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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. Wildlife whodunnit: forensic identification of predators to inform wildlife management and conservation

   https://doi.org/10.1002/wsb.1386  

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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)

   Abstract 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 Stewardship on Tribal Lands

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5. Identifying datasets for global wildlife trafficking

   https://doi.org/10.5281/zenodo.7096921  

   Gore, Meredith; Keskin, Burcu; Macdonald, John; Ferber, Aaron; Griffin, Emily; Prell, Oakley; Dilkina, Bistra; Hilend, Rowan (January 2022, Zenodo)

   We describe a novel database on wildlife trafficking that can be used for exploring supply chain coordination via game-theoretic collaboration models, geographic spread of wildlife products trafficked via multi-item knapsack problems, or illicit network interdiction via multi-armed bandit problems.</p> A publicly available visualization of this dataset is available at: https://public.tableau.com/views/IWTDataDirectory-Gore/Sheet2?:language=en-US&:display_count=n&:origin=viz_share_link 

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