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How functionally diverse are fish in the deep? A comparison of fish communities in deep and shallow‐water systems

Cite this dataset

Carrington, Victoria Grace et al. (2021). How functionally diverse are fish in the deep? A comparison of fish communities in deep and shallow‐water systems [Dataset]. Dryad.


Aim: Functional diversity metrics inform how species’ traits relate to ecosystem functions, useful for quantifying how exploitation and disturbance impact ecosystems. We compare the functional diversity of entire fish communities in a shallow-water region with a deep-sea region for further insight into the differences between these ecosystem types.

Location: The regions compared in this study were selected to represent a shallow-water coastal region, Tasman and Golden Bays (TBGB), and a deep-sea region, Chatham Rise (CR), in New Zealand.

Methods: Functional diversity was assessed using four metrics: functional richness, evenness, divergence, and dispersion. We compared these metrics across four key functions: habitat use, feeding, locomotion and life history.

Results: Our results showed that overall, the shallow-water and deep-sea ecosystems had equal diversity. When focusing on the four ecological functions, the two ecosystems exhibited equal diversity metrics across most analyses. Of the significantly different results, the deep-sea had higher functional richness for habitat use and locomotion traits, lower functional dispersion for feeding, and lower functional evenness for life history.

Main Conclusions: Differences across the functions highlight higher diversity of habitat utilisation by deep-sea fish, while lower diversity in feeding suggests deep-sea fish tend towards generalist diets, likely driven by low food availability. Deep-sea fish displayed an increased range of locomotive traits in our analyses, but this conflicts with existing evidence and warrants further study. Life history results suggests deep-sea fish exhibit higher clustering of traits, indicating potential under-utilisation of life history strategies in the deep-sea. Our results demonstrate that although deep-sea fish communities have similar levels of diversity to shallow-water communities, the traits that structure this diversity differ, and therefore, the systems may respond to exploitation differently.


Study Regions and species

The regions compared in this study were selected to represent a shallow-water coastal region, Tasman Bay and Golden Bay (TBGB), and a deep-sea region, Chatham Rise (CR), in New Zealand (Fig. 1). Tasman Bay (41°15’S, 173°17’ E) and Golden Bay (40°40’S, 172°50’ E) are located on the north-western coastline of the South Island, with a depth of 0 m to 70 m (Newcombe et al. 2015). Chatham Rise (42°30’S - 44°46’S, 173°30’E - 174°30’W) stretches roughly 1100 km off the east coast of the South Island, with a depth ranging from 250 m to 1500 m, with the exception of seamounts to the west and the Chatham Islands to the east (Bowden & Leduc 2017). The aim was to compare entire ecosystems and these regions were selected because both have been extensively sampled in bottom-trawl surveys (Stevenson & MacGibbon 2015; Stevens et al. 2018). Trawl surveys are carried out every two years in both regions by the Ministry for Primary Industries New Zealand (MPI) and are used to inform fisheries stock assessments and management advice (Stevenson 2012; Stevens et al. 2013).

All fish species caught in the last five MPI trawl surveys in each region were included in the analysis (Stevenson 2012; MacGibbon & Stevenson 2013; Stevens et al. 2013, 2014, 2015, 2017, 2018; Stevenson & MacGibbon 2015, 2018; MacGibbon 2019). Trawls were carried out using similar gear and trawling procedures, with the survey area divided into a two-phase stratified randomised design following Francis (1984). The specifications for each trawl survey are provided in Supplementary Material Appendix S1, such as trawl density, depth range, mesh size, and tow distance. Owing to mesh size and trawling tactic, survey data primarily identifies species directly relevant to pelagic fisheries, as it may not capture species that reside in complex, benthic habitats. The TBGB trawl surveys were carried out on R.V. Kaharoa (Stevenson & MacGibbon 2015) and the CR trawl surveys were carried out on R.V. Tangaroa (Stevens et al. 2018). Species accumulation curves were used to calculate the percentage of observed species, relative to the estimated total number of species in the system, and these showed that both systems were sufficiently sampled, such that the majority (>75%) of species were observed (Appendix S2).

