How to analyse a continuous data which is non-linear, heteroskedastic and is spatially autocorrelated? I have data which is non-linear, heteroscedastic and is spatially autocorrelated. The predictor and response are continuous variables. Quantile regression accounts for the heteroscedasticity but I am not sure how to account for the autocorrelation. As one of the assumptions for quantile regression is the independence of data points.
Any leads will be much appreciated on how to examine heteroskedastic and autocorrelated data?
EDIT
I haven't considered robust estimator and will consider it.
More info regarding the model, I am regressing
Species_richness ~ Latitude + annual_precipitation + elevation
Currently, I am exploring this with the quantreg package in R. Can any of the methods be used for error estimation using this?
This thread suggests bootstrapping but unsure how it works.
(Quantile regression) Which standard error for heteroscedasticity & serial correlation

Quantile regression is not especially efficient in its use of data, and there are other solutions to the problem of heteroscedasticity that are likely better in your case.
Specifically, you can fit your model as a standard linear regression, and then calculate heteroscedasticity- and autocorrelation-consistent (HAC) standard errors such as the Newey-West estimator. This can be done in R using the sandwich package.
