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77 References# Linking demography with drivers: Climate and competition

Abstract

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.

Preprint

In both plant and animal systems, size can determine whether an individual survives and grows under different environmental conditions. However, it is less clear whether and when size-dependent responses to the environment affect population dynamics. Size-by-environment interactions create pathways for environmental fluctuations to influence population dynamics by allowing for negative... [Show full abstract]

Article

We discuss future challenges in developing statistical theory for Random Forests. In particular, we suggest that an analysis of bias and extrapolation is vital to understanding the statistical properties of variable importance measures. We further point to the incorporation of random forests within larger statistical models as an important tool for high-dimensional statistical inference.

Article

This paper examines the use of a residual bootstrap for bias correction in
machine learning regression methods. Accounting for bias is an important
obstacle in recent efforts to develop statistical inference for machine
learning methods. We demonstrate empirically that the proposed bootstrap bias
correction can lead to substantial improvements in both bias and predictive
accuracy. In the... [Show full abstract]

Article

1. A change in a climate variable may alter a species’ abundance not only through a direct effect on that species’ vital rates, but also through ‘indirect’ effects mediated by species interactions. While recent work has highlighted cases in which indirect effects overwhelm the direct effects of climate, we lack robust generalizations to predict the strength of indirect effects.
2. For... [Show full abstract]

Conference Paper

There has historically been very little concern with extrapolation in Machine Learning, yet extrapolation can be critical to diagnose. Predictor functions are almost always learned on a set of highly correlated data comprising a very small segment of predictor space. Moreover, flexible predictors, by their very nature, are not controlled at points of extrapolation. This becomes a problem for... [Show full abstract]