Project

General

Profile

Firstwrkshnotes » History » Revision 12

Revision 11 (Corinna Gries, 03/10/2014 03:04 PM) → Revision 12/20 (Corinna Gries, 03/10/2014 03:12 PM)

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. 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 
 # Rare species as background noise 


 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 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  
 How do you figure out if 3 or more spp are changing together? 
 ## Temporal Developing/adopting/adapting new techniques 

 h2. Report back from working groups 

 h3. Group 1 and 2 

 * Downloaded datasets, created common format 
 * Eric made script to calculate metrics over time 
 * lots of time to clean up and reformat the data 
 * Dataformat most useful: long format, some standardization is useful: NAN, date format, separate file with site conditions, spelling of words, outliers, how to deal with large missing data chunks, functional groups for species 
 * How to deal with data where individuals have not been keyed to species, but higher and varying taxonomic ranks 
 * more exploration is necessary 
 * specific site conditions were partly unknown (e.g. CAP landuse) 
 * Continue work on development of workflows for data cleanup 
 * Ecological analysis of temporal change data sets 

 h3. Group 3 

 * Eric's LTER project to develop the R model for analyzing more spatial variability, could collaborate with long term data 

 h3. Group 4 

 *Temporal and spatial variance* 
 * ecosystems strongly influenced by temporal and spatial variation bi-variate plot 
 * limitations: other data, e.g. biochemical conditions 
 * time turnover 
 * spatial heterogeneity 
 * cross site LTER datasets to see if there is some traction to the idea 
 * datasets in long form because they need to be manipulated a little for each new exploratory step 
 * Work on common data format for various communities 
 * Work on script to calculate variance 



 in space and time metrics for each of those communities  
 * One limitation was that many available data don't lend themselves to both the spatial and temporal analyses 

 *Variance partitioning* 
 * climate, intra annual seasonal, inter annual 
 * new metrics: variance components analysis 
 * explore different temporal scale 
 * which part of the annual 'weather' is important