Landsat – Earth observing satellite missions managed jointly by NASA and the US Geological Survey
Restore’s community of organizations
Sentinel – A fleet of Earth observing satellites managed by the European Space Agency and the European Commission.
Serge Wich’s Research & Projects
WILDLABS resources (case studies, project updates, news stories, articles, opportunities, and more)
Select Publications by Experts featured in this Issue
Jackman A, Millner N, Cunliff AM, Laumonier Y, Lunstrum E, Paneque-Gálvez J, Wich SA. 2023. Protecting people and wildlife from the potential harms of drone use in biodiversity conservation: interdisciplinary dialogues Global Social Challenges Journal, 20.
Fergus P, Chalmers C, Longmore SN, Wich S, Warmenhove C, Swart J, Ngongwane T, Burger A, Ledgard J, Meijaard E. 2023. Empowering Wildlife Guardians: An Equitable Digital Stewardship and Reward System for Biodiversity Conservation Using Deep Learning and 3/4G Camera Traps. Remote Sensing, 15.
Chalmers C, Fergus P, Wich SA, Longmore SN, Walsh ND, Stephens PA, Sutherland C, Matthews N, Mudde J, Nuseibeh A. 2023. Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep. Learning Remote Sensing, 15 :2638-2638.
L.P. Koh and S. Wich. Dawn of Drone Ecology: Low-Cost Autonomous Aerial Vehicles for Conservation. Tropical Conservation Science. Vol. 5, June 1, 2012.
Speaker T, O’Donnell S, Wittemyer G, Bruyere B, Loucks C, Dancer A, Carter M, Fegraus E, Palmer J, Warren E, Solomon J. A global community-sourced assessment of the state of conservation technology. Conserv Biol. 2022 Jun;36(3):e13871.
Wich, Serge A., and Alex K. Piel (eds), Conservation Technology (Oxford, 2021; online edn, Oxford Academic, 18 Nov. 2021).
References from “Conservation Technology: Beware the Pitfalls” by Jennifer Dowdell
Arts, K., van der Wal, R. & Adams, W.M. Digital technology and the conservation of nature. Ambio 44 (Suppl 4), 661–673 (2015).
Berger-Tal, O. and Lahoz-Monfort, J.J. (2018). Conservation technology: the next generation. Conserv. Lett. 11: 1–15.
Sandbrook Chris, Clark Douglas D., Toivonen Tuuli, Simlai Trishant, O’Donnell Stephanie, Cobbe Jennifer, Adams William M.. 2021. “Principles for the socially responsible use of conservation monitoring technology and data.” Conservation Science and Practice 3 (5): e374.
Simlai, Trishant, Negotiating The Gaze. Sanctuary Asia, Vol. 39 No. 12, December 2019
Why did the Luddites Protest? (from the British National Archives)
What the Luddites Can Teach Us about AI by Sophie Bushwick & Elah Feder.
Scientific American podcast.
How do I use Conservation Technology Ethically, a WILDLABS tech tutors panel
References from “Biological Breadcrumbs” by Jessica Norris
Barnes, M.A. and Turner, C.R., 2016. The ecology of environmental DNA and implications for conservation genetics. Conservation genetics, 17(1), pp.1-17.
Corlett, R.T., 2017. A bigger toolbox: biotechnology in biodiversity conservation. Trends in biotechnology, 35(1), pp.55-65.
Ariza, M., Fouks, B., Mauvisseau, Q., Halvorsen, R., Alsos, I.G. and de Boer, H.J., 2023. Plant biodiversity assessment through soil eDNA reflects temporal and local diversity. Methods in Ecology and Evolution, 14(2), pp.415-430.
Fediajevaite, J., Priestley, V., Arnold, R. and Savolainen, V., 2021. Meta‐analysis shows that environmental DNA outperforms traditional surveys, but warrants better reporting standards. Ecology and Evolution, 11(9), pp.4803-4815.
Ruppert, K.M., Kline, R.J. and Rahman, M.S., 2019. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: A systematic review in methods, monitoring, and applications of global eDNA. Global Ecology and Conservation, 17, p.e00547.
Shelton, A.O., Gold, Z.J., Jensen, A.J., D′ Agnese, E., Andruszkiewicz Allan, E., Van Cise, A., Gallego, R., Ramón‐Laca, A., Garber‐Yonts, M., Parsons, K. and Kelly, R.P., 2023. Toward quantitative metabarcoding. Ecology, 104(2), p.e3906.
Additional Sites and Publications
The non-profit Wild Me created a cloud-based platform Wildbook, which uses computer vision and deep learning algorithms to scan millions of crowdsourced wildlife images to identify species and individual animals based on their unique patterns, including stripes, spots or other defining physical features such as scars.
Chapman Melissa, Xu Lily, Lapeyrolerie Marcus, and Boettinger Carl. 2023 Bridging adaptive management and reinforcement learning for more robust decisions. Phil. Trans. R. Soc. B 378: 20220195
Schulz, Andrew & Shriver, Cassie & Patka, Anika & Greiner, Caroline & Seleb, Benjamin & Hull, Rebecca & Sullivan, Carol & Sonnenberg-Klein, Julia & Moore, Roxanne. (2023). Intradisciplinary Growth of Sustainability-Minded Engineers through Conservation Technology. 10.1101/2023.07.03.546429.
Parris-Piper, Naomi & Dressler, Wolfram & Satizábal, Paula & Fletcher, Rob. (2023). Automating violence? The anti-politics of ‘smart technology’ in biodiversity conservation. Biological Conservation. 278. 109859.
Tuia, D., Kellenberger, B., Beery, S. et al. Perspectives in machine learning for wildlife conservation. Nat Commun. 13, 792 (2022).
Glossary
Artificial Intelligence: Artificial intelligence is the ability for computers to imitate cognitive human functions such as learning and problem-solving. Through AI, a computer system uses math and logic to simulate the reasoning that people use to learn from new information and make decisions. (source: MIT)
Machine Learning: Machine learning is when we teach computers to extract patterns from collected data and apply them to new tasks that they may not have completed before. (Source: MIT)
Sensor: A device that measures a physical quantity and converts it into a signal which can be read by an observer or by an instrument. A sensor is a device which responds to an input quantity by generating a functionally related output usually in the form of an electrical or optical signal. (Source: National Institute of Standards and Technology)