Latitudinal variation in genome size in crustaceans

Latitudinal variation in genome size in crustaceans Abstract In animals, genome size is correlated with many traits that also vary with latitude, such as body size and developmental rate. Crustaceans have highly variable genome sizes (ranging nearly 650-fold), and some polar crustaceans have exceptionally large genomes. Here, we investigate whether genome size is positively correlated with latitude across 275 species of decapods, amphipods and copepods. We also test whether this relationship is independent of other factors that co-vary with genome size and latitude (body size, habitat and larval development) using phylogenetic generalized least-squares methods and model selection using Akaike’s information criterion. In amphipods, all well-supported models explaining genome size included latitude, whereas in copepods the supported models included body size, but not latitude. In decapods, there was a significant correlation between genome size and number of larval stages; however, model testing indicated that no single factor was most well supported in explaining genome size correlations. Although genome size generally increases with latitude across crustaceans, it is clearly a complex trait that differs among taxonomic groups. INTRODUCTION Genome size, the bulk quantity of DNA contained in a single set of a species’ chromosomes, is known to vary extensively among eukaryotes without any obvious relationship to overall organismal complexity (Mirsky & Ris, 1951; Gregory, 2005). Genome size diversity is not distributed at random, however, and many patterns have emerged after nearly 70 years of study. Notably, genome size is known to correlate positively with cell size and inversely with cell division rate across a variety of taxa (Gregory, 2001). This, in turn, may lead to associations between genome size and a number of phenotypic and ecological traits; large genomes are associated with slower metabolic rates (Vinogradov, 1995; Gregory, 2002), slower developmental rates (McLaren, Sevigny & Corkett, 1988; White & McLaren, 2000; Wyngaard et al., 2005), larger body sizes (Gregory, 2005; Hessen & Persson, 2009) and other traits. In addition, it is apparent that some groups (e.g. birds; Kapusta, Suh & Feschotte, 2017) are relatively constrained with regard to genome size differences among species, whereas others (e.g. amphibians) exhibit genome sizes that vary over a wide range (Pagel & Johnstone, 1992). Among invertebrates, crustaceans display some of the more variable genome sizes, ranging from ~0.1 to ~65 pg (Bachmann & Rheinsmith, 1973; Bonnivard et al., 2009; Jeffery, 2015). Genome size is correlated with phenotypic traits within various crustacean groups, including a positive association with body size in amphipods and copepods (Gregory, 2005; Hessen & Persson, 2009; Hessen, 2015), a negative association with body size in brachyuran crabs (Hessen & Persson, 2009; Jeffery, 2015) and an inverse correlation with developmental rate in copepods (McLaren et al., 1988; White & McLaren, 2000; Wyngaard et al., 2005). Intriguingly, many of the phenotypic traits associated with large genomes, such as large body size and slower development, show gradients with latitude that form the basis of many so-called ‘ecogeographical rules’. In homeotherms, Bergmann’s rule predicts that species or populations at higher latitudes should be larger, because larger individuals can conserve heat more efficiently in colder climates (Bergmann, 1847; Lomolino, 2006; Salewski & Watt, 2016), and increases in body size with latitude have been well documented within mammals and birds (Blackburn et al., 1999; Ashton, Tracy & de Queiroz, 2000). Many poikilotherms also attain larger body sizes when raised at cooler temperatures, a phenomenon typically termed the ‘temperature–size rule’ (e.g. Lee & Boulding, 2010; reviewed by Atkinson, 1994). Paradoxically, this occurs even though growth is generally slower at cooler temperatures, and there is still debate as to the physiological mechanism driving this pattern (Atkinson & Sibly, 1997; Angilletta & Dunham, 2003). Some studies have also demonstrated that the temperature–size rule holds among species, e.g. poikilothermic species living at higher latitudes tend to be larger (Poulin, 1995; Poulin & Hamilton, 1995; Ashton & Feldman, 2003). Irrespective of taxonomic scale, the wealth of literature on this topic indicates that body size is a crucial factor to take into account when examining any correlations with latitude. In addition to changes in body size, Thorson (1936) noted a shift in the prevalence of different reproductive strategies among some marine invertebrates—including increased frequency of larger eggs, direct (non-planktonic) development and/or non-feeding development—in high-latitude and/or deep-water species (Thorson, 1936; Mileikovsky, 1971; Lomolino, 2006). However, this trend clearly does not hold true for echinoderms (Pearse, 1994), which vary primarily among regions in the proportion of species with feeding vs. non-feeding larvae, and many subsequent authors have shown that patterns of larval development are more complex and clearly affect phylogeny, habitat differences, larval food availability and other factors (Clarke, 1992; Gallardo & Penchaszadeh, 2001; Laptikhovsky, 2006; Pearse, Mooi & Lockhart, 2009). Crustaceans represent a useful group in which to explore patterns in body size and larval development, as they exhibit a variety of reproductive and developmental modes. In addition to direct development (in which all planktonic larval stages are suppressed), many decapods show ‘abbreviated development’, where one or more of the typical larval stages are suppressed and the species proceeds through a reduced number of larval stages before settlement (Dobkin, 1969; Knowlton, 1973; Vogt, 2012). Abbreviated development has been reported from several freshwater species, and species from low-temperature habitats, such as the sub-Antarctic and the deep sea (summarized by Vogt, 2012). Embryonic development has been reported to be slower for some crustacean species with abbreviated development (Knowlton, 1973), and for crustaceans raised at lower temperatures (Steele & Steele, 1975). As larger-bodied animals, and possibly slower-developing animals, are more common near the poles, a clear prediction would be that polar species should have larger genomes than their temperate or tropical relatives. Indeed, the largest crustacean genome sizes reported so far have been found in polar species of krill (Jeffery, 2012), amphipods (Ampelisca macrocephala Liljeborg, 64.62 pg; Rees et al., 2007) and caridean shrimps [Sclerocrangon ferox (Sars G.O.), 40.89 pg; Rees et al., 2008)] as well as deep-sea crustaceans (Bonnivard et al., 2009; Ritchie et al., 2017). Hessen and Persson (2009) compared genome size of arctic vs. non-arctic crustaceans, and found that arctic species had significantly larger genomes for calanoid copepods, caridean shrimp and amphipods. Alfsnes, Leinaas & Hessen (2017) used regression models to examine relationships between maximal latitude, depth, body length and genome size across crustaceans (~140 species) and insects, and found larger and deeper-dwelling crustacean species tended to have significantly larger genomes. Here, we expand upon previous studies of crustacean genome size diversity and its phenotypic and ecological correlates by comparing genome size, latitude, body size and number of larval developmental stages for 275 species across three major crustacean groups: the orders Amphipoda and Decapoda and the subclass Copepoda. Within each group, we examined how genome size was related to latitudinal midpoint using correlations of species means and phylogenetic independent contrasts, and how body size was related to latitude. In decapods, we additionally examined whether the number of larval developmental stages was correlated with genome size. Finally, to test whether associations between genome size and latitude were independent of other factors in our study (body size, habitat and larval development), we used a model selection approach to evaluate models incorporating different combinations of these variables in phylogenetically controlled analyses. MATERIAL AND METHODS Data collection For genome size, we used data from the animal genome size database (Gregory, 2016), and collected data on ~165 additional species. Genome size was estimated using either Feulgen image analysis densitometry (FIAD) or flow cytometry (FCM). The diploid tissues used in each method varied; gill tissue was used for FIAD in decapods and amphipods, either gill or muscle was used for FCM (for detailed methods, see Jeffery, 2015), and whole copepods were used in each method. Briefly, for FIAD, air-dried microscope slides containing prepared tissues underwent hydrolysis followed by staining in Schiff reagent according to best practice protocols (Hardie, Gregory & Hebert, 2002). All slides were co-stained and analysed with reference standard slides of domestic chicken blood [Gallus domesticus (Linnaeus), 1C (haploid nuclear DNA content) = 1.25 pg] and rainbow trout blood [Oncorhynchus mykiss (Walbaum), 1C = 2.60 pg]. The integrated optical density (IOD) was measured for a minimum of 30 stained nuclei using the Bioquant image analysis package, and the ratio of the average IOD to the IOD of the standard species is then multiplied by the genome size of