Dear all:

Thank you for your comments.I am thinking about and working out the code for three possibilities (### Methods A, B, C). What do you think? Any additional thoughts are much appreciated.


I would like to test if the variation in three response variables (i) species 
richness,  (ii) species composition (presence/absence), (iii) species 
composition (abundance) is explained by a disturbance regime "gradient" (6 levels, categorical variable). I plan to "block" disturbance regime by study area; the six disturbance regime plots are nested in eight study areas. In addition, it is important to know if covariates (slope, aspect, etc.) influence the relationship between the response variables and disturbance regime. 


### Method A: Compare two MANCOVAs for a blocked experimental design - using lme() of the nlme package:
(i)MANCOVA including all response variables** with the explanatory variable as levels of disturbance only (this I will call "D") 

(ii) MANCOVA with "D" and covariates included 

(iii) Compare MANCOVA 1 (M1) vs. MANCOVA 2 (M2) with an F-test

i.e., anova(M1,M2) **species richness and species composition (presence/absence, abundance). Six plots of a particular disturbance level would be compared with dissimilarity indices (Jaccard Index for presence/absence, and Sorensen Index for abundance [using the mean value of each cover categories to create quantitative values]) to test for differences of species composition among disturbance regimes in a fashion akin to the methods mentioned in  - albeit with MANCOVA rather than an ANOVA - "Regional vs. local processes in determining zooplankton composition of Little Rock Lake, Wisconsin USA" Journal of plankton research, Lukaszewski et al. (1999).


### Method B: Instead of the F-test in Method A (iii), I am considering model selection tools such as AIC to examine the fit of alternative models. 


### Method C: Instead of the dissimilarity calculations (Jaccard and Sorensen)used in Methods A and B , I am considering quantifying community composition dissimilarity in a way that corrects for variation in alpha via the null model methods described here: "Using null models to disentangle variation in community dissimilarity from variation in a-diversity" Ecosphere, Chase et al. (2011). 


Thank you for your time and consideration, 

Laura





________________________________

To: "r-sig-ecology@r-project.org" <r-sig-ecology@r-project.org> 
Sent: Sunday, January 15, 2012 11:10:00 AM
Subject: Recommended R package for analyzing community data set? - nested design, stratified random sampling, covariates


Dear all:

May you recommend an R package for analyzing this data set? I would greatly appreciate any thoughts you can provide.


I. Study goals

This study examines soil crust (lichens and bryophytes) recovery and succession in fields that underwent different levels of disturbance.


II. Variables
Response variables of interest: soil crust cover (categorical scale - described below), species richness, species composition
Explanatory variable of interest: disturbance regime (categorical variable)
Environmental variables measured (covariates - mix of categorical and numerical variables): cover of mineral soil, litter, vascular plant bases, stones, or rocks,
slope, aspect

III. Study sampling and design
Eight
 research areas (BR, CB, CC, JL, PC, PL, SL, TR)
Within each research area subplots were assigned six disturbance treatments (NC, NS, OC, OS, SC, SS) based on disturbance history 
A single transect was placed randomly in the center of each subplot and sampled in twenty 20 x 20 cm plots at 1 m intervals along the transect 
47 of 48 possible treatment subplots were sampled (n=6 for 7 sites, n=5 for 1 site)


Sampling cover scale: 

Scale value    Representative % cover 

1                    <= 1
2                    >1-4
3                    >4-10
4                    >10-25
5                    >25-50
6                    >50-75
7                    >75-95
8                    >95-100


There were three different sampling times (spread over
 two years), but time of sampling was not considered as a confounding factor given the way sampling was conducted with the particular communities studied (soil crust communities).

Total species positively identified: 33 taxa (species and species groups), 15 of these were in four or more
of 47 subplots (n=6 x 7, n=5 x 1)
Unidentified collected taxa: less than 0.5%
Approximate taxa pool (species observed in entire areas, but not necessarily in sample plots): 26 lichen + 21 bryophytes
= 47 taxa

Thank you for your time and consideration,
Laura
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