Workshop Notes¶

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

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?

Issues:¶

#length of time series relative to lifespan of organisms

WMI toolbox

#high frequency data needed

sample too frequently then don't see signal, sample to far about miss all dynamics

#type of variable being measured

abundance, biomass, production

1. Rare species as background noise

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

not yet coded:

• state-space models and community level resilience