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Temporally-scheduled ROV-based monitoring to detect behavioural rhythms of deep-sea megafaunaNARA Subscribed
Animal activity rhythms, especially expressed as periodic displacement in the case of motile megafauna, can affect the detection of species in a certain spot during a short sampling window, and thus affect our perception of local biodiversity. However, this temporal aspect of animal behavior is rarely included in the data collection strategies of ecological monitoring programs, potentially leading to biased scientific outcomes...
Aim The urgency for remote, reliable and scalable biodiversity monitoring amidst mounting human pressures on ecosystems has sparked worldwide interest in Passive Acoustic Monitoring (PAM), which can track life underwater and on land. However, we lack a unified methodology to report this sampling effort and a comprehensive overview of PAM coverage to gauge its potential as a global research and monitoring tool. To address this...
The re-use of offshore platforms as ecological observatoriesNARA Subscribed
Burrow emergence rhythms of deep-water Mediterranean Norway lobsters (Nephrops norvegicus) revealed by acoustic telemetryNARA Subscribed
N. norvegicus supports one of the most commercially-important fisheries in the Mediterranean, and there is considerable interest in developing non-invasive sampling stock assessment methods. Currently, stock assessments are conducted via trawling or by UnderWater TeleVision (UWTV) surveys with limited capacity to provide direct population data due to the burrowing behavior of the species. Here, we used acoustic telemetry to ch...
The Norway lobster, Nephrops norvegicus , supports a key European fishery. Stock assessments for this species are mostly based on trawling and UnderWater TeleVision (UWTV) surveys. However, N. norvegicus are burrowing organisms and these survey methods are unable to sample or observe individuals in their burrows. To account for this, UWTV surveys generally assume that “1 burrow system = 1 animal”, due to the territorial behavi...
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