5  Environmental Variables

This project wanted to look at associations between Willow Tit occurrence and environmental variables to see if any relationships were identified that could then be used as predictors of distribution in areas that were not sampled. Such an approach would potentially help identify sites that could be managed for Willow Tits.

However, it is worth noting that my knowledge of, and experience with, species distribution models is very limited. Therefore, I have relied heavily on Strimas-Mackey (2023) for guidance and recommendations in applying these methods to the data. As well, the following sections have been undertaken more as a means of teaching myself species distribution modeling using a case study, and so the results may not reflect a robust methodology for Willow Tit data analysis.

First, MODIS files were downloaded and processed into a raster file (see Processing_MODIS_files.R), then the variables were matched to the checklist locations and a prediction grid was created (see EnvironmentalVariables.R).

5.1 Landcover

The MODIS MCD12Q1 v061 product was the chosen dataset for landcover variables as it provides global coverage at 500m spacial resolution from 2001-present. The University of Maryland classification system was used. It provides fifteen classes:

class name label description
0 Water bodies water At least 60% of area is covered by permanent water bodies.
1 Evergreen Needleleaf Forests evergreen_needleleaf Dominated by evergreen conifer trees (canopy >2m). Tree cover >60%.
2 Evergreen Broadleaf Forests evergreen_broadleaf Dominated by evergreen broadleaf and palmate trees (canopy >2m). Tree cover >60%.
3 Deciduous Needleleaf Forests deciduous_needleleaf Dominated by deciduous needleleaf (e.g. larch) trees (canopy >2m). Tree cover >60%.
4 Deciduous Broadleaf Forests deciduous_broadleaf Dominated by deciduous broadleaf trees (canopy >2m). Tree cover >60%.
5 Mixed Forests mixed_forest Dominated by neither deciduous nor evergreen (40-60% of each) tree type (canopy >2m). Tree cover >60%.
6 Closed Shrublands closed_shrubland Dominated by woody perennials (1-2m height) >60% cover.
7 Open Shrublands open_shrubland Dominated by woody perennials (1-2m height) 10-60% cover.
8 Woody Savannas woody_savanna Tree cover 30-60% (canopy >2m).
9 Savannas savanna Tree cover 10-30% (canopy >2m).
10 Grasslands grassland Dominated by herbaceous annuals (<2m).
12 Croplands cropland At least 60% of area is cultivated cropland.
13 Urban and Built-up Lands urban At least 30% impervious surface area including building materials, asphalt, and vehicles.
15 Non-Vegetated Lands nonvegetated At least 60% of area is non-vegetated barren (sand, rock, soil) or permanent snow and ice with less than 10% vegetation.
255 Unclassified unclassified Has not received a map label because of missing inputs.

University of Maryland classes

The landcover class was extracted for each checklist location by creating a 3km area around the checklist location. This is considered sufficient to account for spatial precision. Then, two metrics were calculated: a measure of composition (percentage of cover type) and a measure of configuration (edge density). These are reliable metrics for calculating environmental covariates in distribution models.

5.2 Elevation

Elevation data was downloaded from the EarthEnv website at 1km resolution. Then, both the mean and standard deviation of the elevation for the 3km checklist neighborhood was calculated.

The landcover and elevation variables were joined to the checklist data.

5.3 Prediction grid

A grid of habitat variables was created from which to make predictions.

The prediction grid was saved as a .csv file, but the raster (or .tiff) file can be used to map the variables. This is useful as a way to understand the classification scheme and how it relates to UK habitats.

For example, the variable “savanna” defined as “tree cover 30-60% (canopy >2m), was more relateable when mapped:

Example of mean elevation: