To assess the prevalence of endo- and ectoparasites in Alaska brown bears (Ursus arctos), blood and fecal samples were collected during 2013 – 2016 from five locations: Gates of the Arctic National Park and Preserve (GAAR), Katmai National Park (KATM), Lake Clark National Park and Preserve (LACL), Yakutat Forelands (YAK), and Kodiak Island (KOD). Standard fecal centrifugal-flotation was used to screen for gastrointestinal parasites, molecular techniques were used to test blood for the presence of Bartonella and Babesia spp., and an enzyme-linked immunosorbent assay (ELISA) was used to detect antibodies to Sarcoptes scabiei, a species of mite recently associated with mange in American black bears (Ursus americanus). From fecal flotations (n=160), we identified the following helminths: Uncinaria sp. (n=16, 10.0%), Baylisascaris sp. (n=5, 3.1%), Dibothriocephalus sp. (n=2, 1.2%), and taeniid-type eggs (n=1, 0.6%). Molecular screening for intraerythrocytic parasites (Babesia spp.) and intracellular bacteria (Bartonella spp.) was negative for all bears tested. We detected antibodies to S. scabiei in six out of 59 (10.2%) individuals. The data set contains 238 rows, each row representing a capture/sampling event for an individual bear. The location of the bear, month and year of sampling, and bear demographic information (ID number, sex, and age) are provided for each entry, as well as as which of the three tests (fecal flotation, Bartonella/Babesia PCR, Sarcoptes ELISA) were performed on samples collected during that capture event. Results are provided for each test when it was performed. For fecal flotation, there are columns for presence of the four detected parasite genera (1= present, 0 = absent), as well as a column for other fecal findings. For Bartonella/Babesia testing, a positive or negative result is provided when the tests were performed. For the Sarcoptes ELISA, results are provided based on bear positive controls and dog positive controls. Samples were reported as positive when they were positive when run with both positive controls. Methods As part of ongoing inter-agency research, personnel from the Alaska Department of Fish and Game, National Park Service, U.S. Fish and Wildlife Service, and U.S. Geological Survey sampled 166 brown bears during July 2013–July 2017 at five locations: Gates of the Arctic National Park and Preserve (GAAR), Katmai National Park (KATM), Kodiak Island (KOD), Lake Clark National Park and Preserve (LACL), and the Yakutat Forelands. Bears were captured and handled as reported by Ramey et al. (2019), with all capture, handling, and sampling procedures approved by Animal and Care Use Committees for Alaska Department of Fish and Game (2013-028), NPS (2014.A2, 2014.A3), USFWS (2012-14), and USGS (2014-1, 2014-10, 2015-4, 2015-6). Feces were collected opportunistically from the rectums of 114 anesthetized bears from GAAR, KATM, KOD, and LACL one to five times during the study period, stored in 70% ethanol, then processed using double centrifugal flotation with Sheather’s sucrose solution (specific gravity 1.25). Blood was collected as described by Ramey et al. (2019) from 156 bears from all sites and tested for Bartonella and Babesia species, with 44 bears screened twice. Genomic DNA was extracted using a DNeasy® Blood and Tissue Kit (Qiagen, Hilden, Germany) following the manufacturer’s protocol. Nested PCR was performed using GoTaq® Flexi DNA Polymerase (Promega, Madison, Wisconsin, USA). For Bartonella spp., the ITS gene was targeted using the primers and cycling conditions described by Trataris et al. (2012). Primary PCR primers were QHVE-1 and QHVE-3; secondary primers were QHVE-12 and QHVE-14b. For Babesia spp., the 18S gene was targeted using primers and cycling conditions described by Yabsley et al. (2005). Primary PCR primers were 3.1 and 5.1; secondary primers were RLB-F and RLB-R. Amplicons were purified using the QIAquick gel extraction kit (Qiagen) and submitted for bi-directional sequencing at the Georgia Genomics and Bioinformatics Core (Athens, Georgia, USA). Serum samples (n=59) were collected from 53 individual bears in 2016 and 2017 from GAAR, LACL, and KATM and tested for antibodies to Sarcoptes scabiei using a commercial indirect ELISA kit designed for domestic dogs (Sarcoptes-ELISA 2001, Afosa, Germany). Modifications for use in black bears (Ursus americanus) were implemented as described (Niedringhaus et al. 2020).

