Understanding how environmental factors influence reproductive output may improve model predictions of coyote population dynamics.
Since many types of distributions, for example, are used in modeling wind characteristics it should be expected that Kum-G families of distributions may improve modeling results.
Collection and incorporation of data that appear not to exist for Boophilus ticks, such as larval lipid content and lipid-use rates, may improve model accuracy.
Understanding of the statistical variability of SSP and its correlation with ambient noise may improve modeling efforts and design generalized mitigation strategy.
Incorporating auxiliary biophysical predictors, such as land-use, land-cover or vegetation structure information may improve model accuracy, by reducing the spectral variability within those biophysical predictors [e.g. see 10, 67, 72].
However, providing the collinearity between predictors can be identified, quantified and built into the model their synergistic effect may improve model predictive capability.
The results suggest that the use of historical, in addition to modern, range information and randomly sampled pseudo-presence points may improve model accuracy.
The inclusion of the percentage of women exclusively breastfeeding may improve model predictions and therefore warrants consideration.
Future research may improve model prediction by sampling in different seasons and measuring a more diverse and larger number of subjects.
Therefore, including contiguity as based on field edges rather than on area-weighted tessellation around farm premises point locations may improve model accuracy.
