Dochtermann, N. A. | bioRxiv Science

bioRxiv2021

The role of trade-offs and feedbacks in shaping integrated plasticity, behavioral syndromes, and behavioral correlations

How behaviors vary among individuals and covary with other behaviors has been a major topic of interest over the last two decades. Unfortunately, proposed theoretical and conceptual frameworks explaining the seemingly ubiquitous observation of behavioral (co)variation have rarely successfully generalized. Two observations perhaps explain this failure: First, phenotypic correlations between behaviors are more strongly influenced by correlated and reversible plastic changes in behavior than by "behavioral syndromes". Second, while trait correlations are frequently assumed to arise via trade-offs, the observed pattern of correlations is not consistent with simple pair-wise trade-offs. A possible resolution to the apparent inconsistency between observed correlations and a role for trade-offs is provided by state-behavior feedbacks. This is critical because the inconsistency between data and theory represents a major failure in our understanding of behavioral evolution. These two primary observations emphasize the importance of an increased research focus on correlated reversible plasticity in behavior--frequently estimated and then disregarded as within-individual covariances. LAY SUMMARYCorrelations between behaviors are common but observed patterns of these correlations are, at least superficially, inconsistent with expectations of trade-offs. This mismatch is potentially resolved via feedbacks between behaviors and energy availability, suggesting important new research directions. DATA AND MODEL AVAILABILITYModel code, as well as the data associated with Figures 2 & 3, are available at github.com/DochtermannLab/FeedbacksModel. Both code and data will be made available at Dryad if accepted. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=66 SRC="FIGDIR/small/453877v2_fig2.gif" ALT="Figure 2"> View larger version (19K): org.highwire.dtl.DTLVardef@f3814forg.highwire.dtl.DTLVardef@ae702dorg.highwire.dtl.DTLVardef@46be9borg.highwire.dtl.DTLVardef@8be692_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 2.C_FLOATNO Relationship of within-individual behavioral correlations (rW) with (A) among-individual correlations (rA) and (B) genetic correlations (rG). The sign and magnitude of rA and rG are highly concordant with rW across behaviors and taxa. Diagonal lines indicate 1:1 relationship. Horizontal and vertical dashed lines divide plots into sign mismatches (top left, bottom right) and sign matches (top right, bottom left). Within-individual correlations were the same sign as among-individual and genetic correlations in 62% and 79% of cases respectively. Data in A are from Brommer and Class (2017). Data in B are from Dochtermann (2011). C_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/453877v2_fig3.gif" ALT="Figure 3"> View larger version (19K): org.highwire.dtl.DTLVardef@ce931dorg.highwire.dtl.DTLVardef@1c8798org.highwire.dtl.DTLVardef@10a418eorg.highwire.dtl.DTLVardef@55e6ae_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 3.C_FLOATNO (A) Simple model structure combining Sih et al.s (2015) feedback model with Houles (1991) y-model to include feedbacks between behavior and state. TO (the trade-off parameter) is the proportion of state energy (S1 at time t) allocated to behavior B1, making 1-TO the energy allocated to B2. {lambda} is the feedback strength and the conversion rate of a behavior (B1 or B2) into energy that can be used at a later time. (B) Magnitude of among-(purple) and within-individual (green) correlations under feedbacks ({lambda}) of different strength. Shaded regions indicate those feedback strengths that produce correlations of the same sign. Repeatability is not set a priori and is instead an emergent property of the model. Individuals within a population expressed behaviors according to A over ten time steps, with 250 individuals per population. Fifty populations were simulated at each of five levels of feebacks ({lambda} in B). rA and rW were estimated using the MCMCglmm package in R (Hadfield 2010). C_FIG

evolutionary biology↗

bioRxiv2020

Misalignment of selection, plasticity, and among-individual variation: A test of theoretical predictions with Peromyscus maniculatus.

Genetic variation and phenotypic plasticity are predicted to align with selection surfaces, a prediction that has rarely been empirically tested. Understanding the relationship between sources of phenotypic variation, i.e. genetic variation and plasticity, with selection surfaces improves our ability to predict a populations ability to adapt to a changing environment and our understanding of how selection has shaped phenotypes. Here, we estimated the (co)variances among three different behaviors (activity, aggression, and anti-predator response) in a natural population of deer mice (Peromyscus maniculatus). Using multi-response generalized mixed effects models, we divided the phenotypic covariance matrix into among- and within-individual matrices. The among-individual covariances includes genetic and permanent environmental covariances (e.g. developmental plasticity) and is predicted to align with selection. Simultaneously, we estimated the within-individual (co)variances, which include reversible phenotypic plasticity. To determine whether genetic variation, plasticity and selection align in multivariate space we calculated the dimensions containing the greatest among-individual variation and the dimension in which most plasticity was expressed (i.e. the dominant eigenvector for the among- and within-individual covariance matrices respectively). We estimated selection coefficients based on survival estimates from a mark-recapture model. Alignment between the dominant eigenvectors of behavioural variation and the selection gradient was estimated by calculating the angle between them, with an angle of 0 indicating perfect alignment. The angle between vectors ranged from 68{degrees} to 89{degrees}, indicating that genetic variation, phenotypic plasticity, and selection are misaligned in this population. This misalignment could be due to the behaviors being close to their fitness optima, which is supported by low evolvabilities, or because of low selection pressure on these behaviors.

animal behavior and cognition↗