R-tools to analyse bird data_Henk sierdsema_Bird numbers 2016
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This presentation shows how trends, distribution maps and trend maps can be made from monitoring data, atlas data and casual observations with R. The presentation shows examples for birds, but the techniques implemented in R-package TRIMmaps can also be used for other species groups.
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R-tools to analyse bird data_Henk sierdsema_Bird numbers 2016
- 1. R-tools to analyse bird data Henk Sierdsema Christian Kampichler
- 2. Bird data • Monitoring data • Atlas data • Casual observations and lists Saxifraga – Piet Munsterman
- 3. Bird data analysis • Trends • Distribution maps • Trend maps / change maps Saxifraga – Piet Munsterman
- 4. Analysis tools • Dedicated programs – Trends: TRIM – Maps: GIS (ArcGis, QGis), Maxent • Scripting Saxifraga – Piet Munsterman
- 6. • Free • http://www.r-project.org/ • Large user community • Many plugins called ‘packages’ dedicated to specific statistical issues
- 7. • You can perfom very basic and very advanced statistics with R • But it’s also very useful if you have to (almost) the same thing many times, like: – Make trends graphs for 100 species – Make correlation plots for 100 species and 50 variables – Make distribution maps for 100 species • And if you want to redo an analysis because you got new data
- 8. Trend analysis of monitoring data • Most organisations use TRIM to calculate trends
- 9. TRIM • Data files needed for TRIM – Observations – For automated runs (batches): Command-file
- 10. TRIM-observation file • Complete matrix: – Positive observations – Zero-observations – Missing values – (Covariates) 5 1976 -1 5 1977 -1 5 1978 -1 5 1979 -1 5 1980 0 5 1981 -1 5 1982 0 5 1983 1 5 1984 0 5 1985 0 5 1986 5 5 1987 0
- 11. Statistics in TRIM • Generalized Estimating Equations – Modelling of repeated/correlated observations – Robust regression method – Estimating procedure, no likelihood function
- 12. TRIM output • Estimated numbers per site per year • Trend estimates • Basic graphs in interactive mode
- 13. BirdStats • Facilitate data preparation, trend calculation and retrieval of output • Access-database
- 14. BirdStats • However, also requires data-processing before data can be read into BirdStats • Limited flexibility • Other option: make R-script to prepare data and retrieval of output
- 15. TRIM R-script • Retrieve data directly from database or read .csv-files • Set number of parameters like: – Time period (can be species specific) – Species selection – Site-selection (shape or csv-file)
- 16. TRIM R-script • Run TRIM in batch mode with tcf- command files • Retrieve TRIM-output • Makes observations maps and maps Saxifraga – Mark Zekhuis
- 17. TRIM R-script • Retrieve data directly from Postgres- database or read .csv-files: – Sites – Visits – Observations
- 18. Examples • Make files for TRIM – make.TRIM.files() • Run TRIM • Import TRIM-output – Import.TRIM.output()
- 19. Examples • Read data ## ROUTE LOCATIONS pktrute <- read.csv2("pktrute.csv", as.is = TRUE, fileEncoding = "UTF8") ## SPECIES CODES AND NAMES speciescodes <- read.csv2("speciescodes.csv", as.is = TRUE, fileEncoding = "UTF8") • Set parameters minyear <- 1976 maxyear <- 2015 map.format <- "pdf“ test.run <- TRUE
- 20. Output: maps with observations © Danish bird data: DOF
- 22. MAPS
- 23. Spatial modeling with TRIMmaps Saxifraga – Piet Munsterman
- 24. TRIMmaps • Facilitate production of maps from monitoring data and casual observations • R-programme • Open source / freeware
- 25. Statistics TRIMmaps • Combination of – Regression type statistics – Spatial interpolation of residuals • Uses TRIM-input (data-files) or -output (F1-files) and a number of other formats
- 26. Interpolation of residuals • Inversed Distance Weighting (IDW) • Kriging Include observed local deviations from regression model Better maps
- 28. What do you need to run TRIMmaps ?
- 29. Observations • Counts • Presence-absence • Presence-only • Local trend data • CSV-file • TRIM F1- or dat-files
- 30. Site/location info • X- and Y-coordinates
- 31. Environmental maps • Raster maps – Like: • Climate • Land use • Landscape
- 32. Optional: site specific covariates • Environmental characteristics for sites that have a different spatial extend than the prediction grids – Like: • buffers around points • Transects with distance bands • Plots with natural bounderies • Squares that are bigger than the prediction grid • Have to be pre-prepared with GIS
- 33. And some basic R-knowledge We even made a small course for you
- 34. Running TRIMmaps
- 35. Zeroes • Add zeroes for TRIM F1- or dat-files • Generate zeroes with Maxent
- 36. Overlay with environmental data sets • Climate • Altitude (DTM) • Land cover • …
- 37. Regression models • GLM • GAM • MARS • Mixed models • Boosted Regression Trees • Random Forests • ..
- 38. Output • Maps – Observations – Predictions regression – Residuals – Combined map • Regression models • Model validations • Shape-files, ASCII-grids and geoTIFF with predictions
- 39. Observations
- 42. Distribution maps Regression predictions and interpolated residuals Regression predictions Interpolated residuals
- 43. Variable contributions Variable Relative importance (%) Precipitation Seasonality 18.8 Precipitation of Driest Month 17.4 Arable land 15.6 Precipitation of Warmest Quarter 8.5 Temperature Seasonality 6.1 Potential evapotranspiration 5.8 Silt soil 4.7 Mean Temperature of Driest Quarter 3.9 Complex cultivation patterns 3.6
- 44. Response curves 18 20 22 24 26 28 30 246810 bio_15 Predictedcount 19.9 % 0.0 0.2 0.4 0.6 0.8 1.0 23456 c00_f12_1km Predictedcount 16.6 % 30 35 40 45 345678 bio_14 Predictedcount 15.9 % 160 180 200 220 3.03.23.43.6 bio_18 Predictedcount 8.8 % 5600 5800 6000 6200 6400 3.13.23.33.43.5 bio_4 Predictedcount 6 % 480 520 560 600 3.03.43.84.2 pet_he_yr Predictedcount 5.4 %
- 45. Model quality • Explained deviance • Error statistics (MAE, MFE) • ROC-plots • Cross-validation • Covariate maps • QQ-plots, etc
- 47. Trend maps • Standardized difference between the average count in the last three years and the first three years of the period of interest Nend of period – Nstart of period (N1+N2)/2
- 51. Difference maps • Difference between predicted distribution in two time periods Saxifraga – Piet Munsterman
- 53. Important remarks • Bird observed Birds present ! • Assumes minor influence of differences in detection probabilities between habitats • Next steps: account for imperfect detection
- 54. Comparison with other R- packages • Site-specific covariates – like buffer around point or transect – downscaling larger grids • Account for spatial correlation in residuals • Use TRIM-files for input
- 56. Download TRIMmaps or simply google ‘trimmaps’
- 57. Installing TRIMmaps • Install R • Install 64-bits Java manually! • Run ‘install_TRIMmaps.r’ • Check dependent packages with firstLibraryStart() • That’s it!
- 58. Thank you for your attention… And we are happy to help you with the use of TRIMmaps! henk.sierdsema@sovon.nl An extented version of this presentation can be download from Slideshare.net Saxifraga – Mark Zekhuis