environmental, evolutionary, and ecological drivers of slow growth … · fitness of a ‘slow’...

MARINE ECOLOGY PROGRESS SERIESMar Ecol Prog Ser

Vol. 658: 1–26, 2021https://doi.org/10.3354/meps13591

Published January 21

© The authors 2021. Open Access under Creative Commons byAttribution Licence. Use, distribution and reproduction are un -restricted. Authors and original publication must be credited.

Publisher: Inter-Research · www.int-res.com

*Corresponding author: [email protected]

FEATURE ARTICLE

Environmental, evolutionary, and ecological driversof slow growth in deep-sea demersal teleosts

Jesse A. Black1,*, Anna B. Neuheimer1,2, Peter L. Horn3, Di M. Tracey3, Jeffrey C. Drazen1

1Department of Oceanography, School of Ocean Earth Science and Technology, University of Hawaii, Honolulu 96822, USA2Aarhus Institute of Advanced Studies & Department of Biology, Aarhus University, Aarhus 8000, Denmark

3National Institute of Water & Atmospheric Research Ltd (NIWA), Wellington 6241, New Zealand

ABSTRACT: The deep sea (>500 m ocean depth) isthe largest global habitat, characterized by cool tem-peratures, low ambient light, and food-poor condi-tions relative to shallower waters. Deep-sea teleostsgenerally grow more slowly than those inhabitingshallow water. However, this is a generalization, andeven amongst deep-sea teleosts, there is a broadcontinuum of growth rates. The importance of poten-tial drivers of growth rate variability amongst deep-sea species, such as temperature, food availability,oxygen concentration, metabolic rate, and phylo -geny, have yet to be fully evaluated. We present ameta-ana lysis in which age and size data were col-lected for 53 species of teleosts whose collectivedepth ranges span from surface waters to 4000 m. Wecalculated growth metrics using both calendar andthermal age, and compared them with environmen-tal, ecological, and phylogenetic variables. Tempera-ture alone ex plained up to 30% of variation in thevon Bertalanffy growth coefficient (K, yr−1), and 21%of the variation in the average annual increase inmass (AIM, %), a metric of growth prior to maturity.After correcting for temperature effects, depth wasstill a significant driver of growth, explaining up to 20and 10% of the remaining variation in K and AIM,respectively. Oxygen concentration also explained~11% of re maining variation in AIM following tem-perature correction. Relatively minor amounts ofvariation may be ex plained by food availability,phylo geny, and the locomotory mode of the teleosts.We also found strong correlation between growthand metabolic rate, which may be an underlyingdriver also related to temperature, depth, and otherfactors, or the 2 para meters may simply covary as aresult of being linked by evolutionary pressures.Evaluating the influence of ecological and/or envi-

ronmental drivers of growth is a vital step in under-standing both the evolution of life history parametersacross the depth continuum as well as their implica-tions for species’ resilience to increasing anthropo -genic stressors.

KEY WORDS: Fish · Teleost · Growth · Pace of life ·Evolution · Life history · Metabolism · Temperature ·Deep sea · Demersal · Marine

OPENPEN ACCESSCCESS

Wide variation in growth, habitat, and ecological role can befound across deep-sea fishes like the abyssal (bottom: Cory -phaenoides armatus, Barathrites iris, Bassozetus nasus), theespecially long-lived (top left: Hoplostethus atlanticus), andthe shelf/slope (top right: Anoplopoma fimbria) species.

Photos: Bottom: Jeff Drazen and Astrid Leitner; Top left:Di Tracey; Top right: NOAA

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Mar Ecol Prog Ser 658: 1–26, 2021

1. INTRODUCTION

Deep-sea teleosts (referred to hereafter as fishes)generally have high ages-at-maturity, slower growthrates, lower metabolic rates, and greater longevity thanshallow-water fishes (Koslow 1996, Caillet et al. 2001,Drazen & Haedrich 2012). Despite these gen eraliza -tions, life history parameters vary considerably acrossthe depth continuum. For example, the Pata go niantoothfish Dissostichus eleginoides (median depth of~1100 m) matures between 5 and 7 yr of age (Everson& Murray 1999, Horn 2002), while the black cardinal-fish Epigonus telescopus (median depth ~800 m) ma-tures around 36 yr (Tracey et al. 2000, 2017, Dunn2009). Differences in longevity, maturity, and growthrate may reflect the cumulative influence of phylogeny,evolutionary pressures such as predation, and a vari-ety of environmental drivers such as pressure, temp -er ature, oxygen, and food availability, all of whichchange with depth in most ocean habitats.

A variety of environmental drivers, temperatureand food supply in particular, may affect growth ratesof shallow-water fishes. Temperature has been shownto be a primary driver. The growing degree-day (GDD°C × day), i.e. the product of habitat temperature andcalendar age of fish in days, has successfully ex -plained differences in growth rates across popula-tions of Atlantic cod Gadus morhua and other fishes(Braten & Guy 2002, Neuheimer & Taggert 2007,2010, Venturelli et al. 2010, Shackell et al. 2019). Inmuch of the deep sea, tem per ature and growth ratesde cline rapidly with depth, and so temperature hasbeen frequently hypo thesized to drive the observedpatterns (Drazen & Haedrich 2012, McClain et al.2012). Flux of particulate organic carbon (POC), theprimary basis of the food web in the deep ocean, alsodeclines exponentially with depth (e.g. Martin et al.1987, Lutz 2007). Thus, the declining availability offood with depth may also play a role in growth (e.g.Rigby & Simpfen dorfer 2015), as accumulating massrequires energy intake and, at least in aquaculturesettings, feeding level has a large influence ongrowth rates (reviewed in Persson & De Roos 2006).In fishes’ natural environment, however, ecosystem-level changes in food supply may primarily constrainpopulation size be fore affecting interspecific growthrate; as available food increases, the carrying capac-ity of a given population increases, while the amountof energy available to individuals for growth andother biological processes may remain relatively con-stant, depending on relationships between averagebody size and foraging efficiency (Sebens 1987,McClain et al. 2012).

Metabolic rate has been found to correlate stronglywith growth rates. Metabolic rate, which scales tobody mass and temperature, represents the rate ofbiochemical reactions in the organism (Brown et al.2004, McClain et al. 2012), including the distributionof biochemical resources into the processes of growth,reproduction, and survival. It is unclear whether meta bolic rate mechanistically constrains maximumgrowth rate (the ‘performance model’), or rather rep-resents the fraction of energy devoted to maintenancecosts, with the remaining energy diverted to growthand activity (the ‘allocation model’), as studies onteleosts have found support for both models (reviewedin Careau et al. 2008). If metabolic rate does positivelyand causally correlate to growth, then any environ-mental conditions affecting metabolism would there-fore indirectly affect growth rate. Temperature clearlydeclines with depth in most ocean basins which couldthen reduce metabolic rates with concurrent reduc-tions in growth rate. Oxygen is a key reactant in aero-bic metabolism, and if growth rate is indeed con-strained by or linked to metabolic rate, it would followthat aerobic metabolic reactions that facilitate gener-ation of new biomass can only operate as quickly asoxygen can be made available. However, metabolicrate has been shown to be more strongly associatedwith gill surface area than the ambient concentrationof oxygen itself, and thus rapid metabolism andgrowth in oxygen-poor waters may simply be a matterof possessing specific adaptations for efficient oxygenextraction and delivery (Friedman et al. 2012). Finally,the visual interactions hypothesis suggests that afteraccounting for temperature and body size effects, thedecline in metabolism with depth in fishes (and severalmidwater groups) to a depth of about 1000 m reflectsa decrease in selective pressure for rapid locomotorycapacity as visual predation becomes less commonwith decreasing light levels (Childress 1995). In short,temperature, oxygen, and light levels may all affectmetabolic rate, depending largely on the depth inquestion, and may therefore be indirect but importantdrivers of growth.

Many of these variables change with depth, oftencovarying with one another and/or varying betweenregions of the ocean (Paulmier & Ruiz-Pino 2009).Therefore, distinguishing the importance of onefactor over another will require knowledge of life his-tories from multiple regions of the oceans to untangleas much covariation as possible. Studies of age andgrowth require extensive sampling effort, time, andlabor, and these logistical demands increase sharplyas deeper dwelling organisms are targeted. Theprogress of age and growth studies for many deep-sea

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Black et al.: Drivers of growth in deep-sea teleosts

species may therefore be outpaced by anthropogenicimpacts, and as such, a predictive framework ofgrowth is required in the short term. To address thesefactors, we compared life history parameters fromstudies on populations of deep-sea demersal fishesand some phylogenetically related shallow-waterspecies to environmental variables from each species’habitat to establish a framework for estimatinggrowth patterns and life histories. Similar analyseshave been performed on octopods, freshwater fishes,and sharks (Rigby & Simpfendorfer 2015, Schwarz etal. 2018, de Santana et al. 2020) to ex plain the effect oftemperature and other ecological and environmentalvariables on life history parameters, but to our knowl-edge, this is the first meta-analysis to incorporate en-vironmental conditions in explaining growth ofmarine demersal fishes across depth.

It may be that variation in growth across demersalfishes is not directly explained by environmental con-ditions. Rather, it may relate to the relatively higherfitness of a ‘slow’ life history strategy with increasingdepth, with the deep sea favoring slow metabolism,low growth rates, and long maturation times relativeto the shallows. A spectrum of ‘fast vs. slow’ life his-tory strategies has been observed across a diversity ofanimals, with ‘faster’ organisms tending to have asuite of traits including higher growth rates, fastermetabolisms, shorter maturation times, in creasedmortality, bolder behavior, etc. relative to ‘slower’ or-ganisms (Mittelbach et al. 2014, Auer et al. 2018,Damsgård et al. 2019; reviewed in Réale et al. 2010).Much of this research has focused on intraspecificpatterns, but it may apply to interspecific comparisonstoo. We discuss the implications of evolutionary dri -vers even though their quantification is much morechallenging than environmental determinants in thedeep sea or any environment.

