Patterns in Micronekton Diversity Across the North Pacific Subtropical Gyre Observed from the Diet of Longnose Lancetfish (Alepisaurus Ferox)

Home , Alepisaurus ferox, Amphitretidae, Phronima sedentaria

Author’s Accepted Manuscript

Patterns in micronekton diversity across the North Pacific Subtropical Gyre observed from the diet of longnose lancetfish (Alepisaurus ferox)

Elan J. Portner, Jeffrey J. Polovina, C. Anela Choy

www.elsevier.com

PII: S0967-0637(16)30357-0 DOI: http://dx.doi.org/10.1016/j.dsr.2017.04.013 Reference: DSRI2784 To appear in: Deep-Sea Research Part I Received date: 28 October 2016 Revised date: 7 March 2017 Accepted date: 18 April 2017 Cite this article as: Elan J. Portner, Jeffrey J. Polovina and C. Anela Choy, Patterns in micronekton diversity across the North Pacific Subtropical Gyre observed from the diet of longnose lancetfish (Alepisaurus ferox) , Deep-Sea Research Part I, http://dx.doi.org/10.1016/j.dsr.2017.04.013 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Patterns in micronekton diversity across the North Pacific Subtropical Gyre observed from the diet of longnose lancetfish (Alepisaurus ferox)

Elan J. Portnera*, Jeffrey J. Polovinab, C. Anela Choyc aHopkins Marine Station, Stanford University, 120 Ocean View Blvd., Pacific Grove, CA 93950, USA bNOAA Pacific Islands Fisheries Science Center 1845 Wasp Blvd., Honolulu, HI 96818, USA cMonterey Bay Aquarium Research Institute 7700 Sandholdt Road, Moss Landing, CA 95039, USA [email protected] [email protected] [email protected] *Corresponding author

Abstract: We examined the diet of a common midwater predator, the longnose lancetfish (Alepisaurus ferox, n= 1371), with respect to fork length, season, and capture location within the North Pacific Subtropical Gyre (NPSG). While A. ferox fed diversely across 98 prey families, approximately 70% of its diet by wet weight was contributed by seven prey families (fishes: Sternoptychidae, Anoplogastridae, Omosudidae, Alepisauridae; hyperiid amphipods: Phrosinidae; octopods: Amphitretidae; polychaetes: Alciopidae). Altogether, these micronekton prey families constitute a poorly known forage community distinct from those exploited by other pelagic predators and poorly sampled by conventional methods. We demonstrate ontogenetic variation in diet of A. ferox between two size classes of a bimodal size structure of our specimens (<97cm fork length = “small”, ≥97cm fork length = “large”). Large A. ferox consumed more fish and octopods, fewer crustaceans, and were more cannibalistic than small A. ferox. Multiple observations supported ontogenetic shifts in vertical foraging habitat, including that large A. ferox consume more mesopelagic and larger prey overall, than small A. ferox. Spatial and

1 seasonal variation in the diet of A. ferox is consistent with expected patterns of variation in prey distribution with respect to oceanographic features of the NPSG. Within both size classes, the diets of specimens collected from the oligotrophic core of the NPSG were more diverse than those collected near the boundaries of the gyre and appeared to track seasonal variation in the position of the northern boundary of the gyre. Our data suggest seasonal and spatial variability in the midwater forage communities exploited by A. ferox across the NPSG, and demonstrate that sustained monitoring of diet would provide valuable insights into the long-term changes in these understudied communities.

Keywords – Pelagic ecology, trophic dynamics, mesopelagic predator, diet analysis, biological sampler, lancetfish, micronekton, North Pacific Subtropical Gyre

1. Introduction Pelagic ecosystems are the largest on the planet in terms of volume (Ramirez-Llodra et al., 2010; Robison 2009) and support numerous species which are commercially harvested (e.g. tunas, billfishes, sharks, and squids; FAO 2012) or protected (e.g. whales, dolphins, and birds; Moore et al., 2009). Many of these species are direct predators of micronekton – actively swimming fishes, crustaceans, cephalopods, and gelatinous organisms approximately 2-20cm in length, which comprise a large biomass in pelagic ecosystems and link production at the base of the food web to top predators (Brodeur & Yamamura, 2005; Dambacher et al., 2010; Moteki, et al., 2001). The composition of micronekton communities varies between ocean basins (Brodeur and Yamamura, 2005), and diel migration of many micronekton species between the surface and mesopelagic depths (~200-1000m) creates vertical variability in within ocean basins (Maas et al., 2014; Tont 1976; Young 1978). Although spatial and temporal variation in micronekton communities has been detected at the scale of ocean basins, there have been few community- scale studies of micronekton ( Maynard et al., 1975; De Forest and Drazen, 2009; Drazen et al., 2011) and many species are able to actively avoid trawls, limiting our ability to thoroughly sample these dynamic midtrophic communities with conventional methods (Clarke, 1973; Kaartvedt et al., 2012). As such, natural variation in micronekton communities and their

2 responses to perturbations represent a critical gap in our understanding of pelagic ecosystem dynamics (Lehodey et al., 2010; Young et al., 2015). For pelagic fish predators, diet analysis is commonly used to study vertical distributions (Choy et al., 2013; Moteki, et al., 2001), variability in trophic ecology over space and time (Kuhnert et al., 2012; Olson and Galván-Magaña, 2002; Watanabe et al., 2009), and has been used in concert with acoustic and trawl surveys to assess temporal variability in prey availability and selectivity (Bertrand et al., 2002). Examining prey communities through diet is inherently biased by foraging behaviors, but provides access to prey that escape capture by midwater trawls. In the case of mesopelagic predators, diet analysis augments our understanding of deep-dwelling communities difficult to sample at high spatial or temporal resolutions. Previous work has shown that large pelagic fish can serve as biological samplers of micronekton and diet analysis can be used to detect large-scale changes in pelagic food webs (Olson et al., 2014; Overholtz et al., 2000). Most of the effort to elucidate pelagic trophic dynamics has been focused on commercially important apex predators, such as tunas and billfishes, many of which are metabolically tied to warm waters at the ocean’s surface (Block et al., 1992, 1997; Dewar et al., 2011, Olson et al., 2016). Examining the trophic ecology of deeper-dwelling, midtrophic predators could greatly augment our understanding of variability in the structure of mesopelagic micronekton communities by increasing the diversity of forage communities sampled. The longnose lancetfish, Alepisaurus ferox, is a midtrophic, mesopelagic predator found circumglobally at tropical and subtropical latitudes (Orlov & Ul’chenko, 2002), and is known mostly from reports of incidental catch in tuna and swordfish longline fisheries (Carruthers et al., 2009; Jantz et al., 2013; Uchiyama et al., 2003) and onshore records of dead individuals (Kubota & Uyeno, 1970; Orlov & Ul’chenko, 2002). Very little is known about the growth rate or life history of A. ferox; its maximum reported length is 215cm (Robins & Ray, 1986) and exploratory histological studies suggest that A. ferox is a simultaneous hermaphrodite, although the functionality of each gonad throughout its life history is unknown (Gibbs 1960, Smith & Atz, 1973). Alepisaurus ferox has a large, blind-sac gut typical of fishes in the suborder Alepisauroidea, but appears to store food in its stomach for extended periods with minimal digestion (Wassersug & Johnson, 1976), allowing for detailed prey identification. Diet studies from multiple ocean basins demonstrate that A. ferox consumes diverse prey (e.g. fishes, crustaceans, cephalopods, etc.) and have generally classified A. ferox as an opportunistic predator

3 (Moteki et al., 1993; Potier et al., 2007a, 2007b; Romanov et al., 2008). However, A. ferox from different locations have comparable diets (Choy et al., 2013, Romanov et al., 2008), suggesting that a better understanding of diet selectivity by A. ferox and the trophic niche it occupies will require more spatially expansive diet studies. Alepisaurus ferox plays important roles in pelagic ecosystems as both predator and prey (Moteki, et al., 2001; Potier et al., 2007a; Young et al., 2010). In the south- and central-western Pacific Ocean, A. ferox has been identified as a “key player” based on high numbers of trophic linkages and the negative modeled effects of its removal from these ecosystems (Dambacher et al., 2010). Within the North Pacific Subtropical Gyre (NPSG), which lies between 20º and 35º N latitude (Howell et al., 2012), the prevalence of A. ferox has increased over the past two decades in parallel with the expansion of the oligotrophic core of the NPSG (Polovina et al., 2008, 2011) and large-scale changes in the size structure and abundance of primary producers and top predators (Polovina et al., 2009; Barnes et al., 2011; Woodworth-Jefcoats et al., 2013). Fisheries observer data show that A. ferox was the most commonly captured species in the Hawaii-based, deep-set longline fishery between 2005 and 2015, but it is unclear how these trends relate to competitive release, reduction in predation, fisheries movement, prey selectivity, or interactions between these factors. Because of its abundance, unusual digestive physiology, and broad consumption of mesopelagic micronekton, A. ferox is a good candidate for a biological sampler of micronekton communities in the NPSG. Quantifying variability in these midtrophic communities is critical to our understanding of the cumulative effects of natural and anthropogenic pressures on pelagic ecosystems and to the predictive power of ecosystem models used to inform effective ecosystem- based fisheries management (Young et al., 2015; Choy et al., 2016; Hunsicker et al., 2016). The goals of this study were to 1) assess variation in the trophic ecology of A. ferox across the NPSG and 2) evaluate more thoroughly the utility of A. ferox as a biological sampler of variability in micronekton communities across large spatial scales. We provide the most extensive diet study of A. ferox to date and discuss the utility of continued monitoring of lancetfish diet towards improving our understanding of spatial and temporal variation in understudied micronekton communities.

