| Title: | Community Dynamics Metrics |
|---|---|
| Description: | Univariate and multivariate temporal and spatial diversity indices, rank abundance curves, and community stability measures. The functions implement measures that are either explicitly temporal and include the option to calculate them over multiple replicates, or spatial and include the option to calculate them over multiple time points. Functions fall into five categories: static diversity indices, temporal diversity indices, spatial diversity indices, rank abundance curves, and community stability measures. The diversity indices are temporal and spatial analogs to traditional diversity indices. Specifically, the package includes functions to calculate community richness, evenness and diversity at a given point in space and time. In addition, it contains functions to calculate species turnover, mean rank shifts, and lags in community similarity between two time points. Details of the methods are available in Hallett et al. (2016) <doi:10.1111/2041-210X.12569> and Avolio et al. (2019) <doi:10.1002/ecs2.2881>. |
| Authors: | Lauren Hallett [aut]  ,
Meghan L. Avolio [aut] ,
Ian T. Carroll [aut] ,
Sydney K. Jones [aut],
A. Andrew M. MacDonald [aut] ,
Dan F. B. Flynn [aut] ,
Peter Slaughter [aut] ,
Julie Ripplinger [aut] ,
Scott L. Collins [aut] ,
Corinna Gries [aut] ,
Matthew B. Jones [aut, cre] |
| Maintainer: | Matthew B. Jones <[email protected]> |
| License: | Apache License (== 2.0) |
| Version: | 2.0.5 |
| Built: | 2024-11-11 05:31:10 UTC |
| Source: | https://github.com/nceas/codyn |
Univariate and multivariate temporal and spatial diversity indices, rank abundance curves, and community stability metrics for ecologists.
The functions in codyn implement metrics that are either explicitly temporal and include the option to calculate them over multiple replicates, or spatial and include the option to calculate them over multiple time points.
Functions fall into five categories: static diversity indices, temporal diversity indices, spatial diversity indices, rank abundance curves, and community stability metrics.
The diversity indices in codyn are temporal and spatial analogs to traditional diversity indices.
Specifically, codyn includes functions to calculate community richness, evenness and diversity at a given point in space and time. In addition, codyn contains functions to calculate species turnover, mean rank shifts, and lags in community similarity between two time points. For the components of rank abundance curves, the shape of the rank abundance curve, species abundances and multivariate metrics of community composition, codyn contains functions to calculate these metrics either between time points or a single time point between two paired replicates.
The community stability metrics in codyn calculate overall stability and patterns of species covariance and synchrony over time.
Finally, codyn contains vignettes that describe methods and reproduce figures from published papers to help users contextualize and apply functions to their own data.
Work on this package was supported by National Science Foundation grant #1262458 to the National Center for Ecological Analysis and Synthesis (NCEAS), the University of Wisconsin, and the University of New Mexico, and a SESYNC Synthesis Postdoctoral Fellowship to MLA.
turnover: Calculates species turnover between time periods
rank_shift: Calculates the mean relative change in species rank abundances
rate_change: Calculates the rate change in a community over time
rate_change_interval: Produces a data frame containing differences in species composition between samples at increasing time intervals
community_stability: Calculates community stability over time
variance_ratio: Computes the ratio of the variance of aggregate species abundances in a community
synchrony: Calculates the degree synchrony in species abundances
temporal_torus_translation: Calculates a test statistic on a null ecological community created via cyclic shifts. confint provides mean and confidence intervals of this null distribution
community_structure: Calculates richness and evenness (using specified metric) for a replicate
community_diversity: Calculates diversity (using specified metric) for a replicate
RAC_change:Calculates changes in species richness, evenness, species' ranks, gain, and losses for a replicate over time
abundance_change: For each species in a replicate, calculates changes in abundance over time
curve_change: Calculates changes in the shape of the RAC curve for each replicate over time
multivariate_change: Calculates changes in community composition and dispersion over time
RAC_difference: Calculates differences in species richness, evenness, species' ranks, shared species between paired samples at a single point in time
abundance_difference: Calculates differences in abundance for each species in paired samples at a single point in time
curve_difference: Calculates differences in the shape of the RAC between paired samples at a single point in time
multivariate_difference: Calculates differences in community composition and dispersion of all replicates between treatments at a single point in time
Lauren Hallett [email protected]
Meghan L. Avolio [email protected]
Ian T. Carroll [email protected]
Sydney K. Jones [email protected]
A. Andrew A. MacDonald [email protected]
Dan F. B. Flynn [email protected]
Peter Slaughter [email protected]
Julie Ripplinger [email protected]
Scott L. Collins [email protected]
Corinna Gries [email protected]
Matthew B. Jones [email protected]
Calculates the abundance change for species in a replicate between two time points. Changes are on abundance values provided, if relative data is used, then changes in relative abundance will be calculated.
abundance_change( df, time.var, species.var, abundance.var, replicate.var = NULL, reference.time = NULL )abundance_change( df, time.var, species.var, abundance.var, replicate.var = NULL, reference.time = NULL )
df |
A data frame containing time, species, and abundance columns and an optional columns of replicates. |
time.var |
The name of the time column. |
species.var |
The name of the species column. |
abundance.var |
The name of the abundance column. |
replicate.var |
The name of the optional replicate column. If specified, replicate must be unique within the dataset and cannot be nested within treatments or blocks. |
reference.time |
The name of the optional time point that all other time points should be compared to (e.g. the first year of data). If not specified, each comparison is between consecutive time points (the first and second year, second and third year, etc.) |
The abundance_change function returns a data frame with a subset of the following columns:
replicate.var: A column with the specified replicate.var, if it is specified.
time.var: A column with the specified time.var and a second column, with '2' appended to the name. Time is subtracted from time2
species.var: A column with the specified species.var.
change: A numeric column of the change in abundance between time points. A positive value occurs when a species increases in abundance over time, and a negative value when a species decreases in abundance over time.
Avolio et al. Submitted
data(pplots) # Without replicates df <- subset(pplots, plot == 25) abundance_change(df = df, species.var = "species", abundance.var = "relative_cover", time.var = "year") # With replicates df <- subset(pplots, year < 2004 & plot %in% c(6, 25, 32)) abundance_change(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year") # With reference year df <- subset(pplots, year < 2005 & plot %in% c(6, 25, 32)) abundance_change(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year", reference.time = 2002)data(pplots) # Without replicates df <- subset(pplots, plot == 25) abundance_change(df = df, species.var = "species", abundance.var = "relative_cover", time.var = "year") # With replicates df <- subset(pplots, year < 2004 & plot %in% c(6, 25, 32)) abundance_change(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year") # With reference year df <- subset(pplots, year < 2005 & plot %in% c(6, 25, 32)) abundance_change(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year", reference.time = 2002)
Calculates the abundance difference for species between two samples. Differences are on abundance values provided, if relative data is used, then differences in relative abundance will be calculated. There are three ways differences can be calculated. 1) Between treatments within a block (note: block.var and treatment.var need to be specified). 2) Between treatments, pooling all replicates into a single species pool (note: pool = TRUE, treatment.var needs to be specified, and block.var = NULL). 3) All pairwise combinations between all replicates (note: block.var = NULL, pool = FALSE and specifying treatment.var is optional. If treatment.var is specified, the treatment that each replicate belongs to will also be listed in the output).
abundance_difference( df, time.var = NULL, species.var, abundance.var, replicate.var, treatment.var = NULL, pool = FALSE, block.var = NULL, reference.treatment = NULL )abundance_difference( df, time.var = NULL, species.var, abundance.var, replicate.var, treatment.var = NULL, pool = FALSE, block.var = NULL, reference.treatment = NULL )
df |
A data frame containing a species, abundance, and replicate columns and optional time, treatment, and block columns. |
time.var |
The name of the optional time column. |
species.var |
The name of the species column. |
abundance.var |
The name of the abundance column. |
replicate.var |
The name of the replicate column. Replicate identifiers must be unique within the dataset and cannot be nested within treatments or blocks. |
treatment.var |
The name of the optional treatment column. |
pool |
An argument to allow abundance values to be pooled within a treatment. The default value is "FALSE", a value of "TRUE" averages abundance of each species within a treatment at a given time point. |
block.var |
The name of the optional block column. |
reference.treatment |
The name of the optional treatment that all other treatments will be compared to (e.g. only controls will be compared to all other treatments). If not specified all pairwise treatment comparisons will be made. |
The abundance_difference function returns a data frame with a subset of the following columns:
species.var: A column that has same name and type as the species.var column.
