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

The spatiotemporal context of individual specialization in resource use and environmental associations

1. Individual niche specialization is widespread in natural populations and has key implications for higher levels of biological organization. This phenomenon, however, has been primarily quantified in resource niche axes, overlooking individual variation in environmental associations (i.e. abiotic conditions organisms experience). 2. Here, we explore what we can learn from a multidimensional perspective of individual niche specialization that integrates resource use and environmental associations into a common framework. 3. By combining predictions from theory and simple simulations, we illustrate how (i) multidimensional intraspecific niche variation and (ii) the spatiotemporal context of interactions between conspecifics scale up to shape emergent patterns of the population niche. 4. Contemplating individual specialization as a multidimensional, unifying concept across biotic and abiotic niche axes is a fundamental step towards bringing this concept closer to the n-dimensional niche envisioned by Hutchinson.

A systematic review of animal personality in conservation science

Although animal personality research may have applied uses, this suggestion has yet to be evaluated by assessing empirical studies examining animal personality and conservation. To address this knowledge gap, we performed a systematic review of the peer-reviewed literature relating to conservation science and animal personality. Criteria for inclusion in our review included access to full text, primary research articles, and relevant animal conservation or personality focus (i.e., not human personality studies). Ninety-two articles met these criteria. We summarized the conservation contexts, testing procedures (including species and sample size), analytical approach, claimed personality traits (activity, aggression, boldness, exploration, and sociability), and each report's key findings and conservation-focused suggestions. Although providing evidence for repeatability in behavior is crucial for personality studies, repeatability quantification was implemented in only half of the reports. Nonetheless, each of the 5 personality traits were investigated to some extent in a range of conservations contexts. The most robust studies in the field showed variance in how personality relates to other ecologically important variables across species and contexts. Moreover, many studies were first attempts at using personality for conservation purposes in a given study system. Overall, it appears personality is not yet a fully realized tool for conservation. To apply personality research to conservation problems, we suggest researchers think about where individual differences in behavior may affect conservation outcomes in their system, assess where there are opportunities for repeated measures, and follow the most current methodological guides on quantifying personality.

Estimating heritability of song considering within-individual variance in a wild songbird: The collared flycatcher

Heritable genetic variation is a prerequisite for adaptive evolution; however, our knowledge about the heritability of plastic traits, such as behaviors, is scarce, especially in wild populations. In this study, we investigated the heritability of song traits in the collared flycatcher (Ficedula albicollis), a small oscine passerine with complex songs involved in sexual selection. We recorded the songs of 81 males in a natural population and obtained various measures describing the frequency, temporal organization, and complexity of each song. As we had multiple songs from each individual, we were able to statistically account for the first time for the effect of within-individual variance on the heritability of song. Heritability was calculated from the variance estimates of animal models relying on a genetic similarity matrix based on Single Nucleotide Polymorphism screening. Overall, we found small additive genetic variance and heritability values in all song traits, highlighting the role of environmental factors in shaping bird song.

Simulating physiological flexibility in the acute glucocorticoid response to stressors reveals limitations of current empirical approaches

Wild animals often experience unpredictable challenges that demand rapid and flexible responses. The glucocorticoid mediated stress response is one of the major systems that allows vertebrates to rapidly adjust their physiology and behavior. Given its role in responding to challenges, evolutionary physiologists have focused on the consequences of between-individual and, more recently, within-individual variation in the acute glucocorticoid response. However, empirical studies of physiological flexibility are severely limited by the logistical challenges of measuring the same animal multiple times. Data simulation is a powerful approach when empirical data are limited, but has not been adopted to date in studies of physiological flexibility. In this article, I develop a simulation that can generate realistic acute glucocorticoid response data with user specified characteristics. Simulated animals can be sampled continuously through an acute response and across as many separate responses as desired, while varying key parameters. Using the simulation, I develop several scenarios that address key questions in physiological flexibility. These scenarios demonstrate the conditions under which a single glucocorticoid trait can be accurately assessed with typical experimental designs, the consequences of covariation between different components of the acute stress response, and the way that context specific differences in variability of acute responses can influence the power to detect relationships between the strength of the acute stress response and fitness. I also describe how to use the simulation tools to aid in the design and evaluation of empirical studies of physiological flexibility.

