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Search by author or title:
Best practices for using drones in seabird monitoring and research
Citation
EDNEY, A.J., HART, T., JESSOPP, M.J., BANKS, A., CLARKE, L.E., CUGNIÈRE, L., ELLIOT, K.H., JUAREZ MARTINEZ, I., KILCOYNE, A., MURPHY, M., NAGER, R.G., RATCLIFFE, N., THOMPSON, D.L., WARD, R.M. & WOOD, M.J. 2023. Best practices for using drones in seabird monitoring and research. Marine Ornithology 51: 265 - 280
Received 23 March 2023, accepted 23 June 2023
Date Published: 2023/10/15
Date Online: 2023/10/12
Key words: drones, seabirds, remote sensing, monitoring, disturbance
Abstract
Over the past decade, drones have become increasingly popular in environmental biology and have been used to study wildlife on all continents. Drones have become of global importance for surveying breeding seabirds by providing opportunities to transform monitoring techniques and allow new research on some of the most threatened birds. However, such fast-changing and increasingly available technology presents challenges to regulators responding to requests to carry out surveys and to researchers ensuring their work follows best practice and meets legal and ethical standards. Following a workshop convened at the 14th International Seabird Group Conference and a subsequent literature search, we collate information from over 100 studies and present a framework to ensure drone-seabird surveys are safe, effective, and within the law. The framework comprises eight steps: (1) Objectives and Feasibility; (2) Technology and Training; (3) Site Assessment and Permission; (4) Disturbance Mitigation; (5) Pre-deployment Checks; (6) Flying; (7) Data Handling and Analysis; and (8) Reporting. The audience is wide-ranging with sections having relevance for different users, including prospective and experienced drone-seabird pilots, landowners, and licensors. Regulations vary between countries and are frequently changing, but common principles exist. Taking-off, landing, and conducting in-flight changes in altitude and speed at ≥ 50 m from the study area, and flying at ≥ 50 m above ground-nesting seabirds/horizontal distance from vertical colonies, should have limited disturbance impact on many seabird species; however, surveys should stop if disturbance occurs. Compared to automated methods, manual or semi-automated image analyses are, at present, more suitable for infrequent drone surveys and surveys of relatively small colonies. When deciding if drone-seabird surveys are an appropriate monitoring method long-term, the cost, risks, and results obtained should be compared to traditional field monitoring where possible. Accurate and timely reporting of surveys is essential to developing adaptive guidelines for this increasingly common technology.
References
ALBORES-BARAJAS, Y.V., SOLDATINI, C., RAMOS-RODRÍGUEZ, A., ALCALA-SANTOYO, J.E., CARMONA, R. & DELL’OMO, G. 2018. A new use of technology to solve an old problem: Estimating the population size of a burrow nesting seabird. PLoS One 13: e0202094. doi:10.1371/journal.pone.0202094
ANDREW, M.E. & SHEPHARD, J.M. 2017. Semi-automated detection of eagle nests: an application of very high-resolution image data and advanced image analyses to wildlife surveys. Remote Sensing in Ecology and Conservation 3: 66–80. doi:10.1002/rse2.38
ARNEILL, G.E., PERRINS, C.M., WOOD, M.J. ET AL. 2019. Sampling strategies for species with high breeding-site fidelity: A case study in burrow-nesting seabirds. PLoS One 14: e0221625. doi:10.1371/journal.pone.0221625
ARTETA, C., LEMPITSKY, V. & ZISSERMAN, A. 2016. Counting in the wild. European Conference on Computer Vision 9911: 483–498. doi:10.1007/978-3-319-46478-7_30
BARNAS, A., CHABOT, D., HODGSON, A., JOHNSTON, D.W., BIRD, D.M. & ELLIS-FELEGE, S.N. 2020. A standardized protocol for reporting methods when using drones for wildlife research. Journal Of Unmanned Vehicle Systems 8: 89–98. doi:10.1139/juvs-2019-0011
BARR, J.R., GREEN, M.C., DEMASO, S.J. & HARDY, T.B. 2020. Drone surveys do not increase colony-wide flight behaviour at waterbird nesting sites, but sensitivity varies among species. Scientific Reports 10: 3781. doi:10.1038/s41598-020-60543-z
BENEMANN, V.R.F., ARAÚJO, L.D., FABBRIS, A.Z., MONTONE, R.C. & PETRY, M.V. 2022. Nesting distribution of Masked Booby Sula dactylatra at Trindade Island, western South Atlantic Ocean. Marine Ornithology 50: 189–195.
