# Supplementary data to Sarabeev, V.L., Balbuena, J.A., Morand, S. Aggregation patterns of helminths populations in the introduced fish, Liza haematocheilus (Teleostei: Mugilidae): disentangling host-parasite relationships. Int. J. Parasitol.

## Description

The data file contains Supplementary Tables S1 and S2.

## Files

## Steps to reproduce

Four measures to quantify the infection and aggregation parameters of helminth populations were used: prevalence, mean abundance, exponent k of the negative binomial distribution (NBD) and the slope b of Taylor’s power law. Prevalence and mean abundance were calculated according to Bush et al. (1997). - Prevalence describes the portion of a sample observed to have a particular parasite and was expressed here as a percentage. - Mean abundance is a number of individuals of all parasite individuals of the given species in a sample divided by the total number of hosts of that sample examined (including both infected and uninfected hosts). - The exponent k of the NBD was obtained using maximum-likelihood estimation. A web-based tool provided by Reiczigel and Rózsa (http://www.zoologia.hu/qp/) was used to compute k and its associated χ2 p-value, determining whether or not the parasite data fit the NBD. - A bootstrapping technique developed by Boag et al. (2001) was used to calculate b, its associated standard deviation (SD) and the coefficient of determination of the model was computed here with a modification. Differently from Boag et al. (2001) and Sherrard-Smith et al. (2015), a higher number of bootstrap samples and replications were used, which yields more stable parameters from calculation to calculation. In the present study 100 parasite infra-populations were sampled with replacement to obtain the variance-mean pair. This was repeated 500 times to estimate b and its associated statistics calculated from the linear regression of log (variance+1) onto log (mean+1). The bootstrapping technique was performed with package ‘boot’ (Canty and Ripley, 2017) in R. The code used in R is presented below: sink("filename.txt") z<-c (X1,...,Xi) for(i in 1:500){ z1<-c(sample(z, size=100, replace = TRUE)) boots <-boot (z1, var, R=100) mean(z1) var(z1) print(mean(z1)); print(var(z1)) } sink() Where z is a numeric vector here. References Boag, B., Lello, J., Fenton, A., Tompkins, D.M., Hudson, P.J., 2001. Patterns of parasite aggregation in the wild European rabbit (Oryctolagus cuniculus). Int. J. Parasitol. 31, 1421–1428. doi:10.1016/S0020-7519(01)00270-3 Bush, A.O., Lafferty, K.D., Lotz, J.M., Shostak, A.W., 1997. Parasitology meets ecology on its own terms: Margolis et al. revisited. J. Parasitol. 83, 575–583. doi:10.2307/3284227 Canty, A., Ripley, B., 2017. boot: Bootstrap R (S-Plus) Functions. Sherrard-Smith, E., Perkins, S.E., Chadwick, E.A., Cable, J., 2015. Spatial and seasonal factors are key determinants in the aggregation of helminths in their definitive hosts: Pseudamphistomum truncatum in otters (Lutra lutra). Int. J. Parasitol. 45, 75–83. doi:10.1016/j.ijpara.2014.09.004