difference: A numeric column of the abundance differences between the two samples being compared (replicates or treatments). A numeric column of the change in abundance between consecutive timepoints. A positive value occurs when a species has greater abundance in replicate.var2 than in replicate.var and/or in treatment.var2 than in treatment.var.
replicate.var: A column that has same name and type as the replicate.var column, represents the first replicate being compared. Note, a replicate column will be returned only when pool = FALSE or block.var = NULL.
replicate.var2: A column that has the same type as the replicate.var column, and is named replicate.var with a 2 appended to it, represents the second replicate being compared. Note, a replicate.var column will be returned only when pool = FALSE and block.var = NULL.
time.var: A column that has the same name and type as the time.var column, if time.var is specified.
treatment.var: A column that has same name and type as the treatment.var column, represents the first treatment being compared. A treatment.var column will be returned when pool = TRUE, block.var is specified, or treatment.var is specified.
treatment.var2: A column that has the same type as the treatment.var column, and is named treatment.var with a 2 appended to it, represents the second treatment being compared. A treatment.var column will be returned when pool = TRUE, block.var is specified, or treatment.var is specified.
block.var: A column that has same name and type as the block.var column, if block.var is specified.
Avolio et al. Submitted
data(pplots) # With block and no time df <- subset(pplots, year == 2002 & block < 3) abundance_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = "treatment", block.var = "block", replicate.var = "plot") # With blocks and time df <- subset(pplots, year < 2004 & block < 3) abundance_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = "treatment", block.var = "block", replicate.var = "plot", time.var = "year") # With blocks, time and reference treatment df <- subset(pplots, year < 2004 & block < 3) abundance_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = "treatment", block.var = "block", replicate.var = "plot", time.var = "year", reference.treatment = "N1P0") # Pooling by treatment with time df <- subset(pplots, year < 2004) abundance_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = "treatment", pool = TRUE, replicate.var = "plot", time.var = "year") # All pairwise replicates with treatment df <- subset(pplots, year < 2004 & plot %in% c(21, 25, 32)) abundance_difference(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year", treatment.var = "treatment") # All pairwise replicates without treatment df <- subset(pplots, year < 2004 & plot %in% c(21, 25, 32)) abundance_difference(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year")data(pplots) # With block and no time df <- subset(pplots, year == 2002 & block < 3) abundance_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = "treatment", block.var = "block", replicate.var = "plot") # With blocks and time df <- subset(pplots, year < 2004 & block < 3) abundance_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = "treatment", block.var = "block", replicate.var = "plot", time.var = "year") # With blocks, time and reference treatment df <- subset(pplots, year < 2004 & block < 3) abundance_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = "treatment", block.var = "block", replicate.var = "plot", time.var = "year", reference.treatment = "N1P0") # Pooling by treatment with time df <- subset(pplots, year < 2004) abundance_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = "treatment", pool = TRUE, replicate.var = "plot", time.var = "year") # All pairwise replicates with treatment df <- subset(pplots, year < 2004 & plot %in% c(21, 25, 32)) abundance_difference(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year", treatment.var = "treatment") # All pairwise replicates without treatment df <- subset(pplots, year < 2004 & plot %in% c(21, 25, 32)) abundance_difference(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year")
A dataset of tallgrass prairie plant composition at one annually burned and one unburned site over time at the Konza Prairie LTER, Manhattan Kansas (Collins et al. 2008).
data(collins08)data(collins08)
A data frame with 2058 rows and 4 variables
A data frame containing a column for replicate, year, species and abundance:
replicate: A factor column of spatial replicates with two levels ("annually burned" and "unburned")
year: An integer column of sampling time points
species: A factor column of species sampled
abundance: A numeric column of abundance values
Collins, Scott L., Katharine N. Suding, Elsa E. Cleland, Michael Batty, Steven C. Pennings, Katherine L. Gross, James B. Grace, Laura Gough, Joe E. Fargione, and Christopher M. Clark. (2008) "Rank clocks and plant community dynamics." Ecology 89, no. 12: 3534-41.
Calculates Shannon's or Inverse Simpson's diversity of a community, but only one measure of diversity can be calculated at a time and must be specified.
community_diversity( df, time.var = NULL, abundance.var, replicate.var = NULL, metric = c("Shannon", "InverseSimpson") )community_diversity( df, time.var = NULL, abundance.var, replicate.var = NULL, metric = c("Shannon", "InverseSimpson") )
df |
A data frame containing species and abundance columns and optional columns of time and/or replicate. |
time.var |
The name of the optional time column |
abundance.var |
The name of the abundance column |
replicate.var |
The name of the optional replicate column. If specified, replicate must be unique within the dataset and cannot be nested within treatments or blocks. |
metric |
The diversity measure to return:
|
The community_diversity function returns a data frame with the following attributes:
time.var: A column that has the same name and type as the time.var column, if time.var is specified.
replicate.var: A column that has same name and type as the replicate.var column, if replicate.var is specified.
Shannon: A numeric column of Shannon's diversity if metric = "Shannon"
InverseSimpson: A numeric column of the inverse of Simpson's diversity if metric = "InverseSimpson"
Magurran, A.E. 2004. Measuring Biological Diversity. Blackwell Publishing, Malden MA, USA.
data(pplots) #Example with both time and replicates df <- subset(pplots, plot == 25 | plot == 6) community_diversity(df, time.var="year", replicate.var = "plot", abundance.var = "relative_cover") # for Shannon's diversity measure df <- subset(pplots, plot == 25 | plot == 6) community_diversity(df, time.var="year", replicate.var = "plot", abundance.var = "relative_cover", metric = "InverseSimpson") # for Inverse of Simpson's diversity measure #Example with no replicates df <- subset(pplots, plot == 25) community_diversity(df, time.var="year", abundance.var = "relative_cover") # for Shannon's diversity measure #Example with no time or replicate df <- subset(pplots, plot == 25 & year == 2002) community_diversity(df, abundance.var = "relative_cover") # for Shannon's diversity measuredata(pplots) #Example with both time and replicates df <- subset(pplots, plot == 25 | plot == 6) community_diversity(df, time.var="year", replicate.var = "plot", abundance.var = "relative_cover") # for Shannon's diversity measure df <- subset(pplots, plot == 25 | plot == 6) community_diversity(df, time.var="year", replicate.var = "plot", abundance.var = "relative_cover", metric = "InverseSimpson") # for Inverse of Simpson's diversity measure #Example with no replicates df <- subset(pplots, plot == 25) community_diversity(df, time.var="year", abundance.var = "relative_cover") # for Shannon's diversity measure #Example with no time or replicate df <- subset(pplots, plot == 25 & year == 2002) community_diversity(df, abundance.var = "relative_cover") # for Shannon's diversity measure
Calculates the stability of the overall community over time as the temporal mean / temporal standard deviation of aggregate species abundances (Tilman 1999).
community_stability(df, time.var, abundance.var, replicate.var = NA)community_stability(df, time.var, abundance.var, replicate.var = NA)
df |
A data frame containing time, species and abundance columns and an optional column of replicates |
time.var |
The name of the time column |
abundance.var |
The name of the abundance column |
replicate.var |
The name of the optional replicate column |
The input data frame needs to contain columns for time, species and abundance; time.var, species.var and abundance.var are used to indicate which columns contain those variables. If multiple replicates are included in the data frame, that column should be specified with replicate.var. Each replicate should reflect a single experimental unit - there should be a single community represented within each time point and replicate.
The community_stability function returns a numeric stability value unless a replication column is specified in the input data frame. If replication is specified, the function returns a data frame with the following columns:
stability: A numeric column with the stability values.
replicate.var: A column that shares the same name and type as the replicate.var column in the input data frame.