2. Data and Methods

4 2.1 Stomach collection and data preparation Stomachs of A. ferox (n = 1371) were collected from 2009-2015 by federal fisheries observers in the Hawaii-based longline deep-set (n = 1319, median max hook depth = 248m) and shallow-set fisheries (n = 42, median max hook depth = 60m; fishery of origin is unknown for 10 stomachs) operating within the NPSG, approximately between 135-180ºW and 0-40ºN (Bigelow et al., 2006) (Table 1). Sample density and distribution across the fishing grounds, binned into 5x5 degree cells, are displayed in Figure 1. To reduce the risk of pseudo-replication of samples from the same sampling event (longline set), we limited the number of specimens to n=4 per set (number of sets = 702, mean number of specimens per set = 1.474, sd = 0.538; Hunsicker et al., 2012). Observers recorded specimen fork length (FL) to the nearest centimeter (range = 20 – 168cm FL, mean = 107.6cm FL, Figure 2), excised the stomach and esophagus, and stored the samples at -20ºC. In the lab, stomachs were dissected following Choy et al. (2013). Prey items were identified to the lowest possible taxon, assigned a digestive state (1 = fully intact; 2 = minimally digested, has most of skin; 3 = partially digested, much of skin missing and some muscle tissue digested; 4 = heavily digested, much or all of the soft tissue digested), and were grouped by prey type and digestion state before being enumerated and weighed. Individual length and weight measurements were collected for each prey type per digestive state from a maximum of three individuals of a representative size-range (smallest, largest, and individual of median size). Prey identifications were made using published keys (Aizawa et al., 2002; Jereb and Roper, 2010; Jereb et al., 2016) and with the help of expert taxonomists.

Table 1: Number of stomachs collected per season by year. The number of stomachs in each sampling period that contained soft-tissue prey and were included in our analyses are given in parentheses (See section 2.1). Maps of the spatial distribution of our samples by season are given in Figure S1.

Season Year Winter: Spring: Summer: Fall: Total Jan-Mar Apr-Jun Jul-Sep Oct-Dec 2009 1 (1) - 23 (16) 95 (63) 119 (80) 2010 11 (8) 13 (12) - 2 (0) 26 (20) 2011 9 (2) 10 (1) 4 (0) - 23 (3) 2014 - 228 (170) 375 (321) 263 (196) 866 (687) 2015 191 (125) 115 (92) 30 (22) 1 (1) 337 (240) Total 212 (136) 366 (275) 432 (359) 361 (260) 1371 (1030)

Pelagic salps (1.71% weight of total stomach contents) were considered to have been consumed dead because they lacked internal structure or organs, unusual for salps found in fish stomachs (Henschke et al., 2016) and the origin of much of their caloric value (Madin et al., 1981). This can be explained by the symbiotic relationship between salps and some species of hyperiid amphipods, such as Phronima sedentaria, which have been reported to feed on salps, use them as feeding platforms, and demarsupiate their broods into salp-derived barrels (Diebel, 1988; Laval, 1980; Madin & Harbison, 1977). Additionally, 92.2% (by number) of the salps were found in stomachs containing P. sedentaria and that salps and P. sedentaria were found in similar numbers in the diet (n = 1417 P. sedentaria, n= 1169 salps). As such, pelagic salps (with the exception of Thetys sp., which was always found with internal organs intact) were removed from our dataset prior to analysis. All analyses discussed here utilize proportional weight to compare diet between sample groups. Most prey items were only minimally or partially digested (41.17% and 51.73% respectively), thus proportional weight measurements should be minimally distorted by differential digestion rates between prey types and accurately reflect consumption (Liao et al., 2001). Cephalopod beaks (n = 458 halves, 0.02% total prey weight) and inorganic items (n = 759 pieces, 5.13% total prey weight) were removed from the dataset prior to analysis. Because they are retained in the stomach for longer periods of time than soft tissue prey, the inclusion of hard parts would overrepresent their numerical importance and underrepresent their gravimetric importance relative to soft tissue prey. Cephalopod prey identified from beaks are reported in Figure S2. Inorganic items, referring to human products including plastics (50% total inorganic item weight), and fishing line or twine (43% total inorganic item weight) were found in 30% of our stomach samples and are addressed elsewhere. To facilitate a more holistic understanding of the relative contribution of prey groups to the diet of A. ferox, we report, in addition to percent weight (%W), the percent number (%N), and the frequency of occurrence (%FO) of prey groups in the overall diet (Bowen, 1996). Unless otherwise stated, analyses were performed using packages in R (R Core Team, 2015).

6 2.2 CART analysis Diet composition was quantified as the proportional wet weight of prey items within an individual stomach. Prey items were grouped by family, and families that contributed more than 1% to the total proportional prey wet weight were included in the analyses (after Kuhnert et al., 2012; Olson et al., 2014). Prey families that contributed less than 1% to the total proportional prey weight were grouped together by a broad taxonomic identifier (e.g. “fish”, “crustacean”, “squid”, etc.) and treated together as an “other” prey group. Prey items identified to a lower taxonomic resolution than family were considered unidentified and removed from the dataset prior to analysis (1.48% of total prey wet weight). In the case of crustacean prey, only families of hyperiid amphipods contributed more than 1% of the proportional diet weight so all other crustacean prey identified at least to class were combined into a single prey group. A diet table listing the prey group designations for all prey types is provided as supplementary material (Table S1). To examine the effects of collection latitude, longitude, year, and season (January-March = winter, April-June = spring, etc.), as well as FL on diet composition of A. ferox, we used classification and regression tree (CART) analysis in “diet” (Kuhnert and Duffy 2013). CART is described in detail in Breiman et al. (1984) and Kuhnert et al. (2012). Ten-fold cross validation was performed on the full tree, which was then pruned to a size that minimized the cross- validated prediction error rate. Following the “1-SE” rule (Breiman et al., 1984), we selected the least complex pruned tree within one standard error of the tree with the minimum cross-validated error rate. The importance of each variable for explaining the variance of our dataset was determined as described in Kuhnert et al. (2012) and is reported relative to the explanatory variable that most accurately predicted the splits at all nodes throughout the tree. To improve the accuracy with which our selected tree predicted the diet composition in each node, we performed traditional bootstrap aggregation (n=500) in “diet”. To determine whether the prediction error was affected by spatial dependence of our samples, we fitted a variogram to the residuals of spatially stratified (1x1 degree) bootstrap predictions in “diet” (n=500) (Dale and Fortin, 2014; Kuhnert et al., 2012; Olson et al., 2014). The Gini Index of Diversity, (D, Gini, 1912) is reported for each terminal node of the pruned regression tree. Set-type was initially included in our CART analysis, but was found not to contribute to the predictive accuracy of our analysis (Relative importance = 0) and was excluded as an explanatory variable.

2.3 Diet similarity To quantify the diet similarity between samples within nodes identified by the CART analysis, we calculated the Morisita-Horn Dissimilarity Index (ĈMH, Horn, 1966) between each node using the package “vegan” (Oksanen et al., 2015). ĈMH is an appropriate metric of diet similarity between groups with different sample sizes for data reported as proportional biomass (Krebs, 1999). We performed analysis of similarity (ANOSIM, Clarke, 1993) on the diet dissimilarity matrix and examined the prey groups responsible for driving the differences using similarity percent (SIMPER, Clarke, 1993) in PRIMER 6 (Clarke and Gorley, 2006).

2.4 Assessment of behavioral variation Differences in vertical habitat use of A. ferox between nodes were examined by re- grouping prey types by depth habitat (Table 2) based on trawl records from the literature (Table S1). Prey items that exhibit variable habitat use throughout their life cycles were assigned to the depth habitat of the developmental stage (e.g., larval, juvenile, adult) corresponding to the mean size of that prey type in our dataset (Table S1). One drawback of utilizing a single depth habitat designation for each prey type is that it does not account for ontogenetic variation in depth habitats. To more thoroughly examine the possibility that A. ferox exhibits ontogenetic descent with respect to foraging depth, we examined how prey size changed with A. ferox FL for select prey types that are known to utilize variable depth habitats throughout their life. Ontogenetic descent is well described for several species of squids in the family Chiroteuthidae, as well amphitretid octopods in the subfamily Bolitaeninae (Young, 1978), and prey in these groups were selected to examine size-based evidence for ontogenetic descent in A. ferox.

Table 2: Descriptions of criteria used for depth habitat designations. The epipelagic and mesopelagic zones are typically considered to be 0-200m and 200-1000m respectively (e.g. Sutton, 2013), but many species that spend most of their time in these regions and are considered “epipelagic” or “mesopelagic”, have also been captured outside of the lower depth limits of these

8 regions (Mundy, 2005; Choy et al. 2009, Sutton, 2013). To account for these cases, we extended the lower boundaries of our definitions for epipelagic and mesopelagic habitats by 50% of their conventional width (100m and 400m in the case of epipelagic and mesopelagic zones respectively). Although these depth designations create some overlap between zones, they allow us to better describe prey vertical habitat when applied to the deepest records of individual species, such that animals sometimes found just outside a conventional depth zone are not named in a way that suggests utilization of a deeper habitat. See table S1 for references of depth habitats for each prey type.

Depth Habitat Description epipelagic Found strictly between the surface and 300m. epi/upper mesopelagic Utilizes the water column between the surface and 800m depth. Is not known to undergo rhythmic vertical migration. upper mesopelagic migrator Exhibits diel migration between epipelagic waters (0-300m) at night and upper mesopelagic waters (200-800m) during the day. epi/lower mesopelagic Utilizes the water column between the surface and 1400m depth. Is not known to undergo rhythmic vertical migration. lower mesopelagic migrator Exhibits diel migration between epipelagic waters at (0-300m) night and lower mesopelagic waters (800-1400m) during the day. epi/bathypelagic Utilizes the water column between the surface and >1400m depth. Is not known to undergo rhythmic vertical migration. mesopelagic Found strictly between 200 and 1400m depth meso/bathypelagic Utilizes the water column between 200m and >1400m depth. Is not known to undergo rhythmic vertical migration. bathypelagic Found strictly below 1000m.

