Abstract
AbstractMany different factors influence animal activity. Often, the value of an environmental variable may influence significantly the upper or lower tails of the activity distribution. For describing relationships with heterogeneous boundaries, quantile regressions predict a quantile of the conditional distribution of the dependent variable. A quantile count model extends linear quantile regression methods to discrete response variables, and is useful if activity is quantified by trapping, where there may be many tied (equal) values in the activity distribution, over a small range of discrete values. Additionally, different environmental variables in combination may have synergistic or antagonistic effects on activity, so examining their effects together, in a modeling framework, is a useful approach. Thus, model selection on quantile counts can be used to determine the relative importance of different variables in determining activity, across the entire distribution of capture results. We conducted model selection on quantile count models to describe the factors affecting activity (numbers of captures) of cane toads (Rhinella marina) in response to several environmental variables (humidity, temperature, rainfall, wind speed, and moon luminosity) over eleven months of trapping. Environmental effects on activity are understudied in this pest animal. In the dry season, model selection on quantile count models suggested that rainfall positively affected activity, especially near the lower tails of the activity distribution. In the wet season, wind speed limited activity near the maximum of the distribution, while minimum activity increased with minimum temperature. This statistical methodology allowed us to explore, in depth, how environmental factors influenced activity across the entire distribution, and is applicable to any survey or trapping regime, in which environmental variables affect activity.
Highlights
Animal activity is influenced by a complex web of factors (Tester and Figala 1990), including a range of environmental variables (ChamailleJammes et al 2007, Upham and Hafner 2013)
We examined the distributions of toad captures using model selection on quantile count models to examine which environmental variables affected toad activity at different parts of the activity distribution during different seasons
We suggest that model selection on quantile count models is applicable to any trapping regime for which several environmental variables affect the number of individuals captured, especially if those effects occur near the lower or upper tails of the distribution
Summary
Animal activity is influenced by a complex web of factors (Tester and Figala 1990), including a range of environmental variables (ChamailleJammes et al 2007, Upham and Hafner 2013). Animal activity can vary widely in response to a variety of different environmental variables, but rather than determining the mean number of active animals, such variables may impose a limit on the maximum or minimum number of active animals. In such cases, it should be more appropriate to analyze particular portions of an activity distribution, rather than describing the rate of change of the mean, which may or may not change with the variable of interest. For example, a particular measured variable imposes a limit on activity, the organism’s response cannot increase to more than the upper limit set by that factor; it can be any value less than that, for example, if other, unmeasured, factors are influencing activity (Cade and Noon 2003)
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