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1. Advancing interdisciplinary science for disrupting wildlife trafficking networks

   Gore, M.L. (January 2023, Proceedings of the National Academy of Sciences of the United States of America)

   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. 

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2. Opportunities for Transdisciplinary Science to Mitigate Biosecurity Risks From the Intersectionality of Illegal Wildlife Trade With Emerging Zoonotic Pathogens

   https://doi.org/10.3389/fevo.2021.604929  

   Aguirre, A. Alonso; Gore, Meredith L.; Kammer-Kerwick, Matt; Curtin, Kevin M.; Heyns, Andries; Preiser, Wolfgang; Shelley, Louise I. (February 2021, Frontiers in Ecology and Evolution)

   null (Ed.)

   Existing collaborations among public health practitioners, veterinarians, and ecologists do not sufficiently consider illegal wildlife trade in their surveillance, biosafety, and security (SB&S) efforts even though the risks to health and biodiversity from these threats are significant. We highlight multiple cases to illustrate the risks posed by existing gaps in understanding the intersectionality of the illegal wildlife trade and zoonotic disease transmission. We argue for more integrative science in support of decision-making using the One Health approach. Opportunities abound to apply transdisciplinary science to sustainable wildlife trade policy and programming, such as combining on-the-ground monitoring of health, environmental, and social conditions with an understanding of the operational and spatial dynamics of illicit wildlife trade. We advocate for (1) a surveillance sample management system for enhanced diagnostic efficiency in collaboration with diverse and local partners that can help establish new or link existing surveillance networks, outbreak analysis, and risk mitigation strategies; (2) novel analytical tools and decision support models that can enhance self-directed local livelihoods by addressing monitoring, detection, prevention, interdiction, and remediation; (3) enhanced capacity to promote joint SB&S efforts that can encourage improved human and animal health, timely reporting, emerging disease detection, and outbreak response; and, (4) enhanced monitoring of illicit wildlife trade and supply chains across the heterogeneous context within which they occur. By integrating more diverse scientific disciplines, and their respective scientists with indigenous people and local community insight and risk assessment data, we can help promote a more sustainable and equitable wildlife trade. 

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3. Predicting Wildlife Trafficking Routes with Differentiable Shortest Paths

   Ferber, A. (May 2023, Part of the Lecture Notes in Computer Science book series (LNCS,volume 13884))

   Cire, A.A. (Ed.)

   Wildlife trafficking (WT), the illegal trade of wild fauna, flora, and their parts, directly threatens biodiversity and conservation of trafficked species, while also negatively impacting human health, national security, and economic development. Wildlife traffickers obfuscate their activities in plain sight, leveraging legal, large, and globally linked transportation networks. To complicate matters, defensive interdiction resources are limited, datasets are fragmented and rarely interoperable, and interventions like setting checkpoints place a burden on legal transportation. As a result, interpretable predictions of which routes wildlife traffickers are likely to take can help target defensive efforts and understand what wildlife traffickers may be considering when selecting routes. We propose a data-driven model for predicting trafficking routes on the global commercial flight network, a transportation network for which we have some historical seizure data and a specification of the possible routes that traffickers may take. While seizure data has limitations such as data bias and dependence on the deployed defensive resources, this is a first step towards predicting wildlife trafficking routes on real-world data. Our seizure data documents the planned commercial flight itinerary of trafficked and successfully interdicted wildlife. We aim to provide predictions of highly-trafficked flight paths for known origin-destination pairs with plausible explanations that illuminate how traffickers make decisions based on the presence of criminal actors, markets, and resilience systems. We propose a model that first predicts likelihoods of which commercial flights will be taken out of a given airport given input features, and then subsequently finds the highest-likelihood flight path from origin to destination using a differentiable shortest path solver, allowing us to automatically align our model’s loss with the overall goal of correctly predicting the full flight itinerary from a given source to a destination. We evaluate the proposed model’s predictions and interpretations both quantitatively and qualitatively, showing that the predicted paths are aligned with observed held-out seizures, and can be interpreted by policy-makers 

