DOI: 10.5061/dryad.f7m0cfxwf
title: Fast life-histories are associated with larger brain size in killifishes

description: The high energetic demands associated with the vertebrate brain are
 proposed to result in a trade-off between the pace of life-history and
 relative brain size. However, because both life-history and brain size
 also have a strong relationship with body size, any associations between
 the pace of life-history and relative brain size may be confounded by
 coevolution with body size. Studies on systems where contrasts in the pace
 of life-history occur without concordant contrasts in body size could
 therefore add to our understanding of the potential coevolution between
 relative brain size and life-history. Using one such system - 21 species
 of killifish - we employed a common garden design across two ontogenetic
 stages to investigate the association between relative brain size and the
 pace of life-history. Contrary to predictions, we found that relative
 brain size was larger in adult fast-living killifishes, compared to
 slow-living species. Although we found no differences in relative brain
 size between juvenile killifishes. Our results suggest that fast- and
 slow-living killifishes do not exhibit the predicted trade-off between
 brain size and life-history. Instead, fast and slow-living killifishes
 could differ in the ontogenetic timing of somatic vs. neural growth or
 inhabit environments that differ considerably in cognitive demands.
description: During 2017-2018, we reared individuals from 21 species of
 killifish (Aplocheiloidei) from the egg stage to either non-sexually
 mature juveniles (in their linear growth phase, Sowersby et al. preprint)
 or sexually mature adults (adults: Nspp= 17, Nind = 234; juveniles: Nspp.
 = 18, Nind = 110; see Supplementary Information for a description of fish
 maintenance, and Table 1 and Supplementary Tables S1 to S7 for details on
 sample sizes, body size and age at sampling, and population origin). As
 environmental factors can induce plastic responses in brain size in fishes
 (Gonda et al. 2011), we kept all individuals in an environmentally
 standardized, common garden setting. The species included in the study
 were purposely selected to represent the five major evolutionary
 transitions between fast-living (annual) and slow-living (non-annual)
 life-history strategies (see Table 1; Table S1-S2), characterized by the
 presence or absence of eggs capable of entering embryonic diapause (as per
 Furness et al. 2015). Previous studies have confirmed that fast-living
 species exhibit faster life-history traits, compared to slow-living
 species (reproduction: Eckerstrm- Liedholm et al. 2017, development and
 growth: Sowersby et al. preprint), and that these traits are strongly
 correlated across species (the dominant eigenvector of a principal
 component analysis explain 75.4% of the total variation; Eckerstrm-
 Liedholm et al. 2019). After hatching, a subset of individuals were reared
 in 0.75-L plastic boxes (although some individuals were then moved to 13-L
 tanks in species that grew quickly), and were weighed and dissected as
 juveniles (at ~1 cm total body length) before they had reached sexual
 maturity (mean wet mass: 0.10 g; range: 0.015 to 0.34, no gonad tissue was
 observed during dissection). The remainder of the individuals were reared
 identically but were transferred to 13-L tanks 10 to 14 days after
 hatching . Each tank contained a mixed-sexed group of conspecifics of up
 to eight individuals, because some species produced more hatchlings, the
 number of groups and sample sizes are therefore not equal across species.
 Upon reaching sexually maturity these individuals were weighed and then
 dissected (i.e. after sexual maturity, but prior to showing signs of
 senescence). Sexual maturity was determined by the presence of
 species-specific male coloration. All individuals, juveniles and adults,
 were euthanized with a lethal dose of benzocaine solution, with
 experimental procedures approved by the Ethical Committee in Stockholm,
 Sweden (license N132/15). Individuals were euthanized, blotted with a
 paper towel, and their wet mass was recorded (precision: 1 mg; XS105,
 Mettler-Toledo GmbH, Giessen, Germany). Brains were fixed inside the head
 for 5-7 days in 4% phosphate-buffered formaldehyde, after which they were
 transferred to a phosphate- buffered saline solution and stored until
 dissection. Brains were dissected and photographed using a stereo
 microscope with a built-in 3 Mpixel digital camera (Leica EZ 4HD; Leica
 Application Suite EZ 3.4; Leica Microsystems GmbH, Wetzlar, Germany).
 Nerves were cut to ca. 0.3 mm length, the spinal cord was cut posterior to
 medulla oblongata, and fatty tissue, meninx, and blood vessels were
 removed to the extent possible. Brains were then photographed dorsally,
 ventrally, and laterally (left and right). Photographs were calibrated
 using a reference photo of a digital caliper (Absolute, Mitutoyo,
 Takatsu-ku Kawasaki, Japan) set at 6.00 mm, and measurements were taken
 using ImageJ 1.49 (Schneider et al. 2012). Length (L), width (W), and
 height (H) of olfactory bulbs, telencephalon, optic tectum, cerebellum,
 medulla oblongata and hypothalamus were recorded and used to calculate
 volumes (V) for each sub-region, using the idealized ellipsoid model
 (Huber et al. 1997): V = (L ∙ W ∙ H) / 6 For olfactory bulbs,
 telencephalon, optic tectum, and hypothalamus, the measurements of the two
 lobes were taken separately and thereafter added together; while the
 cerebellum and medulla oblongata were treated as single-lobed structures.
 The total brain volume was calculated by summing all sub-region volume
 estimates. Adult brains were blotted and weighed after photography
 (precision: 0.01 mg; MT5, Mettler-Toledo GmbH, Giessen, Germany).
