From the blue whale to the Bengal tiger, there are many endangered species that share the risk of extinction. A new mathematical model could save these species.
The risk of extinction varies between endangered species, depending on how the individuals within the species reproduce and how long they survive. A knowledge of the dynamics of survival and reproduction supports management actions to improve a species chance of survival. Statistical and mathematical models have become useful tools to provide this knowledge of survival and reproductive dynamics. A new mathematical model provides more accurate predictions.
The necessity of a new mathematical model
Associate professor Fernando Colchero, who is the author of the new paper published in Ecology Letters, explains: “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 focused on recreating population dynamics using an improved understanding of a species demography. He worked on construction stochastic population models, which predict how a certain population will change over time.
The models include mathematical factors describing the effect of the species’ environment, survival rates, and reproduction on population size and growth. Some assumptions are necessary for practical reasons.
Colchero challenge two commonly held assumptions, which are:
- The assumption that survival and reproduction are constant with age; and
- High survival in the species goes hand in hand with reproduction across all age groups within a species.
By accounting for age-specific survival and reproduction, and trade-offs between survival and reproduction, he was able to challenge the assumptions.
The outcome was a significantly improved mathematical model that had more accurate predictions for a species’ population growth.
Will it prevent the extinction of endangered species?
Despite the technical nature, this work could have practical applications in providing qualified explanations for the underlying reasons for extinction. This can be used to take better management actions which may help to prevent the extinction of endangered species.