Generalized Single Index Models and Jensen Effects on Reproduction and Survival

TL;DR

This paper extends the Jensen Effect analysis to reproduction and survival data using generalized single index models, allowing testing of nonlinear environmental effects on count and binary outcomes in ecological studies.

Contribution

It introduces a novel method to test the direction of environmental variance effects on reproduction and survival, expanding previous models to new data types.

Findings

The proposed test has reasonable power under mild alternatives.

Sample size requirements are larger than typical for ecological data.

Application to Idaho steppe data demonstrates method utility.

Abstract

Environmental variability often has substantial impacts on natural populations and communities through its effects on the performance of individuals. Because organisms' responses to environmental conditions are often nonlinear (e.g., decreasing performance on both sides of an optimal temperature), the mean response is often different from the response in the mean environment. Ye et. al. 2020, proposed testing for the presence of such variance effects on individual or population growth rates by estimating the "Jensen Effect", the difference in average growth rates under varying versus fixed environments, in functional single index models for environmental effects on growth. In this paper, we extend this analysis to effect of environmental variance on reproduction and survival, which have count and binary outcomes. In the standard generalized linear models used to analyze such data the direction of the Jensen Effect is tacitly assumed a priori by the model's link function. Here we extend the methods of Ye et. al. 2020 using a generalized single index model to test whether this assumed direction is contradicted by the data. We show that our test has reasonable power under mild alternatives, but requires sample sizes that are larger than are often available. We demonstrate our methods on a long-term time series of plant ground cover on the Idaho steppe.