BEVAN, E., WIBBELS, T., NAJERA, B.M. ET AL. 2015. Unmanned aerial vehicles (DRONEs) for monitoring sea turtles in near-shore waters. Marine Turtle Newsletter 145: 19–22.
BIBBY, C.J., BURGESS, N.D., HILL, D.A. & MUSTOE, S. 2000. Bird Census Techniques. London, UK: Elsevier.
BISHOP, A.M., BROWN, C.L., CHRISTIE, K.S. ET AL. 2022. Surveying cliff-nesting seabirds with unoccupied aircraft systems in the Gulf of Alaska. Polar Biology 45: 17031714. doi:10.1007/s00300-022-03101-9
BLIGHT, L.K., BERTRAM, D.F. & KROC, E. 2019. Evaluating drone-based techniques to census an urban-nesting gull population on Canada’s Pacific coast. Journal of Unmanned Vehicle Systems 7: 312–324. doi:10.1139/juvs-2019-0005
BORRELLE, S.B. & FLETCHER, A.T. 2017. Will drones reduce investigator disturbance to surface-nesting seabirds? Marine Ornithology 45: 89–94.
BOWLER, E., FRETWELL, P.T., FRENCH, G. & MACKIEWICZ, M. 2020. Using deep learning to count albatrosses from space: assessing results in light of ground truth uncertainty. Remote Sensing 12: 2026. doi:10.3390/rs12122026
BRINKMAN, M.P. & GARCELON, D.K. 2020. Applying UAV Systems in Wildlife Management. Proceedings of the Vertebrate Pest Conference 29.
BRISSON-CURADEAU, É., BIRD, D., BURKE, C. ET AL. 2017. Seabird species vary in behavioural response to drone census. Scientific Reports 7: 1–9. doi:10.1038/s41598-017-18202-3
BUCKLAND, S.T., BURT, M.L., REXSTAD, E.A., MELLOR, M., WILLIAMS, A.E. & WOODWARD, R. 2012. Aerial surveys of seabirds: the advent of digital methods. Journal of Applied Ecology 49: 960–967. doi:10.1111/j.1365-2664.2012.02150.x
CARAVAGGI, A., CUTHBERT, R.J., RYAN, P.G., COOPER, J. & BOND, A.L. 2019. The impacts of introduced House Mice on the breeding success of nesting seabirds on Gough Island. Ibis 161: 648–661. doi:10.1111/ibi.12664
CARNEY, K. M. & SYDEMAN, W. J. 1999. A review of human disturbance effects on nesting colonial waterbirds. The International Journal of Waterbird Biology 22: 68–79. doi:10.1111/jofo.12171
CHABOT, D. & FRANCIS, C.M. 2016. Computer-automated bird detection and counts in high-resolution aerial images: a review. Journal of Field Ornithology 87: 343–359. doi:10.1111/jofo.12171
CHABOT, D., CRAIK, S.R. & BIRD, D.M. 2015. Population census of a large common tern colony with a small unmanned aircraft. PLoS One 10: e0122588. doi:10.1371/journal.pone.0122588
CHAPMAN, A. 2014. It's okay to call them drones. Journal of Unmanned Vehicle Systems 2: iii-v. doi:10.1139/juvs-2014-0009
CHRISTIN, S., HERVET, É. & LECOMTE, N. 2019. Applications for deep learning in ecology. Methods in Ecology and Evolution 10: 1632–1644. doi:10.1111/2041-210X.13256
COMNAP (COUNCIL OF MANAGERS OF NATIONAL ANTARCTIC PROGRAMS). 2021. Antarctic Flight Information Manual (AFIM). [Accessed at https://www.comnap.aq/air-operations on 31 March 2022.]
CORCORAN, E., WINSEN, M., SUDHOLZ, A. & HAMILTON, G. 2021. Automated detection of wildlife using drones: Synthesis, opportunities and constraints. Methods in Ecology and Evolution 12: 1103–1114. doi:10.1111/2041-210X.13581
CORREGIDOR-CASTRO, A. & VALLE, R.G. 2022. Semi-Automated counts on drone imagery of breeding seabirds using free accessible software. Ardea 110: 89–97. doi:10.5253/arde.v110i1.a7
CORREGIDOR-CASTRO, A., HOLM, T.E. & BREGNBALLE, T. 2021. Counting breeding gulls with unmanned aerial vehicles: camera quality and flying height affects precision of a semi-automatic counting method. Ornis Fennica 98: 33–45.
