Biology subjects

Krishnan, A. G.

Publications and source records attributed to Krishnan, A. G..

3 recordsLinked to original sources

bioRxiv2025

Estimating population range distributions from animal tracking data

O_LIQuantifying the space requirements of a population is a fundamental problem in spatial ecology, particularly as it relates to the identification of important utilization areas and the designation of protected areas for conservation and wildlife management. C_LIO_LITraditionally, population space use estimation techniques scale up from the individual to the population level by aggregating individual animal tracks and then using a single pooled distribution estimator like minimum convex polygons (MCP) or kernel density estimation (KDE). These techniques fail to account for the high levels of temporal autocorrelation in modern tracking datasets, and estimates are often sensitive to the number of individuals sampled. C_LIO_LIWe introduce a new population kernel density estimator (PKDE) that accounts for temporal autocorrelation in tracking data, propagates uncertainty from the individual to the population level, accounts for inter-individual variation when scaling up to the population level, and is not highly sensitive to the number of individuals tracked. Through a combination of simulated data and empirical GPS tracking datasets from three species: (a) grizzly bear (Ursus arctos horribilis); (b) lowland tapir (Tapirus terrestris); and (c) bobcat (Lynx rufus), we demonstrate that PKDE produces minimally biased estimates of population-level space use compared to conventional methods like MCP and KDE. C_LIO_LIThe use of conventional estimators can lead to substantial underestimation of population space usage, making them unsuitable for area-based conservation planning. The statistically efficient PKDE estimator provides relatively unbiased estimates of space use with fewer individuals sampled. This method has been made available as a function in the ctmm R package. C_LI

ecology↗

bioRxiv2024

RNA binding protein Musashi1 interacts with the viral genomic RNA and restricts SARS-CoV-2 infection by repressing translation

Musashi RNA binding proteins are important post-transcriptional regulators of stem cell homeostasis and are known to be involved in viral infections. However, their role in SARS-CoV-2 infection remains largely unknown. Using computational studies, in vivo RNA immunoprecipitation and biochemical assays, here, we establish that Musashi1 (Msi1) interacts with viral genomic RNA through direct binding to the SARS-CoV-2 3UTR. Importantly, binding of Msi1 to the viral 3UTR results in translational repression mediated by inhibition of Poly (A) binding protein (PABP). Conversely, Msi1 knockout promotes robust viral replication and increased viral protein expression. Using 2D cell cultures, stem cells and 3D organoids, we show that depletion of Msi1 in intestinal cells augments infection. This finding explains why the human intestine serves as a reservoir for the SARS-CoV-2 virus, wherein differentiated enterocytes, which have negligible levels of Msi1, are highly affected. Contrarily, stem cells which are enriched for Msi1 expression, are known to be less permissive to SARS-CoV-2 infection despite expressing the entry receptors. Our findings show how translation repression of SARS-CoV-2 by stem cell RNA binding proteins such as Msi1 could help evasion of infection.

cell biology↗

bioRxiv2023

Intraspecific encounters can induce home-range shifts

Direct encounters, in which two or more individuals are physically close to one another, are a topic of increasing interest as more and better movement data become available. Recent progress, including the development of statistical tools for estimating robust measures of changes in animals space use over time, facilitates opportunities to link direct encounters between individuals with the long-term consequences of those encounters. Working with movement data for coyotes (Canis latrans) and grizzly bears (Ursus arctos horribilis), we investigate whether close intraspecific encounters were associated with spatial shifts in the animals range distributions, as might be expected if one or both of the individuals involved in an encounter were seeking to reduce or avoid conflict over space. We analyze the movement data of a pair of coyotes in detail, identifying how a shift in home range location resulting from altered movement behavior was apparently a consequence of a close intraspecific encounter. With grizzly bear movement data, we approach the problem from the perspective of a set of encounter pairs within a population. We find support for the hypotheses that 1) close intraspecific encounters between bears are, on average, associated with subsequent shifts in range distributions and 2) encounters defined at finer spatial scales are followed by greater changes in space use. Our results suggest that animals can undertake long-term, large-scale spatial shifts in response to close intraspecific encounters that have the potential for conflict. These results lend support for existing theory on the evolution of territories and space use (e.g., Maynard-Smiths bourgeois strategy regarding low-conflict coexistence). Overall, we find that analyses of movement data in a pairwise context can 1) identify distances at which individuals proximity to one another may alter behavior and 2) facilitate testing of population-level hypotheses concerning the potential for direct encounters to alter individuals space use. Open Research StatementMovement data for the coyotes and grizzly bears are posted on Movebank.org as datasets 1614661371 and 1044288582, respectively. Statistical tools for estimating, manipulating, and comparing home ranges from movement data are implemented in the open-source R package ctmm. R scripts used to carry out specific analyses for this study are openly available on GitHub at https://github.com/anagkrish/encounter_homerangeshift.

ecology↗