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At least 199 records · Page 11Linked to original sources
USGS2015

Markov decision processes in natural resources management: observability and uncertainty

The breadth and complexity of stochastic decision processes in natural resources presents a challenge to analysts who need to understand and use these approaches. The objective of this paper is to describe a class of decision processes that are germane to natural resources conservation and management, namely Markov decision processes, and to discuss applications and computing algorithms under different conditions of observability and uncertainty. A number of important similarities are developed in the framing and evaluation of different decision processes, which can be useful in their applications in natural resources management. The challenges attendant to partial observability are highlighted, and possible approaches for dealing with it are discussed.

Ecological Modelling↗
USGS2018

Evaluating trade-offs in bull trout reintroduction strategies using structured decision making

Structured decision making allows reintroduction decisions to be made despite uncertainty by linking reintroduction goals with alternative management actions through predictive models of ecological processes. We developed a decision model to evaluate the trade-offs between six bull trout ( Salvelinus confluentus ) reintroduction decisions with the goal of maximizing the number of adults in the recipient population without reducing the donor population to an unacceptable level. Sensitivity analyses suggested that the decision identity and outcome were most influenced by survival parameters that result in increased adult abundance in the recipient population, increased juvenile survival in the donor and recipient populations, adult fecundity rates, and sex ratio. The decision was least sensitive to survival parameters associated with the captive-reared population, the effect of naivety on released individuals, and juvenile carrying capacity of the reintroduced population. The model and sensitivity analyses can serve as the foundation for formal adaptive management and improved effectiveness, efficiency, and transparency of bull trout reintroduction decisions.

Canadian Journal of Fisheries and Aquatic Sciences↗
USGS2023

Low-complexity floodplain inundation model performs well for ecological and management applications in a large river ecosystem

Flooding is a dominant physical process that drives the form and function of river-floodplain ecosystems. Efficiently characterizing flooding dynamics can be challenging, especially over geographically broad areas or at spatial and temporal scales relevant for ecosystem management activities. Here, we empirically evaluated a low-complexity geospatial model of floodplain inundation in six study segments of the Upper Mississippi River System (UMRS) by pairing spatially extensive, temporally limited and spatially limited, temporally extensive sampling designs. We found little evidence of systematic bias in model performance although discrepancies between model predictions and empirical data did occur locally. Assessments of model predictions revealed low segment-wide discrepancies of wetted extent under contrasting flow conditions and agreement for inundation event detection and duration. Model performance for predicting event frequency and duration was similar among sites expected to exhibit contrasting patterns of hydrologic connectivity with the main channel. Our results suggest that low-complexity models can efficiently characterize a critical physical process across geographically broad, complex river-floodplain ecosystems. Such tools have the potential for advancing scientific understanding of landscape-scale ecological patterns and for prioritizing management actions in large, complex river-floodplain ecosystems like the UMRS.

Illinois, Iowa, Minnesota, Missouri, Wisconsin↗
USGS2000

Breeding season ecology: Chapter 6 in Status, ecology and conservation of the Southwestern Willow Flycatcher (RMRS-GTR-60)

The willow flycatcher ( Empidonax traillii ) breeds across much of the conterminous United States and in portions of extreme southern Canada. As might be expected in such a wide-ranging species, willow fly- catchers in different portions of the range exhibit differences in appearance, song, and ecological characteristics. The intent of this chapter is to provide information on the breeding-season ecology of the southwestern subspecies, E.t. extimus . However, most ecological studies to date have dealt with other willow flycatcher subspecies. Relatively few studies have been published on E.t. extimus , and much of what is currently known is presented in unpublished literature (e.g., agency and consulting firm reports); these sources are relied upon heavily in this chapter. This chapter does not address habitat characteristics in depth, other than for nest sites (refer to A Survey of Current Breeding Habitats for additional details). Although southwestern willow flycatchers are frequent victims of nest parasitism by brown-headed cowbirds ( Molothrus ater ), this chapter will not address the topic of parasitism and its effect on breeding ecology (refer to The Ecology of Brown-headed Cowbirds and their Effects on Southwestern Willow Flycatchers for details). Readers interested in more details of willow flycatcher biology and ecology are encouraged to read McCabe’s (1991) treatise, which is based on over a decade of willow flycatchers research in Wisconsin and includes comparisons with other populations and subspecies.