CORREGIDOR-CASTRO, A., RIDDERVOLD, M., HOLM, T. E. & BREGNBALLE, T. 2022. Monitoring colonies of large gulls using UAVs: from individuals to breeding pairs. Micromachines 13: 1844. doi:10.3390/mi13111844
CROXALL, J.P., BUTCHART, S.H.M., LASCELLES, B. ET AL. 2012. Seabird conservation status, threats and priority actions: a global assessment. Bird Conservation International 22: 1–34. doi:10.1017/S0959270912000020
CUNNINGHAM, E.J.A., GAMBLE, A., HART, T. ET AL. 2022. The incursion of Highly Pathogenic Avian Influenza (HPAI) into North Atlantic seabird populations: an interim report from the 15th International Seabird Group conference. Seabird 34.
DEWAR, M. L., DR, WILLE, M., GAMBLE, A. ET AL. 2022. The Risk of Avian Influenza in the Southern Ocean: A Practical Guide. EcoEvoRxiv Preprints. doi:10.32942/osf.io/8jrbu
DIAS, M.P., MARTIN, R., PEARMAIN, E.J. ET AL. 2019. Threats to seabirds: A global assessment. Biological Conservation 237: 525–537. doi:10.1016/j.biocon.2019.06.033
DICKENS, J., HOLLYMAN, P.R., HART, T. ET AL. 2021. Developing UAV monitoring of South Georgia and the South Sandwich Islands’ iconic land-based marine predators. Frontiers in Marine Science 8: 630. doi:10.3389/fmars.2021.654215
DJI. 2022. Mavic 2 Enterprise Series. Shenzen, China: DJI. [Accessed at https://www.dji.com/uk/mavic-2-enterprise/specs on 31 March 2022.]
DOUKARI, M., KATSANEVAKIS, S., SOULAKELLIS, N. & TOPOUZELIS, K. 2021. The effect of environmental conditions on the quality of UAS orthophoto-maps in the coastal environment. ISPRS International Journal of Geo-Information 10: 18. doi:10.3390/ijgi10010018
DUFFY, J. P., CUNLIFFE, A. M., DEBELL, L. ET AL. 2018. Location, location, location: considerations when using lightweight drones in challenging environments. Remote Sensing in Ecology and Conservation 4: 7–19. doi:10.1002/rse2.58
DUJON, A.M., IERODIACONOU, D., GEESON, J.J. ET AL. 2021. Machine learning to detect marine animals in UAV imagery: effect of morphology, spacing, behaviour and habitat. Remote Sensing in Ecology and Conservation 7: 341–354. doi:10.1002/rse2.205
DUKOWITZ, Z. 2019. Drones in National Parks: What Every Drone Pilot Needs to Know. Nashville, USA: The UAV Coach. [Accessed at https://uavcoach.com/drones-in-national-parks/ on 24 August 2021.]
DUNN, M.J., ADLARD, S., TAYLOR, A.P., WOOD, A.G., TRATHAN, P.N. & RATCLIFFE, N. 2021. Un-crewed aerial vehicle population survey of three sympatrically breeding seabird species at Signy Island, South Orkney Islands. Polar Biology 44: 717–727. doi:10.1007/s00300-021-02831-6.
DUPORGE, I., SPIEGEL, M.P., THOMSON, E.R. ET AL. 2021. Determination of optimal flight altitude to minimise acoustic drone disturbance to wildlife using species audiograms. Methods in Ecology and Evolution 12: 2196–2207. doi:10.1111/2041-210X.13691
EDNEY, A.J. & WOOD, M.J. 2021. Applications of digital imaging and analysis in seabird monitoring and research. Ibis 163: 317–337. doi:10.1111/ibi.12871
ELLETT, L., GIBBONS, S., GILBERT, J., CRUZ, J.G. & ISLAM, A. 2021. Navigating assumptions of wildlife viewing impacts. Parks Stewardship Forum 37: 546–551.
