Thousands of Undiscovered Mammal Species May Be Hidden in Plain Sight
Taxonomythe study of the relationships between living organisms as species, has been around since the 1700s. Although scientists and philosophers have long debated what makes a species a species, taxonomists treat each species as a group of organisms that share common biological characteristics.
The discovery and description of new species is essential for biological researchers and conservationists because they use species as the unit of analysis. The species are also economically important for agriculture, hunting and fishing, and have special legal status, such as under the US Endangered Species Act.
Despite this, scientists have only been able to name and formally describe one estimated 10% of species on the planet, based on trends in discovery over the years.
This knowledge gap is known as the Linnean deficit. It remains uncertain whether poor research methodologydisagreements about how to define a speciesor other factors are responsible for this discrepancy.
We are scientists in evolutionary biology, and finding ways to better identify species is at the heart of our research. Thanks to genetic analysis and artificial intelligence, we were able to untangle the hidden species that were lumped into a single group and predict where and what types they might be. Our findings also identify a potential cause for this lack of species identification: an underinvestment in the science of taxonomy. Determining what makes a species can get complicated.
Hidden species remain to be discovered
For this study, we chose to focus on mammals. Due to their relatively large size and importance to humans as a source of food, companionship and entertainment, we predicted that it was more likely that a large proportion of mammalian species had already been identified .
Our first task was to identify known species that might actually contain two or more species. To do this, we analyzed 1 million gene sequences from 4,300 named species, identifying clusters of sequences with high genetic diversity and fitting the data to an evolutionary model.
We have found potentially hundreds of hidden species that were previously categorized into a single group. This result was expected because it reflects results of previous studiesbut on a larger scale.
Where and what are these hidden species?
Once we identified the presence of these potentially hidden species, our second task was to determine what specific traits they had in common. To do this, we used a data science technique called random forest analysis, a form of machine learning that draws information from a large number of different variables in order to make a prediction about a particular outcome. It is similar to technical than Netflix uses to suggest shows you might be interested in. Random Forests is a machine learning algorithm that makes predictions using multiple decision trees.
In our case, we wanted to predict whether a known species contained hidden species. The predictor variables we used covered environmental factors, such as the climate of common mammal habitats, and species-specific factors, such as physical traits, geographical scope, modes of reproduction and survival. We have also included factors based on research into the techniques used by scientists to conduct their studies. In total, we collected some 3.8 million data points to build our model.
Based on our model, we found that three types of predictor variables stood out the most.
The first type included attributes of the species itself, such as body mass and geographic distribution. These results suggest that small mammals with relatively large ranges are more likely to have hidden species. This makes sense because, all things being equal, it is harder for scientists to recognize physical differences in small animals than in larger ones.
The second type was climate – there are probably more hidden species in humid, warm areas with a large difference in daytime and nighttime temperatures. This probably reflects the fact that tropical rainforests tend to have very high levels of mammalian diversity.
The third type was research effort, including the geographic dispersion of specimens in museum collections and the number of recent publications mentioning the scientific name of a known species. This implies that researchers are generally good at identifying new mammals because the scientific community’s attention to a specific mammal predicts whether that creature is identified. This is supported by how the general characteristics we have identified correspond to the new mammalian species described during the course. the last 30 yearsas well as the fact that our model recognizes areas that scientists are already surveying for hidden species.
Unknown species threatened with extinction
At a time when the Earth faces its greatest extinction crisis since an asteroid killed the dinosaurs, we believe that identifying and describing the many undiscovered species on Earth is crucial to help preserve its biodiversity.
Even though our study still found a large number of mammals to discover, the diversity of mammals is already relatively well captured compared to that of other species. We found that about 80% of existing mammalian species have already been described, a much higher proportion than for non-mammals. groups with even more diversity such as beetles or mites.
Discovering and describing new species, as in all scientific research, takes an entire village. Natural history museums are largely responsible for collecting the raw data we analyzed, and genetic and biodiversity the databases provided the infrastructure to make it accessible to us. A culture of information sharing between peers and large computer networks supported the thousands of hours of computing time we needed. Our work has only been made possible by the pursuit investments in taxonomic research.
Biodiversity scientists race to better understand the processes that create and maintain biodiversity in the middle of the planet sixth mass extinction, one that is entirely caused by human actions. Taxonomists face the challenge of describing the species around us before they disappear. As our results suggest, there is still a long way to go.
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