1Dominance determines fish in a temperate seagrass

2ecosystem

3

4Aaron M. Eger*1,3, Rebecca J. Best2, Julia K. Baum1

51 University of Victoria, Department of Biology, V8P 5C2, Victoria, BC, Canada.

62 Northern Arizona University, School of Earth and Sustainability, 86011, Flagstaff, AZ, 7USA

83 Present Address: University of New South Wales, School of Biological, Earth, and 9Environmental Sciences, 2033, Sydney, NSW, Australia

10*Corresponding author: Aaron Eger, [email protected]. +610487523824

11ORCID IDs:

12Aaron M. Eger: 0000-0003-0687-7340

13Rebecca J. Best: 0000-0003-2103-064X

14Julia K. Baum: 0000-0002-9827-1612

15

16Keywords: , function, eelgrass, functional diversity, community 17ecology

18Running head: Ecosystem function in eelgrass fish communities

1 19Abstract

20Biodiversity and ecosystem function are often correlated, but there are multiple hypotheses 21about the mechanisms underlying this relationship. Ecosystem functions such as primary or 22secondary production may be maximized by richness, evenness in species 23abundances, or the presence or dominance of species with certain traits. Here, we combined 24surveys of natural fish communities (conducted in July and August, 2016) with 25morphological trait data to examine relationships between diversity and ecosystem function 26(quantified as fish community biomass) across 14 subtidal eelgrass meadows in the Northeast 27Pacific (54° N 130° W). We employed both taxonomic and functional trait measures of 28diversity to investigate if ecosystem function is driven by (complementarity 29hypothesis) or by the presence or dominance of species with particular trait values (selection 30or dominance hypotheses). After controlling for environmental variation, we found that fish 31community biomass is maximized when taxonomic richness and functional evenness is low, 32and in communities dominated by species with particular trait values – those associated with 33benthic and prey capture. While previous work on fish communities has found that 34species richness is positively correlated with ecosystem function, our results instead highlight 35the capacity for regionally prevalent and locally dominant species to drive ecosystem 36function in moderately diverse communities. We discuss these alternate links between 37community composition and ecosystem function and consider their divergent implications for 38ecosystem valuation and conservation prioritization.

2 39Introduction

40 Understanding the mechanisms underlying biodiversity – ecosystem function (BEF) 41relationships is both critical for developing predictive ecological theory and effective 42ecosystem management. Each hypothesis has a distinct implication about the “value” of a 43species to an ecosystem. For instance, high ecosystem functioning can result from the distinct 44actions of all species present in a diverse community. Under this “complementarity 45hypothesis” (Tilman et al. 2001), a greater number of species with distinct traits will be able 46to utilize a greater range of resources and perform a greater range of functions (Davies et al. 472011, Jenkins 2015). Thus, the loss of any single species would negatively impact ecosystem 48function. In contrast, other hypotheses suggest that a few species contribute 49disproportionately to ecosystem function due to their large or ecological 50impacts. Under the “selection hypothesis”, for example, higher increases 51ecosystem function only because it increases the chance that a community will include one of 52these high-impact species, with additional species contributing only marginal gains to 53ecosystem function (Loreau & Hector 2001). Extending this mechanism from presence- 54absence to relative , the “biomass ratio” or “dominance” hypothesis (Grime 1998), 55predicts that ecosystem function will increase with the relative abundance or dominance of 56those particularly important species (Finegan et al. 2015, Zhu et al. 2016). If ecosystem 57management seeks to maximize function, the implication of either of these latter hypotheses 58is that the identification and protection of specific species is more important than maintaining 59the greatest number of species. Finally, the effects of species diversity may be outweighed by 60direct effects of abiotic factors on ecosystem functioning (Huston & McBride 2002). In this 61case, management actions would need to focus on mitigating stressors like temperature or 62pollution (Srivastava & Vellend 2005) or selecting areas with preferable abiotic conditions 63(Keppel et al. 2012). Because of their distinct implication for management, quantifying the 64relative importance of these proposed mechanisms is important for accurately assessing 65losses of ecosystem function, prioritizing future conservation efforts (Steffen et al. 2007, 66Cardinale et al. 2012) and planning restoration efforts that achieve ecosystem function goals 67(Laughlin 2014).

68

69 One way to improve our ability to identify mechanisms linking species diversity and 70ecosystem function is to incorporate organismal traits to BEF analyses, in addition to

3 71taxonomic metrics. Although taxonomic metrics can be proxies for the niche space occupied 72by community members, they do not directly quantify niche diversity or overlap, a central 73element of BEF hypotheses (Hooper et al. 2005, Fargione et al. 2007). In contrast, functional 74(i.e. trait-based) metrics such as functional richness (the size of trait space occupied by a 75community), functional evenness (how abundance is distributed amongst trait values), and 76functional dispersion (the dissimilarity of trait values) directly incorporate niche overlap 77among species (Villéger et al. 2008, Laliberté & Legendre 2010, McPherson et al. 2018). In 78addition, examining the community weighted mean (CWM) values of individual traits 79(Swenson 2014) allows us to infer how EF is related to particular trait values.