3. Results 3.1 General diet description Of the 1371 stomachs sampled, 238 were empty (17.36%), and 69 contained only cephalopod hard parts and inorganic items (5.03%). Of the remaining 1064 stomachs containing soft-tissue prey, 1022 (75.13%) contained identifiable prey suitable for inclusion in the CART analysis (see section 2.1 and 2.2, Figure 3). A total of 248 prey types belonging to 98 families were identified. Prey in 89 of these families (52 fish, 13 crustacean, 15 squid, 3 octopod, 1 polychaete, 1 heteropod) representing 97.31% of the total prey wet weight, were treated as 23 prey groups (8 fish, 6 crustacean, 4 squid, 3 octopod, 1 polychaete, 1 heteropod) and included in the analyses. Prey in the remaining 9 families, treated as 6 prey groups, contributed 1.13% of the total prey wet weight and were excluded by the CART analysis. Unidentified prey items

9 represented 1.56% of the total prey wet weight and were removed prior to analyses. Together the hyperiid amphipod Phrosina semilunata (C-Psin); fishes in the families Sternoptychidae (F-Ster), Anoplogastridae (F-Anop), Omosudidae (F-Omos), and Alepisauridae (F-Alep); polychaetes in the family Alciopidae (P-Alc), and octopods in the family Amphitretidae (O-Amph), contributed 70.26% of the total prey wet weight and 59.51% of the proportional prey weight. The relative importance of omosudid and alepisaurid fishes, as well as amphitretid octopods increased with increasing FL, and the importance of amphipods in the family Phrosinidae decreased (Figure 4). The relative importance of sternoptychid and anoplogastrid fishes, as well as alciopid polychaetes showed no distinct trends with respect to FL across the size range of A. ferox sampled. With increasing latitude, the relative importance of Platyscelidae (hyperiid amphipod), Phrosinidae, Sternoptychidae, and Anoplogastridae increased. Octopods in the family Amphitretidae and omosudid fishes demonstrated decreased importance with increasing latitude. Cannibalism contributed higher proportions to the overall diet of specimens collected in the center of the NPSG between 20 and 30ºN compared to specimens collected near the edges of the gyre (Figure 5). 3.2 CART analysis and diet similarity The most parsimonious tree generated by the CART analysis within one standard error of the tree with the minimum cross-validated error rate was selected (Breiman et al., 1984) and included 1022 stomachs (Figure 6a). Latitude was the most important predictor of diet (Rank = 1.00), followed by season (Rank = 0.574), and FL (Rank = 0.546). Neither year (Rank = 0.176) nor longitude (Rank = 0.052) were strong predictors of diet (Figure 6b). An experimental variogram of the spatial bootstrap prediction residuals exhibited a small nugget effect (due to sampling error, or variation at scales smaller than the sampling distance) and demonstrated a minimal effect of distance on variance (Figure 6c), suggesting that spatial autocorrelation between individual diets is negligible and that traditional bootstrap methods sufficiently estimate error of diet predictions (Dale and Fortin, 2014; Kuhnert et al., 2012). There was a shift in diet at 97cm FL, as well as variable spatial and temporal structure for individuals smaller than 97cm FL (hereafter referred to as “small”, n=342) and individuals larger than or equal to 97cm FL (hereafter referred to as “large”, n=680). Large A. ferox consumed significantly more fish and octopus prey (t-tests; t = 4.48, df = 482.13, p = 1.08e-5; t = 6.98, df = 169.66, p = 6.28e-11, respectively), and significantly less crustacean prey than small A. ferox (t-test; t = -7.97, df =

10 437.94, p = 1.40e-14). Large A. ferox were significantly more cannibalistic than small A. ferox (t- test; t = 12.72, df = 175.95, p = 2.20 e-16) Small A. ferox were split by season into Node 5 (season = winter, n= 38), and further by latitude into Node 10 (season ≠ winter, latitude ≥ 23.22ºN, n=217) and Node 9 (season ≠ winter, latitude < 23.22ºN, n=91). Diet diversity in Node 5 (D = 0.673) was lower than in either Node 10 (D = 0.808) or Node 9 (D = 0.844). Node 10 was further split about 25.48ºN latitude into Nodes 20 and 21, however the differences detected between Nodes 20 and 21 were not large enough for subsequent ANOSIM to detect, so we stepped backwards up the tree and used Node 10 for all comparisons between nodes. Large A. ferox exhibited more complex spatial and temporal structure than small A. ferox. Samples were split at 18.25ºN latitude into Node 7 (latitude < 18.25ºN, n= 106, D = 0.746) and all other nodes of large A. ferox were further split by season. Stomachs collected in the first half of the year, north of 18.25ºN were distinguished solely by season as Node 24 (latitude ≥ 18.25ºN, season = spring, n = 103, D = 0.779) and Node 25 (latitude ≥ 18.25ºN, season = winter, n = 79, D = 0.729). Samples of large A. ferox collected in summer and fall exhibited a higher degree of spatial structure than stomachs collected in the first half of the year and were split about 25.13ºN latitude into Node 26 (18.25ºN ≤ latitude < 25.13ºN, season = summer or fall, n = 154, D = 0.809) and Node 27 (latitude ≥ 25.13ºN, season = summer or fall, n = 241, D = 0.791). Table 3 lists the proportional weight of each prey group for the entire dataset and per node. Maps of the distribution and number of stomachs in each node are given in Figure S3. Table 3: Gravimetric (%W), numeric (%N), and frequency (%FO) contributions of each prey family included in the CART analysis. For each diet metric, the five most important prey groups are bolded. All three diet metrics are provided for each node in Table S2.

All stomachs %W by Node CART Family Code %W %N %FO 05 09 10 07 24 25 26 27 Crustaceans Lanceolidae C-Lanc 0.22 1.43 3.10 0.01 0.86 0.29 0.19 0.19 0.05 0.22 0.21 Phronimidae C-Pnim 2.30 7.23 9.27 0.80 0.74 3.15 0.79 3.39 0.38 1.12 0.73 Phrosinidae C-Psin 3.99 18.38 11.22 1.33 4.59 15.52 0.26 2.78 0.65 1.90 2.92 Platyscelidae C-Plat 0.98 4.34 7.82 8.56 0.40 2.89 0.02 0.87 0.33 2.92 3.57 Uristiidae C-Uris 0.23 1.70 2.60 1.83 1.03 0.97 0.44 0.05 0.06 0.02 0.01 Other C-Other 0.22 1.07 2.28 0.22 1.69 0.39 0.11 0.12 0.00 0.56 0.05 Fishes Alepisauridae F-Alep 27.56 2.46 5.98 5.59 5.93 5.12 20.34 22.27 39.41 36.36 35.97 Anoplogastridae F-Anop 11.59 6.12 3.36 10.68 1.93 16.75 1.04 29.13 15.79 5.50 7.28 Bramidae F-Bram 0.83 0.41 1.24 - 8.99 2.45 0.26 0.27 - 0.17 0.50 Gempylidae F-Gemp 1.60 0.57 1.95 2.83 9.20 2.56 0.37 0.19 0.73 2.41 1.56 Omosudidae F-Omos 5.00 0.67 2.25 - 0.87 3.96 10.39 0.41 4.91 10.13 4.25 Paralepididae F-Para 0.65 0.25 0.89 1.65 5.49 0.76 1.38 0.15 0.21 0.89 0.12

11 Sternoptychidae F-Ster 13.18 12.53 12.48 23.91 13.63 17.56 13.89 9.42 8.02 6.24 17.10 Other F-Other 6.26 2.55 6.20 12.96 16.48 8.84 13.92 1.96 6.44 7.40 3.11 Octopods Amphitretidae O-Amph 6.66 1.46 4.64 3.31 4.71 1.52 17.47 2.98 7.16 9.70 5.20 Argonautidae O-Argo 2.34 0.34 1.00 9.93 1.51 1.17 1.57 1.41 5.10 3.13 2.26 Tremoctopodidae O-Trem 2.28 0.31 0.76 - 2.69 4.74 1.79 5.63 - 0.26 1.46 Squids Chiroteuthidae S-Chir 1.00 0.29 1.08 - 0.09 0.03 2.01 1.74 1.48 1.74 0.29 Ommastrephidae S-Omma 0.75 0.34 1.04 10.79 5.73 1.44 0.91 0.21 0.05 0.79 0.14 Onychoteuthidae S-Onyc 2.31 0.44 1.45 0.68 6.90 2.51 4.39 0.40 2.15 2.72 1.91 Other S-Other 4.01 0.80 3.01 4.03 2.27 1.11 6.50 11.76 4.76 0.97 1.38 Heteropods Carinariidae M-Cari 1.98 6.07 5.72 0.51 0.45 2.04 0.47 3.60 1.87 1.44 2.15 Polychaetes Alciopidae P-Alci 4.08 30.26 10.66 0.39 3.83 4.22 1.48 1.05 0.45 3.38 7.84

Pairwise Morisita-Horn dissimilarity indices were calculated to quantify the degree of diet dissimilarity between nodes and are listed in Table 4. Nodes of large A. ferox were generally more similar to each other than to nodes of small A. ferox and vice versa. When compared to all nodes in the opposite size class, diets were always most similar when collected at a similar time of year and/or latitude (e.g. Node 5 most similar to Node 25, Node 7 most similar to Node 9, etc.). Pairwise ANOSIM between nodes demonstrated that the diet compositions in all nodes were significantly different from each other (Table 4). SIMPER analysis showed that for all comparisons, an average of 60.19% (SE = 1.38) of the difference in diet between nodes could be accounted for by variation in seven prey groups (fishes: Alepisauridae, Anoplogastridae, Omosudidae, Sternoptychidae; hyperiid amphipods: Phrosinidae; polychaetes: Alciopidae; and octopods: Amphitretidae).