ENGLER, R.E. 2012. The complex interaction between marine debris and toxic chemicals in the ocean. Environmental Science and Technology 46: 12302–12315. doi:10.1021/es3027105
ESPÍNDOLA, W. D., CRUZ‐MENDOZA, A., GARRASTAZÚ, A. ET AL. 2023. Estimating population size of red‐footed boobies using distance sampling and drone photography. Wildlife Society Bulletin 47: e1406. doi:10.1002/wsb.1406
FRETWELL, P.T., LARUE, M.A., MORIN, P. ET AL. 2012. An emperor penguin population estimate: the first global, synoptic survey of a species from space. PLoS One 7: e33751. doi:10.1371/journal.pone.0033751
FRETWELL, P.T., SCOFIELD, P. & PHILLIPS, R.A. 2017. Using super-high resolution satellite imagery to census threatened albatrosses. Ibis 159: 481–490. doi:10.1111/ibi.12482
GELDART, E.A., BARNAS, A.F., SEMENIUK, C.A.D. ET AL. 2022. A colonial-nesting seabird shows no heart-rate response to drone-based population surveys. Scientific Reports 12: 18804. doi:10.1038/s41598-022-22492-7
GOEBEL, M.E., PERRYMAN, W.L., HINKE, J.T. ET AL. 2015. A small unmanned aerial system for estimating abundance and size of Antarctic predators. Polar Biology 38: 619–630. doi:10.1007/s00300-014-1625-4
GOV.UK. 2015. Protected Species: When to Apply for a Licence To Survey, Film or Photograph Them. London, UK: GOV.UK. [Accessed at https://www.gov.uk/guidance/protected-species-when-to-apply-for-a-licence-to-survey-film-or-photograph-them on 09 July 2021.]
GREGORY, R.D., GIBBONS, D.W. & DONALD, P.F. 2004. Bird census and survey techniques. In: SUTHERLAND, W.J., NEWTON, I. & RHYS, G. (Eds.) Bird Ecology and Conservation: A Handbook of Techniques. Oxford, UK: Oxford University Press, pp. 17–52.
GRENZDÖRFFER, G.J. 2013. UAS-based automatic bird count of a common gull colony. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1/W2: 169–174. doi:10.5194/isprsarchives-XL-1-W2-169-2013
GRIMALDI, W.W., SEDDON, P.J., LYVER, P.O., NAKAGAWA, S. & TOMPKINS, D.M. 2015. Infectious diseases of Antarctic penguins: current status and future threats. Polar Biology 38: 591–606. doi:10.1007/s00300-014-1632-5
HAYES, M.C., GRAY, P.C., HARRIS, G. ET AL. 2021. Drones and deep learning produce accurate and efficient monitoring of large-scale seabird colonies. Ornithological Applications 123: 1–16. doi:10.1093/ornithapp/duab022
HODGSON, J. C., MOTT, R., BAYLIS, S. M. ET AL. 2018. Drones count wildlife more accurately and precisely than humans. Methods in Ecology and Evolution 9: 1160–1167. doi:10.1111/2041-210X.12974
HODGSON, J.C. & KOH, L.P. 2016. Best practice for minimising unmanned aerial vehicle disturbance to wildlife in biological field research. Current Biology 26: R404–R405. doi:10.1016/j.cub.2016.04.001
HODGSON, J.C., BAYLIS, S.M., MOTT, R., HERROD, A. & CLARKE, R.H. 2016. Precision wildlife monitoring using unmanned aerial vehicles. Scientific Reports 6: 1–7. doi:10.1038/srep22574
HOLLINGS, T., BURGMAN, M., ANDEL, M. VAN, GILBERT, M., ROBINSON, T. & ROBINSON, A. 2018. How do you find the green sheep? A critical review of the use of remotely sensed imagery to detect and count animals. Methods in Ecology and Evolution 9: 881–892. doi:10.1111/2041-210X.12973
HURFORD, C. 2017. Improving the Accuracy of Bird Counts Using Manual and Automated Counts in ImageJ: An Open-Source Image Processing Program. In: DIAZ-DELGADO R., LUCAS R., HURFORD C. (Eds.) The Roles of Remote Sensing in Nature Conservation. New York, USA: Springer International Publishing.
