Living with Leopards: Implications of human-leopard interaction on food security and public health in the foothills of the Himalayas
In Nepal, human-leopard conflict threatens food security of local communities as a result of livestock loss and causes injuries and death to both humans and leopards. We aim to understand key drivers and impacts of human-leopard interactions across a rural-urban gradient and to generate well-informed policy-led interventions for sustainable conservation actions. We will conduct an analysis of leopard diet to better understand the extent to which leopards prey on livestock and feral species and we will develop a spatial conflict risk model that will help to manage or mitigate human-leopard interactions. In partnership with two national conservation champions, we seek to secure national policy commitment by developing a National Policy document to enable site-specific sustainable conflict management responses, and promote local stewardship for the survival of leopards in shared landscapes without compromising human well-being.
Collaborators: Dr. Martin Gilbert, College of Veterinary Medicine, Cornell University and Dr. Richard Stedman, Department of Natural Resources, Cornell University; Shashank Poudel, Cornell University
The next frontier in bioacoustics: modeling sound attenuation and individual space usage to estimate density of animal populations
Growing economies in developing countries frequently come at the expense of conservation priorities, such as the protection of endangered species, the maintenance of ecosystem services, and the long-term sustainability of resources. Assessing the persistence of native wildlife, a measure of ecosystem health, in rapidly changing landscapes is challenging due to current limitations in methods to survey wildlife populations and analyze the resulting data. Population density is a metric commonly used to assess species status; however, estimating the density of species that are difficult to identify visually is challenging. Passive acoustic monitoring is a new survey method with the potential to provide quick and reliable population density estimates for species that are easy to detect acoustically; however, statistical methods that reliably produce in situ density estimates from acoustic data that are not dependent on human interpretation of call rates are currently undeveloped. We propose to extend recent developments in statistical models for other noninvasive sampling methods to the use of acoustic monitoring devices for density estimation by incorporating spatial information about the spatial structure of the population to produce estimates of call rate in situ, and in turn estimate density. In addition, we will expand on existing automated detection and classification techniques for processing the acoustic data which will allow us to incorporate information about caller identity into our estimates. As a case study in use of these methods, we are applying the methods to endangered gibbons in Borneo, Indonesia. This application will demonstrate how bioacoustics detectors can be used to estimate the density of other species which can be detected acoustically, leading to more reliable conservation and land use decisions worldwide for any vocalizing species.
Collaborators: Dr. J. Andrew Royle, U.S. Geological Survey; Dr. Holger Klinck, Cornell Lab of Ornithology; Dr. Edward Game, The Nature Conservancy; Mohamad Rifqi, The Nature Conservancy; Dr. Ben Augustine, Cornell University; Dr. Dena Clink, Cornell University
Conservation and management of Andean bears from regional to local scales: occupancy, density, connectivity, and threats
The Andean bear is the only extant species of bear in South America and is considered threatened across its range due to habitat loss, fragmentation, and illegal hunting. Typically, Andean bear inhabit natural areas with little to no human presence/activity, occurring between 200-4700 m elevation. Nevertheless, in Colombia non-protected areas have historically had a high level of human presence/activity. Consequently, Andean bear populations are isolated, and exposed to a diverse degree of human related threats, including human-bear conflict in the form of retaliatory hunting. Monitoring changes in the Andean bear population, and understanding their relationship with threats and environmental variables is necessary for informing management decisions.
The objectives of this study are to 1) evaluate the relationship between real/perceived damage caused by Andean bears, farmers’ attitudes about bears, and bear killing; 2) Evaluate landscape factors and species threats that contribute to regional occupancy of Andean bears. 3) Estimate density and connectivity of Andean bears in priority conservation areas, and evaluate the relationship between density and occupancy.
Spatial Risk Mapping: A Tool to Plan and Implement Human- Andean Bear Conflict Mitigation in Ecuador
The Chocó-Andean region of Ecuador lies at the convergence of two of the world’s top 25 biodiversity hotspots and is home to more endemic species than any other hotspot on Earth. Unfortunately, half of this region has been deforested and the expansion of agriculture, development, and recently granted mining concessions threatens remaining forest. Social-ecological systems are linked systems of people and nature, emphasizing that humans must be seen as a part of, not apart from, nature. We will use a socio-ecological system approach and generate alternative strategies to guide the design of public policies that can help communities to cope with the effects of environmental changes. Specific objectives include, 1) Assess the level of knowledge and perception of local communities about the state of their natural resources and the benefits that they obtain from them, 2) Identify strategies and preferences that local communities use for their subsistence (land uses: livestock, agriculture, ecotourism, recreation, ecotourism, conservation) 3) Assess the social capacity of communities to engage in conservation activities, 4) Understand the motivations that influence land use and development decisions, 5) Engage community members in the co-development of a tool to allow growing productive and sustainable agricultural crops in landscapes inhabited by Andean bears, and 6) Develop a spatial risk/benefit map that identifies areas with a high potential for agricultural crop damage by Andean bears. This will serve as a decision-making tool to preemptively avoid conflict with Andean bears and to identify areas for management interventions (e.g., sustainable crop management practices).
