five

Scale-dependent foraging ecology of a marine top predator modelled using passive acoustic data

收藏
NIAID Data Ecosystem2026-03-07 收录
下载链接:
http://datadryad.org/dataset/doi%253A10.5061%252Fdryad.83h07
下载链接
链接失效反馈
官方服务:
资源简介:
1. Understanding which environmental factors drive foraging preferences is critical for the development of effective management measures, but resource use patterns may emerge from processes that occur at different spatial and temporal scales. Direct observations of foraging are also especially challenging in marine predators, but passive acoustic techniques provide opportunities to study the behavior of echolocating species over a range of scales. 2. We used an extensive passive acoustic dataset to investigate the distribution and temporal dynamics of foraging in bottlenose dolphins using the Moray Firth (Scotland, UK). Echolocation buzzes were identified with a mixture model of detected echolocation inter-click intervals, and used as a proxy of foraging activity. A robust modelling approach accounting for autocorrelation in the data was then used to evaluate which environmental factors were associated with the observed dynamics at two different spatial and temporal scales. 3. At a broad scale, foraging varied seasonally, and was also affected by sea-bed slope and shelf-sea fronts. At a finer scale, we identified variation in seasonal use and local interactions with tidal processes. Foraging was best predicted at a daily scale, accounting for site-specificity in the shape of the estimated relationships. 4. This study demonstrates how passive acoustic data can be used to understand foraging ecology in echolocating species, and provides a robust analytical procedure for describing spatio-temporal patterns. Associations between foraging and environmental characteristics varied according to spatial and temporal scale, highlighting the need for a multi-scale approach. Our results indicate that dolphins respond to coarser-scale temporal dynamics, but have a detailed understanding of finer-scale spatial distribution of resources.
创建时间:
2013-06-18
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作