Workshop Notes¶

are on etherpad: https://epad.nceas.ucsb.edu/p/commdyn-20140105

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}

Metrics¶

1. Diversity (all of these are generally in R, mostly in vegan)
   1. Jaccard index
   2. Simpson's diversity
   3. Shannons index
   4. Turnover - different ways to calculate
   5. Dominance
   6. Evenness
   7. Richness
   8. Rank abundance shift
   9. Proportion of overall diversity
   10. Beta diversity
2. Community metrics/ordination
   1. NMDS (vegan)
   2. PCA (vegan)
   3. Bray curtis (vegan)
   4. Variance tracking, quantify variability change
   5. Position in ordination-space
3. Spatial
   1. patch scale
   2. spatial autoregression
   3. Endemism
   4. Summary of species' positions within their ranges
   5. meta community statistics
4. Mechanistic models
   1. 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
   2. MANOVA (vegan? Also, permanova is in vegan)
   3. Ecosystem function (e.g. N deposition)
   4. interaction population models - inter specific competition (Ben Bolker's book and corresponding package)
   5. Economically/legally relevant metrics (e.g. Maximum sustainable yield)
5. Food webs
   1. connectance
   2. network analysis
6. Traits/phylogentic
   1. functional/phylogenetic diversity
   2. species aggregation (functional groups, trophic levels
   3. phylogenetic dispersion
   4. Native/exotic
   5. Phylogeographic history
7. Temporal indices
   1. species turnover
   2. rate of return
   3. Variance ratio
   4. Mean-variance scaling
   5. Spectral analysis
   6. Regresssion windows (strucchange)
   7. time series models of abundance -- metric would be parameters of model
8. null models
9. Comparative analysis of small noise vs large noise systems. What drives differences?

Issues:¶

1. length of time series relative to lifespan of organisms

WMI toolbox

1. high frequency data needed

sample too frequently then don't see signal, sample to far about miss all dynamics

1. type of variable being measured

abundance, biomass, production

1. Rare species as background noise

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

Breakout Session 2: Identify research questions:¶

1. Data set transformation to allow compute of many metrics
2. Time series analysis of community level metrics (consider higher freq data too)(earlywarnings R package)
3. New R code for capturing climate variance at seasonal and interannual scales and residuals
4. Review of non-stationarity
   1. How do you figure out if 3 or more spp are changing together?
   2. Developing/adopting/adapting new techniques

Report back from working groups¶

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

Group 3¶

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

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