Collaborators: Dr. Richard Stedman, Department of Natural Resources, Cornell University; CONDESAN; Foundation Cambugan; Santiago Garcia, Cornell University
Carnivore Occupancy and Intraguild Interactions Across New York State
The distribution and abundance of carnivore species can have significant impacts on ecological communities through top-down and cascading trophic effects. Several carnivore species occur in New York, and in addition to their ecological importance, they have economic and recreational value to humans as fur-bearing species. Understanding the factors that influence their spatial distribution can help managers ensure the maintenance of sustainable populations. These factors can include environmental variables that determine the suitability of habitat for a particular species or their main prey, as well as the potential for negative interspecific interactions arising from competition and intraguild predation in areas where they occur in sympatry. Occupancy models are a useful tool to determine the occurrence of species as a function of environmental covariates across the landscape, while accounting for imperfect detection. In addition, more recently developed multispecies occupancy models can elucidate the effects of interspecific interactions on species occupancy. From 2013-2015, we collaborated with the New York State Department of Environmental Conservation (NYSDEC) to conduct a non-invasive survey across western portions of New York. Results from the fisher survey resulted in the opening of conservative trapping seasons (6 days) in new wildlife management units (WMU) previously closed to trapping, based on a minimum threshold predicted occupancy level of 0.41. We are using the same fisher detection data, along with additional data on bobcats, coyotes, and red foxes in multispecies occupancy models to explore hypotheses regarding interspecific interactions and environmental correlates in determining species occupancy. These studies demonstrate the efficiency and value of large-scale camera-trapping surveys, which can detect multiple species at once. Both single-species and multispecies occupancy models can provide managers with useful information that can be used to guide decisions on harvest, conservation of habitat, and population management.
Collaborators: New York State Department of Environmental Conservation; Dr. Jennifer Brazeal, Cornell University
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 actually derived quantities (or synthetic data) and subject to various important sources of bias and error. If the derived quantities are used to make ecological determinations without consideration of these biases, those inferences which inform monitoring, conservation, and management will be flawed. We propose to develop the concept of coupled classification in which statistical classification models are linked to ecological models of species abundance or occurrence. In this new framework, classification (e.g., species identification) takes into account the local structure of populations, communities and landscapes and does not assume that where a sample is collected is independent of the class structure of the population, as all current classification methods do. Our work addresses a significant bottleneck in the utilization of data from new technologies for monitoring and assessment of populations and communities – the lack of formal statistical frameworks (which fully propagate uncertainty) for automatically integrating observed digital monitoring data to ecological objectives of scientific and management concern. Our work is transformative because it provides a mechanism for directly integrating remotely sensed “big data” with ecological models while accounting for misclassification. With a coupled classification system there stands the possibility of fully automated data collection and processing systems.
Collaborators: Dr. J. Andrew Royle, U.S. Geological Survey
Spatial Optimization of Invasive Species Management in New York
Managing invasive species across large areas often requires multiple objective decisions involving numerous species with a wide range of biological characteristics, impacts to valued goods and services, and a large number of treatment options. Although there have been advancements in models informing the management of invasive species to reduce their impacts, few approaches are available that address the issue of spatially optimizing the allocation of treatments for multiple species subject to a budget constraint and that explicitly considers difficult tradeoffs. Structured decision making provides a framework for informing such complex decisions that is robust, transparent, and values-focused.
We are using a structured decision making approach to aid invasive species management decisions, and are developing a novel decision tool that mangers can use to identify where and which treatments to apply for multiple invasive species that accounts for species-specific impacts, invasive pathways, and treatment feasibility. We are applying our approach to the management of invasive species in New York, considering alternatives for prevention, surveillance, control, and education.
We are working with the New York State Department of Environmental Conservation (NYSDEC)and leaders from the 8 NY Partnerships for Regional Invasive Species Management (PRISM). We are developing a tool that builds on the work of NY Heritage Program, NYSDEC, and others to help invasive species managers prioritize management actions based on species, areas, and projects statewide, with flexibility to tailor actions at the regional level. Ultimately, our approach will guide managers in determining which species should prioritized, where those species should be managed, and the best approach to managing them. We will also include metrics of treatment feasibility into the prioritization to ensure management dollars are well spent.