Relevent Citations: Niedringhaus KD, Brown JD, Ternent M, Peltier SK, Van Wick P, Yabsley MJ. 2020. Serology as a tool to investigate sarcoptic mange in American black bears (Ursus americanus) J Wildl Dis 56:350-358. Ramey AM, Cleveland CA, Hilderbrand GV, Joly K, Gustine DD, Mangipane B, Leacock WB, Crupi AP, Hill DE, Dubey JP, Yabsley MJ. 2019. Exposure of Alaska brown bears (Ursus arctos) to bacterial, viral, and parasitic agents varies spatiotemporally and may be influenced by age. J Wildl Dis 55:576-588. Trataris AN, Rossouw J, Arntzen L, Karstaedt A, Frean J. 2012. Bartonella spp. in human and animal populations in Gauteng, South Africa, from 2007 to 2009. J Vet Res 79:E1–8 Yabsley MJ, Davidson WR, Stallknecht DE, Varela AS, Swift PK, Devos Jr. JC, Dubay SA. 2005. Evidence of tick-borne organisms in Mule deer (Odocoileus hemionus) from the Western United States. Vector Borne Zoonotic Dis 5:351-362.

AbstractThe urban-wildland interface is expanding and increasing the risk of human-wildlife conflict. Some wildlife species adapt to or avoid living near people, while others select for anthropogenic resources and are thus more prone to conflict. To promote human-wildlife coexistence, wildlife and land managers need to understand how conflict relates to habitat and resource use in the urban-wildland interface. We investigated black bear (Ursus americanus) habitat use across a gradient of human disturbance in a North American hotspot of human-black bear conflict. We used camera traps to monitor bear activity from July 2018 to July 2019, and compared bear habitat use to environmental and anthropogenic variables and spatiotemporal probabilities of conflict. Bears predominantly used areas of high vegetation productivity, avoided higher human densities, and increased their nocturnality near people. Still, bears used more high-conflict areas in summer and autumn, specifically rural lands with ripe crops. Our results suggest that bears are generally modifying their behaviours in the urban-wildland interface through spatial and temporal avoidance of humans, which may facilitate coexistence. However, conflict still occurs, especially in autumn when hyperphagia and peak crop availability attract bears to abundant rural food resources. To improve conflict mitigation practices, we recommend targeting seasonal rural attractants such as with pre-emptive fruit picking, bear-proof compost containment, and other forms of behavioural deterrence. By combining camera-trap monitoring of a large carnivore along an anthropogenic gradient with conflict mapping, we provide a framework for evidence-based improvements in human-wildlife coexistence., MethodsWe set 54 camera traps within a 80 km2 area in and adjacent to Sooke, Vancouver Island, BC, Canada to assess spatial and temporal variation in bear distribution and habitat use along a gradient of human disturbance from urban to wild. We deployed cameras following a stratified random design to representatively allocate cameras based on the proportion of the survey area falling within each of three strata: urban (n = 11 cameras), rural (n = 19), or wild (n = 24). We aimed for >200 m between neighboring camera sites (mean = 446 m, range = 147-1467 m) to maintain spatial independence. Within strata, sampling distribution was randomized where possible. Due to the abundance of private land, urban and rural camera sites were selected from a candidate list of participating landowners provided by the local environmental non-governmental organization. Rural sites were either within agricultural land cover or low development areas, while urban sites were in town and close to other homes. Wild sites were in forested areas with minimal disturbance from human development, consisting of 21 in Sea to Sea Regional Park and three on undeveloped T’Sou-ke Nation lands. To randomize sampling locations within the main accessible block of the regional park, a 500 by 500 m grid was overlaid on park trail maps and cameras were placed in 10 random cells that contained a trail. The T’Sou-ke Nation forest sites and regional park sites on the northwest edge were only accessible by a single hiking trail, so cameras were set a minimum of 200 m apart. To avoid excessive human photos and privacy concerns, we avoided setting cameras directly on the main hiking trails in the park and T’Sou-ke Nation lands, and either targeted adjacent game and low-use human trails within the selected cell or set cameras off the main trail. Deployment occurred between July 18- August 20, 2018. To detect any seasonal variation in black bear habitat use, all cameras remained deployed for approximately one year, and were retrieved between July 16-19, 2019. We used a combination of three camera trap models (Reconyx PC900, Reconyx HC600, and Browning Strike Force HD Pro) randomly allocated across strata to reduce potential effects of different detectability between camera models. We set cameras at locations to maximize the probability of detecting bears that occurred there, using local knowledge of where bears moved across urban or rural properties, or the presence of animal trails and sign. Per site, one camera was set on a tree, approximately one metre above the ground, at high sensitivity, with a one second delay between triggers (one image per trigger as bears are large enough to be captured without a sequence and this saves battery and memory card space), and facing open spaces such as meadows, lawns, or trails. Black bears have shown a preference for using low-use human paths because of the ease of movement and increased shrub vegetation containing berries. Where possible, cameras faced an intersection of multiple animal and/or low-use human trails. We visited camera traps every 2... Visit https://dataone.org/datasets/sha256%3A494dbb159169867e98065de4e2002a26799bd09f45910ad01adb83edaee50a88 for complete metadata about this dataset.