Table 4: Pair-wise Morisita-Horn dissimilarity indices (0 = completely similar, 1 = infinitely dissimilar) between nodes (see Figure 6) for family-level diet composition based on proportional weights are given below the diagonal line. R statistics from pairwise ANOSIM are given above the diagonal. For all the R statistics, p = 0.001, unless indicated with a “*”, for which 0.001 < p < 0.05 (global R = 0.148, p = 0.001).

Nodes 5 9 10 7 24 25 26 27 5 0.206 0.163 0.128 0.192 0.070* 0.193 0.302 9 0.555 0.125 0.130 0.185 0.169 0.097 0.254 10 0.302 0.276 0.266 0.088 0.123 0.132 0.076 7 0.475 0.406 0.496 0.187 0.077 0.130 0.288 24 0.434 0.507 0.284 0.513 0.057 0.056* 0.119 25 0.198 0.518 0.255 0.347 0.232 0.061 0.129

12 26 0.550 0.342 0.407 0.321 0.341 0.243 0.098 27 0.378 0.408 0.204 0.380 0.234 0.087 0.148

Differences in the proportions of sternoptychid fish prey between nodes (ANOVA; F = 10.62, p = 2.33e-12) were driven by higher proportions in individuals collected in the northern NPSG, or during the winter (Nodes 5, 10, 25, and 27), than in those collected in the central or southern NPSG, or spring-fall (Nodes 7, 9, 24, and 26). Thirteen of the sixteen possible pairwise comparisons between these groups of nodes were significantly different (Tukey’s HSD, p-adj. = 4.0e-7 – 0.04). Fishes in the family Anoplogastridae were consumed in similar proportions in all nodes, with the exception of Node 24, which had significantly higher proportions of this fish than any other node (ANOVA; F = 16.53, p = 7.51e-16, Tukey’s HSD, p-adj = 1.0e-8 – 0.01 for all comparisons). Fish families pooled in the “other” prey group were consumed in higher proportions by A. ferox collected in the southern portion of our sampling area (ANOVA; F = 8.90, p = 1.30e-9). Individuals in Node 09 consumed significantly more “other” fish than individuals in all other nodes except Node 07 and Node 05 (Tukey’s HSD, p-adj= 1.0e-8 – 0.004). 3.3 Variation in vertical habitat Large A. ferox consumed more mesopelagic, epi/bathypelagic, and meso/bathypelagic -4 13 prey (t-tests; t = 3.62, df = 422.26, p-value = 3.28e ; t = 7.68, df = 446.12, p-value = 1.01e- ; t = 4.96, df = 214.06, p-value = 1.43e-6, respectively); and less epi/upper mesopelagic, epi/lower mesopelagic, lower mesopelagic migratory, and bathypelagic prey (t-tests; t = -2.61, df = 117.48, p-value = 0.01; t = -4.848, df = 240.67, p-value = 1.15e-5; t = -4.82, df = 85.96, p = 6.22e-6; t = - 6.47, df = 349.83, p = 3.22e-10, respectively) than small A. ferox. There were no differences in the proportions of epipelagic or upper mesopelagic migratory prey consumed by large and small A. ferox (t-tests, t = -0.64, df = 234.57, p = 0.53; t = -0.92, df = 56.30, p = 0.36, respectively). Although there was a weakly significant difference in the amount of epipelagic prey consumed between nodes (ANOVA, F = 2.08, p = 0.04), none of the pairwise comparisons were significantly different. Large A. ferox also consumed significantly larger individuals of mesopelagic octopods in the subfamily Bolitaeninae than small A. ferox (t-test, t = 2.306, df = 29.24, p = 0.028), and a significantly higher proportion of mesopelagic squids in the family Chiroteuthidae (t-test, t = 6.035, df = 44.89, p = 2.761e-7) (Figure 7).

13 Though small A. ferox consumed significantly more bathypelagic prey than large A. ferox, there was a weak relationship between node identity and the proportion of bathypelagic prey (ANOVA, F = 2.16, p = 0.043), and none of the pairwise comparisons between nodes were significant. Re-grouping prey according to depth habitat resulted in general trends in diet similarity between nodes of large and small A. ferox similar to those observed for prey families and pairwise ANOSIM demonstrated that the diet composition between nodes was significantly different in all except three of the 28 comparisons (Table 5). However, size was a worse predictor of diet similarity when prey were grouped by depth habitat than by family.

3.4 Spatial and seasonal variation in diet composition Specimens collected from the center of the NPSG had the highest diet diversity among nodes (Node 9, D = 0.849; Node 26, D = 0.809) and specimens collected in winter or the southernmost portion of the NPSG had the lowest diversity among nodes (Node 5, D = 0.673; Node 25, D = 0.729; Node 7, D = 0.746). The diets of large and small A. ferox collected in the northern NPSG or during winter were most similar to large A. ferox collected during spring. Large A. ferox collected in winter or spring or at the southern boundary of the NPSG had diets that were most dissimilar to small A. ferox collected from the southern portion of the NPSG. Regardless of FL, A. ferox collected in the northern portion of the NPSG consumed more mesopelagic (ANOVA, F = 15.6, p = 2.16e-16; Tukey’s HSD, p-adj = 3.8e-6 – 0.002) and less epi/lower mesopelagic prey (ANOVA, F = 7.49, p = 2.16e-8; Tukey’s HSD, p-adj = 3.0e-7 – 0.01) than A. ferox collected in the southern portion of the gyre.

Table 5: Pair-wise Morisita-Horn dissimilarity indices (0 = completely similar, 1 = infinitely dissimilar) between nodes (see Figure 6) for diet composition based on proportional weights of prey by depth habitat are given below the diagonal line. R statistics from pairwise ANOSIM are given above the diagonal. For the R statistics; all comparisons had p values = 0.001 unless indicated with a “**”, for which 0.001 < p ≤ 0.01, “*” for which 0.01 < p ≤ 0.05, or highlighted in grey for which p > 0.05 (global R = 0.101, p = 0.001).

Nodes 5 9 10 7 24 25 26 27 5 0.133 0.129** 0.079* 0.153** 0.110* 0.225 0.306 9 0.295 0.094 0.149 0.100 0.112 0.044** 0.221 10 0.154 0.139 0.132 -0.011 0.075** 0.085 0.079

14 7 0.130 0.326 0.175 0.054 0.003 0.129 0.175 24 0.123 0.218 0.071 0.079 0.023* 0.026* 0.035 25 0.155 0.280 0.153 0.044 0.025 0.067 0.095 26 0.415 0.109 0.188 0.263 0.167 0.185 0.086 27 0.285 0.209 0.132 0.147 0.047 0.056 0.077

4. Discussion 4.1 General findings The diet of Alepisaurus ferox was significantly structured across the NPSG with respect to FL, latitude, and season. Increased prey diversity in the diet of A. ferox collected from the center of the NPSG relative to those collected closer to its edges is consistent with global trends of subtropical peaks in species diversity in the ocean (Rutherford et al., 1999; Worm et al., 2005). Though overall diet diversity was high relative to other pelagic fishes (Olson et al., 2014; Williams et al., 2015), approximately 70% of the prey wet weight was contributed by only seven prey families, supporting the observation by Choy et al. (2013) that A. ferox exhibit a high degree of selectivity over large oceanic regions. This selectivity appears to be for a specific forage community (dominated by mesopelagic fishes and octopods, as well as migratory amphipods), whose constituents vary with respect to species-specific latitudinal gradients in distribution across the NPSG. Fishes in the families Anoplogastridae and Omosudidae, monospecific prey families that are two of the seven most important prey types for A. ferox, also utilize this forage community, consuming large amounts of sternoptychid fishes and amphipods in the families Phrosinidae and Platyscelidae (pers. observation). It appears that A. ferox share a portion of their forage community with these prey fishes and large A. ferox incorporate them, as well as juvenile A. ferox, into their diets as they grow.

4.2 Ontogenetic variation in trophic ecology There was a distinct shift in diet between A. ferox smaller than 97cm FL and larger than 97cm FL. Increased importance of mesopelagic prey in the diets of large relative to small specimens suggests that ontogenetic descent to deeper foraging grounds is at least partially responsible for the diet differences observed between the size-classes of A. ferox sampled in the NPSG. Though small A. ferox consumed more bathypelagic prey than large A. ferox, the only prey item in the bathypelagic category was the gamariid amphipod, Hirondellea gigas, a resident of hadal trenches not well known in the central Pacific (France, 1993). Ontogenetic descent

15 between size classes was also supported by the presence of larger amphitretid octopods in the subfamily Bolitaeninae with increasing A. ferox FL and the increased importance of relatively deep dwelling amphitretid octopods and chiroteuthid squid in large relative to small A. ferox. The ontogenetic shift in diet composition occurs between modes of the bimodal size distribution of our samples (Figure 2). Romanov and Zamorov (2007) also report significant differences in prey composition between small (<100cm FL) and large (>= 100cm FL) lancetfish from the Indian Ocean, however only specimens from one of their sub-regions exhibited size- bimodality. Bimodal size distributions in animal populations have been attributed to a variety of inherent and imposed factors including cannibalism (Claessen et al., 2002; Hammar, 2000); hermaphroditism (Huston and DeAngelis, 1987); temporal and spatial variation in food availability (DeAngelis and Coutant, 1982; Nicieza et al., 1991); and sampling bias (Finstad and Berg, 2004). Alepisaurus ferox is both hermaphroditic and cannibalistic, and the size bimodality of our specimens was observed throughout our sampling region which comprises habitats of variable primary productivity and prey density (Irigoien et al., 2014; Lehodey et al., 2015). Future work to describe the life history of A. ferox, define size-specific gear selectivity, and to examine the effects of cannibalism and mesopredator release due to commercial fishing (Crowder et al., 2008) will be useful to elucidate drivers of the bimodal size structure of A. ferox in the Indian Ocean and NPSG.