The relative speed of the glucocorticoid stress response varies independently of scope and is predicted by environmental variability and longevity across birds

The acute glucocorticoid response is a key mediator of the coordinated vertebrate response to unpredictable challenges. Rapid glucocorticoid increases initiate changes that allow animals to cope with stressors. The scope of the glucocorticoid response - defined here as the absolute increase in glucocorticoids - is associated with individual differences in performance and varies across species with environment and life history. In addition to varying in scope, responses can differ enormously in speed; however, relatively little is known about whether speed and absolute glucocorticoid levels covary, how selection shapes speed, or what aspects of speed are important. We used corticosterone samples collected at 5 time points from 1750 individuals of 60 species of birds to ask i) how the speed and scope of the glucocorticoid response covary and ii) whether variation in absolute or relative speed is predicted by environmental context or life history. Among species, faster absolute glucocorticoid responses were strongly associated with a larger scope. Despite this covariation, the relative speed of the glucocorticoid response (standardized within species) varied independently of absolute scope, suggesting that selection could operate on both features independently. Species with faster relative glucocorticoid responses lived in locations with more variable temperature and had shorter lifespans. Our results suggest that rapid changes associated with the speed of the glucocorticoid response, such as those occurring through non-genomic receptors, might be an important determinant of coping ability and we emphasize the need for studies designed to measure speed independently of absolute glucocorticoid levels.

Frontiers in quantifying wildlife behavioural responses to chemical pollution

Animal behaviour is remarkably sensitive to disruption by chemical pollution, with widespread implications for ecological and evolutionary processes in contaminated wildlife populations. However, conventional approaches applied to study the impacts of chemical pollutants on wildlife behaviour seldom address the complexity of natural environments in which contamination occurs. The aim of this review is to guide the rapidly developing field of behavioural ecotoxicology towards increased environmental realism, ecological complexity, and mechanistic understanding. We identify research areas in ecology that to date have been largely overlooked within behavioural ecotoxicology but which promise to yield valuable insights, including within- and among-individual variation, social networks and collective behaviour, and multi-stressor interactions. Further, we feature methodological and technological innovations that enable the collection of data on pollutant-induced behavioural changes at an unprecedented resolution and scale in the laboratory and the field. In an era of rapid environmental change, there is an urgent need to advance our understanding of the real-world impacts of chemical pollution on wildlife behaviour. This review therefore provides a roadmap of the major outstanding questions in behavioural ecotoxicology and highlights the need for increased cross-talk with other disciplines in order to find the answers.

Individual variation in natural or manipulated corticosterone does not covary with circulating glucose in a wild bird

Animals respond to sudden challenges with a coordinated set of physiological and behavioral responses that enhance the ability to cope with stressors. While general characteristics of the vertebrate stress response are well described, it is not as clear how individual components covary between- or within-individuals. A rapid increase in glucocorticoids coordinates the stress response and one of the primary downstream results is an increase in glucose availability via reduced glucose utilization. Here, we asked whether between- and within-individual variation in corticosterone directly predicted variation in glucose. We collected 2,673 paired glucose and corticosterone measures from 776 tree swallows (Tachycineta bicolor) from four populations spanning the species range. In adults, glucose and corticosterone both increased during a standardized restraint protocol in all four populations. Moreover, in one population experimentally increasing a precursor that stimulates corticosterone release resulted in a further increase in both measures. In contrast, nestlings did not show a robust glucose response to handling or manipulation. Despite this group level variation, there was very little evidence in any population that between-individual variation in corticosterone predicted between-individual variation in glucose regulation. Glucose was moderately repeatable within-individuals, but within-individual variation in glucose and corticosterone were unrelated. Our results highlight the fact that a strong response in one aspect of the coordinated acute stress response (corticosterone) does not necessarily indicate that specific downstream components, such as glucose, will show similarly strong responses. These results have implications for understanding the evolution of integrated stress response systems.