Tilman, D. "The Ecological Consequences of Changes in Biodiversity: A Search for General Principles." Ecology 80, no. 5 (July 1999): 1455-74. doi:10.1890/0012-9658(1999)080[1455:TECOCI]2.0.CO;2.
data(knz_001d) community_stability(knz_001d[knz_001d$subplot=="A_1",], time.var = "year", abundance.var = "abundance") # for one subplot community_stability(knz_001d, time.var = "year", abundance.var = "abundance", replicate.var = "subplot") # across all subplotsdata(knz_001d) community_stability(knz_001d[knz_001d$subplot=="A_1",], time.var = "year", abundance.var = "abundance") # for one subplot community_stability(knz_001d, time.var = "year", abundance.var = "abundance", replicate.var = "subplot") # across all subplots
Calculates species richness and evenness of a community. Evenness may be calculated as Simpson's (1/D/S), EQ, or Evar, but only one metric of evenness can be calculated at a time and must be specified.
community_structure( df, time.var = NULL, abundance.var, replicate.var = NULL, metric = c("Evar", "SimpsonEvenness", "EQ") )community_structure( df, time.var = NULL, abundance.var, replicate.var = NULL, metric = c("Evar", "SimpsonEvenness", "EQ") )
df |
A data frame containing species and abundance columns and optional columns of time and/or replicate. |
time.var |
The name of the optional time column |
abundance.var |
The name of the abundance column |
replicate.var |
The name of the optional replicate column. If specified, replicate must be unique within the dataset and cannot be nested within treatments or blocks. |
metric |
The measure of evenness to return:
|
The community_structure function returns a data frame with the following attributes:
time.var: A column that has the same name and type as the time.var column, if time.var is specified.
replicate.var: A column that has same name and type as the replicate.var column, if specified.
richness: A numeric column of species richness
Evar: A numeric column of Evar if evenness = "Evar"
EQ: A numeric column of EQ if evenness = "EQ"
SimpsonEvenness: A numeric column of Simpson's evenness if evenness = "SimpsonEveness"
Smith, B. and Wilson, J. B. 1996. A consumer's guide to evenness indices. Oikos 76: 70-82.
data(pplots) #Example with both time and replicates df <- subset(pplots, plot == 25 | plot == 6) community_structure(df, time.var="year", replicate.var = "plot", abundance.var = "relative_cover") # for Evar evenness measure df <- subset(pplots, plot == 25 | plot == 6) community_structure(df, time.var="year", replicate.var = "plot", abundance.var = "relative_cover", metric = "SimpsonEvenness") # for Simpson's evenness measure #Example with no replicates df <- subset(pplots, plot == 25) community_structure(df, time.var="year", abundance.var = "relative_cover", metric = "EQ") # for EQ evenness measure #Example with only a single time point and no replicates df <- subset(pplots, plot == 25 & year == 2002) community_structure(df, abundance.var = "relative_cover") # for Evar evenness measuredata(pplots) #Example with both time and replicates df <- subset(pplots, plot == 25 | plot == 6) community_structure(df, time.var="year", replicate.var = "plot", abundance.var = "relative_cover") # for Evar evenness measure df <- subset(pplots, plot == 25 | plot == 6) community_structure(df, time.var="year", replicate.var = "plot", abundance.var = "relative_cover", metric = "SimpsonEvenness") # for Simpson's evenness measure #Example with no replicates df <- subset(pplots, plot == 25) community_structure(df, time.var="year", abundance.var = "relative_cover", metric = "EQ") # for EQ evenness measure #Example with only a single time point and no replicates df <- subset(pplots, plot == 25 & year == 2002) community_structure(df, abundance.var = "relative_cover") # for Evar evenness measure
Calculates confidence intervals for the S3 object produced by cyclic_shift
## S3 method for class 'cyclic_shift' confint(object, parm = "out", level = 0.95, ...)## S3 method for class 'cyclic_shift' confint(object, parm = "out", level = 0.95, ...)
object |
An object of class |
parm |
which parameter is to be given a confidence interval. At present there is only one option: the mean of the null distribution. Defaults to "out", referring to the null distribution in objects of class |
level |
the confidence level required. |
... |
further arguments to |
A dataframe with the following columns:
lowerCI: A numeric column with the lowest confidence interval value.
upperCI: A numeric column with the highest confidence interval value.
nullMean: A numeric column with the average value of the specified test statistic when calculated on a null community.
Hallett, Lauren M., Joanna S. Hsu, Elsa E. Cleland, Scott L. Collins, Timothy L. Dickson, Emily C. Farrer, Laureano A. Gherardi, et al. "Biotic Mechanisms of Community Stability Shift along a Precipitation Gradient." Ecology 95, no. 6 (2014): 1693-1700.
Harms, Kyle E., Richard Condit, Stephen P. Hubbell, and Robin B. Foster. "Habitat Associations of Trees and Shrubs in a 50-Ha Neotropical Forest Plot." Journal of Ecology 89, no. 6 (2001): 947-59.
# Calculate a covariance matrix on a null community data(knz_001d) a1_cyclic <- cyclic_shift(subset(knz_001d, subplot == "A_1"), time.var = "year", species.var = "species", abundance.var = "abundance", FUN = cov, bootnumber = 10) # Return CI on a1_cyclic confint(a1_cyclic)# Calculate a covariance matrix on a null community data(knz_001d) a1_cyclic <- cyclic_shift(subset(knz_001d, subplot == "A_1"), time.var = "year", species.var = "species", abundance.var = "abundance", FUN = cov, bootnumber = 10) # Return CI on a1_cyclic confint(a1_cyclic)
Calculates the area difference between two rank abundance curves between two time periods. If replicate is specified, it must be measured in both time points, otherwise it will be dropped for that time period comparison.
curve_change( df, time.var, species.var, abundance.var, replicate.var = NULL, reference.time = NULL )curve_change( df, time.var, species.var, abundance.var, replicate.var = NULL, reference.time = NULL )
df |
A data frame containing time, species, and abundance columns and an optional columns of replicates. |
time.var |
The name of the time column. |
species.var |
The name of the species column. |
abundance.var |
The name of the abundance column. |
replicate.var |
The name of the optional replicate column. If specified, replicate must be unique within the dataset and cannot be nested within treatments or blocks. |
reference.time |
The name of the optional time point that all other time points should be compared to (e.g. the first year of data). If not specified, each comparison is between consecutive time points (the first and second year, second and third year, etc.) |
The curve_change function returns a data frame with the following attributes:
time.var: A column with the specified time.var and a second column, with '2' appended to the name. Time is subtracted from time2.
curve_change: A numeric column of the change in curves between time points.
replicate.var: A column that has same name and type as the replicate.var column, if specified.
Avolio et al. Submitted
data(pplots) # Without replicates df <- subset(pplots, plot == 25) curve_change(df = df, species.var = "species", abundance.var = "relative_cover", time.var = "year") # With replicates df <- subset(pplots, year < 2004 & plot %in% c(6, 25, 32)) curve_change(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year") # With reference year df <- subset(pplots, year < 2005 & plot %in% c(6, 25, 32)) curve_change(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year", reference.time = 2002)data(pplots) # Without replicates df <- subset(pplots, plot == 25) curve_change(df = df, species.var = "species", abundance.var = "relative_cover", time.var = "year") # With replicates df <- subset(pplots, year < 2004 & plot %in% c(6, 25, 32)) curve_change(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year") # With reference year df <- subset(pplots, year < 2005 & plot %in% c(6, 25, 32)) curve_change(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year", reference.time = 2002)
Calculates the area difference between two rank abundance curves. There are three ways differences can be calculated. 1) Between all treatments within a block (note: block.var and treatment.var need to be specified. 2) Between treatments, pooling all replicates into a single species pool (note: pool = TRUE, treatment.var needs to be specified, and block.var = NULL. 3) All pairwise combinations between all replicates (note:block.var = NULL, pool = FALSE and specifying treatment.var is optional. If treatment.var is specified, the treatment that each replicate belongs to will also be listed in the output).
