PSicalc | R Documentation |

A measure of individual specialization proposed by Bolnick et al. (2003), based on the average pairwise overlap of the niche distribution of individuals and the population.

PSicalc(dataset, pop.diet = "sum", exclude = FALSE, replicates=999, precision = 1e-9)

`dataset` |
Object of class RInSp with data. |

`pop.diet` |
Option specifying the way to calculate the population diet. Valid values are “sum” or “average”. |

`exclude` |
A logical value to exclude or not the individual in variance estimates. Defalut is FALSE, i.e. keep all individuals. |

`replicates` |
Number of replicates for Monte Carlo resampling. |

`precision` |
a filter to remove from the PS matrix all values which are lower than "precision". In this way some interactions are removed due to their small weight in the PS matrix. |

Both Schoener (1968) and Feinsinger et al. (1981) advocated using Czekanowski's proportional similarity index (PS) for interspecific niche studies. Adapted to individual-level analyses, the diet overlap between an individual and the population is:

* PS_i = 1 - 0.5 ∑_j{|p_{ij} - q_j|} *

where *p_{ij}* is the frequency of category j in the individual i's diet, and *q_j* is the frequency of category j in the population as a whole.
The prevalence of individual specialization (IS) in the population is then measured by the average of individuals' PS values:

*IS = \frac{∑_i(PS_i)}{N}*

Monte Carlo resampling for hypothesis testing is implemented for the case when all individuals are kept.

The user has the option of calculating each individual's *PS_i* and *Var(PS_i)* using *q_j* values calculated by either including or excluding individual i.

The function returns a list composed by:

`PSi` |
A column vector with the individual proportional similarity index for each individual i. |

`IS` |
The value of the prevalence of individual specialization. |

`PSi.montecarlo` |
Monte Carlo resampling simulation values for |

`Var.montecarlo` |
Monte Carlo resampling simulation values for |

`VarPSi` |
A column vector of the estimated variance for each individual. |

`population.diet` |
A vector describing the population diet. A matrix is returned when “exclude” is TRUE. |

`IS.pvalue` |
Probability value for the observed IS against the Monte Carlo resampling simulation. Consider to use more than 1000 simulations. |

`montecarlo` |
Monte Carlo resampling simulation values for |

`num.individuals` |
The number of individuals of the data set. |

`parmeter` |
The value points to the column of the “montecarlo” matrix for plotting results. |

`Precission` |
The value of the selected threshold for precision. |

Dr. Nicola ZACCARELLI

Bolnick, D.I., L.H. Yang, J.A. Fordyce, J.M. Davis, and Svanback, R. 2002. Measuring individual-level resource specialization. *Ecology* **83**: 2936-2941.

Feinsinger, P., Spears, E. E., and Poole, R. W. 1981. A simple measure of niche breadth. *Ecology* **62**: 27-32.

Schoener, T.W. 1968. The *Anolis* lizards of Bimini: resource partitioning in a complex fauna. *Ecology* **49**: 704-726.

# PSicalc example using stickleback data from Bolnick and Paull 2009 data(Stickleback) # Select a single spatial sampling site (site A) GutContents_SiteA <- import.RInSp(Stickleback, row.names = 1, info.cols = c(2:13), subset.rows = c("Site", "A")) # Warning, the number of replicates is set low to speed up he example! # Note, for real analyses we recommend to set replicates => 999 PSi <- PSicalc(GutContents_SiteA, exclude = FALSE, replicates = 99) sumMC.RInSp(PSi) rm(list=ls(all=TRUE))

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.