General Technical Report↗
USGS2019

A landscape model of variable social-ecological fire regimes

Fire regimes are now recognized as the product of social processes whereby fire on any landscape is the product of human-generated drivers: climate change, historical patterns of vegetation manipulation, invasive species, active fire suppression, ongoing fuel management efforts, prescribed burning, and accidental ignitions. We developed a new fire model (Social-Climate Related Pyrogenic Processes and their Landscape Effects: SCRPPLE) that emphasizes the social dimensions of fire and enables simulation of fuel-treatment effects, fire suppression, and prescribed fires. Fire behavior was parameterized with daily fire weather, ignition, and fire-boundary data. SCRPPLE was initially parameterized and developed for the Lake Tahoe Basin (LTB) in California and Nevada, USA although its behavior is general and could be applied worldwide. We demonstrate the behavior and utility of our model via four simple scenarios that emphasize the social dimensions of fire regimes: a) Recent Historical: simulated recent historical patterns of lightning and accidental fires and current patterns of fire suppression, b) Natural-Fire-Regime: simulated wildfire without suppression, accidental fires, or prescribed fires, holding all other factors the same as Recent Historical, c) Enhanced Suppression: simulated a doubling of the effectiveness of suppression, holding all other factors the same as Recent Historical, and d) Reduced Accidental Ignitions: within which the number of accidental fires was reduced by half, holding all other factors the same as Recent Historical. Results indicate that SCRPPLE can recreate past fire regimes, including size, intensity, and locations. Furthermore, our results indicate that the ‘Enhanced Suppression’ and ‘Reduced Accidental Ignitions’ scenarios had similar capacity to reduce fire and related tree mortality over time, suggesting that within the broad outlines of the scenarios, reducing accidental fires can be as effective as substantially increasing resources for suppression.

California, Nevada↗
USGS2013

Mapping monkeypox transmission risk through time and space in the Congo Basin

Monkeypox is a major public health concern in the Congo Basin area, with changing patterns of human case occurrences reported in recent years. Whether this trend results from better surveillance and detection methods, reduced proportions of vaccinated vs. non-vaccinated human populations, or changing environmental conditions remains unclear. Our objective is to examine potential correlations between environment and transmission of monkeypox events in the Congo Basin. We created ecological niche models based on human cases reported in the Congo Basin by the World Health Organization at the end of the smallpox eradication campaign, in relation to remotely-sensed Normalized Difference Vegetation Index datasets from the same time period. These models predicted independent spatial subsets of monkeypox occurrences with high confidence; models were then projected onto parallel environmental datasets for the 2000s to create present-day monkeypox suitability maps. Recent trends in human monkeypox infection are associated with broad environmental changes across the Congo Basin. Our results demonstrate that ecological niche models provide useful tools for identification of areas suitable for transmission, even for poorly-known diseases like monkeypox.

PLoS ONE↗
USGS2024

Evaluating density-weighted connectivity of black bears (Ursus americanus) in Glacier National Park with spatial capture–recapture models

