Geology topics

Angela K. Fuller

Publications and source records attributed to Angela K. Fuller.

At least 55 records · Page 3Linked to original sources

USGS2019

Integration of social and ecological sciences for natural resource decision making: Challenges and opportunities

The last 25 years have witnessed growing recognition that natural resource management decisions depend as much on understanding humans and their social interactions as on understanding the interactions between non-human organisms and their environment. Decision science provides a framework for integrating ecological and social factors into a decision, but challenges to integration remain. The decision-analytic framework elicits values and preferences to help articulate objectives, and then evaluates the outcomes of alternative management actions to achieve these objectives. Integrating social science into these steps can be hindered by failing to include social scientists as more than stakeholder-process facilitators, assuming that specific decision-analytic skills are commonplace for social scientists, misperceptions of social data as inherently qualitative, timescale mismatches for iterating through decision analysis and collecting relevant social data, difficulties in predicting human behavior, and failures of institutions to recognize the importance of this integration. We engage these challenges, and suggest solutions to them, helping move forward the integration of social and biological/ecological knowledge and considerations in decision-making.

Environmental Management↗

USGS2018

Lions and leopards coexist without spatial, temporal or demographic effects of interspecific competition

1. Although interspecific competition plays a principle role in shaping species behaviour and demography, little is known about the population-level outcomes of competition between large carnivores, and the mechanisms that facilitate coexistence. 2. We conducted a multi-landscape analysis of two widely distributed, threatened large carnivore competitors to offer insight into coexistence strategies and assist with species-level conservation. 3. We evaluated how interference competition affects occupancy, temporal activity and population density of a dominant competitor, the lion (Panthera leo), and its subordinate competitor, the leopard (Panthera pardus). We collected camera-trap data over three years in ten study sites covering 5,070 km2. We used multispecies occupancy modelling to assess spatial responses in varying environmental and prey conditions and competitor presence, and examined temporal overlap and the relationship between lion and leopard densities across sites and years. 4. Results showed that both lion and leopard occupancy was independent of – rather than conditional on – their competitor’s presence across all environmental covariates. Marginal occupancy probability for leopard was higher in areas with more bushy, ‘hideable’ habitat, human (tourist) activity and topographic ruggedness, whereas lion occupancy decreased with increasing hideable habitat and increased with higher abundance of very large prey. Temporal overlap was high between carnivores and there was no significant relationship between species densities. 4. Lions pose a threat to the survival of individual leopards, but they exerted no tractable influence on leopard spatial or temporal dynamics. Furthermore, lions did not appear to suppress leopard populations, suggesting that intraguild competitors can coexist in the same areas without population decline. Aligned conservation strategies that promote functioning ecosystems, rather than target individual species, are therefore suggested to achieve cost- and space-effective conservation.

Journal of Animal Ecology↗

USGS2018

Large-scale variation in density of an aquatic ecosystem indicator species

Monitoring indicator species is a pragmatic approach to natural resource assessments, especially when the link between the indicator species and ecosystem state is well justified. However, conducting ecosystem assessments over representative spatial scales that are insensitive to local heterogeneity is challenging. We examine the link between polychlorinated biphenyl (PCB) contamination and population density of an aquatic habitat specialist over a large spatial scale using non-invasive genetic spatial capture-recapture. Using American mink (Neovison vison), a predatory mammal and an indicator of aquatic ecosystems, we compared estimates of density in two major river systems, one with extremely high levels of PCB contamination (Hudson River), and a hydrologically independent river with lower PCB levels (Mohawk River). Our work supports the hypothesis that the mink densities are substantially (1.64-1.67 times) lower in the contaminated river system. We demonstrate the value of coupling the indicator species concept with well-conceived and spatially representative monitoring protocols. PCBs have demonstrable detrimental effects on aquatic ecosystems, including mink, and these effects are likely to be profound and long-lasting, manifesting as population-level impacts. Through integrating non-invasive data collection, genetic analysis, and spatial capture-recapture methods, we present a monitoring framework for generating robust density estimates across large spatial scales.

New York↗

USGS2018

The use of remote camera trapping to study cheetahs

Remote camera trapping is an efficient noninvasive technique for monitoring rare and elusive species, such as cheetahs. The unique pelage pattern of cheetahs allows for identification of individuals from photographs, providing detection histories that are naturally suited for abundance estimation using capture–recapture methods. Furthermore, the spatial location of photographic detections allows for the use of spatial capture–recapture models, which provide estimates of density. In this chapter, we describe aspects of cheetah ecology that should be considered when designing camera trapping surveys (e.g., social structure, natural densities, and home range size) to estimate cheetah density and provide guidance for future camera trap sampling and analysis.

