White-tailed Deer Distribution and Movement Behavior in South-Central Texas, USA

Providing wildlife managers with reliable population abundance estimates for white-tailed deer (Odocoileus virginianus; deer) is challenging and requires proper evaluation of population surveys. My objectives for this study were to compare capture techniques (drop net, single helicopter, and tandem helicopter) and evaluate deer movement in response to infrared-triggered camera (camera) and spotlight survey methods in relation to potential biases associated with each method. Cost and labor efforts were greater for drop nets than either helicopter method. All techniques were safe and effective methods for deer capture, but results showed tandem helicopter capture was superior for balancing cost-efficiency and safety while minimizing post-capture behavioral impacts. I used movement data to determine if the presence and absence of bait altered deer distributions. For males, the use of bait detracted from percent canopy coverage, which was significant in determining deer distributions prior to the use of bait. This indicates the use of bait evoked a stronger response from males, violating the assumption of equal detectability during camera surveys. This pro-male bait-bias can ultimately result in an underestimation of female deer. I conducted spotlight surveys based on road surface type and disturbance level due to traffic volume. More deer per area were encountered on unimproved (trails) and maintained gravel (gravel) roads than on paved roads, suggesting that deer either shied away from paved roads or congregated near trails and gravel roads. It is more likely deer shied away from paved roads due to high traffic levels resulting in density estimates biased low. Behavioral change attributable to capture technique must be considered when selecting a capture method, and determining the period over which data are biased is critical to wildlife research. I recommend managers either not base harvest quotas on estimates obtained via baited camera surveys or be aware of the potential biases and try to account for underestimates of females and fawns. I also recommend managers either use road types with little traffic disturbance while maximizing visibilities, or incorporate an even distribution of non-overlapping transects for all road types present

White-tailed Deer Distribution and Movement Behavior in South-Central Texas, USA

Providing wildlife managers with reliable population abundance estimates for white-tailed deer (Odocoileus virginianus; deer) is challenging and requires proper evaluation of population surveys. My objectives for this study were to compare capture techniques (drop net, single helicopter, and tandem helicopter) and evaluate deer movement in response to infrared-triggered camera (camera) and spotlight survey methods in relation to potential biases associated with each method. Cost and labor efforts were greater for drop nets than either helicopter method. All techniques were safe and effective methods for deer capture, but results showed tandem helicopter capture was superior for balancing cost-efficiency and safety while minimizing post-capture behavioral impacts. I used movement data to determine if the presence and absence of bait altered deer distributions. For males, the use of bait detracted from percent canopy coverage, which was significant in determining deer distributions prior to the use of bait. This indicates the use of bait evoked a stronger response from males, violating the assumption of equal detectability during camera surveys. This pro-male bait-bias can ultimately result in an underestimation of female deer. I conducted spotlight surveys based on road surface type and disturbance level due to traffic volume. More deer per area were encountered on unimproved (trails) and maintained gravel (gravel) roads than on paved roads, suggesting that deer either shied away from paved roads or congregated near trails and gravel roads. It is more likely deer shied away from paved roads due to high traffic levels resulting in density estimates biased low. Behavioral change attributable to capture technique must be considered when selecting a capture method, and determining the period over which data are biased is critical to wildlife research. I recommend managers either not base harvest quotas on estimates obtained via baited camera surveys or be aware of the potential biases and try to account for underestimates of females and fawns. I also recommend managers either use road types with little traffic disturbance while maximizing visibilities, or incorporate an even distribution of non-overlapping transects for all road types present

Multidisciplinary engagement for fencing research informs efficacy and rancher-to-researcher knowledge exchange

Across much of the Western United States, recovery of large carnivore populations is creating new challenges for livestock producers. Reducing the risks of sharing the landscape with recovering wildlife populations is critical to private working lands, which play an vital role in securing future energy, water, food, and fiber for an ever-expanding human population. Fencing is an important mitigation practice that many ranchers, land managers, and conservationists implement to reduce carnivore-livestock conflict. While fencing strategies have been reviewed in the literature, research seldom incorporates knowledge from the people who utilize fencing the most (i.e., livestock producers). Incorporating producers and practitioners early in the process of producing scientific knowledge is proving to be a critical endeavor for enhancing knowledge exchange, better evaluation of the practice, and more realistic understanding of the costs and benefits. Here, we describe how our multidisciplinary effort of co-producing knowledge informs understanding of the effectiveness of various fencing designs and more importantly provides a better mechanism for transferring this knowledge between producers, researchers, and land managers. We explain the process underway and demonstrate that incorporating producers and practitioners from the onset allows research priorities and expected outcomes to be set collaboratively, gives transparency to the agricultural community of the research process, provides a critical lens to evaluate efficacy and functionality, and will inform the practicality of fencing as a conflict prevention tool. We discuss opportunities and challenges of this co-production process and how it can be applied to other realms of fencing and conflict prevention strategies

AI ATAC 1: An Evaluation of Prominent Commercial Malware Detectors

This work presents an evaluation of six prominent commercial endpoint malware detectors, a network malware detector, and a file-conviction algorithm from a cyber technology vendor. The evaluation was administered as the first of the Artificial Intelligence Applications to Autonomous Cybersecurity (AI ATAC) prize challenges, funded by / completed in service of the US Navy. The experiment employed 100K files (50/50% benign/malicious) with a stratified distribution of file types, including ~1K zero-day program executables (increasing experiment size two orders of magnitude over previous work). We present an evaluation process of delivering a file to a fresh virtual machine donning the detection technology, waiting 90s to allow static detection, then executing the file and waiting another period for dynamic detection; this allows greater fidelity in the observational data than previous experiments, in particular, resource and time-to-detection statistics. To execute all 800K trials (100K files $\times$ 8 tools), a software framework is designed to choreographed the experiment into a completely automated, time-synced, and reproducible workflow with substantial parallelization. A cost-benefit model was configured to integrate the tools' recall, precision, time to detection, and resource requirements into a single comparable quantity by simulating costs of use. This provides a ranking methodology for cyber competitions and a lens through which to reason about the varied statistical viewpoints of the results. These statistical and cost-model results provide insights on state of commercial malware detection

