r-tools to analyse bird data_henk sierdsema_bird numbers 2016

R-tools to analyse bird data

Henk Sierdsema

Christian Kampichler

Bird data

• Monitoring data

• Atlas data

• Casual observations and lists

Saxifraga – Piet Munsterman

Bird data analysis

• Trends

• Distribution maps

• Trend maps / change

maps


Analysis tools

• Dedicated programs

– Trends: TRIM

– Maps: GIS (ArcGis, QGis), Maxent

• Scripting


• Free

• http://www.r-project.org/

• Large user community

• Many plugins called ‘packages’ dedicated

to specific statistical issues

http://www.r-project.org/



• 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

Trend analysis of monitoring data

• Most organisations use TRIM to calculate

trends

TRIM

• Data files needed for TRIM

– Observations

– For automated runs (batches): Command-file

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

Statistics in TRIM

• Generalized Estimating Equations

– Modelling of repeated/correlated

observations

– Robust regression method

– Estimating procedure, no likelihood function

TRIM output

• Estimated numbers per site per year

• Trend estimates

• Basic graphs in interactive mode

BirdStats

• Facilitate data preparation, trend

calculation and retrieval of output

• Access-database

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

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)

TRIM R-script

• Run TRIM in batch mode with tcf-

command files

• Retrieve TRIM-output

• Makes observations maps and maps

Saxifraga – Mark Zekhuis

TRIM R-script

• Retrieve data directly from Postgres-

database or read .csv-files:

– Sites

– Visits

– Observations

Examples

• Make files for TRIM

– make.TRIM.files()

• Run TRIM

• Import TRIM-output

– Import.TRIM.output()

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

Output: maps with observations

© Danish bird data: DOF

Output: trend graphs

Spatial modeling with TRIMmaps


TRIMmaps

• Facilitate production of maps from

monitoring data and casual

observations

• R-programme

• Open source / freeware

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

Interpolation of residuals

• Inversed Distance Weighting (IDW)

• Kriging

Include observed local deviations from regression model

Better maps

Combined map

interpolated residuals

regression model

What do you need to run TRIMmaps ?

Observations

• Counts

• Presence-absence

• Presence-only

• Local trend data

• CSV-file

• TRIM F1- or dat-files

Site/location info

• X- and Y-coordinates

Environmental maps

• Raster maps

– Like:

• Climate

• Land use

• Landscape

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

And some basic R-knowledge

We even made a small course for you

Running TRIMmaps

Zeroes

• Add zeroes for TRIM F1- or dat-files

• Generate zeroes with Maxent

Overlay with environmental data

sets

• Climate

• Altitude (DTM)

• Land cover

• …

Regression models

• GLM

• GAM

• MARS

• Mixed models

• Boosted Regression Trees

• Random Forests

• ..

Output

• Maps

– Observations

– Predictions regression

– Residuals

– Combined map

• Regression models

• Model validations

• Shape-files, ASCII-grids and geoTIFF with predictions

Observations

Distribution maps: regression

predictions

Interpolated residuals

Distribution maps

Regression predictions and

interpolated residuals

Regression predictions

Interpolated residuals

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

Response curves

18 20 22 24 26 28 302

46

81

0

bio_15

Pre

dic

ted

co

un

t

19.9 %

0.0 0.2 0.4 0.6 0.8 1.0

23

45

6

c00_f12_1km

Pre

dic

ted

co

un

t

16.6 %

30 35 40 45

34

56

78

bio_14

Pre

dic

ted

co

un

t

15.9 %

160 180 200 220

3.0

3.2

3.4

3.6

bio_18

Pre

dic

ted

co

un

t

8.8 %

5600 5800 6000 6200 6400

3.1

3.2

3.3

3.4

3.5

bio_4

Pre

dic

ted

co

un

t

6 %

480 520 560 600

3.0

3.4

3.8

4.2

pet_he_yr

Pre

dic

ted

co

un

t

5.4 %

Model quality

• Explained deviance

• Error statistics (MAE, MFE)

• ROC-plots

• Cross-validation

• Covariate maps

• QQ-plots, etc

Combining

trend and distribution:

trend maps

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

Corn Bunting trend 1984-1995

Difference maps

• Difference between predicted

distribution in two time periods


Absolute difference map

2010

2000

Important remarks

• Bird observed Birds present !

• Assumes minor influence of differences in

detection probabilities between habitats

• Next steps: account for imperfect

detection

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

Download TRIMmaps

www.sovon.nl/nl/content/trimmaps

Download TRIMmaps

or simply google ‘trimmaps’

Installing TRIMmaps

• Install R

• Install 64-bits Java manually!

• Run ‘install_TRIMmaps.r’

• Check dependent packages with

firstLibraryStart()

• That’s it!

Thank you for your attention… And we are happy to help you with the use of TRIMmaps!

[email protected]

An extented version of this presentation

can be download from Slideshare.net

Saxifraga – Mark Zekhuis

r-tools to analyse bird data_henk sierdsema_bird numbers 2016

Data & Analytics