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Vallas, B.

Publications and source records attributed to Vallas, B..

2 recordsLinked to original sources

Early-life stress as a potential resilience factor in king penguins

Climate change-driven environmental variability can cause species to shift their phenology, leading to an increase in phenological mismatches. Whether the fitness cost of such mismatches can be mitigated by plasticity remains poorly understood, especially in the context of wild populations. In this study, we quantified responses to phenological mismatch using capture-mark-recapture, morphological, and transcriptomic data from king penguins hatching during and after the seasonal peak of food availability (i.e., born early and late in the breeding season, respectively). Late-born chicks showed reduced survival to fledging and fledged smaller and in slightly poorer body condition than chicks born during the seasonal peak in food availability. Yet post-fledging return rates did not differ between late- and early-born chicks, suggesting that early-life stress was effectively mitigated among individuals that survived to fledging, with no detectable carryover effects in the first years post-fledging. Genes involved in oxidative stress response, homeostasis, and nutrient-sensing were differentially expressed between early- and late-born chicks, consistent with stress mitigation and homeostasis maintenance at the expense of growth in late-born chicks. A co-expression gene cluster whose primary expression pattern was associated with hatching phenology also contained central genes involved in stress mitigation. Together, our findings highlight molecular mechanisms that buffer the fitness costs of phenological mismatch, which may allow species to tolerate mismatch to some extent. Despite higher winter mortality of individuals born in mismatch, the plastic response observed in survivors hints towards molecular adaptations that could allow species resilience under increasing climate-driven environmental unpredictability.

genomics↗

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↗