80

81 The use of both functional and taxonomic metrics to differentiate between BEF 82mechanisms has been widely applied in terrestrial systems (particularly plant communities), 83but much less is known about these relationships in marine . Past BEF work in 84terrestrial systems has found differing results, with some studies’ findings supporting the 85selection hypothesis (Wasof et al. 2018), others supporting complementarity (Duffy et al. 862017), and still others supporting abiotic factors as the main drivers of ecosystem function 87(Van Eekeren et al. 2010). BEF work in coastal marine systems has been particularly limited 88(only 13 publications in van der Plas' 2019 review) and the evidence is dominated by one 89study with thousands of data points (Duffy et al. 2016). These results and others (Danovaro et 90al. 2008, Mora et al. 2011) currently suggest that complementarity is the main driver of 91ecosystem function in marine ecosystems. Further, in both terrestrial and marine systems, 92there is also recognition that more work is needed to assess BEF relationships in natural 93ecological communities, which have real species assemblages and environmental variability, 94unlike controlled experiments, which randomize species compositions and remove abiotic 95drivers (Bannar‐ Martin et al. 2018).

96

97 Globally, coastal seagrass systems provide many important ecosystem functions and 98services that often depend on biodiversity at multiple trophic levels. In addition to water 99filtration, carbon capture, and sediment stabilisation (Larkum et al. 2006, Cullen-Unsworth & 100Unsworth 2013), these habitats provide important fish and invertebrate nursery habitats 101(McDevitt-Irwin et al. 2016) and directly or indirectly support additional levels of algal, 102invertebrate, fish, and bird biodiversity (Phillips 1984, Gilby et al. 2018). Fishes are key

4 103components of seagrass ecosystems, which can directly promote healthy habitats by deterring 104herbivores (Eger & Baum 2020) or indirectly promote grazers of epiphytic algae colonizing 105seagrass blades (Moksnes et al. 2008); their secondary production also feeds higher trophic 106levels that support commercial fisheries (Unsworth et al. 2019). Despite their global 107distribution and contribution to fisheries, there are few studies that test BEF mechanisms in 108seagrass ecosystems (Allgeier et al. 2015, Duffy et al. 2015), particularly for fish 109communities. Thus, while there is evidence that genetic diversity within some seagrasses can 110promote and stability (Hughes & Stachowicz 2004, Abbott et al. 2017), as can 111species diversity in invertebrates such as snails and crustaceans (Duffy et al. 2015), we know 112much less about the mechanisms driving secondary production in the diverse fish 113communities found in seagrasses.

114

115 In this study, we address these gaps in understanding for a critical 116by testing the importance of contrasting BEF mechanisms of fish communities in temperate 117eelgrass (: Zostera) ecosystems. We achieve this goal by considering both functional 118and taxonomic diversity metrics and using the shape and direction of BEF relationships 119(Table 1) to identify the underlying mechanisms in communities that reflect natural variation 120in fish species composition and environmental conditions. If ecosystem function (EF) is 121driven by the complementarity effect, we expect to see positive, non-saturating relationships 122between EF and biodiversity, whether it is measured as richness (Mora et al. 2014), 123taxonomic diversity (Cavanaugh et al. 2014), or through measures of morphological traits 124such as functional dispersion (Gagic et al. 2015) or functional evenness (Mason et al. 2005) 125(Table 1). If EF is instead driven only by the presence of particular species, we expect 126positive but saturating relationships between EF and functional and taxonomic richness, 127taxonomic diversity, and functional dispersion (Mora et al. 2014), and no relationship 128between EF and evenness (Table 1). If it is not just presence but dominance (i.e. high relative 129abundance of certain species) that drives EF, then high diversity and evenness should instead 130be associated with decreased function due to lower contributions from those particularly 131important species (Table 1). In this case we expect negative relationships between EF and 132functional evenness (Grime 1998), taxonomic metrics, and functional dispersion, and strong 133relationships with CWM trait values (Finegan et al. 2015, Zhu et al. 2016). Lastly, if EF is 134driven by abiotic or factors, we expect to find null relationships between EF and the 135diversity metrics and strong relationships between EF and the environmental metrics.

5 136Table 1. Predicted relationships between biodiversity (B) metrics and ecosystem function

137(EF) under each BEF hypothesis.

Selection Dominance (Mass Predictor variable Complementarity Presence Ratio) Taxonomic richness

Taxonomic diversity

Functional richness

Functional dispersion Functional evenness (multi- or one-dimensional) or CWM traits 138

139Materials and Methods

140Fish community surveys

141 In the summer of 2016, we surveyed fish communities in fourteen subtidal eelgrass 142meadows on Canada’s west coast in northern British Columbia (Fig. 1). To minimize 143seasonal variability, we completed all surveys within a five-week period (July 1 to August 5). 144To access the subtidal portion of each meadow, we surveyed at the day’s low-low and 145only on days with a tidal height of less than 1-meter depth as measured by chart datum at 146mean low-low water. Surveying consisted of duplicate beach seines (Guest et al. 2003) using 147a seine that measured 10 m in length, 3 m depth at the centre, tapered to 1 meter depth at each 148end, and with a mesh size of 6 mm as measured along the diagonal. The area sampled was 149approximately 60 m2 per site. After each seine haul, we collected the fish in tubs on shore. To 150avoid re-counting released individuals we conducted both hauls before identification. Once 151both sets were completed, we measured the fork length (to the nearest millimetre) of the first 15220 individuals of each species at each site. We quantified site-level eelgrass density at 11 153sites by collecting all shoots within 9 haphazardly thrown 25x25 cm quadrats at a site and 154counting them in the lab. We also quantified temperature and pH at each site using a Hanna 155HI® meter, and extracted site-level salinity from a GIS layer at a 500 x 500 m spatial 156resolution (Foreman et al. 2008).

