Supplementary Material from Predicting behavioural responses to novel organisms: state-dependent detection theory

Human activity alters natural habitats for many species. Understanding variation in animals' behavioural responses to these changing environments is critical. We show how signal detection theory can be used within a wider framework of state-dependent modelling to predict behavioural responses to a major environmental change: novel, exotic species. We allow thresholds for action to be a function of reserves, and demonstrate how optimal thresholds can be calculated. We term this framework ‘state-dependent detection theory’ (SDDT). We focus on behavioural and fitness outcomes when animals continue to use formerly adaptive thresholds following environmental change. In a simple example, we show that exposure to novel animals which <i>appear</i> dangerous—but are actually safe—(e.g. ecotourists) can have catastrophic consequences for ‘prey’ (organisms that respond as if the new organisms are predators), significantly increasing mortality even when the novel species is not predatory. SDDT also reveals that the effect on reproduction can be greater than the effect on lifespan. We investigate factors that influence the effect of novel organisms, and address the potential for behavioural adjustments (via evolution or learning) to recover otherwise reduced fitness. Although effects of environmental change are often difficult to predict, we suggest that SDDT provides a useful route ahead

Density of zooplankton

Density of zooplankto

Appendix A. Scoring system of crayfish aggregation.

Scoring system of crayfish aggregation

Male dominance stability data

Data for replicating analysis of male stability. Proportion of times male remained dominant were manually calculated from scan samples presented in an earlier publication (Montiglio et al. 2017; DOI: https://doi.org/10.5061/dryad.69dh9

Phenotypic differences between groups

Phenotypic differences between group

Appendix A: Table of mathematical notation from Mast seeding promotes evolution of scatter-hoarding

A list of names and descriptions for the parameters and variables introduced in the main text

R code from Mast seeding promotes evolution of scatter-hoarding

Many plant species worldwide are dispersed by scatter-hoarding granivores: animals that hide seeds in numerous, small caches for future consumption. Yet, the evolution of scatter-hoarding is difficult to explain because undefended caches are at high risk of pilferage. Previous models have attempted to solve this problem by giving cache owners large advantages in cache recovery, by kin selection, or by introducing reciprocal pilferage of ‘shared’ seed resources. However, the role of environmental variability has been so far overlooked in this context. One important form of such variability is masting, which is displayed by many plant species dispersed by scatterhoarders. We use a mathematical model to investigate the influence of masting on the evolution of scatter-hoarding. The model accounts for periodically varying annual seed fall, caching and pilfering behaviour, and the demography of scatterhoarders. The parameter values are based mostly on research on European beech (Fagus sylvatica) and yellow-necked mice (Apodemus flavicollis). Starvation of scatterhoarders between mast years decreases the population density that enters masting events, which leads to reduced seed pilferage. Satiation of scatterhoarders during mast events lowers the reproductive cost of caching (i.e. the cost of caching for the future rather than using seeds for current reproduction). These reductions promote the evolution of scatter-hoarding behaviour especially when interannual variation in seed fall and the period between masting events are large.This article is part of the theme issue ‘The ecology and evolution of synchronized seed production in plants’

File for path analysis

File for path analysi

Appendix B. Tables and figures of the results from the repeated-measures ANOVA for behavior variables.

Tables and figures of the results from the repeated-measures ANOVA for behavior variables

data used in the manuscript and script to reproduce tables and figures.

The zip file contains three data sets used in the study. data.striders.complete.sihetal.csv provides the activity and mating activity of individuals at each observation. data.striders.mating.durations.sihetal.csv provides the durations, individual identities, and other data associated with each mating observed. data.striders.mating.frequencies.sihetal.csv provides information on the number of mating of each individual each day. Each data set is also associated with a text meta-data file describing the meaning of the variables and the structure of the data. The zip file also contains the R script used to produce tables and figures in the manuscript. See the read me file for more information