Biology subjects

Barracho, T.

Publications and source records attributed to Barracho, T..

5 recordsLinked to original sources

bioRxiv2025

Multiannual environmental forcing shapes breeding phenology and success in a subantarctic seabird

Climate-driven phenological mismatches threaten avian reproduction by disrupting food availability during critical breeding stages. In marine ecosystems, time lags between environmental changes and their effects on food webs are challenging to model, yet they can have a profound impact on top-predator reproduction. We disentangle how oceanic and climatic variability influence the breeding phenology and success of a keystone seabird of the Southern Ocean drawing on 24 years of data from 17,000 marked king penguins. We document an exceptional 19-day advancement in breeding phenology, alongside increased breeding success (44% in 2000, 62% in 2023). A sliding-window analysis reveals that sea temperature and primary production in key foraging zones predict both phenology and breeding success, with lags ranging from several weeks to two years. While king penguins appear to be keeping pace with current changes, their dependence on multi-year environmental conditions underscores the vulnerability of top predators to unpredictable, fast-changing, and more frequent extreme conditions.

ecology↗

bioRxiv2025

Flexible foraging effort shapes Adelie penguin reproductive success across Antarctica and sea ice conditions

Understanding how species deal with diverse environmental conditions is crucial for predicting their future responses to climate change. In Antarctica, changing sea ice dynamics threaten ice-dependent species. Whilebehavioural adjustmentscould help mitigate these threats, their effectiveness under future environmental conditions and potential thresholds remain unclear. Long-term studies and circumpolar approaches could address this challenge. Our 15-year study (2010-2024) of over 23,000 foraging trips from Adelie penguins (Pygoscelis adeliae) in East Antarctica, identified a non-linear relationship between sea ice concentration (SIC) and foraging effort when landfast ice was absent, a situation anticipated to define the species future habitat during chick-rearing. Minimal effort occurred at 10-20% SIC, but more than doubled with the presence of landfast ice or heavy pack-ice, with females showing greater sensitivity to these ice changes. Acircumpolar analysis of similar data demonstrated that foraging effort during chick-guarding predicted reproductive success across ten Adelie penguin populations. Reproductive success remained high until foraging trips exceeded approximately 29 hours, after which performance declined sharply. This indicates that while behavioural flexibility can buffer against moderate environmental changes, it has clear limits. Our findings establish foraging trip duration as a valuable early warning indicator for population responses to environmental change.

ecology↗

bioRxiv2024

Declining juvenile survival of Adelie penguins in Antarctica

As summer sea ice around Antarctica reaches modern lows, quantifying the demographic response of polar species to such environmental changes becomes critical. To achieve this, synthesizing results across species ranges and elucidating the environmental factors driving population dynamics are key. Adelie penguins are considered reliable indicators of changes in Antarctica but the processes through which sea ice and other environmental factors shape their population dynamics are still unclear, especially for critical age groups such as juveniles. Using a 17-year dataset of Adelie penguins electronically tagged in Adelie Land (Antarctica), we found that juvenile survival probability was most impacted by sea ice concentration near their natal colony right after fledging, with lower ice concentrations detrimental to survival. Importantly, we found that juvenile survival declined by 32% from 2007 to 2020, mirroring trends at other distant colonies. The emergence of similar patterns at opposite ends of the continent may be an early signal for shifts in population trends expected from climate change.

ecology↗

bioRxiv2023

Remote sensing of emperor penguin abundance and breeding success

Emperor penguins (Aptenodytes forsteri) are under increasing environmental pressure. Monitoring colony size and trends of this Antarctic seabird relies primarily on satellite imagery recorded near the end of the breeding season, when illumination levels are sufficient to capture images, but colony occupancy is highly variable. To correct population estimates for this variability, we develop a phenological model that accurately predicts the number of breeding pairs and fledging chicks, as well as key phenological events such as arrival, hatching and foraging times, from as few as six data points from a single season. The ability to extrapolate occupancy from sparse data makes the model particularly useful for monitoring remotely sensed animal colonies where ground-based population estimates are very rare or unavailable. TeaserThe Emperor penguin becomes the Southern Oceans canary in a coal mine through remote sensing its annual breeding success.

ecology↗

bioRxiv2023

RFIDeep: Unfolding the Potential of Deep Learning forRadio-Frequency Identification

O_LIAutomatic monitoring of wildlife is becoming a critical tool in the field of ecology. In particular, Radio-Frequency IDentification (RFID) is now a widespread technology to assess the phenology, breeding, and survival of many species. While RFID produces massive datasets, no established fast and accurate methods are yet available for this type of data processing. Deep learning approaches have been used to overcome similar problems in other scientific fields and hence might hold the potential to overcome these analytical challenges and unlock the full potential of RFID studies. C_LIO_LIWe present a deep learning workflow, coined "RFIDeep", to derive ecological features, such as breeding status and outcome, from RFID mark-recapture data. To demonstrate the performance of RFIDeep with complex datasets, we used a long-term automatic monitoring of a long-lived seabird that breeds in densely packed colonies, hence with many daily entries and exits. C_LIO_LITo determine individual breeding status and phenology and for each breeding season, we first developed a one-dimensional convolution neural network (1D-CNN) architecture. Second, to account for variance in breeding phenology and technical limitations of field data acquisition, we built a new data augmentation step mimicking a shift in breeding dates and missing RFID detections, a common issue with RFIDs. Third, to identify the segments of the breeding activity used during classification, we also included a visualisation tool, which allows users to understand what is usually considered a "black box" step of deep learning. With these three steps, we achieved a high accuracy for all breeding parameters: breeding status accuracy = 96.3%; phenological accuracy = 86.9%; breeding success accuracy = 97.3%. C_LIO_LIRFIDeep has unfolded the potential of artificial intelligence for tracking changes in animal populations, multiplying the benefit of automated mark-recapture monitoring of undisturbed wildlife populations. RFIDeep is an open source code to facilitate the use, adaptation, or enhancement of RFID data in a wide variety of species. In addition to a tremendous time saving for analyzing these large datasets, our study shows the capacities of CNN models to autonomously detect ecologically meaningful patterns in data through visualisation techniques, which are seldom used in ecology. C_LI

ecology↗