Neoliberal Redistributive Policy: The U.S. Net Social Wage in the 21st Century

In this paper, I examine the trends of fiscal transfers between the state and workers during 1959 - 2012 to understand the net impact of redistributive policy in the United States. This paper presents original net social wage data from and analysis based on the replication and extension of Shaikh and Tonak (2002). The paper investigates the appearance of a post-2001 variation in the net social wage data. The positive net social wage in the 21st century is the result of a combination of factors including the growth of income support, healthcare inflation, neoliberal tax reforms, and macroeconomic instability. Growing economic inequality does not appear to alter the results of the net social wage methodology

Appendix C. Principal components analysis of environmental variables.

Principal components analysis of environmental variables

Appendix B. Correlation between environmental variables and abundance-weighted mean plot value for plant functional traits.

Correlation between environmental variables and abundance-weighted mean plot value for plant functional traits

Appendix D. Additional plots of the relationships between environmental variables and traits.

Additional plots of the relationships between environmental variables and traits

Appendix A. Covariance stucture of leaf, stem, and root traits for 54 woody species.

Covariance stucture of leaf, stem, and root traits for 54 woody species

Supplement 2. Community assembly simulation code for use in the R programming language.

<h2>File List</h2><blockquote> <p><a href="sup_files/community_assembly_simulations.txt">community_assembly_simulations.txt</a> </p> </blockquote><h2>Description</h2><blockquote> <p>A set of functions in the R programming language for simulating community assembly randomly (with or without weighting species by abundance), by trait-based competition, or by trait-based habitat filtering. The linked text file contains a series of R functions, a brief demonstration of the functions, commentary on the algorithms, and references. There are three key functions, described below, and a series of smaller functions that support them. More detailed descriptions can be found as in the file itself.</p> random_assembly(pool, final_richness, abund=NULL)<br> <blockquote> <p>Randomly samples final_richness number of species from a vector of species names given in pool. Sampling is occurence weighted if a vector of abundances is specified in the abund argument- default is no abundance weighting. </p> </blockquote> compete_until(nfinal, community)<br> <blockquote> <p>Takes a data frame community with species names in column 1 and traits in subsequent columns and runs a competition algorithm to cull the community unitl nfinal taxa remain. At each step, the algorithm identifies the most similar pair of species based on trait similarity and randomly removes one. Ties are broken randomly.</p> </blockquote> filter_until(nfinal, community, optima) <blockquote> <p>Takes a data frame community with species names in column 1 and traits in subsequent columns and runs a habitat filtering algorithm to cull the community unitl nfinal taxa remain. At each step, the algorithm identifies the species that is farthest from the trait optima and removes it. Ties are broken randomly. The community can have from 1 to many traits, but the optima vector must have the same number of elements as the number of traits in the community dataframe.</p> </blockquote> </blockquote

Information on local assemblages included in analysis including name of contributor, number of sites provided, location and size of area sampled, duration of sampling, <i>Bombus</i> richness in sites, regional species pool as determined using Williams(1996) equal area grid cells and publication information.

<p>Information on local assemblages included in analysis including name of contributor, number of sites provided, location and size of area sampled, duration of sampling, <i>Bombus</i> richness in sites, regional species pool as determined using Williams(1996) equal area grid cells and publication information.</p

Z scores and p-values of relatedness and tongue length for various scales and measures of similarity.

<p>Local assemblages (n = 110) represent co-occurring species and the species pool is the regional gridcell the assemblage is within. Regional assemblages (n = 45) are the species in each grid cell compared to a species pool of all Nearctic Species. The continental assemblage (n = 1) consists of all Nearctic species compared to all <i>Bombus</i> globally.</p

Filtering across Spatial Scales: Phylogeny, Biogeography and Community Structure in Bumble Bees

<div><p>Despite the expansion of phylogenetic community analysis to understand community assembly, few studies have used these methods on mobile organisms and it has been suggested the local scales that are typically considered may be too small to represent the community as perceived by organisms with high mobility. Mobility is believed to allow species to mediate competitive interactions quickly and thus highly mobile species may appear randomly assembled in local communities. At larger scales, however, biogeographical processes could cause communities to be either phylogenetically clustered or even. Using phylogenetic community analysis we examined patterns of relatedness and trait similarity in communities of bumble bees (<i>Bombus)</i> across spatial scales comparing: local communities to regional pools, regional communities to continental pools and the continental community to a global species pool. Species composition and data on tongue lengths, a key foraging trait, were used to test patterns of relatedness and trait similarity across scales. Although expected to exhibit limiting similarity, local communities were clustered both phenotypically and phylogenetically. Larger spatial scales were also found to have more phylogenetic clustering but less trait clustering. While patterns of relatedness in mobile species have previously been suggested to exhibit less structure in local communities and to be less clustered than immobile species, we suggest that mobility may actually allow communities to have more similar species that can simply limit direct competition through mobility.</p> </div

Bi-plot of the phylogeny of species with trait values (n = 79) and the associated tongue length measured in ln(mm).

<p>Grey bars indicate species found in the Nearctic. Short faced (SF) and long faced (LF) sister clades are labeled to demonstrate the association with tongue length. Taxa labels are available in the Supplementary <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060446#pone-0060446-t001" target="_blank">Table 1</a> with trait values.</p