 Correlation between brain mass and calculated total brain volume was high
 (r = 0.93, N = 234), but due to the adhesive nature of some brains, which
 led to slight damage and loss of tissue, we proceeded to analyze brain
 volume rather than mass. Juvenile brains were not weighed.
 <strong>Phylogeny</strong> To
 control for phylogenetic non-independence, a phylogenetic effect was added
 to all analyses (Freckleton et al. 2002). For this purpose, we used a
 previously published time-calibrated phylogeny (Furness et al. 2015), with
 an additional four species inserted into said phylogeny (see Figure 1).
 These additional species were inserted into the phylogeny based on
 taxonomic information and other published phylogenies. Specifically:
 Ophthalmolebias constanciae was placed within the main Simpsonichthys
 clade (Pohl et al. 2015), Nothobranchius kadleci alongside its sister
 species N. furzeri (Dorn et al. 2015), Scriptaphyosemion cauveti and
 Rachovia aff. brevis (Monteria population) in their respective genera, and
 Millerichthys robustus was placed as a sister species of Rivulus
 cylindraceus (Gonzalez-Voyer et al. in prep). After these species were
 added, the tree was then pruned, leaving the overall structure of the tree
 unchanged. Therefore, the position of the additional species reflects the
 phylogenetic relationships that would be recovered from a tree with
 complete species sampling. <strong>Statistical
 analyses</strong> Relative brain-/sub-region size
 To test for differences in relative brain- and sub-region size between the
 two life- history strategies, we fit models with absolute brain volume
 (mm3; log10-transformed) as a response variable, and the explanatory
 variables: log10-transformed body mass (mean centered based on species
 means), life-history strategy (fast and slow), the interaction between
 body mass and life-history strategy, and sex (female and male). In
 addition, species identity and phylogenetic distance were added as random
 effects (Felsenstein 1985; Gelman 2007). Since the adult allometric
 slopes, between brain and body size, differed for different species (DIC =
 25.7 for adults, DIC = 1.5 for juveniles, random slopes equal slopes), we
 chose to use random slope models. Adult and juvenile brain volumes were
 analyzed separately, but the models were identically applied, except for
 excluding sex as an explanatory factor in the juvenile analysis. In both
 adults and juveniles, we analyzed differences in intercepts and slopes of
 evolutionary static allometric slopes. As we only had one sampling point
 per individual, we did not obtain any estimates of ontogenetic slopes. In
 order to confirm that body size did not differ across the fast- and
 slow-living killifishes, we tested the effect of life-history strategy and
 sex (only for the adult model) on log10- transformed body mass, with
 species identity and phylogenetic relationship as random effects. Further,
 in the analysis above we used the classification of annual and non- annual
 as a proxy for fast and slow-living species. To validate our use of this
 proxy, we repeated the analysis (as described above), using the scores
 along the dominant eigenvector from a principal component analysis on
 growth rate, development time and reproductive rate (see
 Eckerstrm-Liedholm et al. 2019; Sowersby et al. preprint). These scores
 represent indirect measures of the pace-of-life and were available for all
 but two species. To analyze the relative volume of specific subregions of
 the brain (i.e. log10-transformed volumes of the olfactory bulbs,
 telencephalon, optic tectum, cerebellum, medulla oblongata and
 hypothalamus), we fitted one model per sub-region. Each model contained
 the following explanatory variables: log10-transformed brain volume (mean
 centered based on species means), life-history strategy (fast or slow),
 and their interaction. Species identity and the phylogeny were added as
 random effects. All models were analyzed using the MCMCglmm package
 Hadfield 2010 in R. Fixed effects were fitted with flat priors, while
 random effects were fitted with parameter-expanded locally non-informative
 priors (Murphy 2007). The parameter sampling was run for 2.01M iterations
 (burn-in: 10 000; thinning-interval: 2 000; posterior samples: 1 000).
 Autocorrelations between parameter estimates were within the interval of
 -0.1 and 0.1 for all analyses. For all analyses, the assumption of
 normally distributed residuals was assessed using visual examination. In
 the results, statistics from the Bayesian models are presented with
 parameter estimates () followed by their 95% credibility intervals (lower
 bound; upper bound), and Bayesian P-values (PMCMC).
 <em>Growth rate and brain size</em>
 We examined relative brain size under stages and conditions where
 energetic investment into life-history traits were realized; i.e. the
 subject fish were growing juveniles in isolation and reproducing (i.e.
 sexually mature) adults in mixed-sex groups. However, rearing density has
 previously been found to suppress growth rates in fish, both in the
 laboratory (Ribas et al. 2017) and in the wild (Lorenzen and Enberg 2002;
 Vrtlek et al. 2019), and if body size has a stronger plastic response than
 brain size, this could potentially affect relative brain size. As the
 fast- and slow-living species differ in terms of growth (Sowersby et al.
 preprint), it is plausible that plastic effects (suppression of growth)
 will be stronger in those species with faster growth rates (Auld et al.
 2010). Hence, consistent differences in relative brain size among the
 fast-living and the slow-living groups could arise due to plastic effects
 of growth changes, rather than differential energetic budgets. To explore
 these options, we analyzed the relationship between brain size and growth
 rate (species means, cm ∙ day-1; Sowersby et al. preprint) using total
 brain volume as a response variable in a phylogenetic generalized least
 squares (PGLS) model with body mass and growth rate used as predictor
 variables, using a pruned version of the phylogenetic tree described
 above, with maximum likelihood estimation of (the strength of the
 phylogenetic signal; Freckleton et al. 2002). The model was fitted with
 the function pgls in the package caper (Orme 2018) in R.