A global Bayesian sensitivity analysis of the 1d SimSphere soil–vegetation–atmospheric transfer (SVAT) model using Gaussian model emulation

Sensitivity analysis consists of an integral and important validatory check of a computer simulation model before the code is used in performing any kind of analysis operation. The present paper demonstrates the use of a relatively new method and tool for conducting global sensitivity analysis (GSA) for environmental models, providing simultaneously the first GSA study of the widely used 1d soil–vegetation–atmospheric transfer (SVAT) model named SimSphere. A software platform called the Gaussian emulation machine for sensitivity analysis (GEM SA), which has been developed for performing a GSA via Bayesian theory, is applied to SimSphere model in order to identify the most responsive model inputs to the simulation of key model outputs, detect their interactions and derive absolute sensitivity measures concerning the model structure. This study is also very timely in that, use of this particular SVAT model is currently being considered to be used in a scheme being developed for the operational retrieval of the soil surface moisture content by National Polar-orbiting Operational Environmental Satellite System (NPOESS), in a series of satellite platforms that are due to be launched in the next 12 years starting from 2016.The employed GSA method was found capable of identifying the most responsive SimSphere inputs and also of capturing their key interactions for each of the simulated target quantities on which the GSA was conducted. The most sensitive model inputs were the topography parameters (slope, aspect) as well as the fractional vegetation cover and soil surface moisture availability. The implications of these findings for the future use of SimSphere are discussed.     

Savviness of prey to introduced predators

The prey naivety hypothesis posits that prey are vulnerable to introduced predators because many generations in slow gradual coevolution are needed for appropriate avoidance responses to develop. It predicts that prey will be more responsive to native than introduced predators and less responsive to introduced predators that differ substantially from native predators and from those newly established. To test these predictions, we conducted a global meta-analysis of studies that measured the wariness responses of small mammals to the scent of sympatric mammalian mesopredators. We identified 26 studies that met our selection criteria. These studies comprised 134 experiments reporting on the responses of 36 small mammal species to the scent of six introduced mesopredators and 12 native mesopredators. For each introduced mesopredator, we measured their phylogenetic and functional distance to local native mesopredators and the number of years sympatric with their prey. We used predator and prey body mass as a measure of predation risk. Globally, small mammals were similarly wary of the scent of native and introduced mesopredators; phylogenetic and functional distance between introduced mesopredators and closest native mesopredators had no effect on wariness; and wariness was unrelated to the number of prey generations, or years, since first contact with introduced mesopredators. Small mammal wariness was associated with predator-prey body mass ratio, regardless of the nativity. The one thing animals do not seem to recognize is whether their predators are native.