Reviews in Fish Biology and Fisheries

, Volume 25, Issue 3, pp 537–549 | Cite as

Influences of size and sex on invasive species aggression and native species vulnerability: a case for modern regression techniques

Original Research


Animal behaviour is of fundamental importance but is often overlooked in biological invasion research. A problem with such studies is that they may add pressure to already threatened species and subject vulnerable individuals to increased risk. One solution is to obtain the maximum possible information from the generated data using a variety of statistical techniques, instead of solely using simple versions of linear regression or generalized linear models as is customary. Here, we exemplify and compare the use of modern regression techniques which have very different conceptual backgrounds and aims (negative binomial models, zero-inflated regression, and expectile regression), and which have rarely been applied to behavioural data in biological invasion studies. We show that our data display overdispersion, which is frequent in ecological and behavioural data, and that conventional statistical methods such as Poisson generalized linear models are inadequate in this case. Expectile regression is similar to quantile regression and allows the estimation of functional relationships between variables for all portions of a probability distribution and is thus well suited for modelling boundaries in polygonal relationships or cases with heterogeneous variances which are frequent in behavioural data. We applied various statistical techniques to aggression in invasive mosquitofish, Gambusia holbrooki, and the concomitant vulnerability of native toothcarp, Aphanius iberus, in relation to individual size and sex. We found that medium sized male G. holbrooki carry out the majority of aggressive acts and that smaller and medium size A. iberus are most vulnerable. Of the regression techniques used, only negative binomial models and zero-inflated and expectile Poisson regressions revealed these relationships.


Gambusia holbrooki Aphanius iberus Aggressive interactions Expectile Poisson regression Negative binomial models Size 

Supplementary material

11160_2015_9391_MOESM1_ESM.txt (2 kb)
Table S1. Raw data used in this manuscript (TXT 2 kb)
11160_2015_9391_MOESM2_ESM.docx (15 kb)
Appendix S1. R script to exemplify the use of four regression techniques (DOCX 14 kb)


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.GRECO, Institute of Aquatic EcologyUniversity of GironaGironaSpain

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