4.3 Spatial and seasonal variation in trophic ecology We showed that A. ferox from the oligotrophic core of the NPSG had distinct diets from samples collected closer to the northern and southern edges of the NPSG, suggesting that basin- scale variation in habitat conditions may be driving the observed diet variation. More specifically, the 18.25º N latitude separation for large A. ferox diet corresponds to the northern boundary of the North Equatorial Current and separates the equatorial biome from the NPSG (Howell et al., 2012). While surface measurements of temperature and chlorophyll suggest a spatially homogeneous NPSG, subsurface temperature, nutrients, chlorophyll, and oxygen profiles start to shoal at about 25º N (Boyer et al., 2013). Thus a separation at 25º N latitude would capture depth-integrated northern and southern environmental differences. Furthermore, A. ferox from the southern NPSG, corresponding with the oligotrophic core of the gyre, consumed a smaller proportion of deeper-dwelling prey than A. ferox from the northern NPSG

16 (Figure 8). This apparent increase in habitat depth utilization by A. ferox may reflect a shoaling of midwater prey with increased productivity and turbidity outside of the oligotrophic core of the gyre (Howell et al., 2010; Tont, 1976) rather than a change in vertical habitat use by A. ferox. Seasonal differences in diet within our sampling area also suggest that diet variation is driven by habitat variability across the NPSG. Sea surface temperatures (SSTs) in the North Pacific are at their annual minima during winter and the transition zone chlorophyll front (TZCF), which forms a dynamic boundary between oligotrophic waters to the south (<0.2mg C/m3) and more productive waters to the North (>0.2mg C/m3, Polovina et al., 2015), is at its southernmost extent between 30-35ºN (Polovina et al., 2001). In 2014, SST at 30ºN 155ºW ranged between ~16ºC in winter (February) and ~26ºC in summer (September), whereas SST at 18.25ºN 155ºW ranged between ~25ºC in winter (February) and ~27.5ºC in summer (September) (Figure S5). When compared to individuals collected at other times, winter-caught A. ferox are always more similar to those collected in the northern region. This pattern in diet similarity could be expected if a subtropical forage community extended southward during boreal spring. Large A. ferox collected south of 18.25ºN did not exhibit seasonal variation in their diet, perhaps reflecting minimal variation in a tropical forage community in the absence of large seasonal variation in SST or productivity (Figure S5). Understanding the relationship between oceanographic conditions and A. ferox diet could validate the hypothesis that seasonal and spatial variation in A. ferox diet is driven changes in the distribution of habitats supporting distinct micronekton communities. Large A. ferox exhibited a higher degree of spatial structure in their diet than small A. ferox, but also exhibited a higher degree of within size-class diet similarity, suggesting less- pronounced shifts in the diets of large relative to small A. ferox across the NPSG. Elevated dietary similarity within large relative to small A. ferox could be explained by more extensive movements between feeding events by larger individuals. As large A. ferox generally consume more mesopelagic prey than small A. ferox, this trend could also reflect differences in responses of prey to seasonal changes in the upper portion of the ocean. Epipelagic, epi/upper mesopelagic, and epi/lower mesopelagic prey experience higher variability in conditions with respect to latitude than mesopelagic prey (Iwasaka et al., 2006; Lee et al., 2015), perhaps leading to higher variability in the distribution of epipelagic than mesopelagic prey across small spatial scales. The difference in degree of seasonal structure between nodes of large and small A. ferox may also

17 support this assertion. Small A. ferox exhibit seasonal variation only in winter, while large A. ferox exhibit independent seasonal structure in the winter and spring. If mesopelagic prey respond more slowly than epipelagic prey to seasonal changes in the environment at the surface, we would expect the diet of large A. ferox to change more gradually. It is unclear whether the spatial and seasonal differences in familial prey composition reflect variation in the availability of prey, or variable competition with other pelagic predators (Moteki, et al., 2001). Though there is a lack of trawl data to demonstrate the spatial distribution of these common prey items, which are thought to readily avoid pelagic trawls, diet data from other predators in the NPSG show low degrees of diet similarity (Choy et al., 2013; Moteki et al., 2001). In the case of swordfish and bigeye tuna from Hawaiian waters, which exhibit low diet overlap with A. ferox driven by fishes in the family Sternoptichydae (Moteki et al., 2001), the trend in consumption with latitude is the opposite of what we would expect if A. ferox were competing with these predators for sternoptychid prey. Alepisaurus ferox consume increasing proportions of sternoptychids with increasing latitude even as overlap with suitable habitat for bigeye tuna and swordfish increases (Reygondeau et al., 2012). Given successful prey partitioning by mesopelagic predators, it is unlikely that the diet variation is driven by variation in competition across the boundaries defined by our analyses, and it would be worthwhile to examine other data that may further clarify the possibility that changes in diet are largely driven by changes in prey availability across the NPSG.

4.4 Potential as biological samplers An ideal biological sampler would be a predator that consistently provides high quality samples of prey, feeds broadly within a forage community such that variation in prey composition would be detectable across small spatial and temporal scales, and does not exhibit a high degree of diet overlap with other predators. Alepisaurus ferox appear to consistently exploit a specific forage community across the NPSG, with seven dominant prey families comprising ~70% of diet and a broad diversity of prey comprising the other 30% of their diet by wet weight across the NPSG. Many of the prey species in this forage community (e.g. Amphitretidae, Omosudidae, Anoplogastridae, Alepisauridae, Paralepididae) are poorly sampled by midwater trawls (Williams and Koslow, 1997; Hidaka et al., 2003). Previous studies have demonstrated low diet overlap between A. ferox and other pelagic fish predators (Moteki, et al., 2001; Choy et

18 al., 2013). In this study, A. ferox diet reflects spatial and seasonal variability in oceanographic conditions at relatively small scales. The Hawaii-based longline fishery consistently catches large quantities of A. ferox and a federally-mandated fisheries observer program greatly facilitates the collection of large numbers of samples from these fisheries. Diet analysis is a relatively low cost sampling method that allows for higher spatial and temporal resolution sampling than trawl- or ROV-based sampling, and in the case of A. ferox provides prey items that are minimally digested and readily identifiable. Taken together, these factors demonstrate great potential for A. ferox to act as biological samplers of a poorly-known micronekton forage community in the NPSG.

5. Summary and Conclusions We demonstrated significant spatial, temporal, and ontogenetic variation in the trophic ecology of A. ferox in the NPSG. This work represents the most thorough description of the diet of this circumglobal predator to date and is the first to quantify diet variability in a mesopelagic fish across an ocean basin. Given the low degree of diet overlap with other pelagic predators for which diet data are available, it is likely that prey availability, rather than competition drives this variation and that A. ferox inhabit a distinct ecological niche. It would be worthwhile to utilize other data sources, including trawl samples and other diet studies, to verify this hypothesis. Alepisaurus ferox should be considered as a useful biological sampler of a poorly known mesopelagic micronekton community in the NPSG, and possibly throughout the global distribution of this common midwater predator. These data on the natural ontogenetic and spatial variation in A. ferox diet can be used as the basis for sampling programs to monitor changes in mesopelagic micronekton communities over time (Olson et al., 2014), helping to address a critical gap in our understanding of pelagic ecosystem dynamics by providing a cost-effective tool for monitoring midtrophic organisms. Alepisaurus ferox can also act as a useful biological sampler of plastic debris, as well as provide new ecological information on common prey species and basic biogeographical information on prey species that are poorly known from trawls. Given the low diet overlap between A. ferox and other pelagic predators it is likely that continued investigation of the trophic ecology of this midwater predator will help explain its prevalence in pelagic fisheries across the NPSG despite increasing fishing pressure and ocean warming.

19 Acknowledgements We would like to express our gratitude to the observers of the Pacific Islands Regional Observer Program (PIRO) who diligently provide us with a steady stream of samples. We also thank L. Jantz, K. Busscher, and J. Kelly of PIRO who have facilitated ongoing collections and offered assistance with data retrieval. R. Young, B. Mundy, and M. Wicksten provided valuable assistance with prey identification. We thank L. Duffy and P. Kuhnert for sharing their R- package and providing implementation guidance. P. Woodworth-Jefcoats, M. Abecassis, J. Wong-Ala, J. Gold and J. Drazen assisted with sample processing and provided discussion towards further development of this project. J. Mason and two anonymous reviewers provided thoughtful comments to improve the manuscript. E. Portner was supported in part by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE- 114747, as well as the Joint Institute of Marine and Atmospheric Research (JIMAR). C.A. Choy acknowledges the support of the David and Lucile Packard Foundation, as well as JIMAR.

