Q2. How many equal interval bins were used to determine bin size and number?
To determine bin size and number, the authors divided predictions into 20 equalinterval bins scaled between the minimum and maximum scores.
Q3. What is the main concern with the used-versus-unused approach to fitting RSF models?
A concern with the used-versus-unused approach to fitting RSF models is that it may be difficult to demonstrate non-use, especially for mobile and cryptic animals.
Q4. What is the way to evaluate RSF models?
The authors propose a method that evaluates prediction success from RSF models built with presence/ available data using a form of k -fold cross validation.
Q5. What is the importance of understanding the relationships between habitats and species?
Understanding such relationships is of crucial importance in natural resource management and conservation, because managers and conservationists are asked to provide habitat-based models describing the influence of changing land-use activities on sensitive or rare species (cumulative effects assessments, population viability analyses, climate change models, etc.).
Q6. What were the environmental predictor variables for the grizzly bear?
Environmental predictor variables included elevation from a digital elevation model (DEM), the square (Gaussian transformation) of elevation, greenness derived from a tasseled-cap transformation of spectral reflectance from a Landsat image, and habitat cover type following aggregate functional habitats outlined by Mattson et al. (1998).
Q7. Why is the Kappa technique important in wildlife RSF modeling?
This is particularly important when the prevalence of the species is low (Fielding and Bell, 1997)*/a frequent phenomenon in wildlife RSF modeling because models often are developed for rare, threatened, or endangered species.
Q8. What is the alternative to withholding a fraction of the data?
An alternative is to withhold a fraction of the data using a k -fold partitioning of the original samples (Fielding and Bell, 1997), where k represents the number of partitions ranging from 2 to N /1 (number of observations minus one).
Q9. What are the powerful approaches for selecting a model from a set of alternative plausible models?
Information criteria such as AIC and BIC are the most powerful approaches for model selection from a set of alternative plausible models (Burnham and Anderson, 1998).
Q10. What is the main difference between the two types of RSF models?
given the spatially and temporally dynamic nature of habitat selection common to many species, robust RSF models are notnecessarily expected.
Q11. What was the method used to develop the presence/absence models?
All presence/absence models (GLM, logistic regression) were developed using the same general approach the authors recently used to develop abundance models (Vernier et al., 2002; GLM, Poisson regression), where the set of variables included for each species’ model was selected from among five alternative habitat model formulations using AIC).