Rodents in the arena: a critical evaluation of methods measuring personality traits

<p>The Open Field Test (OFT) and Mirror Image Stimulation (MIS) are used to measure behaviours related to an individual’s personality. These tests, carried out in a same novel arena, have been used for different taxa, but only a few papers underline the importance of method validation. Here we investigate how Eurasian red squirrels (<i>Sciurus vulgaris</i>) and Eastern grey squirrels (<i>Sciurus carolinensis</i>) behave during OFT and MIS. Next, we compare the performance between three analytical methods: the Principal Component Analysis (PCA), the Factor Analysis (FA) and an expert-based (EB) method. The EB approach classifies behaviours in groups relating on researchers’ knowledge and returns personality-trait values for each individual facilitating comparisons over studies and/or with new datasets. The comparison between the three methods gave similar results and high repeatabilities in some expert-based personality traits as well as PCA components and FA factors, showing that all three methods were valid to measure activity using OFT (both species) and sociability using MIS (grey squirrel). Repeatabilities of the other traits were less strong. Proportion of time spent in different behaviours did not differ with test duration, since shorter tests yielded valid measures of individual differences in personality. Shorter tests reduce operator time in the field, and are likely to reduce stress and arena-habituation of the animals. Test sequence affected the outcome of OFT: squirrels tested for the first time were more active than squirrels tested a second time. For the two squirrel species investigated, we recommend an OFT of 4 min and a MIS test of three and suggest to test an individual no more than 2 times per season with at least 2 months between repetitions.</p

Minimum selected model of the effects of host characteristics and environmental variables on parasite abundance (no. of parasites/host).

<p>Minimum selected model of the effects of host characteristics and environmental variables on parasite abundance (no. of parasites/host).</p

Variation of <i>S. robustus</i> abundance by host body mass.

<p>Relationship between <i>S. robustus</i> abundance and host body mass: observed values (blank circles) and values predicted by the model at different host densities (lines). Host body mass had a positive effect on <i>S. robustus</i> abundance (p = 0.0005; parameter estimate: 0.0059±0.0017 SE).</p

Helminth species infecting grey squirrels in Piedmont and Lombardy populations.

<p>N: number of host examined; n: number of infected hosts; p: prevalence; mI: mean intensity (no. parasites infected/hosts; when number of infected hosts <5, worm counts in italic).</p

Variation of <i>C. sciurorum</i> abundance by season (A) and host density (B).

<p>Mean abundance of <i>C. sciurorum</i> (sample size above standard error bars) varied during different seasons (p<0.0001) and at different host densities (p<0.0001). Squirrels trapped in spring were more infested than in autumn and winter (both sequential Bonferroni adjusted p<0.0001) and animals living in high-density sites were more infested then those living in medium- and low-density populations (both adjusted p<0.008).</p

Most prevalent gastro-intestinal helminths and arthropods (excluding mites) parasitizing grey squirrels in North America.

<p>Only parasites that were recorded by more than one author and with maximum prevalence >5% are reported. Studies with sample size <50 hosts were excluded.</p

Variation of <i>S. robustus</i> abundance by host density.

<p>Mean abundance of <i>S. robustus</i> (sample size above standard error bars) varied with density of hosts in the site (p<0.0001). Squirrels living in high-density sites were more infested than individuals living in medium- and low-density sites (both sequential Bonferroni adjusted p<0.0001) and squirrels living in medium-density sites were more infested than in low-density sites (adjusted p = 0.0008).</p

Taxonomic ranks and their relationships in a molecular-based taxonomic study.

<p>In this schematic view the taxonomic ranks can be grouped in four different areas discriminated by their information content: individuals lie in the less informative level; a single taxonomic approach identifies morphotypes, MOTU and UCS; integration of data allows the definition of DCL, IOTU and CCS; the last and more informative level contains species. Individuals represent the first level of observation (1). These organisms are grouped on the basis of morphological similarities (2), in a classical taxonomic approach, which may lead to the identification of a species (2a), but can also be one of the inputs of the IOTU (2b). Molecular variability observed among individuals can lead to the definition of MOTUs (3) that, with the addition of more data, can be elevated to the level of DCL (3a) or IOTU (3b). However, in many published works MOTUs are identified within nominal species without additional taxonomic data (3c), being in this sense synonyms of UCS. As a consequence, the information content of MOTU and UCS is variable as identified by the dotted arrows between them. UCS is identified within a species (4), if further taxonomic data are provided it can be elevated to a DCL (4a) or an IOTU (4b). When two or more nominal species are similar at the molecular level for the chosen marker we call this situation Multi Taxa - Molecular Operational Taxonomic Units (MT-MOTUs) (4c). IOTU is the rank reached by a biological entity defined by molecular data coherently coupled with other source of information. When IOTU has reached a sufficient level of information it can be elevated to the rank of a CCS (5), which following a formal description will become species (6). The “+” in the left up corner of each box indicates that within each taxonomic rank, more than a single entity belonging to that rank can occur. MOTU is defined according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040122#pone.0040122-Floyd1" target="_blank">[7]</a>; UCS, DCL and CCS are defined according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040122#pone.0040122-Vieites1" target="_blank">[17]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040122#pone.0040122-Padial1" target="_blank">[18]</a>.</p

Subdivision of echolocating bats in the different taxonomic ranks.

<p>How to properly call all the different entities identified in our work of integrated taxonomy on Italian echolocating bats. It is important to observe the raise of information content proceeding from left to right.</p

BOLD-IDS and OT identification of unknown samples.

<p>List of identification results for 41 unrecognized bats sampled in Italy. Identification was performed by the IDS (identification engine on BOLD System <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040122#pone.0040122-Aliabadian1" target="_blank">[12]</a>) and OT <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040122#pone.0040122-Buhay1" target="_blank">[55]</a> approaches. Identity score and indecision cases returned by IDS are reported for each sample.</p