the standard species, providing a genome size estimate for the species in question. For flow cytometry, tissues were homogenized in LB01 buffer (Doležel, Binarová & Lucretti, 1989) and co-stained with a reference standard using propidium iodide. All FCM samples were analysed on an FC500 flow cytometer using a 488 nm laser (Beckman-Coulter), and the fluorescence signal of a minimum of 1000 nuclei was measured for comparison with the fluorescence signal of the standard species. Data on latitudinal extent, larval development and body size were obtained from an extensive review of the literature (data available in Supporting Information, Table S1). We used the World Register of Marine Species database (WoRMS Editorial Board, 2017) to confirm taxonomy and to classify habitat (freshwater or marine). We then collected data on maximal body size, number of larval stages (for decapods) and latitudinal range. We used several sources, including species descriptions, taxonomic studies and databases, including the Encyclopedia of Life (www.eol.org) and the Ocean Biogeographic Information System (www.iobis.org). We included representative crustacean species from both the Northern and Southern Hemispheres (and several low-latitude species that spanned both Hemispheres; see Supporting Information, Table S1), and we calculated the latitudinal midpoint as the absolute value of the average of the latitudinal maximum and minimum. For amphipods and copepods, body size was measured as total body length. Given the variation in body shape within amphipods and copepods, body lengths may not be directly comparable among species in these groups. Thus, we converted body length (in millimetres) to biomass (in milligrams) using curves from the literature (see Supporting Information, Table S2.1) for amphipods (Benke et al., 1999) and copepods (Bottrell et al., 1976). For decapods, we coded length using the most common body size convention in the literature: carapace width (Brachyura) or carapace length (Achelata, Caridea, Astacidea, Dendrobranchiata and Anomura). As with the amphipods and copepods, we converted all lengths to approximate biomass values using standard curves, calculated from measurements of preserved museum specimens at the Oxford University Museum of Natural History, building curves for each common infraorder: Achelata, Astacidea, Brachyura, Anomura and Caridea/ Dendrobranchiata (Dendrobranchiata and Caridea had nearly identical curves). For each of these groups, we measured the body length (in millimetres) and blotted wet weight (to 0.001 g) of 15–20 specimens across several different families and a wide range of body sizes, and fitted power equations (Supporting Information, Table S2.1; r2 = 0.97–0.99). Best-fit curves are presented in the Supporting Information (Figure S2.2). For decapod crustaceans, we also collected data on larval development. For indirect developers (species having at least one planktonic larval stage), we recorded the number of larval stages observed for each species. For direct-developing species (in which young hatch directly from eggs as miniature adults), we coded the number of larval stages as zero. We did not include developmental mode in models for amphipods (that have exclusively direct development) or copepods (with exclusively indirect development). Phylogeny construction For phylogenetically controlled analyses, we constructed separate Bayesian phylogenies for each group (amphipods, decapods and copepods) using genetic data for three or four loci per species from GenBank (Supporting Information, Table S1). For amphipods and decapods, we used the partial regions of the mitochondrial loci COI (~650 bp) and 16S (~450 bp) and the nuclear locus 18S (~800 bp); for copepods, we used COI, 16S and the nuclear loci 18S and 28S (~700 bp). For amphipods and copepods, we included a species in the phylogenetic tree if there were sequence data available for at least two loci; for the decapod tree, we included a species if there were data for 18S and at least one mitochondrial locus (16S or COI), as including taxa with missing 18S data resulted in trees with non-monophyletic infraorders. We aligned data for each locus using Muscle (Edgar, 2004) implemented in Mega 7 (Tamura et al., 2007). We used GBlocks 0.91 (Talavera & Castresana, 2007) to exclude ambiguous areas of the 18S and 28S alignments, using semi-conservative gap alignment parameters (allowed gap positions = with half, minimal block length = 5). We used the program jModelTest2 (Darriba et al., 2012) implemented on the CIPRES web server (Miller, Pfeiffer, & Schwartz, 2010), and used the Akaike information criterion (AIC) for model selection (nucleotide substitution model: nst = 6 for all loci and taxa). We then constructed concatenated nexus files for the amphipods (1880 bp), copepods (2838 bp) and decapods (2983 bp). Outgroups included the amphipod Crangonyx pseudogracilis Bousfield for the copepod tree, the amphipod Gammarus tigrinus Sexton for the decapod tree, and the isopod Atlantoserolis vemae (Menzies) for the amphipod tree). We ran partitioned Bayesian analyses on MrBayes 3.2.6 (Nylander et al., 2004; Ronquist et al., 2012), using the CIPRES server (Miller et al., 2010), with four chains and two runs, and discarding the first 25% of the samples as burn-in to ensure run convergence (total number of generations: Amphipoda, 1 × 108; Copepoda, 2.5 × 108; and Decapoda, 5 × 107). Phylogenetic trees for each crustacean group are presented in the Supporting Information (Appendix S3, Figs S3.1–S3.3) and are available on Treebase (treebase 21804: http://purl.org/phylo/treebase/phylows/study/TB2:S21804). Data selection and statistical analyses As much of our original dataset (40 of 215 species, or 18.6%) for the decapod crustaceans consisted of different species of Synalpheus from a recent study (Jeffery et al., 2016), we used only a subset of those data (one randomly sampled species from each of the five major clades). Likewise, as much of our amphipod sample (39%) consisted of amphipods from Lake Baikal from a recent study (Jeffery, Yampolsky & Gregory, 2017), we used only one randomly sampled species of amphipod per genus from Lake Baikal. Genome size and biomass were log10-transformed for all analyses. We elected to run correlations using both raw species means and phylogenetic independent contrasts (PIC), as we were able to include only 42–63% of our sample in phylogenetically controlled analyses. For each of the three crustacean groups, we first tested for correlations between genome size and latitudinal midpoint (our primary variable of interest) using JMP 12.2 (SAS Institute) for raw correlations, and the PDAP module on Mesquite 3.2 (Maddison & Maddison, 2017) for PIC. Within the decapods, we examined the genome size–latitude correlation within separate infraorders, limiting these additional analyses to datasets with > 20 taxa (Anomura, Brachyura and Caridea). We also tested whether genome size and number of larval stages were correlated, and whether decapod species with fewer larval stages lived at higher latitudes. In all three groups, we tested whether biomass increased with latitude, using species correlations and PIC. For these analyses, we used Bonferroni–Holm P-values to correct for multiple comparisons. As the relationship between body size and genome size has been exhaustively documented elsewhere (Hessen & Persson, 2009; Alfsnes et al., 2017), we did not run correlations between genome size and body size, but included size as a variable in combined models. Finally, we examined the effects of multiple variables (biomass, latitude, habitat, and number of larval stages for the decapods, and all interactions) on genome size using a phylogenetic generalized least-squares (PGLS) analysis in the nlme and ape packages in R (Paradis, Claude & Strimmer, 2004; Pinheiro et al., 2015), with a modified Brownian motion model (Pagel, 1999) and the phylogeny of each group as the correlation structure. There were no significant interactions between any of these variables, so interaction terms were removed. We examined the effect of each of these factors in a combined PGLS model, and then compared models using different combinations of these variables using the AICc, the small-sample approximation of the AIC (Burnham & Anderson, 2004). This method assigns an AIC value for each model, and calculates the change in AIC (ΔAIC) for each model i (Δi, where Δi = AICi − AICmin); Burnham and Anderson’s (2004) criteria specify that a model i has substantial support if Δi < 2, and substantially less support if Δi > 4. As these variable combinations resulted in many possible models for each group, we evaluated each single-parameter model first, used the ‘dredge’ function in R to make a full list of models, and selected a subset of these models (with Δi < 10) to evaluate in R using the model.sel function in the R package MuMIn (Barton, 2016). RESULTS There were positive relationships between genome size and latitude (Table 1) for the amphipods (P < 0.0001; PIC, P = 0.0054; Fig. 1A) and the copepods (P < 0.0001; PIC, P = 0.0015; Fig. 1B), but not for the decapods after correcting for multiple comparisons (P = 0.046; PIC, P = 0.08; Fig. 1C). Within the decapod infraorders, there were significant latitude–genome size correlations for carideans (P = 0.0042; Fig. 2C), but not for anomurans (P = 0.013; Fig. 2A) or brachyurans (P = 0.599; Fig. 2B, Table 1). We did not run