Background Improved understanding of wildlife population connectivity among protected area networks can support effective planning for the persistence of wildlife populations in the face of land use and climate change. Common approaches to estimating connectivity often rely on small samples of individuals without considering the spatial structure of populations, leading to limited understanding of how individual movement links to demography and population connectivity. Recently developed spatial capture-recapture (SCR) models provide a framework to formally connect inference about individual movement, connectivity, and population density, but few studies have applied this approach to empirical data to support connectivity planning. Methods We used mark-recapture data collected from 924 genetic detections of 598 American black bears ( Ursus americanus ) in 2004 with SCR ecological distance models to simultaneously estimate density, landscape resistance to movement, and population connectivity in Glacier National Park northwest Montana, USA. We estimated density and movement parameters separately for males and females and used model estimates to calculate predicted density-weighted connectivity surfaces. Results Model results indicated that landscape structure influences black bear density and space use in Glacier. The mean density estimate was 16.08 bears/100 km 2 (95% CI 12.52–20.6) for females and 9.27 bears/100 km 2 (95% CI 7.70–11.14) for males. Density increased with forest cover for both sexes. For male black bears, density decreased at higher grizzly bear ( Ursus arctos ) densities. Drainages, valley bottoms, and riparian vegetation decreased estimates of landscape resistance to movement for male and female bears. For males, forest cover also decreased estimated resistance to movement, but a transportation corridor bisecting the study area strongly increased resistance to movement presenting a barrier to connectivity. Conclusions Density-weighed connectivity surfaces highlighted areas important for population connectivity that were distinct from areas with high potential connectivity. For black bears in Glacier and surrounding landscapes, consideration of both vegetation and valley topography could inform the placement of underpasses along the transportation corridor in areas characterized by both high population density and potential connectivity. Our study demonstrates that the SCR ecological distance model can provide biologically realistic, spatially explicit predictions to support movement connectivity planning across large landscapes.

Montana↗
USGS2018

Recent advances in environmental flows science and water management—Innovation in the Anthropocene

The implementation of environmental flow regimes offers a promising means to protect and restore riverine, wetland and estuarine ecosystems, their critical environmental services and cultural/societal values. This Special Issue expands the scope of environmental flows and water science in theory and practice, offering 20 papers from academics, agency researchers and non‐governmental organisations, each with fresh perspectives on the science and management of environmental water allocations. Contributions confront the grand challenge for environmental flows and water management in the Anthropocene—the urgent need for innovations that will help to sustain the innate resilience of social–ecological systems under dynamic and uncertain environmental and societal futures. Basin‐scale and regional assessments of flow requirements mark a necessary advance in environmental water science in the face of rapid changes in water‐resource management activities worldwide (e.g. increases in dams, diversions, retention and reuse). Techniques for regional‐scale hydrological and ecohydrological modelling support ecological risk assessment and identification of priority flow management and river restoration actions. Changing flood–drought cycles, long‐term climatic shifts and associated effects on hydrological, thermal and water quality regimes add enormous uncertainty to the prediction of future ecological outcomes, regardless of environmental water allocations. An improved capacity to predict the trajectories of ecological change in rivers degraded by legacies of past impact interacting with current conditions and future climate change is essential. Otherwise, we risk unrealistic expectations from restoration of river and estuarine flow regimes. A more robust, dynamic and predictive approach to environmental water science is emerging. It encourages the measurement of process rates (e.g. birth rate, colonisation rate) and species traits (e.g. physiological requirements, morphological adaptations) as well as ecosystem states (e.g. species richness, assemblage structure), as the variables representing ecological responses to flow variability and environmental water allocations. Another necessary development is the incorporation of other environmental variables such as water temperature and sedimentary processes in flow–ecological response models. Based on contributions to this Special Issue, several recent compilations and the wider literature, we identify six major scientific challenges for further exploration, and seven themes for advancing the management of environmental water. We see the emerging frontier of environmental flows and water science as urgent and challenging, with numerous opportunities for reinvigorated science and methodological innovation in the expanding enterprise of environmental water linked to ecological sustainability and social well‐being.