2. METHODS

2.1. Growth data and metrics

Data on fish growth and life history were extractedfrom a variety of sources (see Table 1). Forty pub-lished studies and 2 national oceanographic institu-tions (Northwest Fisheries Science Center [NWFSC;accessed via https://www.nwfsc.noaa.gov/data/map]operating out of the USA, and the National Instituteof Water and Atmospheric Research [NIWA] out ofNew Zealand) also provided data. Raw data wereused when possible. When raw data were unavail-able, data were digitized from plots of age vs. size, or

from tables containing mean size-at-age. When avail-able, other relevant spatial and environmental datawere also collected, including depth, latitude andlongitude, and temperature and/or oxygen concen-tration measured on the fishing gear. We at temptedto minimize the effects of covariation be tween depthand temperature by collecting data from a global setof locations, including the Mediterranean Sea, wheretemperature remains relatively constant (~13°C) acrossdepth. Only teleost fishes were included in this analy-sis. Chondrichthyans were omitted as there is a scarcityof age data for this group and they have fundamen-tally different life histories and physiologies (e.g. Tre-berg & Speers-Roesch 2016).

All age data used in this meta-analysis originatedfrom counts of otolith annuli. To be accepted for usein our meta-analysis, otoliths must have been at leastpolished/ ground/ broken to reveal the central nucle -us before age estimation. Age data from whole oto -liths has been widely shown to severely underesti-mate actual fish age for multiple species, particularlythose that are long-lived (reviewed in Campana2001). For this reason, we did not include age data formature fishes derived from whole otolith age counts,with the only exception being Beryx splendens.Studies have validated the use of whole otoliths as anaccurate aging method for B. splendens via marginalfrequency analysis and direct comparison of annulicounts from whole and sectioned otoliths (Massey &Horn 1990, Lehodey & Grandperrin 1996). Somestudies in this analysis used whole otoliths to esti-mate ages of young fish but switched to other meth-ods (break and burn or transverse sectioning) for fishabove a particular size. Only studies with age datafor at least 90 individual fish were used in this analy-sis. In some cases, data for more than one populationof the same species was collected, and growth para-meters and environmental conditions were estimatedseparately for these distinct populations. The only ex -ception is Coryphaenoides acrolepis. For this species,in order to obtain enough observations to fit growthparameters to age and size data, data were combinedfrom Andrews et al. (1999) and Matsui et al. (1990),which were collected primarily from the Californiaslope with some data from off Oregon and Washing-ton. The catch locations for 7 species from theNWFSC Groundfish Survey, which spans the entirewest coast of the USA, were initially split into Northand South sections to test for differences in environ-mental parameters and growth rates between the 2regions. All these species are mobile, and no evidencefor multiple, isolated populations of these fishes alongthe western USA was found in the literature. Of these

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7 species, only Merluccius productus showed signifi-cant differences in growth metrics (~30% difference)between fish from north and south of 42.5° N, sothese groups were parameterized as different popu-lations (see Table 1). The rest of the species from theNWFSC Groundfish Survey were parameterized asone contiguous population because no significantdifferences in growth parameters were found.

Growth rate in fish has been traditionally estimatedusing the von Bertalanffy (1938) growth curve: Lt = L∞

(1 − e −K[t−t0]), where Lt is length of the fish at time t, thex-intercept t0 is the theoretical age at a length of zero,and K represents the exponential coefficient of growthuntil a maximum asymptotic length (L∞). A larger valueof K corresponds to a more rapid increase in length tothe maximum length of the fish. These parameterswere calculated for each population. Bootstrapped95% confidence intervals were calculated for eachparameter. For data sets with few young fish, thismethod can produce biologically improbable curves(e.g. a population with predicted lengths of ~20 cm atage 0). For this meta-analysis, any von Bertalanffycurve with a t0 with absolute value >2.5 was recalcu-lated with a t0 fixed at −0.5. Fixing the t0 parameter at−0.1 or −0.5 has been employed in growth studies onspecies with a relative scarcity of juvenile data in orderto ensure predicted fish length is positive and nonzeroat hatching, producing more biologically realistic val-ues of K (Horn et al. 2012, Horn & Sutton 2015). Here,we chose −0.5 as the fixed value, though fixing at −0.1did not yield significantly different K values. Still,parameter values should be observed with some cau-tion (Lorenzen 1996). See Supplement 1 for growthcurves and Supplement 2 for parameter values andconfidence intervals with and without fixed values of t0

(all Supplements for this article are at www. int-res.com/ articles/ suppl/ m658 p001_ supp/).

Growth was also calculated as an average increasein mass (AIM; as %) per year during the prematurityphase, to provide an estimate of the maximum rates atwhich each species can accumulate mass prior to ex-periencing the energy expenditures associated withreproduction. When available, age at 50% maturitywas collected from the literature (Supplement 2).When age at 50% maturity was not available, weused the median between minimum and maximumage at maturity or estimated a likely age at maturityfrom published lengths at maturity using von Berta-lanffy growth curves (see Table 1). If the age at matu-rity differed between sexes, the younger age wasused as the cut-off for the end of the prematurityphase. When mass data were not available, predictedmass was calculated from fish length using published

length−weight conversions (Supplement 2). For eachspecies, the relationship between age and predictedor observed mass was modeled using a generalizedadditive model (GAM) to predict mass-at-age duringthe prematurity phase of growth (error distribution:Gamma). These GAM fits were assessed for their abil-ity to give rise to the data by comparing residuals be-tween data and simulation estimates from 1000 simu-lations to check for uniformity or outlier errors usingthe R package ‘DHARMa’ (Hartig 2019). To avoidoverfitting, the complexity of the curve shape was in-creased incrementally until simulated fits did not pro-duce these errors. A more detailed description of theGAM fitting process is in the ‘Supplementary Methods’(Supplement 3). Curves fit for the Atlantic populationsof the abyssal Coryphaenoides armatus, the deepestspecies in our data set, did not meet our thresholds forany fitting approach due to a small data set. However,data for this population was retained to investigate theinfluence of this abyssal fish on patterns in growth. Re-sults for AIM are reported with and without inclusionof C. armatus. To capture only the phase of rapidgrowth during prematurity, any decline in predictedmass observed as the fish approached maturity wastrimmed. The remaining values of predicted mass-at-age were then used to calculate the AIM.

Depth ranges were summarized from the literaturefor each species (Supplement 2). Whenever possible,we used published depth ranges that were estimatedin the same general area as the catch locations forstudies used for this meta-analysis. Several of thespecies included in this meta-analysis have improba-bly wide absolute minimum and maximum depths ofoccurrence. For example, Antimora rostrata has beenobserved/collected at depths from 350−3000 m, butgenerally lives at 1200−2500 m (Cohen et al. 1990,Froese & Pauly 2019). In order to constrain predic-tions of temperature, oxygen, and other environmen-tal parameters to conditions likely experienced bymost members of each species, we used these nar-rower, ‘usual’ depth ranges of high abundance foreach species in our analyses rather than the absoluteobserved depth range (as in Drazen & Haedrich 2012).When modal histograms of abundance vs. depth wereavailable in the literature, the minimum and maxi-mum depth range was set to where abundance was25% of the maximum abundance. This procedure wasalso used if histograms of presence in percent of towsvs. depth were available (i.e. Anderson et al. 1998).The median value between minimum and maximumusual depths was also used as a predictor. Thus, foreach population, this analysis included 3 depth metrics:minimum, median, and maximum usual depth.

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As temperature has been shown to explain a greatdeal of variation in growth rate in shallow-water spe-cies, it was predicted to be a primary driver in bothgrowth metrics, K and AIM. To decouple the effect oftemperature from other potential drivers that alsoclosely correlate with increasing depth, temperature-adjusted K and AIM were created by calculating ver-sions of each growth metric using growing degree-years (GDY, i.e. the integrated thermal age), insteadof calendar age. Here we use GDY, the product offish age in years and predicted temperature of habi-tat, to obtain a metric of thermal age for each fish.Thermal ages were then used to create temperature-adjusted versions of K and AIM, denoted here asKGDY and AIMGDY. These 2 metrics were analyzedalongside K and AIM in statistical analyses withenvironmental and ecological variables.

Temperature at the location of fish captures wasnot available in most studies. An average tempera-ture experienced by the fish throughout their life-span was predicted using data from World OceanAtlas (WOA) (1° horizontal resolution with 5 m verti-cal resolution near the surface down to 100 m resolu-tion at abys sal depths, decadal averages from 1955−2012; Locar nini et al. 2013). Temperature observa-tions across each species’ usual depth range at thelocation of catch were averaged. Species that live inhabitats that contradict the inverse relationship ofdepth and temperature in the world ocean were clas-sified as follows for comparison: cold, shallow-waterfishes (≤300 m maximum usual depth and <4°C aver-age temperature of habitat) and deep, warm-waterfishes (≥300 m minimum usual depth and >10°Caverage temperature of habitat).

Oxygen concentration (ml l−1) data were gatheredfrom WOA (1° horizontal resolution with 5 m verticalresolution near the surface down to 100 m resolutionat abyssal depths, decadal averages from 1955−2012;García et al. 2013). Oxygen concentration observa-tions across each species’ usual depth range at thecapture locations were averaged.

Estimating food availability is a great challenge anddepends upon species-specific feeding preferencesand foraging strategies (Drazen & Sutton 2017) aswell as prey population dynamics. This informationwas lacking for nearly all species in this study. There-fore, we used POC flux to the seafloor estimated bythe Lutz model (Lutz et al. 2007) as a proxy for foodavailability. POC flux has been used as a proxy forfood supply in similar, global meta-analyses (Woolleyet al. 2016). POC flux is the basal energy source formost seafloor habitats (except chemosynthetic ecosys-tems), and therefore supports the production of bio-

mass in benthic habitats, which is theoretically avail-able for fishes to feed upon. The procedure for esti-mating POC flux to the seafloor for each individualspecies’ catch location was adapted from methods de-scribed by Lutz et al. (2007). Net primary production(NPP) was estimated using global Standard VerticallyGeneralized Production Model (VGPM) data fromOregon State University’s Ocean Productivity portal(www.science.oregonstate.edu/ ocean. productivity/custom. php) at a resolution of 1/6th of a degree. Ba-thymetry was extracted from the Navy ETOPO database (ETOPO5 5 × 5 minute bathy metry; http:// iridl.ldeo.columbia.edu/ SOURCES/ . WORLD BATH /. bath/datafiles.html), and any observations of chlorophyllalong the 30 m isobath or shallower were omitted toavoid unnecessary backscatter, as done in previouspublished estimations of primary production (Gove etal. 2016). NPP at 2 yr intervals across the years 2007−2017 was averaged across the catch location boundsfor each population, resulting in a de cadal averagevalue of NPP for each population. Seasonal VariationIndex was calculated as described in Lutz et al. (2007).A global average euphotic zone depth of 110 m wasused (Lima et al. 2013). Three separate Lutz POCfluxes were predicted for the minimum, median, andmaximum depths for each population within theircatch boundaries.

