Biology subjects

Larsen, S. N.

Publications and source records attributed to Larsen, S. N..

2 recordsLinked to original sources

bioRxiv2024

Investigating Honey Bee Pollen Foraging Patterns Across a Season Using DNA Metabarcoding

The western honey bee, or Apis mellifera, is a vital pollinator, essential for both ecosystems and agriculture worldwide. As human influences on these bees become more pronounced, understanding their pollen foraging behavior is increasingly important. Pollen provides crucial nutrients for A. mellifera such as protein and lipids that are necessary for hive health and prosperity. Pollen DNA metabarcoding allows for the longitudinal analysis of the floral sources of corbicular (bee-collected) pollen. In this study we employ the use of metabarcoding and Oxford Nanopore Sequencing technology to analyze the floral composition of pollen samples collected on a weekly basis from two closely related hives. In total, we identified 74 distinct plant taxa (at the genus level) between the two hives. Although both hives had equivalent values of taxonomic richness and diversity, the majority of taxa identified were unique to the individual hives. This indicates a large degree of variation in pollen foraging despite the hives relative proximity. In addition to this interhive analysis, we also analyzed changes in pollen origin of a single hive across the course of five months. We identified 59 distinct plant taxa within samples from this hive whose presence and relative abundance differ drastically on a weekly basis with no taxa being present in all samples across the observed time period. In addition, even though there was a median of 8 to 9 taxa detected in each sample, each sample was composed primarily of 1 to 2 taxa indicating a degree of foraging preference that changes on a regular basis. Further quantitative and qualitative comparisons completed in this study demonstrate the degree of variation in pollen foraging patterns both throughout the season and between hives.

ecology↗

bioRxiv2021

Vocal markers of Autism Spectrum Disorder: assessing the generalizability of machine learning models

Machine learning (ML) approaches show increasing promise in their ability to identify vocal markers of autism. Nonetheless, it is unclear to what extent such markers generalize to new speech samples collected e.g., using a different speech task or in a different language. In this paper, we systematically assess the generalizability of ML findings across a variety of contexts. We train promising published ML models of vocal markers of autism on novel cross-linguistic datasets following a rigorous pipeline to minimize overfitting, including cross-validated training and ensemble models. We test the generalizability of the models by testing them on i) different participants from the same study, performing the same task; ii) the same participants, performing a different (but similar) task; iii) a different study with participants speaking a different language, performing the same type of task. While model performance is similar to previously published findings when trained and tested on data from the same study (out-of-sample performance), there is considerable variance between studies. Crucially, the models do not generalize well to different, though similar, tasks and not at all to new languages. The ML pipeline is openly shared. Generalizability of ML models of vocal markers of autism is an issue. We outline three recommendations for strategies researchers could take to be more explicit about generalizability and improve it in future studies. LAY SUMMARYMachine learning approaches promise to be able to identify autism from voice only. These models underestimate how diverse the contexts in which we speak are, how diverse the languages used are and how diverse autistic voices are. Machine learning approaches need to be more careful in defining their limits and generalizability.

bioinformatics↗