PIC correlations for these three infraorders owing to incomplete phylogenetic sampling of the datasets (11–17 contrasts for each group). Table 1. Pairwise correlations between log10 genome size and (A) latitudinal midpoint or (B) number of decapod larval stages (A) Genome size vs. latitude    Raw species means  Phylogenetic independent contrasts    N  Slope  R2  P-value  Number of contrasts  R2  F-value  P-value  Amphipoda  79  0.018  0.327  < 0.0001  36  0.1713  7.23  0.0054  Copepoda  44  0.014  0.354  < 0.0001  26  0.30289  10.862  0.0015  Decapoda  129  0.004  0.03109  0.0456  58  0.033  2.02  0.08  Anomura  29  0.006  0.208  0.0127          Brachyura  47  −0.001  0.00617  0.5997          Caridea  34  0.005  0.229  0.0042          (B) Genome size vs. number of larval stages                N  Slope  R2  P-value  Number of contrasts  R2  F-value  Two-tailed P-value  Decapoda  103  −0.038  0.12504  0.0002  37  0.4971  35.6  < 0.001  (A) Genome size vs. latitude    Raw species means  Phylogenetic independent contrasts    N  Slope  R2  P-value  Number of contrasts  R2  F-value  P-value  Amphipoda  79  0.018  0.327  < 0.0001  36  0.1713  7.23  0.0054  Copepoda  44  0.014  0.354  < 0.0001  26  0.30289  10.862  0.0015  Decapoda  129  0.004  0.03109  0.0456  58  0.033  2.02  0.08  Anomura  29  0.006  0.208  0.0127          Brachyura  47  −0.001  0.00617  0.5997          Caridea  34  0.005  0.229  0.0042          (B) Genome size vs. number of larval stages                N  Slope  R2  P-value  Number of contrasts  R2  F-value  Two-tailed P-value  Decapoda  103  −0.038  0.12504  0.0002  37  0.4971  35.6  < 0.001  Significant P-values (using Bonferroni–Holm corrections for multiple comparisons) are given in bold. View Large Figure 1. View largeDownload slide Correlations between genome size (log10-transformed) and latitudinal midpoint, using phylogenetic independent contrasts, for the three major groups of crustaceans in our study. Figure 1. View largeDownload slide Correlations between genome size (log10-transformed) and latitudinal midpoint, using phylogenetic independent contrasts, for the three major groups of crustaceans in our study. Figure 2. View largeDownload slide Correlations between genome size (log10) and latitudinal midpoint, using raw species correlations, for the three major decapod infraorders. Figure 2. View largeDownload slide Correlations between genome size (log10) and latitudinal midpoint, using raw species correlations, for the three major decapod infraorders. For the decapods, there were significant negative relationships between genome size and number of larval stages (e.g. larger genomes in species with abbreviated or direct development) using the raw species means (P = 0.0002; Table 1) and PIC (P < 0.001; Table 1, Fig. 3). Figure 3. View largeDownload slide Correlations between genome size (log10) and number of decapod larval stages, using phylogenetic independent contrasts. Figure 3. View largeDownload slide Correlations between genome size (log10) and number of decapod larval stages, using phylogenetic independent contrasts. We also tested whether biomass increased with latitude (summarized in Table 2, Fig. 4). In general, there were positive relationships between biomass and latitude in the amphipods (P < 0.0001) and copepods (P = 0.0016), although these patterns were not significant when we corrected for phylogeny and multiple comparisons (amphipod PIC, P = 0.428; copepod PIC, P = 0.019; Fig. 4). There were no significant correlations between biomass and latitude in the decapods (P = 0.191; PIC, P = 0.392; Fig. 4C), nor within the brachyuran (P = 0.527) or anomuran (P = 0.816) infraorders, but there was a positive latitude–biomass correlation in the infraorder Caridea (P = 0.001; Table 2, data not shown). In the decapods, latitude was also negatively correlated with number of larval stages (with fewer larval stages in higher latitudes) using species means (P < 0.0001), but not PIC (P = 0.065, data not shown; Table 2). Table 2. Pairwise correlations between latitudinal midpoint and (A) log10 biomass or (B) number of larval stages (A) Latitude vs. biomass    Raw species means  Phylogenetic independent contrasts    N  Slope  R2  P-value  Number of contrasts  R2  Two-tailed P-value  Amphipoda  75  0.03  0.19  < 0.0001  36  0.00  0.428  Copepoda  43  0.01  0.22  0.0016  26  0.160  0.019  Decapoda  127  0.00  0.01  0.1909  59  0.00  0.392  Anomura  29  0.00  0.00  0.816        Brachyura  44  0.00  0.01  0.527        Caridea  35  0.01  0.28  0.001        (B) Latitude vs. number of larval stages              N  Slope  R2  P-value  Number of contrasts  R2  Two-tailed P-value  Decapoda  103  −0.07  0.16  < 0.0001  44  0.062  0.065  (A) Latitude vs. biomass    Raw species means  Phylogenetic independent contrasts    N  Slope  R2  P-value  Number of contrasts  R2  Two-tailed P-value  Amphipoda  75  0.03  0.19  < 0.0001  36  0.00  0.428  Copepoda  43  0.01  0.22  0.0016  26  0.160  0.019  Decapoda  127  0.00  0.01  0.1909  59  0.00  0.392  Anomura  29  0.00  0.00  0.816        Brachyura  44  0.00  0.01  0.527        Caridea  35  0.01  0.28  0.001        (B) Latitude vs. number of larval stages              N  Slope  R2  P-value  Number of contrasts  R2  Two-tailed P-value  Decapoda  103  −0.07  0.16  < 0.0001  44  0.062  0.065  Significant P-values (using Bonferroni–Holm corrections for multiple comparisons) are given in bold. View Large Figure 4. View largeDownload slide Correlations between biomass (log10-transformed) and latitudinal midpoint, using phylogenetic independent contrasts, for the three major groups of crustaceans in our study. Figure 4. View largeDownload slide Correlations between biomass (log10-transformed) and latitudinal midpoint, using phylogenetic independent contrasts, for the three major groups of crustaceans in our study. Finally, using a PGLS approach to control for phylogeny, we evaluated how multiple variables (biomass, latitude, habitat; number of larval stages in decapods) affected genome size in combined models and using AICc (Table 3). For the amphipods, a combined PGLS model showed significant effects of latitude alone (P < 0.0001), but no significant effects of biomass (P = 0.1454) or habitat (P = 0.8336). Using the AIC, a model with latitude alone was the most well-supported model; the second best model included latitude and habitat, but was not substantially supported (ΔAICc = 3.71; Table 3). For the copepods, a combined PGLS model showed significant effects of only biomass (P = 0.0014), and no significant effects of latitude (P = 0.1637) or habitat (P = 0.3275). Using the AIC, a model with biomass alone (ΔAICc = 0) and a model including biomass and habitat (ΔAICc = 0.58) were both substantially supported (Table 3). In the decapods, there were no significant effects of latitude (P = 0.3983), habitat (P = 0.4313), biomass (P = 0.1177) or number of larval stages (P = 0.5325) in a combined PGLS model, and a null model (intercept only) was the only model with substantial support using the AIC (Table 3). Table 3. Assessment of different models explaining variation in genome size, using the Akaike information criterion   d.f.  Model  AICc  ΔAICc  Weight  Amphipoda  4  Latitude  69.9  0  0.791    5  Latitude + Habitat  73.6  3.71  0.124  Copepoda  4  log(Biomass)  73.7  0  0.545    5  log(Biomass) + Habitat  74.3  0.58  0.408  Decapoda  3  Null (intercept only)  29.6  0  0.674    4  Habitat  32.2  2.63  0.18    4  Biomass  32.7  3.06  0.146    d.f.  Model  AICc  ΔAICc  Weight  Amphipoda  4  Latitude  69.9  0  0.791    5  Latitude + Habitat  73.6  3.71  0.124  Copepoda  4  log(Biomass)  73.7  0  0.545    5  log(Biomass) + Habitat  74.3  0.58  0.408  Decapoda  3  Null (intercept only)  29.6  0  0.674    4  Habitat  32.2  2.63  0.18    4  Biomass  32.7  3.06  0.146  All models with change in corrected Akaike information criterion (ΔAICc) < 4 are included; models with ΔAICc < 2 have substantial support and are indicated in bold. View Large DISCUSSION In this study, we demonstrate significant relationships between genome size and different variables (latitude, biomass and larval development) within three major crustacean groups. In amphipods and copepods, there were strong positive correlations between latitude and genome size, with the largest genomes being found in polar species. Importantly, this strong positive correlation was still present when we corrected for the close evolutionary relationships between taxa using phylogenetic independent contrasts. However, our analyses also showed that latitude–genome size correlations cannot be considered independently of biomass; biomass was also correlated with latitude using species means in all groups. When we considered latitude, biomass, habitat and development in a phylogenetically controlled, model selection context, different variables emerged as important in different crustacean groups. Genome size and phenotypic traits Amphipods and copepods were the two groups with the clearest results. For the amphipods, phylogenetically controlled comparisons indicated that in combined models, there were significant effects of latitude (but not biomass or habitat), and AIC indicated that only the model with latitude alone had substantial support, suggesting that latitude has important correlations with genome size independent of biomass (Table 3). This pattern does not appear to be driven by inclusion of ‘outlier’ species, as the Arctic