An inexpensive passive acoustic system for recording and localizing wild animal sounds

2000 ◽  
Vol 107 (6) ◽  
pp. 3552-3555 ◽  
Author(s):  
Sean A. Hayes ◽  
David K. Mellinger ◽  
Donald A. Croll ◽  
Daniel P. Costa ◽  
J. Fabrizio Borsani
Keyword(s):  
Wild Animal ◽  
Acoustic System ◽  
Passive Acoustic

Active–passive acoustic system for underwater port protection

2013 ◽  
Vol 134 (5) ◽  
pp. 4077-4077 ◽  
Author(s):  
Hady Salloum ◽  
Andrew Meeecham ◽  
Alexander Sutin
Keyword(s):  
Acoustic System ◽  
Passive Acoustic

Detection localization and tracking of aircraft using Sparse distributed passive acoustic system

2017 ◽  
Vol 142 (4) ◽  
pp. 2555-2555
Author(s):  
Alexander Sedunov ◽  
Hady Salloum ◽  
Alexander Sutin ◽  
Nikolay Sedunov ◽  
David Masters
Keyword(s):  
Acoustic System ◽  
Passive Acoustic ◽  
Localization And Tracking

Stevens Passive Acoustic system for surface and underwater threat detection

2013 ◽  
Author(s):  
Alexander Sutin ◽  
Hady Salloum ◽  
Michael DeLorme ◽  
Nikolay Sedunov ◽  
Alexander Sedunov ◽  
...  
Keyword(s):  
Threat Detection ◽  
Acoustic System ◽  
Passive Acoustic

Stevens Passive Acoustic System for underwater surveillance

2010 ◽  
Author(s):  
Alexander Sutin ◽  
Barry Bunin ◽  
Alexander Sedunov ◽  
Nikolay Sedunov ◽  
Laurent Fillinger ◽  
...  
Keyword(s):  
Acoustic System ◽  
Passive Acoustic

Passive acoustic system for tracking low‐flying aircraft

2016 ◽  
Vol 10 (9) ◽  
pp. 1561-1568 ◽  
Author(s):  
Alexander Sedunov ◽  
Alexander Sutin ◽  
Nikolay Sedunov ◽  
Hady Salloum ◽  
Alexander Yakubovskiy ◽  
...  
Keyword(s):  
Acoustic System ◽  
Passive Acoustic

Spatiotemporal distribution of foraging in a marine predator: behavioural drivers of hotspot formation

2020 ◽  
Vol 635 ◽  
pp. 187-202
Author(s):  
T Brough ◽  
W Rayment ◽  
E Slooten ◽  
S Dawson
Keyword(s):  
Time Of Day ◽  
Spatiotemporal Distribution ◽  
Temporal Effects ◽  
Marine Predator ◽  
Marine Predators ◽  
Additive Mixed Models ◽  
Density Analysis ◽  
The Times ◽  
Passive Acoustic ◽  
Spatial Locations

Many species of marine predators display defined hotspots in their distribution, although the reasons why this happens are not well understood in some species. Understanding whether hotspots are used for certain behaviours provides insights into the importance of these areas for the predators’ ecology and population viability. In this study, we investigated the spatiotemporal distribution of foraging behaviour in Hector’s dolphin Cephalorhynchus hectori, a small, endangered species from New Zealand. Passive acoustic monitoring of foraging ‘buzzes’ was carried out at 4 hotspots and 6 lower-use, ‘reference areas’, chosen randomly based on a previous density analysis of visual sightings. The distribution of buzzes was modelled among spatial locations and on 3 temporal scales (season, time of day, tidal state) with generalised additive mixed models using 82000 h of monitoring data. Foraging rates were significantly influenced by all 3 temporal effects, with substantial variation in the importance and nature of each effect among locations. The complexity of the temporal effects on foraging is likely due to the patchy nature of prey distributions and shows how foraging is highly variable at fine scales. Foraging rates were highest at the hotspots, suggesting that feeding opportunities shape fine-scale distribution in Hector’s dolphin. Foraging can be disrupted by anthropogenic influences. Thus, information from this study can be used to manage threats to this vital behaviour in the locations and at the times where it is most prevalent.

DETECTION OF XENOBIOTIC SUBSTANCES IN MUTE SWANS’ (CYGNUS OLOR) BLOOD

2015 ◽  
Author(s):  
Jolanta STANKEVIČIŪTĖ ◽  
Solveiga Marija BARKAUSKAITĖ ◽  
Gediminas BRAZAITIS
Keyword(s):  
Animal Species ◽  
Wild Animal ◽  
Wild Populations ◽  
New Methods ◽  
Negative Results ◽  
Cygnus Olor ◽  
Ecological Scale ◽  
Literature Reviews ◽  
Baseline Information ◽  
Mute Swans

During recent years the attention towards the effects of xenobiotic substances on wild nature has been steadily increasing. Literature reviews have revealed that active hormone-disintegrating substances might affect the reproduction of some wild animal species. Research shows anomalies of reproduction and development in various animal groups such as birds, fish, invertebrates and reptiles. Species inhabiting water and its surroundings cause the highest concern. Due to insufficient baseline information it is difficult to determine the extent of the problem in these wild populations on an ecological scale. The research described in this article is the first attempt to analyse xenobiotic substances and evaluate possible accumulation of pharmaceuticals in animals higher up in the food chain in Lithuania. This research tests new methods for to analyse for xenobiotics substances, which might be used in the future. Blood samples of 7 swans were examined using liquid chromatography, however, no xenobiotics were detected. Negative results do not eliminate the necessity for further investigate of larger samples, other species or to search for non-pharmaceutical xenobiotics.

Battery-Powered Wild Animal Detection Nodes with Deep Learning

2020 ◽  
Vol E103.B (12) ◽  
pp. 1394-1402
Author(s):  
Hiroshi SAITO ◽  
Tatsuki OTAKE ◽  
Hayato KATO ◽  
Masayuki TOKUTAKE ◽  
Shogo SEMBA ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wild Animal ◽  
Animal Detection
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