Firstwrkshnotes » History » Revision 10
Revision 9 (Corinna Gries, 03/10/2014 02:16 PM) → Revision 10/20 (Corinna Gries, 03/10/2014 03:02 PM)
h1. Workshop Notes are on etherpad: https://epad.nceas.ucsb.edu/p/commdyn-20140105 h1. Breakout session 1: Metrics brainstorming 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: # 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 > How do you figure out if 3 or more spp are changing together? > 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