Freshwater Biology↗
USGS2017

Developing multi-model ensemble projections of ecologically relevant climate variables for Puerto Rico and the US Caribbean

The global increases in surface air temperature are the most widespread and direct consequence of anthropogenic climate change. However, while 21st century temperatures are projected to increase in the Caribbean, the low variability and high average temperatures suggest that impacts on ecosystems and water resources are more likely through changes to the availability, timing, and pattern of moisture. The lack of local-scale climate model information that can resolve the complex topography and small scale climate features hinders the development of robust adaptation strategies. The goal of this project was to develop a suite of local-scale climate projections using dynamic downscaling to aid the development of adaptation strategies in Puerto Rico and the U.S. Virgin Islands (USVI). This project began by engaging the ecologists, hydrologists, and conservation biologists in the region to determine the most valuable types of information to aid research and decision making. The final product provides projections of future climate at a 2km horizontal resolution based on three global climate models and two regional climate models for a scenario with high greenhouse gas emissions. Results from the projections suggest that for Puerto Rico, annual temperature would increase between 1°C and 1.3°C by mid-century with larger temperature increases located in the interior portion of the island. Precipitation totals decrease for much of the island with island average decline between 12% and 19%, with some potentially large localized decreases exceeding 30%. The projected changes for the USVI are dominated by the surrounding ocean environment. The resulting projections will be provided to stakeholders in the region via the USGS and the CLCC.

Puerto Rico↗
USGS2016

Prestoration: Using species in restoration that will persist now and into the future

Climate change presents new challenges for selecting species for restoration. If migration fails to keep pace with climate change, as models predict, the most suitable sources for restoration may not occur locally at all. To address this issue we propose a strategy of “prestoration”: utilizing species in restoration for which a site represents suitable habitat now and into the future. Using the Colorado Plateau, USA as a case study, we assess the ability of grass species currently used regionally in restoration to persist into the future using projections of ecological niche models (or climate envelope models) across a suite of climate change scenarios. We then present a technique for identifying new species that best compensate for future losses of suitable habitat by current target species. We found that the current suite of species, selected by a group of experts, is predicted to perform reasonably well in the short-term, but that losses of prestorable habitat by mid-century would approach 40%. Using an algorithm to identify additional species, we found that fewer than ten species could compensate for nearly all of the losses incurred by the current target species. This case study highlights the utility of integrating ecological niche modeling and future climate forecasts to predict the utility of species in restoring under climate change across a wide range of spatial and temporal scales.

Arizona, Colorado, New Mexico, Utah↗
USGS2016

Model calibration criteria for estimating ecological flow characteristics

Quantification of streamflow characteristics in ungauged catchments remains a challenge. Hydrological modeling is often used to derive flow time series and to calculate streamflow characteristics for subsequent applications that may differ from those envisioned by the modelers. While the estimation of model parameters for ungauged catchments is a challenging research task in itself, it is important to evaluate whether simulated time series preserve critical aspects of the streamflow hydrograph. To address this question, seven calibration objective functions were evaluated for their ability to preserve ecologically relevant streamflow characteristics of the average annual hydrograph using a runoff model, HBV-light, at 27 catchments in the southeastern United States. Calibration trials were repeated 100 times to reduce parameter uncertainty effects on the results, and 12 ecological flow characteristics were computed for comparison. Our results showed that the most suitable calibration strategy varied according to streamflow characteristic. Combined objective functions generally gave the best results, though a clear underprediction bias was observed. The occurrence of low prediction errors for certain combinations of objective function and flow characteristic suggests that (1) incorporating multiple ecological flow characteristics into a single objective function would increase model accuracy, potentially benefitting decision-making processes; and (2) there may be a need to have different objective functions available to address specific applications of the predicted time series.

Book chapter↗
USGS2024

Visualizing wading bird optimal foraging decisions with aggregation behaviors using individual-based modeling

