Montana's IMBCR Program: Utility of Seven Years of Statewide Landbird Monitoring Data

The 2016 field season marks the 7th consecutive year of statewide implementation of the Integrated Monitoring in Bird Conservation Regions program (IMBCR) for monitoring bird populations in the state of Montana.  Using a spatially-balanced, hierarchical study design, the IMBCR program provides density and occupancy estimates for bird species at various geographic extents (strata) across the western U.S.  Based largely on agency investment, primary sampling occurs in all USFS R1 National Forests and extensive grassland/sagebrush habitats on Montana BLM lands statewide.  Significant sampling also occurs in various habitats on private lands.  Using these data, as well as tools available on the Rocky Mountain Avian Data Center web console, agencies and NGO partners can evaluate avian distribution and population dynamics statewide.  As an effective monitoring program, the IMBCR program informs research questions, landscape-level management and conservation action.  The design and current applications of the IMBCR program are summarized

Producers’ perceptions of large carnivores and nonlethal methods to protect livestock from depredation: findings from a multistate federal initiative

We examined livestock producers’ perceptions of wolves, grizzly bears, black bears, and mountain lions, as well as their experiences with using nonlethal methods to protect livestock from depredation. All producers in the study received nonlethal predator management assistance in 2020 from USDA-APHIS-Wildlife Services (“WS”) pursuant to a federally funded initiative focused on the use of nonlethal methods to protect livestock. Such methods included assistance from range riders (i.e., individuals who monitor livestock and carnivores), fladry (i.e., flags hung from rope that serve as a repellent), and other audio/visual deterrents (e.g., Foxlights). The producers did not specifically seek nonlethal assistance from WS; rather, they sought assistance from WS with controlling depredation of livestock, and WS personnel determined that nonlethal methods were an appropriate fit for the circumstances. In some cases, lethal methods may have been used prior to, following, or in combination with, nonlethal methods on a producer’s operation. In addition, producers may have employed other nonlethal methods themselves, including fencing and livestock guardian animals. Our objectives were to understand the producers’ (1) experiences with, and attitudes toward, the four carnivores of interest; (2) perceptions of the effectiveness of all management methods (lethal and nonlethal) used their operations in 2020; and (3) levels of interest in using nonlethal methods, both before and after receiving assistance from WS in 2020. Data were collected using a self-administered, mail-back questionnaire. The questionnaire was sent to all producers in 10 US states1 who received nonlethal predator management assistance from WS in 2020 (n = 89). We received 40 responses (45% response rate), nearly three-quarters of which were from Montana (n = 13), Minnesota (n = 10), and Wisconsin (n = 6). A majority of respondents produced cattle (n = 28), followed by horses/mules (n = 11), sheep/goats (n = 6), honeybees (n = 3), and chickens (n = 2). Ten respondents produced multiple livestock types

Memory usage verification using Hip/Sleek.

Embedded systems often come with constrained memory footprints. It is therefore essential to ensure that software running on such platforms fulfils memory usage specifications at compile-time, to prevent memory-related software failure after deployment. Previous proposals on memory usage verification are not satisfactory as they usually can only handle restricted subsets of programs, especially when shared mutable data structures are involved. In this paper, we propose a simple but novel solution. We instrument programs with explicit memory operations so that memory usage verification can be done along with the verification of other properties, using an automated verification system Hip/Sleek developed recently by Chin et al.[10,19]. The instrumentation can be done automatically and is proven sound with respect to an underlying semantics. One immediate benefit is that we do not need to develop from scratch a specific system for memory usage verification. Another benefit is that we can verify more programs, especially those involving shared mutable data structures, which previous systems failed to handle, as evidenced by our experimental results