Book chapter↗

USGS2018

Wildlife management is science based: Myth or reality?

In the January/February issue of The Wildlife Professional , a group of wildlife leaders discussed what they considered "myths" in wildlife management and invited other wildlife professionals to contribute their favorites. Here, five wildlife professionals take up that theme with their discussions of the scientific basis of wildlife management.

The Wildlife Professional↗

USGS2017

Unifying population and landscape ecology with spatial capture-recapture

Spatial heterogeneity in the environment induces variation in population demographic rates and dispersal patterns, which result in spatio‐temporal variation in density and gene flow. Unfortunately, applying theory to learn about the role of spatial structure on populations has been hindered by the lack of mechanistic spatial models and inability to make precise observations of population state and structure. Spatial capture–recapture (SCR) represents an individual‐based analytic framework for overcoming this fundamental obstacle that has limited the utility of ecological theory. SCR methods make explicit use of spatial encounter information on individuals in order to model density and other spatial aspects of animal population structure, and they have been widely adopted in the last decade. We review the historical context and emerging developments in SCR models that enable the integration of explicit ecological hypotheses about landscape connectivity, movement, resource selection, and spatial variation in density, directly with individual encounter history data obtained by new technologies (e.g. camera trapping, non‐invasive DNA sampling). We describe ways in which SCR methods stand to advance the study of animal population ecology.

Ecography↗

USGS2017

Evaluating population expansion of black bears using spatial capture-recapture

The population of American black bears ( Ursus americanus ) in southern New York, USA has been growing and expanding in range since the 1990s. This has motivated a need to anticipate future patterns of range expansion. We conducted a non-invasive, genetic, spatial capture-recapture (SCR) study to estimate black bear density and identify spatial patterns of population density that are potentially associated with range expansion. We collected hair samples in a 2,519-km 2 study area in southern New York with barbed-wire hair snares and identified individuals and measured genetic diversity using 7 microsatellite loci and 1 sex-linked marker. We estimated a mean density of black bears in the region of 13.7 bears/100 km 2 , and detected a slight latitudinal gradient in density consistent with the documented range expansion. However, elevation and the amounts of forest, crop, and developed landcover types did not influence density, suggesting that bears are using a diversity of resources in this heterogeneous landscape outside their previously described distribution. These results provide the first robust baseline estimates for population density and distribution associated with different landcover types in the expanded bear range. Further, genetic diversity was comparable to that of non-expanding black bear populations in the eastern United States, and in combination with the latitudinal density gradient, suggest that the study area is not at the colonizing front of the range expansion. In addition, the diversity of landcover types used by bears in the study area implies a possible lack of constraints for further northern expansion of the black bear range. Our non-invasive, genetic, spatial capture-recapture approach has utility for studying populations of other species that may be expanding in range because SCR allows for the testing of explicit, spatial ecological hypotheses.

New York↗

USGS2017

Estimating occupancy probability of moose using hunter survey data

Monitoring rare species can be difficult, especially across large spatial extents, making conventional methods of population monitoring costly and logistically challenging. Citizen science has the potential to produce observational data across large areas that can be used to monitor wildlife distributions using occupancy models. We used citizen science (i.e., hunter surveys) to facilitate monitoring of moose ( Alces alces ) populations, an especially important endeavor because of their recent apparent declines in the northeastern and upper midwestern regions of the United States. To better understand patterns of occurrence of moose in New York, we used data collected through an annual survey of approximately 11,000 hunters between 2012 and 2014 that recorded detection–non-detection data of moose and other species. We estimated patterns of occurrence of moose in relation to land cover characteristics, climate effects, and interspecific interactions using occupancy models to analyze spatially referenced moose observations. Coniferous and deciduous forest with low prevalence of white-tailed deer ( Odocoileus virginianus ) had the highest probability of moose occurrence. This study highlights the potential of data collected using citizen science for understanding the spatial distribution of low-density species across large spatial extents and providing key information regarding where and when future research and management activities should be focused.