6 157

158Fig 1 Study area with surveyed sites on the northern coast of British Columbia, Canada. 159Source: Canadian Hydrographic Service

160

161Functional trait measures

162 We calculated seven species-level functional traits: mouth length, oral gape position, 163eye size, eye position, pectoral fin position, caudal peduncle throttling, and body length (Fig. 1642). We chose these specific traits because of their connection to acquisition, either 165through size, visual acuity, mobility, or position in the water column, which should combine 166to determine each species’ niche within the community (Villéger et al. 2010). To obtain these 167morphometric trait measures, we photographed representative individuals of each species 168(although not every species was photographed at every site) and used the image processing 169software ImageJ (Rasband 1997) to make the measurements (Villéger et al. 2010). Each trait 170(except for body length) is size-standardized and thus constrained between zero and one. 171Species-level functional trait values were obtained by taking the mean value of each trait 172across all individuals measured for each species (372 total measures, mean per species =

7 1739.57, standard deviation = 11.91). One species, Cymatogaster aggregata, was an exception 174because it was found in both juvenile and adult stages. As a result, a single mean would mask 175bimodal variation in traits and we therefore considered these two stages as functionally 176distinct and calculated separate morphological metrics for them. Photos and trait measures 177were unobtainable for five of the rarest species (Myoxocephalus polyacanthocephalus (N = 1783), Rimicola muscarum (N =1), Ascelichthys rhodorus (N = 1), Sebastes miniatus (N = 1), 179and Rhacochilus vacca (N = 1), so they were dropped from the taxonomic and functional 180analyses.

181

182Fig 2 Morphometric traits measured from photographs of individuals of each of the 34 183species. All traits (except for body length) are expressed relative to other body parts (total 184head, body size, or caudal peduncle depth) and are therefore independent of variation in total 185size. Trait abbreviations are as follows: Ed: Eye depth Mo: Mouth opening; Ml: Mouth 186length; Hd: Head depth; Eh: Eye height; PFb: Body depth at pectoral fin; PFi: Height of 187pectoral fin; Cpd: Caudal peduncle depth; CFd: Caudal fin depth; Bl: Body length

188

189Functional diversity

190 We quantified three metrics of multivariate functional diversity (functional richness, 191functional evenness, and functional dispersion) using the “FD” package (Laliberté & 192Legendre 2010) in R. Calculation of each of these multivariate metrics first involved 193performing a Principal Coordinates Analysis (PCoA). Functional richness is the volume of 194the polygon that connects the exterior points of this transformation (Villéger et al. 2008). 195Functional evenness, which is independent of functional richness, is a measure of how

8 196regularly traits are distributed throughout the trait space and how abundance is distributed 197across those traits (Mason et al. 2005). Functional dispersion is calculated as the average 198distance to the abundance weighted centroid with the traits represented in n-dimensional 199space (Laliberté & Legendre 2010). The calculations for evenness and dispersion were done 200using the abundance weighted Gower’s dissimilarity measure and included all the traits. The 201calculation for functional richness was not weighted by abundance and included six of the 202seven traits: caudal peduncle throttling was dropped from this calculation because it was 203found to be redundant in the PCoA. The resulting quality of the reduced space representation, 204which is analogous to R2, was 0.93. Prior to conducting the PCoA, we standardized the 205morphometric traits to a mean of zero and standard deviation of one and gave all traits equal 206weighting.

207

208 To better understand which individual traits underlay the relationships between 209biomass and the multivariate functional diversity metrics, we also calculated two univariate 210metrics. We focused on understanding the evenness (and conversely the dominance) of 211individual traits because functional evenness was the only functional diversity metric to show 212a significant relationship with ecosystem function. First, we calculated univariate indices of 213functional evenness (i.e. functional regularity (Mouillot et al. 2005). Second, because we also 214wanted to understand the directional influence of specific trait values on community biomass, 215we calculated the community weighted mean (CWM, Swenson 2014) of each trait. The

216CWM is calculated by taking the average trait value of the individuals at each site (e.g. S1 =

21710cm, S2 = 15 cm, S3 = 20 cm, CWM = 15 cm).

218

219Taxonomic metrics

220 We used two taxonomic metrics, Chao’s species richness and Chao’s Shannon’s 221diversity (hereafter ‘taxonomic diversity’). Both metrics are rarefied measures of taxonomic 222diversity, which use sample-based rarefaction that incorporate abundance data. We rarefied 223our diversity metrics to account for the non-random distribution of fishes in the water column 224and for differences in species’ abundance at each site (Gotelli & Colwell 2011, Colwell et al. 2252012). The final values reported are the asymptotic estimates of diversity at each site. The 226calculations were done using the “iNEXT” function in the “iNEXT” (Hsieh et al. 2016) 227package in R.