Lutz POC flux is dependent on measurements fromsediment traps, which have been found to underesti-mate the inputs of organic carbon to the seafloor, par-ticularly on continental slopes (Smith 1992, Smith &Demopoulos 2003). For this reason, we also gatheredsediment community oxygen consumption (SCOC)rate data, a measure of remineralization rates oforganic material, which has also been used to esti-mate ecosystem food availability (Jahnke 1996, Strat-mann et al. 2019). We used the SCOC database col-lated by Stratmann et al. (2019) to collect SCOC rateswithin the bounds of each species’ catch location andaveraged them at 50 m intervals across the depthrange of each species. These 50 m binned averageswere then averaged to provide a mean SCOC valuerepresentative of each entire habitat. As these dataare sparse geographically and rely on individualmeasurements rather than extrapolation from satel-lite color data, representative lifespan SCOC ratescould only be calculated for 21 species.

For a subset of fishes, published data from studieson fish metabolic rate were available for comparisonto growth metrics. Specifically, published activities ofcitrate synthase (CS) enzyme activity for various spe-cies were collected (Sullivan & Somero 1980, Siebe-naller et al. 1982, Drazen et al. 2015, Saavedra et al.

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2016, C. N. Trueman & D. Shores unpubl. data). CS isan enzyme of the Krebs cycle, and its activity hasbeen used in past studies as a proxy for whole animalresting aerobic metabolic rates in fishes (Somero &Childress 1980, Somero 1990; reviewed in Dahlhoff2004 and Drazen & Seibel 2007). Enzyme activitieswere all measured at a temperature of 10°C (denotedCS10), and normalized to a mass of 500 g in order tocorrect for the scaling effects of body mass on meta -bolism as described in Gillooly et al. (2001) and Brownet al. (2004). See ‘Adjusted metabolic rates to temper-ature and mass of habitat’ in ‘Supplementary Meth-ods’ in Supplement 3 for equations used. As biochem-ical reaction rates increase with temperature, CSvalues were also calculated to reflect metabolic ratesat habitat temperature (denoted CSH), as per previouswork (Gillooly et al. 2001, Brown et al. 2004, McClainet al. 2012; see Supplement 3 and Section 3.2 below).

The fishes in this meta-analysis, though all demer-sal, occur on a wide spectrum of activity levels, fromthe sluggish, sedentary Sebastolobus alascanus toactive swimmers like Anoplopoma fimbria. As an ad -ditional proxy for activity level, we included a roughcategorization of activity level based on locomotorymode: each fish was characterized as benthic if theypredominately remain on the seafloor or bentho -pelagic if they are typically actively swimming abovethe seafloor. This descriptor was used as a categoricalvariable in statistical analysis. Locomotory modeswere generally assigned based on descriptions foreach species in FishBase (see Supplement 2 for allsources and justifications used).

2.2. Ontogenetic migrators

Ten species in this meta-analysis are known toontogenetically migrate deeper with age/size (seeTable 1), complicating estimation of environmentalpredictors. For these fishes, estimation of environ-mental predictors was modified to account forchanges in depth across the fish’s lifespan. Environ-mental predictors were estimated individually, basedon the unique depth of each individual fish given itscapture depth (or mean size-at-age data point, whenonly mean size-at-age was available). When individ-ual depth-of-capture was not available, publishedregressions were used to estimate likely depth at agiven size/age. For S. alascanus, published probabil-ities of depth by length (Jacobson et al. 2001) wereused to model predicted length vs. depth (GAM;family: gamma; link: log). When detailed depth infor-mation was available (primarily for data from

NWFSC), temperature was estimated for each onto-genetic migrator by collecting WOA temperaturedata within each study location and using a model(GAM; family: gamma; link: log) to predict tempera-ture based on each ontogenetic migrator’s predictedor observed depth and latitude/ longitude of catch.This process was repeated to estimate oxygen con-centration for each migrator. Lutz POC flux was cal-culated at the predicted or observed depth of eachindividual ontogenetic migrator (or mean size-at-agedatapoint when only mean data were available).Thus, for each ontogenetically migrating species,mean conditions were available for each year of thespecies as it moved downslope.

The descriptors ‘prematurity’ and ‘lifespan’ areused next to environmental predictors to specifywhether the underlying data for ontogenetic migra-tors reflects their early, relatively shallow-waterperiod of life, or their entire lifespan, respectively.For ontogenetic migrators, these predictors are eacheffectively weighted by time (years) spent at particu-lar depths/ conditions. For non-ontogenetic migra-tors, these 2 versions are identical, as these fish areexpected to be distributed relatively uniformly inage/ size across their depth range. Collected SCOCrates within the catch locations and depth ranges ofontogenetic migrators were not numerous enough tocreate regressions of SCOC vs. depth and predictSCOC at the depth of each individual ontogeneticmigrator. To resolve this issue, we averaged SCOC atthe prematurity depth range of each ontogeneticmigrator (average individual depth of capture or pre-dicted depth during prematurity, ±SD of the depth ofcapture). Data were only sufficient to calculate theseprematurity SCOC rates for ontogenetic migratorsliving along the US west coast (S. alascanus and spe-cies collected in the NWFSC Groundfish Trawl Sur-vey; see Table 1). For metabolic rate comparisons, 2versions of temperature-adjusted CS activities werecreated by adjusting CS10 values to the temperatureexperienced by each species across its lifespan (‘life-span CSH’), and CS activity rate adjusted to the pre-maturity temperature experienced by each species(‘prematurity CSH’; see ‘Adjusted metabolic rates totemperature and mass of habitat’ in ‘SupplementaryMethods’ in Supplement 3 for the equations used).

In terms of calculating thermal age for ontogeneticmigrators, GDY was calculated to represent shiftingtemperature regimes across their lifespan by fitting ageneralized linear model (GLM) of age vs. predictedor observed temperature (family: gamma; link func-tion: log). In multiple species, males and femaleswere observed to have statistically different (p <

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0.05) age vs. temperature curves, suggesting differ-ent rates of downslope migration between sexes. Forthis reason, separate GLMs were fit to males andfemales for most ontogenetic migrators. To calculatethe cumulative GDY for a fish species, the curve pro-duced by the respective GLM equation for the fish’sspecies and/or sex was integrated with bounds be -ginning from age 0 up to the individual fish age. Thisintegration produces the area under the age vs. tem-perature curve, and thus calculates a thermal age forthe individual fish that accounts for the shifting ther-mal regime encountered by these ontogenetic migra-tors. See Fig. S1 in Supplement 3 for an example ofhow GDY was calculated for ontogenetic migratorsand non-migrators.

2.3. Statistical analysis

All growth metrics were compared with all envi-ronmental predictors in 2 ways: pairwise GLMs (withgamma error distribution), each containing only onegrowth metric as response and one environmentalvariable, and with multivariate generalized linearmixed models (GLMMs; gamma error distribution),each containing one growth metric as response, mul-tiple environmental variables, and a random effectfor phylogenetic family. The former method was usedprimarily to identify which metric of each predictor(i.e. maximum depth vs. median depth, prematuritytemperature vs. lifespan temperature, etc.) best ex -plained growth and informed which should beincluded in subsequent GLMM analysis. As such, theresults of these pairwise comparisons are not dis-cussed at length here. The latter approach was usedto determine which combinations of predictors (in -cluding the null model) best explained the growthrate data. In these analyses, a gamma error distribu-tion was chosen because the long-term growth ratesof these animals must be greater than zero, and thiserror distribution does not allow residuals to be neg-ative. Due to paucity of SCOC and metabolic ratedata, these predictors were only analyzed in pairwiseregressions, and were not included in the full GLMManalysis. However, to see how much variation couldpotentially be explained by including metabolic ratedata, the top model for explaining each growth ratewas rerun including prematurity CSH, the metabolicrate predictor with the strongest relationships withgrowth rate in pairwise analysis, as a predictor. Envi-ronmental predictors estimated from both the prema-turity phase of life and across the entire lifespan wereused in the pairwise regressions, as well as in GLMM

analysis for K and KGDY. Only prematurity predictorswere used in multivariate analysis for AIM andAIMGDY, as these growth metrics reflect only the pre-maturity phase of growth. For numerical predictors,either a log link function or inverse link function wasused depending on which form of the GLM createdresiduals that were more evenly distributed andwithout clear patterns. When a clear difference inresidual patterns across link functions was not ob -served, the model favored by Akaike’s informationcriterion with correction for small sample size (AICc)was chosen. AICc is a scoring method used to rankmodels without favoring those that are overly com-plex, as the AICc is decreased with increasing ex -planatory power but is increased by model complex-ity. Models with AICc scores within 2 of one anotherare assumed to be equally likely to be the ‘true’model. AICc was used in these analyses in the modelselection process to maintain balance between over-fitting and underfitting the data.

Mixed model analysis and subsequent model se -lection was repeated for each growth metric with andwithout inclusion of the abyssal grenadier C. arma-tus, as this is the only abyssal fish in the data set andis thus a valuable point of comparison to other fishes,but is also a notable outlier in AIM and AIMGDY. Forthe mixed models where there was more than oneversion of a predictor (i.e. minimum usual depth vs.median usual depth vs. maximum usual depth), theone that explained the most variation for eachgrowth metric in their respective pairwise regression(see Table 2) was used.

To reduce the influence of covariation among pre-dictors in mixed models, predictors were stepwiseremoved from each GLMM until the variation infla-tion factor (VIF) of each predictor was below 3. VIFthresholds of lower than 10 have been suggested,with more conservative cutoffs in high-collinearitydata sets set at 2 or 3 (Zuur et al. 2010). The remain-ing GLMs were each run through the R function‘dredge’ (package ‘MuMIn’; Barton 2019), which cre-ates an individual GLMM for every possible permu-tation of predictors in the global model and returns atable with the models ranked by AICc.

