minor changes on tutorial

Author: Lisa Bald

docs/_docs/030_spatialMaxent_short_introduction.md

@@ -66,5 +66,3 @@ If you are working on the command line these are the aditional parameters to use
 
 A full list of all possible command line parameters can be found [here](https://github.com/envima/spatialMaxent/blob/main/src/density/parameters.csv).
 
-If you are following the Tutorial with the downloaded preporcessed data [go to the Tutorial page next](../040_tutorial). If you are reproducing the whole workflow from front to back including preprocessing the data [go to the page data preprocessing next](../070_preprocessing).
-

docs/_docs/040_data_structure.md

@@ -92,3 +92,12 @@ The background file should then look like this:
 |8|Bradypus variegatus	|-70.0833333333333|	0.75	|			-9999	|25.5028228759766|	9.06110382080078|	85.6801605224609|	52.3916168212891|	30.8559989929199|	20.2805004119873|	10.5754985809326|	25.3150825500488|	25.891040802002	|	26.0111656188965|	24.7291259765625|	3280|	364|	182|23.4312362670898|	1062|	618	|	626	|973|
 |9|Bradypus variegatus	|-39.75|			-6.25	|			-9999	|25.156063079834|	10.2133750915527|	75.6560287475586|	105.416397094727|	32.3040008544922|	18.8042507171631|	13.4997501373291|	24.747917175293	|	25.6065845489502|	26.4520835876465|	23.9066257476807|	830	|	215|	7|	101.104278564453|	517	|	28	|	58	|148|
 |10|Bradypus variegatus	|-51.9166666666667|	-16.25	|			-9999	|24.5825939178467|	12.3339786529541|	65.1170349121094|	143.526840209961|	32.556999206543	|	13.6157503128052|	18.9412498474121|	25.3168754577637|	22.6622085571289|	25.9228324890137|	22.478084564209|	1568|	289|	8|	81.0324325561523|	778	|	37	|	424|	50|
+
+
+
+### What´s next?
+
+* If you just want to do the short standalone workflow go [here](../050_standalone_workflow).
+* If you are following the tutorial with the downloaded preporcessed data go to the [Tutorial page](../100_tutorial) next. 
+* If you are reproducing the whole tutorial workflow from front to back including preprocessing the data go to the [page data preprocessing](../060_preprocessing) next.
+

docs/_docs/090_bg.md

@@ -7,7 +7,7 @@ header:
 
 As a last step we prepare the background points (BP) we need to train Maxent. We will use 10,000 background points but not distribute them randomly over the whole study area. Instead we use `conditioned latin hypercube sampling` (CLHS) [(Minasny & McBratney, 2006) ]( 10.1016/j.cageo.2005.12.009) as implemented in the [R package clhs]( https://cran.r-project.org/web/packages/clhs/index.html).  The conditioned latin hypercube sampling distributes the points in a way over the study area that all variables of the environmental data are represented as well as possible.
 
-To do this you first need to download the environmental grids for Ontario, Canada from [here]( https://osf.io/kwc4v/) if you have not already done it and save them in the `layers` folder of your project. Then execute the script below.
+To do this you first need to download the environmental grids for Ontario, Canada from [here]( https://osf.io/kwc4v/files/osfstorage) if you have not already done it and save them in the `layers` folder of your project. You can find the raster files on the OSF page under data/Environment/CAN. Download the complete CAN folder into your data/layers folder and don´t forget to transorm the format to one that is supported by Maxent (see [here](../060_preprocessing/#download-environmental-layers) how to do this). Then execute the script below.
 
 <script src="https://gist.github.com/Baldl/863fe4abdb6a5afb868d604b2c787636.js"></script>