References: 1. Aizawa, M., Akihito, T., Aonuma, Y., Hatooka, K., Hayashi, M., Hosoya, K., Ikeda, Y., Nakabo, T., Sakamoto, K., Senou, H., Shimada, K., Sugiyama, K., Yamada, U., Yoshino, T., 2002. Fishes of Japan with pictorial keys to the species, English Ed. ed. Tokai University Press, Tokyo. 2. Barnes, C., Irigoien, X., De Oliveira, J.A.A., Maxwell, D., Jennings, S., 2011. Predicting marine phytoplankton community size structure from empirical relationships with remotely sensed variables. J. Plankton Res. 33, 13–24. doi:10.1093/plankt/fbq088 3. Bertrand, A., Bard, F.X., Josse, E., 2002. Tuna food habits related to the micronekton distribution in French Polynesia. Mar. Biol. 140, 1023–1037. doi:10.1007/s00227-001-0776-3 4. Bigelow, K., Musyl, M.K., Poisson, F., Kleiber, P., 2006. Pelagic longline gear depth and shoaling. Fish. Res. 77, 173–183. doi:10.1016/j.fishres.2005.10.010 5. Block, B.A., Booth, D.T., Carey, E.G., 1992. Depth and temperature of the blue marlin, Makaira nigricans, observed by acoustic telemetry. Mar. Biol. 114, 175–183. 6. Block, B.A., Keen, J.E., Castillo, B., Dewar, H., Freund, E.V., Marcinek, D.J., Brill, R.W., Farwell, C., 1997. Environmental preferences of yellowfin tuna (Thunnus albacares) at the northern extent of its range. Mar. Biol. 130, 119–132. 7. Bowen, S., 1996. Quantitative description of the diet, in: Murphy, B.R., Willis, D.W. (Eds.), Fisheries Techniques. American Fisheries Society, Bethesda, Maryland, pp. 513–532.

20 8. Boyer, T.P., J. I. Antonov, O. K. Baranova, C. Coleman, H. E. Garcia, A. Grodsky, D. R. Johnson, R. A. Locarnini, A. V. Mishonov, T.D. O'Brien, C.R. Paver, J.R. Reagan, D. Seidov, I. V. Smolyar, and M. M. Zweng, 2013. World Ocean Database 2013, NOAA Atlas NESDIS 72, S. Levitus, Ed., A. Mishonov, Technical Ed.; Silver Spring, MD, 209 pp., http://doi.org/10.7289/V5NZ85MT 9. Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A. 1984. Classification and regression trees. Wadsworth, California. 10. Brodeur, R., Yamamura, O., 2005. PICES Scientific Report No. 30 Micronekton of the North Pacific PICES Working Group 14 Final Report. PICES. 11. Carruthers, E.H., Schneider, D.C., Neilson, J.D., 2009. Estimating the odds of survival and identifying mitigation opportunities for common bycatch in pelagic longline fisheries. Biol. Conserv. 142, 2620–2630. doi:10.1016/j.biocon.2009.06.010 12. Choy, C.A., Popp, B.N., Kaneko, J.J., and Drazen, J.C. (2009). The influence of depth on mercury levels in pelagic fishes and their prey. Proc. Natl. Acad. Sci. U. S. A. 106, 13865– 13869. 13. Choy, C.A., Portner, E., Iwane, M., Drazen, J.C., 2013. Diets of five important predatory mesopelagic fishes of the central North Pacific. Mar. Ecol. Prog. Ser. 492, 169–184. doi:10.3354/meps10518 14. Choy, C., Wabnitz, C., Weijerman, M., Woodworth-Jefcoats, P., Polovina, J., 2016. Finding the way to the top: how the composition of oceanic mid-trophic micronekton groups determines apex predator biomass in the central North Pacific. Mar. Ecol. Prog. Ser. 549, 9– 25. doi:10.3354/meps11680 15. Claessen, D., Van Oss, C., De Roos, A.M., Persson, L., 2002. The impact of size-dependent predation on population dynamics and individual life history. Ecology 83, 1660–1675. 16. Clarke, T.A., 1973. Some aspects of the ecology of lanternfishes (myctophidae) in the Pacific Ocean near Hawaii. Fish. Bull. 71, 401–434. 17. Clarke, K.R., 1993. Non-parametric multivariate analyses of changes in community structure. Australian Journal of Ecology 18: 117-143. 18. Clarke, K.R., Gorley, R.N., 2006. PRIMER v6: User Manual/Tutorial. PRIMER-E, Plymouth, 192pp. 19. Crowder, L.B., Hazen, E.L., Avissar, N., Bjorkland, R., Latanich, C., Ogburn, M.B., 2008. The Impacts of Fisheries on Marine Ecosystems and the Transition to Ecosystem-Based Management. Annu. Rev. Ecol. Evol. Syst. 39, 259–278. doi:10.1146/annurev.ecolsys.39.110707.173406 20. Dale, M.R., Fortin, M., 2014. Spatial analysis: A guide for ecologists, 2nd ed. Cambridge University Press, Cambridge. 21. Dambacher, J.M., Young, J.W., Olson, R.J., Allain, V., Galván-Magaña, F., Lansdell, M.J., Bocanegra-Castillo, N., Alatorre-Ramírez, V., Cooper, S.P., Duffy, L.M., 2010. Analyzing pelagic food webs leading to top predators in the Pacific Ocean: A graph-theoretic approach. Prog. Oceanogr. 86, 152–165. doi:10.1016/j.pocean.2010.04.011 22. De Forest, L., Drazen, J., 2009. The influence of a Hawaiian seamount on mesopelagic micronekton. Deep Sea Res. Part I Oceanogr. Res. Pap. 56, 232–250. doi:10.1016/j.dsr.2008.09.007 23. DeAngelis, D.L., Coutant, C.C., 1982. Genesis of bimodal size distributions in species cohorts. Trans. Am. Fish. Soc. 111, 384–388. 24. Dewar, H., Prince, E.D., Musyl, M.K., Brill, R.W., Sepulveda, C., Luo, J., Foley, D.,

21 Orbesen, E.S., Snodgrass, D., Laurs, R.M., Hoolihan, J.P., Block, B.A., Mcnaughton, L.M., 2011. Movements and behaviors of swordfish in the Atlantic and Pacific Oceans examined using pop-up satellite archival tags. Fish. Oceanogr. 20, 219–241. doi:10.1111/j.1365- 2419.2011.00581.x 25. Diebel, C.E., 1988. Observations on the anatomy and behavior of Phronima sedentaria. J. Crustac. Biol. 8, 79–90. 26. Drazen, J.C., De Forest, L.G., Domokos, R., 2011. Micronekton abundance and biomass in Hawaiian waters as influenced by seamounts, eddies, and the moon. Deep Sea Res. Part I Oceanogr. Res. Pap. 58, 557–566. doi:10.1016/j.dsr.2011.03.002 27. FAO, 2012. The state of world fisheries and aquaculture. Rome, Italy. 28. Finstad, A.G., Berg, O.K., 2004. Bimodal population size distributions and biased gillnet sampling. Can. J. Fish. Aquat. Sci. 61, 2151–2157. doi:10.1139/F04-157 29. France, S.C., 1993. Geographic variation among three isolated popuations of the hadal amphipod Hirondellea gigas (Crustacea: Amphipoda: Lysianassoidea). Mar. Ecol. Prog. Ser. 92, 277–287. 30. Gibbs, R. H. JR., 1960. Alepisaurus brevirostris a new species of Lancetfish from the Western North Atlantic. Breviora No. 123:1-14. 31. Gini, C., 1912. Variabilita e mutabilita. Stud; Economico-Giuridici della facolta di Giurisprudenza dell Universita di Cagliari, a Ill, Parte 1. 32. Hammar, J., 2000. Cannibals and parasites: conflicting regulators of bimodality in high latitude Arctic char, Sal7elinus alpinus. OIKOS 88, 33–47. 33. Henschke, N., Everett, J.D., Richardson, A.J., Suthers, I.M., 2016. Rethinking the Role of Salps in the Ocean. Trends Ecol. Evol. 31, 720–733. doi:10.1016/j.tree.2016.06.007 34. Hidaka, K., Kawaguchi, K., Tanabe, T., Takahashi, M., Kubodera, T., 2003. Biomass and taxonomic composition of micronekton in the western tropical-subtropical Pacific. Fish. Oceanogr. 12, 112–125. doi:10.1046/j.1365-2419.2003.00230.x 35. Horn, H.S., 1966. Measurement of overlap in comparative ecological studies. Am. Nat. 100 (914), 419–424. 36. Howell, E.A., Bograd, S.J., Seki, M.P., Polovina, J.J., 2012. On North Pacific circulation and associated marine debris concentration. Mar. Pollut. Bull. 65, 16–22. doi:10.1016/j.marpolbul.2011.04.034 37. Howell, E.A., Hawn, D.R., Polovina, J.J., 2010. Progress in Oceanography Spatiotemporal variability in bigeye tuna ( Thunnus obesus ) dive behavior in the central North Pacific Ocean. Prog. Oceanogr. 86, 81–93. doi:10.1016/j.pocean.2010.04.013 38. Hunsicker, M.E., Kappel, C.V, Selkoe, K.A., Halpern, B.S., Scarborough, C., Mease, L., Amrhein, A., 2016. Characterizing driver – response relationships in marine pelagic ecosystems for improved ocean management. Ecol. Appl. 26, 651–663. doi:10.1890/14-2200 39. Hunsicker, M.E., Olson, R.J., Essington, T.E., Maunder, M.N., Duffy, L.M., Kitchell, J.F., 2012. Potential for top-down control on tropical tunas based on size structure of predator−prey interactions. Mar. Ecol. Prog. Ser. 445, 263–277. doi:10.3354/meps09494 40. Huston, M.A., Deangelis, D.L., 1987. Am. Nat. 129, 678–707. 41. Iwasaka, N., Kobashi, F., Kinoshita, Y., Ohno, Y., 2006. Seasonal Variations of the Upper Ocean in the Western North Pacific Observed by an Argo Float. J. Oceanogr. 62, 481–492. 42. Irigoien, X., Klevjer, T.A., Røstad, A., Martinez, U., Boyra, G., Acuña, J.L., Bode, A., Echevarria, F., Gonzalez-Gordillo, J.I., Hernandez-Leon, S., Agusti, S., Aksnes, D.L.,