New York↗

USGS2017

Examining the occupancy–density relationship for a low-density carnivore

The challenges associated with monitoring low-density carnivores across large landscapes have limited the ability to implement and evaluate conservation and management strategies for such species. Non-invasive sampling techniques and advanced statistical approaches have alleviated some of these challenges and can even allow for spatially explicit estimates of density, one of the most valuable wildlife monitoring tools. For some species, individual identification comes at no cost when unique attributes (e.g. pelage patterns) can be discerned with remote cameras, while other species require viable genetic material and expensive laboratory processing for individual assignment. Prohibitive costs may still force monitoring efforts to use species distribution or occupancy as a surrogate for density, which may not be appropriate under many conditions. Here, we used a large-scale monitoring study of fisher Pekania pennanti to evaluate the effectiveness of occupancy as an approximation to density, particularly for informing harvest management decisions. We combined remote cameras with baited hair snares during 2013–2015 to sample across a 70 096-km 2 region of western New York, USA. We fit occupancy and Royle–Nichols models to species detection–non-detection data collected by cameras, and spatial capture–recapture (SCR) models to individual encounter data obtained by genotyped hair samples. Variation in the state variables within 15-km 2 grid cells was modelled as a function of landscape attributes known to influence fisher distribution. We found a close relationship between grid cell estimates of fisher state variables from the models using detection–non-detection data and those from the SCR model, likely due to informative spatial covariates across a large landscape extent and a grid cell resolution that worked well with the movement ecology of the species. Fisher occupancy and density were both positively associated with the proportion of coniferous-mixed forest and negatively associated with road density. As a result, spatially explicit management recommendations for fisher were similar across models, though relative variation was dampened for the detection–non-detection data. Synthesis and applications . Our work provides empirical evidence that models using detection–non-detection data can make similar inferences regarding relative spatial variation of the focal population to models using more expensive individual encounters when the selected spatial grain approximates or is marginally smaller than home range size. When occupancy alone is chosen as a cost-effective state variable for monitoring, simulation and sensitivity analyses should be used to understand how inferences from detection–non-detection data will be affected by aspects of study design and species ecology.

Journal of Applied Ecology↗

USGS2017

Addressing wild turkey population declines using structured decision making

We present a case study from New York, USA, of the use of structured decision making (SDM) to identify fall turkey harvest regulations that best meet stakeholder objectives, in light of recent apparent declines in abundance of wild turkeys in the northeastern United States. We used the SDM framework to incorporate the multiple objectives associated with turkey hunting, stakeholder desires, and region-specific ecological and environmental factors that could influence fall harvest. We identified a set of 4 fall harvest regulations, composed of different season lengths and bag limits, and evaluated their relative achievement of the objectives. We used a stochastic turkey population model, statistical modeling, and expert elicitation to evaluate the consequences of each harvest regulation on each of the objectives. We conducted a statewide mail survey of fall turkey hunters in New York to gather the necessary information to evaluate tradeoffs among multiple objectives associated with hunter satisfaction. The optimal fall harvest regulation was a 2-week season and allowed for the harvest of 1 bird/hunter. This regulation was the most conservative of those evaluated, reflecting the concerns about recent declines in turkey abundance among agency wildlife biologists and the hunting public. Depending on the region of the state, the 2-week, 1-bird regulation was predicted to result in 7–32% more turkeys on the landscape after 5 years. The SDM process provided a transparent framework for setting fall turkey harvest regulations and reduced potential stakeholder conflict by explicitly taking the multiple objectives of different stakeholder groups into account.

New York↗

USGS2017

Model-based estimators of density and connectivity to inform conservation of spatially structured populations

Conservation and management of spatially structured populations is challenging because solutions must consider where individuals are located, but also differential individual space use as a result of landscape heterogeneity. A recent extension of spatial capture–recapture (SCR) models, the ecological distance model, uses spatial encounter histories of individuals (e.g., a record of where individuals are detected across space, often sequenced over multiple sampling occasions), to estimate the relationship between space use and characteristics of a landscape, allowing simultaneous estimation of both local densities of individuals across space and connectivity at the scale of individual movement. We developed two model-based estimators derived from the SCR ecological distance model to quantify connectivity over a continuous surface: (1) potential connectivity—a metric of the connectivity of areas based on resistance to individual movement; and (2) density-weighted connectivity (DWC)—potential connectivity weighted by estimated density. Estimates of potential connectivity and DWC can provide spatial representations of areas that are most important for the conservation of threatened species, or management of abundant populations (i.e., areas with high density and landscape connectivity), and thus generate predictions that have great potential to inform conservation and management actions. We used a simulation study with a stationary trap design across a range of landscape resistance scenarios to evaluate how well our model estimates resistance, potential connectivity, and DWC. Correlation between true and estimated potential connectivity was high, and there was positive correlation and high spatial accuracy between estimated DWC and true DWC. We applied our approach to data collected from a population of black bears in New York, and found that forested areas represented low levels of resistance for black bears. We demonstrate that formal inference about measures of landscape connectivity can be achieved from standard methods of studying animal populations which yield individual encounter history data such as camera trapping. Resulting biological parameters including resistance, potential connectivity, and DWC estimate the spatial distribution and connectivity of the population within a statistical framework, and we outline applications to many possible conservation and management problems.