amphipod, A. macrocephala, with a genome size of 64.62 pg (Rees et al., 2007) was not included in our phylogenetically controlled analyses. In copepods, the model indicated that there were significant effects of biomass (but not latitude or habitat), and AIC indicated that a biomass model and a biomass plus habitat model had substantial support. This suggests that latitude–genome size correlations might be driven, in part, by larger body sizes in high-latitude copepods, in support of the temperature–size pattern seen in other groups. Previous studies on copepods have shown significant relationships between biomass and latitude for some groups (Poulin, 1995), and our study documented increases in biomass with latitude using PIC, although the correlation was not significant after controlling for multiple comparisons. Although previous studies have documented larger genomes in arctic vs. temperate copepods and amphipods and in more deep-water crustaceans (Hessen & Persson, 2009; Alfsnes et al., 2017; Jeffery et al., 2017; Ritchie et al., 2017), our study uses a greatly expanded dataset and is the first to consider latitude, habitat and biomass together in a model-selection framework using phylogenetically controlled analyses of different crustacean groups. In contrast, within the decapods there were only weak correlations between latitude and genome size, consistent with patterns from previous studies (Hessen & Persson, 2009; Jeffery, 2015), and no single variable emerged with the highest explanatory power. Some of this variability is clearly attributable to differences among the decapod infraorders; for example, there were positive latitude–genome size correlations in the caridean shrimps and anomuran crabs, but not in the brachyuran crabs (Table 1, Fig. 2). Although it is unclear why brachyuran crabs have no correlation between genome size and latitude, this might be related to the comparatively low variability in genome size in this group (Jeffery, 2015). Our study is the first to demonstrate correlations between larval development and genome size in the decapods, specifically larger genome sizes in species with direct development or few larval stages. In copepods, which all share the same defined larval developmental stages, genome size is correlated with developmental rate; specifically, species with faster development have smaller genomes (McLaren et al., 1988; White & McLaren, 2000; Wyngaard et al., 2005). This suggests that genome size may be correlated with various aspects of embryonic and/or larval development in different crustacean groups. Although few studies have examined developmental rate in relationship to the number of larval stages in decapods, Knowlton (1973) noted that alpheid shrimp species with direct or abbreviated development had very slow embryonic development relative to species with extended larval development, suggesting that our results may be consistent with developmental patterns seen in copepods. However, in phylogenetically controlled analyses of multiple variables in decapod analyses, no single trait emerged with the most explanatory value, and clearly more data need to be collected on decapod genome size to test rigorously how larval development relates to genome size. Hypotheses to explain the observed patterns Unfortunately, little is known about the specific genomic content or architecture of large-genomed crustaceans. In addition to the effects of large-scale duplications, increases in genome size are thought to be associated with the accumulation of non-coding DNA, especially transposable elements (Kidwell & Lisch, 2000; Lynch & Conery, 2003; Dufresne & Jeffery, 2011; Ritchie et al., 2017). Additional data on the genomic architecture of species with varying genome sizes would be very useful in elucidating the genome-level mechanisms responsible for the wide range in crustacean DNA contents. In terms of phenotypic and ecological constraints that may shape patterns of genome size variability in crustaceans, the large genome sizes in high-latitude crustaceans documented here and in previous studies (Rees et al., 2007, 2008) could be related to slower metabolism at the lower temperatures in these marine habitats (Hessen & Persson, 2009), as some studies have revealed that larger genomes are correlated with lower body-mass-corrected metabolic rates in mammals (Vinogradov, 1995) and birds (Gregory, 2002). The pattern of larger genome sizes at higher latitudes and in deeper habitats is also consistent with r–K–a selection (Greenslade, 1983), where high-latitude species are under a- or ‘adverse’ selection, associated with production of fewer, larger eggs (or direct development), late maturity and greater longevity (see also Rees et al., 2007). A similar pattern can be drawn with deep-water species; large genomes in crustaceans have also been recorded from the deep sea (Ritchie et al., 2017) and the depths of Lake Baikal (Jeffery et al., 2017). Martens (1997) drew parallels between the polar oceans and deep-water-dwelling species in ancient lakes and oceans, showing convergent evolution in a number of traits, such as body armature and gigantism. Martens (1997) states that these environments are highly stable and isolated, and the phenotypes exhibited in the deep sea and ancient lakes are hypothesized to be indicative of a stable, cold environment. It may be that similar evolutionary processes, which led to this convergent evolution, also led to the shared large genomes between these different habitats. However, the causal directionality of any such relationships remains unknown and may be somewhat complex. It is possible that genomes may become larger through the proliferation of non-coding elements in polar environments, because of either selection for larger body sizes through an increased genome size or a lack of selective pressures for a rapid developmental and/or metabolic rate, which would act to constrain expansion of the genome. It is also plausible that large genomes impose constraints in terms of development or other factors that make species with large genomes more suited to high latitudes. Habitat differences represent an additional confounding factor in latitude-based comparisons, such as the one presented here. This is especially true of marine vs. freshwater environments in polar regions, the latter of which typically freeze seasonally and may therefore represent ephemeral habitats. These habitats differ in other ways as well, including nutrient levels, salinity and community composition, all of which may be additional confounding variables. In plants, whereas temperate species tend to have larger genomes and/or larger chromosomes than tropical species (Bennett, 1976; Levin & Funderburg, 1979), many very high-latitude plants have small genomes, possibly because of constraints related to developmental rate during short growing seasons (Grime & Mowforth, 1982). If species living in polar freshwater habitats experience similar developmental constraints, we may expect different patterns between genome size and latitude in freshwater and marine habitats. Although we found few significant effects of habitat (marine vs. freshwater) in our study, we had very few freshwater species in some groups (e.g. decapods), and more data are necessary to test explicitly the genome size–latitude relationship between marine and freshwater species. In summary, increases in genome size with latitude appear to be associated with a suite of traits typical of crustaceans living at high latitudes, including large body size and shifts in developmental mode. Given the variable patterns in genome size seen within some of our crustacean groups (e.g. decapod infraorders), these additional variables may be particularly valuable to examine in directed studies of certain crustacean families or genera in a phylogenetically controlled, model-testing approach. Given their extraordinary diversity in genome sizes, physical traits and ecological lifestyles, crustaceans should feature prominently in future research in genome evolution from perspectives ranging from subgenomic to global-scale studies. SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article at the publisher's web-site: Table S1. Data on habitat, genome size, latitudinal midpoint, and body size used in this study. Habitat includes freshwater (FW) or marine. Genbank accession numbers are given for all species used in phylogenies. Under C-value method, FD = Feulgen densitometry and FCM = flow cytometry; n=1 unless noted otherwise. Body size is given in mm, as entire length (Amphipoda and Copepoda), carapace length (Caridea, Astacidea, Dendrobranchiata), shield or carapace length (Anomura), or carapace width (Brachyura). Fig. S2.2. Best-fit curves of biomass as a func3on of body length for differentdecapod infraorders. Figure S3: Bayesian consensus trees used in phylogene3c analyses for decapods (Fig. S3.1), amphipods (S3.2), and copepods (S3.3). For all trees, numbers by each node indicate Bayesian posterior probability values; scale bar indicates subs3tu3ons/site. For the decapoda (a), infraorders are indicated. Fig. S3.1. Decapod tree Fig. S3.2. Amphipod tree Fig. S3.3. Copepod tree ACKNOWLEDGEMENTS This work was funded by a Natural Sciences grant to K.M.H. from the Murdock Charitable Trust. 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Latitudinal variation in genome size in crustaceans

