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Angela Fuller, PhD

Unit Leader - New York Cooperative Fish and Wildlife Research Unit 

Research Interests

The single unifying theme of Angela's research program that transcends all projects is that they provide information that contributes to the conservation or management of wildlife species and that has an impact on the way species are managed. The first area of her research is on how spatial variation in the environment influences resource use, movements, and population ecology of mammals. This research is focused primarily on mammalian carnivores, which have many traits that make them especially susceptible to landscape change (e.g., large home ranges, relatively low population densities, and long dispersal distances). She frequently uses non-invasive sampling methods that do not require direct capture of individuals. Using these noninvasively collected samples from carnivores, she employs methods for population estimation that incorporate spatial or landscape processes to help explain the density of species across a landscape. The second major program area of her research is in applying structured decision making (SDM) and adaptive management to guide natural resource management and policy outcomes. SDM is a process for helping to make management or policy decisions in a clear and transparent way, and involves evaluating how well alternative management strategies do at achieving objectives that have been identified by the decision maker and stakeholders. This work integrates quantitative modeling to help predict outcomes of the management strategies that were developed to achieve the stated objectives. Both of her research areas involve the spatial ecology of species, investigating how spatial landscape patterns influence the distribution, density, or dynamics of animal populations.

Teaching Interests

  • Decision Making for Natural Resources
  • Landscape Ecology
  • Habitat Ecology
  • Forest Landscape Planning

Professional Experience

  • Unit Leader, New York Cooperative Fish and Wildlife Research Unit, 2014-

  • Assistant Unit Leader, New York Cooperative Fish and Wildlife Research Unit, 2009-2014

Education and Certifications

  • Ph D University of Maine 2006

  • MS University of Maine 1999

  • BS University of Maine at Machias 1996

Science and Products

Filter Total Items: 41

Citizen science data collection for integrated wildlife population analyses

Citizen science, or community science, has emerged as a cost-efficient method to collect data for wildlife monitoring. To inform research and conservation, citizen science sampling designs should collect data that match the robust statistical analyses needed to quantify species and population patterns. Further increasing the contributions of citizen science, integrating citizen science data with o
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Cooperative Research Units

Engaging hunters in selecting duck season dates using decision science: Problem framing, objective setting, devising management alternatives

Waterfowl hunters have an important economic impact on local, state, and national economies, and are important stakeholders in decisions regarding waterfowl harvest season dates. Individual states are responsible for annually setting duck season dates that conform to the migratory game bird season frameworks as set by the U.S. Fish and Wildlife Service. The federal framework specifies season lengt
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Cooperative Research Units

Using structured decision making to incorporate ecological and social values into harvest decisions: Case studies of white-tailed deer and walleye

Harvest decisions for fish and wildlife populations often include conflicting ecological, economic, and social values. Using decision analysis, such as structured decision making and adaptive management, as a framework to aid decision makers in multi-objective decision making for setting harvest regulations can lead to a more transparent and resilient decision. The process includes opportunities f
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Cooperative Research Units

Accelerating ecological sciences from above: Spatial contrastive learning for remote sensing

The rise of neural networks has opened the door for automatic analysis of remote sensing data. A challenge to using this machinery for computational sustainability is the necessity of massive labeled data sets, which can be cost-prohibitive for many non-profit organizations. The primary motivation for this work is one such problem; the efficient management of invasive species -- invading flora and
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Cooperative Research Units

Learning augmented methods for matching: Improving invasive species management and urban mobility

With the success of machine learning, integrating learned models into real-world systems has become a critical chal- lenge. Naively applying predictions to combinatorial opti- mization problems can incur high costs, which has motivated researchers to consider learning augmented algorithms that can make use of faulty or incomplete predictions. Inspired by two matching problems in computational sust
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Cooperative Research Units

Trends in cheetah Acinonyx jubatus density in north-central Namibia

Assessing trends in abundance and density of species of conservation concern is vital to inform conservation and management strategies. The remaining population of the cheetah (Acinonyx jubatus) largely exists outside of protected areas, where they are often in conflict with humans. Despite this, the population status and dynamics of cheetah outside of protected areas have received relatively limi
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Cooperative Research Units

Ratcheting up rigor in wildlife management decision making

The wildlife management institution has been transforming to ensure relevance and positive conservation outcomes into the future. Continuous improvement of decision making is one aspect of this transformation, but many obstacles hinder systematic approaches to decision making. One can point to examples of formal decision science applications by state and federal agencies in the United States, but
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Cooperative Research Units

Computational sustainability: Computing for a better world and a sustainable future

Computational sustainability aims to develop computational methods to help solve environmental, economic, and societal problems and thereby facilitate a path towards a sustainable future. Sustainability problems are unique in scale, impact, complexity, and richness, offering challenges but also opportunities for the advancement of the state of the art of computing and information science.
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Cooperative Research Units

Reserve design to optimize functional connectivity and animal density

Ecological distance-based spatial capture–recapture models (SCR) are a promising approach for simultaneously estimating animal density and connectivity, both of which affect spatial population processes and ultimately species persistence. We explored how SCR models can be integrated into reserve-design frameworks that explicitly acknowledge both the spatial distribution of individuals and their sp
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Cooperative Research Units

Incorporating citizen science data in spatially explicit integrated population models

Information about population abundance, distribution, and demographic rates is critical for understanding a species’ ecology and for effective conservation and management. To collect data over large spatial and temporal extents for such inferences, especially for species with low densities or wide distributions, citizen science can be an efficient approach. Integrated models have also emerged as a
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Cooperative Research Units

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 deci
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Cooperative Research Units

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
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Cooperative Research Units
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Conceptual formulation of the coupled classification/ecological model.

Capture-recapture meets big data: integrating statistical classification with ecological models of species abundance and occurrence

Advances in new technologies such as remote cameras, noninvasive genetics and bioacoustics provide massive quantities of electronic data. Much work has been done on automated (“machine learning”) methods of classification which produce “sample class designations” (e.g., identification of species or individuals) that are regarded as observed data in ecological models. However, these “data” are actu...
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John Wesley Powell Center for Analysis and Synthesis
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Capture-recapture meets big data: integrating statistical classification with ecological models of species abundance and occurrence

Advances in new technologies such as remote cameras, noninvasive genetics and bioacoustics provide massive quantities of electronic data. Much work has been done on automated (“machine learning”) methods of classification which produce “sample class designations” (e.g., identification of species or individuals) that are regarded as observed data in ecological models. However, these “data” are actu...
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Spatial Capture-Recapture by Royle,Chandler, Sollmann, & Gardner

Spatial Capture-Recapture Models to Estimate Abundance and Density of Animal Populations

The Challenge: For decades, capture-recapture methods have been the cornerstone of ecological statistics as applied to population biology. While capture-recapture has become the standard sampling and analytical framework for the study of population processes (Williams, Nichols & Conroy 2002) it has advanced independent of and remained unconnected to the spatial structure of the population or the...
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Ecosystems Mission Area, Species Management Research Program, Eastern Ecological Science Center
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Spatial Capture-Recapture Models to Estimate Abundance and Density of Animal Populations

The Challenge: For decades, capture-recapture methods have been the cornerstone of ecological statistics as applied to population biology. While capture-recapture has become the standard sampling and analytical framework for the study of population processes (Williams, Nichols & Conroy 2002) it has advanced independent of and remained unconnected to the spatial structure of the population or the...
Learn More

Science and Products