A Novel Framework to Protect Animal Data in a World of Ecosurveillance

Lennox, Robert J.; Harcourt, Robert; Bennett, Joseph; Davies, Alasdair; Ford, Adam T.; Frey, Remo Manuel; Hayward, Matt; Hussey, Nigel E.; Iverson, Sara J.; Kays, Roland; Kessel, Steven T.; Muelbert, Mônica M. C.; Murray, Taryn S.; Nguyen, Vivian M.; Pye, Jonathan; Roche, Dominique G.; Whoriskey, Frederick G.; Young, Nathan; Cooke, Steven J.

doi:10.1093/biosci/biaa035

Cited by 26 publications

(23 citation statements)

References 34 publications

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“…It is important to understand the limits of open data sharing in animal conservation in nature parks. In some cases, it is imperative that the privacy of the data be preserved, for instance to avoid giving poachers access to locations of animals in near-real-time 134 . Security of rangers themselves is also at stake; for example, the flight path of drones might be backtracked to reveal their location.…”

Section: Attention Points and Opportunitiesmentioning

confidence: 99%

Perspectives in machine learning for wildlife conservation

et al. 2022

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Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into relevant information. We argue that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge. Incorporating machine learning into ecological workflows could improve inputs for ecological models and lead to integrated hybrid modeling tools. This approach will require close interdisciplinary collaboration to ensure the quality of novel approaches and train a new generation of data scientists in ecology and conservation.

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Section: Attention Points and Opportunitiesmentioning

confidence: 99%

Perspectives in machine learning for wildlife conservation

et al. 2022

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“…However, despite Argo's global fleet of around 4000 profiling floats, the high polar seas, shallow coastal shelves and high energy boundary currents remain under-sampled (Figure 2). This combined with the political challenges of sampling within Exclusive Economic Zones (Kraska et al, 2015;McLaughlin, 2015;Lennox et al, 2020), logistical difficulties and expense of accessing remote regions for deployments, and the high cost of some autonomous vehicles constrains where observations are made. Marine animals carrying oceanographic sensors offer realistic solutions to many of these issues because animals inhabit many of these poorly sampled regions and swim multiple times per day through the water column to depths of several hundred meters (Figures 1, 2) and are able to sample temperature and salinity at appropriate resolutions to complement observing networks such as Argo (Table 2).…”

Section: Mapping Past Present and Future Coveragementioning

confidence: 99%

“…They provide a direct way to investigate the link between physical oceanographic and biological processes at meso and sub-mesoscales and how physics structures biological fields (animal habitats) horizontally and vertically. Animal-borne observations provide information on EOVs/ECVs in areas and corridors of great importance to biodiversity (Harcourt et al, 2019;Hays et al, 2019;Hindell et al, 2020;Lennox et al, 2020). AniBOS will also contribute to marine management and policy decision making.…”

Section: Ecological Inferencesmentioning

confidence: 99%

“…While data sharing within the marine ecology community has previously occurred intermittently a number of studies illustrate and demonstrate the increased productivity by data sharers within the animal tracking community (Nguyen et al, 2017;Lennox et al, 2020); several independent initiatives where multiple datasets have already been shared for large synthetic studies, e.g., the Retrospective Analysis of Antarctic Tracking Data (Hindell et al, 2020;Ropert-Coudert et al, 2020) the Global Shark Movement project (Queiroz et al, 2019); MegaMove (Sequeira et al, 2018) as well as mandated data sharing by data providers (Hill et al, 2010). In addition, some data centers are now including the minting of DOI's as part of their data archiving service (e.g., the U.S. ATN Data Assembly Center).…”

Section: Description Referencesmentioning

confidence: 99%

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Animal Borne Ocean Sensors – AniBOS – An Essential Component of the Global Ocean Observing System

et al. 2021

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Marine animals equipped with biological and physical electronic sensors have produced long-term data streams on key marine environmental variables, hydrography, animal behavior and ecology. These data are an essential component of the Global Ocean Observing System (GOOS). The Animal Borne Ocean Sensors (AniBOS) network aims to coordinate the long-term collection and delivery of marine data streams, providing a complementary capability to other GOOS networks that monitor Essential Ocean Variables (EOVs), essential climate variables (ECVs) and essential biodiversity variables (EBVs). AniBOS augments observations of temperature and salinity within the upper ocean, in areas that are under-sampled, providing information that is urgently needed for an improved understanding of climate and ocean variability and for forecasting. Additionally, measurements of chlorophyll fluorescence and dissolved oxygen concentrations are emerging. The observations AniBOS provides are used widely across the research, modeling and operational oceanographic communities. High latitude, shallow coastal shelves and tropical seas have historically been sampled poorly with traditional observing platforms for many reasons including sea ice presence, limited satellite coverage and logistical costs. Animal-borne sensors are helping to fill that gap by collecting and transmitting in near real time an average of 500 temperature-salinity-depth profiles per animal annually and, when instruments are recovered (∼30% of instruments deployed annually, n = 103 ± 34), up to 1,000 profiles per month in these regions. Increased observations from under-sampled regions greatly improve the accuracy and confidence in estimates of ocean state and improve studies of climate variability by delivering data that refine climate prediction estimates at regional and global scales. The GOOS Observations Coordination Group (OCG) reviews, advises on and coordinates activities across the global ocean observing networks to strengthen the effective implementation of the system. AniBOS was formally recognized in 2020 as a GOOS network. This improves our ability to observe the ocean’s structure and animals that live in them more comprehensively, concomitantly improving our understanding of global ocean and climate processes for societal benefit consistent with the UN Sustainability Goals 13 and 14: Climate and Life below Water. Working within the GOOS OCG framework ensures that AniBOS is an essential component of an integrated Global Ocean Observing System.

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“…For these reasons and more, it might not always be possible or beneficial for all methods or data from conservation research to be open. In these cases, access control or partially open methods, data, and code will be the next best option (Lowe et al 2017;Lennox et al 2020).…”

Section: Challenges and Opportunities For Interpretable Research Resultsmentioning

confidence: 99%

Closing the knowledge‐action gap in conservation with open science

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Kerr

Rytwinski

et al. 2021

Conservation Biology

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The knowledge-action gap in conservation science and practice occurs when research outputs do not result in actions to protect or restore biodiversity. Among the diverse and complex reasons for this gap, three barriers are fundamental: knowledge is often unavailable to practitioners,to interpret, and/or difficult to use. Problems of availability, interpretability, and useability are solvable with open science practices. We consider the benefits and challenges of three open science practices for use by conservation scientists and practitioners. First, open access publishing makes the scientific literature available to all. Second, open materials (methods, data, code, and software) increase the transparency and (re)use potential of research findings. Third, open education resources allow conservation professionals (scientists and practitioners) to acquire the skills needed to make use of research outputs. The long-term adoption of open science practiceswould help researchers and practitioners achieve conservation goals more quickly and efficiently, in addition to reducing inequities in information sharing. However, short-term costs for individual researchers (insufficient institutional incentives to engage in open science and knowledge mobilization) remain a challenge to overcome. Finally, we caution against a passive approach to sharing that simply involves making information available. We advocate for a proactive stance towards transparency, communication, collaboration, and capacity building that involves seeking out and engaging with potential users to maximize the environmental and societal impact of conservation science.

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A Novel Framework to Protect Animal Data in a World of Ecosurveillance

Cited by 26 publications

References 34 publications

Perspectives in machine learning for wildlife conservation

Perspectives in machine learning for wildlife conservation

Animal Borne Ocean Sensors – AniBOS – An Essential Component of the Global Ocean Observing System

Closing the knowledge‐action gap in conservation with open science

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