curve_difference( df, time.var = NULL, species.var, abundance.var, replicate.var, treatment.var = NULL, pool = FALSE, block.var = NULL, reference.treatment = NULL )curve_difference( df, time.var = NULL, species.var, abundance.var, replicate.var, treatment.var = NULL, pool = FALSE, block.var = NULL, reference.treatment = NULL )
df |
A data frame containing a species, abundance, and replicate columns and optional time, treatment, and block columns. |
time.var |
The name of the optional time column. |
species.var |
The name of the species column. |
abundance.var |
The name of the abundance column. |
replicate.var |
The name of the replicate column. Replicate identifiers must be unique within the dataset and cannot be nested within treatments or blocks. |
treatment.var |
The name of the optional treatment column. |
pool |
An argument to allow abundance values to be pooled within a treatment. The default value is "FALSE", a value of "TRUE" averages abundance of each species within a treatment at a given time point. |
block.var |
The name of the optional block column. |
reference.treatment |
The name of the optional treatment that all other treatments will be compared to (e.g. only controls will be compared to all other treatments). If not specified all pairwise treatment comparisons will be made. |
The curve_difference function returns a data frame with the following attributes:
curve_diff: A numeric column of the area difference in curves between the two samples being compared (replicates or treatments).
replicate.var: A column that has same name and type as the replicate.var column, represents the first replicate being compared. Note, a replicate column will be returned only when pool is FALSE or block.var = NULL.
replicate.var2: A column that has the same type as the replicate.var column, and is named replicate.var with a 2 appended to it, represents the second replicate being compared. Note, a replicate.var column will be returned only when pool is FALSE and block.var = NULL.
time.var: A column that has the same name and type as the time.var column, if time.var is specified.
treatment.var: A column that has same name and type as the treatment.var column, represents the first treatment being compared. A treatment.var column will be returned when pool is TRUE or block.var is specified, or treatment.var is specified.
treatment.var2: A column that has the same type as the treatment.var column, and is named treatment.var with a 2 appended to it, represents the second treatment being compared. A treatment.var column will be returned when pool is TRUE or block.var is specified, or treatment.var is specified.
block.var: A column that has same name and type as the block.var column, if block.var is specified.
Avolio et al. Submitted
data(pplots) # With block and no time df <- subset(pplots, year == 2002 & block < 3) curve_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = "treatment", block.var = "block", replicate.var = "plot") # With blocks and time df <- subset(pplots, year < 2004 & block < 3) curve_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = "treatment", block.var = "block", replicate.var = "plot", time.var = "year") # With blocks, time, and reference treatment df <- subset(pplots, year < 2004 & block < 3) curve_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = "treatment", block.var = "block", replicate.var = "plot", time.var = "year", reference.treatment = "N1P0") # Pooling by treatment with time df <- subset(pplots, year < 2004) curve_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = "treatment", pool = TRUE, replicate.var = "plot", time.var = "year") # All pairwise replicates with treatment df <- subset(pplots, year < 2004 & plot %in% c(21, 25, 32)) curve_difference(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year", treatment.var = "treatment") # All pairwise replicates without treatment df <- subset(pplots, year < 2004 & plot %in% c(21, 25, 32)) curve_difference(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year")data(pplots) # With block and no time df <- subset(pplots, year == 2002 & block < 3) curve_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = "treatment", block.var = "block", replicate.var = "plot") # With blocks and time df <- subset(pplots, year < 2004 & block < 3) curve_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = "treatment", block.var = "block", replicate.var = "plot", time.var = "year") # With blocks, time, and reference treatment df <- subset(pplots, year < 2004 & block < 3) curve_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = "treatment", block.var = "block", replicate.var = "plot", time.var = "year", reference.treatment = "N1P0") # Pooling by treatment with time df <- subset(pplots, year < 2004) curve_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = "treatment", pool = TRUE, replicate.var = "plot", time.var = "year") # All pairwise replicates with treatment df <- subset(pplots, year < 2004 & plot %in% c(21, 25, 32)) curve_difference(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year", treatment.var = "treatment") # All pairwise replicates without treatment df <- subset(pplots, year < 2004 & plot %in% c(21, 25, 32)) curve_difference(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year")
Performs a user-specified function on a null ecological community created via cyclic shift permutations (Harms et al. 2001, Hallett et al. 2014). The null community is formed by randomly selected different starting years for the time series of each species. This generates a null community matrix in which species abundances vary independently but within-species autocorrelation is maintained. The user-specified function must require a species x time input.
cyclic_shift( df, time.var, species.var, abundance.var, replicate.var = NA, FUN, bootnumber )cyclic_shift( df, time.var, species.var, abundance.var, replicate.var = NA, FUN, bootnumber )
df |
A data frame containing time, species and abundance columns and an optional column of replicates |
time.var |
The name of the time column |
species.var |
The name of the species column |
abundance.var |
The name of the abundance column |
replicate.var |
The name of the replicate column. Defaults to |
FUN |
A function to calculate on the null community |
bootnumber |
Number of null simulations to run |
The input data frame needs to contain columns for time, species and abundance; time.var, species.var and abundance.var are used to indicate which columns contain those variables.
The cyclic_shift function returns an S3 object of class "cyclic_shift" and parameter "out". The length of the "out" parameter is the number of null iterations as specified by bootnumber. If multiple replicates are specified, null values are averaged among replicates for each iteration, but a different cyclic shift permutation is applied to each replicate within an iteration.
Hallett, Lauren M., Joanna S. Hsu, Elsa E. Cleland, Scott L. Collins, Timothy L. Dickson, Emily C. Farrer, Laureano A. Gherardi, et al. "Biotic Mechanisms of Community Stability Shift along a Precipitation Gradient." Ecology 95, no. 6 (2014): 1693-1700.
Harms, Kyle E., Richard Condit, Stephen P. Hubbell, and Robin B. Foster. "Habitat Associations of Trees and Shrubs in a 50-Ha Neotropical Forest Plot." Journal of Ecology 89, no. 6 (2001): 947-59.
# Calculate a covariance matrix on a null community data(knz_001d) a1_cyclic <- cyclic_shift(subset(knz_001d, subplot == "A_1"), time.var = "year", species.var = "species", abundance.var = "abundance", FUN = cov, bootnumber = 10)# Calculate a covariance matrix on a null community data(knz_001d) a1_cyclic <- cyclic_shift(subset(knz_001d, subplot == "A_1"), time.var = "year", species.var = "species", abundance.var = "abundance", FUN = cov, bootnumber = 10)
Plant composition within multiple replicates at an annually burned tallgrass prairie site in the Konza Prairie LTER, Manhattan KS (Watershed 001d).
data(knz_001d)data(knz_001d)
A data frame with 8768 rows and 4 variables
A data frame containing a column for species, year, subplot and abundance:
species: A factor column of species sampled
year: An integer column of sampling time points
subplot: A factor column of spatial replicates with 20 levels
abundance: A numeric column of abundance values
Konza Prairie LTER Dataset ID: PVC02, watershed 1D
Collins, S. L. (2000) Disturbance frequency and community stability in native tallgrass prairie. American Naturalist 155:311-325.
A measure of the relative change in species rank abundances, which indicates shifts in relative abundances over time (Collins et al. 2008). Mean rank shifts are calculated as the average difference in species' ranks between consecutive time periods, among species that are present across the entire time series.
mean_rank_shift( df, time.var, species.var, abundance.var, replicate.var = as.character(NA) )mean_rank_shift( df, time.var, species.var, abundance.var, replicate.var = as.character(NA) )
df |
A data frame containing time, species and abundance columns and an optional column of replicates |
time.var |
The name of the time column |
species.var |
The name of the species column |
abundance.var |
The name of the abundance column |
replicate.var |
The name of the optional replicate column |
The input data frame needs to contain columns for time, species and abundance; time.var, species.var and abundance.var are used to indicate which columns contain those variables. If multiple replicates are included in the data frame, that column should be specified with replicate.var. Each replicate should reflect a single experimental unit - there must be a single abundance value per species within each time point and replicate.
mean_rank_shift returns a data frame with the following columns:
time.var_pair: A factor column that returns the two time periods being compared, separated by a dash. The name of this column is the same as the time.var column in the input dataframe followed by "_pair".
MRS: A numeric column with the mean rank shift values.
replicate.var: A column that has same name and type as the replicate.var column, if replication is specified.
