Ned A. Dochtermann

Defining behavioural syndromes and the role of ‘syndrome deviation’ in understanding their evolution

This commentary highlights multivariate tools that have been used by evolutionary biologists in the study of syndromes and their evolution and discusses the insights that these methods provide into evolutionary processes relative to the metric ‘syndrome deviation’ that has recently been proposed by Herczeg and Garamszegi (Behav Ecol Sociobiol 66:161–169, 2012). We clarify that non-zero phenotypic correlations arise from the joint influences of within- and between-individual correlations, whereas only non-zero between-individual correlations represent behavioural syndromes, and discuss how acknowledgement of this subtle difference between phenotypic and between-individual correlations affects the applicability of syndrome deviation for the study of behavioural syndromes.

A method for exploring the structure of behavioural syndromes to allow formal comparison within and between data sets

Research on behavioural syndromes (consistent individual differences in suites of correlated behaviours) requires formal statistical methods to describe and compare syndrome structures. We detail the shortcomings of current methods aimed at describing variation in behavioural syndromes, such as multiple pairwise correlations and principal components analysis (PCA). In their place we propose an alternative statistical framework involving: (1) calculation of trait variance–covariance and correlation matrices within each data set; (2) statistical evaluation of specific hypotheses regarding how behaviours covary within a behavioural syndrome; and (3) statistical comparison of behavioural covariances across data sets using structural equation modelling (SEM). Given their unfamiliarity to most behavioural ecologists, we illustrate these methods using an already published data set for two groups of populations of three-spined stickleback, Gasterosteus aculeatus, living in ponds with and without fish predators. Previous analyses suggested a lack of behavioural syndrome structure for stickleback that lived in the absence of fish predators. However, by evaluating a priori hypotheses of how behaviours might covary using SEM, we were able to demonstrate that the two types of populations differed specifically in covariance patterns for aggression, exploration of novel food sources and altered environments, but not for exploration of novel environments and activity. Such detailed inferences cannot readily be made based on conventional statistical approaches alone, and so the methods we outline here should become standard in studies concerning the evolution of behavioural syndromes within and between populations.

The contribution of additive genetic variation to personality variation: heritability of personality.

Individual animals frequently exhibit repeatable differences from other members of their population, differences now commonly referred to as ‘animal personality’. Personality differences can arise, for example, from differences in permanent environmental effects―including parental and epigenetic contributors―and the effect of additive genetic variation. Although several studies have evaluated the heritability of behaviour, less is known about general patterns of heritability and additive genetic variation in animal personality. As overall variation in behaviour includes both the among-individual differences that reflect different personalities and temporary environmental effects, it is possible for personality to be largely genetically influenced even when heritability of behaviour per se is quite low. The relative contribution of additive genetic variation to personality variation can be estimated whenever both repeatability and heritability are estimated for the same data. Using published estimates to address this issue, we found that approximately 52% of animal personality variation was attributable to additive genetic variation. Thus, while the heritability of behaviour is often moderate or low, the heritability of personality is much higher. Our results therefore (i) demonstrate that genetic differences are likely to be a major contributor to variation in animal personality and (ii) support the phenotypic gambit: that evolutionary inferences drawn from repeatability estimates may often be justified.

Behavioural syndromes in Merriam's kangaroo rats (Dipodomys merriami): a test of competing hypotheses

Behavioural syndromes, correlations of behaviours conceptually analogous to personalities, have been a topic of recent attention due to their potential to explain trade-offs in behavioural responses, apparently maladaptive behaviour and limits to plasticity. Using Merriams kangaroo rats (Dipodomys merriami), we assessed the explanatory power and generality of hypothesized syndrome structures derived from the literature and the natural history of the species. Several aspects of functionally distinct behavioural responses of D. merriami were quantified. Syndrome structures were compared using structural equation modelling and model selection procedures. A domain-general behavioural syndrome incorporating cross-functional relationships between measures of boldness, agonistic behaviour, flexibility and food hoarding best explained the data. This pattern suggests that D. merriami behaviours should not be viewed as discrete elements but as components of a multivariate landscape. Our results support arguments that a lack of independence between behaviours may be a general aspect of behavioural phenotypes and suggest that the ability of D. merriamis behaviour to respond to selection may be constrained by underlying connections.

Developing multiple hypotheses in behavioral ecology

Researchers in behavioral ecology are increasingly turning to research methods that allow the simultaneous evaluation of hypotheses. This approach has great potential to increase our scientific understanding, but researchers interested in the approach should be aware of its long and somewhat contentious history. Also, prior to implementing multiple hypothesis evaluation, researchers should be aware of the importance of clearly specifying a priori hypotheses. This is one of the more difficult aspects of research based on multiple hypothesis evaluation, and we outline and provide examples of three approaches for doing so. Finally, multiple hypothesis evaluation has some limitations important to behavioral ecologists; we discuss two practical issues behavioral ecologists are likely to face.

Behaviour, metabolism and size: phenotypic modularity or integration in Acheta domesticus ?

The pace-of-life hypothesis predicts that among-individual differences in behaviour should integrate with a wide variety of morphological, metabolic and life-history traits along a slow to fast pace-of-life continuum. Support for the pace-of-life hypothesis has been mixed, in part because most empirical tests have been conducted strictly at the phenotypic level and have thus conflated genetic and environmental sources of covariance among traits. In the present study, we tested the hypothesis that, according to the predictions of the pace-of-life hypothesis, body mass, routine metabolic rate, activity and exploratory propensity are positively integrated in the house cricket Acheta domesticus (Orthoptera: Gryllidae). Using modified open field behavioural tests and flow-through respirometry, we determined whether among-individual differences are correlated across morphology, behaviour and metabolism in 50 male house crickets. All traits were repeatable, but we found poor evidence for overall integration across traits. Instead we found evidence for modularity, with behavioural traits covarying independently from mass and routine metabolic rate. Modularity, like that found here between activity and exploratory propensity, has been suggested to facilitate adaptive evolutionary change by coupling functionally related traits into suites on which selection can act more rapidly.

