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Understanding how environmental factors influence reproductive output may improve model predictions of coyote population dynamics.
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.
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.
Similar(49)
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.
Understanding how such traits link to large scale features of the environment may improve models that account for variation in strength of consumer effects across landscapes.
Understanding of the statistical variability of SSP and its correlation with ambient noise may improve modeling efforts and design generalized mitigation strategy.
Including other interneighborhood differences in highways and other streets, buildings, and roadside structures (e.g., elevation, street canyons, and shape) may also improve model transferability and generalizability.
Including interaction terms in an eQTL model may improve the model fit but also increases the model complexity.
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