DOI: 10.5061/dryad.xgxd254gt
title: High functional diversity in deep-sea fish communities and increasing intra-specific trait variation with increasing latitude

description: Variation in both inter- and intra-specific traits affect community
 dynamics, yet we know little regarding the relative importance of external
 environmental filters vs internal biotic interactions that shape the
 functional space of communities along broad-scale environmental gradients,
 such as latitude, elevation or depth. We examined changes in several key
 aspects of functional alpha-diversity for marine fishes along depth and
 latitude gradients by quantifying intra- and inter-specific richness,
 dispersion and regularity in functional trait space. We derived eight
 functional traits related to food acquisition and locomotion, and
 calculated seven complementary indices of functional diversity for 144
 species of marine ray-finned fishes along large-scale depth (50 m – 1200
 m) and latitudinal gradients (29° – 51° S) in New Zealand waters. Traits
 were derived from morphological measurements taken directly from footage
 obtained using Baited Remote Underwater Stereo-Video systems and museum
 specimens. We partitioned functional variation into intra- and
 inter-specific components for the first time using a PERMANOVA approach.
 We also implemented two tree-based diversity metrics in a functional
 distance-based context for the first time: namely, the variance in
 pairwise functional distance, and the variance in nearest-neighbour
 distance. Functional alpha diversity increased with increasing depth, and
 decreased with increasing latitude. More specifically, the dispersion and
 mean nearest-neighbour distances among species in trait space, and
 intra-specific trait variability all increased with depth, whereas
 functional hypervolume (richness) was stable across depth. In contrast,
 functional hypervolume, dispersion and regularity indices all decreased
 with increasing latitude; however, intra-specific trait variation
 increased with latitude, suggesting that intra-specific trait variability
 becomes increasingly important at higher latitudes. These results suggest
 that competition within and among species are key processes shaping
 functional multi-dimensional space for fishes in the deep sea. Increasing
 morphological dissimilarity with increasing depth may facilitate niche
 partitioning to promote coexistence, whereas abiotic filtering may be the
 dominant process structuring communities with increasing latitude.
description: We observed 144 species of marine ray-finned fishes (Class
 Actinopterygii) on Baited Remote Underwater stereo-Video (stereo-BRUV)
 footage. We analysed data on the basis of the species present (observed in
 video footage) within each of the 47 depth-by-location cells. There were
 144 species recorded, and 509 species-by-cell occurrences (many species
 naturally occurred in more than one cell). Our original dataset was
 comprised of a complete set of 15 raw morphological measurements for a
 total of 722 individuals (136 of these required some random-forest
 imputation, and missing traits were remeasured for 4 individuals) obtained
 directly from Stereo-BRUV footage.  From this original
 dataset, we calculated all species-level functional metrics (i.e., FHV,
 MPFD, MNND, VPFD and VNND; see descriptions below). We created 100 tables
 of 509 unique species-by-cell occurrences (rows) for the 8 traits
 (columns; eye position, pectoral fin position, caudal peduncle throttling,
 elongation, eye size, oral gape position, jaw length/head length, and
 total length) by randomly drawing 1 individual from the list of all
 complete individuals for each species. To maintain any spatial structures
 in trait variability as well as possible, we drew an individual for each
 species within each cell from conspecific individuals that were (in order
 of preference): a) within that depth-by-location cell, b) at the same
 depth, or c) from anywhere within the Stereo-BRUV study design (n = 722)
 or d) from a museum specimen (n = 291). All species-level functional
 metrics were calculated for each replicate table, and we calculated the
 mean across all 100 tables for every metric for subsequent
 analyses. All functional metrics were calculated using
 8 normalised continuous traits. We calculated the following species-level
 metrics for each depth-by-latitude cell, for each of the 100
 species-by-trait (509 x 8) data matrices after calculating Euclidean
 distances: (i) mean pairwise functional distance (MPFD; (Clarke &
 Warwick 1998; Somerfield et al. 2008; Swenson 2014), (ii) mean
 nearest-neighbour distance (MNND; Swenson & Weiser 2014), (iii)
 variance in pairwise functional distance (VPFD; adapted from Clarke and
 Warwick (2001) and Somerfield et al. (2008)), and (iv) variance in
 nearest-neighbour distance (VNND; Swenson (2014). We also performed
 principal component analysis (PCA) on the normalised traits in order to
 calculate functional hypervolume (FHV; Blonder et al. 2014; Blonder et al.
 2018). FHV was calculated using the first 4 principal component axes
 (which accounted for 70.2 – 74.4 % of the total variation in the 8D
 functional trait space across the 100 species-by-trait tables). We did not
 retain all 8 dimensions due to difficulties associated with calculating
 FHV when few species were present. FHV has been used as a proxy to
 estimate niche space, including high-dimensional, irregular spaces
 (Lamanna et al. 2014; Cooke, Eigenbrod & Bates 2019). 
 For metrics focusing on intra-specific trait variability we used
 data solely from the in-situ stereo-BRUV footage (i.e., the dataset
 comprising 722 individuals). Due to the inability to measure every species
 observed on the stereo-BRUVs, this dataset represents a reduced number of
 species (62 out of 144), and cells (43 out of 47). Within this dataset, we
 were able to measure intraspecific trait variability for 42 species (2 or
 more individuals per species per cell). There were, on average, 3.34
 species per cell (min = 1, max = 10, sd = 1.86) and 4.32 individuals per
 species per cell (min = 2, max = 15, sd = 2.5) to measure the
 intra-specific trait variability.  We calculated mean
 pairwise functional distance (MPFD.I) directly, considering only the
 intra-specific distances. In addition, partitioning was done by performing
 a PERMANOVA on the Euclidean distances among all complete individuals
 separately within each cell. Different species were treated as different
 levels of the factor “Species”, and individuals within each species were
 treated as replicates in a one-factor design. Prop.I is equal to the
 proportion of total trait variation (within each cell) attributable to
 individual-level variation (i.e., Prop.I).<br>Prop.I and MPFD.I
 could only be calculated when there were two or more individuals
 representing the same species within a depth-by-location
 cell.<br> 
description: We provide everything else that is needed to know in the metadata
 documents for each data table.