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Revision 3 (Corinna Gries, 02/07/2014 10:35 AM) → Revision 4/20 (Corinna Gries, 03/10/2014 01:50 PM)

h1. 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} 

 # 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 
 # NMDS (vegan) 
 # PCA (vegan) 
 # Bray curtis (vegan) 
 # Variance tracking, quantify variability change 
 # And spatial... 
 # patch scale  
 # spatial autoregression 
 # 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) 
 # interaction population models - inter specific competition (Ben Bolker's book and corresponding package) 
 # Food webs 
 # connectance 
 # network analysis 
 # Traits/phylogentic 
 # species aggregation (functional groups, trophic levels 
 # phylogenetic dispersion (ape etc. -- this stuff is all in R) 
 # Native/exotic 
 # Temporal indices 
 # Variance ratio 
 # Mean-variance scaling 
 # Spectral analysis 
 # Regresssion windows (strucchange) 
 # null models 

 issues identified by four breakout groups: 
 1. length of time series relative to lifespan of organisms 
     WMI toolbox 

 2. high frequency data needed 
     sample too frequently then don't see signal, sample to far about miss all dynamics 

 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?