Ecosphere↗

USGS2017

Use of spatial capture–recapture to estimate density of Andean bears in northern Ecuador

The Andean bear ( Tremarctos ornatus ) is the only extant species of bear in South America and is considered threatened across its range and endangered in Ecuador. Habitat loss and fragmentation is considered a critical threat to the species, and there is a lack of knowledge regarding its distribution and abundance. The species is thought to occur at low densities, making field studies designed to estimate abundance or density challenging. We conducted a pilot camera-trap study to estimate Andean bear density in a recently identified population of Andean bears northwest of Quito, Ecuador, during 2012. We compared 12 candidate spatial capture–recapture models including covariates on encounter probability and density and estimated a density of 7.45 bears/100 km 2 within the region. In addition, we estimated that approximately 40 bears used a recently named Andean bear corridor established by the Secretary of Environment, and we produced a density map for this area. Use of a rub-post with vanilla scent attractant allowed us to capture numerous photographs for each event, improving our ability to identify individual bears by unique facial markings. This study provides the first empirically derived density estimate for Andean bears in Ecuador and should provide direction for future landscape-scale studies interested in conservation initiatives requiring spatially explicit estimates of density.

Ursus↗

USGS2017

Dynamic optimization of landscape connectivity embedding spatial-capture-recapture information

Maintaining landscape connectivity is increasingly important in wildlife conservation, especially for species experiencing the effects of habitat loss and fragmentation. We propose a novel approach to dynamically optimize landscape connectivity. Our approach is based on a mixed integer program formulation, embedding a spatial capture-recapture model that estimates the density, space usage, and landscape connectivity for a given species. Our method takes into account the fact that local animal density and connectivity change dynamically and non-linearly with different habitat protection plans. In order to scale up our encoding, we propose a sampling scheme via random partitioning of the search space using parity functions. We show that our method scales to realworld size problems and dramatically outperforms the solution quality of an expectation maximization approach and a sample average approximation approach.

Conference Paper↗

USGS2016

Structured decision making as a framework for large-scale wildlife harvest management decisions

Fish and wildlife harvest management at large spatial scales often involves making complex decisions with multiple objectives and difficult tradeoffs, population demographics that vary spatially, competing stakeholder values, and uncertainties that might affect management decisions. Structured decision making (SDM) provides a formal decision analytic framework for evaluating difficult decisions by breaking decisions into component parts and separating the values of stakeholders from the scientific evaluation of management actions and uncertainty. The result is a rigorous, transparent, and values-driven process. This decision-aiding process provides the decision maker with a more complete understanding of the problem and the effects of potential management actions on stakeholder values, as well as how key uncertainties can affect the decision. We use a case study to illustrate how SDM can be used as a decision-aiding tool for management decision making at large scales. We evaluated alternative white-tailed deer ( Odocoileus virginianus ) buck-harvest regulations in New York designed to reduce harvest of yearling bucks, taking into consideration the values of the state wildlife agency responsible for managing deer, as well as deer hunters. We incorporated tradeoffs about social, ecological, and economic management concerns throughout the state. Based on the outcomes of predictive models, expert elicitation, and hunter surveys, the SDM process identified management alternatives that optimized competing objectives. The SDM process provided biologists and managers insight about aspects of the buck-harvest decision that helped them adopt a management strategy most compatible with diverse hunter values and management concerns.

Ecosphere↗

USGS2016

Participatory modeling and structured decision making

Structured decision making (SDM) provides a framework for making sound decisions even when faced with uncertainty, and is a transparent, defensible, and replicable method used to understand complex problems. A hallmark of SDM is the explicit incorporation of values and science, which often includes participation from multiple stakeholders, helping to garner trust and ultimately result in a decision that is more likely to be implemented. The core steps in the SDM process are used to structure thinking about natural resources management choices, and include: (1) properly defining the problem and the decision context, (2) determining the objectives that help describe the aspirations of the decision maker, (3) devising management actions or alternatives that can achieve those objectives, (4) evaluating the outcomes or consequences of each alternative on each of the objectives, (5) evaluating trade-offs, and (6) implementing the decision. Participatory modeling for SDM includes engaging stakeholders in some or all of the steps of the SDM process listed above. In addition, participatory modeling often is crucial for creating qualitative and quantitative models of how the system works, providing data for these models, and eliciting expert opinion when data are unavailable. In these ways, SDM provides a framework for decision making in natural resources management that includes participation from stakeholder groups throughout the process, including the modeling phase.

