Tracking Pacific bluefin tuna (Thunnus thynnus orientalis)in the northeastern Pacific with an automated algorithm that estimates latitude by matching sea-surface-temperature data from satellites with temperature data from tags on fish

图努斯 金枪鱼 纬度 地理定位 经度 卫星 海面温度 地理 海洋学 渔业 地质学 大地测量学 计算机科学 生物 工程类 万维网 航空航天工程
作者
Michael L. Domeier,Dale A. Kiefer,Nicole Nasby-Lucas,Adam Wagschal,F. B. O. O'Brien
摘要

Data recovered from 11 popup satellite archival tags and 3 surgically implanted archival tags were used to analyze the movement patterns of juvenile northern bluefin tuna (Thunnus thynnus orientalis) in the eastern Pacific. The light sensors on archival and pop-up satellite transmitting archival tags (PSATs) provide data on the time of sunrise and sunset, allowing the calculation of an approximate geographic position of the animal. Light-based estimates of longitude are relatively robust but latitude estimates are prone to large degrees of error, particularly near the times of the equinoxes and when the tag is at low latitudes. Estimating latitude remains a problem for researchers using light-based geolocation algorithms and it has been suggested that sea surface temperature data from satellites may be a useful tool for refining latitude estimates. Tag data from bluefin tuna were subjected to a newly developed algorithm, called “PSAT Tracker,” which automatically matches sea surface temperature data from the tags with sea surface temperatures recorded by satellites. The results of this algorithm compared favorably to the estimates of latitude calculated with the lightbased algorithms and allowed for estimation of fish positions during times of the year when the lightbased algorithms failed. Three near one-year tracks produced by PSAT tracker showed that the fish range from the California−Oregon border to southern Baja California, Mexico, and that the majority of time is spent off the coast of central Baja Mexico. A seasonal movement pattern was evident; the fish spend winter and spring off central Baja California, and summer through fall is spent moving northward to Oregon and returning to Baja California.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
希望天下0贩的0应助rrrr采纳,获得10
1秒前
1秒前
Akim应助zzz采纳,获得10
2秒前
cdercder应助Huyq采纳,获得10
2秒前
666发布了新的文献求助30
2秒前
2秒前
躺平的洋仔完成签到,获得积分10
3秒前
3秒前
毛毛雨的老豆完成签到,获得积分10
3秒前
斯文败类应助windtalker采纳,获得10
4秒前
Nole应助daniel666采纳,获得10
4秒前
洛洛薇完成签到 ,获得积分10
4秒前
肚子发布了新的文献求助10
5秒前
wangxw发布了新的文献求助10
5秒前
6秒前
7秒前
moshang发布了新的文献求助10
7秒前
轻松的璎完成签到,获得积分10
7秒前
summer应助思思采纳,获得10
8秒前
zz完成签到,获得积分10
8秒前
真实的亦竹完成签到,获得积分10
8秒前
冰然发布了新的文献求助10
9秒前
Rich发布了新的文献求助30
9秒前
9秒前
11秒前
12秒前
13秒前
14秒前
十七完成签到,获得积分20
14秒前
Akim应助amelie采纳,获得10
15秒前
15秒前
拾三发布了新的文献求助10
15秒前
16秒前
蛋烘糕完成签到,获得积分10
16秒前
17秒前
小巧秋天发布了新的文献求助10
17秒前
windtalker发布了新的文献求助10
18秒前
张欢馨应助凡是空间采纳,获得10
18秒前
zzz发布了新的文献求助10
19秒前
20秒前
高分求助中
Markov Chain Monte Carlo 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
How to Use Machine Learning in Chemistry: An Introduction 1000
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
Handbuch Trainingswissenschaft – Trainingslehre 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7583462
求助须知:如何正确求助?哪些是违规求助? 9162196
关于积分的说明 19606301
捐赠科研通 7165505
什么是DOI,文献DOI怎么找? 3266283
关于科研通互助平台的介绍 2431182
邀请新用户注册赠送积分活动 2257737