Ants are little critters with a big impact on our world but in something you might have never thought you needed to know – very few have any legal protection.
Ants perform several crucial ecological functions, including removing waste, aerating soil, dispersing seeds and nutrients, and as both predator and prey in complex food chains. In fact, ants and other similar invertebrates might be considered the true backbone of various ecospheres.
So, they need to be researched and, in many cases, protected.
To improve our understanding of the global diversity of ants, researchers from the Okinawa Institute of Science and Technology (OIST) Graduate University, combined machine learning methods with information from online repositories, museums and approximately 10,000 scientific publications to produce a global map of ant diversity down to about 20 km2.
A second map highlighting “species rarity” was constructed to display populations of ant species that are highly localised. They are particularly susceptible to environmental change and therefore of special concern for conservation.
Some areas, such as Okinawa in southern Japan, proved to be home to many species of ants in a single localised area– some 1000 times smaller than species across North America and Europe.
The researchers found that very few of these localised ant species were on land with any sort of legal protection like conservation parks or reserves.
This new information, which has taken a decade to compile, could be critical in conserving ant biodiversity.
Professor Evan Economo, who leads the Biodiversity and Biocomplexity Unit at OIST says this project helps to “add ants and other terrestrial invertebrates in general to the discussion on biodiversity conservation. We need to know the locations of high diversity centres of invertebrates so we know the areas that can be the focus of future research and environmental protection.”
More than 14,000 (of the 15,000 currently known) ant species were included in the study, which trawled through a variety of sources for descriptions of sampling locations and involved the contribution of international researchers to identify and correct errors.
In many cases, records were not specific enough to allow for mapping, so computational estimates were made from available data, which then underwent a range-estimation process, during which researchers modelled via constructing shapes around data points or statistical modelling.
There are some areas of the world that have been sampled significantly more than others, affecting the estimates of ant diversity and distribution. To overcome this sample bias, the researchers employed machine learning, teaching an algorithm to predict how ant diversity would change if all areas of the world were sampled equally. The machine learning process resulted in a kind of “treasure map”, says Economo, because it identified areas where many unknown, unsampled species were expected to exist. We can be guided “to where we should explore next and look for new species with restricted ranges”.