3. RESULTS

In total, 74 teleost growth studies or data sourceswere considered for this meta-analysis. Of these, 21were eliminated due to use of only whole otoliths and/or scales in the aging process. Another 12 were elimi-nated for quality control reasons outlined in the Sup-

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plementary Methods (Supplement 3). Thus, datawere included from 42 sources: 40 published studiesand 2 national oceanographic institutions (NIWA andNWFSC), representing 53 species over 7 orders ofteleost fishes (Table 1). Among these species, 22 areshallow-water fishes (maximum usual depth <500 m),with most others with median depths of 500−3150 m,and a maximum depth of 4000 m (Fig. 1). Tempera-tures experienced by these fishes ranged from1.7−25°C (endmembers are Cory pha eno ides armatusand Platy cephalus indicus, respectively), with 17 spe-cies inhabiting waters >10°C (Fig. 1). See Fig. S2 inSupplement 3 for depth and temperature distributionsfor the 3 most populous orders in our data set. Averageoxygen concentrations ranged from 0.5−7.0 ml l−1

(Ano plo po ma fimbria and Hippoglossus hippo glossus,respectively; Table 1). Estimated Lutz POC fluxes toeach species’ habitat ranged from 1.02− 26.7 g C m−2

yr−1 (Table 1). SCOC rates spanned from 0.45−13.3 mmol O2 m−2 d−1 (Supplement 2). Catch locationsranged in latitude from 80° S to 66° N, across the In-dian, Atlantic, Pacific, and Southern Ocean regions(Fig. 2). Most catch data were focused in the NorthAtlan tic, Northeast Pacific, and Southwest Pacific.The Mediterranean Sea was the next-most sampledregion with 19 growth studies, but as several studiesused whole otoliths for age estimation, 15 wererejected for quality control reasons. The low latitudes(~20° S to 20° N) are relatively under-represented, re-flecting the availability of data for this study fromEnglish-language peer-reviewed literature based inthe Asian region. Phylogenetic orders are evenly dis-persed across the areas of highest concentration ofstudies (Fig. 2) but some orders are not found in shal-low water (Fig. 1). The age and size data available forsome of the species in this meta-analysis were insuf -ficient to robustly compute growth metrics (K or AIM).These cases are identified in Table 1 and wereomitted from analyses.

Moderate covariation was found between someenvironmental predictors (Fig. 3). Pearson correla-tion coefficients of temperature and depth had valuesbetween −0.43 and −0.51, depending on the depthbound and whether prematurity or lifespan tempera-tures were used for ontogenetic migrators. As ex -pected, NPP and POC metrics were all highly posi-tively correlated (Pearson correlation coefficients:81−87%). Among all predictors, POC metrics werethe most strongly correlated with depth (Pearson cor-relation coefficients: −0.48 and −0.6). Oxygen wasstrongly (negatively) correlated only with NPP. Prin-cipal component analysis of the prematurity predic-tors showed NPP and oxygen concentration oriented

along the same axis (Fig. 3). Temperature andmedian depth were fairly aligned and negativelycorre lated. PC1 and PC2 explained 48.1 and 27.8%of the variation among environmental predictors,respectively. No obvious clustering was observedamong phylogenetic orders, but species inhabitingdeep, warm environments (minimum usual depth>300 m and average temperature >10°C) were clus-tered tightly. These species were all caught in theMediterranean, likely driving their similar positionacross environmental characteristics. The 2 shallow,cold-water fish, Gadus morhua and Sebastes marinus(maximum usual depth <300 m and average temper-ature <4°C), were also clustered closely.

3.1. Pairwise GLM comparisons: growth rates have strong relationships with temperature, depth,

and metabolic rate

As pairwise comparisons with environmental pre-dictors were primarily used to inform which predic-tors should be included in the mixed-model analysisand may be highly susceptible to covariation, the re -sults are not discussed at length here (see Figs. S3−S5 & S16 in Supplement 3). We do report re sults ofpairwise comparisons with metabolic rates, as al -though these data were insufficient to include in thefull mixed-model analysis, we found stronger corre-lations with depth than expected.

Metabolic rate positively correlated with metrics ofgrowth. K and KGDY in creased with CS activity, bothmeasured at 10°C and adjusted to each species’ habi-tat temperature (R2 = 17− 32%, p < 0.05; Fig. 4). CS ac-tivity ad justed to prematurity temperatures ex plainedthe greatest amount of variation among the metabolicrate pre dictors (R2 = 27%, p < 0.05 with C. armatus,32% without C. armatus). Beryx splendens was a no-table outlier in metabolic rate relative to all metrics ofgrowth (Fig. 4). When B. splendens was omitted fromthese analyses, metabolic rate was a stronger predic-tor of K (p < 0.05 for all metabolic rate metrics, R2 = 20,36, and 41% for CS10, lifespan CSH, and prematurityCSH, respectively) and KGDY (p < 0.01 for all metabolicrate metrics, R2 = 30, 40, and 48% for CS10, lifespanCSH, and prematurity CSH, respectively). CS activitieswere not significantly related to AIM or AIMGDY

unless B. splendens was removed from the analysis, inwhich case there were significant positive relation-ships (p < 0.001, R2 = 37 and 34% for AIM vs. lifespanCSH and prematurity CSH, respectively; p < 0.01, R2 =29, 42, and 37% for AIMGDY vs. CS10, lifespan CSH,and prematurity CSH, respectively; Fig. 4).

8

Black et al.: Drivers of growth in deep-sea teleosts 9

Depth Lutz POC flux O2 T K KGDY AIM AIMGDY CSH AM n Min. Median Max. (mg C m−2 d−1) (ml l−1) (°C) (%) (%) (m) (m) (m) SCOC (O2 m−2 d−1)

BeryciformesBeryx decadactlyusCharleston Bump 368 432 496 7.7/7.3/7.0 4 15.2 0.12 0.007 * * – 4 155

(Friess & Sedberry 2011)

Beryx splendensPalliser Bank, NZ (NIWA) 200 500 800 33/26/21 5 9 0.22 0.021 33.1 2.34 3.41 5b 2310 10

Hoplostethus atlanticusWalter’s Shoal, SW Indian 1000 1125 1250 6.9/6.4/5.9 4.5 5.4 0.06 0.007 * * 0.5 22 409

Ocean (NIWA)

Hoplostethus mediterraneusEastern Mediterranean 388 642.5 897 8.2/7.0/6.0 4.4 13.9 0.21 0.013 72.7 4.33 – 4 419

(D’Onghia et al. 1998)

GadiformesAntimora rostrataNorth Atlantic 1300 1900 2500 9.5 6.6 3.5 0.06 0.007 24.2 3.2 0.21 16 340

(Orlov et al. 2018) [880] [960] [1045] [12.4] [3.7]

Coelorinchus caelorhincusEastern Mediterranean 350 450 550 8.4/7.9/7.4 4.5 14.1 0.05 0.003 116.3 4.58 – 6 244

(Labropoulou & Papaconstantinou 2000)

Coryphaenoides acrolepisWest coast USA (Matsui et al. 700 1350 2000 18/11/7.5 0.9 3.1 0.05 0.007 9.7 1.5 0.51 36a 1461990, Andrews et al. 1999) 2.4

Coryphaenoides armatus

Mid-Atlantic Ridge 2000 3000 4000 6.9/4.0/2.8 6.3 2.9 0.04 0.007 36.5 5.23 1.04 20c 317(Bergstad 2013)

Station M 2000 3150 4300 6.3/4.3/3.5 2.7 1.7 * * 15.3 3.04 1.04 20c 107(Gerringer et al. 2018) 0.45

Coryphaenoides rupestrisBritish Isles 400 950 1500 34.4/21/13 5.5 7.6 0.04 0.004 17.1 1.48 0.3 10 2814

(Allain & Lorance 2000)

Gadus morhuaNorth Atlantic 50 125 200 34/32/30 6.2 3.2 0.13 0.022 40 6.47 – 5 7 781 062

(Rideout et al. 2016)

Hymenocephalus italicusEastern Mediterranean 305 601 897 9.7/8.0/6.6 4.4 13.9 0.18 0.011 78 3.46 – 3 408

(D’Onghia et al. 2000)

Table 1. Depths, habitat variables, and growth metrics for each fish population (see Methods 2.1 and Supplement 2 for sources and explanationof depth ranges). K: von Bertallanfy growth coefficient calculated with length vs. calendar age in years. KGDY: von Bertallanfy growth coefficientcalculated with length vs. thermal age in growing degree-years. AIM: average increase in fish mass between calendar years during the prema-turity growth phase. AIMGDY: average increase in fish mass between thermal growing degree-years during the prematurity growth phase. AM:age at 50% maturity unless otherwise noted with the following subscripts: a: median of known minimum and maximum ages at maturity; b: esti-mated using a published length at 50% maturity and the von Bertalanffy curve; c: general time at maturity. Lutz particulate organic carbon(POC) flux is reported as 3 values separated left to right by slashes as values at minimum, median, and maximum depths; sediment communityoxygen consumption (SCOC) values are below POC flux in italics. CSH: citrate synthase activity (units per gram wet weight of white muscle tis-sue at each species’ average prematurity temperature). Ontogenetic migrators are underlined, and the prematurity version of applicable predic-tors are in square brackets. Sebastes mentella is not classified as an ontogenetic migrator here, but sampling was performed at 2 locations withslightly different temperatures, and so has a different prematurity temperature. Asterisks denote where there was insufficient data to calculategrowth metrics; dashes indicate data not available. Number of fish represented in data from each study listed under n. Only observations con-taining both age and size are counted here. Data for M. productus contained many more observations of mass-at-age than of length-at-age,

denoted with (L) for length observations and (M) for mass

Table continued on next page



Lepidion equesNorth Atlantic 500 700 900 31/26/21 6.1 5.8 0.1 0.006 43.9 3.99 – 9 295

(Magnússon 2001) [6.4]

Macrourus carinatusHeard Island 500 750 1000 11/8.7/6.9 4.4 2.3 0.07 0.013 19.3 3.43 – 11 142

(Van Wijk et al. 2003)

Macruronus novaezelandiaeOff New Zealand 200 500 800 39/31/25 5.1 9.2 0.17 0.014 43 2.94 – 6 9569

(Horn & Sullivan 1996) 4.6

Merluccius australisWest Coast South Island 300 700 1100 25/18/14 4.6 7.7 0.22 0.02 49.6 3.6 – 6 3884

(NIWA) 3.1

Merluccius hubbsiOff Brazil 100 150 200 16/16/15 5 18 0.19 0.009 * 5.67 – 2 685

(Vaz-dos-Santos et al. 2007)

Merluccius merlucciusIberian Peninsula 70 220 370 41/36/31 6.6 12.6 0.1 0.007 * * – 3 965

