Biology subjects

Katija, K.

Publications and source records attributed to Katija, K..

4 recordsLinked to original sources

bioRxiv2023

Nectophore coordination and kinematics by physonect siphonophores

Siphonophores are ubiquitous and often highly abundant members of pelagic ecosystems throughout the open ocean. They are unique among animal taxa in that they use multiple jets for propulsion. Little is known about kinematics of the individual jets produced by nectophores or how the jets are coordinated during normal swimming behavior. Using remotely operated vehicles and SCUBA, we video recorded the swimming behavior of several physonect species in their natural environment. The pulsed kinematics of the individual nectophores that comprise the siphonophore nectosome were quantified and, based on these kinematics, we examined the coordination of adjacent nectophores. We found for all species that nectophores sharing the same side of the nectosomal axis were coordinated metachronally. However, this coordination was not shared with nectophores on the opposite side of the nectosomal axis. For most species, the metachronal contraction waves of nectophores were initiated by the apical nectophores and traveled dorsally. However, the metachronal wave of Apolemia rubriversa traveled in the opposite direction. Although nectophore groups on opposite sides of the nectosome were not coordinated, they pulsed with similar frequencies. This enabled siphonophores to maintain relatively linear trajectories during swimming. The timing and characteristics of the metachronal coordination of pulsed jets affects how the jet wakes interact and may provide important insight into how interacting jets may be optimized for efficient propulsion.

zoology↗

bioRxiv2023

Flow Physics Explains Morphological Diversity of Ciliated Organs

Organs that pump fluids by the coordinated beat of motile cilia through the lumen are integral to animal physiology. Such organs include the human air-ways, brain ventricles, and reproductive tracts. Although cilia organization and duct morphology vary drastically in the animal kingdom, ducts are typically classified as either carpet or flame designs. The reason behind this di-chotomy and how duct design relates to fluid pumping remain unclear. Here, we demonstrate that two structural parameters - lumen diameter and cilia-to-lumen ratio - organize the observed duct diversity into a continuous spectrum that connects carpets to flames across all animal phyla. Using a unified fluid model, we show that carpet and flame designs maximize flow rate and pressure generation, respectively. We propose that convergence of ciliated organ designs follows functional constraints rather than phylogenetic distance, along with universal design rules for ciliary pumps.

biophysics↗

bioRxiv2022

Demystifying image-based machine learning: A practical guide to automated analysis of field imagery using modern machine learning tools

Image-based machine learning methods are quickly becoming among the most widely-used forms of data analysis across science, technology, and engineering. These methods are powerful because they can rapidly and automatically extract rich contextual and spatial information from images, a process that has historically required a large amount of manual labor. The potential of image-based machine learning methods to change how researchers study the ocean has been demonstrated through a diverse range of recent applications. However, despite their promise, machine learning tools are still under-exploited in many domains including species and environmental monitoring, biodiversity surveys, fisheries abundance and size estimation, rare event and species detection, the study of wild animal behavior, and citizen science. Our objective in this article is to provide an approachable, application-oriented guide to help researchers apply image-based machine learning methods effectively to their own research problems. Using a case study, we describe how to prepare data, train and deploy models, and avoid common pitfalls that can cause models to underperform. Importantly, we discuss how to diagnose problems that can cause poor model performance on new imagery to build robust tools that can vastly accelerate data acquisition in the marine realm. Code to perform our analyses is provided at https://github.com/heinsense2/AIO_CaseStudy

ecology↗

bioRxiv2019

Augmenting biologging with supervised machine learning to study in situ behavior of the medusa Chrysaora fuscescens

Zooplankton occupy critical roles in marine ecosystems, yet their fine-scale behavior remains poorly understood due to the difficulty of studying individuals in situ. Here we combine biologging with supervised machine learning (ML) to demonstrate a pipeline for studying in situ behavior of larger zooplankton such as jellyfish. We deployed the ITAG, a biologging package with high-resolution motion sensors designed for soft-bodied invertebrates, on 8 Chrysaora fuscescens in Monterey Bay, using the tether method for retrieval. Using simultaneous video footage of the tagged jellyfish, we develop ML methods to 1) identify periods of tag data corrupted by the tether method, which may have compromised prior research findings, and 2) classify jellyfish behaviors. Our tools yield characterizations of fine-scale jellyfish activity and orientation over long durations, and provide evidence that developing behavioral classifiers on in situ rather than laboratory data is essential.\n\nSummary StatementHigh-resolution motion sensors paired with supervised machine learning can be used to infer fine-scale in situ behavior of zooplankton for long durations.

animal behavior and cognition↗