Early Online Release

Oceanography in the Age of Intelligent Robots and a Changing Climate By Chris Scholin

Oceanography | Early Online Release

seafloor (e.g.,  Robison et  al., 2005; Brierley, 2014; Honjo et  al.,

2014; K.L. Smith et  al., 2017; Archibald et  al., 2019). The same

processes can also transport microplastics, which has led to the

suggestion that a large, previously unknown reservoir of marine

microplastics may be contained within animal communities living

in the deep sea. (Choy et al., 2019).

Comprehensively investigating the players and processes that

transform and transport organic matter from the sea surface to

the seafloor over decades is not easy (e.g.,  Messié et  al., 2023).

The distribution and behavior of the participants and the mate­

rial they transform and produce varies tremendously in time and

space, challenging our ability to model biologically driven carbon

flux and resultant climate influence. Persistent observations of the

ocean using a variety of tools is a necessary step toward meeting

that grand challenge (e.g., Karl, 2014; Chavez et al., 2021a).

LISTENING AND DECODING WHAT ANIMALS

LEAVE IN THEIR WAKE

It is truly amazing what you can learn by listening. The history

of ocean soundscape analysis is a great example. In the mid-

4th century BCE, the ancient Greek philosopher Aristotle in his

landmark work History of Animals noted that sea creatures pro­

duced sounds (see Thompson, 1910). Ancient mariners also mar­

veled at the mysterious noises that occasionally resonated through

the hulls of their ships. Over millennia, these astute observations

gave way to curiosity-driven research and wartime pursuits that

exploited ocean sound. Following World War II, revelations about

the lives and vocalizations of charismatic megafauna piqued the

public’s interest, popularizing the idea of an ocean soundscape and

highlighting the mysteries of marine mammal communication

(e.g., Schevill and Lawrence, 1949; Payne and McVay, 1971). In an

FIGURE 6. Videos are processed

using an integration of MBARI’s

Video Annotation and Reference

System (VARS) with advanced

machine learning tools (VARS-ML)

to identify and track animals

(a) on the seafloor and (b) in the

water column. The VARS-ML ini­

tiative combines the expertise of

marine scientists, engineers, and

data scientists. Source: Lonny

Lundsten and Nancy Jacobsen

Stout. Images © 2025 MBARI