In total, 239 species were included in the dataset, 210 from CR and 60 from TBGB, with 31 species occurring in both regions (Appendix S3). Particular families or genera which include difficult to distinguish species with similar ecology were grouped together and treated as groups of confamilial or congeneric species (Appendix S3). It is important to note that species abundance within each system was only recorded as absence or presence (0 or 1) as accurate abundance data were not available for many species.

Trait selection

Ten traits (Table 1) were selected to cover four underlying functions (habitat use, feeding, locomotion, life history), prioritising those with direct ecological importance and ensuring that at least four traits represented each function (Pease et al. 2012) (Appendix S4 contains sources explaining the ecological function of traits). To avoid trait redundancy (Laughlin 2014), a correlation matrix of all ten traits was used to check for strongly correlated pairs of traits by their occurrence. No strong correlations were observed (i.e. all weaker than ±0.5) (Appendix S5).

Data collection primarily relied on fish guides for New Zealand (Francis 2001; Patterson 2016; McMillan et al. 2019) and global fish guides (Hubbs & Nelson 2006; Froese et al. 2019). Additional trait information came from extensive literature searches for specific species (Appendix S6 provides data sources and corresponding traits) as well as photographs from the Ministry for Primary Industries (MPI) January 2018 Trawl Survey of Chatham Rise (Ladds et al. 2018; Stevens et al. 2018). There were missing data for a number of species and in these situations traits were determined from literature or photographs (Ladds et al. 2018). For diet and schooling behaviour, where data from literature were unavailable, expert opinion was used (fisheries scientist P. Horn, NIWA). Finally, for maximum age, and relative age at maturity, unknown values for species were estimated using the average value of the most closely related species. The possibility of diverging trait values within taxonomic groups was not considered here.

Categorical trait modalities (Table 1) were based on those commonly used in similar functional trait studies (Boyle & Horn 2006; Farré et al. 2016; Patterson 2016). Continuous traits, such as maximum size and age at maturity, were collected in continuous form and then transformed into categorical traits by assigning them to classes (Table 1). Maximum size in its continuous form was measured using different methods (i.e. standard length, fork length and total length); assigning them to classes reduced the impact of this. The purpose of transforming maximum age and age at maturity, as opposed to keeping them in continuous form, was to reduce the impact of unknown trait values that were estimated with trait values of close relatives. Maximum body size values were assigned to five discrete classes ranging from <20 cm (S1) to ≥160 cm (S5). Maximum age values were similarly categorized in six classes ranging from “very short-lived” (<5 yrs, MA1) through to “very long-lived” (≥65 yrs (MA6)) (Table 1). Relative age at maturity values were calculated as the age at maturity divided by maximum age, with five modalities from fast-maturing at <10% of lifespan (MT1) through to slow-maturing, >40% of lifespan (MT5).

Diet modalities were assigned by the dietary components (Table 1). Modalities most commonly used in functional trait-based studies (Albouy et al. 2011; Brandl et al. 2016; Henriques et al. 2017; Valls et al. 2017; Yeager et al. 2017; Dewi et al. 2018) were modified to best suit the range of diets in this dataset. The classical method of categorizing diet, assigning species to discrete categories (herbivore, planktivore, omnivore, invertivore, piscivore, and generalist), lacks precision when species fall into multiple categories. That method separates generalist species from categories they overlap with, therefore measures of similarities, used to calculate functional diversity metrics, would fail to classify similarities in diets between species accurately. This was an important consideration for this species pool, where species labelled as “generalists” showed high diversity in prey items (from amphipods to marine mammals). Diet modalities were the prey items (e.g. benthic invertebrates), and species were assigned to multiple modalities if required; this method more accurately depicts the diversity and overlap of species’ diets.