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Traditional models underestimate extinction rates

Last year, the World Conservation Union reported an unprecedented decline in biodiversity, with nearly 16,000 species facing extinction. The biggest threat to most species is loss of habitat. And as habitat loss and degradation proceed nearly unabated, the need to accurately predict the population dynamics and extinction risk of potentially endangered species has never been greater. In a new study, John Drake tests models traditionally used to estimate the likelihood of extinction and shows that because they ignore a critical parameter in projecting risk, they underestimate extinction rates.

Standard models for predicting extinction assume that population growth and decline are governed by random, or stochastic, variables. But few scientists have actually tested the accuracy of these models with empirical data. To do this, Drake first manipulated the available food sources in 281 populations of water fleas, because it's generally assumed that fluctuating environments, a given in the natural world, increase a species's chance of extinction. Drake then used half of the experimental data generated from testing the effects of environmental variability on water flea survival to select his models and estimate a range of parameters that might affect extinction, and the other half to test the models' reliability. From the estimated parameters, Drake wrote a computer program to simulate all the possible population outcomes and predict extinction rates. However, one set of simulations included a parameter for density-dependent random interactions and another did not. The idea is that if organisms interact with each other in their environments--which of course they do--then these interactions will likely affect an individual's probability of dying or reproducing, which ultimately affects species survival. Drake calls this variable density-dependent demographic stochasticity. Only when density-dependence was included did the models match the observed extinction rates in
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Source:Public Library of Science


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