A comparison in species distribution model performance of succulents using key species and subsets of environmental predictors.

Identifying the environmental drivers of the global distribution of succulent plants using the Crassulacean acid metabolism pathway of photosynthesis has previously been investigated through ensemble-modeling of species delimiting the realized niche of the natural succulent biome. An alternative approach, which may provide further insight into the fundamental niche of succulent plants in the absence of dispersal limitation, is to model the distribution of selected species that are globally widespread and have become naturalized far beyond their native habitats. This could be of interest, for example, in defining areas that may be suitable for cultivation of alternative crops resilient to future climate change. We therefore explored the performance of climate-only species distribution models (SDMs) in predicting the drivers and distribution of two widespread CAM plants, Opuntia ficus-indica and Euphorbia tirucalli. Using two different algorithms and five predictor sets, we created distribution models for these exemplar species and produced an updated map of global inter-annual rainfall predictability. No single predictor set produced markedly more accurate models, with the basic bioclim-only predictor set marginally out-performing combinations with additional predictors. Minimum temperature of the coldest month was the single most important variable in determining spatial distribution, but additional predictors such as precipitation and inter-annual precipitation variability were also important in explaining the differences in spatial predictions between SDMs. When compared against previous projections, an a posteriori approach correctly does not predict distributions in areas of ecophysiological tolerance yet known absence (e.g., due to biotic competition). An updated map of inter-annual rainfall predictability has successfully identified regions known to be depauperate in succulent plants. High model performance metrics suggest that the majority of potentially suitable regions for these species are predicted by these models with a limited number of climate predictors, and there is no benefit in expanding model complexity and increasing the potential for overfitting.

Retrieving the real refractive index of mono- and polydisperse colloids from reflectance near the critical angle.

We investigate the accuracy in retrieving the real refractive index of submicron aerosol particles, at a visible wavelength, from near critical angle reflectance measurements of a dilute suspension of the aerosol. A coherent scattering model (CSM) is used to model the coherent reflectance from the colloidal suspension. We use an extension of the model for polydisperse particles to properly account for the modified size distribution close to the incident medium to colloid interface. We perform a rigorous sensitivity analysis, for both the monodisperse and polydisperse models, to determine how experimental uncertainties propagate into uncertainty in the retrieval of real refractive index. The effect of non-spherical scattering was included in the sensitivity analysis by using T-matrix methods. Experimental reflectance data, at a wavelength of 635 nm, were obtained for monodisperse spherical latex particles, a polydisperse sand sample and a polydisperse volcanic ash sample. We show that the retrieved real refractive index for these particles is consistent with values obtained using other techniques.