Calculates the changes in composition and dispersion based off a Bray-Curtis dissimilarity matrix. Composition change is the pairwise distance between centroids of compared time periods and ranges from 0-1, where identical communities give 0 and completely different communities give 1. Dispersion change is the difference between time periods in the dispersion of replicates, i.e. the average distance between a replicate and its centroid.
multivariate_change( df, time.var, species.var, abundance.var, replicate.var, treatment.var = NULL, reference.time = NULL )multivariate_change( df, time.var, species.var, abundance.var, replicate.var, treatment.var = NULL, reference.time = NULL )
df |
A data frame containing time, species, abundance and replicate columns and an optional column of treatment. |
time.var |
The name of the time column. |
species.var |
The name of the species column. |
abundance.var |
The name of the abundance column. |
replicate.var |
The name of the replicate column. Replicate identifiers must be unique within the dataset and cannot be nested within treatments or blocks. |
treatment.var |
The name of the optional treatment column. |
reference.time |
The name of the optional time point that all other time points should be compared to (e.g. the first year of data). If not specified, each comparison is between consecutive time points (the first and second year, second and third year, etc.) |
The multivariate_change function returns a data frame with the following attributes:
time.var: A column with the specified time.var and a second column, with '2' appended to the name. Time is subtracted from time2 for dispersion change.
composition_change: A numeric column that is the distance between the centroids of two time points, or NA if a real distance could not be calculated.
dispersion_change: A numeric column that is the difference in the average dispersion of the replicates around the centroid for the two time periods. A negative value indicates replicates are converging over time (there is less dispersion at time period 2 than time period 1) and a positive value indicates replicates are diverging over time (there is more dispersion at time period 2 than time period 1.
treatment.var: A column that has same name and type as the treatment.var column, if treatment.var is specified.
Avolio et al. 2015; Avolio et al. Submitted, Marti Anderson et al. 2006.
data(pplots) # With treatment multivariate_change(pplots, time.var="year", replicate.var = "plot", treatment.var = "treatment", species.var = "species", abundance.var = "relative_cover") # In each year there are 6 replicates and there are 4 years of data for 3 # time comparisons, thus 24 total observations in each treatment. # With treatment and reference year multivariate_change(pplots, time.var="year", replicate.var = "plot", treatment.var = "treatment", species.var = "species", abundance.var = "relative_cover", reference.time = 2002) # In each year there are 6 replicates and there are 4 years of data for 3 # time comparisons, thus 24 total observations in each treatment. # Without treatment df <- subset(pplots, treatment == "N1P0") multivariate_change(df, time.var="year", replicate.var = "plot", species.var = "species", abundance.var = "relative_cover") # In each year there are 6 replicates and there are 4 years of data for 3 # time comparisons, thus 24 total observations.data(pplots) # With treatment multivariate_change(pplots, time.var="year", replicate.var = "plot", treatment.var = "treatment", species.var = "species", abundance.var = "relative_cover") # In each year there are 6 replicates and there are 4 years of data for 3 # time comparisons, thus 24 total observations in each treatment. # With treatment and reference year multivariate_change(pplots, time.var="year", replicate.var = "plot", treatment.var = "treatment", species.var = "species", abundance.var = "relative_cover", reference.time = 2002) # In each year there are 6 replicates and there are 4 years of data for 3 # time comparisons, thus 24 total observations in each treatment. # Without treatment df <- subset(pplots, treatment == "N1P0") multivariate_change(df, time.var="year", replicate.var = "plot", species.var = "species", abundance.var = "relative_cover") # In each year there are 6 replicates and there are 4 years of data for 3 # time comparisons, thus 24 total observations.
Calculates the difference in composition and dispersion between treatments based off a Bray-Curtis dissimilarity matrix at a single point in time. Composition difference is the pairwise distance between centroids of compared treatments and ranges from 0-1, where identical communities give 0 and completely different communities give 1. Dispersion difference is the difference between treatments in the dispersion of replicates, i.e. the average distance between a replicate and its centroid.
multivariate_difference( df, time.var = NULL, species.var, abundance.var, replicate.var, treatment.var, reference.treatment = NULL )multivariate_difference( df, time.var = NULL, species.var, abundance.var, replicate.var, treatment.var, reference.treatment = NULL )
df |
A data frame containing a species, abundance, replicate, and treatment columns and optional time column. |
time.var |
The name of the optional time column. |
species.var |
The name of the species column. |
abundance.var |
The name of the abundance column. |
replicate.var |
The name of the replicate column. Replicate identifiers must be unique within the dataset and cannot be nested within treatments or blocks. |
treatment.var |
The name of the treatment column. |
reference.treatment |
The name of the optional treatment that all other treatments will be compared to (e.g. only controls will be compared to all other treatments). If not specified all pairwise treatment comparisons will be made. |
The multivariate_difference function returns a data frame with the following attributes:
treatment.var: A column that has same name and type as the treatment.var column, if treatment.var is specified.
treatment.var2: A column that has the same type as the treatment.var column, and is named treatment.var with a 2 appended to it.
composition_diff: A numeric column that is the euclidean distance between the centroids of two treatments at a single point in time.
abs_dispersion_diff: A numeric column that is the absolute value of the difference in the average dispersion of the replicates around the centroid for the two treatments.
trt_greater_disp: A column that has same type as the treatment.var column, and specifies which of the two treatments has greater dispersion.
time.var: A characteristic column that has the same name and type as the time.var column, if specified.
Avolio et al. Submitted, Avolio et al. 2015, Marti Anderson et al. 2006
data(pplots) # Without time df <- subset(pplots, year == 2002) multivariate_difference(df, replicate.var = "plot", treatment.var = "treatment", species.var = "species", abundance.var = "relative_cover") # There are 6 replicates for each of three treatments, thus 18 total # observations. # Without time and with reference treatment df <- subset(pplots, year == 2002) multivariate_difference(df, replicate.var = "plot", treatment.var = "treatment", species.var = "species", abundance.var = "relative_cover", reference.treatment = "N1P0") # There are 6 replicates for each of three treatments, thus 18 total # observations. # With time multivariate_difference(pplots, time.var = "year", replicate.var = "plot", species.var = "species", abundance.var = "relative_cover", treatment.var = "treatment") # In each year there are 6 replicates for each of three treatments, for a # total of 18 observations.data(pplots) # Without time df <- subset(pplots, year == 2002) multivariate_difference(df, replicate.var = "plot", treatment.var = "treatment", species.var = "species", abundance.var = "relative_cover") # There are 6 replicates for each of three treatments, thus 18 total # observations. # Without time and with reference treatment df <- subset(pplots, year == 2002) multivariate_difference(df, replicate.var = "plot", treatment.var = "treatment", species.var = "species", abundance.var = "relative_cover", reference.treatment = "N1P0") # There are 6 replicates for each of three treatments, thus 18 total # observations. # With time multivariate_difference(pplots, time.var = "year", replicate.var = "plot", species.var = "species", abundance.var = "relative_cover", treatment.var = "treatment") # In each year there are 6 replicates for each of three treatments, for a # total of 18 observations.
A dataset of tallgrass prairie plant composition in a nitrogen and phosphorus addition experiment at Konza Prairie, Manhattan Kansas (Avolio et al. 2014). This dataset is a subset of the full dataset.
data(pplots)data(pplots)
A data frame with 1232 rows and 6 variables
A data frame containing a column for replicate, year, species, abundance, block and treatment :
plot: An integer column of spatial replicates with 18 levels (6-48)
year: An integer column of sampling time points
species: A factor column of species sampled
relative_cover: A numeric column of relative cover values
block: An integer column of dummy blocking variable, grouping treatment plots into blocks
treatment: A factor column of nitrogen and phosphorus treatments applied to the plots
Avolio, ML, Koerner, S, La Pierre, K, Wilcox, K, Wilson, GTW, Smith, MD, Collins, S. 2014. Changes in plant community composition, not diversity, to a decade of nitrogen and phosphorus additions drive changes in aboveground productivity in a tallgrass prairie. Journal of Ecology. 102: 1649-1660.
Calculates change of the five aspects of rank abundance curves (richness, evenness, rank, species gains, and species losses) for a replicate between two time points.