Book chapter↗

USGS2016

Estimating population density and connectivity of American mink using spatial capture-recapture

Estimating the abundance or density of populations is fundamental to the conservation and management of species, and as landscapes become more fragmented, maintaining landscape connectivity has become one of the most important challenges for biodiversity conservation. Yet these two issues have never been formally integrated together in a model that simultaneously models abundance while accounting for connectivity of a landscape. We demonstrate an application of using capture–recapture to develop a model of animal density using a least-cost path model for individual encounter probability that accounts for non-Euclidean connectivity in a highly structured network. We utilized scat detection dogs ( Canis lupus familiaris ) as a means of collecting non-invasive genetic samples of American mink ( Neovison vison ) individuals and used spatial capture–recapture models (SCR) to gain inferences about mink population density and connectivity. Density of mink was not constant across the landscape, but rather increased with increasing distance from city, town, or village centers, and mink activity was associated with water. The SCR model allowed us to estimate the density and spatial distribution of individuals across a 388 km 2 area. The model was used to investigate patterns of space usage and to evaluate covariate effects on encounter probabilities, including differences between sexes. This study provides an application of capture–recapture models based on ecological distance, allowing us to directly estimate landscape connectivity. This approach should be widely applicable to provide simultaneous direct estimates of density, space usage, and landscape connectivity for many species.

Ecological Applications↗

USGS2016

Management decision making for fisher populations informed by occupancy modeling

Harvest data are often used by wildlife managers when setting harvest regulations for species because the data are regularly collected and do not require implementation of logistically and financially challenging studies to obtain the data. However, when harvest data are not available because an area had not previously supported a harvest season, alternative approaches are required to help inform management decision making. When distribution or density data are required across large areas, occupancy modeling is a useful approach, and under certain conditions, can be used as a surrogate for density. We collaborated with the New York State Department of Environmental Conservation (NYSDEC) to conduct a camera trapping study across a 70,096-km 2 region of southern New York in areas that were currently open to fisher ( Pekania [ Martes ] pennanti ) harvest and those that had been closed to harvest for approximately 65 years. We used detection–nondetection data at 826 sites to model occupancy as a function of site-level landscape characteristics while accounting for sampling variation. Fisher occupancy was influenced positively by the proportion of conifer and mixed-wood forest within a 15-km 2 grid cell and negatively associated with road density and the proportion of agriculture. Model-averaged predictions indicated high occupancy probabilities (>0.90) when road densities were low (<1 km/km 2 ) and coniferous and mixed forest proportions were high (>0.50). Predicted occupancy ranged 0.41–0.67 in wildlife management units (WMUs) currently open to trapping, which could be used to guide a minimum occupancy threshold for opening new areas to trapping seasons. There were 5 WMUs that had been closed to trapping but had an average predicted occupancy of 0.52 (0.07 SE), and above the threshold of 0.41. These areas are currently under consideration by NYSDEC for opening a conservative harvest season. We demonstrate the use of occupancy modeling as an aid to management decision making when harvest-related data are unavailable and when budgetary constraints do not allow for capture–recapture studies to directly estimate density.

Journal of Wildlife Management↗

USGS2016

Combining landscape variables and species traits can improve the utility of climate change vulnerability assessments

Conservation organizations worldwide are investing in climate change vulnerability assessments. Most vulnerability assessment methods focus on either landscape features or species traits that can affect a species vulnerability to climate change. However, landscape features and species traits likely interact to affect vulnerability. We compare a landscape-based assessment, a trait-based assessment, and an assessment that combines landscape variables and species traits for 113 species of birds, herpetofauna , and mammals in the northeastern United States. Our aim is to better understand which species traits and landscape variables have the largest influence on assessment results and which types of vulnerability assessments are most useful for different objectives. Species traits were most important for determining which species will be most vulnerable to climate change. The sensitivity of species to dispersal barriers and the species average natal dispersal distance were the most important traits. Landscape features were most important for determining where species will be most vulnerable because species were most vulnerable in areas where multiple landscape features combined to increase vulnerability, regardless of species traits. The interaction between landscape variables and species traits was important when determining how to reduce climate change vulnerability. For example, an assessment that combines information on landscape connectivity, climate change velocity, and natal dispersal distance suggests that increasing landscape connectivity may not reduce the vulnerability of many species. Assessments that include landscape features and species traits will likely be most useful in guiding conservation under climate change.

Biological Conservation↗