Studying diet is fundamental to animal ecology and scat analysis, a widespread approach, is considered a reliable dietary proxy. Nonetheless, this method has weaknesses such as non-random sampling of habitats and individuals, inaccurate evaluation of excretion date, and lack of assessment of inter-individual dietary variability. We coupled GPS telemetry and scat analyses of black bears Ursus americanus Pallas to relate diet to individual characteristics and habitat use patterns while foraging. We captured 20 black bears (6 males and 14 females) and fitted them with GPS/Argos collars. We then surveyed GPS locations shortly after individual bear visits and collected 139 feces in 71 different locations. Fecal content (relative dry matter biomass of ingested items) was subsequently linked to individual characteristics (sex, age, reproductive status) and to habitats visited during foraging bouts using Brownian bridges based on GPS locations prior to feces excretion. At the population level, diet composition was similar to what was previously described in studies on black bears. However, our individual-based method allowed us to highlight different intra-population patterns, showing that sex and female reproductive status had significant influence on individual diet. For example, in the same habitats, females with cubs did not use the same food sources as lone bears. Linking fecal content (i.e., food sources) to habitat previously visited by different individuals, we demonstrated a potential differential use of similar habitats dependent on individual characteristics. Females with cubs-of-the-year tended to use old forest clearcuts (6–20 years old) to feed on bunchberry, whereas females with yearling foraged for blueberry and lone bears for ants. Coupling GPS telemetry and scat analyses allows for efficient detection of inter-individual or inter-group variations in foraging strategies and of linkages between previous habitat use and food consumption, even for cryptic species. This approach could have interesting ecological implications, such as supporting the identification of habitats types abundant in important food sources for endangered species targeted by conservation measures or for management actions for depredating animals.

The removal of individuals through hunting can destabilize social structure, potentially affecting population dynamics. Although previous studies have shown that hunting can indirectly reduce juvenile survival through increased sexually selected infanticide (SSI), very little is known about the spatiotemporal effects of male hunting on juvenile survival. Using detailed individual monitoring of a hunted population of brown bears (Ursus arctos) in Sweden (1991–2011), we assessed the spatiotemporal effect of male removal on cub survival. We modelled cub survival before, during and after the mating season. We used three proxies to evaluate spatial and temporal variation in male turnover; distance and timing of the closest male killed and number of males that died around a female's home range centre. Male removal decreased cub survival only during the mating season, as expected in seasonal breeders with SSI. Cub survival increased with distance to the closest male killed within the previous 1·5 years, and it was lower when the closest male killed was removed 1·5 instead of 0·5 year earlier. We did not detect an effect of the number of males killed. Our results support the hypothesis that social restructuring due to hunting can reduce recruitment and suggest that the distribution of the male deaths might be more important than the overall number of males that die. As the removal of individuals through hunting is typically not homogenously distributed across the landscape, spatial heterogeneity in hunting pressure may cause source–sink dynamics, with lower recruitment in areas of high human-induced mortality.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Brown Group, Inc. (BWNG): Has the Bear Market Hit the Bottom?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Dataset includes the following potential covariates: elevation. The dependent variable (“Conflict”) is binary; “1” denotes a human-black bear conflict, “0” denotes a randomly generated non-conflict location. See Table 1 for more information about each covariate. (CSV)