Scientists have tracked animals for decades, to learn more about their behaviour and also to identify the environmental niches they prefer to inhabit. Contrasting this knowledge with intersecting human activities can inform conservation and management practices for threatened species.
Dr Ana M M Sequeira from the UWA Oceans Institute and School of Biological Sciences is expanding this approach to the global scale. Combining the efforts of hundreds of researchers around the world, she has compiled tracking datasets for over 100 different species across the global oceans, and is using this information to map biodiversity hotspots and identify areas of potential conservation value for our most migratory animals.
Effective conservation and protection of highly migratory species like sharks and whales is challenged by the fact that they move through different ecosystems and across national jurisdictions; two-thirds of the global oceans are effectively international waters. They can spend different seasons in entirely different environments, impacted by a range of human activities that may go unrecognised from a local or even national perspective.
Dr Sequeira is spearheading a global initiative to address this limitation by building an animal-tracking database and models that can combine information from many isolated tracking studies, to provide a global picture of animal activity and environmental preferences across our oceans.
“We’ve got a network of researchers around the globe that are providing their tracking data going back as far as 30 years, says Dr Sequeira. “Together, we have assembled many thousands of animal tracks containing millions of locations over time showing where individuals from over 100 species have travelled. The species range from polar bears, whales, seals and turtles to penguins and albatrosses, as well as a wide range of sharks.”
Dr Sequeira is using this information to match animal activity with a range of environmental conditions measured at the same locations and times. Factors like temperature, chlorophyll concentration and current speed can be extracted from satellite data and oceanographic models, representing millions of additional measurements over time.
“We’re using Pawsey to prepare for this large analytical effort,” explains Dr Sequeira. “As a starting point, we ran simulations to make sure we can cope with the variability and biases that can be found when doing these types of studies.”
Different tracking datasets come with their own experimental biases. For example, animal locations measured using Global Positioning System (GPS) technology are inherently more accurate than datasets
acquired using other technologies, such as light geolocation, which results in only two positions recorded per day. Data quality also depends on what animal is being tracked. For satellite tags to transmit positional data, the tag’s antenna needs to breach the water surface – so the frequency of positions recorded for an air-breathing animal like a whale or turtle will be much higher than for a shark.
There are also gaps in the environmental data. “Because we’re looking at a global scale, there are both temporal and spatial resolution limits to the data we can access. For example, with satellite data, it is often hard to get daily chlorophyll information for all areas we are interested in. For such cases, averaged weekly or monthly data might be more useful.”
Using simulation datasets, results for models using specific combinations of known biases can be obtained in a controlled way. To prepare for the real dataset the team has run over 100,000 different simulations, each representing a set of tracks containing hundreds to thousands of positions over time, and identified local environmental information associated with each of those positions. The results from these simulations have been used to define the best strategy to cope with all of the real-world tracking data assembled.
“We’re now ready to run our models with the real animal tracking and environmental data, at a global scale,” enthuses Dr Sequeira. “
This will be the first global study incorporating tracking data from so many different species across many different taxa.”
The aim is to be able to predict the areas of highest biodiversity and environmental value around the world for highly migratory species such as whales, sharks and turtles.
“Being able to overlay areas of ecological importance for a range of species with existing human impacts like shipping, fishing and other industrial operations will provide an evidence base to work from to assist understanding which areas might be critical to the survival of each species or the health of entire ecosystems.”