(Piñeiro & Saínza 2003)

Merluccius productusNorthern West Coast USA 50 275 500 67 [67] 3.1 7.1 0.27 0.025 122.6 7.22 1.52 3 5501 [M]

(NWFSC Groundfish [120] [170] [220] 7.3 [7.7] [3.0] [7.2] 556 [L]Trawl Survey)

Southern West Coast USA 50 275 500 36 [37] 2.4 7.5 0.2 0.019 88.3 5.91 1.65 4475 [M](NWFSC Groundfish [170] [190] [210] 11 [4.4] [2.6] [7.9] 3 687 [L]Trawl Survey)

Micromesistius australisOff Argentina 70 200 620 64/56/37 6.4 4.5 0.17 0.023 81.2 8.16 – 4b 4919(Cassia 2000)

Off New Zealand 400 500 600 13/12/11 5.8 5.8 0.2 0.022 96.7 7.76 – 3 7920(NIWA)

Mora moroChatham Rise 500 750 1000 23/18/15 5 6.3 0.13 0.014 42.8 3.81 0.34 8c 294

(Sutton et al. 2010)

Nezumia sclerorhynchusEastern Mediterranean 350 550 750 8.4/7.4/6.6 4.5 14.1 0.15 0.009 42.5 1.97 – 11 200

(Labropoulou & Papaconstantinou 2000)

Trachyrhynchus scabrusWestern Mediterranean 700 875 1050 11/9.4/8.4 4.3 13.3 0.15 0.009 * * – 4b 263

(Massutí et al. 1995)

OphidiiformesGenypterus blacodesCampbell Plateau, NZ (NIWA) 200 500 800 13/10/8 5.8 6.6 0.15 0.015 65.7 5.31 – 6 5254

Chatham Rise, NZ (NIWA) 200 500 800 28/22/17 5.5 7.3 0.12 0.011 66.8 5.05 – 6 5751 4.2

PerciformesCheilodactylus spectabilisTasmania 0 25 50 34/33/33 5.6 15.3 0.39 0.026 57.7 2.54 – 3 198

(Ewing et al. 2007)

Table 1 (continued)




Epigonus telescopusOff New Zealand 500 800 1000 15/12/10 4.8 7.2 0.04 0.004 5.7 0.54 – 36 554

(Tracey et al. 2000) 100

Hyperoglyphe perciformisCharleston Bump 200 363 526 10/8.8/7.8 4.1 16.1 0.21 0.012 9.2 0.46 – 6 710

(Filer & Sedberry 2008)

PleuronectiformesAtheresthes stomiasWest coast USA 27 148.5 270 51 [55] 1.4 6.4 0.14 0.012 33.2 2.68 0.46 7 3525

(NWFSC Groundfish [130] [170] [210] 7.7 [7.6] [1.7] [7.6]Trawl Survey)

Cynoglossus zanzibarensisSouth Africa 30 230 430 59/50/43 4.7 14.8 0.44 0.025 100.6 4.05 – 2 180

(Booth & Walmsley-Hart 2000)

Eopsetta jordaniWest coast USA 160 205 250 46 [51] 3.1 8.4 0.13 0.009 39.7 2.75 0.64 5 4745


Hippoglossus hippoglossusNorth Atlantic 55 81.5 108 47/45/44 7 6.2 0.13 0.014 186.1 6.69 – 6 2429

(Armsworthy & Campana 2010)

Microstomus pacificusWest coast USA 55 777.5 1500 31 [43] 1.1 5.3 0.17 0.017 23.3 2.1 0.78 7 6020


Parophrys vetulusWest coast USA 40 120 200 49 [51] 3.8 9.2 0.35 0.028 61.6 3.31 0.85 4a 772(NWFSC Groundfish [70] [90] [110] 11 [6.4] [4.2] [10.6]Trawl Survey)

Reinhardtius hippoglossoidesNorth Atlantic 500 750 1000 23/18/14 6.7 2 0.07 0.015 58.4 9.78 – 10 266

(Dwyer et al. 2016)

ScorpaeniformesAnoplopoma fimbriaWest coast USA 200 600 1000 26 [40] 0.7 4.3 0.43 0.045 61.4 4.87 1.67 5 3772

(NWFSC Groundfish [140] [270] [390] 4.4 [6.2] [2.7] [7.4] Trawl Survey)

Chelidonichthys capensisSouth Africa 30 115 200 42/39/37 4.7 14.8 0.07 0.004 75.2 3.31 – 4 383

(McPhail 1998)

Chelidonichthys quekettiSouth Africa 50 100 150 38/36/35 4.7 14.8 0.4 0.022 * * – 2 227

(Booth 1997)

Helicolenus percoidesOff New Zealand 100 400 700 37/29/22 5.4 8.8 0.1 0.009 53.9 3.73 – 5 1338

(Paul & Horn 2009) 4.7

Platycephalus indicusJapan 0 15 30 12/12/2012 4.8 25 0.38 0.014 118.7 2.78 – 2 351

(Akita & Tachihara 2019)

Table 1 (continued)




Pterois volitansGulf of Mexico 50 85 120 24/23/22 4.2 20.8 0.44 0.018 163.5 5.92 – 1 1576

(Fogg 2017) 5.3

Scorpaena notataEastern Mediterranean 30 365 700 51/38/29 5.1 13.4 0.32 0.019 59 2.85 – 2.5 225

(Scarcella et al. 2011) 13

Sebastes crameriWest coast USA 140 175 210 73/71/69 2.9 7.6 0.2 0.019 75.9 5.66 0.49 5 1060

(Nichol 1990) 6.3

Sebastes ensifer West coast USA 45 205.5 366 62/54/47 3.1 8.7 0.13 0.012 23.7 1.91 – 9 171

(Love et al. 2018)

Sebastes fasciatusFlemish Cap 130 315 500 34/28/24 6.3 3.9 0.13 0.018 * * – 6 5174

(Saborido-Rey et al. 2004)

Sebastes marinusFlemish Cap 100 200 300 35/32/29 6.3 3.9 0.07 0.009 52.1 6.15 – 7 6016

(Saborido-Rey et al. 2004)

Sebastes melanostomus West coast USA 250 425 600 40/35/30 1 6.4 0.06 0.007 7.9 0.82 0.32 34 332

(Stevens et al. 2004) 3.1

Sebastes mentellaIrminger 300 600 900 29/22/16 6.7 3.8 0.13 0.02 10.2 1.58 – 10 359

(Stransky et al. 2005) [4.1]

Newfoundland 300 600 900 29/22/17 6.5 3.7 0.11 0.016 27.8 3.64 – 10 8930(Saborido-Rey et al. 2004)

Sebastes mystinusBritish Isles 0 45 90 70/67/65 5.2 11.1 0.22 0.015 89.9 4.67 0.76 6 1245

(Laidig et al. 2003)

Sebastes simulatorWest coast USA 99 274.5 450 59/51/44 1.7 7.8 0.1 0.009 30.4 2.53 – 11 156

(Love et al. 2018)

Sebastolobus alascanusOff California 90 750 1500 31 [42] 0.5 4.4 0.02 0.002 21.8 2.26 0.4 12 1140

(Kline 1996) [380] [380] [380] 4.3 [1.16] [6.0]

Sebastolobus altivelisOff California 400 950 1500 47/30/21 0.7 4.2 0.06 0.008 * * 0.21 6c 342(Kline 1996) 1.4

ZeiformesAllocyttus nigerChatham Rise 700 900 1300 17 [18] 4.8 3.9 0.23 0.023 3.7 0.54 – 27c 2056(NIWA) [644] [840] [1039] 2.7 [4.9] [4.2]

Allocyttus verrucosusOff Tasmania 1000 1200 1400 11/10/8.8 4.2 3.6 0.06 0.01 12.4 1.79 – 24 102

(Stewart et al. 1995)

Neocyttus rhomboidalisSouth East Australia 600 700 800 17/15/14 5.2 7.6 0.05 0.005 8.5 0.77 – 33b 97

(Smith & Stewart 1994)

Table 1 (continued)


3.2. Multivariate GLMM analysis

The GLMM for prematurity analysisexplained more variation in all growthmetrics than that for lifespan. There-fore, we report only the prematurityGLMM analysis here. See Figs. S9 &S11, Tables S7 & S8 in Supplement 3 forGLMM results upon ex cluding C. ar-matus. K was best explained by a nega-tive relationship with minimum depthand a positive relationship with tem-perature (R2 = 47%, AICc = −127.9, hierarchical partitioning: 71% fordepth, 28% for temperature, predictorsremoved: NPP; Table 2, Fig. 5). Fiveadditional models were just as likely asthe best model by ΔAICc < 2, includingsome combination of minimum depth,temperature, Lutz POC flux, and loco-motory mode (Table 2). The second-best model indicated that K was nearlyjust as likely to be ex plained by depthalone (R2 = 45%, AICc = −127.7, pre-dictors re moved: NPP). Random-effectintercepts for each phylogenetic familyall showed overlapping confidence intervals, with sebas tids appearingslightly farther from the mean (Fig. S6in Supplement 3). This is likely due tothe large variability of growth ratewithin the family and the especiallyslow growth of Sebas to lobus alascanus

13

Fig. 1. Numbers of populations of fishes in this study organized by (A) lifespandepth category and (B) average temperature of habitat. Median depths across

the entire fish lifespan were used to categorize each species

Fig. 2. World map highlightedwith catch locations from eachstudy used for the meta-analysis.Catch locations are colored by

Order


(Figs. S12− S15 in Supplement 3). When a random ef-fect for phylogenetic family was not included, locomo-tory mode was also included, along with depth andtemperature, as an important predictor in the bestmodel, with benthopelagic fishes growing morequickly than benthic fishes (Table S2 in Supple-ment 3). However, hierarchical partitioning revealedthat depth and temperature were by far the primarydrivers (R2 = 49%, weight = 0.36, hierarchical partition-ing: 64% for depth, 30% for temperature, and 6% for

locomotory mode, predictors removed: NPP). Rerun-ning the best model with the addition of prematurityCSH as a predictor increased R2 to 61% but limited thesample size to 19 and increased the maximum VIF ofthe predictors to 2.64.

