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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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A data directory to facilitate investigations on worldwide wildlife trafficking
Wildlife trafficking is a global phenomenon posing many negative impacts on socio-environmental systems. Scientific exploration of wildlife trafficking trends and the impact of interventions is signifi-cantly encumbered by a suite of data reuse challenges. We describe a novel, open-access data directory on wildlife trafficking and a corresponding visualization tool that can be used to identify data for multiple purposes, such as exploring wildlife trafficking hotspots and convergence points with other crime, discovering key drivers or deterrents of wildlife trafficking, and uncovering structural patterns. Keyword searches, expert elicitation, and peer- reviewed publications were used to search for extant sources used by industry and non-profit organizations, as well as those leveraged to publish academic research articles. The open-access data direc-tory is designed to be a living document and searchable according to multiple measures. The directory can be instrumental in the data- driven analysis of unsustainable illegal wildlife trade, supply chain structure via link prediction models, the value of demand and supply reduction initiatives via multi-item knapsack problems, or trafficking behavior and transportation choices via network inter-diction problems.
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- PAR ID:
- 10403307
- Date Published:
- Journal Name:
- Big Earth Data
- ISSN:
- 2096-4471
- Page Range / eLocation ID:
- 1 to 11
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Agrawal, A. (Ed.)Wildlife trafficking, whether local or transnational in scope, undermines sustainable development efforts, degrades cultural resources, endangers species, erodes the local and global economy, and facilitates the spread of zoonotic diseases. Wildlife trafficking networks (WTNs) occupy a unique gray space in supply chains—straddling licit and illicit networks, supporting legitimate and criminal workforces, and often demonstrating high resilience in their sourcing flexibility and adaptability. Authorities in different sectors desire, but frequently lack knowledge about how to allocate resources to disrupt illicit wildlife supply networks and prevent negative collateral impacts. Novel conceptualizations and a deeper scientific understanding of WTN structures are needed to help unravel the dynamics of interaction between disruption and resilience while accommodating socioenvironmental context. We use the case of ploughshare tortoise trafficking to help illustrate the potential of key advancements in interdisciplinary thinking. Insights herein suggest a significant need and opportunity for scientists to generate new science-based recommendations for WTN-related data collection and analysis for supply chain visibility, shifts in illicit supply chain dominance, network resilience, or limits of the supplier base.more » « less
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Abstract We have more data about wildlife trafficking than ever before, but it remains underutilized for decision-making. Central to effective wildlife trafficking interventions is collection, aggregation, and analysis of data across a range of source, transit, and destination geographies. Many data are geospatial, but these data cannot be effectively accessed or aggregated without appropriate geospatial data standards. Our goal was to create geospatial data standards to help advance efforts to combat wildlife trafficking. We achieved our goal using voluntary, participatory, and engagement-based workshops with diverse and multisectoral stakeholders, online portals, and electronic communication with more than 100 participants on three continents. The standards support data-to-decision efforts in the field, for example indictments of key figures within wildlife trafficking, and disruption of their networks. Geospatial data standards help enable broader utilization of wildlife trafficking data across disciplines and sectors, accelerate aggregation and analysis of data across space and time, advance evidence-based decision making, and reduce wildlife trafficking.more » « less
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Wildlife trafficking remains a critical global issue, significantly impacting biodiversity, ecological stability, and public health. Despite efforts to combat this illicit trade, the rise of e-commerce platforms has made it easier to sell wildlife products, putting new pressure on wild populations of endangered and threatened species. The use of these platforms also opens a new opportunity: as criminals sell wildlife products online, they leave digital traces of their activity that can provide insights into trafficking activities as well as how they can be disrupted. The challenge lies in finding these traces. Online marketplaces publish ads for a plethora of products, and identifying ads for wildlife-related products is like finding a needle in a haystack. Learning classifiers can automate ad identification, but creating them requires costly, time-consuming data labeling that hinders support for diverse ads and research questions. This paper addresses a critical challenge in the data science pipeline for wildlife trafficking analytics: generating quality labeled data for classifiers that select relevant data. While large language models (LLMs) can directly label advertisements, doing so at scale is prohibitively expensive. We propose a cost-effective strategy that leverages LLMs to generate pseudo labels for a small sample of the data and uses these labels to create specialized classification models. Our novel method automatically gathers diverse and representative samples to be labeled while minimizing the labeling costs. Our experimental evaluation shows that our classifiers achieve up to 95% F1 score, outperforming LLMs at a lower cost. We present real use cases that demonstrate the effectiveness of our approach in enabling analyses of different aspects of wildlife trafficking.more » « less
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### Access Data files can be accessed and downloaded from the directory via: [https://arcticdata.io/data/10.18739/A2M03Z00G](https://arcticdata.io/data/10.18739/A2M03Z00G) ### Overview Emergence of beavers as ecosystem engineers in the New Arctic project focuses on establishing field sites at tundra beaver ponds to study the implications of beaver engineering on ecosystems. We established three game camera sites at beaver-impacted streams on the Baldwin Peninsula from August 2023-April 2024. We aimed to collect information regarding ice formation phenology, overflow dynamics, and wildlife interactions. Two cameras were deployed adjacent to beaver dams, and another was deployed at a "control" site in a part of a stream that remains unimpacted by beavers. Cameras were set in a hybrid setting, collecting images through timelapse and trigger settings. Two cameras (Moultrie brand) lost power in early December, and one (Bushnell brand) maintained power over the entire study period. Cameras captured ice formation dynamics in early fall, as well as a series of overflow events. From this rudimentary data set, we did not detect differences in ice formation between ponds and the control site. We were also able to detect a dam bursting event following an August rain storm, which beavers did not repair before winter. Cameras captured a variety of wildlife, including red foxes, moose, brown bears, Canada geese, green-winged teal, and, of course, beavers.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/20964471.2023.2193281
