Community Dynamics Toolbox

h1. Workshop Notes

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

h1. 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}

h2. Metrics

# *Diversity* (all of these are generally in R, mostly in vegan)

## Jaccard index

## Simpson's diversity 

## Shannons index

## Turnover - different ways to calculate

## Dominance 

## Evenness

## Richness

## Rank abundance shift

## Proportion of overall diversity

## Beta diversity

# *Community metrics/ordination*

## NMDS (vegan)

## PCA (vegan)

## Bray curtis (vegan)

## Variance tracking, quantify variability change

## Position in ordination-space

# *Spatial*

## patch scale 

## spatial autoregression

## Endemism

## Summary of species' positions within their ranges

## meta community statistics

# *Mechanistic models*

## 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

## MANOVA (vegan? Also, permanova is in vegan)

## Ecosystem function (e.g. N deposition)

## interaction population models - inter specific competition (Ben Bolker's book and corresponding package)

## Economically/legally relevant metrics (e.g. Maximum sustainable yield)

# *Food webs*

## connectance

## network analysis

# *Traits/phylogentic*

## functional/phylogenetic diversity

## species aggregation (functional groups, trophic levels

## phylogenetic dispersion

## Native/exotic

## Phylogeographic history

# *Temporal indices*

## species turnover

## rate of return

## Variance ratio

## Mean-variance scaling

## Spectral analysis

## Regresssion windows (strucchange)

## time series models of abundance -- metric would be parameters of model

# *null models*

# *Comparative analysis of small noise vs large noise systems. What drives differences?*

h2. 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

h1. Breakout Session 2: Identify research questions

# Data set transformation to allow compute of many metrics

# Time series analysis of community level metrics (consider higher freq data too)(earlywarnings R package)

# New R code for capturing climate variance at seasonal and interannual scales and residuals

# Review of non-stationarity

## Variance partitioning 

## Temporal and spatial variance