KGDY was best explained by minimum depth and locomotory mode, showing declines with depth andin benthic fishes in the GLMM analysis (R2 = 27%,AICc = −399.2, hierarchical partitioning: 83% fordepth, 17% for locomotory mode, weight = 0.64, pre-dictors removed: NPP; Table 3, Fig. 6). Only one addi-tional model was equally likely (i.e. ΔAICc < 2), andcontained POC flux, minimum depth, and locomotorymode as important predictors (R2 = 29%, AICc =−398, predictors re moved: NPP; Table 3). No changesin important predictors chosen occurred when phylo-genetic family was not included in the analysis as arandom effect. Random-effect intercepts for eachphylogenetic family all showed overlapping confi-dence intervals (Fig. S7 in Supplement 3). Rerunning

14

Fig. 3. (A) Correlation plot of all continuous growth predic-tors used in this meta-analysis. Deeper colors indicate largermagnitudes of the Pearson correlation coefficient. Bluehues: positive correlation; red hues: negative correlation. (B)PCA plots of prematurity predictors. Points are colored byunique thermal habitats (if applicable) that contrast the typi-cal ‘colder, deeper’ paradigm found in the world oceans (seeSection 2.1). (C) PCA plot of prematurity predictors. Points

are colored by order


this best model with prematurity CSH as a predictorincreased R2 to 47% but limited the sample size to 19.

AIM was best explained by temperature anddepth, but the importance of depth was sensitive toC. armatus inclusion. When C. armatus was in -cluded, GLMM analysis indicated that variation inAIM is best explained by temperature, Lutz POCflux, and O2, in that rank order (R2 = 38%, weight =0.37, hierarchical partitioning: 49% for temperature,39% for Lutz POC flux, and 12% for O2, predictorsre moved: NPP; Table 4, Fig. 7). Three additionalmodels were equally likely by ΔAICc < 2, each con-taining some combination of POC flux, mediandepth, O2, and temperature (R2 = 32−38%, predictorsremoved: NPP; Table 4). Random-effect interceptsfor each phylogenetic family all showed overlappingconfidence intervals (Fig. S8 in Supplement 3). Whenthe random effect for phylogeny was removed,median depth, temperature, and O2 were importantpredictors of variation in AIM (R2 = 38%, weight =

0.34, hierarchical partitioning: 72%for depth, 20% for temperature, and8% for O2, predictors removed: NPP).There were again 3 additional modelswith ΔAICc < 2, each containing somecombination of these same predictors(R2 = 33−40%, predictors re moved:NPP; Table S3 in Supplement 3).Rerunning this best model with pre-maturity CSH as a predictor increasedR2 slightly to 40% but limited samplesize to 18, prevented the use of phy-logeny as a random ef fect, and themaximum VIF of the predictors in -creased to 3.11.

AIMGDY declined with decreasingO2, increasing depth, and in benthicfishes, but results are sensitive to C.armatus inclusion. When C. armatuswas in cluded in the analysis, AIMGDY

was found to be best explained by O2

and Lutz POC flux (R2 = 22%, AICc =73.3, weight = 0.40, hierarchical parti-tioning: 80% for O2, 20% for LutzPOC flux, predictors removed: NPP;Table 5, Fig. 8). Two additional mod-els were equally likely by ΔAICc < 2:one model containing O2 alone (R2 =18%, AICc = 73.3, weight = 0.40;Table 5) and another model contain-ing O2, locomotory mode, and LutzPOC flux (R2 = 24%, AICc = 74.7,weight = 0.20; Table 5). Random-

effect intercepts for each phylogenetic family allshowed overlapping confidence intervals, but thereare a few apparent outliers in the model residuals(Fig. S10 in Supplement 3). When random ef fectswere excluded, these patterns were similar, but witha drop in R2 for one of the 3 output models (R2 =13−20%; Table S6 in Supplement 3). Rerunning thisbest model with prematurity CSH as a predictorincreased R2 to 32% but limited sample size to 18.

4. DISCUSSION

This study presents the first comprehensive exam-ination of the environmental and physiologicaldrivers of demersal fish growth rates across thedepth continuum. The general decline in the vonBertalanffy growth co efficient K with depth ob -served by Drazen & Haedrich (2012) was foundagain here (Fig. 5). Prematurity depths for ontoge-

15

Fig. 4. Growth metrics vs. citrate synthase activity adjusted to average prema-turity temperature and normalized to a body mass of 500 g. See ‘Adjustedmetabolic rates to temperature and mass of habitat’ in Supplement 3 for adjust-ment equations. Blue lines: best-fit regression; grey bands: SE. Top row: valuesof K (A) and KGDY (B) vs. CSH activity, colored by order. Bottom row: values ofAIM (C) and AIMGDY (D) vs. CSH activity, colored by order and shown withoutBeryx splendens. Note that the relationships of AIM and AIMGDY vs. CSH activ-ity are only significant at the p < 0.05 level when B. splendens is omitted from

the analysis. See Table 1 for definitions of terms


netic migrators explained more variation in K andKGDY than when lifespan-averaged depths wereused (Table S1 in Supplement 3). This is not sur-prising, given that most fish growth occurs in theprematurity phase of life and thus habitat variablesmust be matched accordingly. Average percentincrease in mass during the prematurity phase (i.e.AIM) also declined with depth, though this wassensitive to the inclusion of Coryphaenoides arma-tus (Table S1 in Supplement 3, Fig. 7). However,observing the depth pattern alone does not lead toan understanding of the underlying drivers be -

cause depth is not a direct eco-logical driver, and there is stillconsiderable variation in growth(~60− 90%, de pending on themetric) that is unexplained bydepth alone (Table S1 in Sup-plement 3).

Temperature during the pre-maturity phase was found to bean important predictor of K andAIM (Table S1 in Supplement 3,Figs. 5 & 7). Our findings rein-force the exponential increasesin interspecific growth rate as afunction of temperature hypoth-esized in prior work (Van der

Have & De Jong 1996, Jobling 1997, Neu heimer &Grønkjær 2012, Rall et al. 2012). It should be notedthat the patterns with temperature in this interspe-cific meta-analysis do not reflect those found in intra-specific studies. While populations of a given speciesin warmer temperatures have been shown to growfaster than conspecifics inhabiting colder tempera-tures, this intraspecific growth response is unimodal:growth rate initially increases with temperature toan optimum and decreases be yond this threshold(Braaten & Guy 2002, Pörtner & Knust 2007, Thresheret al. 2007, Neu heimer et al. 2011, Rypel 2012). Asimilar meta-analysis of growth in freshwater fishesacross South America by de Santana et al. (2020) pri-marily focused on the effects of latitude as a proxy fortemperature but did not find a significant relationshipwith interspecific growth rate, although they foundthat intraspecific asymptotic length decreases withlatitude. Their meta-analysis differed in that it fo -cused on freshwater habitat, used latitude as a proxyfor temperature (which is imprecise), and incorpo-rated ageing methods not included here (scales, ver-tebrae, length−frequency analysis, etc.).

Contrary to expectations, the apparent influenceof depth was still present even after the signal of temperature was accounted for with temperature-adjusted growth rates. Multivariate mixed modelsshowed that 26% of the remaining variability in KGDY

is explained by depth and locomotory mode. Thiswould suggest that temperature is not the sole driverof variation in K across depth. Compared to patternsseen with KGDY, depth was found to be a lesser, butstill important, predictor of variation in AIMGDY,though only upon exclusion of C. armatus from thedata set. The sensitivity of these patterns in AIM andAIMGDY to inclusion of C. armatus likely reflectsinherent uncertainty as to this species’ age-at-matu-

16

Models Int. Loc. Min. Lutz Temp. R2 AICc ΔAICc Weight Mode Depth POC flux

1 7.02 3.345 −0.6743 0.472 −127.9 0 0.2052 6.846 3.881 0.4461 −127.7 0.26 0.183 8.049 + 3.61 −0.7151 0.4916 −127.5 0.4 0.1684 8.149 + 2.835 −0.7975 −1.027 0.5144 −127.5 0.45 0.1645 6.977 2.641 −0.7279 −0.9488 0.49 −127.3 0.57 0.1546 7.825 + 4.144 0.4631 −127 0.93 0.129

Table 2. Results of dredge analysis of K vs. prematurity predictors with phylogeneticfamily as a random effect, showing models with a ΔAICc score of <2 relative to thetop model. For numerical predictors, the intercept (Int.) is shown if the predictor is inthe model. A ‘+’ is shown if locomotory (Loc.) mode is included. Global model call:glmer(formula = K ~ scale(min depth) + scale(Lutz POC flux to max depth) + scale(O2)+ locomotory mode + (1 | phyl. family), data = meta.sub, family = Gamma,

na.action = ‘na.fail’). Predictors removed: NPP

Fig. 5. Effects of the top mixed model of K vs. minimum depthwhen temperature is held at the median (green, dashed line),first quartile (yellow, dotted line), and third quartile (blue,dashed line). Rug values for each data point are colored according to closest proximity to median, first quartile, or

third quartile


rity, as well as a lack of data for other abyssal species,which is discussed in more detail below. Neverthe-less, the persistent significance of depth in explain-ing KGDY and AIMGDY suggests the influence of addi-tional variables associated with depth in determininggrowth.

The estimates of food availability used here werenot found to be important predictors of interspecificgrowth rates (Table 2). Lutz POC flux was a second-ary predictor of AIM and AIMGDY, but only when theoutlier C. armatus was included. These results echothe findings of the meta-analysis by McClain et al.(2012) in which the amount of chemical energy pres-ent in the base of the food web was not found to con-trol interspecific patterns in rockfish growth rates, butin stead constrains population- or ecosystem-levelprocesses such as abundance, biodiversity, and total

biomass. In deed, fisheries re -search has found that wild popu-lations increase in abundanceuntil reaching a carrying capac-ity determined at least in part byavailability of prey (re viewed inAnderson 1988 and Le Pape &Bonhommeau 2015). Popu lationsizes of abyssal fishes are thoughtto track long-term fluctuations inprey abundance, with interan-nual changes in population den-sities likely due to migration

rather than in creased reproduction rates (Drazen etal. 2012, Milligan et al. 2020). Studies on the energet-ics of individual growth in fishes have establishedthat an increase in food ration leads to more rapidgrowth, and intraspecific variation in growth rates onshort timescales, such as in aquaculture settings,clearly affects fish growth rates (Persson & De Roos2006). Despite food’s importance as an intraspecificdriver of individual growth in aquaculture settings, itap pears that interspecific patterns in growth acrossdepth are relatively unaffected by variability in foodsupply as measured via Lutz POC, NPP, or SCOC.However, we were unable to incorporate these 2 pre-dictors into GLMM analysis due to covariation andlack of data, respectively. It is possible, given moredata on SCOC rates or other food supply metrics, thata significant relationship with growth would beapparent.