HYUN, C.-U., PARK, M. & LEE, W.Y. 2020. Remotely Piloted Aircraft System (RPAS)-based wildlife detection: a review and case studies in maritime Antarctica. Animals 10: 2387. doi:10.3390/ani10122387
IRIGOIN-LOVERA, C., LUNA, D.M., ACOSTA, D.A. & ZAVALAGA, C.B. 2019. Response of colonial Peruvian guano birds to flying UAVs: effects and feasibility for implementing new population monitoring methods. PeerJ 7: e8129. doi:10.7717/peerj.8129
JARRETT, D., CALLADINE, J., COTTON, A., WILSON, M. W. & HUMPHREYS, E. 2020. Behavioural responses of non-breeding waterbirds to drone approach are associated with flock size and habitat. Bird Study 67: 190–196. doi:10.1080/00063657.2020.1808587
JOHNSTON, D.W. 2019. Unoccupied aircraft systems in marine science and conservation. Annual Review of Marine Science 11: 439–463. doi:10.1146/annurev-marine-010318-095323
JONES, F.M., ALLEN, C., ARTETA, C. ET AL. 2018. Time-lapse imagery and volunteer classifications from the Zooniverse Penguin Watch project. Scientific Data 5: 180124. doi:10.1038/sdata.2018.124
JONES, F.M., ARTETA, C., ZISSERMAN, A., LEMPITSKY, V., LINTOTT, C.J. & HART, T. 2020. Processing citizen science- and machine-annotated time-lapse imagery for biologically meaningful metrics. Scientific Data 7: 1–15. doi:10.1038/s41597-020-0442-6
JUNDA, J., GREENE, E. & BIRD, D. M. 2015. Proper flight technique for using a small rotary-winged drone aircraft to safely, quickly, and accurately survey raptor nests. Journal of Unmanned Vehicle Systems 3: 222–236. doi:10.1139/juvs-2015-0003
KELLENBERGER, B., VEEN, T., FOLMER, E. & TUIA, D. 2021. 21 000 birds in 4.5 h: efficient large-scale seabird detection with machine learning. Remote Sensing in Ecology and Conservation 7: 445–460. doi:10.1002/rse2.200
KORCZAK-ABSHIRE, M., ZMARZ, A., RODZEWICZ, M., KYCKO, M., KARSZNIA, I. & CHWEDORZEWSKA, K.J. 2019. Study of fauna population changes on Penguin Island and Turret Point Oasis (King George Island, Antarctica) using an unmanned aerial vehicle. Polar Biology 42: 217–224. doi:10.1007/s00300-018-2379-1
KRAUSE, D.J., HINKE, J.T., GOEBEL, M.E. & PERRYMAN, W.L. 2021. Drones minimize Antarctic predator responses relative to ground survey methods: an appeal for context in policy advice. Frontiers in Marine Science 8: 648772. doi:10.3389/fmars.2021.648772
LEE, W.Y., PARK, M. & HYUN, C.-U. 2019. Detection of two Arctic birds in Greenland and an endangered bird in Korea using RGB and thermal cameras with an unmanned aerial vehicle (UAV). PLoS One 14: e0222088. doi:10.1371/journal.pone.0222088
LETHBRIDGE, M., STEAD, M., WELLS, C., LETHBRIDGE, M., STEAD, M. & WELLS, C. 2019. Estimating kangaroo density by aerial survey: a comparison of thermal cameras with human observers. Wildlife Research 46: 639–648. doi:10.1071/WR18122
LIEBER, L., LANGROCK, R. & NIMMO-SMITH, W.A.M. 2021. A bird’s-eye view on turbulence: seabird foraging associations with evolving surface flow features. Proceedings of the Royal Society B. 288: 20210592. doi:10.1098/rspb.2021.0592
LIEBER, L., NIMMO-SMITH, W.A.M., WAGGITT, J.J. & KREGTING, L. 2019. Localised anthropogenic wake generates a predictable foraging hotspot for top predators. Communications Biology 2: 123. doi:10.1038/s42003-019-0364-z
LINCHANT, J., LISEIN, J., SEMEKI, J., LEJEUNE, P. & VERMEULEN, C. 2015. Are unmanned aircraft systems (UASs) the future of wildlife monitoring? A review of accomplishments and challenges. Mammal Review 45: 239–252. doi:10.1111/mam.12046
LYONS, M.B., BRANDIS, K.J., MURRAY, N.J. ET AL. 2019. Monitoring large and complex wildlife aggregations with drones. Methods in Ecology and Evolution 10: 1024–1035. doi:10.1111/2041-210X.13194
MALLORY, M.L., DEY, C.J., MCINTYRE, J. ET AL. 2020. Long-term declines in the size of Northern Fulmar (Fulmarus glacialis) colonies on eastern Baffin Island, Canada. Arctic 73: 187–194.
MAPES, K.L., PRICOPE, N.G., BAXLEY, J.B., SCHAALE, L.E. & DANNER, R.M. 2020. Thermal imaging of beach-nesting bird habitat with unmanned aerial vehicles: considerations for reducing disturbance and enhanced image accuracy. Drones 4: 12. doi:10.3390/drones4020012
MARTIN, A.R. & RICHARDSON, M.G. 2017. Rodent eradication scaled up: clearing rats and mice from South Georgia. Oryx 53: 27–35. doi:10.1017/S003060531700028X
MATTERN, T., REXER-HUBER, K., PARKER, G. ET AL. 2021. Erect-crested penguins on the Bounty Islands: population size and trends determined from ground counts and drone surveys. Notornis 68: 37–50. doi:10.6084/m9.figshare.19709476
MCCLELLAND, G.T., BOND, A.L., SARDANA, A. & GLASS, T. 2016. Rapid population estimate of a surface-nesting seabird on a remote island using a low-cost unmanned aerial vehicle. Marine Ornithology 44: 215–220.
