Bird abundance and distribution data for emergency response.

Tracking data

• Get metadata
• Build kernels 2 usage grid function

Abundance data

• Get metadata
• Kernels vs. rqss GAMs

##PROS and CONS of different methods

###GAMs

PROS

• Easily interpretable value (group size; number of birds seen when going to a site, by quantiles)

• Independent of the number of observers/checklists (but needs a certain number of observations to give a meaningful result)

CONS

• Can be difficult to implement

• Sensible to smoothing value

• May give extreme results when few observations are available and quantile is high (e.g. 90%)

• Need cells for predictions

###KERNELS

PROS

• Easier to implement and to use (e.g. risk: high, medium, low)

• Easy to output to polygons

CONS

• Not easy to interpret real meaning (group size, number of birds concerned?)

• Strongly depends on the amount of observers and checklists (lightly mitigated by a weigthing method based on numbers)

• Sensible to bandwith value and different parameters

##NOTES and IDEAS

• Use GAMs to predict quantile group size in each kernel polygons

• Need to sum species by groups when doing GAMs

• Integrate ECSAS, tracking and ebird data within the same kernel output

##TODO

• Winter Eiders, check email for using whites and adding browns which are in % ?
• Take out groups and observations flagged red in FB dynamic excel file (in downloads)
• Add EPOQ data, but only what is older than 2012 (?) to reduce overlap with EBIRD data
• Verify which seasons to use
• Add data from SOMEC (2012 and over ?) to the ECSAS data
• Use 50-70-90% kernels
• Shanti wants tracking data? (email 01-27)
• BIOMQ never was included in the kernel output (email 01-27)
• Adjust seasons according to (email 01-27)
• Check list of things left to do on google drive
• Make sure last version of ECSASconnect is used to produce RData with ECSAS data
• Check FB commentary on putting seabird survey data with ECSAS and shorebird data with ebird