Oxygen was found to be an important predictor ofAIM, though relatively less so than depth and tem-perature. In GLMM analyses, O2 only explainedabout 5% of variation in AIM. After correcting fortemperature, the influence of O2 was much stronger,becoming the most important predictor of AIMGDY

with or without the inclusion of C. armatus. However,this pattern with AIMGDY was driven by several oxy-gen minimum zone (OMZ)-dwelling fishes inhabit-ing O2 concentrations of less than 2 ml l−1. Oxygenhas been linked with growth in the literature: overallpopulation biomass, maximum body size, and abun-dance tends to correlate with oxygen levels withinOMZs (reviewed in Gallo & Levin 2016). Intraspecificstudies have shown a link between metabolism, oxy-gen concentration, and growth, with hypoxic condi-tions suppressing growth and metabolic rate inactively swimming fishes, though the critical con-centration at which suppression occurs is species-specific (reviewed in Gray et al. 2002). Moreover, theheightened longevity observed in deeper dwellingfishes has been attributed to lower concentrations of

Fig. 6. Effects of top mixed model of KGDY vs. minimum depthfor benthic (orange, dashed line) and benthopelagic (green,solid line) fishes. Rug values for each data point are colored

according to locomotory mode

Models Int. Loc. Min. Lutz R2 AICc ΔAICc Weight Mode Depth POC flux

1 92.19 + 26.97 0.2732 −399.2 0 0.6442 92.23 + 22.82 −5.888 0.2902 −398 1.19 0.356

Table 3. Results of dredge analysis of KGDY vs. prematurity predictors with phyloge-netic family as a random effect, showing models with a ΔAICc score of <2 relative tothe top model. For numerical predictors, the intercept (Int.) is shown if the predictor isin the model. A ‘+’ is shown if locomotory (Loc.) mode is included. Global model call:glmer(formula = KGDY ~ scale(min depth) + scale(Lutz POC flux to mid depth) +scale(O2) + locomotory mode + (1 | phyl. family), family = Gamma, na.action =

‘na.fail’). Predictors removed: NPP


oxygen and thus free radical damage to cells, thoughhigh longevity is not necessarily coupled to growthrate (Cailliet et al. 2001). Generally, less is known re -garding the effects of oxygen on interspecific growthrates. High O2 demand has been associated with rap-idly growing, ‘high performance’ fishes such as tunaand mahi mahi (Brill 1996). However, prior work ondemersal species has not shown a clear patternbetween environmental O2 concentration and rate ofmetabolism. Rather, a strong positive correlation be -tween high metabolism and mass-specific gill sur-face area has been found (Friedman et al. 2012), anddeep-sea species ap pear to have scaling exponentsof metabolic rate that indicate they are less limited bythe geometric constraints of oxygen transport than

shallower fish (Killen et al.2010). Our analysis shows thatsablefish live at low concentra-tions of oxygen (~0.7− 2.7 ml l−1)and have high rates of metabo-lism and growth, but also werefound by Friedman et al. (2012)to have relatively higher gill sur-face area than several of theslow-growing OMZ-dwellers inour data set, such as Sebas-tolobus alascanus, S. altivelis,and Microstomus pacificus. Thepositive relationship be tweengrowth and O2 we found was

driven en tirely by these slow-growing OMZ-dwellers, which may indicate a threshold response inthat oxygen only limits maximum growth rate at con-centrations lower than about 2 ml l−1, and only infishes without specific adaptations for high gill sur-face area.

The influence of phylogeny on growth appears tobe minor, though we were unable to conclusively andquantitatively determine its effect on growth. Acrossphylogenetic orders, no significant differences werefound in K or KGDY. Only Zeiformes (the oreos) werefound to grow significantly slower than other orders interms of AIM and AIMGDY. This may be due simply toa lack of sampling in this order because all 3 speciesanalyzed are deep living, with median lifespan

18

Models Int. Loc. Min. Lutz Temp R2 AICc ΔAICc Weight Mode Depth POC flux

1 1.361 −0.3944 −0.3187 −0.3689 0.3799 81.2 0 0.3672 1.337 0.5791 −0.2696 −0.1653 0.3687 82.1 0.87 0.2373 1.377 0.3014 −0.3561 −0.2198 −0.2738 0.4012 82.2 1.02 0.224 1.292 0.7638 −0.2156 0.3261 82.7 1.47 0.176

Table 4. Results of dredge analysis of AIM vs. prematurity predictors includingCoryphaenoides armatus and with phylogenetic family as a random effect, showingmodels with a ΔAICc score of <2 relative to the top model. For numerical predictors, theintercept (Int.) is shown if the predictor is in the model. Global model call: glmer(for-mula = AIM ~ scale(med. depth) + scale(Lutz POC flux to max depth) + scale(O2) +scale(temperature) + locomotory mode + (1 | phyl. family), family = Gamma, na.action =

‘na.fail’). Predictors removed: NPP

Fig. 7. (A) Effects of the top mixed model of AIM vs. temperature when oxygen is held constant at the median (green, solidline), first quartile (blue, dashed line), and third quartile (yellow, dotted line) values. Particulate organic carbon (POC) flux isheld constant at the median value. (B) Effects of the top mixed model of AIM vs. temperature when POC flux is held constantat the median (green, solid line), first quartile (blue, dashed line), and third quartile (yellow, dotted line) values, and oxygenconcentration is held constant at the median value (4.8 ml l−1). Rug values for each data point are colored according to closest

proximity to median, first quartile, or third quartile


depths >600 m. We had insufficient data to correct forvariability in responses to our environmental variablesat the species level using random effects but wereable do so at the family level. Important predictors ofvariation in AIM change when phylogeny is incorpo-rated as a random effect (see results for GLMM analy-sis and Tables S4 & S5 in Supplement 3), but these aresmall shifts from one covarying factor to another (e.g.Lutz POC flux vs. depth). The effect of phylogeny isdifficult to address, particularly because growth dataare not available for shallow-water members of somedeep-sea families, such as the Oreo soma tidae andMacrouridae (Drazen & Haedrich 2012, Priede &

Froese 2013). We also includedthe shallow dwelling ophidiidGeny pte rus blacodes but wereunable to find age and growthdata for deep-sea members ofthis family. Similar meta-analyseson chondr ichthyan growth rateshave investigated the importanceof phylo geny as a predictor: Gar-cía et al. (2008) found correlationswith growth rates and genus,and Rigby & Simpfen dorfer(2015) found that after account-ing for phylo geny, the influenceof environmental conditions on

growth was still significant. Overall, the effect ofphylo geny is inconclusive, but its incorporation intothese analyses as a random effect appears to accountfor some underlying variation in growth. Our findingsreinforce those of Rigby & Simpfendorfer (2015), thatwhile phylogeny can be important, other environ-mental drivers seem to be more so.

Temperature and depth fall out from our analysesas the primary, indirect drivers of growth, and inaddition we found a strong positive relationship tometabolic rate, though it is not clear whether it is adriver or covarying energetic parameter across thedepth continuum. All 3 CS activity predictors — non-adjusted, adjusted to average temperature, and ad -justed to average prematurity temperature — werefound to be significantly related to nearly all thegrowth metrics in this study (Fig. 4). While there wasinsufficient data to include metabolic rate as a pre-dictor in the full mixed-model analysis, adding it tothe top mixed model for each growth metric drasti-cally increased the variation explained. Metabolicrate in demersal fishes has been found to declinewith depth even after accounting for temperatureand body size effects (Sullivan & Somero 1980,Drazen & Seibel 2007, Drazen et al. 2015). It is yetunclear whether the depth-related decline in meta-bolic rate is mechanistically driving the patterns ob -served in growth rate or simply correlates to de creasedmaintenance costs and activity levels associated withincreasing depth (Killen et al. 2010). Careau et al.(2008) suggest 2 possible models for energy budget-ing of growth and metabolism. In the allocationmodel, metabolic rate reflects the baseline energeticcost of cellular maintenance, and leftover energy isfunneled into activity and/or growth (i.e. growth ratecan be negatively correlated with meta bo lism). In theperformance model, the metabolic rate representsthe mechanistic rates of biochemical processes

19

Fig. 8. Effects of the top mixed model of AIMGDY vs. oxygenconcentration when particulate organic carbon (POC) flux isheld constant at the median (green, solid line), first quartile(blue, dashed line), and third quartile (yellow, dotted line)values. Rug values for each data point are colored accordingto closest proximity to median, first quartile, or third quartile

Models Int. Loc. Min. Lutz R2 AICc ΔAICc Weight Mode Depth POC flux

1 1.176 −0.2506 −0.1428 0.217 73.3 0 0.4012 1.204 −0.2006 0.176 73.3 0.02 0.3983 1.343 + −0.2493 −0.1635 0.2362 74.7 1.39 0.201

Table 5. Results of dredge analysis of AIMGDY vs. prematurity predictors with phylo-genetic family as a random effect when Coryphaenoides armatus is included. Onlymodels with a ΔAICc score of <2 relative to the top model are shown. For numericalpredictors, the intercept (Int.) is shown if the predictor is in the model. A ‘+’ is shownif locomotory (Loc.) mode is included. Global model call: glmer(formula = AIM ~scale(med. depth) + scale(Lutz POC flux to max depth) + scale(O2) + locomotorymode + (1 | phyl. family), data = meta.sub, family = Gamma, na.action = ‘na.fail’).

Predictors removed: NPP


including channeling of energy into growth, result-ing in positive correlations between growth andmetabolic rates. Both models have found some sup-port in intraspecific studies, with relationships be -tween growth and metabolic rate changing acrosslow, intermediate, and ad libitum food levels (Careauet al. 2008, Jobling 2010, Killen 2014, Auer et al.2015). Our findings of positive correlations betweengrowth and metabolic rates generally support theperformance model. Our findings also align with abroad pattern of ‘fast vs. slow’ life history adaptationsobserved across and within animal taxa. That is, vari-ation in life history parameters can be partly ex -plained by a fast vs. slow dichotomy, with organismswith a relatively faster pace of life having moreproactive behavior, short times-to-maturity, higheractivity levels, shorter lifespan, and more rapidgrowth than ‘slower’ organisms. (Mittelbach et al.2014, Auer et al. 2018, Damsgård et al. 2019; revie-wed in Réale et al. 2010).

