Workshop Notes¶

are on etherpad: https://epad.nceas.ucsb.edu/p/commdyn-20140105

Breakout session 1: Metrics brainstorming¶

• what are you currently using
• what would you like to use
• how widely is it used
• can it be applied to different biological community datasets (sampling approach)
• is it already coded {in R}

Metrics¶

1. Diversity (all of these are generally in R, mostly in vegan)
   1. Jaccard index
   2. Simpson's diversity
   3. Shannons index
   4. Turnover - different ways to calculate
   5. Dominance
   6. Evenness
   7. Richness
   8. Rank abundance shift
   9. Proportion of overall diversity
   10. Beta diversity
2. Community metrics/ordination
   1. NMDS (vegan)
   2. PCA (vegan)
   3. Bray curtis (vegan)
   4. Variance tracking, quantify variability change
   5. Position in ordination-space
3. Spatial
   1. patch scale
   2. spatial autoregression
   3. Endemism
   4. Summary of species' positions within their ranges
   5. meta community statistics
4. Mechanistic models
   1. MAR, needs driver matrix, problem auto-corelation, mostly fresh water or marine (Eli Holmes has state-space MAR in R implemented, not sure if it's on CRAN) http://cran.r-project.org/web/packages/MARSS/index.html
   2. MANOVA (vegan? Also, permanova is in vegan)
   3. Ecosystem function (e.g. N deposition)
   4. interaction population models - inter specific competition (Ben Bolker's book and corresponding package)
   5. Economically/legally relevant metrics (e.g. Maximum sustainable yield)
5. Food webs
   1. connectance
   2. network analysis
6. Traits/phylogentic
   1. functional/phylogenetic diversity
   2. species aggregation (functional groups, trophic levels
   3. phylogenetic dispersion
   4. Native/exotic
   5. Phylogeographic history
7. Temporal indices
   1. species turnover
   2. rate of return
   3. Variance ratio
   4. Mean-variance scaling
   5. Spectral analysis
   6. Regresssion windows (strucchange)
   7. time series models of abundance -- metric would be parameters of model
8. null models
9. Comparative analysis of small noise vs large noise systems. What drives differences?

Coded in R¶

• Richness/diversity metrics: http://cran.r-project.org/web/packages/vegan/index.html
• Diversity metrics (alpha, beta, gamma): http://cran.r-project.org/web/packages/vegetarian/index.html
• Hubble metrics: http://cran.r-project.org/web/packages/untb/index.html
• Leading indicators, variance, autocorrelation, skew, heteroscedasticity: http://cran.at.r-project.org/web/packages/earlywarnings/index.html

• state-space models and community level resilience

Breakout Session 2: Identify research questions¶

1. Data set transformation to allow compute of many metrics
2. Time series analysis of community level metrics (consider higher freq data too)(earlywarnings R package)
3. New R code for capturing climate variance at seasonal and interannual scales and residuals
4. Review of non-stationarity
   1. Variance partitioning
   2. Temporal and spatial variance