Seabird Sessions 18

grant-humphriesGrant RW Humphries
  • 10 Aug

Hi all,

After a week long vacation, Seabird Sessions 18 is coming up this week! Wednesday August 12th at 1600 GMT. David G is in the field this week (lucky guy) - so we will be joined by Virginia Morera-Pujol.

Our two papers this week:

Bastos, R., Martins, B., Cabral, J.A., Ceia, F.R., Ramos, J.A., Paiva, V.H., Luís, A. and Santos, M., 2020. Oceans of stimuli: an individual-based model to assess the role of olfactory cues and local enhancement in seabirds’ foraging behaviour. Animal Cognition, pp.1-14.

Abstract: Oceans are extremely dynamic environments, which poses challenges for top-predators such as seabirds to find food resources. Yet, seabirds evolved sensorial abilities (olfactory senses) along with complex behaviours (social information transfer through local enhancement) to improve foraging efficiency. Using the Cory’s shearwater (Calonectris borealis) as a model species, we developed an individual-based model to explore the complementary role of different searching mechanisms (olfactory foraging and local enhancement) for the optimal foraging behaviour of pelagic seabirds during 1-day foraging trips around breeding colonies. Model outputs were compared with observed patterns of Cory’s shearwaters distribution during local foraging trips. Also, the foraging efficiency of virtual individuals was analysed considering hypothetical scenarios of foraging conditions and densities of foraging individuals around breeding colonies. The results support the use of a combination of searching strategies by Cory's shearwaters, which produced representative patterns of space use from tracked individuals, including spatial foraging segregation of neighbouring sub-colonies. Furthermore, while the mechanisms underpinning local enhancement played a key role in mitigating sub-optimal foraging conditions, the use of olfactory senses conferred great adaptive foraging advantages over a wide range of environmental conditions. Our results also indicate a synergistic effect between the two strategies, which suggests that a multimodal foraging strategy is useful to forage in extremely dynamic environments. The developed model provides a basis for further investigation regarding the role of foraging mechanisms in the population dynamics of colonial animals, including the adaptive foraging behaviour of marine top predators to dynamic environmental conditions.


Bowler, E., Fretwell, P.T., French, G. and Mackiewicz, M., 2020. Using Deep Learning to Count Albatrosses from Space: Assessing Results in Light of Ground Truth Uncertainty. Remote Sensing, 12(12), p.2026.

ABSTRACT: Many wildlife species inhabit inaccessible environments, limiting researchers ability to conduct essential population surveys. Recently, very high resolution (sub-metre) satellite imagery has enabled remote monitoring of certain species directly from space; however, manual analysis of the imagery is time-consuming, expensive and subjective. State-of-the-art deep learning approaches can automate this process; however, often image datasets are small, and uncertainty in ground truth labels can affect supervised training schemes and the interpretation of errors. In this paper, we investigate these challenges by conducting both manual and automated counts of nesting Wandering Albatrosses on four separate islands, captured by the 31 cm resolution WorldView-3 sensor. We collect counts from six observers, and train a convolutional neural network (U-Net) using leave-one-island-out cross-validation and different combinations of ground truth labels. We show that (1) interobserver variation in manual counts is significant and differs between the four islands, (2) the small dataset can limit the networks ability to generalise to unseen imagery and (3) the choice of ground truth labels can have a significant impact on our assessment of network performance. Our final results show the network detects albatrosses as accurately as human observers for two of the islands, while in the other two misclassifications are largely caused by the presence of noise, cloud cover and habitat, which was not present in the training dataset. While the results show promise, we stress the importance of considering these factors for any study where data is limited and observer confidence is variable.

The ZOOM link will be available online at 1545 GMT on August 12th.

Looking forward to seeing you there!