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