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© 2018 The Linnean Society of London, Biological Journal of the Linnean Society
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0024-4066
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Abstract

Abstract In animals, genome size is correlated with many traits that also vary with latitude, such as body size and developmental rate. Crustaceans have highly variable genome sizes (ranging nearly 650-fold), and some polar crustaceans have exceptionally large genomes. Here, we investigate whether genome size is positively correlated with latitude across 275 species of decapods, amphipods and copepods. We also test whether this relationship is independent of other factors that co-vary with genome size and latitude (body size, habitat and larval development) using phylogenetic generalized least-squares methods and model selection using Akaike’s information criterion. In amphipods, all well-supported models explaining genome size included latitude, whereas in copepods the supported models included body size, but not latitude. In decapods, there was a significant correlation between genome size and number of larval stages; however, model testing indicated that no single factor was most well supported in explaining genome size correlations. Although genome size generally increases with latitude across crustaceans, it is clearly a complex trait that differs among taxonomic groups. INTRODUCTION Genome size, the bulk quantity of DNA contained in a single set of a species’ chromosomes, is known to vary extensively among eukaryotes without any obvious relationship to overall organismal complexity (Mirsky & Ris, 1951; Gregory, 2005). Genome size diversity is not distributed at random, however, and many patterns have emerged after nearly 70 years of study. Notably, genome size is known to correlate positively with cell size and inversely with cell division rate across a variety of taxa (Gregory, 2001). This, in turn, may lead to associations between genome size and a number of phenotypic and ecological traits; large genomes are associated with slower metabolic rates (Vinogradov, 1995; Gregory, 2002), slower developmental rates (McLaren, Sevigny & Corkett, 1988; White & McLaren, 2000; Wyngaard et al., 2005), larger body sizes (Gregory, 2005; Hessen & Persson, 2009) and other traits. In addition, it is apparent that some groups (e.g. birds; Kapusta, Suh & Feschotte, 2017) are relatively constrained with regard to genome size differences among species, whereas others (e.g. amphibians) exhibit genome sizes that vary over a wide range (Pagel & Johnstone, 1992). Among invertebrates, crustaceans display some of the more variable genome sizes, ranging from ~0.1 to ~65 pg (Bachmann & Rheinsmith, 1973; Bonnivard et al., 2009; Jeffery, 2015). Genome size is correlated with phenotypic traits within various crustacean groups, including a positive association with body size in amphipods and copepods (Gregory, 2005; Hessen & Persson, 2009; Hessen, 2015), a negative association with body size in brachyuran crabs (Hessen & Persson, 2009; Jeffery, 2015) and an inverse correlation with developmental rate in copepods (McLaren et al., 1988; White & McLaren, 2000; Wyngaard et al., 2005). Intriguingly, many of the phenotypic traits associated with large genomes, such as large body size and slower development, show gradients with latitude that form the basis of many so-called ‘ecogeographical rules’. In homeotherms, Bergmann’s rule predicts that species or populations at higher latitudes should be larger, because larger individuals can conserve heat more efficiently in colder climates (Bergmann, 1847; Lomolino, 2006; Salewski & Watt, 2016), and increases in body size with latitude have been well documented within mammals and birds (Blackburn et al., 1999; Ashton, Tracy & de Queiroz, 2000). Many poikilotherms also attain larger body sizes when raised at cooler temperatures, a phenomenon typically termed the ‘temperature–size rule’ (e.g. Lee & Boulding, 2010; reviewed by Atkinson, 1994). Paradoxically, this occurs even though growth is generally slower at cooler temperatures, and there is still debate as to the physiological mechanism driving this pattern (Atkinson & Sibly, 1997; Angilletta & Dunham, 2003). Some studies have also demonstrated that the temperature–size rule holds among species, e.g. poikilothermic species living at higher latitudes tend to be larger (Poulin, 1995; Poulin & Hamilton, 1995; Ashton & Feldman, 2003). Irrespective of taxonomic scale, the wealth of literature on this topic indicates that body size is a crucial factor to take into account when examining any correlations with latitude. In addition to changes in body size, Thorson (1936) noted a shift in the prevalence of different reproductive strategies among some marine invertebrates—including increased frequency of larger eggs, direct (non-planktonic) development and/or non-feeding development—in high-latitude and/or deep-water species (Thorson, 1936; Mileikovsky, 1971; Lomolino, 2006). However, this trend clearly does not hold true for echinoderms (Pearse, 1994), which vary primarily among regions in the proportion of species with feeding vs. non-feeding larvae, and many subsequent authors have shown that patterns of larval development are more complex and clearly affect phylogeny, habitat differences, larval food availability and other factors (Clarke, 1992; Gallardo & Penchaszadeh, 2001; Laptikhovsky, 2006; Pearse, Mooi & Lockhart, 2009). Crustaceans represent a useful group in which to explore patterns in body size and larval development, as they exhibit a variety of reproductive and developmental modes. In addition to direct development (in which all planktonic larval stages are suppressed), many decapods show ‘abbreviated development’, where one or more of the typical larval stages are suppressed and the species proceeds through a reduced number of larval stages before settlement (Dobkin, 1969; Knowlton, 1973; Vogt, 2012). Abbreviated development has been reported from several freshwater species, and species from low-temperature habitats, such as the sub-Antarctic and the deep sea (summarized by Vogt, 2012). Embryonic development has been reported to be slower for some crustacean species with abbreviated development (Knowlton, 1973), and for crustaceans raised at lower temperatures (Steele & Steele, 1975). As larger-bodied animals, and possibly slower-developing animals, are more common near the poles, a clear prediction would be that polar species should have larger genomes than their temperate or tropical relatives. Indeed, the largest crustacean genome sizes reported so far have been found in polar species of krill (Jeffery, 2012), amphipods (Ampelisca macrocephala Liljeborg, 64.62 pg; Rees et al., 2007) and caridean shrimps [Sclerocrangon ferox (Sars G.O.), 40.89 pg; Rees et al., 2008)] as well as deep-sea crustaceans (Bonnivard et al., 2009; Ritchie et al., 2017). Hessen and Persson (2009) compared genome size of arctic vs. non-arctic crustaceans, and found that arctic species had significantly larger genomes for calanoid copepods, caridean shrimp and amphipods. Alfsnes, Leinaas & Hessen (2017) used regression models to examine relationships between maximal latitude, depth, body length and genome size across crustaceans (~140 species) and insects, and found larger and deeper-dwelling crustacean species tended to have significantly larger genomes. Here, we expand upon previous studies of crustacean genome size diversity and its phenotypic and ecological correlates by comparing genome size, latitude, body size and number of larval developmental stages for 275 species across three major crustacean groups: the orders Amphipoda and Decapoda and the subclass Copepoda. Within each group, we examined how genome size was related to latitudinal midpoint using correlations of species means and phylogenetic independent contrasts, and how body size was related to latitude. In decapods, we additionally examined whether the number of larval developmental stages was correlated with genome size. Finally, to test whether associations between genome size and latitude were independent of other factors in our study (body size, habitat and larval development), we used a model selection approach to evaluate models incorporating different combinations of these variables in phylogenetically controlled analyses. MATERIAL AND METHODS Data collection For genome size, we used data from the animal genome size database (Gregory, 2016), and collected data on ~165 additional species. Genome size was estimated using either Feulgen image analysis densitometry (FIAD) or flow cytometry (FCM). The diploid tissues used in each method varied; gill tissue was used for FIAD in decapods and amphipods, either gill or muscle was used for FCM (for detailed methods, see Jeffery, 2015), and whole copepods were used in each method. Briefly, for FIAD, air-dried microscope slides containing prepared tissues underwent hydrolysis followed by staining in Schiff reagent according to best practice protocols (Hardie, Gregory & Hebert, 2002). All slides were co-stained and analysed with reference standard slides of domestic chicken blood [Gallus domesticus (Linnaeus), 1C (haploid nuclear DNA content) = 1.25 pg] and rainbow trout blood [Oncorhynchus mykiss (Walbaum), 1C = 2.60 pg]. The integrated optical density (IOD) was measured for a minimum of 30 stained nuclei using the Bioquant image analysis package, and the