The roles of competition and environmental heterogeneity in the maintenance of behavioral variation and covariation

Many models of selection predict that populations will lose variation in traits that affect fitness. Nonetheless, phenotypic variation is commonly observed in natural populations. We tested the influences of competition and spatial heterogeneity on behavioral variation within and among populations of Merriams kangaroo rats (Dipodomys merriami) and tested for the differential expression of trait correlations. We found that populations of D. merriami exhibited more aggression at sites with more competition. Contrary to theoretical predictions and empirical results in other systems, the sites with the greatest spatial heterogeneity and highest levels of competition did not exhibit the most behavioral variation among individuals. However, the greatest within-individual behavioral variability in boldness (response to cues of predator presence) was exhibited where spatial heterogeneity was highest. Aggression and boldness of D. merriami were highly repeatable, that is, individuals behaved in a consistent manner over time, and the two behaviors were also highly correlated. Interestingly, the strength of this correlation was greatest where the competitive community was least diverse. These findings add to increasing evidence that natural populations of animals exhibit patterns of behavioral covariance, or personality structure, and suggest that competitive variation may act to erode personality structure.

Coexisting desert rodents differ in selection of microhabitats for cache placement and pilferage

Abstract Seed caching by desert rodents in the family Heteromyidae is an important behavioral adaptation for animals living in environments with limited and unpredictable food resources. Heteromyids cache seeds throughout their home ranges, either concentrated in 1 location (larder hoard) or in multiple, small seed piles (scatter hoards). To minimize cache pilferage by other rodents, coexisting species may scatter hoard seeds in distinct microhabitats. We examined interspecific differences in caching and pilfering behaviors of 3 coexisting heteromyid rodents, Merriams kangaroo rat (Dipodomys merriami), the pale kangaroo mouse (Microdipodops pallidus), and the little pocket mouse (Perognathus longimembris), to determine if caching microhabitat affects likelihood of pilferage. In outdoor enclosures we tracked cache placement and measured pilferage of artificial caches using radiolabeled Indian ricegrass (Achnatherum hymenoides) seeds. M. pallidus and P. longimembris placed seed caches mostly under shrubs, whereas D. merriami placed caches predominately in open microhabitat. However, D. merriami showed a significant preference for pilfering caches under shrubs, whereas P. longimembris did not show a significant preference for pilfering caches in either open or undershrub microhabitats. The 2 species pilfered similar numbers of caches. Coexisting heteroymid rodent species may contribute to spatial heterogeneity of available resources by caching in different microhabitats, thereby reducing but not eliminating cache pilferage by other species.

Differences in population size variability among populations and species of the family Salmonidae

1. How population sizes vary with time is an important ecological question with both practical and theoretical implications. Because population size variability corresponds to the operation of density-dependent mechanisms and the presence of stable states, numerous researchers have attempted to conduct broad taxonomic comparisons of population size variability. 2. Most comparisons of population size variability suggest a general lack of taxonomic differences. However, these comparisons may conflate differences within taxonomic levels with differences among taxonomic levels. Further, the degree to which intraspecific differences may affect broader inferences has generally not been estimated and has largely been ignored. 3. To address this uncertainty, we examined intraspecific differences in population size variability for a total of 131 populations distributed among nine species of the Salmonidae. We extended this comparison to the interspecific level by developing species level estimates of population size variability. 4. We used a jackknife (re-sampling) approach to estimate intra- and interspecific variation in population size variability. We found significant intraspecific differences in how population sizes vary with time in all six species of salmonids where it could be tested as well as clear interspecific differences. Further, despite significant interspecific variation, the majority of variation present was at the intraspecific level. Finally, we found that classic and recently developed measures of population variability lead to concordant inferences. 5. The presence of significant intraspecific differences in all species examined suggests that the ability to detect broad taxonomic patterns in how population sizes change over time may be limited if variance is not properly partitioned among and within taxonomic levels.

Statistical Quantification of Individual Differences (SQuID): an educational and statistical tool for understanding multilevel phenotypic data in linear mixed models

1. Phenotypic variation exists in and at all levels of biological organization: variation exists among species, among-individuals within-populations, and in the case of l within-populations abile traits, within-individuals. Mixed-effects models represent ideal tools to quantify multilevel measurements of traits and are being increasingly used in evolutionary ecology. Mixed-effects models are relatively complex, and two main issues may be hampering their proper usage: (i) the relatively few educational resources available to teach new users how to implement and interpret them and (ii) the lack of tools to ensure that the statistical parameters of interest are correctly estimated. In this paper, we introduce Statistical Quantification of Individual Differences (SQuID), a simulation-based tool that can be used for research and educational purposes. SQuID creates a virtual world inhabited by subjects whose phenotypes are generated by a user-defined phenotypic equation, which allows easy translation of biological hypotheses into quantifiable parameters. Statistical Quantification of Individual Differences currently models normally distributed traits with linear predictors, but SQuID is subject to further development and will adapt to handle more complex scenarios in the future. The current framework is suitable for performing simulation studies, determining optimal sampling designs for user-specific biological problems and making simulation-based inferences to aid in the interpretation of empirical studies. Statistical Quantification of Individual Differences is also a teaching tool for biologists interested in learning, or teaching others, how to implement and interpret linear mixed-effects models when studying the processes causing phenotypic variation. Interface-based modules allow users to learn about these issues. As research on effects of sampling designs continues, new issues will be implemented in new modules, including nonlinear and non-Gaussian data.