not incorporate browsing effects. Further, model estimates of moose population density and available forage biomass were similar to previously published field estimates at Isle Royale and in other moose-boreal forest systems. Our simulations suggest that neglecting effects of browsing when modeling forest succession in ecosystems known to be influenced by ungulates may result in flawed predictions of aboveground biomass and tree species composition.

Ecological Modelling↗
USGS2005

Influence of topographic complexity on solar insolation estimates for the Colorado River, Grand Canyon, AZ

Rugged topography along the Colorado River in Glen and Grand Canyons, exemplifies features common to canyon-bound streams and rivers of the arid southwest. Physical relief influences regulated river systems, especially those that are altered, and have become partially reliant on aquatic primary production. We measured and modeled instantaneous solar flux in a topographically complex environment to determine where differences in daily, seasonal and annual solar insolation occurred in this river system. At a system-wide scale, topographic complexity generates a spatial and temporal mosaic of varying solar insolation. This solar variation is a predictable consequence of channel orientation, geomorphology, elevation angles and viewshed. Modeled estimates for clear conditions corresponded closely with observed measurements for both instantaneous photosynthetic photon flux density (PPFD: ??mol m-2 s-1) and daily insolation levels (relative error 2.3%, CI ??0.45, S.D. 0.3, n = 29,813). Mean annual daily insolation levels system-wide were estimated to be 36 mol m-2 d -1 (17.5 S.D.), and seasonally varied on average from 13.4-57.4 mol m-2 d-1, for winter and summer, respectively. In comparison to identical areas lacking topographic effect (idealized plane), mean daily insolation levels were reduced by 22% during summer, and as much as 53% during winter. Depending on outlying topography, canyon bound regions having east-west (EW) orientations had higher seasonal variation, averaging from 8.1 to 61.4 mol m-2 d-1, for winter and summer, respectively. For EW orientations, 70% of mid-channel sites were obscured from direct incidence during part of the year; and of these sites, average diffuse light conditions persisted for 19.3% of the year (70.5 days), and extended upwards to 194 days. This predictive model has provided an initial quantitative step to estimate and determine the importance of autotrophic production for this ecosystem, as well as a broader application for other canyon systems. ?? 2004 Published by Elsevier B.V.

Ecological Modelling↗
USGS2019

Dynamic N-mixture models with temporal variability in detection probability

In theory parameters of dynamic N-mixture models can be estimated with multiple years of data without the robust design under the assumption of constant detection probability. However, such an assumption can rarely be met in long-term studies, and the consequences of violating this assumption in the inferences of dynamic N-mixture models have not been assessed. In this study we used simulation studies to evaluate inferences of the original dynamic N-mixture model and two of its spatial extensions in the face of temporal variability in detection probability. We first evaluated the dynamic N-mixture models when detection probability that varied temporally was wrongly treated as a constant. We then evaluated if the robust design was necessary for dynamic N-mixture models to provide valid parameter estimates when detection probability was correctly assumed to vary temporally. Our results showed that, when detection probability that varied temporally was wrongly treated as a constant, biases were introduced in the parameter estimates of dynamic N-mixture models. When detection probability was correctly assumed to vary temporally, the models could provide valid parameter estimates with the robust design. The model could also provide valid parameter estimates when detection probability was a random effect, even without the robust design. Based on our results, we strongly recommended considering temporal variability in detection probability when using dynamic N-mixture models to analyze long-term data and adopting the robust design in long-term surveys. Our work here is not only useful for data analysis but also important for research design, and thus are relevant to a wide range of studies.

Ecological Modelling↗
USGS1996

Comparison of alternative spatial resolutions in the application of a spatially distributed biogeochemical model over complex terrain

Spatially distributed biogeochemical models may be applied over grids at a range of spatial resolutions, however, evaluation of potential errors and loss of information at relatively coarse resolutions is rare. In this study, a georeferenced database at the 1-km spatial resolution was developed to initialize and drive a process-based model (Forest-BGC) of water and carbon balance over a gridded 54976 km2 area covering two river basins in mountainous western Oregon. Corresponding data sets were also prepared at 10-km and 50-km spatial resolutions using commonly employed aggregation schemes. Estimates were made at each grid cell for climate variables including daily solar radiation, air temperature, humidity, and precipitation. The topographic structure, water holding capacity, vegetation type and leaf area index were likewise estimated for initial conditions. The daily time series for the climatic drivers was developed from interpolations of meteorological station data for the water year 1990 (1 October 1989-30 September 1990). Model outputs at the 1-km resolution showed good agreement with observed patterns in runoff and productivity. The ranges for model inputs at the 10-km and 50-km resolutions tended to contract because of the smoothed topography. Estimates for mean evapotranspiration and runoff were relatively insensitive to changing the spatial resolution of the grid whereas estimates of mean annual net primary production varied by 11%. The designation of a vegetation type and leaf area at the 50-km resolution often subsumed significant heterogeneity in vegetation, and this factor accounted for much of the difference in the mean values for the carbon flux variables. Although area wide means for model outputs were generally similar across resolutions, difference maps often revealed large areas of disagreement. Relatively high spatial resolution analyses of biogeochemical cycling are desirable from several perspectives and may be particularly important in the study of the potential impacts of climate change.

