testing new links

cran-comments.md

@@ -3,3 +3,5 @@
 0 errors | 0 warnings | 1 note
 
 * This is a new release.
+* `urlchecker::url_check()` is intermittently returing 403s. I have verified and 
+double-checked the links involved.

vignettes/augmented_models.Rmd

@@ -430,7 +430,7 @@ summary(fm)
 #> scale reduction factor on split chains (at convergence, Rhat = 1).
 ```
 
-`flocker` enables users to fit data-augmented models using arbitrary `brms` formulas for the occupancy and detection components. However, we caution that continuous covariates in the occupancy sub-model can lead to pitfalls in interpretation. A seemingly straightforward application, for example, might be to ask how many species are present along an elevational gradient, fitting species-specific quadratic elevation-occupancy relationships. In our experience, a data-augmented model that includes quadratic elevation terms will place an arbitrarily large number of pseudo-species along the gradient, but will do so by placing pseudospecies' estimated elevational ranges entirely outside the range covered by the sampling effort ([Socolar et al 2022](https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.9328)). The model is in effect trying to estimate how many species occur in a landscape with elevations ranging from negative to positive infinity. This extrapolation is unprincipled, most obviously because it does not account for hard limits imposed by the physical termina of the gradient (valley floor and mountain peak). Thus, although `flocker` provides functionality to fit continuous covariates in the occupancy term, we recommend extreme caution in interpreting patterns estimated for never-observed species.
+`flocker` enables users to fit data-augmented models using arbitrary `brms` formulas for the occupancy and detection components. However, we caution that continuous covariates in the occupancy sub-model can lead to pitfalls in interpretation. A seemingly straightforward application, for example, might be to ask how many species are present along an elevational gradient, fitting species-specific quadratic elevation-occupancy relationships. In our experience, a data-augmented model that includes quadratic elevation terms will place an arbitrarily large number of pseudo-species along the gradient, but will do so by placing pseudospecies' estimated elevational ranges entirely outside the range covered by the sampling effort ([Socolar et al 2022](https://onlinelibrary.wiley.com/doi/10.1002/ece3.9328)). The model is in effect trying to estimate how many species occur in a landscape with elevations ranging from negative to positive infinity. This extrapolation is unprincipled, most obviously because it does not account for hard limits imposed by the physical termina of the gradient (valley floor and mountain peak). Thus, although `flocker` provides functionality to fit continuous covariates in the occupancy term, we recommend extreme caution in interpreting patterns estimated for never-observed species.
 
 <center>
 

vignettes/augmented_models.Rmd.orig

@@ -58,7 +58,7 @@ summary(fm)
 
 ```
 
-`flocker` enables users to fit data-augmented models using arbitrary `brms` formulas for the occupancy and detection components. However, we caution that continuous covariates in the occupancy sub-model can lead to pitfalls in interpretation. A seemingly straightforward application, for example, might be to ask how many species are present along an elevational gradient, fitting species-specific quadratic elevation-occupancy relationships. In our experience, a data-augmented model that includes quadratic elevation terms will place an arbitrarily large number of pseudo-species along the gradient, but will do so by placing pseudospecies' estimated elevational ranges entirely outside the range covered by the sampling effort ([Socolar et al 2022](https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.9328)). The model is in effect trying to estimate how many species occur in a landscape with elevations ranging from negative to positive infinity. This extrapolation is unprincipled, most obviously because it does not account for hard limits imposed by the physical termina of the gradient (valley floor and mountain peak). Thus, although `flocker` provides functionality to fit continuous covariates in the occupancy term, we recommend extreme caution in interpreting patterns estimated for never-observed species.
+`flocker` enables users to fit data-augmented models using arbitrary `brms` formulas for the occupancy and detection components. However, we caution that continuous covariates in the occupancy sub-model can lead to pitfalls in interpretation. A seemingly straightforward application, for example, might be to ask how many species are present along an elevational gradient, fitting species-specific quadratic elevation-occupancy relationships. In our experience, a data-augmented model that includes quadratic elevation terms will place an arbitrarily large number of pseudo-species along the gradient, but will do so by placing pseudospecies' estimated elevational ranges entirely outside the range covered by the sampling effort ([Socolar et al 2022](https://onlinelibrary.wiley.com/doi/10.1002/ece3.9328)). The model is in effect trying to estimate how many species occur in a landscape with elevations ranging from negative to positive infinity. This extrapolation is unprincipled, most obviously because it does not account for hard limits imposed by the physical termina of the gradient (valley floor and mountain peak). Thus, although `flocker` provides functionality to fit continuous covariates in the occupancy term, we recommend extreme caution in interpreting patterns estimated for never-observed species.
 
 <center>
 

vignettes/flocker_tutorial.Rmd

@@ -774,7 +774,7 @@ transect, yielding a one-dimensional analog of a spatial autologistic occupancy
 model.
 
 A second caution is to remind users that in multi-species models, users will 
-likely want to fit separate spatial terms by species ([Doser et al 2022](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13897)). 
+likely want to fit separate spatial terms by species ([Doser et al 2022](https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13897)). 
 For Gaussian processes, this can be achieved via the `by` argument to 
 `brms::gp()`. For some conditional autoregressive structures (those that allow 
 disconnected islands), this can be achieved by passing a block-diagonal