Linking demography with drivers: climate and competition. Issue 2 (February 2016)

• Record Type: Journal Article
• Authors: Teller, Brittany J.; Adler, Peter B.; Edwards, Collin B.; Hooker, Giles; Ellner, Stephen P.
• Editors: Metcalf, Jessica
• Is Part Of: Methods in ecology and evolution. Volume 7:Issue 2(2016:Feb.)
• Page Start: 171
• Page End: 183
• Publication Date: 2016-02
• Subjects: climate -- competition -- demography -- drivers -- estimation -- functional data analysis -- machine learning -- splines; Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210X.12486
• Languages: English
• ISSNs: 2041-210X

Abstract

Summary: In observational demographic data, the number of measured factors that could potentially drive demography (such as daily weather records between two censuses) can easily exceed the number of independent observations. Thus, identifying the important drivers requires alternatives to standard model selection and variable selection methods. Spline methods that estimate smooth functions over continuous domains (such as space or time) have the potential to resolve high‐dimensional problems in ecological systems. We consider two examples that are important for many plant populations: competition with neighbours that vary in size and distance from the focal individual and climate variables during a window of time before a response (growth, survival, etc.) is measured. For competition covariates, we use a simulation study based on empirical data to show that a monotone spline estimate of competition kernels via approximate AIC returns very accurate estimates. We then apply the method to long‐term, mapped quadrat data on the four dominant species in an Idaho (US) sagebrush steppe community. For climate predictors and their temporal lags, we use simulated data sets to compare functional smoothing methods with competing linear (LASSO) or machine learning (random forests) methods. Given sufficient data, functional smoothing methods outperformed the other two methods. Functional smoothing methods can advance data‐driven population modelling by providing alternatives to specifying competition kernels a priori and to arbitrarily aggregating continuous environmental covariates. However, there are important open questions related to modelling of nonlinear climate responses and size × climate interactions.