9 228Ecosystem function

229 Following Mora et al. (2011) and Duffy et al. (2016) we used community standing 230stock biomass (hereafter ‘community biomass’) as our measure of ecosystem function. 231Though this is not a rate, fisheries landings are often viewed as a type of ecosystem function 232or service (Holmlund & Hammer 1999) and the biomass of an individual frequently predicts 233its contribution to a range of other ecosystem functions such as suspension feeding, nutrient 234uptake, and gross productivity (Davies et al. 2011). We estimated each individual’s mass 235using its recorded length and published length-weight relationships, most of which were 236specific to fishes in eelgrass meadows in British Columbia (Siegle et al. 2014); data for five 237species were only available from Fishbase.org (Froese & Pauly 2010). Lengths were 238measured for the first 20 individuals of each species at each site. As such, lengths were not 239available for all individuals, so truncated density distributions based on maximum and 240minimum observed fish lengths were created using the “fitdistrplus” package in R 241(Delignette-Muller & Dutang 2015). The unmeasured individual lengths were then randomly 242sampled from these distributions. Lastly, biomass was summed at each site and logged to 243normalize its variance.

244

245Statistical analyses

246 To examine the relationships between fish community biomass and each taxonomic 247and functional metric we fitted generalized linear models (GLM) with a gamma distribution. 248We chose a gamma distribution because the data were non-normal and contained positive 249non-zero values. We examined the univariate relationships between each of four 250environmental variables (temperature, salinity, pH, eelgrass density) and fish community 251biomass, but did not include these in our GLMs to avoid over-parameterizing our model (i.e. 252by including multiple correlated fixed effects with a low sample size; Appendix 1). Instead, 253we included the geographic location (longitude) of the survey sites as a second fixed effect in 254each GLM. Because longitude was strongly correlated with the abiotic variables (Appendix 2551), with sites farther from the coast tending to have lower temperature, higher salinity, higher 256eelgrass density, and less human , we considered it a proxy for the environmental 257relationships with fish biomass. We also tested for spatial autocorrelation in the biotic and 258abiotic variables amongst the surveyed sites using the Durbin-Watson test, but found no 259significant effects of spatial autocorrelation. The models were then evaluated for relative fit

10 260using the proportion of the deviance explained, which is measured in comparison to a null 261model that includes only the intercept (D2). This measure of fit was calculated using the R 262package “modEvA” (Barbosa et al. 2016) and variables were assessed for statistical 263significance based on their P value (P < 0.05). All other statistical operations were also 264performed using the R programming language (R Core Development Team V 4.0, 2020). All 265data and associated code for graphing and analysis are archived in a GitHub repository and 266on Open Science Framework (https://github.com/baumlab, DOI 10.17605/OSF.IO/6SQRF).

267

268Results

269 Species richness and community biomass varied substantially across study sites. 270Unrarefied richness ranged from 7 to 23 species per site, with a regional pool of 34 species. 271Of these 34 species, over three-quarters (N = 26) were found at more than one site within the

272region. Community biomass ranged ten-fold across sites, from 1,216 to 12,774 grams (log10, 2733.08 to 4.10) per site (approximately 60 m2). All sites were dominated (>50% of abundance) 274by three or fewer species (Appendix 2), and only 16 of the 34 species had median site 275biomass proportion values above 0.01 (Appendix 3). Of these 16 species, the two with the 276highest median proportion of site biomass were the surfperch, Cymatogaster aggregata 277(0.34) and the , Leptocottus armatus (0.21, Appendix 3).

278

279Biodiversity – ecosystem function

280 Fish community biomass was negatively related to functional evenness (P = 0.01, Fig. 2813), and this relationship explained the most deviance of any biodiversity metric considered 282(D2 = 0.51, Fig. 3). The only other statistically significant relationship was between species 283richness and community biomass, with sites with the lowest species richness having the 284highest biomass (P = 0.03, D2 = 0.43, Fig. 3). Taxonomic diversity, functional richness, and 285functional dispersion were not significant predictors of fish community biomass (P = 0.26, P 286= 0.30, and P = 0.74 respectively, Fig. 3) and explained much less deviance (D2 < 0.22, Fig. 2873). Additionally, none of the four environmental variables were significant predictors of fish 288community biomass in the univariate regressions (and all of them explained very little 289variance, D2 < 0.2, Fig. 4); longitude (our environmental proxy) was only significant in the

11 290species richness model (P = 0.02), where sites with lower longitude (i.e. those further from 291the coast) had greater community biomass.

292

293Fig 3 Relationships between the log site-level fish community biomass and a) functional 294richness b) functional evenness c) functional dispersion d) taxonomic richness and e) 295taxonomic diversity. The solid line (shown only for the significant relationships) is the 296regression line and the dashed lines are twice the standard error.

