Authors
MAYA E. PHILIPP1,2, ABRAM B. FLEISHMAN3, JEFF SCHLUETER3, GIANNIRA ÁLVAREZ4, BENJAMIN GALLARDO4, PABLO GUTIÉRREZ4, RONNY PEREDO4, BRAD KEITT5, & FERNANDO MEDRANO4
1Department of Biology, Syracuse University, 114 Life Sciences Complex, Syracuse, New York, 13244, USA (mayaphilipp1001@gmail.com)
2Scripps Institution of Oceanography, University of California - San Diego, 8622 Kennel Way, La Jolla, California, 92037, USA
3Conservation Metrics, Inc, 145 McAllister Way, Santa Cruz, California, 95060, USA
4Red de Observadores de Aves y Vida Silvestre de Chile, Chile
5American Bird Conservancy, 4301 Connecticut Ave, NW Suite 451, Washington D.C., 20008, USA
Citation
Philipp, M. E., Fleishman, A. B., Schlueter, J., Álvarez, G., Gallardo, B., Gutiérrez, P., Peredo, R., Keitt, B., & Medrano, F. (2026). Diel and seasonal patterns of Markham’s Storm Petrel Hydrobates markhami.
Marine Ornithology, 54(1),
1-9.
http://doi.org/10.5038/2074-1235.54.1.1675
Key words: automated recording unit, bioacoustics, Convolutional Neural Network (CNN), ecology, passive acoustic monitoring, storm petrel
Abstract
Markham's Storm Petrel Hydrobates markhami is endemic to the Humboldt Current, breeding inland in Chile and Peru. Its nesting habitat in Atacama Desert saltpeter deposits overlaps with light pollution, roadways, and other anthropogenic disturbances—factors that may impact the species' populations. Owing to its remote nesting locations and nocturnal behavior, finding and monitoring its colonies is challenging. In fact, the first colonies were discovered only recently. In this study, we assessed whether passive acoustic monitoring of this species detects the same activity patterns as active nest monitoring. Acoustic monitors were placed at eight sites within colonies at Caleta Buena (Tarapacá Region) and Pampa Chaca (Arica Region), both located in the Atacama Desert, Chile. Acoustic data were processed with a trained Convolutional Neural Network detection model and analyzed to investigate diel and seasonal patterns. We then compared the call rates in areas with different burrow densities to assess this approach against active nest monitoring. We found that the mean call rate for each site was correlated with estimated nest densities. In both colonies, the seasonal pattern of vocal activity was similar to that reported in the literature based on active monitoring. This study also provides the first description of the species' diel activity: birds of Caleta Buena became active between 102.9 ± 49.6 and 344.6 ± 47.9 min after sunset, and birds from Pampa Chaca were active between 86. 7 ± 23.0 and 187.2 ± 66.6 min after sunset. We conclude that passive acoustic monitoring may be effective for estimating the relative density of Markham's Storm Petrel, especially in the early breeding season. Future monitoring in known colony areas should deploy recorders for longer periods to clarify breeding seasons and compare across colonies.
References
Ainley, D. G., Divoky, G. J., Baird, P., & Spencer, G. C. (2024). 'Floating populations' of seabirds: The bane of demographic modelers and managers.
Marine Ornithology,
52(2), 375-382.
http://doi.org/10.5038/2074-1235.52.2.1589
Arneill, G. E., Critchley, E. J., Wischnewshi, S., Jessopp, M. J., & Quinn, J. L. (2020). Acoustic activity across a seabird colony reflects patterns of within‐colony flight rather than nest density.
Ibis,
162(2), 416-428.
https://doi.org/10.1111/ibi.12740
Barros, R., Medrano, F., Norambuena, H. V., Peredo, R., Silva, R., de Groote, F., & Schmitt, F. (2019). Breeding phenology, distribution and conservation status of Markham's Storm-Petrel
Oceanodroma markhami in the Atacama Desert.
Ardea,
107(1), 75-84.
https://doi.org/10.5253/arde.v107i1.a1
Borker, A. L., McKown, M. W., Ackerman, J. T., Eagles‐Smith, C. A., Tershy, B. R., & Croll, D. A. (2014). Vocal activity as a low cost and scalable index of seabird colony size.
Conservation Biology,
28(4), 1100-1108.
https://doi.org/10.1111/cobi.12264
Brooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A., Berg, C. W., Nielsen, A., Skaug, H. J., Maechler, M., & Bolker, B. M. (2017). glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling.
The R Journal,
9(2), 378-400.
https://doi.org/10.32614/RJ-2017-066
Buxton, R. T., & Jones, I. L. (2012). Measuring nocturnal seabird activity and status using acoustic recording devices: Applications for island restoration.
Journal of Field Ornithology,
83(1), 47-60.
https://doi.org/10.1111/j.1557-9263.2011.00355.x
Cichy, R., Khosla, A., Pantazis, D., Torralba, A., & Oliva, A. (2016). Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence.
Scientific Reports,
6, Article 27755.
https://doi.org/10.1038/srep27755
Deng, L., Hinton, G., & Kingsbury, B. (2013). New types of deep neural network learning for speech recognition and related applications: An overview.
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 8599-8603.
https://doi.org/10.1109/ICASSP.2013.6639344
Dunleavy, K., Roberts, P., Fleishman, A., & McKown, M. (2018). Automated acoustic surveys for Ancient Murrelet (Synthliboramphus antiquus), Fork-tailed Storm-petrel (Oceanodroma furcata), and Leach's Storm-petrel (Oceanodroma leucorhoa) at 5 colonies in Haida Gwaii, British Columbia [Unpublished report]. Environment and Climate Change Canada.
Gallardo, B., Vizcarra, J. K., Peredo, R., Gutiérrez, P., Contardo, N., Arcco, A., & Medrano, F. (2023). Descubrimiento del primer sitio de reproducción de Golondrina de Mar Negra (Hydrobates markhami) en el extremo sur del Perú.
Hornero,
38(2), 63-69.
https://dx.doi.org/10.56178/eh.v38i2.1438
Harrison, X. A., Donaldson, L., Correa-Cano, M. E., Evans, J., Fisher, D. N., Goodwin, C. E. D., Robinson, B. S., Hodgson, D. J., & Inger, R. (2018). A brief introduction to mixed effects modelling and multi-model inference in ecology.
PeerJ,
6, Article e4794.
https://doi.org/10.7717/peerj.4794
Hill, A. P., Prince, P., Snaddon, J. L., Doncaster, C. P., & Rogers, A. (2019).
AudioMoth: A low-cost acoustic device for monitoring biodiversity and the environment. HardwareX, 6, Article e00073.
https://doi.org/10.1016/j.ohx.2019.e00073