Collaborators: Carrie Brown-Lima, Invasive Species Research Institute, Cornell University; Jennifer Dean, New York Natural Heritage Program; Dr. Carla Gomes, Department of Computer Science, Cornell University; Dr. Jennifer Price Tack, Cornell University
Leveraging partial identity information to advance noninvasive genetic, remote camera, and bioacoustics sampling of animal populations and improve conservation decision making
Over the past two decades, new technologies have affected the way we study and understand animal populations. New, noninvasive methods for monitoring wildlife species such as genetic data from hair or scat samples, remote cameras, and bioacoustic monitoring, have allowed researchers to collect more abundant data than was previously possible. However, to estimate population parameters relevant to conservation decisions such as population density and growth rates, individuals must be individually identifiable which is only possible for small subset of species for which individual identities are easily determined such as the flank patterns of tigers seen in photographs or species that yield high quality DNA samples. The vast majority of noninvasive applications do not always provide an unambiguous determination of individual identity.
Estimation methods that incorporate partial identity information have only recently been developed and have not been extended to accommodate most types of partial identity problems that arise with noninvasive sampling. Further, the importance of the spatial location where a noninvasive sample is collected in determining individual identity has only recently been recognized and this information greatly improves the utility of noninvasive methods and introduces new, more efficient, study design options. The key idea of what we termed “spatial partial identity” is that because animal populations are spatially structured, the location where a noninvasive sample was collected contains information about its individual identity.
Ben Augustine’s work will generalize and adapt the spatial partial identity model to accommodate three other types of noninvasive sampling methods— genetic material from scat or hair samples, remote camera studies of species with more ambiguous natural marks (e.g. pumas), and bioacoustics surveys—with the principal goal of extending the utility of noninvasive methods for improving conservation decisions to a wider range of threatened species.
Collaborators: Dr. J. Andrew Royle, U.S. Geological Survey; Dr. Ben Augustine, Cornell University
Occupancy of dhole in Chitwan National Park, Nepal
The dhole (Cuon alpinus), or Asiatic wild dog, is endangered across its range in Nepal. We will estimate occupancy of dhole in Chitwan National Park to better understand factors that influence their occurrence.
Choosing an optimal duck season: integrating hunter values with duck migration data
State wildlife agencies have long struggled to identify the optimal hunting season dates for migratory game bird species that meet the diverse and often competing interests of stakeholders. Many approaches have been used to ensure the regulated community is involved in the decision-making process including public hearings, hunter season-date preference surveys, and hunter task forces or committees. Although these approaches include portions of the regulated community (i.e., typically the most avid hunters) they may not necessarily reflect the opinions and values of all stakeholders. Additionally, these approaches rely heavily on limited anecdotal observations that may be unduly influenced by hunter avidity (e.g. days spent afield), hunter density, and access. To address these challenges, we are using a structured decision making framework that includes a duck hunter survey of a representative sample of the regulated community in each duck hunting zone in New York State. Rather than asking duck hunters about their specific season date preferences, we ask them to rank six objectives describing what they value in their hunting experience (e.g., maximizing the opportunity to see mallards and black duck, maximizing the number of weekend days, etc.). Four of the six objectives described duck species availability (i.e., abundance or immigration). We are using eBird’s Spatio-Temporal Exploratory Models to estimate abundance and immigration rates of ducks in each waterfowl zone. We are evaluating up to nine unique season date alternatives developed by the duck hunter task forces to determine which season date alternative best satisfies the competing objectives of duck hunters in each zone.
Collaborators: Josh Stiller, New York State Department of Environmental Conservation; William Siemer, Department of Natural Resources, Cornell University; Kelly Perkins, Cornell University
Integrated Population Model for Black Bears in Maine
To assist the Maine Department of Inland Fisheries and Wildlife in developing a cost-efficient monitoring program to inform their black bear management, we are developing an integrated model that estimates spatially explicit population size, movement patterns, and demographic rates of the Maine black bear population. Integrated models (IMs) unite data from multiple sources that are informative about demographic variables. IMs offer several important advantages over analyzing single datasets by themselves. IMs can estimate parameters that are inestimable from single data sets and do so more precisely. Multiple datasets can also overcome biases present in individual datasets, thus leading to more accurate estimates. IMs also can make use of datasets collected for different monitoring purposes at different times and locations, uniting seemingly disparate information to offer multi-scale inference on population patterns. IMs are therefore an efficient way to understand wildlife population dynamics, including survival and recruitment processes by making best use of all available information.
Collaborators: Dr. Sarah Converse, USGS and University of Washington; Maine Department of Inland Fisheries and Wildlife