RAC_change( df, time.var, species.var, abundance.var, replicate.var = NULL, reference.time = NULL )RAC_change( df, time.var, species.var, abundance.var, replicate.var = NULL, reference.time = NULL )
df |
A data frame containing time, species, and abundance columns and an optional columns of replicates. |
time.var |
The name of the time column. |
species.var |
The name of the species column. |
abundance.var |
The name of the abundance column. |
replicate.var |
The name of the optional replicate column. If specified, replicate must be unique within the dataset and cannot be nested within treatments or blocks. |
reference.time |
The name of the optional time point that all other time points should be compared to (e.g. the first year of data). If not specified, each comparison is between consecutive time points (the first and second year, second and third year, etc.) |
The RAC_change function returns a data frame with a subset of the following columns:
replicate.var: A column that has same name and type as the replicate.var column, if replicate.var is specified.
time.var: A column with the specified time.var and a second column, with '2' appended to the name. Time is subtracted from time2.
richness_change: A numeric column that is the change in richness between the two time periods for a replicate divided by the total number of unique species in both time periods. A positive value occurs when a there is an increase in species richness over time, and a negative value when there is a decreases in species richness over time.
evenness_change: A numeric column that is the change in evenness(measured with Evar) between the two time periods for a replicate. A positive value occurs when evenness increases over time, and a negative value when evenness decreases in over time.
rank_change: A numeric column that is the absolute value of the average change in species ranks between the two time periods for a replicate divided by the total number of unique species in both time periods. Species that are not present in both time periods are given the S+1 rank in the sample it is absent in, where S is the number of species in that sample.
gains: A numeric column of the number of species that are present at time period 2 that were not present at time period 1 for a replicate divided by the total number of unique species in both time periods. This is equivalent to the turnover function with metric = "appearances".
losses: A numeric column of the number of species that are not present at time period 2 but were present at time period 1 for a replicate divided by the total number of unique species in both time periods. This is equivalent to the turnover function with metric = "disappearance".
Avolio et al. Submitted
data(pplots) # Without replicates df <- subset(pplots, plot == 25) RAC_change(df = df, species.var = "species", abundance.var = "relative_cover", time.var = "year") # With replicates df <- subset(pplots, year < 2004 & plot %in% c(6, 25, 32)) RAC_change(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year") # With reference year df <- subset(pplots, year < 2005 & plot %in% c(6, 25, 32)) RAC_change(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year", reference.time = 2002)data(pplots) # Without replicates df <- subset(pplots, plot == 25) RAC_change(df = df, species.var = "species", abundance.var = "relative_cover", time.var = "year") # With replicates df <- subset(pplots, year < 2004 & plot %in% c(6, 25, 32)) RAC_change(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year") # With reference year df <- subset(pplots, year < 2005 & plot %in% c(6, 25, 32)) RAC_change(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year", reference.time = 2002)
Calculates differences between two samples for four comparable aspects of rank abundance curves (richness, evenness, rank, species composition). There are three ways differences can be calculated. 1) Between treatments within a block (note: block.var and treatment.var need to be specified). 2) Between treatments, pooling all replicates into a single species pool (note: pool = TRUE, treatment.var needs to be specified, and block.var will be NULL). 3) All pairwise combinations between all replicates (note: block.var = NULL, pool = FALSE and specifying treatment.var is optional. If treatment.var is specified, the treatment that each replicate belongs to will also be listed in the output).
RAC_difference( df, time.var = NULL, species.var, abundance.var, replicate.var, treatment.var = NULL, pool = FALSE, block.var = NULL, reference.treatment = NULL )RAC_difference( df, time.var = NULL, species.var, abundance.var, replicate.var, treatment.var = NULL, pool = FALSE, block.var = NULL, reference.treatment = NULL )
df |
A data frame containing a species, abundance, and replicate columns and optional time, treatment, and block columns. |
time.var |
The name of the optional time column. |
species.var |
The name of the species column. |
abundance.var |
The name of the abundance column. |
replicate.var |
The name of the replicate column. Replicate identifiers must be unique within the dataset and cannot be nested within treatments or blocks. |
treatment.var |
The name of the optional treatment column. |
pool |
An argument to allow abundance values to be pooled within a treatment. The default value is "FALSE", a value of "TRUE" averages abundance of each species within a treatment at a given time point. |
block.var |
The name of the optional block column. |
reference.treatment |
The name of the optional treatment that all other treatments will be compared to (e.g. only controls will be compared to all other treatments). If not specified all pairwise treatment comparisons will be made. |
The RAC_difference function returns a data frame with the following attributes:
time.var: A column that has the same name and type as the time.var column, if time.var is specified.
block.var: A column that has same name and type as the block.var column, if block.var is specified.
replicate.var: A column that has same name and type as the replicate.var column, represents the first replicate being compared. Note, a replicate column will be returned only when pool is FALSE or block.var = NULL.
replicate.var2: A column that has the same type as the replicate.var column, and is named replicate.var with a 2 appended to it, represents the second replicate being compared. Note, a replicate.var column will be returned only when pool is FALSE and block.var = NULL.
treatment.var: A column that has the same name and type as the treatment.var column, represents the first treatment being compared. A treatment.var column will be returned when pool is TRUE or block.var is present, or treatment.var is specified.
treatment.var2: A column that has the same type as the treatment.var column, and is named treatment.var with a 2 appended to it, represents the second treatment being compared. A treatment.var column will be returned when pool is TRUE or block.var is present, or treatment.var is specified.
richness_diff: A numeric column that is the difference between the compared samples (treatments or replicates) in species richness divided by the total number of unique species in both samples. A positive value occurs when there is greater species richness in replicate.var2 than replicate.var or treatment.var2 than treatment.var.
evenness_diff: A numeric column of the difference between the compared samples (treatments or replicates) in evenness (measured by Evar). A positive value occurs when there is greater evenness in replicate.var2 than replicate.var or treatment.var2 than treatment.var.
rank_diff: A numeric column of the absolute value of average difference between the compared samples (treatments or replicates) in species' ranks divided by the total number of unique species in both samples.Species that are not present in both samples are given the S+1 rank in the sample it is absent in, where S is the number of species in that sample.
species_diff: A numeric column of the number of species that are different between the compared samples (treatments or replicates) divided by the total number of species in both samples. This is equivalent to the Jaccard Index.
Avolio et al. Submitted
data(pplots) # With block and no time df <- subset(pplots, year == 2002 & block < 3) RAC_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = 'treatment', block.var = "block", replicate.var = "plot") # With blocks and time df <- subset(pplots, year < 2004 & block < 3) RAC_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = 'treatment', block.var = "block", replicate.var = "plot", time.var = "year") # With blocks, time and reference treatment df <- subset(pplots, year < 2004 & block < 3) RAC_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = 'treatment', block.var = "block", replicate.var = "plot", time.var = "year", reference.treatment = "N1P0") # Pooling by treatment with time df <- subset(pplots, year < 2004) RAC_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = 'treatment', pool = TRUE, replicate.var = "plot", time.var = "year") # All pairwise replicates with treatment df <- subset(pplots, year < 2004 & plot %in% c(21, 25, 32)) RAC_difference(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year", treatment.var = "treatment") # All pairwise replicates without treatment df <- subset(pplots, year < 2004 & plot %in% c(21, 25, 32)) RAC_difference(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year")data(pplots) # With block and no time df <- subset(pplots, year == 2002 & block < 3) RAC_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = 'treatment', block.var = "block", replicate.var = "plot") # With blocks and time df <- subset(pplots, year < 2004 & block < 3) RAC_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = 'treatment', block.var = "block", replicate.var = "plot", time.var = "year") # With blocks, time and reference treatment df <- subset(pplots, year < 2004 & block < 3) RAC_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = 'treatment', block.var = "block", replicate.var = "plot", time.var = "year", reference.treatment = "N1P0") # Pooling by treatment with time df <- subset(pplots, year < 2004) RAC_difference(df = df, species.var = "species", abundance.var = "relative_cover", treatment.var = 'treatment', pool = TRUE, replicate.var = "plot", time.var = "year") # All pairwise replicates with treatment df <- subset(pplots, year < 2004 & plot %in% c(21, 25, 32)) RAC_difference(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year", treatment.var = "treatment") # All pairwise replicates without treatment df <- subset(pplots, year < 2004 & plot %in% c(21, 25, 32)) RAC_difference(df = df, species.var = "species", abundance.var = "relative_cover", replicate.var = "plot", time.var = "year")
A measure of the relative change in species rank abundances, which indicates shifts in relative abundances over time (Collins et al. 2008). Mean rank shifts are calculated as the average difference in species' ranks between consecutive time periods, among species that are present across the entire time series.
rank_shift( df, time.var, species.var, abundance.var, replicate.var = as.character(NA) )rank_shift( df, time.var, species.var, abundance.var, replicate.var = as.character(NA) )
df |
A data frame containing time, species and abundance columns and an optional column of replicates |
time.var |
The name of the time column |
species.var |
The name of the species column |
abundance.var |
The name of the abundance column |
replicate.var |
The name of the optional replicate column |
The input data frame needs to contain columns for time, species and abundance; time.var, species.var and abundance.var are used to indicate which columns contain those variables. If multiple replicates are included in the data frame, that column should be specified with replicate.var. Each replicate should reflect a single experimental unit - there must be a single abundance value per species within each time point and replicate.
rank_shift returns a data frame with the following columns:
time.var_pair: A factor column that returns the two time periods being compared, separated by a dash. The name of this column is the same as the time.var column in the input dataframe followed by "_pair".