The Potential for Physiological Performance Curves to Shape Environmental Effects on Social Behavior

As individual animals are exposed to varying environmental conditions, phenotypic plasticity will occur in a vast array of physiological traits. For example, shifts in factors such as temperature and oxygen availability can affect the energy demand, cardiovascular system, and neuromuscular function of animals that in turn impact individual behavior. Here, we argue that nonlinear changes in the physiological traits and performance of animals across environmental gradients—known as physiological performance curves—may have wide-ranging effects on the behavior of individual social group members and the functioning of animal social groups as a whole. Previous work has demonstrated how variation between individuals can have profound implications for socially living animals, as well as how environmental conditions affect social behavior. However, the importance of variation between individuals in how they respond to changing environmental conditions has so far been largely overlooked in the context of animal social behavior. First, we consider the broad effects that individual variation in performance curves may have on the behavior of socially living animals, including: (1) changes in the rank order of performance capacity among group mates across environments; (2) environment-dependent changes in the amount of among- and within-individual variation, and (3) differences among group members in terms of the environmental optima, the critical environmental limits, and the peak capacity and breadth of performance. We then consider the ecological implications of these effects for a range of socially mediated phenomena, including within-group conflict, within- and among group assortment, collective movement, social foraging, predator-prey interactions and disease and parasite transfer. We end by outlining the type of empirical work required to test the implications for physiological performance curves in social behavior.

Quantifying Glucocorticoid Plasticity Using Reaction Norm Approaches: There Still is So Much to Discover!

Hormones are highly responsive internal signals that help organisms adjust their phenotype to fluctuations in environmental and internal conditions. Our knowledge of the causes and consequences of variation in circulating hormone concentrations has improved greatly in the past. However, this knowledge often comes from population-level studies, which generally tend to make the flawed assumption that all individuals respond in the same way to environmental changes. Here, we advocate that we can vastly expand our understanding of the ecology and evolution of hormonal traits once we acknowledge the existence of individual differences by quantifying hormonal plasticity at the individual level, where selection acts. In this review, we use glucocorticoid (GC) hormones as examples of highly plastic endocrine traits that interact intimately with energy metabolism but also with other organismal traits like behavior and physiology. First, we highlight the insights gained by repeatedly assessing an individual's GC concentrations along a gradient of environmental or internal conditions using a “reaction norm approach.” This study design should be followed by a hierarchical statistical partitioning of the total endocrine variance into the among-individual component (individual differences in average hormone concentrations, i.e., in the intercept of the reaction norm) and the residual (within-individual) component. The latter is ideally further partitioned by estimating more precisely hormonal plasticity (i.e., the slope of the reaction norm), which allows to test whether individuals differ in the degree of hormonal change along the gradient. Second, we critically review the published evidence for GC variation, focusing mostly on among- and within-individual levels, finding only a good handful of studies that used repeated-measures designs and random regression statistics to investigate GC plasticity. These studies indicate that individuals can differ in both the intercept and the slope of their GC reaction norm to a known gradient. Third, we suggest rewarding avenues for future work on hormonal reaction norms, for example to uncover potential costs and trade-offs associated with GC plasticity, to test whether GC plasticity varies when an individual's reaction norm is repeatedly assessed along the same gradient, whether reaction norms in GCs covary with those in other traits like behavior and fitness (generating multivariate plasticity), or to quantify GC reaction norms along multiple external and internal gradients that act simultaneously (leading to multidimensional plasticity). Throughout this review, we emphasize the power that reaction norm approaches offer for resolving unanswered questions in ecological and evolutionary endocrinology.