22 Duarte, C.M., Kaartvedt, S., 2014. Large mesopelagic fishes biomass and trophic efficiency in the open ocean. Nat. Commun. 5, 3271. doi:10.1038/ncomms4271 43. Jacobsen Stout, N., Kuhnz, L., Lundsten, L., Schlining, B., Schlining, K., von Thunn, S. (Eds.). The Deep-Sea Guide (DSG). Monterey Bay Aquarium Research Institute (MBARI). Consulted on: 2017-02-04. 44. Jantz, L.A., Morishige, C.L., Bruland, G.L., Lepczyk, C.A., 2013. Ingestion of plastic marine debris by longnose lancetfish (Alepisaurus ferox) in the North Pacific Ocean. Mar. Pollut. Bull. 69, 97–104. doi:10.1016/j.marpolbul.2013.01.019 45. Jereb, P., Roper, C., Norman, M., Finn, J. (Eds.), 2016. FAO Cephalopods of the World No. 4 Vol. 3: Octopods and Vampire Squids. Rome. 46. Jereb, P., Roper, C.F.E. (Eds.), 2010. FAO Cephalopods of the World No. 4 Vol. 2: Oegopsid and Myopsid squids. Rome. 47. Kaartvedt, S., Staby, A., Aksnes, D., 2012. Efficient trawl avoidance by mesopelagic fishes causes large underestimation of their biomass. Mar. Ecol. Prog. Ser. 456, 1–6. doi:10.3354/meps09785 48. Krebs, C.J., 1999. Ecological Methodology, 2nd edn. Addison Wesley Longman, Inc., Menlo Park, CA. 49. Kubota, T., Uyeno, T., 1970. Food habits of lancetfish Alepisaurus ferox (Order Myctophiformes) in Suruga Bay, Japan. Japanese J. Ichthyol. 17, 22–28. 50. Kuhnert, P.M., Duffy, L.M., Young, J.W., Olson, R.J., 2012. Predicting fish diet composition using a bagged classification tree approach: a case study using yellowfin tuna (Thunnus albacares). Mar. Biol. 159, 87–100. doi:10.1007/s00227-011-1792-6 51. Kuhnert, P., Duffy, L., 2013. diet: Performs an analysis of diet data using univariate trees. R package version 0.933. 52. Laval, P., 1980. Hyperiid amphipods as crustacean parasitoids associated with gelatinous zooplankton. Oceanogr. Mar. Biol. Annu. Rev. 18, 11–56. 53. Lee, E., Noh, Y., Qiu, B., Yeh, S.-W., 2015. Seasonal variation of the upper ocean responding to surface heating in the North Pacific. J. Geophys. Res. Ocean. 120, 5631–5647. doi:10.1002/2015JC010800 54. Lehodey, P., Conchon, A., Senina, I., Domokos, R., Calmettes, B., Jouanno, J., Hernandez, O., Kloser, R., 2015. Optimization of a micronekton model with acoustic data. ICES J. Mar. Sci. 55. Lehodey, P., Murtugudde, R., Senina, I., 2010. Bridging the gap from ocean models to population dynamics of large marine predators: A model of mid-trophic functional groups. Prog. Oceanogr. 84, 69–84. doi:10.1016/j.pocean.2009.09.008 56. Liao, H., Pierce, C.L., Larscheid, J.G., 2001. Empirical Assessment of Indices of Prey Importance in the Diets of Predacious Fish. Trans. Am. Fish. Soc. 130, 583–591. 57. Maas, A.E., Frazar, S.L., Outram, D.M., Seibel, B.A., Wishner, K.F., 2014. Fine-scale vertical distribution of macroplankton and micronekton in the Eastern Tropical North Pacific in association with an oxygen minimum zone. J. Plankton Res. 36, 1557–1575. doi:10.1093/plankt/fbu077 58. Madin, L.P., Cetta, C.M., McAlister, V.L., 1981. Elemental and biochemical composition of salps (Tunicata:Thaliacea). Mar. Biol. 63, 217–226. doi:10.1007/BF00395990 59. Madin, L.P., Harbison, G.R., 1977. The associations of Amphipoda Hyperiidea with gelatinous zooplankton--I. Associations with Salpidae. Deep Sea Res. 24, 449–463.

23 60. Maynard, S.D., Riggs, F. V, and Walters, J.F. (1975). Mesopelagic micronekton in Hawaiian waters: faunal composition, standing stock, and diel vertical migration. Fish. Bull. 73, 726– 736. 61. Moore, J.E., Wallace, B.P., Lewison, R.L., Žydelis, R., Cox, T.M., Crowder, L.B., 2009. A review of marine mammal, sea turtle and seabird bycatch in USA fisheries and the role of policy in shaping management. Mar. Policy 33, 435–451. doi:10.1016/j.marpol.2008.09.003 62. Moteki, M., Arai, M., Tsuchiya, K., Okamoto, H., 2001. Composition of piscine prey in the diet of large pelagic fish in the eastern tropical Pacific Ocean. Fish. Sci. 67, 1063–1074. doi:10.1046/j.1444-2906.2001.00362.x 63. Moteki, M., Fujita, K., Kohno, H., 1993. Stomach contents of longnose lancetfish, Alepisaurus ferox, in Hawaiian waters and central equatorial Pacific waters. J. Tokyo Univ. Fish. 80, 121–137. 64. Mundy, B.M., 2005. Checklist of the fishes of the Hawaiian Archipelago. Bish. Museum Bull. Zool. 6, 704. 65. Nicieza, A.G., Braña, F., Toledo, M.M., 1991. Development of length-bimodality and smolting in wild stocks of Atlantic salmon, Salmo salar L., under different growth conditions. J. Fish Biol. 38, 509–523. doi:10.1111/j.1095-8649.1991.tb03138.x 66. Oksanen, J., Blanchet, G., Kindt, R., Legendre, P., Minchin, P., O’Hara, R., Simpson, G., Solymos, P., Henry Stevens, M., Wagner, H., 2015. vegan: Community Ecology Package. R package version 2.3-2. http://CRAN.R-project.org/package=vegan 67. Olson, R.J., Duffy, L.M., Kuhnert, P.M., Galván-Magaña, F., Bocanegra-Castillo, N., Alatorre-Ramírez, V., 2014. Decadal diet shift in yellowfin tuna Thunnus albacares suggests broad-scale food web changes in the eastern tropical Pacific Ocean. Mar. Ecol. Prog. Ser. 497, 157–178. doi:10.3354/meps10609 68. Olson, R.J., Galván-Magaña, F., 2002. Food habits and consumption rates of common dolphinfish (Coryphaena hippurus) in the eastern Pacific Ocean. Fish. Bull. 100, 279–298. 69. Olson, R.J., Young, J.W., Ménard, F., Potier, M., Allain, V., Goñi, N., Logan, J.M., Galván- Magaña, F., 2016. Chapter Four – Bioenergetics, Trophic Ecology, and Niche Separation of Tunas, in: Advances in Marine Biology. pp. 199–344. doi:10.1016/bs.amb.2016.06.002 70. Orlov, A.M., Ul’chenko, V.A., 2002. A hypothesis to explain onshore records of long-nose lancetfish Alepisaurus ferox (Alepisauridae, Teleostei) in the North Pacific Ocean. Mar. Freshw. Res. 53, 303–306. 71. Overholtz, W.J., Link, J.S., Suslowicz, L.E., 2000. Consumption of important pelagic fish and squid by predatory fish in the northeastern USA shelf ecosystem with some fishery comparisons. ICES J. Mar. Sci. 57, 1147–1159. doi:10.1006 72. Pinkas, L., Oliphant, M.S., Iverson, I.L.K., 1971. Food habits of Albacore, Bluefin Tuna, and Bonito in California waters. Calif. Dep. Fish Game Fish Bull. 152, 1–105. 73. Polovina, J.J., Abecassis, M., Howell, E.A., Woodworth, P.A., 2009. Increases in the relative abundance of mid-trophic level fishes concurrent with declines in apex predators in the subtropical North Pacific , 1996 – 2006. Fish. Bull. 107, 523–531. 74. Polovina, J.J., Dunne, J.P., Woodworth, P.A., Howell, E.A., 2011. Projected expansion of the subtropical biome and contraction of the temperate and equatorial upwelling biomes in the North Pacific under global warming 68, 986–995. 75. Polovina, J.J., Howell, E.A., Abecassis, M., 2008. Ocean’s least productive waters are expanding. Geophys. Res. Lett. 35, L03618. doi:10.1029/2007GL031745 76. Polovina, J.J., Howell, E. a., Kobayashi, D.R., Seki, M.P., 2015. The Transition Zone

