DOI: 10.5061/dryad.wpzgmsc05
title: Data from: Resource partitioning among pelagic predators remains stable despite annual variability in diet composition

description: Diet data are critical for describing resource use and partitioning among
 competitors. However, timeseries of diet data needed to properly assess
 variability in resource use and partitioning are limited, especially in
 pelagic (open ocean) ecosystems where predators and prey make broad use of
 horizontal and vertical habitats. We examined a diet timeseries spanning
 two decades (1998-2018), consisting of 2,749 stomachs from ten pelagic
 predators in the southern California Current Ecosystem (SCCE): albacore
 tuna (Thunnus alalunga), Pacific bluefin tuna (Thunnus orientalis),
 swordfish (Xiphias gladius), blue shark (Prionace glauca), shortfin mako
 (Isurus oxyrinchus), common thresher shark (Alopias vulpinus), bigeye
 thresher shark (Alopias superciliosus), short-beaked common dolphin
 (Delphinus delphis), long-beaked common dolphin (Delphinus capensis), and
 northern right whale dolphin (Lissodelphis borealis). We examined feeding
 habits with respect to prey taxonomy, length, vertical habitat, and
 horizontal habitat. From 1998-2015, each predator exhibited diet
 variability but maintained consistent resource partitioning with the other
 predators. Across years, the diets of predators feeding mostly on
 shallow-living prey (&lt;200 m) were more variable than those feeding
 on deeper-dwelling prey (&gt;200 m). Following increased abundance of
 northern anchovy (Engraulis mordax) starting in 2016, the ecological
 niches of bluefin tuna and swordfish converged. Both predators fed more
 heavily on northern anchovy and other prey that occupy shallow nearshore
 habitats. Changes in resource availability likely varied with depth across
 our time series. We show that pelagic predators can maintain resource
 partitioning under a wide range of conditions. However, we also observed
 that drastic changes in resource availability can alter the degree of
 niche partitioning among competitors, providing new perspectives on the
 flexibility of predator niches. As climate change continues to alter food
 webs, understanding how predators forage will be essential for
 anticipating changes to pelagic ecosystem structure and services.
description: # Data from Resource partitioning among pelagic predators remains stable
 despite annual variability in diet composition
 [https://doi.org/10.5061/dryad.wpzgmsc05](https://doi.org/10.5061/dryad.wpzgmsc05) ## Description of the data and file structure We examined a diet timeseries spanning two decades (1998-2018), consisting of 2,749 stomachs from ten pelagic predators in the southern California Current Ecosystem (SCCE): albacore tuna (*Thunnus alalunga*), Pacific bluefin tuna (*Thunnus orientalis*), swordfish (*Xiphias gladius*), blue shark (*Prionace glauca*), shortfin mako (*Isurus oxyrinchus*), common thresher shark (*Alopias vulpinus*), bigeye thresher shark (*Alopias superciliosus*), short-beaked common dolphin (*Delphinus delphis*), long-beaked common dolphin (*Delphinus capensis*), and northern right whale dolphin (*Lissodelphis borealis*). We examined feeding habits with respect to prey taxonomy, length, vertical habitat, and horizontal habitat. Intact and partial remains of prey items (e.g., fish bones, cephalopod beaks, crustacean appendages) were identified to the finest possible taxonomic resolution using published keys. Due to variable states of prey digestion within stomachs and among predator taxa, prey were rarely weighed, and all analyses are based on prey abundance. Lengths were measured for fresh and partially digested prey when possible and reported in mm as standard or fork length for fishes, mantle length for cephalopods, and total length for crustaceans. Whole lengths from partial remains (teleost vertebrae, cephalopod beaks) were estimated using published regressions ### Files and variables #### File: hms\_prey\_taxonomy.csv **Description:**  ##### Variables * Prey_LPT: Prey identification to lowest taxonomic level possible * Prey_Kingdom: Kingdom * Prey_Phylum: Phylum * Prey_Class: Class * Prey_Order: Order * Prey_Family: Family * Prey_Genus: Genus #### File: hms\_predator\_lengths\_subset.csv **Description:**  ##### Variables * Predator_ID: Unique identifier * Predator_Species: Species * Predator_Measurement: Length type (e.g. eye fork length, total length) * Predator_Length: Length in cm * Year: Year of collection * Region: Region of collection * Prey_N: Number of prey #### File: sppHulls.rds **Description:** Polygons covering collection locations #### File: spMatrix\_0.025\_familyAggyes\_1998\_2018.rds **Description:** Species matrixes for multivariate analyses, generated by code in public repo #### File: hms\_predator\_depths.csv **Description:**  ##### Variables * Predator_Species: Species * region: Region of study * startYr: Earliest year of study * endYr: Last year of study * pred_max_depth: Maximum depth of predator if available * pred_max_depth_day: Daytime maximum depth * pred_max_depth_night: Nighttime maximum depth * pred_depth_mode_day: Daytime modal depth if available * pred_depth_mode_night: Nighttime modal depth if available * pred_depth_mean_day: Daytime mean depth if available * pred_depth_mean_night: Nighttime mean depth if available * diel_foraging: Times of day when foraging has been recorded * references.depth: Citations for depth information * doi.depth: DOI for citations * references.diel.foraging: Citations for diel foraging behavior * doi.diel.foraging: DOI for citations * notes: Any helpful notes #### File: hms\_prey\_lengths\_subset.csv **Description:**  ##### Variables * Predator_ID: Unique predator ID * Prey_LPT: Prey identification to lowest taxonomic level possible * Prey_Length: Prey length in mm * group: Prey type * Year: Year of collection * Prey_Measurement: Measurement type (mantle length, standard length, total length, fork length) * Predator_Species: Species of predator * lenMat: Length at maturity * life_stage: Assumed life stage based on length at maturity #### File: hms\_prey\_withTraits.csv **Description:**  ##### Variables * Prey_LPT: Prey identification to lowest taxonomic level possible * Predator_ID: Unique predator identifier * Prey_ID: Unique prey identifier * Prey_N: Number of prey * Predator_Species: Species of predator * Predator_Measurement: Type of predators measurement (e.g. eye fork length, total length) * Predator_Length: Length in cm * Year: Year of collection * Region: Region of collection * Prey_Kingdom: Kingdom * Prey_Phylum: Phylum * Prey_Class: Class * Prey_Order: Order * Prey_Family: Family * Prey_Genus: Genus * life_stage_final: Assumed prey life stage * species_lifestage: Life stage of prey species if available * genus_lifestage: Life stage of prey genus if available * family_lifestage: Life stage of prey family if species/genus not available * depth_mode_day: Prey depth mode daytime if available from literature * depth_mode_night: Prey depth mode nighttime if available from literature * vert_habitat: Final vertical habitat classification * inOff_habitat: Final inshore/offshore habitat classification ## Code/software All analyses are run in R. Public repo of code is available at [https://github.com/barbmuhling/predatorDietsPublic](https://github.com/barbmuhling/predatorDietsPublic) ## Access information Other publicly accessible locations of the data: * None Data was derived from the following sources: * Original analyses of predator gut contents