Ecological Modelling↗
USGS2012

Plant toxins and trophic cascades alter fire regime and succession on a boral forest landscape

Two models were integrated in order to study the effect of plant toxicity and a trophic cascade on forest succession and fire patterns across a boreal landscape in central Alaska. One of the models, ALFRESCO, is a cellular automata model that stochastically simulates transitions from spruce dominated 1 km2 spatial cells to deciduous woody vegetation based on stochastic fires, and from deciduous woody vegetation to spruce based on age of the cell with some stochastic variation. The other model, the ‘toxin-dependent functional response’ model (TDFRM) simulates woody vegetation types with different levels of toxicity, an herbivore browser (moose) that can forage selectively on these types, and a carnivore (wolf) that preys on the herbivore. Here we replace the simple succession rules in each ALFRESCO cell by plant–herbivore–carnivore dynamics from TDFRM. The central hypothesis tested in the integrated model is that the herbivore, by feeding selectively on low-toxicity deciduous woody vegetation, speeds succession towards high-toxicity evergreens, like spruce. Wolves, by keeping moose populations down, can help slow the succession. Our results confirmed this hypothesis for the model calibrated to the Tanana floodplain of Alaska. We used the model to estimate the effects of different levels of wolf control. Simulations indicated that management reductions in wolf densities could reduce the mean time to transition from deciduous to spruce by more than 15 years, thereby increasing landscape flammability. The integrated model can be useful in estimating ecosystem impacts of wolf control and moose harvesting in central Alaska.

Alaska↗
USGS2016

A model for the interaction of frog population dynamics with Batrachochytrium dendrobaties , Janthinobacterium lividium and temperature and its implication for chytridiomycosis management

Chytridiomycosis is an emerging disease caused by the fungal pathogen Batrachochytrium dendrobatidis ( Bd ) that poses a serious threat to frog populations worldwide. Several studies have shown that inoculation of bacterial species Janthinobacterium lividum ( Jl ) can mitigate the impact of the disease. However, there are many questions regarding this interaction. A mathematical model of a frog population infected with chytridiomycosis is developed to investigate how the inoculation of Jl could reduce the impact of Bd disease on frogs. The model also illustrates the important role of temperature in disease dynamics. The model simulation results suggest possible control strategies for Jl to limit the impact of Bd in various scenarios. However, a better knowledge of Jl life cycle is needed to fully understand the interaction of Jl , Bd , temperature and frogs.

Ecological Modelling↗
USGS2014

Measuring and predicting abundance and dynamics of habitat for piping plovers on a large reservoir

Measuring habitat and understanding habitat dynamics have become increasingly important for wildlife conservation. Using remotely-sensed data, we developed procedures to measure breeding habitat abundance for the federally listed piping plover (Charadrius melodus) at Lake Sakakawea, North Dakota, USA. We also developed a model to predict habitat abundance based on past and projected water levels, vegetation colonization rates, and topography. Previous studies define plover habitat as flat areas (<10% slope) with ≤30% obstruction of bare substrate. Compared to ground-based data, remotely-sensed habitat classifications (≤30/>30% bare-substrate obstruction) were 76% correct and omission and commission errors were equal. Due to water level fluctuations, habitat abundance varied markedly among years (1986–2009) ranging from 9 to 5195 ha. The proportion bare substrate declined with the number of years since a contour was inundated until 5 years (&beta; = -0.65, SE = 0.05), then it stabilized near zero, and the decline varied by shoreline segment (5, 50, and 95 percentile were &beta; = -0.19, SE = 0.05, &beta; = -0.63, SE = 0.05, and &beta; = -0.91, SE = 0.05, respectively). Years since inundated predicted habitat abundance well at shoreline segments (R 2 = 0.77), but it predicted better for the whole lake (R 2 = 0.86). The vastness and dynamics of plover habitat on Lake Sakakawea suggest that this is a key area for conservation of this species. Model-based habitat predictions can benefit resource conservation because they can (1) form the basis for a sampling stratification, (2) help allocate monitoring efforts among areas, and (3) help inform management through simulations or what-if scenarios.

North Dakota↗