Geology topics

Tomoharu Eguchi

Publications and source records attributed to Tomoharu Eguchi.

4 recordsLinked to original sources

USGS2022

Mismatches in scale between highly mobile marine megafauna and marine protected areas

Marine protected areas (MPAs), particularly large MPAs, are increasing in number and size around the globe in part to facilitate the conservation of marine megafauna under the assumption that large-scale MPAs better align with vagile life histories; however, this alignment is not well established. Using a global tracking dataset from 36 species across five taxa, chosen to reflect the span of home range size in highly mobile marine megafauna, we show most MPAs are too small to encompass complete home ranges of most species. Based on size alone, 40% of existing MPAs could encompass the home ranges of the smallest ranged species, while only < 1% of existing MPAs could encompass those of the largest ranged species. Further, where home ranges and MPAs overlapped in real geographic space, MPAs encompassed < 5% of core areas used by all species. Despite most home ranges of mobile marine megafauna being much larger than existing MPAs, we demonstrate how benefits from MPAs are still likely to accrue by targeting seasonal aggregations and critical life history stages and through other management techniques.

Frontiers in Marine Science↗

USGS2021

Species and population specific gene expression in blood transcriptomes of marine turtles

Background Transcriptomic data has demonstrated utility to advance the study of physiological diversity and organisms’ responses to environmental stressors. However, a lack of genomic resources and challenges associated with collecting high-quality RNA can limit its application for many wild populations. Minimally invasive blood sampling combined with de novo transcriptomic approaches has great potential to alleviate these barriers. Here, we advance these goals for marine turtles by generating high quality de novo blood transcriptome assemblies to characterize functional diversity and compare global transcriptional profiles between tissues, species, and foraging aggregations. Results We generated high quality blood transcriptome assemblies for hawksbill ( Eretmochelys imbricata ) , loggerhead ( Caretta caretta ), green ( Chelonia mydas ), and leatherback ( Dermochelys coriacea ) turtles. The functional diversity in assembled blood transcriptomes was comparable to those from more traditionally sampled tissues. A total of 31.3% of orthogroups identified were present in all four species, representing a core set of conserved genes expressed in blood and shared across marine turtle species. We observed strong species-specific expression of these genes, as well as distinct transcriptomic profiles between green turtle foraging aggregations that inhabit areas of greater or lesser anthropogenic disturbance. Conclusions Obtaining global gene expression data through non-lethal, minimally invasive sampling can greatly expand the applications of RNA-sequencing in protected long-lived species such as marine turtles. The distinct differences in gene expression signatures between species and foraging aggregations provide insight into the functional genomics underlying the diversity in this ancient vertebrate lineage. The transcriptomic resources generated here can be used in further studies examining the evolutionary ecology and anthropogenic impacts on marine turtles.

BMC Genomics↗

USGS2019

Drift and beaching patterns of sea otter carcasses and car tire dummies

Enumerating and examining marine animal carcasses is important for quantifying mortality rates and determining causes of mortality. Drifter experiments are one tool for estimating at‐sea mortality and determining factors affecting carcass drift, but they require validation to confirm drifters accurately replicate the drift characteristics of the species of interest. The goal of this study was to determine whether dummies constructed from car tires were appropriate substitutes for sea otter ( Enhydra lutris ) carcasses. We released 33 sets of targets (carcasses and dummies) in a one‐to‐one ratio on 15 randomly chosen dates between January 1995 and December 1996. They were telemetrically tracked until they beached or were no longer detected. Beaching rates were similar between carcasses (69.7%) and dummies (66.7%). Our results indicated that there was no statistical difference in the drifting pattern, as measured by distance traveled and location, between carcasses and dummies, and that cumulative wind speed, days since release, and release month were predictors of drift patterns. We concluded that dummies constructed from car tires do imitate sea otter carcasses and could be used to estimate at‐sea mortality of sea otters, or, if released during or after an oil spill, could be used to direct search efforts for carcasses.

California↗

USGS2016

Daniel Goodman’s empirical approach to Bayesian statistics

Bayesian statistics, in contrast to classical statistics, uses probability to represent uncertainty about the state of knowledge. Bayesian statistics has often been associated with the idea that knowledge is subjective and that a probability distribution represents a personal degree of belief. Dr. Daniel Goodman considered this viewpoint problematic for issues of public policy. He sought to ground his Bayesian approach in data, and advocated the construction of a prior as an empirical histogram of “similar” cases. In this way, the posterior distribution that results from a Bayesian analysis combined comparable previous data with case-specific current data, using Bayes’ formula. Goodman championed such a data-based approach, but he acknowledged that it was difficult in practice. If based on a true representation of our knowledge and uncertainty, Goodman argued that risk assessment and decision-making could be an exact science, despite the uncertainties. In his view, Bayesian statistics is a critical component of this science because a Bayesian analysis produces the probabilities of future outcomes. Indeed, Goodman maintained that the Bayesian machinery, following the rules of conditional probability, offered the best legitimate inference from available data. We give an example of an informative prior in a recent study of Steller sea lion spatial use patterns in Alaska.

PeerJ↗