Eighteen-month Postdoctoral Scholar awards are offered to recipients of new or recent doctorates in the fields of chemistry, engineering, geology, geophysics, mathematics, meteorology, physics, and bi...

YouTube video by La salud de la humanidad

For the first time, we asked the world what it wants from a future where AI helps us understand animal communication. Surveying 1,057 people across 67 countries, we discovered remarkable global consen...

Understanding the role of humans as ‘ecosystem engineers’ requires a deep-time perspective rooted in evolutionary history and the fossil record. However, no conceptual framework exists for studying the rise of ecosystem engineering in deep time, requiring us to consider effects that fall outside the scope of traditional definitions. Here, we present a new framework applicable to both modern and ancient engineering-type effects. We propose a new term – ‘Earth system engineering’ – to describe biological processes that alter the structure and function of planetary spheres, and which combines core tenets of ecosystem engineering, niche construction, and legacy effects. We illustrate this framework using the fossil record, and show how it can be applied across the tree of life, and throughout Earth history.

Biodiversity Citizen Science & Monitoring

Bioacoustic Data Analysis (BIAC05) equips researchers with the skills to analyse animal acoustic signals in R. Covering spectrograms, signal structure, annotation workflows, and acoustic diversity met...

Automated bioacoustic analysis is essential for biodiversity monitoring and conservation, requiring advanced deep learning models that can adapt to diverse bioacoustic tasks. This article presents a comprehensive review of large-scale pretrained bioacoustic foundation models and systematically investigates their transferability across multiple bioacoustic classification tasks. We overview bioacoustic representation learning including major pretraining data sources and benchmarks. On this basis, we review bioacoustic foundation models by thoroughly analysing design decisions such as model architecture, pretraining scheme, and training paradigm. Additionally, we evaluate selected foundation models on classification tasks from the BEANS and BirdSet benchmarks, comparing the generalisability of learned representations under both linear and attentive probing strategies. Our comprehensive experimental analysis reveals that BirdMAE, trained on large-scale bird song data with a self-supervised objective, achieves the best performance on the BirdSet benchmark. On BEANS, BEATs$_{NLM}$, the extracted encoder of the NatureLM-audio large audio model, is slightly better. Both transformer-based models require attentive probing to extract the full performance of their representations. ConvNext$_{BS}$ and Perch models trained with supervision on large-scale bird song data remain competitive for passive acoustic monitoring classification tasks of BirdSet in linear probing settings. Training a new linear classifier has clear advantages over evaluating these models without further training. While on BEANS, the baseline model BEATs trained with self-supervision on AudioSet outperforms bird-specific models when evaluated with attentive probing. These findings provide valuable guidance for practitioners selecting appropriate models to adapt them to new bioacoustic classification tasks via probing.

Aunque diversos responsables del PP valenciano han tratado de usar en su favor diversas frases descontextualizadas de la reciente entrevista a José Ángel Núñez en À Punt, la realidad es que su interve...