24 Chlorophyll Front updated: Advances from a decade of research. Prog. Oceanogr. 1–7. doi:10.1016/j.pocean.2015.01.006 77. Polovina, J.J., Howell, E., Kobayashi, D.R., Seki, M.P., 2001. The transition zone chlorophyll front, a dynamic global feature defining migration and forage habitat for marine resources. Prog. Oceanogr. 49, 469–483. doi:10.1016/S0079-6611(01)00036-2 78. Potier, M., Marsac, F., Cherel, Y., Lucas, V., Sabatié, R., Maury, O., Ménard, F., 2007. Forage fauna in the diet of three large pelagic fishes (lancetfish, swordfish and yellowfin tuna) in the western equatorial Indian Ocean. Fish. Res. 83, 60–72. doi:10.1016/j.fishres.2006.08.020 79. Potier, M., Menard, F., Cherel, Y., Lorrain, A., Sabatie, R., Marsac, F., 2007. Role of pelagic crustaceans in the diet of the longnose lancetfish Alepisaurus ferox in the Seychelles waters. African J. Mar. Sci. 29, 113–122. doi:Doi 10.2989/Ajms.2007.29.1.10.75 80. R Core Team, 2015. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. 81. Ramirez-Llodra, E., Brandt, A., Danovaro, R., De Mol, B., Escobar, E., German, C.R., Levin, L. A., Martinez Arbizu, P., Menot, L., Buhl-Mortensen, P., Narayanaswamy, B.E., Smith, C.R., Tittensor, D.P., Tyler, P. A., Vanreusel, A., Vecchione, M., 2010. Deep, diverse and definitely different: unique attributes of the world’s largest ecosystem. Biogeosciences 7, 2851–2899. doi:10.5194/bg-7-2851-2010 82. Reygondeau, G., Maury, O., Beaugrand, G., Fromentin, J.M., Fonteneau, A., Cury, P., 2012. Biogeography of tuna and billfish communities. J. Biogeogr. 39, 114–129. doi:10.1111/j.1365-2699.2011.02582.x 83. Robins, C., and Ray, G. (1986). A Field Guide to Atlantic Coast Fishes of North America (Boston, MA: Houghton Mifflin Company). 84. Robison, B.H., 2009. Conservation of Deep Pelagic Biodiversity. Conserv. Biol. 23, 847– 858. doi:10.1111/j.1523-1739.2009.01219.x 85. Romanov, E.V., Ménard, F., Zamorov, V.V., Potier, M., 2008. Variability in conspecific predation among longnose lancetfish Alepisaurus ferox in the western Indian Ocean. Fish. Sci. 74, 62–68. doi:10.1111/j.1444-2906.2007.01496.x 86. Romanov, E. V, Zamorov, V. V, 2007. Regional Feeding Patterns of the Longnose Lancetfish (Alepisaurus ferox Lowe, 1833) of the Western Indian Ocean. West. Indian Ocean J. Mar.Sci. 6, 37–56. 87. Rutherford, S., D’Hondt, S., Prell, W., 1999. Environmental controls on the geographic distribution of zooplankton diversity. Nature 400, 749–753. 88. Smith, C.L., Atz, E.H., 1973. Hermaphroditism in the mesopelagic fishes Omosudis lowei and Alepisaurus ferox. Copeia 1, 41–44. 89. Sutton, T.T. (2013). Vertical ecology of the pelagic ocean: classical patterns and new perspectives. J. Fish Biol. 83, 1508–27. Available at: http://www.ncbi.nlm.nih.gov/pubmed/24298949 [Accessed May 26, 2014]. 90. Tont, S.A., 1976. Deep scattering layers: patterns in the pacific. Calif. Coop. Ocean. Fish. Investig. Reports 18. 91. Uchiyama, J., Uchiyama, J., Kazama, T., Kazama, T., 2003. Updated Weight-on-Length Relationships for Pelagic Fishes Caught in the Central North Pacific Ocean and Bottomfishes from the Northwestern Hawaiian Islands. Fish. Sci. 1–34. 92. Wassersug, R.J., Johnson, R.K., 1976. A remarkable pyloric caecum in the evermannellid genus Coccorella with notes on gut structure and function in alepisauroid fishes (Pisces,

25 Myctophiformes). J. Zool. London 179, 273–289. doi:10.1111/j.1469-7998.1976.tb02296.x 93. Watanabe, H., Kubodera, T., Yokawa, K., 2009. Feeding ecology of the swordfish Xiphias glades un the subtropical region and transition zone of the western North Pacific. Mar. Ecol. Prog. Ser. 396, 111–122. doi:10.3354/meps08330 94. Williams, A.J., Allain, V., Nicol, S.J., Evans, K.J., Hoyle, S.D., Dupoux, C., Vourey, E., Dubosc, J., 2015. Vertical behavior and diet of albacore tuna (Thunnus alalunga) vary with latitude in the South Pacific Ocean. Deep Sea Res. Part II Top. Stud. Oceanogr. 113, 154– 169. doi:10.1016/j.dsr2.2014.03.010 95. Williams, A., Koslow, J.A., 1997. Species composition, biomass and vertical distribution of micronekton over the mid-slope region off southern Tasmania, Australia. Mar. Biol. 130, 259–276. doi:10.1007/s002270050246 96. Woodworth-Jefcoats, P.A., Polovina, J.J., Dunne, J.P., Blanchard, J.L., 2013. Ecosystem size structure response to 21st century climate projection: large fish abundance decreases in the central North Pacific and increases in the California Current. Glob. Chang. Biol. 19, 724–33. doi:10.1111/gcb.12076 97. Worm, B., Sandow, M., Oschlies, A., Lotze, H.K., Myers, R.A., 2005. Global patterns of predator diversity in the open oceans. Science 309, 1365–1369. doi:10.1126/science.1113399 98. Young, J.W., Hunt, B.P. V, Cook, T.R., Llopiz, J.K., Hazen, E.L., Pethybridge, H.R., Ceccarelli, D., Lorrain, A., Olson, R.J., Allain, V., Menkes, C., Patterson, T., Nicol, S., Lehodey, P., Kloser, R.J., Arrizabalaga, H., Choy, C.A., 2015. The trophodynamics of marine top predators: Current knowledge, recent advances and challenges. Deep. Res. Part II Top. Stud. Oceanogr. 113, 170–187. doi:10.1016/j.dsr2.2014.05.015 99. Young, J.W., Lansdell, M.J., Campbell, R.A., Cooper, S.P., Juanes, F., Guest, M.A., 2010. Feeding ecology and niche segregation in oceanic top predators off eastern Australia. Mar. Biol. 157, 2347–2368. doi:10.1007/s00227-010-1500-y 100. Young, R., 1978. Vertical distribution and photosensitive vesicles of pelagic cephalopods from Hawaiian waters. Fish. Bull. 76, 583–615.

Figure 1: Map of the distribution of A. ferox specimens collected within the Hawaii-based longline fishing grounds (N = 1371 from both deep and shallow sets). Grey arrows indicate generalized surface current flow in the North Pacific Ocean and the structure of the North Pacific Subtropical Gyre.

Figure 2: Size-distribution of A. ferox specimens in 5cm FL bins. Samples containing soft tissue prey that were included in our analyses are indicated in light grey and samples representing empty stomachs or stomachs containing only hard-tissue prey are indicated in dark grey. The numbers given in parentheses in the figure legend show the number of samples for which length data were not available for each stomach classification.

Figure 3: Distribution of lancetfish specimens, by size class, that had soft-tissue prey in their

26 stomachs, binned into 5x5 degree cells. The size of each bubble corresponds to the sample size in each cell. Length and location data were not available for four and eight of 1022 samples included in the CART analysis, respectively.

Figure 4: Mean bootstrapped proportions of six common prey groups with respect to specimen FL (black line). The grey region indicates the standard error about the mean. Rug plots at the base of each panel show the distribution of the size of A. ferox which contained each prey item. Polychaetes in the family Alciopidae (not shown) had a trend very similar to anoplogastrid fishes (F-Anop).

Figure 5: Mean bootstrapped proportions of six common prey groups with respect to specimen collection latitude (black line). The grey region indicates the standard error about the mean. Rug plots at the base of each panel show the capture latitudes of A. ferox which contained each prey item.

Figure 6: A) Pruned classification tree selected after 10-fold cross validation (CP = 0.0063). Values of the explanatory variables used to split each node are given at each node. The shapes at each terminal node indicate the most important prey type in that node. Nodes 20 and 21 are shown in grey to demonstrate that their parent, Node 10, was used in all subsequent analyses. B) Bar graphs showing the importance of each explanatory variable across the entire tree relative to the explanatory variable that best predicted diet structure across the entire tree. C) Variogram fitted to the residuals of the bootstrap diet predictions (see Kuhnert et al., 2012).

Figure 7: Size of cephalopods consumed by large and small size classes of A. ferox. Octopods in the subfamily Bolitaeninae are shown in panel (A) and chiroteuthid squids are shown in panel (B). In each panel, the vertical dashed line shows the location of the break between size classes at 97cm FL and the horizontal gray bar indicated the range of sizes at which ontogenetic migration from epipelagic to mesopelagic depths is known to occur in each prey group.

Figure 8: Mean proportions of prey, grouped by depth habitat, within the diets of lancetfish from Nodes 10, 9, 26, and 27 (clockwise from the top left, and represented with pictograms of A. ferox

27 size classes and position within the gyre: N = North, S = South). Error bars show the standard error about the mean for each depth habitat. The proportional contributions of prey from each depth habitat for all nodes are displayed in Figure S4.

Figure S1: Seasonal distribution, binned into 5x5 degree cells, of small and large lancetfish specimens that had soft-tissue prey in their stomachs. 1) Winter, 2) Spring, 3) Summer, 4) Fall. The size of each bubble is proportional to the number of samples in each cell. Length and location data were not available for four and eight of our 1022 samples, respectively.

Figure S2: Numeric composition of cephalopod prey as identified from beaks. A) samples grouped into families according to rules for CART analysis. B) Samples grouped according to family, S = squid, O = octopod. The families (O) Vampyroteuthidae, (O) Alloposidae, (S) Ancistrocheiridae, and (S) Mastigoteuthidae were represented by only one beak and were not included in panel B to facilitate visualization.

Figure S3: Maps of the distribution of stomachs in each node binned in 5x5 degree cells. The size of the bubble is proportional to the number of stomachs from each cell and the legends provided in the rightmost panels apply to all panels.

Figure S4: Mean proportions of prey, grouped by depth habitat, within the diets of lancetfish from all nodes. Error bars show the standard error about the mean for each depth habitat.

Figure S5: Maps of (A) temperature (NASA Worldview) and (B) chlorophyll concentration (NASA GES DISC) across our study area during winter (February, left panels) and summer (August, right panels) 2014. Lines indicate latitudinal separation between nodes for large (dashed) and small (dotted) lancetfish

33 Highlights

 Abundant lancetfish consume high diversities of poorly known micronekton prey.  Diet varies in a predictable way across the North Pacific Subtropical Gyre.  Unique prey and cannibalism may help explain prevalence of lancetfish in North Pacific.  Useful as biological samplers of variation in midwater prey communities.