Accounting for sources of uncertainty when forecasting population responses to climate change

Abstract

In Focus: Jaatinen, K., Westerbom, M., Norkko, A., Mustonen, O., & Koons, D. N. (2021). Detrimental impacts of climate change may be exacerbated by density‐dependent population regulation in blue mussels. Journal of Animal Ecology, 90, 562–573, https://doi.org/10.1111/1365-2656.13377. Conservation strategies for threatened species are increasingly dependent on forecasts of population responses to climate change. For such forecasts to be accurate, they must account for multiple sources of uncertainty, including those associated with projections of future climate scenarios and those associated with the models used to describe population dynamics. While many population forecasts incorporate parameter uncertainty in abiotic effects and process variance related to unexplained temporal variation, most forecasts overlook the importance of evaluating uncertainty in the structure of the population model itself. By accounting for structural uncertainties in a model of population growth for blue mussels, Jaatinen et al. (2021) demonstrated that density‐dependent processes are likely to exacerbate adverse effects of climate change and reduce population viability of this keystone species. These findings highlight the importance of incorporating structural unknowns in population forecasts and the value of approaches that account for multiple sources of climate and model uncertainties. Forecasts that capture a range of possible population trajectories under climate change will help ensure efficient allocation of limited conservation resources.

Climate‐mediated population dynamics of a migratory songbird differ between the trailing edge and range core

“…We used statistical forecasting to assess population viability through 2040 (Clark et al, 2001 ; Desforges et al, 2018 ; Hooker et al, 2020 ; Howell et al, 2020 ). Statistical forecasting accounts for process variance (i.e., demographic stochasticity and environmental change) as well as parameter uncertainty (Zylstra & Zipkin, 2021 ). We forecasted dynamics at each study plot using the temporal‐trend and climate‐trend models.…”

Integrated community models: A framework combining multispecies data sources to estimate the status, trends and dynamics of biodiversity

“…Initial steps in determining the utility and value of combining multispecies data sources should focus on the specific information that could be gained from an integrated community model and the quantity and types of available data. Data integration approaches are becoming increasingly popular (Zipkin et al, 2021), and for good reason, as they have immense potential to expand inferential and predictive capabilities from available, yet imperfect, data (Zylstra & Zipkin, 2021). However, data integration is not without its challenges and limitations.…”

Integrated Population Models: Achieving Their Potential

“…For example, [100] provided projections of the monarch butterfly population based on a range of climate change scenarios. However, while these environmental-based techniques provide useful insights into the future, they are not without their issues [101]. As the projection duration increases so does uncertainty in the parameter estimates and forecasts, especially if the future climate scenario is significantly different from the past and current [102].…”

“…However, this can be easily reduced through larger sample sizes, or combining data sources, i.e., through the use of an IPM. In addition, [101] suggests that parameter uncertainty can be reduced, over the near and long term, by collecting targeted data to better understand mechanistic links. Another possibility, which is useful when resources are limited, is to optimize sampling design by investigating the cost-benefit of certain data collection methods, i.e., assessing whether the benefit of using more expensive monitoring methods are worth their possible reduction in uncertainty.…”