This blog post is provided by Giacomo Rosa, Benedikt R. Schmidt, Hugo Cayuela and Stefano Canessa and tells the #StoryBehindThePaper for the article “Frankenstein matrices: among-population life history variation affects the reliability and predictions of demographic models”, which was recently published in the Journal of Animal Ecology. In their study, Rosa and colleagues examine the impacts of using “Frankenstein matrices” in practical population ecology, which can obscure the effects of inter-population variation in vital rates.

Population models are commonly used to predict the effect of conservation management on population viability. Models have to be parameterized with vital rates, such as survival or fecundity. When these vital rates are not available for a specific population or life stage (juveniles, adults), researchers often replace them with estimates from other populations of the same species. This approach, although practical, has an inherent problem, since it can overlook critical differences in life history across populations. We call the resulting population matrices ‘Frankenstein matrices’, given that they have been created using components from different populations. But are these hybrid matrices reliable tools for conservation? To answer this question, we assessed the effect of Frankenstein matrices on demographic inference and management decisions with a real-world application.

We analysed a large dataset of 18 populations from all over Europe (with, on average, 538 animals each) of yellow-bellied toad (Bombina variegata), an amphibian species whose life history depends on human land use. This species can occupy a broad range of natural (intermittent streams, springs and ponds) and anthropogenic habitats (ruts, ditches, seminatural ponds, and drinking troughs). We estimated survival of life stages (juveniles, sub-adults and adults) and recruitment (the rate of new individuals entering the population each year) for 18 populations across different habitat types (natural, forest, quarry and farmland). We then assessed how estimated population growth rates and elasticity (the proportional change in the population growth rate for a proportional change in a vital rate) changed when vital rates of a specific population were replaced by estimates from other populations. We made the replacements choosing randomly or basing on habitat, demographic or geographic proximity.

Our study, based on a real-world dataset, reveals the risks, and potential solutions, of relying on such hybrid approaches. The level of bias for the predictions of Frankenstein matrices depended on the grouping method. Borrowing estimates from geographically close or demographically similar populations substantially reduced the risk of extreme errors. Mean bias was relatively minor also when sampling randomly across all populations, because our large dataset represented the whole range of life histories and errors cancelled out on average. Borrowing populations from similar habitat types could also reduce bias, but results varied depending on the exact habitat types concerned. We showed how differences between populations, both from the environmental and life-history point of view, affect the reliability of population matrices commonly used in evolutionary demography, ecology, and conservation. This means that conservation practitioners should take care when borrowing vital rate estimates from other populations to guide management decisions.

However, the use of Frankenstein matrices cannot be avoided as certain estimates are not always available. For this reason, we recommend creating hybrid matrices on the basis of ecological, demographic, or geographic information. Moreover, our findings highlight the importance of establishing conservation strategies that include the full spectrum of uncertainty in demographic estimates. This method ensures that management plans remain robust even when data is lacking. We can improve the accuracy of population models by promoting transparency and more informed methods, leading to more successful conservation measures that accurately capture the complexity of species life-history. This study offers a roadmap for future research and decision-making in evolutionary demography, ecology, and conservation by concentrating on ecological patterns and ensuring reliable parameter estimates.

Read the paper

Read the full paper here: https://doi.org/10.1111/1365-2656.14243