Modelling reindeer rut activity using on-animal acoustic recorders and machine learning.

For decades, researchers have employed sound to study the biology of wildlife, with the aim to better understand their ecology and behaviour. By utilizing on-animal recorders to capture audio from freely moving animals, scientists can decipher the vocalizations and glean insights into their behaviour and ecosystem dynamics through advanced signal processing. However, the laborious task of sorting through extensive audio recordings has been a major bottleneck.

Microhabitat conditions drive uncertainty of risk and shape neophobic responses in Trinidadian guppies, .

In response to uncertain risks, prey may rely on neophobic phenotypes to reduce the costs associated with the lack of information regarding local conditions. Neophobia has been shown to be driven by information reliability, ambient risk and predator diversity, all of which shape uncertainty of risk. We similarly expect environmental conditions to shape uncertainty by interfering with information availability.

Can species naming drive scientific attention? A perspective from plant-feeding arthropods.

How do researchers choose their study species? Some choices are based on ecological or economic importance, some on ease of study, some on tradition-but could the name of a species influence researcher decisions? We asked whether phytophagous arthropod species named after their host plants were more likely to be assayed for host-associated genetic differentiation (or 'HAD'; the evolution of cryptic, genetically isolated host specialists within an apparently more generalist lineage). We chose 30 arthropod species (from a Google Scholar search) for which a HAD hypothesis has been tested. We traced the etymologies of species names in the 30 corresponding genera, and asked whether HAD tests were more frequent among species whose etymologies were based on host-plant names (e.

Demographic resilience of brook trout populations subjected to experimental size-selective harvesting.

Sustainable management of exploited populations benefits from integrating demographic and genetic considerations into assessments, as both play a role in determining harvest yields and population persistence. This is especially important in populations subject to size-selective harvest, because size selective harvesting has the potential to result in significant demographic, life-history, and genetic changes. We investigated harvest-induced changes in the effective number of breeders ( ) for introduced brook trout populations () in alpine lakes from western Canada.

Varying genetic imprints of road networks and human density in North American mammal populations.

Road networks and human density are major factors contributing to habitat fragmentation and loss, isolation of wildlife populations, and reduced genetic diversity. Terrestrial mammals are particularly sensitive to road networks and encroachment by human populations. However, there are limited assessments of the impacts of road networks and human density on population-specific nuclear genetic diversity, and it remains unclear how these impacts are modulated by life-history traits.

Investigation of XoxF methanol dehydrogenases reveals new methylotrophic bacteria in pelagic marine and freshwater ecosystems.

The diversity and distribution of methylotrophic bacteria have been investigated in the oceans and lakes using the methanol dehydrogenase mxaF gene as a functional marker. However, pelagic marine (OM43) and freshwater (LD28 and PRD01a001B) methylotrophs within the Betaproteobacteria lack mxaF, instead possessing a related xoxF4-encoded methanol dehydrogenase. Here, we developed and employed xoxF4 as a complementary functional gene marker to mxaF for studying methylotrophs in aquatic environment.

BioMiCo: a supervised Bayesian model for inference of microbial community structure.

Background: Microbiome samples often represent mixtures of communities, where each community is composed of overlapping assemblages of species. Such mixtures are complex, the number of species is huge and abundance information for many species is often sparse. Classical methods have a limited value for identifying complex features within such data.