MCDOWALL, P. & LYNCH, H.J. 2017. Ultra-fine scale spatially-integrated mapping of habitat and occupancy using structure-from-motion. PLoS One 12: e0166773. doi:0.1371/journal.pone.0166773
MILLAR, G. 2022. Drone Footage Reveals Devastating Impact of Bird Flu on The Bass Rock Gannets. Glasgow, UK: The National. [Accessed at https://www.thenational.scot/news/20281829.drone-footage-reveals-devastating-impact-bird-flu-bass-rock-gannets/ on 11 August 2022.]
MITCHELL, P.I. & PARSONS, M. 2007. Strategic Review of the UK Seabird Monitoring Programme. Joint Nature Conservation Committee, Unpublished Report. Peterborough, UK: Joint Nature Conservation Committee.
MULERO-PÁZMÁNY, M., JENNI-EIERMANN, S., STREBEL, N., SATTLER, T., NEGRO, J.J. & TABLADO, Z. 2017. Unmanned aircraft systems as a new source of disturbance for wildlife: A systematic review. PLoS One 12: e0178448. doi:10.1371/journal.pone.0178448
MUSTAFA, O., BARBOSA, A., KRAUSE, D.J., PETER, H.-U., VIEIRA, G. & RÜMMLER, M.-C. 2018. State of knowledge: Antarctic wildlife response to unmanned aerial systems. Polar Biology 41: 2387–2398. doi:10.1007/s00300-018-2363-9
MUSTAFA, O., BRAUN, C., ESEFELD, J. ET AL. 2019. Detecting Antarctic seals and flying seabirds by UAV. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2/W5: 141–148. doi:10.5194/isprs-annals-IV-2-W5-141-2019
MUSTAFA, O., ESEFELD, J., GRÄMER, H. ET AL. 2017. Monitoring Penguin Colonies in the Antarctic Using Remote Sensing Data. Dessau-Roßlau, Germany: Umweltbundesamt. [Accessed at http://www.umweltbundesamt.de/publikationen on 31 March 2022.]
NATURAL RESOURCES WALES. 2021. Sites of Special Scientific Interest (SSSI): Responsibilities of Owners and Occupiers. Cardiff, UK: Natural Resources Wales. [Accessed at https://naturalresources.wales/guidance-and-advice/environmental-topics/wildlife-and-biodiversity/protected-areas-of-land-and-seas/sites-of-special-scientific-interest-responsibilities-of-owners-and-occupiers/?lang=en on 25 August 2021.]
NATURE SCOT. 2021. Sites of Special Scientific Interest (SSSIs). Inverness, UK: Nature Scot. [Accessed at https://www.nature.scot/professional-advice/protected-areas-and-species/protected-areas/national-designations/sites-special-scientific-interest-sssis on 25 August 2021.]
NOWAK, M.M., DZIÓB, K. & BOGAWSKI, P. 2019. Unmanned Aerial Vehicles (UAVs) in environmental biology: a review. European Journal of Ecology 4: 56–74. doi:10.2478/eje-2018-0012
O’CONNOR, J., SMITH, M.J. & JAMES, M.R. 2017. Cameras and settings for aerial surveys in the geosciences: Optimising image data. Progress in Physical Geography: Earth and Environment 41: 325–344. doi:10.1177/0309133317703092
OOSTHUIZEN, W.C., KRÜGER, L., JOUANNEAU, W. & LOWTHER, A.D. 2020. Unmanned aerial vehicle (UAV) survey of the Antarctic shag (Leucocarbo bransfieldensis) breeding colony at Harmony Point, Nelson Island, South Shetland Islands. Polar Biology 43: 187–191. doi:10.1007/s00300-019-02616-y
PALECZNY, M., HAMMILL, E., KARPOUZI, V. & PAULY, D. 2015. Population trend of the world’s monitored seabirds, 1950–2010. PLoS One 10: e0129342. doi:10.1371/journal.pone.0129342
PARK, M. 2020. Spatial distribution analysis of Black-legged Kittiwakes and Northern Fulmars in Svalbard coastal cliffs using remotely piloted aircraft system. MSc Thesis. Seoul, South Korea: Seoul National University.
PARKER, G.C. & REXER-HUBER, K. 2020. Drone-based Salvin’s Albatross Population Assessment: Feasibility at the Bounty Islands. Dunedin, New Zealand: Conservation Services Programme, Department of Conservation.
