One thing that species like the blue whale, Bengal tiger and the green turtle have in common is the fact that all three of them are at the brink of extinction and are classified as endangered species.
The risk of extinction varies from species to species depending on how individuals in its populations reproduce. Now, a new study says that understanding the dynamics of survival and reproduction can support management actions to improve a species’ chances of survival.
Notably, mathematical and statistical models have become powerful tools to help explain these dynamics. However, the quality of the information we use to construct such models is crucial to improve our chances of accurately predicting the fate of populations in nature.
According to associate professor Fernando Colchero, author of a new paper published in Ecology Letters, a model that over-simplifies survival and reproduction can give the illusion that a population is thriving when in reality it will go extinct.
Colchero’s research focuses on mathematically recreating the population dynamics by better understanding the demography of a species. His research is based on constructing and exploring stochastic population models that predict how a certain population will change over time.
These models explaining the dynamics include mathematical factors to describe how the species’ environment, survival rates and reproduction determine to the population’s size and growth. Assumptions, however, are a practical necessity.
Two commonly accepted assumptions are that survival and reproduction are constant with age, and that high survival in the species goes hand in hand with reproduction across all age groups within a species.
Colchero, on his part, challenged these assumptions by accounting for age-specific survival and reproduction, and for trade-offs between survival and reproduction. That means sometimes conditions that favour survival will be unfavourable for reproduction, and vice versa.
For his work, Colchero used statistics, mathematical derivations and computer simulations with data from wild populations of 24 vertebrate species. The result was a significantly improved model that had more accurate predictions for a species’ population growth.
Despite the work’s technical nature, the model can have practical implications as they provide qualified explanations for the underlying reasons for extinction. This can be used to take management actions and may help prevent extinction of endangered species.