Resources

Data collection

1. FieldApp iPhone app for collecting, organising and exporting field datasets

Data processing

1. R package for processing rapid light curve data from Walz WaterPAM fluorometers:

See here for a simple R package developed by Chris Williamson to process rapid light curve datasets in R

https://github.com/chrisjw18/rlcs

2. R package for pixel-wise processing of ImagingPAM rapid light curve datasets derived from Walz ImagingPAM fluorometers:

See here for an R package developed by Bruno Jesus and Chris Williamson compiled as an R Shiny Interface that allows full pixel-wise interrogation of ImagingPAM rapid light curve datasets, including imaging smoothing, region of interest delineation (including the full image), initial parameter selection, multiple model fits for RLC datasets, interactive pixel-wise exploration of derived parameters and associated errors of model fits, including further region of interest and transect capabilities. All datasets and derived products can be saved locally or exported into your R environment for further processing.

The associated publication detailing this package is currently under review in Limnology and Oceanography: Methods – please cite the paper once published if you use the package.

https://github.com/bmjesus/pamfit

Field methods

1. In-situ PAM method to measure actual photophysiology of ice-inhabiting algal communities

See here for a new Pulse Amplitude Modulation (PAM) fluorometry method developed by Chris Williamson on the Greenland Ice Sheet that allows to capture the near-actual photophysiology of ice-inhabiting microalgal communities sampled in-situ allowing the first detailed characterisation of their responses to dominant environmental forcing in glacial landscapes:

New method provides first evidence of fine-scale in situ heterogeneity in glacier algal photophysiology

More to come in the near future….