MRS: A numeric column with the mean rank shift values.
replicate.var: A column that has same name and type as the replicate.var column, if replication is specified.
Collins, Scott L., Katharine N. Suding, Elsa E. Cleland, Michael Batty, Steven C. Pennings, Katherine L. Gross, James B. Grace, Laura Gough, Joe E. Fargione, and Christopher M. Clark. (2008) "Rank clocks and plant community dynamics." Ecology 89, no. 12: 3534-41.
# Calculate mean rank shifts within replicates data(knz_001d) myoutput <- rank_shift(knz_001d, time.var = "year", species.var = "species", abundance.var = "abundance", replicate.var = "subplot") # Calculate mean rank shifts for a data frame with no replication myoutput_singlerep <- rank_shift(subset(knz_001d, subplot=="A_1"), time.var = "year", species.var = "species", abundance.var = "abundance")# Calculate mean rank shifts within replicates data(knz_001d) myoutput <- rank_shift(knz_001d, time.var = "year", species.var = "species", abundance.var = "abundance", replicate.var = "subplot") # Calculate mean rank shifts for a data frame with no replication myoutput_singlerep <- rank_shift(subset(knz_001d, subplot=="A_1"), time.var = "year", species.var = "species", abundance.var = "abundance")
Calculates the slope of the differences in species composition within a community over increasing time intervals, which provides a measures of the rate of directional change in community composition. Differences in species composition are characterized by Euclidean distances, which are calculated on pair-wise communities across the entire time series. For example, a data set with six time intervals will have distance values for five one-year time lags (year 1 vs year 2, year 2 vs year 3 ...), four two-year time lags (year 1 vs year 3, year 2 vs year 4 ...) and so forth. These distance values are regressed against the time lag interval. The slope of the regression line is reported as an indication of the rate and direction of compositional change in the community.
rate_change(df, time.var, species.var, abundance.var, replicate.var = NA)rate_change(df, time.var, species.var, abundance.var, replicate.var = NA)
df |
A data frame containing time, species and abundance columns and an optional column of replicates |
time.var |
The name of the time column |
species.var |
The name of the species column |
abundance.var |
The name of the abundance column |
replicate.var |
The name of the optional replicate column |
The input data frame needs to contain columns for time, species and abundance; time.var, species.var and abundance.var are used to indicate which columns contain those variables. If multiple replicates are included in the data frame, that column should be specified with replicate.var. Each replicate should reflect a single experimental unit - there must be a single abundance value per species within each time point and replicate.
The rate_change function uses linear regression to relate Euclidean distances to time lag intervals.
It is recommended that fit of this relationship be verified using rate_change_interval,
which returns the full set of community distance values and associated time lag intervals.
The rate_change function returns a numeric rate change value unless a replication column is specified in the input data frame.
If replication is specified, the function returns a data frame with the following attributes:
rate_change: A numeric column with the synchrony values.
replicate.var: A column that shares the same name and type as the replicate.var column in the input data frame.
Collins, S. L., Micheli, F. and Hartt, L. 2000. A method to determine rates and patterns of variability in ecological communities. - Oikos 91: 285-293.
data(knz_001d) rate_change(knz_001d[knz_001d$subplot=="A_1",], time.var = "year", species.var = "species", abundance.var = "abundance") # for one subplot rate_change(knz_001d, time.var = "year", species.var = "species", abundance.var = "abundance", replicate.var = "subplot") # across all subplotsdata(knz_001d) rate_change(knz_001d[knz_001d$subplot=="A_1",], time.var = "year", species.var = "species", abundance.var = "abundance") # for one subplot rate_change(knz_001d, time.var = "year", species.var = "species", abundance.var = "abundance", replicate.var = "subplot") # across all subplots
Calculates the differences in species composition within a community over increasing time intervals. Differences in species composition are characterized by Euclidean distances, which are calculated on pair-wise communities across the entire time series. For example, a data set with 6 time intervals will have distance values for five one-year time lags (year 1 vs year 2, year 2 vs year 3 ...), 4 two-year time lags (year 1 vs year 3, year 2 vs year 4 ...) and so forth. Returns the full set of community distance values and associated time lag intervals.
rate_change_interval( df, time.var, species.var, abundance.var, replicate.var = NA )rate_change_interval( df, time.var, species.var, abundance.var, replicate.var = NA )
df |
A data frame containing time, species and abundance columns and an optional column of replicates |
time.var |
The name of the time column |
species.var |
The name of the species column |
abundance.var |
The name of the abundance column |
replicate.var |
The name of the optional replicate column |
The rate_change_interval function returns a data frame with the following attributes:
interval: A numeric column containing the interval length between time periods.
distance: A numeric column containing the Euclidean distances.
replicate.var: A column that shares the same name and type as the replicate.var column in the input data frame.
The input data frame needs to contain columns for time, species and abundance; time.var, species.var and abundance.var are used to indicate which columns contain those variables. If multiple replicates are included in the data frame, that column should be specified with replicate.var. Each replicate should reflect a single experimental unit - there must be a single abundance value per species within each time point and replicate.
Collins, S. L., Micheli, F. and Hartt, L. 2000. A method to determine rates and patterns of variability in ecological communities. - Oikos 91: 285-293.
data(knz_001d) rate_change_interval(knz_001d[knz_001d$subplot=="A_1",], time.var = "year", species.var = "species", abundance.var = "abundance") # for one subplot rate_change_interval(knz_001d, time.var = "year", species.var = "species", abundance.var = "abundance", replicate.var = "subplot") # across all subplotsdata(knz_001d) rate_change_interval(knz_001d[knz_001d$subplot=="A_1",], time.var = "year", species.var = "species", abundance.var = "abundance") # for one subplot rate_change_interval(knz_001d, time.var = "year", species.var = "species", abundance.var = "abundance", replicate.var = "subplot") # across all subplots
Calculates the degree synchrony in species abundances within a community over time. Includes the option for two different synchrony metrics. The first, developed by Loreau and de Mazancourt (2008), compares the variance of the aggregated community with the variance of individual components. The second, developed by Gross et al. (2014), compares the average correlation of each individual species with the rest of the aggregated community.
synchrony( df, time.var, species.var, abundance.var, metric = "Loreau", replicate.var = NA )synchrony( df, time.var, species.var, abundance.var, metric = "Loreau", replicate.var = NA )
df |
A data frame containing time, species and abundance columns and an optional column of replicates |
time.var |
The name of the time column |
species.var |
The name of the species column |
abundance.var |
The name of the abundance column |
metric |
The synchrony metric to return:
|
replicate.var |
The name of the optional replicate column |
The input data frame needs to contain columns for time, species and abundance; time.var, species.var and abundance.var are used to indicate which columns contain those variables. If multiple replicates are included in the data frame, that column should be specified with replicate.var. Each replicate should reflect a single experimental unit - there must be a single abundance value per species within each time point and replicate.
The synchrony function returns a numeric synchrony value unless a replication column is specified in the input data frame.
If replication is specified, the function returns a data frame with the following attributes:
synchrony: A numeric column with the synchrony values.
replicate.var: A column that shares the same name and type as the replicate.var column in the input data frame.
Gross, Kevin, Bradley J. Cardinale, Jeremy W. Fox, Andrew Gonzalez, Michel Loreau, H. Wayne Polley, Peter B. Reich, and Jasper van Ruijven. (2014) "Species richness and the temporal stability of biomass production: A new analysis of recent biodiversity experiments." The American Naturalist 183, no. 1: 1-12. doi:10.1086/673915.