Planned missing data designs and methods: Options for strengthening inference, increasing research efficiency and improving animal welfare in ecological and evolutionary research

Ecological and evolutionary research questions are increasingly requiring the integration of research fields along with larger data sets to address fundamental local- and global-scale problems. Unfortunately, these agendas are often in conflict with limited funding and a need to balance animal welfare concerns. Planned missing data design (PMDD), where data are randomly and deliberately missed during data collection, combined with missing data procedures, can be useful tools when working under greater research constraints. Here, we review how PMDD can be incorporated into existing experimental designs by discussing alternative design approaches and demonstrate with simulated data sets how missing data procedures work with incomplete data. PMDDs can provide researchers with a unique toolkit that can be applied during the experimental design stage. Planning and thinking about missing data early can (1) reduce research costs by allowing for the collection of less expensive measurement variables; (2) provide opportunities to distinguish predictions from alternative hypotheses by allowing more measurement variables to be collected; and (3) minimize distress caused by experimentation by reducing the reliance on invasive procedures or allowing data to be collected on fewer subjects (or less often on a given subject). PMDDs and missing data methods can even provide statistical benefits under certain situations by improving statistical power relative to a complete case design. The impacts of unplanned missing data, which can cause biases in parameter estimates and their uncertainty, can also be ameliorated using missing data procedures. PMDDs are still in their infancy. We discuss some of the difficulties in their implementation and provide tentative solutions. While PMDDs may not always be the best option, missing data procedures are becoming more sophisticated and more easily implemented and it is likely that PMDDs will be effective tools for a wide range of experimental designs, data types and problems in ecology and evolution.

Most published selection gradients are underestimated: Why this is and how to fix it

Ecologists and evolutionary biologists routinely estimate selection gradients. Most researchers seek to quantify selection on individual phenotypes, regardless of whether fixed or repeatedly expressed traits are studied. Selection gradients estimated to address such questions are attenuated unless analyses account for measurement error and biological sources of within-individual variation. Estimates of standardized selection gradients published in Evolution between 2010 and 2019 were primarily based on traits measured once (59% of 325 estimates). We show that those are attenuated: bias increases with decreasing repeatability but differently for linear versus nonlinear gradients. Others derived individual-mean trait values prior to analyses (41%), typically using few repeats per individual, which does not remove bias. We evaluated three solutions, all requiring repeated measures: (i) correcting gradients derived from classic models using estimates of trait correlations and repeatabilities, (ii) multivariate mixed-effects models, previously used for estimating linear gradients (seven estimates, 2%), which we expand to nonlinear analyses, and (iii) errors-in-variables models that account for within-individual variance, and are rarely used in selection studies. All approaches produced accurate estimates regardless of repeatability and type of gradient, however, errors-in-variables models produced more precise estimates and may thus be preferable.

Batch Effect Adjustment to Lower the Drug Attrition Rate of MCF-7 Breast Cancer Cells Exposed to Silica Nanomaterial-Derived Scaffolds

Drug attrition rate is the calculation or measure of the clinical efficacy of a candidate drug on a screen platform for a specific period. Determining the attrition rate of a prospective cancer drug is a reliable way of testing the clinical efficacy. A low attrition rate in the last phase of a preclinical trial increases the success of a drug discovery process. It has been reported that the attrition rates of antineoplastic drugs are much higher than for other therapeutic drugs. Among the factors identified for the high attrition rates in antineoplastic drugs are the nature of the screen-based platforms involving human-derived xenografts, extracellular matrix-derived scaffold systems, and the synthetic scaffolds, which all have propensity to proliferate tumor cells at faster rates than in vivo primary tumors. Other factors that affect the high attrition rates are induced scaffold toxicity and the use of assays that are irrelevant, yet affect data processing. These factors contribute to the wide variation in data and systematic errors. As a result, it becomes imperative to filter batch variations and to standardize the data. Importantly, understanding the interplay between the biological milieu and scaffold connections is also crucial. Here the cell viability of MCF-7 (breast cancer cell line) cells exposed to different scaffolds were screened before cisplatin dosing using the calculated p-values. The statistical significance (p-value) of data was calculated using the one-way analysis of variance, with the p-value set as: 0 < p < 0.06. In addition, the half-maximal inhibitory concentration (IC₅₀) of the different scaffolds exposed to MCF-7 cells were calculated with the probit extension model and cumulative distribution (%) of the extension data. The chemotherapeutic dose (cisplatin, 56 mg/m²) reduced the cell viability of MCF-7 cells to 5% within 24 h on the scaffold developed from silica nanoparticles (SNPs) and polyethylene glycol (PEG) formulation (SNP:PEG) mixtures with a ratio of 1:10, respectively.

