Start headings at h2

The document title is typically rendered as a h1 heading. Headings in the document therefore better start at h2.

vignettes/articles/detection-process.Rmd

@@ -28,7 +28,7 @@ library(dplyr)     # data wrangling
 library(ggplot2)   # data visualisation
 ```
 
-# Input
+## Input
 
 The functions are set up such that a single polygon as input is enough to go through this workflow using default arguments.
 The user can change these arguments to allow for more flexibility.
@@ -77,7 +77,7 @@ ggplot() +
   theme_minimal()
 ```
 
-# Detect occurrences
+## Detect occurrences
 
 We have our occurrences, but not all occurrences are generally observed.
 The detection of occurrences depends on the detection probability of a species
@@ -214,7 +214,7 @@ ggplot() +
   theme_minimal()
 ```
 
-# Example
+## Example
 
 Now that we know how the supporting functions work, we can simulate the detection process using the `sample_observations()` function.
 We can for example state that our species has a 0.9 detection probability and this time we say there is a very small chance to detect it on the road.
@@ -246,7 +246,7 @@ ggplot() +
   theme_minimal()
 ```
 
-# Adding coordinate uncertainty
+## Adding coordinate uncertainty
 
 To mimic real life data collection, we can finally add coordinate uncertainty to our observations.
 We only keep the detected occurrences of the previous example.

vignettes/articles/grid-designation-process.Rmd

@@ -29,7 +29,7 @@ library(ggplot2)   # data visualisation
 library(ggExtra)   # enhance data visualisation
 ```
 
-# Input
+## Input
 
 The functions are set up such that a single polygon as input is enough to go through this workflow using default arguments.
 
@@ -173,7 +173,7 @@ ggplot() +
   theme_minimal()
 ```
 
-# Grid designation
+## Grid designation
 
 Now we can make a data cube from our observations while taking into account the uncertainty.
 We can create the grid using the `grid_designation()` function.
@@ -319,7 +319,7 @@ ggExtra::ggMarginal(scatter_normal, type = "histogram")
 
 If no coordinate uncertainty is provided, the original observation point is used for grid designation.
 
-# Example
+## Example
 
 Now we know how to use the randomisation in `grid_designation()`.
 By default we use uniform randomisation.

vignettes/articles/multi_species_approach.Rmd

@@ -27,7 +27,7 @@ library(dplyr)     # data wrangling
 library(ggplot2)   # data visualisation
 ```
 
-# Spatial extend
+## Spatial extend
 
 As input, we create a polygon in which we simulate occurrences.
 It represents the spatial extend of the species.
@@ -99,7 +99,7 @@ ggplot() +
   theme_minimal()
 ```
 
-# Input dataframe
+## Input dataframe
 
 To generate cubes for multiple species efficiently, we create a dataframe where each row represents a different species and where we specify all arguments to be used by the main cube simulation functions, viz `simulate_occurrences()`, `sample_observations()`, `filter_observations()`, `add_coordinate_uncertainty()`, and `grid_designation()`, in separate columns.
 The values within these columns can change between species.
@@ -177,7 +177,7 @@ multi_species_dataset2 <- generate_taxonomy(
 identical(multi_species_dataset1, multi_species_dataset2)
 ```
 
-# Mapping the simulation process over each row/species
+## Mapping the simulation process over each row/species
 
 Each cube simulation function has a mapping companion.
 These mapping functions apply the single-species operations for each row using the `purrr::pmap()` strategy.
@@ -298,7 +298,7 @@ map_occ_cube_df3 <- multi_species_dataset2_renamed %>%
 glimpse(map_occ_cube_df3)
 ```
 
-# Visualise examples
+## Visualise examples
 
 Let's visualise the output for two of the six species.
 

vignettes/articles/occurrence-process.Rmd

@@ -29,7 +29,7 @@ library(ggplot2)   # data visualisation
 library(tidyterra) # visualisation spatraster objects
 ```
 
-# Input
+## Input
 
 The functions are set up such that a single polygon as input is enough to go through this workflow using default arguments.
 The user can change these arguments to allow for more flexibility.
@@ -53,7 +53,7 @@ ggplot() +
   theme_minimal()
 ```
 
-# Simulate occurrences
+## Simulate occurrences
 
 We generate occurrence points within the polygon using the `simulate_occurrences()` function.
 Default arguments ensure that an sf object with POLYGON geometry is sufficient to simulate occurrences.
@@ -64,7 +64,7 @@ Default arguments ensure that an sf object with POLYGON geometry is sufficient t
 
 The options for user defined arguments are demonstrated in the next subsections.
 
-## Changing number of occurrences over time
+### Changing number of occurrences over time
 
 Say we want to have 100 occurrences in our plot over 10 years.
 You can change the trend in the average number of occurrences over time.
@@ -192,7 +192,7 @@ tibble(
     theme_minimal()
 ```
 
-## Changing the degree of spatial clustering
+### Changing the degree of spatial clustering
 
 We can also choose the amount of spatial clustering.
 We visualise this with the supporting functions used in `simulate_occurrences()`.
@@ -317,7 +317,7 @@ ggplot() +
   theme_minimal()
 ```
 
-# Example
+## Example
 
 Now that we know how the supporting functions work, we can generate occurrence points within the polygon using the `simulate_occurrences()` function.
 We can for example sample randomly within the polygon over 6 time points were we use a random walk over time with an initial average number of occurrences equal to 100.