Loreau, Michel, and Claire de Mazancourt. (2008) "Species synchrony and its drivers: Neutral and nonneutral community dynamics in fluctuating environments." The American Naturalist 172, no. 2: E48-66. doi:10.1086/589746.
data(knz_001d) synchrony(knz_001d[knz_001d$subplot=="A_1",], time.var = "year", species.var = "species", abundance.var = "abundance") # for one subplot ## Not run: synchrony(knz_001d, time.var = "year", species.var = "species", abundance.var = "abundance", replicate.var = "subplot") # across all subplots synchrony(knz_001d, time.var = "year", species.var = "species", abundance.var = "abundance", replicate.var = "subplot", metric="Gross") # With Gross et al. (2014) metric. ## End(Not run)data(knz_001d) synchrony(knz_001d[knz_001d$subplot=="A_1",], time.var = "year", species.var = "species", abundance.var = "abundance") # for one subplot ## Not run: synchrony(knz_001d, time.var = "year", species.var = "species", abundance.var = "abundance", replicate.var = "subplot") # across all subplots synchrony(knz_001d, time.var = "year", species.var = "species", abundance.var = "abundance", replicate.var = "subplot", metric="Gross") # With Gross et al. (2014) metric. ## End(Not run)
Computes species turnover between time periods as the proportion of species either gained or lost relative to the total number of species observed across both time periods. Includes an option to compute turnover as just the proportion of species gained (i.e., "appearances") or lost (i.e., "disappearances").
turnover( df, time.var, species.var, abundance.var, replicate.var = NA, metric = "total" )turnover( df, time.var, species.var, abundance.var, replicate.var = NA, metric = "total" )
df |
A data frame containing time, species and abundance columns and an optional column of replicates |
time.var |
The name of the time column |
species.var |
The name of the species column |
abundance.var |
The name of the abundance column |
replicate.var |
The name of the optional replicate column |
metric |
The turnover metric to return:
|
The input data frame needs to contain columns for time, species and abundance; time.var, species.var and abundance.var are used to indicate which columns contain those variables. If multiple replicates are included in the data frame, that column should be specified with replicate.var. Each replicate should reflect a single experimental unit - there must be a single abundance value per species within each time point and replicate.
The turnover function returns a data frame with the following attributes:
turnover: A numeric column with the turnover values. The name of this column is the same as the specified metric (default is "total").
time.var: A column containing the second time point; the name and type of this column is the same as the time.var column in the input dataframe.
replicate.var: A column that has same name and type as the replicate.var column, if replication is specified.
Cleland, Elsa E., Scott L. Collins, Timothy L. Dickson, Emily C. Farrer, Katherine L. Gross, Laureano A. Gherardi, Lauren M. Hallett, et al. (2013) "Sensitivity of grassland plant community composition to spatial vs. temporal variation in precipitation." Ecology 94, no. 8: 1687-96.
data(knz_001d) # Calculate relative total turnover within replicates total.res <- turnover(df=knz_001d, time.var = "year", species.var = "species", abundance.var = "abundance", replicate.var="subplot") # Calculate relative species appearances within replicates appear.res <- turnover(df=knz_001d, time.var = "year", species.var = "species", abundance.var = "abundance", replicate.var="subplot", metric="appearance") # Calculate relative species disappearances within replicates disappear.res <- turnover(df=knz_001d, time.var = "year", species.var = "species", abundance.var = "abundance", replicate.var="subplot", metric="disappearance")data(knz_001d) # Calculate relative total turnover within replicates total.res <- turnover(df=knz_001d, time.var = "year", species.var = "species", abundance.var = "abundance", replicate.var="subplot") # Calculate relative species appearances within replicates appear.res <- turnover(df=knz_001d, time.var = "year", species.var = "species", abundance.var = "abundance", replicate.var="subplot", metric="appearance") # Calculate relative species disappearances within replicates disappear.res <- turnover(df=knz_001d, time.var = "year", species.var = "species", abundance.var = "abundance", replicate.var="subplot", metric="disappearance")
Computes the ratio of the variance of aggregate species abundances in a community to the sum of the variances of individual, component species. A variance ratio = 1 indicates that species do not covary, a variance ratio > 1 indicates predominately positive covariance among species and a variance ratio < 1 indicates predominately negative covariance (Schluter 1984).
Includes a cyclic shift null modeling option to test if the variance ratio significantly differs from 1. The null community is created by randomly selecting different starting points for each species' time series, which generates a community in which species abundances vary independently but within-species autocorrelation is maintained (Hallett et al. 2014). This randomization is repeated a user-specific number of times and confidence intervals are reported for the resultant null distribution of variance ratios. If the dataframe includes multiple replicates, the variance ratios for the actual and null communities are averaged within each iteration unless specified otherwise.
variance_ratio( df, time.var, species.var, abundance.var, bootnumber, replicate.var = NA, average.replicates = TRUE, level = 0.95, li, ui )variance_ratio( df, time.var, species.var, abundance.var, bootnumber, replicate.var = NA, average.replicates = TRUE, level = 0.95, li, ui )
df |
A data frame containing time, species and abundance columns and an optional column of replicates |
time.var |
The name of the time column |
species.var |
The name of the species column |
abundance.var |
The name of the abundance column |
bootnumber |
The number of null model iterations used to calculated confidence intervals |
replicate.var |
The name of the (optional) replicate column |
average.replicates |
If true returns the variance ratio and CIs averaged |
level |
The confidence level for the null mean |
li |
(deprecated) lower confidence interval |
ui |
(deprecated) upper confidence interval across replicates; if false returns the variance ratio and CI for each replicate. Defaults to true. |
The input data frame needs to contain columns for time, species and abundance; time.var, species.var and abundance.var are used to indicate which columns contain those variables. If multiple replicates are included in the data frame, that column should be specified with replicate.var. Each replicate should reflect a single experimental unit - there must be a single abundance value per species within each time point and replicate.
Null model confidence intervals default to the standard lowest 2.5% and upper 97.5% of the null distribution, typically these do not need to be change, but they can be user-modified to set more stringent CIs. @references Hallett, Lauren M., Joanna S. Hsu, Elsa E. Cleland, Scott L. Collins, Timothy L. Dickson, Emily C. Farrer, Laureano A. Gherardi, et al. (2014) "Biotic Mechanisms of Community Stability Shift along a Precipitation Gradient." Ecology 95, no. 6: 1693-1700. doi: 10.1890/13-0895.1
Schluter, Dolph. (1984) "A Variance Test for Detecting Species Associations, with Some Example Applications." Ecology 65, no. 3: 998-1005. doi:10.2307/1938071.
The variance_ratio function returns a data frame with the following attributes:
VR: A numeric column with the actual variance ratio value.
lowerCI: A numeric column with the lowest confidence interval value.
upperCI: A numeric column with the highest confidence interval value.
nullmean: A numeric column with the average null variance ratio value.
replicate.var: A column that has same name and type as the replicate.var column, if replication is specified.
data(knz_001d) # Calculate the variance ratio and CIs averaged within replicates # Here the null model is repeated once, for final use it is recommended to set a # large bootnumber (eg, 10000) res_averagedreplicates <- variance_ratio(knz_001d, time.var = "year", species.var = "species", abundance.var = "abundance", bootnumber = 1, replicate = "subplot") #Calculate the variance ratio and CIs for each replicate res_withinreplicates <- variance_ratio(knz_001d, time.var = "year", species.var = "species", abundance.var = "abundance", bootnumber = 1, replicate = "subplot", average.replicates = FALSE)data(knz_001d) # Calculate the variance ratio and CIs averaged within replicates # Here the null model is repeated once, for final use it is recommended to set a # large bootnumber (eg, 10000) res_averagedreplicates <- variance_ratio(knz_001d, time.var = "year", species.var = "species", abundance.var = "abundance", bootnumber = 1, replicate = "subplot") #Calculate the variance ratio and CIs for each replicate res_withinreplicates <- variance_ratio(knz_001d, time.var = "year", species.var = "species", abundance.var = "abundance", bootnumber = 1, replicate = "subplot", average.replicates = FALSE)