297

298 The relationship between functional evenness and fish community biomass appeared 299to be driven by particular morphological trait values. Of the seven evenness trait values 300considered individually, only two, lower evenness of eye position and oral gape position, 301were significant predictors of higher community biomass (P = 0.04 and D2 = 0.40 for both, 302Fig. 5). These results indicate that community biomass was maximized in communities 303dominated by certain values of these traits. Similarly, the community weighted mean of these 304two traits were the only CWM values that were significant predictors of fish community 305biomass. The CWM for eye position and oral gape position were both negatively related to

12 306community biomass (P = 0.02, D2 = 0.46 and 0.47, Fig. 5). Lastly, we found no relationship 307between the average CWM length and community biomass at our sites (P > 0.05).

308

309 Fig 4 Relationships between the log site-level fish community biomass and site-level abiotic 310variables: a) temperature b) salinity c) pH and d) eelgrass density.

13 311

312Fig 5: Relationships between the log site-level community biomass and a) evenness of eye

313position (EP) b) evenness of oral gape position (OGP), c) Community Weighted Mean 314(CWM) of CWM of eye position d) CWM of oral gape position. Solid lines are regression 315lines; dashed lines are twice the standard error.

316

317Discussion

318 We found clear evidence that, in our system, high standing stock biomass of fish in 319eelgrass meadows was predicted by the dominance of species with particular trait values 320rather than complementary resource use of a diverse species assemblage. Sites with low 321species diversity and high dominance of species with certain traits had the highest ecosystem 322function (total fish community biomass) of eelgrass-associated fish communities. This 323conclusion is supported by five out of six of the relationships predicted in Table 1; 324specifically, community biomass decreased with increasing taxonomic richness and 325functional evenness (both the multivariate and the two univariate measures) and was strongly 326related to two CWM trait measures. Community biomass was also not well predicted by the

14 327environmental factors. These results are contrary to similar studies of fishes in coastal marine 328environments (Mora et al. 2011, Duffy et al. 2016), which found positive relationships 329between community biomass and taxonomic and functional diversity metrics, and a strong 330influence from environmental variables.

331

332Dominance vs. diversity as drivers of ecosystem function

333 Communities with fewer species had the highest levels of ecosystem function among 334our study sites (Fig. 3). In fact, the abundance and distribution of dominant species played a 335role at two spatial scales. First, at the level of an individual community (i.e. a site), 336communities with the highest function had the highest local dominance of high-contributing 337species. Second, the two most dominant species (Cymatogaster aggregata and Leptocottus 338armatus) had a high degree of regional occupancy (Appendix 3), such that greater species 339richness at the community level corresponded to substituting individuals of these dominant 340species for individuals of rare species with overall low biomass that contributed less to 341ecosystem function. This mechanism helps explain why we failed to find a positive 342relationship between function and taxonomic richness, as predicted under the complementary 343hypothesis. Our results suggest that many species contribute very little to certain components 344of ecosystem function and increased species richness correlates with fewer dominant 345individuals.

346

347 Our main result, namely that low levels of species richness and evenness best predict 348high biomass in eelgrass fish communities, is contrary to past research suggesting that more 349marine fish species equates to more biomass (Mora et al. 2011, Duffy et al. 2016). In an 350extensive analysis of 1,844 sites on shallow hard substrate, Duffy et al. (2016) found an 351overall positive effect of species richness on community biomass; more so they found this 352effect was strongest in temperate ecosystems such as ours. Though they studied rocky reefs 353and we studied a seagrass on soft substrate, we do not believe this distinction is enough to 354explain our contrasting results. Instead, we suggest two differences between the two studies 355that may explain the different conclusions. First, the range in species richness was quite 356different: Duffy et al.’s (2016) surveys in temperate regions ranged from ~ 1 to 10 species 357whereas our surveys ranged from 7 to 23 species. Therefore Duffy et al.’s (2016) might have 358arisen because their communities had very few species, in which the addition of even a single

15 359species resulted in a substantial fulfillment of niche space (Stuart-Smith et al. 2013). This 360could occur if species that are important contributors to community biomass are not 361regionally well distributed and are not reliably found in almost all communities at minimum 362species richness levels. Because our study contained moderately species rich ecosystems, the 363species count may have been past the saturation point on the richness-function curve that is 364often seen in BEF relationships (Morris et al. 2014). Second, we used beach seines to survey, 365which are more likely to detect rare, singleton species (Baker et al. 2016) than the visual 366surveys used in Duffy et al. (2016). These rare, singleton species would have contributed 367very little biomass, but still increased the species count, and thus possibly prevented a 368relationship between species richness and community biomass. While our results are 369representative of eelgrass ecosystems in the Pacific Northwest (Robinson & Yakimishyn 3702013), other temperate marine ecosystems with lower local or regional diversity or more 371fragmented habitat connectivity may therefore find alternative results, such as 372complementarity.

