WildCo Data Analysis Intro
1
Introduction
1.1
Ethos
1.2
What this course is
1.3
What this course is not
1.4
How to use this book
1.5
Get in touch
1.6
Acknowledgements
2
Preparing for the course
2.1
Install R
2.2
Install RStudio
2.3
Install the required packages
2.3.1
CRAN packages
2.3.2
Other packages
2.4
Create an R project
2.5
Download the data files
2.6
The example datasets
2.6.1
example_data
2.6.2
your_data
2.7
Practise before the course
3
Preprocessing and labelling
3.1
Data storage
3.2
Preprocessing
3.2.1
Renaming
3.2.2
Automated Labelers
3.2.3
Sensitive images
3.2.4
Timelapse extraction
3.3
Labelling
3.4
End dates and outages
4
Metadata standardisation
4.1
The Wildlife Insights Minimum Metadata Standards
4.1.1
Project data
4.1.2
Image data
4.1.3
Deployment data
4.1.4
Camera inventory
4.1.5
Important note
5
Error checking
5.1
Standardised exploration script
5.2
Formatting dates
5.2.1
Skill check:
lubridate
5.2.2
Deployment dates
5.2.3
Image dates
5.3
Basic trapping summaries
5.4
Error checks
5.4.1
Camera locations
5.4.2
Camera activity checks
5.4.3
Detection check
5.4.4
Taxonomy check
5.4.5
Skill Check: Taxize package
5.5
Diel activity check
5.6
Conclusion
6
Analysis data creation
6.1
Common analysis data formats
6.2
Independent detections
6.3
Effort look-up
6.4
Observations by time interval
6.5
Our data
6.5.1
Filter to target species
6.5.2
Create a daily camera activity lookup
6.5.3
Determine ‘independent’ camera detections
6.5.4
Add additional data
6.6
Creating analysis dataframes
6.6.1
Final data check
7
Analysis covariates
7.1
Species traits
7.2
Camera station covariates
7.2.1
Locally collected covariates
7.2.2
Remotely collected covariates
7.2.3
Extracting data from local rasters
7.2.4
elevatr package
7.2.5
Open Street Maps
7.2.6
Vegetation productivity
7.2.7
Digging deeper
7.3
Convert and save your covariates
7.4
Correlations between predictors
8
Analysis data exploration
8.1
Final locations plot
8.2
Independent detections summary
8.2.1
Total number of captures
8.2.2
Raw occupancy
8.2.3
Comparison plot
8.3
Temporal patterns in capture rates
8.4
Species-specific capture rates
8.5
Spatial patterns in capture rates
8.6
Species co-occurences
8.7
Covariate plots
8.7.1
Continuous predictors
8.7.2
Catagorical predictors
8.7.3
Do your own exploration
9
Community composition
9.1
Observed richness
9.2
Estimated richness
9.2.1
iNext package
9.3
Sampling-unit-based accumulation curves
9.4
Basic results plot
9.4.1
Sampling duration example
9.4.2
On your own
9.5
Other diversity metrics
9.5.1
Simpson and Shannon
9.5.2
More examples in the literature
9.5.3
Multispecies occupancy model
9.6
Community structure
9.6.1
Extracting data for plotting
9.6.2
On your own
9.6.3
Examples in the literature
10
Habitat use
10.1
Calculating capture rate
10.1.1
Examples from the literature
10.2
Single-species models
10.2.1
Simple linear model
10.2.2
Catagorical predictor
10.2.3
Continuous predictor
10.2.4
Model comparisons
10.2.5
Problems with these models
10.2.6
Mixed-effects models
10.2.7
On your own
10.2.8
Advanced mixed-model predictions
10.2.9
Examples in the literature
10.3
Multispecies models
10.3.1
Potential dangers
10.3.2
Further reading
10.3.3
Examples in the literature
11
Occupancy
11.1
Single species occupancy model
11.1.1
Unmarked package
11.1.2
Plotting predictions
11.1.3
On your own
11.2
Spatial occupancy model: spOccupancy
11.2.1
Multi species model
12
Activity
12.1
Independent detections or raw data?
12.2
Data formatting
12.2.1
Accounting for sunrise and sunset
12.3
Species comparisons
12.4
Treatment comparisons
12.4.1
Seasonal comparison
12.5
On your own
12.6
Selected further reading
13
Density
13.1
Individually identifiable individuals
13.2
Unmarked animals
13.2.1
Random encounter model
13.2.2
Moose
13.2.3
Wolf
13.2.4
Time to event / Space to event density estimates
13.2.5
N-mixture model in unmarked
13.2.6
ABMI Method
13.2.7
Unmarked spatial capture recapture (uSCR)
13.3
Future directions
14
Behavior
14.1
Behavioural designations
14.2
Event duration
14.3
Animal speed and day range
14.4
Experimental manipulations
14.5
Interactions
14.5.1
Activity overlap
14.5.2
One species as a predictor of another
14.5.3
Residual co-occurence models
14.5.4
Attractance-Avoidance Ratios (AAR)
14.6
Worked examples
15
On your own
15.1
Working with a new dataset
15.1.1
Read in the data and packages
15.1.2
Format the dates
15.1.3
Plot and correct spatial co-ordinates
15.1.4
Check camera spacing
15.1.5
Check all deployments have image data
15.1.6
5b. Check all images have deployment data
15.1.7
5c. Check deployments occur where you expect
15.1.8
Check images occur within deployments
15.1.9
Check species taxonomy
15.1.10
Check species activity
15.1.11
Filter to target species
15.1.12
Make effort look_up
15.1.13
Create independent data
15.1.14
Create analysis dataframes
15.1.15
Check analysis dataframe counts
15.2
Add your covariates
15.2.1
Species Traits
15.3
Spatial data
15.3.1
Convert to simple features
15.3.2
Get elevation data
15.3.3
Open street map data
15.3.4
NDVI
15.4
Data exploration
15.4.1
Final map
15.4.2
Detection summary
15.4.3
Temporal patterns
15.4.4
Spatial patterns
15.4.5
Species co-occurance
References
Published with bookdown
An Introduction to Camera Trap Data Management and Analysis in R
References