PFEIFER, C., BARBOSA, A., MUSTAFA, O., PETER, H.-U., RÜMMLER, M.-C. & BRENNING, A. 2019. Using fixed-wing UAV for detecting and mapping the distribution and abundance of penguins on the South Shetlands Islands, Antarctica. Drones 3: 39. doi:10.3390/drones3020039
RADJAWALI, I., PYE, O. & FLITNER, M. 2017. Recognition through reconnaissance? Using drones for counter-mapping in Indonesia. Journal of Peasant Studies 44: 817–833. doi:10.1080/03066150.2016.1264937
RAOULT, V., COLEFAX, A.P., ALLAN, B.M., ET AL. 2020. Operational protocols for the use of drones in marine animal research. Drones 4: 64. doi:10.3390/drones4040064
RATCLIFFE, N., GUIHEN, D., ROBST, J., CROFTS, S., STANWORTH, A. & ENDERLEIN, P. 2015. A protocol for the aerial survey of penguin colonies using UAVs. Journal of Unmanned Vehicle Systems 3: 95–101. doi:10.1139/juvs-2015-0006
REINTSMA, K.M., MCGOWAN, P.C., CALLAHAN, C. ET AL. 2018. Preliminary evaluation of behavioral response of nesting waterbirds to small unmanned aircraft flight. Waterbirds 41: 326–331. doi:10.1675/063.041.0314
REXER-HUBER K., PARKER K.A., PARKER G.C. 2020. Campbell Island Seabirds: Operation Endurance November 2019. Dunedin, New Zealand: Marine and Species Threats, Department of Conservation.
ROCHMAN, C.M., BROWNE, M.A., UNDERWOOD, A.J. ET AL. 2016. The ecological impacts of marine debris: unraveling the demonstrated evidence from what is perceived. Ecology 97: 302–312. doi:10.1890/14-2070.1.
ROMAN, L., KASTURY, F., PETIT, S. ET AL. 2020. Plastic, nutrition and pollution; relationships between ingested plastic and metal concentrations in the livers of two Pachyptila seabirds. Scientific Reports 10: 18023. doi:10.1038/s41598-020-75024-6
ROSS, K.E, BALMER, D.E, HUMPHREYS, E., AUSTIN, G., GODDARD, B. & REHFISCH, M. 2016. Urban Breeding Gull Surveys: A Review of Methods and Options for Survey Design. Thetford, UK: British Trust for Ornithology.
RÜMMLER, M.-C., ESEFELD, J., PFEIFER, C. & MUSTAFA, O. 2021. Effects of UAV overflight height, UAV type, and season on the behaviour of Emperor penguin adults and chicks. Remote Sensing Applications: Society and Environment 23: 100558. doi:10.1016/j.rsase.2021.100558
RÜMMLER, M.-C., MUSTAFA, O., MAERCKER, J., PETER, H.-U. & ESEFELD, J. 2016. Measuring the influence of unmanned aerial vehicles on Adélie penguins. Polar Biology 39: 1329–1334. doi:10.1007/s00300-015-1838-1
RÜMMLER, M.-C., MUSTAFA, O., MAERCKER, J., PETER, H.-U. & ESEFELD, J. 2018. Sensitivity of Adélie and Gentoo penguins to various flight activities of a micro UAV. Polar Biology 41: 2481–2493. doi:10.1007/s00300-018-2385-3
RUSH, G.P., CLARKE, L.E., STONE, M. & WOOD, M.J. 2018. Can drones count gulls? Minimal disturbance and semiautomated image processing with an unmanned aerial vehicle for colony-nesting seabirds. Ecology and Evolution 8: 12322–12334. doi:10.1002/ece3.4495
SARDÀ‐PALOMERA, F., BOTA, G., VIÑOLO, C. ET AL. 2012. Fine-scale bird monitoring from light unmanned aircraft systems. Ibis 154: 177–183. doi:10.1111/j.1474-919X.2011.01177.x
SCARTON, F. & VALLE, R. 2021. Drone assessment of habitat selection and breeding success of Gull-billed Tern Gelochelidon nilotica nesting on low-accessibility sites: a case study. Rivista Italiana di Ornitologia 90: 69–76. doi:10.4081/rio.2020.475
SCARTON, F. & VALLE, R. G. 2022. Comparison of drone vs. ground survey monitoring of hatching success in the black-headed gull (Chroicocephalus ridibundus). Ornithology Research 30: 271–280. doi:10.1007/s43388-022-00112-2
SHEWRING, M.P. & VAFIDIS, J.O. 2021. Using UAV-mounted thermal cameras to detect the presence of nesting nightjar in upland clear-fell: A case study in South Wales, UK. Ecological Solutions and Evidence 2: e12052. doi:10.1002/2688-8319.12052
SINCLAIR, N.C., HARRIS, M.P., NAGER, R.G., LEAKEY, C.D.B. & ROBBINS, A.M. 2017. Nocturnal colony attendance by common guillemots Uria aalge at colony in Shetland during the pre-breeding season. Seabird 30: 51–62.
SWANSON, A., KOSMALA, M., LINTOTT, C. & PACKER, C. 2016. A generalized approach for producing, quantifying, and validating citizen science data from wildlife images. Conservation Biology 30: 520–531. doi:10.1111/cobi.12695
THAXTER, C.B. & BURTON, N.H.K. 2009. High Definition Imagery for Surveying Seabirds and Marine Mammals: A Review of Recent Trials and Development of Protocols. Thetford, UK: British Trust for Ornithology.
VACCA, A. & ONISHI, H. 2017. Drones: military weapons, surveillance or mapping tools for environmental monitoring? The need for legal framework is required. Transportation Research Procedia 25: 51–62. doi:10.1016/j.trpro.2017.05.209
VALLE, R.G. & SCARTON, F. 2021a. Drone-conducted counts as a tool for the rapid assessment of productivity of Sandwich Terns (Thalasseus sandvicensis). Journal of Ornithology 162: 621–628. doi:10.1007/s10336-020-01854-w
VALLE, R. G. & SCARTON, F. 2021b. Monitoring the hatching success of gulls Laridae and terns Sternidae: A comparison of ground and drone methods. Acta Ornithologica 56: 241–254. doi:10.3161/00016454AO2021.56.2.010
VAS, E., LESCROËL, A., DURIEZ, O., BOGUSZEWSKI, G. & GRÉMILLET, D. 2015. Approaching birds with drones: first experiments and ethical guidelines. Biology Letters 11: 20140754. doi:10.1098/rsbl.2014.0754
VERFUSS, U.K., ANICETO, A.S., HARRIS, D.V. ET AL. 2019. A review of unmanned vehicles for the detection and monitoring of marine fauna. Marine Pollution Bulletin 140: 17–29. doi:10.1016/j.marpolbul.2019.01.009
VILLEGAS, P., MENA, L., CONSTANTINE, A., VILLALBA, R. & OCHOA, D. 2018. Data imaging acquisition and processing as a methodology for estimating the population of frigates using UAVs. 2018 IEEE ANDESCON, 1–4. doi:10.1109/ANDESCON.2018.8564660
WALSH, P.M., HALLEY, D.J., HARRIS, M.P., DEL NEVO, A., SIM, I.M.W. & TASKER, M.L. 1995. Seabird Monitoring Handbook for Britain and Ireland. Peterborough, UK: JNCC /RSPB /ITE /Seabird Group.
WALUDA, C.M., DUNN, M.J., CURTIS, M.L. & FRETWELL, P.T. 2014. Assessing penguin colony size and distribution using digital mapping and satellite remote sensing. Polar Biology 37: 1849–1855. doi:10.1007/s00300-014-1566-y
WANG, D., SHAO, Q. & YUE, H. 2019. Surveying wild animals from satellites, manned aircraft and Unmanned Aerial Systems (UASs): A Review. Remote Sensing 11: 1308. doi:10.3390/rs11111308
WEIMERSKIRCH, H., PRUDOR, A. & SCHULL, Q. 2018. Flights of drones over sub-Antarctic seabirds show species- and status-specific behavioural and physiological responses. Polar Biology 41: 259–266. doi:10.1007/s00300-017-2187-z
WEINSTEIN, B.G., GARNER, L., SACCOMANNO, V.R., ET AL. 2021. A general deep learning model for bird detection in high resolution airborne imagery. Ecological Applications 32: e2694. doi:10.1002/eap.2694
WITCZUK, J, PAGACZ, S., ZMARZ, A. & CYPEL, M. 2018. Exploring the feasibility of unmanned aerial vehicles and thermal imaging for ungulate surveys in forests - preliminary results. International Journal of Remote Sensing 39: 15–16. doi:10.1080/01431161.2017.1390621
WOOD, M. J. 2022. Using UAVs in seabird research & monitoring: workshop at the 14th International Seabird Group Conference 2018. [Accessed at https://doi.org/10.17605/OSF.IO/2MJVX on 28 January 2023.] doi:10.17605/OSF.IO/2MJVX
ZOONIVERSE 2021. Welcome to the Zooniverse. [Accessed at https://www.zooniverse.org/ on 11 November 2021.]