# Emerging Technologies to Conserve Biodiversity > Satellites, drones, DNA and smartphones are changing how we watch wildlife, but can they save species on their own? This review of conservation technology argues that the answer is no: the right tools must reach the right people. It highlights non-invasive methods such as reading footprints, and names indigenous expert trackers and CyberTracker as part of the future. Summary by Kersey Lawrence, Original Wisdom. Updated 9 November 2026. Page: https://www.originalwisdom.com/tracking-literature/2015-pimm-alibhai-emerging-technologies-conserve-biodiversity/ ## Citation and access - **Authors:** Stuart L. Pimm, Sky Alibhai, Richard Bergl, Alex Dehgan, Chandra Giri, Zoë Jewell, Lucas Joppa, Roland Kays and Scott Loarie - **Year published:** 2015 - **Journal or source:** Trends in Ecology & Evolution - **Full citation:** Pimm, S. L., Alibhai, S., Bergl, R., Dehgan, A., Giri, C., Jewell, Z., Joppa, L., Kays, R. and Loarie, S. 2015. Emerging technologies to conserve biodiversity. Trends in Ecology & Evolution 30(11): 685–696. - **DOI:** 10.1016/j.tree.2015.08.008 - **Access status:** Hosted (freely available elsewhere) - **PDF:** https://www.originalwisdom.com/wp-content/uploads/2026/11/2015_Pimm_and_Alibhai_etal_emerging-technologies-to-conserve-biodiversity.pdf ## Tags - **Tracking:** Hair; Individual identification; Scat; Track photographs; Tracking; Tracks - **Methods and technology:** Aerial surveys; Camera traps; Citizen science; CyberTracker; CyberTracker software; DNA; Footprint Identification Technology (FIT); Geographic Information System (GIS); Global Positioning System (GPS); Indigenous knowledge; Literature review; Machine learning; Smartphones; Telemetry - **Data type:** Review - **Observer qualification:** Not needed - **Discipline:** Citizen science; Indigenous knowledge; Wildlife tracking - **Year published:** 2015 ## Detailed keywords - **Species:** No species studied; this is a review. Examples named: penguin colonies counted from Landsat images (the text says emperor penguins, but the reference it cites is about Adélie penguin colonies); elephants, wildebeest and zebra counted from satellite images; Amur leopard and grizzly bear (scent-detection dogs and non-invasive genetic sampling); white rhino (Ceratotherium simum) and Amur tiger (Panthera tigris altaica) in the footprint identification references; killer whales (Orcinus orca) identified by their calls; whale shark (Rhincodon typus) identified by spot patterns; African wild dog (Lycaon pictus) and cheetah (Acinonyx jubatus) in tourist photo surveys in Kruger National Park; rhinos (poaching); frogs (disease); bobcat (as an example of crowd identification); eastern North American tree leaves (LeafSnap app); chimpanzees (Gombe, in a reference title). Barabaig herders and their herds appear in a photo. - **Location and habitat:** Global review. Places named as examples: Antarctica; Serengeti National Park and Gombe National Park, Tanzania; Kruger National Park, South Africa; sub-Saharan Africa and Asia (MIST); India’s Western Ghats; Mexico (iNaturalist national surveys); tropical moist forests and biodiversity hotspots. - **Topics:** conservation technology; non-invasive monitoring; footprint identification technique (FIT); scent-detection dogs; pelage, skin and whisker pattern recognition; infrared and multispectral sensors; acoustic identification of individuals; non-invasive genetic sampling (hair, faeces, saliva); DNA barcoding; satellite remote sensing and cubesats; drones (unmanned aerial vehicles); camera traps and image processing; big data and data management; crowdsourcing and citizen science (eBird, iNaturalist, Global Biodiversity Information Facility); mobile phones in rural communities; indigenous expert trackers and traditional ecological knowledge (TEK); tourists as data collectors; anti-poaching (CyberTracker, MIST, SMART, gunshot and metal detectors); habitat loss and species range maps; open source research, hackathons, prizes and crowdfunding. - **Statistics or models:** None - **Data type:** Review. No new data. Figures quoted are as of about 2015 (for example, eBird over 100,000 observers and over 100 million observations; GBIF 420 million records; SMART at over 140 sites in over 30 countries). ## Synopsis ### 1. What they did Tools for finding, identifying and following animals, and for watching their habitats from space, have become better, faster and cheaper, just as threats to species are growing. Nine authors from universities, a zoo, museums, the US Geological Survey, Microsoft Research, iNaturalist, WildTrack and Conservation X Labs reviewed these tools briefly and then looked at the harder questions they raise for conserving species. They covered: - Where technology could make the most difference: cheap DNA barcoding, finer satellite images, and drones. - Non-invasive monitoring: ways to identify species and individual animals without catching or collaring them, including footprints, scent-detection dogs, coat and skin patterns, infrared sensors, calls, and DNA from hair, faeces and saliva. - Handling huge amounts of data, from camera traps to global species databases. - Crowdsourced observations and how to make them reliable. - Bringing in new people: tourists, rural communities, and indigenous experts such as trackers. - Fighting poaching with tools suited to remote places with little technical support. - Linking satellite images of habitat loss with records of where species live. - Speeding up new ideas through open source research, hackathons, prizes and crowdfunding. ### 2. What they found Following individual animals - Tracking devices have moved from bulky, expensive radio collars to smaller satellite-based devices and to non-invasive methods. - Devices can be expensive, with monthly data charges. Catching animals is difficult and expensive and may affect the data collected. For endangered species, even the least intrusive attachment may be an unacceptable risk. Non-invasive monitoring (Box 2) - Footprint identification technique (FIT): described as translating “millennia-old tracking practices.” Digital images of footprints are measured and the measurements analysed with a statistical model in JMP software. It identifies species, individual, sex and age class with over 90% accuracy, and has been adapted for plant-eating