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4. People, Projects, Organizations, and Products: Designing a Knowledge Graph to Support Multi-Stakeholder Environmental Planning and Design

   https://doi.org/10.3390/ijgi10120823  

   Gordon, Sean N.; Murphy, Philip J.; Gallo, John A.; Huber, Patrick; Hollander, Allan; Edwards, Ann; Jankowski, Piotr (December 2021, ISPRS International Journal of Geo-Information)

   As the need for more broad-scale solutions to environmental problems is increasingly recognized, traditional hierarchical, government-led models of coordination are being supplemented by or transformed into more collaborative inter-organizational networks (i.e., collaboratives, coalitions, partnerships). As diffuse networks, such regional environmental planning and design (REPD) efforts often face challenges in sharing and using spatial and other types of information. Recent advances in semantic knowledge management technologies, such as knowledge graphs, have the potential to address these challenges. In this paper, we first describe the information needs of three multi-stakeholder REPD initiatives in the western USA using a list of 80 need-to-know questions and concerns. The top needs expressed were for help in tracking the participants, institutions, and information products relevant to the REDP’s focus. To address these needs, we developed a prototype knowledge graph based on RDF and GeoSPARQL standards. This semantic approach provided a more flexible data structure than traditional relational databases and also functionality to query information across different providers; however, the lack of semantic data expertise, the complexity of existing software solutions, and limited online hosting options are significant barriers to adoption. These same barriers are more acute for geospatial data, which also faces the added challenge of maintaining and synchronizing both semantic and traditional geospatial datastores. 

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5. Next generation Arctic vegetation maps: Aboveground plant biomass and woody dominance mapped at 30 m resolution across the tundra biome

   https://doi.org/10.1016/j.rse.2025.114717  

   Orndahl, Kathleen M; Berner, Logan T; Macander, Matthew J; Arndal, Marie F; Alexander, Heather D; Humphreys, Elyn R; Loranty, Michael M; Ludwig, Sarah M; Nyman, Johanna; Juutinen, Sari; et al (June 2025, Remote Sensing of Environment)

   Chen, Jing M (Ed.)

   The Arctic is warming faster than anywhere else on Earth, placing tundra ecosystems at the forefront of global climate change. Plant biomass is a fundamental ecosystem attribute that is sensitive to changes in climate, closely tied to ecological function, and crucial for constraining ecosystem carbon dynamics. However, the amount, functional composition, and distribution of plant biomass are only coarsely quantified across the Arctic. Therefore, we developed the first moderate resolution (30 m) maps of live aboveground plant biomass (g m− 2) and woody plant dominance (%) for the Arctic tundra biome, including the mountainous Oro Arctic. We modeled biomass for the year 2020 using a new synthesis dataset of field biomass harvest measurements, Landsat satellite seasonal synthetic composites, ancillary geospatial data, and machine learning models. Additionally, we quantified pixel-wise uncertainty in biomass predictions using Monte Carlo simulations and validated the models using a robust, spatially blocked and nested cross-validation procedure. Observed plant and woody plant biomass values ranged from 0 to ~6000 g m− 2 (mean ≈350 g m− 2), while predicted values ranged from 0 to ~4000 g m− 2 (mean ≈275 g m− 2), resulting in model validation root-mean-squared-error (RMSE) ≈400 g m− 2 and R2 ≈ 0.6. Our maps not only capture large-scale patterns of plant biomass and woody plant dominance across the Arctic that are linked to climatic variation (e.g., thawing degree days), but also illustrate how fine-scale patterns are shaped by local surface hydrology, topography, and past disturbance. By providing data on plant biomass across Arctic tundra ecosystems at the highest resolution to date, our maps can significantly advance research and inform decision-making on topics ranging from Arctic vegetation monitoring and wildlife conservation to carbon accounting and land surface modeling 

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