Foragers on patchy landscapes must efficiently balance time between searching for and consuming resources to meet their daily energetic requirements. Spatial aggregation foraging behaviors may improve foraging efficiency by sharing information on locations of resource hotspots. Wading birds are an example of patch foragers that form colonial aggregations during the breeding season to obtain sufficient prey energy to sustain themselves and their offspring each day. Here, we describe a spatially-explicit simulation model of wading bird optimal foraging that represents information sharing through visual cues. The overall purpose of the model is to describe how wading bird daily foraging and reproductive success may change with alternative water control management practices that determine spatial availability of prey for wading birds on the landscape, throughout their breeding seasons. Wading birds are simulated as individuals that operate independently, sampling and selecting among patches based on a prey density tolerance threshold, but also use information from other birds to inform their selection decisions. Foraging success is evaluated against the fundamental objectives of (a) fulfilling daily energetic demands and (b) minimizing predation exposure, by tracking individual daily energetic intake and time spent foraging. In this way, the model approximates population level dynamics of wading bird aggregations that emerge through collective decision making of birds simulated at the lower individual level. Key results of this study suggest that aggregation behaviors may improve population-level foraging success rates, and the optimal settling threshold may modulate when resources become more scarce or difficult to find. Thus, the model addresses ecological theory on the advantages of foraging in groups versus independently. This technique is appropriate for evaluating wading bird populations that forage on patchy landscapes, such as seasonally-pulsed wetlands, wherever sufficient information is available to describe (1) foraging behavior (e.g., feeding rate, flight speeds, patch selection decisions), (2) key landscape characteristics, (3) spatial distributions of prey densities among foraging patches, and (4) changes in prey densities through time. The model was designed to predict qualitative, testable spatial patterns of wading bird foraging movements which can be compared with empirical observations and empirically-derived habitat suitability models. These techniques can also be applied to other bird species, such as shorebirds, or more generally to any species that transits between discrete foraging patches.

Ecological Modelling↗
USGS2010

Functional response models to estimate feeding rates of wading birds

Forager (predator) abundance may mediate feeding rates in wading birds. Yet, when modeled, feeding rates are typically derived from the purely prey-dependent Holling Type II (HoII) functional response model. Estimates of feeding rates are necessary to evaluate wading bird foraging strategies and their role in food webs; thus, models that incorporate predator dependence warrant consideration. Here, data collected in a mangrove swamp in Puerto Rico in 1994 were reanalyzed, reporting feeding rates for mixed-species flocks after comparing fits of the HoII model, as used in the original work, to the Beddington-DeAngelis (BD) and Crowley-Martin (CM) predator-dependent models. Model CM received most support (AIC c wi = 0.44), but models BD and HoII were plausible alternatives (AIC c ??? 2). Results suggested that feeding rates were constrained by predator abundance. Reductions in rates were attributed to interference, which was consistent with the independently observed increase in aggression as flock size increased (P < 0.05). Substantial discrepancies between the CM and HoII models were possible depending on flock sizes used to model feeding rates. However, inferences derived from the HoII model, as used in the original work, were sound. While Holling's Type II and other purely prey-dependent models have fostered advances in wading bird foraging ecology, evaluating models that incorporate predator dependence could lead to a more adequate description of data and processes of interest. The mechanistic bases used to derive models used here lead to biologically interpretable results and advance understanding of wading bird foraging ecology.

Waterbirds↗
USGS2023

Assimilating ecological theory with empiricism: Using constrained generalized additive models to enhance survival analyses

1. Integrating ecological theory with empirical methods is ubiquitous in ecology using hierarchical Bayesian models. However, there has been little development focused on integration of ecological theory into models for survival analysis. Survival is a fundamental process, linking individual fitness with population dynamics, but incorporating life history strategies to inform survival estimation can be challenging because mortality processes occur at multiple scales. 2. We develop an approach to survival analysis, incorporating model constraints based on a species' life history strategy using functional analytical tools. Specifically, we structurally separate intrinsic patterns of mortality that arise from age-specific processes (e.g. increasing survival during early life stages due to growth or maturation, versus senescence) from extrinsic mortality patterns that arise over different periods of time (e.g. seasonal temporal shifts). We use shape constrained generalized additive models (CGAMs) to obtain age-specific hazard functions that incorporate theoretical information based on classical survivorship curves into the age component of the model and capture extrinsic factors in the time component. 3. We compare the performance of our modelling approach to standard survival modelling tools that do not explicitly incorporate species life history strategy in the model structure, using metrics of predictive power, accuracy, efficiency and computation time. We applied these models to two case studies that reflect different functional shapes for the underlying survivorship curves, examining age-period survival for white-tailed deer Odocoileus virginianus in Wisconsin, USA and Columbian sharp-tailed grouse T ympanuchus phasianellus columbianus in Colorado, USA.