ratio of the average IOD to the IOD of the standard species is then multiplied by the genome size of the standard species, providing a genome size estimate for the species in question. For flow cytometry, tissues were homogenized in LB01 buffer (Doležel, Binarová & Lucretti, 1989) and co-stained with a reference standard using propidium iodide. All FCM samples were analysed on an FC500 flow cytometer using a 488 nm laser (Beckman-Coulter), and the fluorescence signal of a minimum of 1000 nuclei was measured for comparison with the fluorescence signal of the standard species. Data on latitudinal extent, larval development and body size were obtained from an extensive review of the literature (data available in Supporting Information, Table S1). We used the World Register of Marine Species database (WoRMS Editorial Board, 2017) to confirm taxonomy and to classify habitat (freshwater or marine). We then collected data on maximal body size, number of larval stages (for decapods) and latitudinal range. We used several sources, including species descriptions, taxonomic studies and databases, including the Encyclopedia of Life (www.eol.org) and the Ocean Biogeographic Information System (www.iobis.org). We included representative crustacean species from both the Northern and Southern Hemispheres (and several low-latitude species that spanned both Hemispheres; see Supporting Information, Table S1), and we calculated the latitudinal midpoint as the absolute value of the average of the latitudinal maximum and minimum. For amphipods and copepods, body size was measured as total body length. Given the variation in body shape within amphipods and copepods, body lengths may not be directly comparable among species in these groups. Thus, we converted body length (in millimetres) to biomass (in milligrams) using curves from the literature (see Supporting Information, Table S2.1) for amphipods (Benke et al., 1999) and copepods (Bottrell et al., 1976). For decapods, we coded length using the most common body size convention in the literature: carapace width (Brachyura) or carapace length (Achelata, Caridea, Astacidea, Dendrobranchiata and Anomura). As with the amphipods and copepods, we converted all lengths to approximate biomass values using standard curves, calculated from measurements of preserved museum specimens at the Oxford University Museum of Natural History, building curves for each common infraorder: Achelata, Astacidea, Brachyura, Anomura and Caridea/ Dendrobranchiata (Dendrobranchiata and Caridea had nearly identical curves). For each of these groups, we measured the body length (in millimetres) and blotted wet weight (to 0.001 g) of 15–20 specimens across several different families and a wide range of body sizes, and fitted power equations (Supporting Information, Table S2.1; r2 = 0.97–0.99). Best-fit curves are presented in the Supporting Information (Figure S2.2). For decapod crustaceans, we also collected data on larval development. For indirect developers (species having at least one planktonic larval stage), we recorded the number of larval stages observed for each species. For direct-developing species (in which young hatch directly from eggs as miniature adults), we coded the number of larval stages as zero. We did not include developmental mode in models for amphipods (that have exclusively direct development) or copepods (with exclusively indirect development). Phylogeny construction For phylogenetically controlled analyses, we constructed separate Bayesian phylogenies for each group (amphipods, decapods and copepods) using genetic data for three or four loci per species from GenBank (Supporting Information, Table S1). For amphipods and decapods, we used the partial regions of the mitochondrial loci COI (~650 bp) and 16S (~450 bp) and the nuclear locus 18S (~800 bp); for copepods, we used COI, 16S and the nuclear loci 18S and 28S (~700 bp). For amphipods and copepods, we included a species in the phylogenetic tree if there were sequence data available for at least two loci; for the decapod tree, we included a species if there were data for 18S and at least one mitochondrial locus (16S or COI), as including taxa with missing 18S data resulted in trees with non-monophyletic infraorders. We aligned data for each locus using Muscle (Edgar, 2004) implemented in Mega 7 (Tamura et al., 2007). We used GBlocks 0.91 (Talavera & Castresana, 2007) to exclude ambiguous areas of the 18S and 28S alignments, using semi-conservative gap alignment parameters (allowed gap positions = with half, minimal block length = 5). We used the program jModelTest2 (Darriba et al., 2012) implemented on the CIPRES web server (Miller, Pfeiffer, & Schwartz, 2010), and used the Akaike information criterion (AIC) for model selection (nucleotide substitution model: nst = 6 for all loci and taxa). We then constructed concatenated nexus files for the amphipods (1880 bp), copepods (2838 bp) and decapods (2983 bp). Outgroups included the amphipod Crangonyx pseudogracilis Bousfield for the copepod tree, the amphipod Gammarus tigrinus Sexton for the decapod tree, and the isopod Atlantoserolis vemae (Menzies) for the amphipod tree). We ran partitioned Bayesian analyses on MrBayes 3.2.6 (Nylander et al., 2004; Ronquist et al., 2012), using the CIPRES server (Miller et al., 2010), with four chains and two runs, and discarding the first 25% of the samples as burn-in to ensure run convergence (total number of generations: Amphipoda, 1 × 108; Copepoda, 2.5 × 108; and Decapoda, 5 × 107). Phylogenetic trees for each crustacean group are presented in the Supporting Information (Appendix S3, Figs S3.1–S3.3) and are available on Treebase (treebase 21804: http://purl.org/phylo/treebase/phylows/study/TB2:S21804). Data selection and statistical analyses As much of our original dataset (40 of 215 species, or 18.6%) for the decapod crustaceans consisted of different species of Synalpheus from a recent study (Jeffery et al., 2016), we used only a subset of those data (one randomly sampled species from each of the five major clades). Likewise, as much of our amphipod sample (39%) consisted of amphipods from Lake Baikal from a recent study (Jeffery, Yampolsky & Gregory, 2017), we used only one randomly sampled species of amphipod per genus from Lake Baikal. Genome size and biomass were log10-transformed for all analyses. We elected to run correlations using both raw species means and phylogenetic independent contrasts (PIC), as we were able to include only 42–63% of our sample in phylogenetically controlled analyses. For each of the three crustacean groups, we first tested for correlations between genome size and latitudinal midpoint (our primary variable of interest) using JMP 12.2 (SAS Institute) for raw correlations, and the PDAP module on Mesquite 3.2 (Maddison & Maddison, 2017) for PIC. Within the decapods, we examined the genome size–latitude correlation within separate infraorders, limiting these additional analyses to datasets with > 20 taxa (Anomura, Brachyura and Caridea). We also tested whether genome size and number of larval stages were correlated, and whether decapod species with fewer larval stages lived at higher latitudes. In all three groups, we tested whether biomass increased with latitude, using species correlations and PIC. For these analyses, we used Bonferroni–Holm P-values to correct for multiple comparisons. As the relationship between body size and genome size has been exhaustively documented elsewhere (Hessen & Persson, 2009; Alfsnes et al., 2017), we did not run correlations between genome size and body size, but included size as a variable in combined models. Finally, we examined the effects of multiple variables (biomass, latitude, habitat, and number of larval stages for the decapods, and all interactions) on genome size using a phylogenetic generalized least-squares (PGLS) analysis in the nlme and ape packages in R (Paradis, Claude & Strimmer, 2004; Pinheiro et al., 2015), with a modified Brownian motion model (Pagel, 1999) and the phylogeny of each group as the correlation structure. There were no significant interactions between any of these variables, so interaction terms were removed. We examined the effect of each of these factors in a combined PGLS model, and then compared models using different combinations of these variables using the AICc, the small-sample approximation of the AIC (Burnham & Anderson, 2004). This method assigns an AIC value for each model, and calculates the change in AIC (ΔAIC) for each model i (Δi, where Δi = AICi − AICmin); Burnham and Anderson’s (2004) criteria specify that a model i has substantial support if Δi < 2, and substantially less support if Δi > 4. As these variable combinations resulted in many possible models for each group, we evaluated each single-parameter model first, used the ‘dredge’ function in R to make a full list of models, and selected a subset of these models (with Δi < 10) to evaluate in R using the model.sel function in the R package MuMIn (Barton, 2016). RESULTS There were positive relationships between genome size and latitude (Table 1) for the amphipods (P < 0.0001; PIC, P = 0.0054; Fig. 1A) and the copepods (P < 0.0001; PIC, P = 0.0015; Fig. 1B), but not for the decapods after correcting for multiple comparisons (P = 0.046; PIC, P = 0.08; Fig. 1C). Within the decapod infraorders, there were significant latitude–genome size correlations for carideans (P = 0.0042; Fig. 2C), but