A team led by the University of Exeter in UK said animals carrying sensors can fill many of these gaps through natural behaviour such as diving under ice, swimming in shallow water or moving against currents.
"We want to highlight the massive potential of animal-borne sensors to teach us about the oceans," said lead author David March from the University.
"This is already happening on a limited scale, but there's scope for much more," March said.
Thousands of marine animals are tagged for a variety of research and conservation purposes, but at present the information gathered isn't widely used to track climate change and other shifts in the oceans.
Instead, monitoring is mostly done by research vessels, underwater drones and thousands of floating sensors that drift with the currents. However, large areas of the ocean still remain under-sampled - leaving gaps in our knowledge.
By comparing this with gaps in current observations by drifting profiling sensors (known as Argo floats) the researchers identified poorly sampled areas where data from animal sensors would help fill gaps.
"We looked at 183 species - including tuna, sharks, rays, whales and flying seabirds - and the areas they are known to inhabit. We have processed more than 1.5 million measurements from floating sensors to identify poorly sampled areas (18.6% of the global ocean surface)," March added.
These include seas near the poles (above 60º latitude) and shallow and coastal areas where Argo profilers are at risk of hitting the land.
According to the researchers, the Caribbean and seas around Indonesia, as well as other semi-enclosed seas, are good examples of places where Argo profilers struggle because of these problems.
Tagged seals in the poles have already complemented ocean observing systems because they can reach areas under ice that are inaccessible to other instruments.
The study, published in the journal Global Change Biology, suggests data collected by turtles or sharks could also enhance ocean monitoring in other remote and critical areas such as tropical regions, with large influence on global climate variability and weather.
( With inputs from IANS )