An individual based, multidimensional approach to identify emotional reactivity profiles in inbred mice

BACKGROUND

Despite extensive environmental standardization and the use of genetically and microbiologically defined mice of similar age and sex, individuals of the same mouse inbred strain commonly differ in quantitative traits. This is a major issue as it affects the quality of experimental results. Standard analysis practices summarize numerical data by means and associated measures of dispersion, while individual values are ignored. Perhaps taking individual values into account in statistical analysis may improve the quality of results.

NEW METHOD

The present study re-inspected existing data on emotional reactivity profiles in 125 BALB/cJ and 129 mice, which displayed contrasting patterns of habituation and sensitization when repeatedly exposed to a novel environment (modified Hole Board). Behaviors were re-analyzed on an individual level, using a multivariate approach, in order to explore whether this yielded new information regarding subtypes of response, and their expression between and within strains.

RESULTS

Clustering individual mice across multiple behavioral dimensions identified two response profiles: a habituation and a sensitization cluster.

COMPARISON WITH EXISTING METHOD(S)

These retrospect analyses identified habituation and sensitization profiles that were similar to those observed in the original data but also yielded new information such as a more pronounced sensitization response. Also, it allowed for the identification of individuals that deviated from the predominant response profile within a strain.

CONCLUSIONS

The present approach allows for the behavioral characterization of experimental animals on an individual level and as such provides a valuable contribution to existing approaches that take individual variation into account in statistical analysis.

Temporal autocorrelation: a neglected factor in the study of behavioral repeatability and plasticity

Quantifying individual variation in labile physiological or behavioral traits often involves repeated measures through time, so as to test for consistency of individual differences (often using repeatability, “R”) and/or individual differences in trendlines over time. Another form of temporal change in behavior is temporal autocorrelation, which predicts observations taken closely together in time to be correlated, leading to nonrandom residuals about individual temporal trendlines. Temporal autocorrelation may result from slowly changing internal states (e.g., hormone or energy levels), leading to slowly changing behavior. Autocorrelation is a well-known phenomenon, but has been largely neglected by those studying individual variation in behavior. Here, we provide two worked examples which show substantial temporal autocorrelation (r > 0.4) is present in spontaneous activity rates of guppies (Poecilia reticulata) and house mice (Mus domesticus) in stable laboratory conditions, even after accounting for temporal plasticity of individuals. Second, we show that ignoring autocorrelation does bias estimates of R and temporal reaction norm variances upwards, both in our worked examples and in separate simulations. This bias occurs due to the misestimation of individual-specific means and slopes. Given the increasing use of technologies that generate behavioral and physiological data at high sampling rates, we can now study among- and within-individual changes in behavior in more detailed ways, including autocorrelation, which we discuss from biological and methodological perspectives and provide recommendations and annotated R code to help researchers implement these models on their data.

Causes and Consequences of Phenotypic Plasticity in Complex Environments

Phenotypic plasticity is a ubiquitous and necessary adaptation of organisms to variable environments, but most environments have multiple dimensions that vary. Many studies have documented plasticity of a trait with respect to variation in multiple environmental factors. Such multidimensional phenotypic plasticity (MDPP) exists at all levels of organismal organization, from the whole organism to within cells. This complexity in plasticity cannot be explained solely by scaling up ideas from models of unidimensional plasticity. MDPP generates new questions about the mechanism and function of plasticity and its role in speciation and population persistence. Here we review empirical and theoretical approaches to plasticity in response to multidimensional environments and we outline new opportunities along with some difficulties facing future research.