Phonons from neutron powder diffraction

The spherically averaged structure function \soq obtained from pulsed neutron powder diffraction contains both elastic and inelastic scattering via an integral over energy. The Fourier transformation of \soq to real space, as is done in the pair density function (PDF) analysis, regularizes the data, i.e. it accentuates the diffuse scattering. We present a technique which enables the extraction of off-center phonon information from powder diffraction experiments by comparing the experimental PDF with theoretical calculations based on standard interatomic potentials and the crystal symmetry. This procedure (dynamics from powder diffraction(DPD)) has been successfully implemented for two systems, a simple metal, fcc Ni, and an ionic crystal, CaF$_{2}$. Although computationally intensive, this data analysis allows for a phonon based modeling of the PDF, and additionally provides off-center phonon information from powder neutron diffraction

A social network analysis of actors involved in wild pig (\u3ci\u3eSus scrofa\u3c/i\u3e) management in Missouri

Wild pigs (Sus scrofa) cause significant damage to agriculture and native ecosystems and can transmit diseases to animals and people. Management responses designed to reduce population numbers are needed to mitigate these threats. Identifying networks of key actors, including the ways in which they interact, is valuable for purposes of better understanding opportunities or constraints that generate or impede effective management responses. The goal of our study was to understand the network of organizations, and the personnel working within them, that were active in wild pig management, research, or policy initiatives in Missouri during 2018–2020 by 1) identifying individuals and organizations involved in the network, 2) investigating the attributes of relevant personnel, 3) determining the structural patterns of the network, and 4) examining how the network structure could be optimized to improve communication and collaboration efforts. Results from a social network analysis identified 150 personnel affiliated with 26 organizations actively working on wild pig issues in Missouri. The network was largely homogenous based on respondents\u27 attributes, had low density, and was relatively fragmented, small, decentralized with few ties per node, and separated with few brokers. We emphasize the importance of understanding the strengths and weaknesses of a network\u27s structure in facilitating effective collective action to manage wild pigs

Modelling the spatial distribution of DEM Error

Assessment of a DEM’s quality is usually undertaken by deriving a measure of DEM accuracy – how close the DEM’s elevation values are to the true elevation. Measures such as Root Mean Squared Error and standard deviation of the error are frequently used. These measures summarise elevation errors in a DEM as a single value. A more detailed description of DEM accuracy would allow better understanding of DEM quality and the consequent uncertainty associated with using DEMs in analytical applications. The research presented addresses the limitations of using a single root mean squared error (RMSE) value to represent the uncertainty associated with a DEM by developing a new technique for creating a spatially distributed model of DEM quality – an accuracy surface. The technique is based on the hypothesis that the distribution and scale of elevation error within a DEM are at least partly related to morphometric characteristics of the terrain. The technique involves generating a set of terrain parameters to characterise terrain morphometry and developing regression models to define the relationship between DEM error and morphometric character. The regression models form the basis for creating standard deviation surfaces to represent DEM accuracy. The hypothesis is shown to be true and reliable accuracy surfaces are successfully created. These accuracy surfaces provide more detailed information about DEM accuracy than a single global estimate of RMSE

Machine Learning as a Tool for Wildlife Management and Research: The Case of Wild Pig-Related Content on Twitter

Wild pigs (Sus scrofa) are a non-native, invasive species that cause considerable damage and transmit a variety of diseases to livestock, people, and wildlife. We explored Twitter, the most popular social media micro-blogging platform, to demonstrate how social media data can be leveraged to investigate social identity and sentiment toward wild pigs. In doing so, we employed a sophisticated machine learning approach to investigate: (1) the overall sentiment associated with the dataset, (2) online identities via user profile descriptions, and (3) the extent to which sentiment varied by online identity. Results indicated that the largest groups of online identity represented in our dataset were females and people whose occupation was in journalism and media communication. While the majority of our data indicated a negative sentiment toward wild pigs and other related search terms, users who identified with agriculture-related occupations had more favorable sentiment. Overall, this article is an important starting point for further investigation of the use of social media data and social identity in the context of wild pigs and other invasive species