Abstract
There is a growing need for marine biodiversity baseline and monitoring data to assess ocean ecosystem health, especially in the deep sea, where data are notoriously sparse. Baited cameras are a biological observing method especially useful in the deep ocean to estimate relative abundances of scavenging fishes and invertebrates. The National Geographic Society Exploration Technology Lab developed an autonomous benthic lander platform with a baited camera system to conduct stationary video surveys of deep-sea megafauna. The first-generation landers were capable of sampling to full ocean depth, however, the form factor, power requirements, and cost of the system limited deployment opportunities. Therefore, a miniaturized version (76 cm × 76 cm × 36 cm, 18 kg in air) was developed to provide a cost-effective method to observe ocean life to 6000 m depth. Here, we detail this next-generation deep-sea camera system, including the structural design, scientific payload, and the procedures for deployment. We provide an overview of NGS deep-sea camera system deployments over the past decade with a focus on the performance improvements of the next-generation system, which began field operations in 2017 and have performed 264 deployments. We present example imagery and discuss the strengths and limitations of the instrument in the context of existing complementary survey methods, and for use in down-stream data products. The key operational advantages of this new instrument are spatial flexibility and cost-efficiency. The instrument can be hand-deployed by a single operator from a small craft concurrent with other shipboard operations. The main limitation of the system is battery power, which allows for 6 h of continuous recording, and takes up to 8 h to recharge between deployments. Like many baited-camera methods, this instrument is specialized to measure the relative abundance of mobile megafauna that are attracted to bait, which results in a stochastic snapshot of the species at the deployment location and time. The small size and ease of deployment of this next-generation camera system allows for increased sample replication on expeditions, and presents a path forward to advance cost-effective biological observing and sustained monitoring in the deep ocean.
Highlights
There is a growing need for marine biodiversity baseline and monitoring data to assess ocean ecosystem health, especially in the deep sea, where data is notoriously sparse (Costello et al, 2010; Levin et al, 2019; Rogers et al, 2020)
New biological data obtained from high definition imaging can be used to build the marine Essential Biodiversity Variables (EBVs) (Pereira et al, 2013; Muller-Karger et al, 2018) and biological Essential Ocean Variables (EOVs) (Miloslavich et al, 2018; Bax et al, 2019)
We describe instrument uses over a decade of operation (2010– 2020) and discuss the performance of the next-generation system, which was used from 2017 onward for improved efficiency in deep-ocean benthic observing
Summary
There is a growing need for marine biodiversity baseline and monitoring data to assess ocean ecosystem health, especially in the deep sea, where data is notoriously sparse (Costello et al, 2010; Levin et al, 2019; Rogers et al, 2020). New biological data obtained from high definition imaging can be used to build the marine Essential Biodiversity Variables (EBVs) (Pereira et al, 2013; Muller-Karger et al, 2018) and biological Essential Ocean Variables (EOVs) (Miloslavich et al, 2018; Bax et al, 2019) These variables are necessary for ecosystem indicator development and reporting over the Ocean Decade (Moltmann et al, 2019; Ryabinin et al, 2019; Tanhua et al, 2019), which help to build a better understanding of the health of ocean ecosystems. This enables observers and users to converge on agreed data standards and practices that complement existing approaches (Pearlman et al, 2019) Such transparency and data standards are necessary to ensure that data obtained from emerging methods contribute to the development of ecosystem indicators that address policy and management requirements (e.g., suitable for building EBV/EOV data products) (Hardisty et al, 2019; Jetz et al, 2019)
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