Ultimately, the question is what evolutionary pres-sure is driving slow growth and slow metabolic ratesin deep-sea fishes? Studies on catch-up growth inteleosts have demonstrated that these organisms typi-cally grow at sub-maximal rates, but grow faster uponfeeding after a period of starvation, to the detriment oftheir tissue quality and overall lifespan (Metcalfe etal. 2002, Alvarez & Metcalfe 2005, Jobling 2010, Hec-tor & Nakagawa 2012). Taken together, these patternswould suggest that growth rate is not strictly bound tometabolic rate as a function of temperature and bodymass, but rather the deep sea broadly selects forslower paces of life, typically low growth and meta-bolic rates and long lifespans and maturation times.Within the physiological constraints on growth rateimposed by environmental conditions, the remainingvariation in growth rate is likely driven by the evolu-tionary fitness conferred by slower rates in deeper en-vironments. The visual interactions hypothesis hassuggested that high metabolic rates (and thus high lo-comotory capacity and response rate) are relativelyadvantageous in shallower, more illuminated habitatsto facilitate prey pursuit and predator avoidance overlarger distances (Childress 1995). As light decreaseswith depth to ~1000 m, the accompanying decrease inescape distances, visual detection range, and overallpredator and prey densities favors a slower metabolicrate and perhaps a suite of life history strategies in-cluding slow growth and reactive rather than proac-tive behavior. Predation risk from marine mammalsalso drops sharply with depth (though some marinemammals forage deeper than 1000 m), which mightresult in lower mortality risk and relaxing selective

pressure for both fast growth/early maturity and high-performance swimming ability in prey fishes. The im-portance of locomotory mode as an im portant predic-tor of growth in some of our models may also reflectthe fast vs. slow dichotomy of life history strategies(Réale et al. 2010), as we found the lower activity ben-thic species to grow slightly slower than more activebenthopelagics. Interestingly, Beryx splendens, adeep seamount dweller with rapid metabolism, highactivity, but a very slow growth rate, was an exceptionto our overall findings. This result matches earlierwork suggesting that high metabolic rate but slowgrowth might be common in seamount fishes (Koslow1996). In this case, it is possible that the seamountecosystem, which is more swept by currents and at-tracts large numbers of predators (Morato et al. 2010)in relation to many other deep habitats, favors higherswimming and metabolic activity (Koslow 1996), butwhy this then favors slower growth (given the poten-tially higher predation risk) is unclear. Further, Hoplo -stethus atlanticus, another seamount species in ourdata set, had low growth but did not have a highmetabolic rate. We suggest that further expandingboth metabolic and growth rate measurements onseamount-associated fishes may be a way to moreclearly identify evolutionary pressures on energeticparameters in deep-sea fishes.

The strong relationships found in this meta-analysisprovide an essential first step in understanding thedrivers of growth variation and in locating gaps in thecurrent knowledge for targeted research. However, afully predictive model of growth across the depth con-tinuum or a complete mechanistic understanding ofits drivers will require much more data, a greater un-derstanding of evolutionary pressures on deep-sealife histories, and better estimates of food availability.The potential explanatory power of metabolic rate inour data set, though limited by sample size, is a prom-ising step in a fully predictive framework. Addingmetabolic rate data to the top mixed model increasedR2 values from 47−61% when explaining K, and from27−47% in explaining KGDY. R2 values for explainingAIM and AIMGDY also in creased drastically whenmetabolic rate data was added (38−67% for AIM,22−61% for AIMGDY; B. splendens omitted). Thiswould suggest that while our models are not yet fullypredictive due to a limited data set, given sufficientenvironmental, phylogenetic, and metabolic ratedata, a predictive framework for the growth of deep-sea demersal fishes is plausible.

Hydrostatic pressure is nearly perfectly correlatedwith depth and is a variable not directly explored inthis analysis. Changes in pressure are known to af -

20


fect cell membrane fluidity, enzyme function, andrates of biochemical reactions in fish, and deep-seafishes possess specific adaptations to tolerate highpressures, such as increased proportions of unsatu-rated fatty acids in cell membranes, pressure-adapted enzymes, and high concentrations of intra-cellular enzyme-stabilizing piezolytes (Somero 1990,Yancey & Siebenaller 2015, Gerringer et al. 2017).These adaptations, while necessary for toleratinghigh pressures, have been found to decrease en -zyme efficiency and/or incur associated energeticcosts. For example, fishes living below 500 m pos-sess versions of dehydrogenases with reduced pres-sure sensitivity, but at the cost of catalytic efficiency(Somero 1990). Catalytic efficiency can be increasedby simply producing higher concentrations of en -zyme, but this increased production requires ener-getic input. As growth rate is the summed productof rates of many metabolic reactions, some of theexplanatory power of depth we observed in predict-ing growth rates may arise mechanistically fromincreasing energetic costs associated with maintain-ing enzyme function and catalytic efficiency withincreasing pressure at depth.

It is also worth noting that this work focused ondemersal fishes, a group on which most marinegrowth studies have focused due to their wide com-mercial exploitation relative to deep pelagic species.In the pelagic, there is less research relating growthrates and depth, though Childress et al. (1980) foundthat K values generally followed a ‘slower, deeper’trend. However, bathypelagic fishes grew faster thanshallower mesopelagics in terms of yearly increasesin caloric content. That study compared only 10 spe-cies and estimated age with whole otoliths, whichhave been shown to underestimate age particularlyin deep-sea species, potentially obfuscating patternsof growth (Campana 2001). However, many of thespecies analyzed by Childress et al. (1980) havesmall otoliths, which makes more accurate agingmethods such as transverse sectioning less feasible.Regardless, further growth studies on open-oceanfishes are necessary to determine whether they fol-low similar rates of decline in growth rate with depthas demersal fishes.

A number of past studies could not be includedhere due to procedural issues and lack of age-valida-tion approaches, which hampered the present analy-sis. For instance, further work is needed to untanglesome of the covariation in depth and temperature bycollecting a data set with as many species as possiblefrom the Mediterranean region, where temperatureremains constant and relatively warm with depth.

Twenty sources of age and size data from Mediter-ranean populations were examined, but 75% usedwhole otoliths without validation for age estimationand were rejected for quality control reasons (see‘Supplementary Methods’ in Supplement 3). It is pos-sible that annuli counts from viewing whole otolithsindeed accurately reflect the age for some or even allof these species, but without radiometric or otherindependent validation of the ring periodicity, therisk of underestimating age and overestimatinggrowth rate was concern enough to exclude thesestudies (Campana 2001). More studies of otolith sec-tions or using age validation are needed from warmdeep-sea regions. We also acknowledge that theshallow-water growth literature is much broaderthan synthesized here. We attempted to include asmuch data as possible that fit quality controls and wefocused primarily on including data from phyloge-netic families that had representatives from acrossthe depth gradient.

Determining the drivers of growth across demersalfishes is one component of understanding their re -siliency to anthropogenic impacts, which continue toexpand in the deep sea. Historically, studies of the lifehistories of deep-sea fish focused on direct exploita-tion, with some evidence that faster-growing stocksare more resilient to fishing mortality, though thistrend is highly dependent on how ‘fishing mortality’ isdefined (Koslow et al. 2000, Bailey et al. 2009, Juan-Jordá et al. 2015, Pinsky & Byler 2015, Färber et al.2020). Past age and growth studies have focused onthe upper bathyal (<1500 m), where commerciallyharvested species live. However, future threats to theocean from deep-sea mining (reviewed in Glover &Smith 2003) and climate change (Sweetman et al.2017) will occur at greater depths and present a realneed for more information. For example, deep-seamining is likely to commence at an industrial scalewithin the next few decades, with potentially decadal-scale disruptions to both benthic and pelagic habitats(Stratmann et al. 2018, reviewed in Glover & Smith2003). Though the spatial and temporal extent of dis-ruption is not yet fully clear, at current proposeddepths of operation on seamounts and/ or abyssalplains, it would certainly affect some of the deepest,slowest growing species in our analyses (C. armatus,Anti mora rostrata, H. atlanticus, etc.). In terms ofbroad-scale anthropogenic climate change, recent es-timates suggest abyssal ocean temperature may in-crease by 1°C by the end of the century, with concur-rent decreases in the flux of POC (Sweetman et al.2017). Deep-dwelling Antarctic fishes have no coolerrefuge from warming waters, and may be at highest

21


risk. As such, we require more growth and life historydata from the lower bathyal, abyssal, and hadal zonesto fully understand drivers of growth across the fulldepth gradient and inform assessments of susceptibil-ity to changing habitat conditions.

Deep-sea demersal fishes fulfill critical ecologicalroles as mid-to-top level predators and may beresponsible for direct delivery of carbon from themeso pelagic to the seafloor via consumption of verti-cal migrators (Trueman et al. 2014, Drazen & Sutton2017). These fishes may represent an important com-ponent of biogeochemical cycling through deep car-bon deposition and burial. It is therefore essential toestablish a fully predictive framework with which togauge the susceptibility of these fishes to exploita-tion and future human stressors and to conserve theirroles in both deep-sea ecosystems and the marinecarbon cycle.

Acknowledgements. We thank Allen Andrews for provid-ing thoughts, ideas, and data for this work. We also thankOdd Bergstad for providing data for Coryphaenoides arma-tus, as well as Clive Trueman and Diana Shores from theUniversity of Southampton for their help in providingenzyme data and metadata. We thank Robert O’Malley atOregon State University for help in using Ocean Productiv-ity data. Finally, we thank all the researchers that providedkey data for this meta-analysis: Alisa Abookire, ValérieAllain, Allen Andrews, Steven E. Campana, CassandraBrooks, Graeme Ewing, Chip Cotton, Alex Fogg, MackenzieGerringer, Alison Gould, Sergio Ragonese, Donna Kline,Tom Laidig, Milton Love, Ana Neves, Dan Nichol, IsaacKaplan, Margaret Finch, Cassandra Brooks, Melissa Ma -honey, Nuria Raventos, Mark Belchier, David Sampson,Cara Rodgveller, Kelly Robinson and Christoph Stransky(affiliations available upon request). NSF-OCE providedfunding for this project (Grants #1829612 and #0727135).This is SOEST contribution number 11187.

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environmental, evolutionary, and ecological drivers of slow growth … · fitness of a ‘slow’...

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