Beyond the Hype: A Real-World Evaluation of the Impact and Cost of Machine Learning-Based Malware Detection

There is a lack of scientific testing of commercially available malware detectors, especially those that boast accurate classification of never-before-seen (i.e., zero-day) files using machine learning (ML). The result is that the efficacy and gaps among the available approaches are opaque, inhibiting end users from making informed network security decisions and researchers from targeting gaps in current detectors. In this paper, we present a scientific evaluation of four market-leading malware detection tools to assist an organization with two primary questions: (Q1) To what extent do ML-based tools accurately classify never-before-seen files without sacrificing detection ability on known files? (Q2) Is it worth purchasing a network-level malware detector to complement host-based detection? We tested each tool against 3,536 total files (2,554 or 72% malicious, 982 or 28% benign) including over 400 zero-day malware, and tested with a variety of file types and protocols for delivery. We present statistical results on detection time and accuracy, consider complementary analysis (using multiple tools together), and provide two novel applications of a recent cost-benefit evaluation procedure by Iannaconne & Bridges that incorporates all the above metrics into a single quantifiable cost. While the ML-based tools are more effective at detecting zero-day files and executables, the signature-based tool may still be an overall better option. Both network-based tools provide substantial (simulated) savings when paired with either host tool, yet both show poor detection rates on protocols other than HTTP or SMTP. Our results show that all four tools have near-perfect precision but alarmingly low recall, especially on file types other than executables and office files -- 37% of malware tested, including all polyglot files, were undetected.Comment: Includes Actionable Takeaways for SOC

An evaluation of population estimators and forage availability and nutritional quality for white-tailed deer in Tennessee

Given the white-tailed deer’s (Odocoileus virginianus; deer) popularity and potentially negative impact on forested systems; Arnold Air Force Base (AAFB) in Tullahoma, Tennessee, USA has made minimizing negative deer impacts on biodiversity a priority. To address these management issues, I initiated a study on AAFB to investigate deer survey techniques and the effects of deer density on forage availability across vegetative communities. Current use of infrared-triggered cameras (camera) for estimating deer populations does not provide a measure of precision critical for density estimation. I conducted a camera survey for deer in Wildlife Management Area (WMA) Units 1 and 2 at AAFB, August 2010 and used Program DENSITY to fit a spatial detection function of capture-recapture (spatial modeling) data from the camera surveys of bucks. Spatial modeling can provide reliable estimates of buck density and facilitate our understanding of biases associated with camera surveys for deer. I compared population and precision estimates from spotlight, ground thermal infrared imaging (ground imaging), and aerial vertical-looking infrared (aerial imaging) surveys in the Security Area (SA) of AAFB, January–February 2010. All 3 techniques provided a precise estimate of deer density. However, the high cost of ground imaging does not justify its use. I also found the potential of road bias in distance sampling to invalidate the technique, unless random transects representative of the study area can be applied. Aerial imaging is less susceptible to road bias, but use should be restricted to large areas where high cost can be justified. I evaluated the effects of 2 deer densities on forage availability and quality within 4 vegetative communities on WMA Units 1 and 2, and the SA of AAFB 2010. Forage availability was consistently greater during summer verses winter and within middle-aged and young pine stands at the low deer density site versus the high deer density site. Both crude protein and total digestible nutrient values were similar regardless of deer density. I recommend managers consider implementing management practices that would reduce deer density and increase forage availability when forage availability beings to decline and deer density estimates approach levels seen detrimental in literature

Aerial vertical-looking infrared imagery to evaluate bias of distance sampling techniques for white-tailed deer

Population monitoring requires techniques that produce estimates with low bias and adequate precision. Distance sampling using ground-based thermal infrared imaging (ground imaging) and spotlight surveys is commonly used to estimate population densities of white-tailed deer (Odocoileus virginianus). These surveys are often conducted along roads, which may violate assumptions of distance sampling and result in density estimates that are biased high. Aerial vertical-looking infrared imaging (aerial imaging) is not restricted to roads and therefore enables random sampling and detection. We compared estimates of population density and precision, and evaluated potential sources of bias for these 3 techniques for deer on Arnold Air Force Base in Tennessee, USA, during January-February 2010. Using data from aerial imaging conducted along systematic strip transects, we found that deer were distributed close to roads and deer responded to the landscape along the road edge or to observers driving along roads. As a result of these distributional patterns, estimated deer density based on ground imaging and spotlighting from road-based surveys was 3.0-7.6 times greater than density estimated from strip transects using aerial imaging. Ground imaging did not produce better estimates than spotlighting. Observers on the ground counting all deer seen at test plots with hand-held thermal imagers saw fewer deer than were seen on aerial images, suggesting high detection of deer by aerial imaging. Despite its higher cost (US$10,000) over spotlight surveys, we recommend aerial imaging instead of road-based ground surveys for monitoring populations of deer and discourage the continued use of non-random road-based surveys as a method for estimating white-tailed deer populations. © 2014 The Wildlife Society