and meat-eating mammals. - Scent-detection dogs: have identified individual free-ranging Amur leopards and grizzly bears from natural traces. A dog’s nose may be about 100,000 times more sensitive than a person’s. - Coat, skin and whisker patterns: used to identify individuals in camera-trap images; computer vision may automate this. Camera traps work poorly for animals with no distinctive markings. - Infrared sensors: could identify individual animals at camera traps, through smartphones or from drones. Wider use by citizen scientists, visitors or indigenous expert trackers could add greatly to the amount and quality of data. - Calls: many species have individually distinct voices; killer whale social groups have been worked out this way. - DNA from hair, faeces and saliva: individual identification will improve as methods get better. Data and crowdsourcing - Millions of camera-trap photos are still mostly sorted by hand, for example 1.2 million Serengeti images classified by citizen scientists. - eBird had over 100,000 observers and over 100 million observations. Its sightings rely on observers building a track record, which suits well-known birds but not species that are harder to detect or identify. - iNaturalist uses photo vouchers checked by experts and the crowd, and has logged over a million records. - Data on endangered species need protecting from criminals; iNaturalist hides their locations from unauthorised users. New data collectors (Box 4) - Mobile phone subscriptions in sub-Saharan Africa were expected to grow from 551 million in 2013 to 930 million by 2019. - Two groups are barely used: tourists (eight billion visits a year to protected areas on land) and indigenous communities and experts, such as trackers with traditional ecological knowledge (TEK). Studies have compared survey data from TEK experts favourably with ground sampling. Poaching - Rangers and local people with inside knowledge of their land provide the most important data, but sites often lack technical skills. - CyberTracker has been used for ranger-based data collection in Kruger National Park, South Africa, and elsewhere. MIST (Management Information System) was developed by the Uganda Wildlife Authority. SMART (Spatial Monitoring and Reporting Tool), a free open source tool combining CyberTracker’s easy field data collection with stronger analysis, was in use at over 140 sites in over 30 countries. - Drones useful against poaching would need near-military abilities (infrared sensors, over 5 hours in the air, live video), which were then too expensive and hard to maintain. Habitat loss - Range maps can now be trimmed with satellite data on elevation and remaining habitat. This works best for forest-only species. The authors suggest linking species records directly to satellite data, skipping the intermediate steps. ### 3. What they concluded - New technologies alone do not save species, and new data create new problems. - Against poaching, the challenge is to give appropriate tools to the right people, not necessarily the most advanced tools. Tools should serve a wider conservation strategy, alongside stronger laws, better courts and less demand for wildlife products. - For habitat loss, the challenge is to connect existing remote sensing with species records, quickly enough for people to act. - The most important trends are technologies suited to remote, low-technology places where conservation happens, and including communities whose data have not been used before. - Open questions include: which other non-invasive methods can identify species and individuals and follow their movements; how to involve “poorly educated, but locally wise” people with little technical support while keeping data from those who would do harm; how to avoid drowning in data; and how to invent solutions that do not exist yet. ### 4. How tracking was or was not used This is a review; no tracking was done. Much of the paper uses “tracking” to mean following animals with collars and other devices. Tracks and signs appear in Box 2: the footprint identification technique, which the authors (two of whom, Jewell and Alibhai, are from WildTrack) describe as translating millennia-old tracking practices into a computer method, and the use of scats, hair and other natural traces by scent-detection dogs and genetic sampling. Indigenous expert trackers are named as possible collectors of sensor data and as holders of traditional ecological knowledge. ### 6. Other technologies, skills or innovations - Footprint identification technique (FIT) in JMP software - Scent-detection dogs - DNA barcoding (about US$1 per sample) and non-invasive genetic sampling - Satellite imagery (Landsat, GeoEye, cubesats, Sentinel) and drones - Camera traps with computer vision; infrared and multispectral sensors; 3D mesh models for measuring animal shape - Acoustic identification of individuals; gunshot and metal detectors - Smartphone apps and platforms: eBird, iNaturalist, LeafSnap, Global Biodiversity Information Facility (GBIF) - Ranger data systems: CyberTracker, MIST and SMART ### 7. Implications for conservation (from the paper) - Even the best tools for marking animals may be unacceptable for endangered species; non-invasive alternatives are needed. - Tools should be suited to the people on the front line, including rangers and local communities with little infrastructure. - Data on rare species must be protected from poachers. - Survey methods have been slow to adopt new technology, and their effect on the animals studied and on the quality of the data has not been considered enough. ### 8. Implications for tracking (Original Wisdom's observations) _These are Original Wisdom's observations, not the authors'._ - The paper places footprints and other natural traces among the most promising non-invasive methods for endangered species, where catching and collaring may not be allowed. - It names indigenous expert trackers as a largely untapped source of data, and CyberTracker as an early tool that made field data collection possible in low-technology settings. ## Cite this summary Lawrence, K. 2026. Summary of: Emerging Technologies to Conserve Biodiversity (Pimm and Alibhai et al., 2015). Original Wisdom, Tracking Literature. https://www.originalwisdom.com/tracking-literature/2015-pimm-alibhai-emerging-technologies-conserve-biodiversity/