373 Functional traits structuring standing stock biomass

374 We found support for the dominance hypothesis, specifically that two traits related to 375fish species’ position in the water column predicted the biomass of a community. This result 376suggests certain community level trait values can enhance EF. Here, both eye position and 377oral gape position were negatively correlated with community biomass, suggesting that the 378most productive eelgrass fish communities were those dominated by species such as 379Leptocottus armatus, which are more adapted for a benthic environment. Fish with high 380values for the eye position metric have more dorsally located eyes and are adapted to 381capturing prey from the pelagic environment, whereas fish with low scores for eye position 382have more laterally located eyes and are more adapted for capturing prey from the benthic 383environment (Gatz 1979). Similarly, fish with high gape position scores have dorsally located 384mouths, whereas those with low scores, such as those in the sculpin family (Cottoidea), have 385ventrally located mouths and are best adapted to capturing prey from the benthic environment 386(Karpouzi & Stergiou 2003). Importantly, there was no relationship between body length and 387community biomass, indicating that it is not simply larger bodied communities that contain 388more biomass, but rather these specific ecologically relevant traits. While we focused on 389traits related to species’ feeding abilities, it is possible that traits related to other life history 390characteristics or environmental tolerances could also influence a species’ dominance within

16 391a community (e.g. metabolic rate, parental care habits, pollution tolerance), and this area 392provides grounds for further research.

393

394 The trait values that permit maximum growth and productivity will vary across 395ecosystems, depending on the distribution of available food and habitat resources within them 396(McGill et al. 2006, Keck et al. 2014). Previous work on fish species in coral, , and 397seagrass habitats from the Caribbean and Australian showed that habitat rather than 398geographic region was most related to functional traits (Hemingson & Bellwood 2018). The 399authors reasoned that differences in habitat structure, such as mangrove roots compared to 400seagrass leaves, their related risk, and food availability, outweighed the 401evolutionary distinctiveness found between ocean regions. As such, we would expect our 402findings to be transferable to other eelgrass habitats but not to other habitats within the same 403region. Specifically, prey in eelgrass systems are known to be benthic and epifanual (Phillips 4041984), which corresponds with our findings that communities with trait values associated 405with more benthic prey capture were found to contain the highest community biomass. 406Adaptations to feed in highly structured benthic habitats are common in seagrass-associated 407fish species (Hovel et al. 2016) and may include both morphological and behavioural 408components. An important future opportunity would be to test if habitats with more pelagic 409prey distributions have a positive relationship between community biomass and traits like eye 410and oral gape position. Alternatively, differences in other traits related to predator avoidance 411or tolerance could drive BEF relationships in other ecosystems. In either case, 412centring our understanding of diversity mechanisms on traits allows us to make informative 413comparisons across systems based on quantifiable features of organisms and their 414environment.

415

416Management Implications and Conclusions

417 The importance of a select few species within a community has significant 418implications for the management priorities of these systems. If we wish to prioritize 419conserving fish biomass as a function within this ecosystem, it appears we need to focus on a 420few species that contribute the greatest function. This optimization would simplify the 421conservation process but also raises important questions about applying a BEF approach to 422conservation. For instance, if dominant species are unaffected by human disturbance, there

17 423would be little concern for such disturbances which may negatively affect other, less 424productive species. Alternatively, if dominant species are impacted by disturbance, it could 425be argued that we need only focus on their conservation to protect ecosystem function. In 426both scenarios, many rare, low biomass species would be deemed expendable, as their 427contribution to ecosystem function is low. The ethical considerations of this approach are 428considerable, and conservationists should be aware of unexpected outcomes when promoting 429BEF in management and conservation frameworks. It is also salient to consider how we 430might manage for only one or a few species all sharing the same habitat. If it were more 431resource intensive to only protect these highly functional species, a more prudent approach 432might be to simply protect the habitat, protect all species, and potentially protect the greatest 433number of species and functions.

434

435 We must also acknowledge the limitations of only considering a single function: 436while one or a few species may drive a particular function, as was found in our study, those 437same species may not be responsible for other functions in the ecosystem (Isbell et al. 2011). 438Seagrass ecosystems have well-established links between fish predators, invertebrate grazers, 439epiphytic algae, and the eelgrass itself. For example, certain fish predators can prevent 440herbivorous invertebrates from grazing epiphytic algae off the eelgrass, and the resulting 441increased epiphyte loads reduce the fitness of the seagrass (Heck Jr & Valentine 2006, 442Moksnes et al. 2008). Thus, while fish community assemblages dominated by certain species 443might maximize community biomass, those same assemblages might result in grazer 444communities that do not maximize epiphyte control and seagrass growth.

445

446 While our results highlight the importance of dominance and relative abundance in 447driving ecosystem function in eelgrass fish communities, they also have implications for 448other ecosystems. First, past research has found support for the dominance hypothesis in 449producers in forests and pollination services in bees (Gilbert et al. 2009, Tardif & Shipley 4502012, Winfree et al. 2015), and our results suggest this relationships spans across taxa and 451could be replicated in other vertebrate communities. Second, many systems have steep rank- 452abundance curves, where one or a few species dominate community abundance and most 453species are rare (Ulrich et al. 2010). If these patterns in relative abundance are inherent to the 454distribution of resources in the system and not a recent feature of system disturbance or

18 455invasion, then the contributions of these few species may be central to ecosystem functioning 456and deserve greater attention (Eisenhauer et al. 2016). Third, communities are more likely to 457change in relative abundance than to lose richness, at least in the short term (Ceballos et al. 4582017). Our results suggest these declines can negatively impact ecosystem function even 459without complete species loss. As such, we suggest that ecologists place an increased value 460on studying and maintaining species abundances within a community as opposed to species 461richness alone.

