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1. Extracting and Visualizing Wildlife Trafficking Events from Wildlife Trafficking Reports

   Coughlin, D. ( January 2022 , ACM/IEEE Advances in Social Network Analysis and Mining)

   Abstract—Experts combating wildlife trafficking manually sift through articles about seizures and arrests, which is time consuming and make identifying trends difficult. We apply natural language processing techniques to automatically extract data from reports published by the Eco Activists for Governance and Law Enforcement (EAGLE). We expanded Python spaCy’s pre-trained pipeline and added a custom named entity ruler, which identified 15 fully correct and 36 partially correct events in 15 reports against an existing baseline, which did not identify any fully correct events. The extracted wildlife trafficking events were inserted to a database. Then, we created visualizations to display trends over time and across regions to support domain experts. These are accessible on our website, Wildlife Trafficking in Africa. 

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2. Toward an integrative molecular approach to wildlife disease: Wildlife Disease

   https://doi.org/10.1111/cobi.13083  

   DeCandia, Alexandra L. ; Dobson, Andrew P. ; vonHoldt, Bridgett M. ( August 2018 , Conservation Biology)
3. Collaborative wildlife–snow science: Integrating wildlife and snow expertise to improve research and management

   https://doi.org/10.1002/ecs2.4094  

   Reinking, Adele_K ; Højlund_Pedersen, Stine ; Elder, Kelly ; Boelman, Natalie_T ; Glass, Thomas_W ; Oates, Brendan_A ; Bergen, Scott ; Roberts, Shane ; Prugh, Laura_R ; Brinkman, Todd_J ; et al ( June 2022 , Ecosphere)

   For wildlife inhabiting snowy environments, snow properties such as onset date, depth, strength, and distribution can influence many aspects of ecology, including movement, community dynamics, energy expenditure, and forage accessibility. As a result, snow plays a considerable role in individual fitness and ultimately population dynamics, and its evaluation is, therefore, important for comprehensive understanding of ecosystem processes in regions experiencing snow. Such understanding, and particularly study of how wildlife–snow relationships may be changing, grows more urgent as winter processes become less predictable and often more extreme under global climate change. However, studying and monitoring wildlife–snow relationships continue to be challenging because characterizing snow, an inherently complex and constantly changing environmental feature, and identifying, accessing, and applying relevant snow information at appropriate spatial and temporal scales, often require a detailed understanding of physical snow science and technologies that typically lie outside the expertise of wildlife researchers and managers. We argue that thoroughly assessing the role of snow in wildlife ecology requires substantive collaboration between researchers with expertise in each of these two fields, leveraging the discipline‐specific knowledge brought by both wildlife and snow professionals. To facilitate this collaboration and encourage more effective exploration of wildlife–snow questions, we provide a five‐step protocol: (1) identify relevant snow property information; (2) specify spatial, temporal, and informational requirements; (3) build the necessary datasets; (4) implement quality control procedures; and (5) incorporate snow information into wildlife analyses. Additionally, we explore the types of snow information that can be used within this collaborative framework. We illustrate, in the context of two examples, field observations, remote‐sensing datasets, and four example modeling tools that simulate spatiotemporal snow property distributions and, in some cases, evolutions. For each type of snow data, we highlight the collaborative opportunities for wildlife and snow professionals when designing snow data collection efforts, processing snow remote sensing products, producing tailored snow datasets, and applying the resulting snow information in wildlife analyses. We seek to provide a clear path for wildlife professionals to address wildlife–snow questions and improve ecological inference by integrating the best available snow science through collaboration with snow professionals.

    

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4. Wildlife visitation at abandoned mines

   https://doi.org/10.1002/jwmg.22179  

   Armstrong, Timothy A. ; Lahiri, Chayan ; Moran, Wyatt K. ; Fuller, Brett D. ; Mix, Jose A. ; Cerny, Tyler M. ; Ibarra‐Garibay, Erika J. ( January 2022 , The Journal of Wildlife Management)

   Abandoned mines provide habitat for bats, but their importance to other wildlife is less understood. This descriptive study was designed to answer the following questions with an emphasis on carnivores: are wildlife species other than bats visiting abandoned mines, is wildlife entering abandoned mines, does wildlife visitation at abandoned mines differ seasonally, and does wildlife visitation differ at individual mines? To address these questions, we monitored 50 abandoned mines using remote cameras in the northern Sangre de Cristo Mountains, Colorado, USA, for 25,201 camera days from May 2017 to August 2020. We monitored mines in 2 phases. During phase 1 (May 2017–May 2019), we monitored 30 randomly selected mines to gather baseline data on carnivore visitation and to model carnivore visitation. During phase 2 (May 2019–August 2020), we monitored 27 mines to test the visitation model and to determine if carnivores visited multiple mines as they traveled across the landscape. We observed >48 species of vertebrates at mines, including 11 of 14 carnivore species known to occur in the Sangre de Cristo Mountains. Carnivores ranged in size from ringtails (Bassariscus astutus) to American black bears (Ursus americanus). Pumas (Puma concolor) visited mines most frequently and we observed pairs of adult pumas entering mines, presumably during courtship and mating. We also observed American black bears, pumas, and common gray foxes (Urocyon cinereoargenteus) visiting and entering mines with young. Carnivores visited mines at low levels throughout the year and visitation differed by season, temperature, and carnivore species, size, and family. Our most parsimonious generalized linear models identified mine elevation, entrance (portal) size, land cover type, tree cover, and aspect as significant predictors of visitation. Our top models explained ≥78% of the variation in carnivore visits and indicated that carnivores in the Sangre de Cristo Mountains were most likely to visit small horizontal mines at lower elevations in dense piñon (Pinus edulis)—juniper (Juniperussp.) woodlands. We encourage resource managers to monitor abandoned mines for ≥1 year prior to closing or gating mines to understand which wildlife species might be affected by closures.

    

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5. Desert pastoralists’ negative and positive effects on rare wildlife in the Gobi: Pastoralists’ Effects on Wildlife

   https://doi.org/10.1111/cobi.12881  

   Ekernas, L. Stefan ; Sarmento, Wesley M. ; Davie, Hannah S. ; Reading, Richard P. ; Murdoch, James ; Wingard, Ganchimeg J. ; Amgalanbaatar, Sukh ; Berger, Joel ( April 2017 , Conservation Biology)