Profile repeatability: A new method for evaluating repeatability of individual hormone response profiles

There is broad interest in determining repeatability of individual responses. Current methods calculate repeatability of individual points (initial and/or peak), time to peak value, or a single measure of the integrated total response (area under the curve), rather than the shape of the response profile. Repeatability estimates of response profiles using linear mixed models (LMM) generate an average repeatability for an aggregate of individuals, rather than an estimate of individual repeatability. Here we use a novel ad hoc method to calculate repeatability of individual response profiles and demonstrate the need for a more rigorous assessment protocol. Response profile repeatability has not been defined at the individual level. We do this using a new metric, Profile Repeatability (PR), which incorporates components of variance and the degree to which response profiles cross each other in a time series. Values range from 0 (no repeatability) to 1 (complete repeatability). We created synthetic data to represent a range of apparent time series repeatability, and 20 independent observers visually ranked those data sets by degree of repeatability. We also applied the method to real data on stress responses of European starlings Sturnus vulgaris. We then computed PR scores for the synthetic data and for real data from European starling corticosterone responses over time, and contrast the results to those from LMM. Finally, we assessed the sensitivity of PR to reductions in the number of time points in the corticosterone response, as well as reductions in the number of replicates per individual. We found the average PR scores for a group of individuals to be somewhat robust to reductions in points in the time series; however, the ranks of individuals (PR values relative to one another) could change substantially with reduction in the number of values in a time series. PR showed threshold sensitivity to losing replicate time series between 6 and 4 replicates. Surprisingly, human observers fell into two disparate groups when ranking repeatability of the synthetic data, and the PR score indicated that human observers may underestimate repeatability of data where replicates cross each other. In contrast to the average profile repeatability estimated using LMMs, our approach calculates individual repeatability. From our perspective, LMM does not provide a definitive idea of repeatability at the individual level; in essence, it concludes that suites of time series with low within-individual variance has high repeatability, regardless of replicate trajectories. LMM and PR have non-linear relationships between 0 and 1, but PR has greater discrimination for mid-values of repeatability. Consistent average group repeatability can be associated with substantial differences in individual ranks suggests that estimating individual repeatability is critical. The PR score should be useful in comparing repeatability of any type of nonlinear, including non-monotonic, response profiles over time, which are common in both physiology and behavior, and it demonstrates the specific needs for future improvements of a profile repeatability metric.

Trade-off between tolerance and resistance to infections: an experimental approach with malaria parasites in a passerine bird

Avian malaria parasites are known to have negative effects on their hosts, including consequences for reproductive success and survival. However, the outcome of disease may vary greatly among individuals, due to their particular genetic background, their past history of exposure to infections, or the way they respond to infections at the physiological level. We experimentally reduced parasitemia in naturally infected birds to examine individual-level variation in physiological parameters involved in anti-parasite defense, focusing specifically on disease resistance and tolerance. As a measure of disease resistance, we used circulating levels of IgY, and as a measure of disease tolerance, we estimated haptoglobin concentrations. Our results show individual consistency in the physiological parameters studied during the experiment, that was statistically significant for body condition, and marginally significant for IgY levels, and a trade-off between physiological mechanisms involved in resistance and tolerance that seem to be mediated by parasitemia. The medication experiment with primaquine was successful in reducing parasite intensity, but was not sufficient to clear the infection, and there was a generalized improvement in body condition in all birds maintained in captivity during the experiment. We suggest that the observed changes in the association between resistance and tolerance estimates may be due to the decrease in parasitemia attained through medication, to the improved nutritional status observed during the experiment or to the combined effect of both. Our study adds to the understanding of how wild animals cope with the diseases they are exposed to in their natural environment, and ultimately the consequences of parasitism at the individual level.

A brief introduction to mixed effects modelling and multi-model inference in ecology

The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.