462

463 In this study, we highlight the importance of dominant species, their traits, and their 464distribution within a community for driving ecosystem function in a real-world ecosystem. 465Our work stresses the importance of testing BEF theory in multiple natural ecosystems and 466under different contexts as different mechanisms may underlie BEF relationships in different 467systems. We also stress the importance of examining multiple facets of biodiversity, 468including abundance weighted measures of diversity, in BEF analyses. Specific to functional 469diversity, we have also shown the relevance of the currently underutilized one-dimensional 470functional evenness and CWM metrics, and how they can be used to understand which traits 471and which trait values best explain ecosystem function. Finally, given the importance of 472dominant species in determining ecosystem function in our system, we conclude that 473conservation that prioritizes single ecosystem functions may not always maximize diversity, 474and a greater understanding of how different dimensions of diversity contribute to different 475dimensions of ecosystem functioning and value is needed.

19 476Funding

477We acknowledge funding from the Natural Sciences and Engineering Research Council of 478Canada in the form of a graduate scholarship and Discovery Grant and Engage Grants. 479Finally, we thank Mike Ambach, James Casey, and Bettina Saier from the World Wildlife 480Fund of Canada for collaboration and financial support.

481Competing Interests

482The authors declare that they have no conflict of interest.

483Research Involving

484All applicable international, national, and/or institutional guidelines for the care and use of 485animals were followed. Fish were collected under: a Department of Fisheries of Canada 486Scientific Fishing Permit (XR 10 2016), a University of Victoria Health and Welfare 487Permit (2016-011), a Prince Rupert Port Authority Work Permit, and with consultations with 488local First Nations, Gitxaala, Git’agaat, Metlakatla, Kitselas, Kitsumkalum and Lax 489Kw’alaams, on whose traditional territory the work took place.

490Author contributions section

491All authors conceived the study design, AE led the field data collection and analysis, AE 492wrote the first version of the manuscript, all authors commented and edited subsequent 493versions of the manuscript.

494Data accessibility section

495All the data and the scripts for running the analysis are deposited on Open Science 496Framework at the link: DOI 10.17605/OSF.IO/6SQRF

497Acknowledgements

498We are grateful to the Gitxaala, Git’agaat, Metlakatla, Kitselas, Kitsumkalum and Lax 499Kw’alaams First Nations for granting access to their territory as well as insights into their 500local ecosystems. We additionally thank the Metlakatla and Gitxaala Nation for providing 501research vessels and field researchers, and especially Bruce Watkinson, Ross Wilson, Colin 502Nelson, Cordell Nelson, and Colton Nelson for their support. Next, we thank the Department 503of Fisheries and Oceans Canada and the Northwest Community College for providing a field 504boat. Gratitude is also owed to our field assistants Tella Osler, Quinn Lowen, and Sarah

20 505Friesen, as well as to Peter Freeman for his advice and support in keeping our field boat 506running.

507

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27 706Appendix

707Appendix 1: Correlation values (Pearson’s R) amongst the variables considered.

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28 715Appendix 2: Species rank abundance plots for the survey sites. Different coloured lines represent the

716different models used to fit the curves; the fits were done using the radfit function in the “vegan”

717package in R.

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29 726Appendix 3: All species caught and photographed along with the number of sites where species was

727observed (Regional occupancy, maximum of 14), the median proportion that species contributed to a

728site’s biomass, and the species’ morphological measurements used to calculate trait values

729(abbreviations as in. Fig 2). Species are ordered from highest to lowest median proportion of site

730biomass.