Colorado, Wisconsin↗
USGS2021

Invaders from islands: Thermal matching, potential or flexibility?

Native-range thermal constraints may not reflect the geographical distributions of species introduced from native island ranges in part due to rapid physiological adaptation in species introduced to new environments. Correlative ecological niche models may thus underestimate potential invasive distributions of species from islands. The northern curly-tailed lizard ( Leiocephalus carinatus ) is established in Florida, including populations north of its native range. Competing hypotheses may explain this distribution: Thermal Matching (distribution reflects thermal conditions of the native range), Thermal Potential (species tolerates thermal extremes absent in the native range) and/or Thermal Flexibility (thermal tolerance reflects local thermal extremes). We rejected the Thermal Matching hypothesis by comparing ecological niche models developed from native vs. native plus invasive distributions; L. carinatus exists in areas of low suitability in Florida as predicted by the native-distribution model. We then compared critical thermal limits of L. carinatus from two non-native populations to evaluate the Thermal Potential and Flexibility hypotheses: one matching native range latitudes, and another 160 km north of the native range that experiences more frequent cold weather events. Critical thermal minima in the northern population were lower than in the south, supporting the Thermal Flexibility hypothesis, whereas critical thermal maxima did not differ.

Florida↗
USGS2013

Predicting thermal reference conditions for USA streams and rivers

Temperature is a primary driver of the structure and function of stream ecosystems. However, the lack of stream temperature (ST) data for the vast majority of streams and rivers severely compromises our ability to describe patterns of thermal variation among streams, test hypotheses regarding the effects of temperature on macroecological patterns, and assess the effects of altered STs on ecological resources. Our goal was to develop empirical models that could: 1) quantify the effects of stream and watershed alteration (SWA) on STs, and 2) accurately and precisely predict natural (i.e., reference condition) STs in conterminous USA streams and rivers. We modeled 3 ecologically important elements of the thermal regime: mean summer, mean winter, and mean annual ST. To build reference-condition models (RCMs), we used daily mean ST data obtained from several thousand US Geological Survey temperature sites distributed across the conterminous USA and iteratively modeled ST with Random Forests to identify sites in reference condition. We first created a set of dirty models (DMs) that related STs to both natural factors (e.g., climate, watershed area, topography) and measures of SWA, i.e., reservoirs, urbanization, and agriculture. The 3 models performed well (r 2 = 0.84–0.94, residual mean square error [RMSE] = 1.2–2.0 ° C). For each DM, we used partial dependence plots to identify SWA thresholds below which response in ST was minimal. We then used data from only the sites with upstream SWA below these thresholds to build RCMs with only natural factors as predictors (r 2 = 0.87–0.95, RMSE = 1.1–1.9 ° C). Use of only reference-quality sites caused RCMs to suffer modest loss of predictor space and spatial coverage, but this loss was associated with parts of ST response curves that were flat and, therefore, not responsive to further variation in predictor space. We then compared predictions made with the RCMs to predictions made with the DMs with SWA set to 0. For most DMs, setting SWAs to 0 resulted in biased estimates of thermal reference condition.

Freshwater Science↗
USGS2019

Discoveries and novel insights in ecology using structural equation modeling

As we enter the era of data science (Lortie 2018), quantitative analysis methodologies are proliferating rapidly, leaving ecologists with the task of choosing among many alternatives. The use of structural equation modeling (SEM) by ecologists has increased in recent years, prompting us to ask users a number of questions about their experience with the methodology. Responses indicate an enthusiastic endorsement of SEM. Two major elements of respondent’s experiences seem to contribute to their positive response, (1) a sense that they are obtaining more accurate explanatory understanding through the use of SEM and (2) excitement generated by the discovery of novel insights into their systems. We elaborate here on the detection of indirect effects, offsetting effects, and suppressed effects, and demonstrate how discovering these effects can advance ecology.

Ecology and Evolution↗