Goldfish phenomics reveals commonalities and a lack of universality in the domestication process for ornamentation.

Domestication process effects are manifold, affecting genotype and phenotype, and assumed to be universal in animals by part of the scientific community. While mammals and birds have been thoroughly investigated, from taming to intensive selective breeding, fish domestication remains comparatively unstudied. The most widely bred and traded ornamental fish species worldwide, the goldfish, underwent the effect of long-term artificial selection on differing skeletal and soft tissue modules through ornamental domestication.

Atolls are globally important sites for tropical seabirds.

Seabirds play critical roles on islands. By catalysing terrestrial and marine productivity through guano nutrient input, seabirds support natural island functioning. In the Indo-Pacific, atolls comprise one-third of all islands but only ~0.

Rethinking atoll futures: local resilience to global challenges.

Atoll islands are often perceived as inevitably lost due to rising sea levels. However, unlike other islands, atoll islands are dynamic landforms that have evolved, at least historically, to vertically accrete at a pace commensurate with changing sea levels. Rather than atoll islands' low elevation per se, the impairment of natural accretion processes is jeopardising their persistence.

Liquid argon light collection and veto modeling in GERDA Phase II.

The ability to detect liquid argon scintillation light from within a densely packed high-purity germanium detector array allowed the Gerda experiment to reach an exceptionally low background rate in the search for neutrinoless double beta decay of Ge. Proper modeling of the light propagation throughout the experimental setup, from any origin in the liquid argon volume to its eventual detection by the novel light read-out system, provides insight into the rejection capability and is a necessary ingredient to obtain robust background predictions. In this paper, we present a model of the Gerda liquid argon veto, as obtained by Monte Carlo simulations and constrained by calibration data, and highlight its application for background decomposition.

A general deep learning model for bird detection in high-resolution airborne imagery.

Advances in artificial intelligence for computer vision hold great promise for increasing the scales at which ecological systems can be studied. The distribution and behavior of individuals is central to ecology, and computer vision using deep neural networks can learn to detect individual objects in imagery. However, developing supervised models for ecological monitoring is challenging because it requires large amounts of human-labeled training data, requires advanced technical expertise and computational infrastructure, and is prone to overfitting.