Reference
Boehmke, B., & Greenwell, B. (2019). Hands-On Machine Learning
with R .
Bom, R. A., Bouten, W., Piersma, T., Oosterbeek, K., & van Gils, J. A.
(2014). Optimizing acceleration-based ethograms: the use of
variable-time versus fixed-time segmentation. Movement Ecology,
2 (1), 6. doi:10.1186/2051-3933-2-6
Brown, D. D., Kays, R., Wikelski, M., Wilson, R., & Klimley, A. P.
(2013). Observing the unwatchable through acceleration logging of animal
behavior. Animal Biotelemetry, 1 (1), 20.
doi:10.1186/2050-3385-1-20
Chakravarty, P., Cozzi, G., Ozgul, A., & Aminian, K. (2019). A novel
biomechanical approach for animal behaviour recognition using
accelerometers. Methods in Ecology and Evolution, 10 (6), 802-814.
doi:10.1111/2041-210X.13172
Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting
System. Proceedings of the 22nd acm sigkdd international
conference on knowledge discovery and data mining , 785-794.
doi:10.1145/2939672.2939785
Fehlmann, G., O’Riain, M. J., Hopkins, P. W., O’Sullivan, J., Holton, M.
D., Shepard, E. L. C., & King, A. J. (2017). Identification of
behaviours from accelerometer data in a wild social primate.Animal Biotelemetry, 5 (1), 6. doi:10.1186/s40317-017-0121-3
Gilbert, N. I., Correia, R. A., Silva, J. P., Pacheco, C., Catry, I.,
Atkinson, P. W., . . . Franco, A. M. A. (2016). Are white storks
addicted to junk food? Impacts of landfill use on the movement and
behaviour of resident white storks (Ciconia ciconia) from a partially
migratory population. Movement Ecology, 4 (1), 7.
doi:10.1186/s40462-016-0070-0
Joo, R., Boone, M. E., Clay, T. A., Patrick, S. C., Clusella-Trullas,
S., & Basille, M. (2020). Navigating through the r packages for
movement. Journal of Animal Ecology, 89 (1), 248-267.
doi:10.1111/1365-2656.13116
Kölzsch, A., Neefjes, M., Barkway, J., Müskens, G. J. D. M., van
Langevelde, F., de Boer, W. F., . . . Nolet, B. A. (2016). Neckband or
backpack? Differences in tag design and their effects on
GPS/accelerometer tracking results in large waterbirds. Animal
Biotelemetry, 4 (1), 13. doi:10.1186/s40317-016-0104-9
Korpela, J., Suzuki, H., Matsumoto, S., Mizutani, Y., Samejima, M.,
Maekawa, T., . . . Yoda, K. (2020). Machine learning enables improved
runtime and precision for bio-loggers on seabirds. Commun Biol,
3 (1), 633. doi:10.1038/s42003-020-01356-8
Kröschel, M., Reineking, B. r., Werwie, F., Wildi, F., & Storch, I.
(2017). Remote monitoring of vigilance behavior in large herbivores
using acceleration data. Anim Biotelemetry, 5 (10). doi:DOI
10.1186/s40317-017-0125-z
Ladds, M. A., Thompson, A. P., Kadar, J.-P., J Slip, D., P Hocking, D.,
& G Harcourt, R. (2017). Super machine learning: improving accuracy and
reducing variance of behaviour classification from accelerometry.Animal Biotelemetry, 5 (1), 8. doi:10.1186/s40317-017-0123-1
McInnes, L., Healy, J., Saul, N., & Grossberger, L. (2018). UMAP:
Uniform Manifold Approximation and Projection. Journal of Open
Source Software, 3 , 861. doi:10.21105/joss.00861
Nathan, R., Spiegel, O., Fortmann-Roe, S., Harel, R., Wikelski, M., &
Getz, W. M. (2012). Using tri-axial acceleration data to identify
behavioral modes of free-ranging animals: general concepts and tools
illustrated for griffon vultures. Journal of Experimental Biology,
215 (6), 986-996. doi:10.1242/jeb.058602
Nuijten, R. J. M., Gerrits, T., Shamoun-Baranes, J., & Nolet, B. A.
(2020). Less is more: On-board lossy compression of accelerometer data
increases biologging capacity. Journal of Animal Ecology, 89 (1),
237-247. doi:10.1111/1365-2656.13164
Resheff, Y. S., Rotics, S., Harel, R., Spiegel, O., & Nathan, R.
(2014). AcceleRater: a web application for supervised learning of
behavioral modes from acceleration measurements. Movement Ecology,
2 (1), 27. doi:10.1186/s40462-014-0027-0
Ropert-Coudert, Y., & Wilson, R. P. (2005). Trends and perspectives in
animal-attached remote sensing. Frontiers in Ecology and the
Environment, 3 (8), 437-444. doi:Doi 10.2307/3868660
Sainburg, T., Theilman, B., Thielk, M., & Gentner, T. Q. (2019).
Parallels in the sequential organization of birdsong and human speech.Nature Communications, 10 (1), 3636.
doi:10.1038/s41467-019-11605-y
Shamoun-Baranes, J., Bom, R., van Loon, E. E., Ens, B. J., Oosterbeek,
K., & Bouten, W. (2012). From sensor data to animal behaviour: an
oystercatcher example. Plos One, 7 (5), e37997.
doi:10.1371/journal.pone.0037997
Shepard, E. L., Wilson, R. P., Quintana, F., Laich, A. G., Liebsch, N.,
Albareda, D. A., . . . Myers, A. E. (2008). Identification of animal
movement patterns using tri-axial accelerometry. Endangered
Species Research, 10 , 47-60. doi:10.3354/esr00084
Toloşi, L., & Lengauer, T. (2011). Classification with correlated
features: unreliability of feature ranking and solutions.Bioinformatics, 27 (14), 1986-1994.
doi:10.1093/bioinformatics/btr300
Williams, H. J., Taylor, L. A., Benhamou, S., Bijleveld, A. I., Clay, T.
A., de Grissac, S., . . . Börger, L. (2019). Optimizing the use of
biologgers for movement ecology research. Journal of Animal
Ecology, n/a (n/a). doi:10.1111/1365-2656.13094
Wilson, R., Börger, L., Holton, M., Michael Scantlebury, D., Laich, A.,
Quintana, F., . . . Shepard, E. (2019). Estimates for energy
expenditure in free-living animals using acceleration proxies: A
reappraisal .