Firstwrkshnotes » History » Revision 5
Revision 4 (Corinna Gries, 03/10/2014 01:50 PM) → Revision 5/20 (Corinna Gries, 03/10/2014 01:57 PM)
h1. Workshop Notes are on etherpad: https://epad.nceas.ucsb.edu/p/commdyn-20140105 h1.Metrics 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* 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 ## > # Beta diversity # *Community metrics/ordination* Community metrics/ordination ## # NMDS (vegan) ## # PCA (vegan) ## # Bray curtis (vegan) ## # Variance tracking, quantify variability change # *Spatial* And spatial... ## # patch scale ## # spatial autoregression ## # meta community statistics # *Mechanistic models* 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) ## # interaction population models - inter specific competition (Ben Bolker's book and corresponding package) # *Food webs* Food webs ## # connectance ## # network analysis # *Traits/phylogentic* Traits/phylogentic ## # species aggregation (functional groups, trophic levels ## # phylogenetic dispersion (ape etc. -- this stuff is all in R) ## # Native/exotic # *Temporal indices* Temporal indices ## # Variance ratio ## # Mean-variance scaling ## # Spectral analysis ## # Regresssion windows (strucchange) # *null models* null models h2.Issues: issues identified by four breakout groups: #length 1. length of time series relative to lifespan of organisms WMI toolbox #high 2. high frequency data needed sample too frequently then don't see signal, sample to far about miss all dynamics #type 3. type of variable being measured abundance, biomass, production Rare species as background noise rank abundance curves back again Comparative analysis of small noise vs large noise systems. What drives differences?