12  Lab Intro

Lab 3 Common trends in plankton data

Author

Mark Scheuerell

Published

April 20, 2023

Background

For this lab you will use dynamic factor analysis (DFA) to examine common temporal patterns in multivariate plankton data from Lake Washington. These data are a bit noisy and gappy, so you will need to make some choices about which species and time period(s) to examine.

Teams

  1. Liz Elmstrom (SAFS), Dylan Hubl (SEFS)

  2. Maria Kuruvilla (QERM), Eric French (CEE), Madison Shipley (SAFS)

  3. Nick Chambers (SAFS), Terrance Wang (SAFS), Zoe Rand (QERM)

  4. Emma Timmins-Schiffman (Genome Sci), Karl Veggerby (SAFS), Miranda Mudge (Molecular & Cellular)

Lake Washington data

For reference, here are the columns in the Lake Washington data set:

Indices

  • Year: year
  • Month: month as an integer

Environmental data

  • Temp: water temperature in degrees C
  • TP: total phosphorous concentration in mg m-3
  • pH: pH.

Phytoplankton

  • Cryptomonas: small brown or green algae (edible)
  • Diatoms: small algae rich in silica (edible)
  • Greens: general class of algae (edible)
  • Bluegreens: cyanobacteria that can fix nitrogen (inedible)
  • Unicells: very small algae (edible)
  • Other.algae: generic catch-all for atypical species (edible)

Zooplankton

  • Conochilus: colonial form of rotifer (grazer)
  • Cyclops: cyclopoid copepod (grazer)
  • Daphnia: cladoceran (grazer)
  • Diaptomus: calanoid copepod (grazer)
  • Epischura: very large calanoid copepod (predator)
  • Leptodora: very large cladoceran (predator)
  • Neomysis: opossum shrimp (predator)
  • Non.daphnid.cladocerans: catch-all for other cladocerans (grazers)
  • Non.colonial.rotifers: free-floating rotifers (grazers)

Resources

Lab materials from April 20 [online here]

Chapter 10 Dynamic Factor Analysis. ATSA Lab Book. [online here]