Median Regional Latin name Common name proportion of Bl CPd CFd PF_i PFb PFl Hd Ed Eh Mo Ml occupancy site biomass Cymatogaster aggregata Shiner surfperch-A 14 0.320 10.87 0.47 0.95 1.15 3.49 0.78 1.91 0.87 1.25 1.03 0.27 Leptocottus armatus Staghorn sculpin 13 0.208 13.75 1.31 0.81 0.36 0.80 3.31 0.65 0.31 0.48 0.28 0.32 Platichthys stellatus Starry flounder 10 0.071 15.07 0.33 0.84 3.20 7.16 2.50 2.28 0.71 1.83 1.65 0.56 Hypomesus pretiosus Surf smelt 1 0.069 5.50 0.17 0.50 0.67 0.73 0.15 0.46 0.25 0.19 0.19 0.38 Lepidopsetta bilineata Rock sole 2 0.044 12.10 0.17 1.43 1.84 4.42 1.59 1.44 0.56 0.94 1.05 0.39 Scorpaenichthys Cabezon 2 0.038 10.51 0.71 1.86 1.02 2.51 3.29 1.79 0.66 1.72 0.55 0.94 marmoratus Lumpenus sagitta Snake prickleback 7 0.034 16.20 0.85 3.20 0.52 1.22 1.85 0.94 0.46 0.63 0.26 0.38 Parophrys vetulus English sole 11 0.033 9.20 0.39 1.40 1.28 2.80 1.10 1.09 0.42 0.94 0.84 0.34 Hemilepidotus Red Irish lord 2 0.020 12.60 1.36 1.04 1.18 2.53 2.93 1.71 0.70 1.26 0.64 0.87 hemilepidotus Cymatogaster aggregata Shiner surfperch-J 14 0.020 4.17 0.12 1.48 0.40 1.29 0.33 0.86 0.45 0.41 0.37 0.14 Pholis ornata Crescent gunnel 13 0.019 13.62 0.75 0.39 0.42 1.27 0.60 0.74 0.28 0.55 0.38 0.28 Citharichthys stigmaeus Speckled sanddab 8 0.018 8.45 0.26 1.09 1.30 2.78 1.37 1.27 0.39 0.95 0.89 0.56 Microgadus tomcod Pacific tomcod 1 0.012 15.88 0.42 1.33 1.17 2.97 2.12 1.70 0.79 1.16 0.60 1.25 Citharichthys sordidus Pacific sanddab 6 0.012 9.12 0.84 0.97 1.37 2.66 1.25 1.15 0.41 0.86 0.91 0.46 Artedius fenestralis Padded sculpin 7 0.012 7.62 0.86 2.66 0.58 1.49 1.96 1.12 0.52 0.77 0.45 0.76 Pleuronichthys coenosus CO sole 1 0.011 12.10 0.47 1.39 2.14 3.91 1.34 1.57 0.64 0.99 0.96 0.48 Sebastes paucispinis Bocaccio 1 0.008 9.73 0.77 2.19 0.88 2.71 1.79 1.96 0.76 1.35 0.58 1.24 Sebastes caurinus Copper rockfish 1 0.008 8.42 0.88 0.95 0.91 2.71 2.27 1.65 0.89 1.25 0.98 0.84 Enophrys bison Buffalo sculpin 6 0.006 7.24 0.40 1.48 0.61 1.57 1.69 1.16 0.46 0.88 0.17 0.11 Syngnathus leptorhynchus Bay pipefish 13 0.005 17.14 0.12 0.58 0.28 0.42 0.33 0.35 0.22 0.26 0.28 0.14 Ophiodon elongatus Lingcod 4 0.005 13.39 0.83 1.29 0.72 1.98 2.06 1.29 0.63 0.96 0.48 0.88 Apodichthys flavidus Penpoint gunnel 8 0.005 11.74 0.71 0.76 0.33 1.01 0.32 0.50 0.17 0.35 0.29 0.13 Aulorhynchus flavidus Tubesnout 7 0.005 6.89 0.26 1.84 0.14 0.36 0.68 0.33 0.24 0.15 0.25 0.39 Artedius lateralis Smoothhead sculpin 5 0.005 9.27 0.52 0.84 0.73 1.65 2.16 1.28 0.56 1.05 0.42 0.47 Hemilepidotus spinosus Brown Irish lord 1 0.004 4.58 0.26 0.95 0.41 0.97 1.27 0.83 0.35 0.48 0.26 0.25 snyderi Fluffy sculpin 2 0.003 5.19 0.66 1.54 0.51 1.16 1.62 0.80 0.35 0.60 0.37 0.30 Oncorhynchus kisutch Coho salmon 1 0.002 10.79 0.90 1.43 0.29 2.07 1.36 1.39 0.72 0.79 0.72 0.85 Blepsias cirrhosus Silverspotted sculpin 4 0.002 6.01 0.55 1.39 0.52 1.37 2.07 0.91 0.41 0.63 0.40 0.41 Oncorhynchus keta Chum salmon 1 0.002 9.19 0.72 2.19 0.10 1.66 1.44 1.14 0.65 0.48 0.52 0.60 Gasterosteus wheatlandi Threespine stickleback 4 0.002 5.04 0.39 0.56 0.45 0.89 0.71 0.56 0.32 0.36 0.32 0.14 Tilesina gibbosa Tubenose poacher 1 0.001 6.34 0.09 0.50 0.23 0.42 1.02 0.42 0.26 0.83 0.36 0.15 Oligocottus maculosus 3 0.001 6.13 0.09 0.93 0.44 1.31 1.92 1.03 0.42 0.48 0.24 0.36 Artedius harringtoni Scalyhead sculpin 2 0.001 4.49 0.33 1.09 0.70 1.10 1.59 0.89 0.39 0.73 0.32 0.34 Ammodytes personatus Sandlance 2 0.001 6.22 0.35 1.52 0.26 0.52 0.49 0.36 0.22 0.30 0.28 0.22 Gadus macrocephalus Walleye pollock 1 0.001 5.47 1.17 2.19 0.55 0.97 2.53 0.81 0.41 0.58 0.38 0.51 Max trait value 17.14 1.36 3.20 3.20 7.16 3.31 2.28 0.89 1.83 1.65 1.25 731 Min trait value 4.17 0.09 0.39 0.10 0.36 0.15 0.33 0.17 0.15 0.17 0.11

732

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