not for anomurans (P = 0.013; Fig. 2A) or brachyurans (P = 0.599; Fig. 2B, Table 1). We did not run PIC correlations for these three infraorders owing to incomplete phylogenetic sampling of the datasets (11–17 contrasts for each group). Table 1. Pairwise correlations between log10 genome size and (A) latitudinal midpoint or (B) number of decapod larval stages (A) Genome size vs. latitude    Raw species means  Phylogenetic independent contrasts    N  Slope  R2  P-value  Number of contrasts  R2  F-value  P-value  Amphipoda  79  0.018  0.327  < 0.0001  36  0.1713  7.23  0.0054  Copepoda  44  0.014  0.354  < 0.0001  26  0.30289  10.862  0.0015  Decapoda  129  0.004  0.03109  0.0456  58  0.033  2.02  0.08  Anomura  29  0.006  0.208  0.0127          Brachyura  47  −0.001  0.00617  0.5997          Caridea  34  0.005  0.229  0.0042          (B) Genome size vs. number of larval stages                N  Slope  R2  P-value  Number of contrasts  R2  F-value  Two-tailed P-value  Decapoda  103  −0.038  0.12504  0.0002  37  0.4971  35.6  < 0.001  (A) Genome size vs. latitude    Raw species means  Phylogenetic independent contrasts    N  Slope  R2  P-value  Number of contrasts  R2  F-value  P-value  Amphipoda  79  0.018  0.327  < 0.0001  36  0.1713  7.23  0.0054  Copepoda  44  0.014  0.354  < 0.0001  26  0.30289  10.862  0.0015  Decapoda  129  0.004  0.03109  0.0456  58  0.033  2.02  0.08  Anomura  29  0.006  0.208  0.0127          Brachyura  47  −0.001  0.00617  0.5997          Caridea  34  0.005  0.229  0.0042          (B) Genome size vs. number of larval stages                N  Slope  R2  P-value  Number of contrasts  R2  F-value  Two-tailed P-value  Decapoda  103  −0.038  0.12504  0.0002  37  0.4971  35.6  < 0.001  Significant P-values (using Bonferroni–Holm corrections for multiple comparisons) are given in bold. View Large Figure 1. View largeDownload slide Correlations between genome size (log10-transformed) and latitudinal midpoint, using phylogenetic independent contrasts, for the three major groups of crustaceans in our study. Figure 1. View largeDownload slide Correlations between genome size (log10-transformed) and latitudinal midpoint, using phylogenetic independent contrasts, for the three major groups of crustaceans in our study. Figure 2. View largeDownload slide Correlations between genome size (log10) and latitudinal midpoint, using raw species correlations, for the three major decapod infraorders. Figure 2. View largeDownload slide Correlations between genome size (log10) and latitudinal midpoint, using raw species correlations, for the three major decapod infraorders. For the decapods, there were significant negative relationships between genome size and number of larval stages (e.g. larger genomes in species with abbreviated or direct development) using the raw species means (P = 0.0002; Table 1) and PIC (P < 0.001; Table 1, Fig. 3). Figure 3. View largeDownload slide Correlations between genome size (log10) and number of decapod larval stages, using phylogenetic independent contrasts. Figure 3. View largeDownload slide Correlations between genome size (log10) and number of decapod larval stages, using phylogenetic independent contrasts. We also tested whether biomass increased with latitude (summarized in Table 2, Fig. 4). In general, there were positive relationships between biomass and latitude in the amphipods (P < 0.0001) and copepods (P = 0.0016), although these patterns were not significant when we corrected for phylogeny and multiple comparisons (amphipod PIC, P = 0.428; copepod PIC, P = 0.019; Fig. 4). There were no significant correlations between biomass and latitude in the decapods (P = 0.191; PIC, P = 0.392; Fig. 4C), nor within the brachyuran (P = 0.527) or anomuran (P = 0.816) infraorders, but there was a positive latitude–biomass correlation in the infraorder Caridea (P = 0.001; Table 2, data not shown). In the decapods, latitude was also negatively correlated with number of larval stages (with fewer larval stages in higher latitudes) using species means (P < 0.0001), but not PIC (P = 0.065, data not shown; Table 2). Table 2. Pairwise correlations between latitudinal midpoint and (A) log10 biomass or (B) number of larval stages (A) Latitude vs. biomass    Raw species means  Phylogenetic independent contrasts    N  Slope  R2  P-value  Number of contrasts  R2  Two-tailed P-value  Amphipoda  75  0.03  0.19  < 0.0001  36  0.00  0.428  Copepoda  43  0.01  0.22  0.0016  26  0.160  0.019  Decapoda  127  0.00  0.01  0.1909  59  0.00  0.392  Anomura  29  0.00  0.00  0.816        Brachyura  44  0.00  0.01  0.527        Caridea  35  0.01  0.28  0.001        (B) Latitude vs. number of larval stages              N  Slope  R2  P-value  Number of contrasts  R2  Two-tailed P-value  Decapoda  103  −0.07  0.16  < 0.0001  44  0.062  0.065  (A) Latitude vs. biomass    Raw species means  Phylogenetic independent contrasts    N  Slope  R2  P-value  Number of contrasts  R2  Two-tailed P-value  Amphipoda  75  0.03  0.19  < 0.0001  36  0.00  0.428  Copepoda  43  0.01  0.22  0.0016  26  0.160  0.019  Decapoda  127  0.00  0.01  0.1909  59  0.00  0.392  Anomura  29  0.00  0.00  0.816        Brachyura  44  0.00  0.01  0.527        Caridea  35  0.01  0.28  0.001        (B) Latitude vs. number of larval stages              N  Slope  R2  P-value  Number of contrasts  R2  Two-tailed P-value  Decapoda  103  −0.07  0.16  < 0.0001  44  0.062  0.065  Significant P-values (using Bonferroni–Holm corrections for multiple comparisons) are given in bold. View Large Figure 4. View largeDownload slide Correlations between biomass (log10-transformed) and latitudinal midpoint, using phylogenetic independent contrasts, for the three major groups of crustaceans in our study. Figure 4. View largeDownload slide Correlations between biomass (log10-transformed) and latitudinal midpoint, using phylogenetic independent contrasts, for the three major groups of crustaceans in our study. Finally, using a PGLS approach to control for phylogeny, we evaluated how multiple variables (biomass, latitude, habitat; number of larval stages in decapods) affected genome size in combined models and using AICc (Table 3). For the amphipods, a combined PGLS model showed significant effects of latitude alone (P < 0.0001), but no significant effects of biomass (P = 0.1454) or habitat (P = 0.8336). Using the AIC, a model with latitude alone was the most well-supported model; the second best model included latitude and habitat, but was not substantially supported (ΔAICc = 3.71; Table 3). For the copepods, a combined PGLS model showed significant effects of only biomass (P = 0.0014), and no significant effects of latitude (P = 0.1637) or habitat (P = 0.3275). Using the AIC, a model with biomass alone (ΔAICc = 0) and a model including biomass and habitat (ΔAICc = 0.58) were both substantially supported (Table 3). In the decapods, there were no significant effects of latitude (P = 0.3983), habitat (P = 0.4313), biomass (P = 0.1177) or number of larval stages (P = 0.5325) in a combined PGLS model, and a null model (intercept only) was the only model with substantial support using the AIC (Table 3). Table 3. Assessment of different models explaining variation in genome size, using the Akaike information criterion   d.f.  Model  AICc  ΔAICc  Weight  Amphipoda  4  Latitude  69.9  0  0.791    5  Latitude + Habitat  73.6  3.71  0.124  Copepoda  4  log(Biomass)  73.7  0  0.545    5  log(Biomass) + Habitat  74.3  0.58  0.408  Decapoda  3  Null (intercept only)  29.6  0  0.674    4  Habitat  32.2  2.63  0.18    4  Biomass  32.7  3.06  0.146    d.f.  Model  AICc  ΔAICc  Weight  Amphipoda  4  Latitude  69.9  0  0.791    5  Latitude + Habitat  73.6  3.71  0.124  Copepoda  4  log(Biomass)  73.7  0  0.545    5  log(Biomass) + Habitat  74.3  0.58  0.408  Decapoda  3  Null (intercept only)  29.6  0  0.674    4  Habitat  32.2  2.63  0.18    4  Biomass  32.7  3.06  0.146  All models with change in corrected Akaike information criterion (ΔAICc) < 4 are included; models with ΔAICc < 2 have substantial support and are indicated in bold. View Large DISCUSSION In this study, we demonstrate significant relationships between genome size and different variables (latitude, biomass and larval development) within three major crustacean groups. In amphipods and copepods, there were strong positive correlations between latitude and genome size, with the largest genomes being found in polar species. Importantly, this strong positive correlation was still present when we corrected for the close evolutionary relationships between taxa using phylogenetic independent contrasts. However, our analyses also showed that latitude–genome size correlations cannot be considered independently of biomass; biomass was also correlated with latitude using species means in all groups. When we considered latitude, biomass, habitat and development in a phylogenetically controlled, model selection context, different variables emerged as important in different crustacean groups. Genome size and phenotypic traits Amphipods and copepods were the two groups with the clearest results. For the amphipods, phylogenetically controlled comparisons indicated that in combined models, there were significant effects of latitude (but not biomass or habitat), and AIC indicated that only the model with latitude alone had substantial support, suggesting that latitude has important correlations with genome size independent of biomass (Table 3). This pattern does not appear to be driven by inclusion of ‘outlier’ species, as the Arctic amphipod, A. macrocephala, with a genome size of 64.62 pg (Rees et al., 2007) was not included in our phylogenetically controlled analyses. In copepods, the model indicated that there were significant effects of biomass (but not latitude or habitat), and AIC indicated that a biomass model and a biomass plus habitat model had substantial support. This suggests that latitude–genome size correlations might be driven, in part, by larger body sizes in high-latitude copepods, in support of the temperature–size pattern seen in other groups. Previous studies on copepods have shown significant relationships between biomass and latitude for some groups (Poulin, 1995), and our study documented increases in biomass with latitude using PIC, although the correlation was not significant after controlling for multiple comparisons. Although previous studies have documented larger genomes in arctic vs. temperate copepods and amphipods and in more deep-water crustaceans (Hessen & Persson, 2009; Alfsnes et al., 2017; Jeffery et al., 2017; Ritchie et al., 2017), our study uses a greatly expanded dataset and is the first to consider latitude, habitat and biomass together in a model-selection framework using phylogenetically controlled analyses of different crustacean groups. In contrast, within the decapods there were only weak correlations between latitude and genome size, consistent with patterns from previous studies (Hessen & Persson, 2009; Jeffery, 2015), and no single variable emerged with the highest explanatory power. Some of this variability is clearly attributable to differences among the decapod infraorders; for example, there were positive latitude–genome size correlations in the caridean shrimps and anomuran crabs, but not in the brachyuran crabs (Table 1, Fig. 2). Although it is unclear why brachyuran crabs have no correlation between genome size and latitude, this might be related to the comparatively low variability in genome size in this group (Jeffery, 2015). Our study is the first to demonstrate correlations between larval development and genome size in the decapods, specifically larger genome sizes in species with direct development or few larval stages. In copepods, which all share the same defined larval developmental stages, genome size is correlated with developmental rate; specifically, species with faster development have smaller genomes (McLaren et al., 1988; White & McLaren, 2000; Wyngaard et al., 2005). This suggests that genome size may be correlated with various aspects of embryonic and/or larval development in different crustacean groups. Although few studies have examined developmental rate in relationship to the number of larval stages in decapods, Knowlton (1973) noted that alpheid shrimp species with direct or abbreviated development had very slow embryonic development relative to species with extended larval development, suggesting that our results may be consistent with developmental patterns seen in copepods. However, in phylogenetically controlled analyses of multiple variables in decapod analyses, no single trait emerged with the most explanatory value, and clearly more data need to be collected on decapod genome size to test rigorously how larval development relates to genome size. Hypotheses to explain the observed patterns Unfortunately, little is known about the specific genomic content or architecture of large-genomed crustaceans. In addition to the effects of large-scale duplications, increases in genome size are thought to be associated with the accumulation of non-coding DNA, especially transposable elements (Kidwell & Lisch, 2000; Lynch & Conery, 2003; Dufresne & Jeffery, 2011; Ritchie et al., 2017). Additional data on the genomic architecture of species with varying genome sizes would be very useful in elucidating the genome-level mechanisms responsible for the wide range in crustacean DNA contents. In terms of phenotypic and ecological constraints that may shape patterns of genome size variability in crustaceans, the large genome sizes in high-latitude crustaceans documented here and in previous studies (Rees et al., 2007, 2008) could be related to slower metabolism at the lower temperatures in these marine habitats (Hessen & Persson, 2009), as some studies have revealed that larger genomes are correlated with lower body-mass-corrected metabolic rates in mammals (Vinogradov, 1995) and birds (Gregory, 2002). The pattern of larger genome sizes at higher latitudes and in deeper habitats is also consistent with r–K–a selection (Greenslade, 1983), where high-latitude species are under a- or ‘adverse’ selection, associated with production of fewer, larger eggs (or direct development), late maturity and greater longevity (see also Rees et al., 2007). A similar pattern can be drawn with deep-water species; large genomes in crustaceans have also been recorded from the deep sea (Ritchie et al., 2017) and the depths of Lake Baikal (Jeffery et al., 2017). Martens (1997) drew parallels between the polar oceans and deep-water-dwelling species in ancient lakes and oceans, showing convergent evolution in a number of traits, such as body armature and gigantism. Martens (1997) states that these environments are highly stable and isolated, and the phenotypes exhibited in the deep sea and ancient lakes are hypothesized to be indicative of a stable, cold environment. It may be that similar evolutionary processes, which led to this convergent evolution, also led to the shared large genomes between these different habitats. However, the causal directionality of any such relationships remains unknown and may be somewhat complex. It is possible that genomes may become larger through the proliferation of non-coding elements in polar environments, because of either selection for larger body sizes through an increased genome size or a lack of selective pressures for a rapid developmental and/or metabolic rate, which would act to constrain expansion of the genome. It is also plausible that large genomes impose constraints in terms of development or other factors that make species with large genomes more suited to high latitudes. Habitat differences represent an additional confounding factor in latitude-based comparisons, such as the one presented here. This is especially true of marine vs. freshwater environments in polar regions, the latter of which typically freeze seasonally and may therefore represent ephemeral habitats. These habitats differ in other ways as well, including nutrient levels, salinity and community composition, all of which may be additional confounding variables. In plants, whereas temperate species tend to have larger genomes and/or larger chromosomes than tropical species (Bennett, 1976; Levin & Funderburg, 1979), many very high-latitude plants have small genomes, possibly because of constraints related to developmental rate during short growing seasons (Grime & Mowforth, 1982). If species living in polar freshwater habitats experience similar developmental constraints, we may expect different patterns between genome size and latitude in freshwater and marine habitats. Although we found few significant effects of habitat (marine vs. freshwater) in our study, we had very few freshwater species in some groups (e.g. decapods), and more data are necessary to test explicitly the genome size–latitude relationship between marine and freshwater species. In summary, increases in genome size with latitude appear to be associated with a suite of traits typical of crustaceans living at high latitudes, including large body size and shifts in developmental mode. Given the variable patterns in genome size seen within some of our crustacean groups (e.g. decapod infraorders), these additional variables may be particularly valuable to examine in directed studies of certain crustacean families or genera in a phylogenetically controlled, model-testing approach. Given their extraordinary diversity in genome sizes, physical traits and ecological lifestyles, crustaceans should feature prominently in future research in genome evolution from perspectives ranging from subgenomic to global-scale studies. SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article at the publisher's web-site: Table S1. Data on habitat, genome size, latitudinal midpoint, and body size used in this study. Habitat includes freshwater (FW) or marine. Genbank accession numbers are given for all species used in phylogenies. Under C-value method, FD = Feulgen densitometry and FCM = flow cytometry; n=1 unless noted otherwise. Body size is given in mm, as entire length (Amphipoda and Copepoda), carapace length (Caridea, Astacidea, Dendrobranchiata), shield or carapace length (Anomura), or carapace width (Brachyura). Fig. S2.2. Best-fit curves of biomass as a func3on of body length for differentdecapod infraorders. Figure S3: Bayesian consensus trees used in phylogene3c analyses for decapods (Fig. S3.1), amphipods (S3.2), and copepods (S3.3). For all trees, numbers by each node indicate Bayesian posterior probability values; scale bar indicates subs3tu3ons/site. For the decapoda (a), infraorders are indicated. Fig. S3.1. Decapod tree Fig. S3.2. Amphipod tree Fig. S3.3. Copepod tree ACKNOWLEDGEMENTS This work was funded by a Natural Sciences grant to K.M.H. from the Murdock Charitable Trust. 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