Many 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, or different environmental variables in combination may have synergistic or antagonistic effects on activity,so examining their effects together, in a modeling framework, or is a useful approach. Thus,model choice 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 choice on quantile count models to record the factors affecting activity (numbers of captures) of cane toads (Rhinella marina) in response to several environmental variables (humidity, and 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 choice on quantile count models suggested that rainfall positively affected activity, especially near the lower tails of the activity distribution. In the wet season, and 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, or how environmental factors influenced activity across the entire distribution,and is relevant to any survey or trapping regime, in which environmental variables affect activity.
Source: usgs.gov