DOI: 10.5061/dryad.x0k6djhtq
title: Can niche plasticity mediate species persistence under ocean acidification?

description: Global change stressors can modify ecological niches of species, and hence
 alter ecological interactions within communities and food webs. Yet, some
 species might take advantage of a fast-changing environment, and allow
 species with high niche plasticity to thrive under climate change. We used
 natural CO2 vents to test the effects of ocean acidification on niche
 modifications of a temperate rocky reef fish assemblage. We quantified
 three ecological niche traits (overlap, shift, and breadth) across three
 key niche dimensions (trophic, habitat, and behavioural). Only one species
 increased its niche width along multiple niche dimensions (trophic and
 behavioural), shifted its niche in the remaining (habitat), and was the
 only species to experience a highly increased density (i.e. doubling) at
 vents. The other three species that showed slightly increased or declining
 densities at vents only displayed a niche width increase in one (habitat
 niche) out of seven niche metrics considered. This niche modification was
 likely in response to habitat simplification (transition to a system
 dominated by turf algae) under ocean acidification. We further show that
 at the vents, the less abundant fishes have a negligible competitive
 impact on the most abundant and common species. Hence, this species
 appears to expand its niche space overlapping with other species,
 consequently leading to lower abundances of the latter under elevated CO2.
 We conclude that niche plasticity across multiple dimensions could be a
 potential adaptation in fishes to benefit from a changing environment in a
 high-CO2 world. 
description: We used a small hand net and a mixture of ethanol and clove oil
 to collect the four species of interest (<em>Forsterygion
 lapillum</em><em>, Notoclinops
 yaldwyni, </em><em>Notoclinops segmentatus
 </em>and<em> Parablennius laticlavius</em>) at both
 control and vent sites over four years.
 For<strong> stable isotope analysis,</strong> white
 muscle tissue was extracted from each fish and oven-dried at 60 °C. The
 dried tissue was subsequently ground using a ball mill. Powdered muscle
 tissue from each fish was individually weighed into tin capsules and
 analysed for stable δ<sup> 15</sup>N and
 δ<sup>13</sup>C  isotopes. Samples were combusted in an
 elemental analyser (EuroVector, EuroEA) coupled to a mass spectrometer (Nu
 Instruments Horizon) at the University of Adelaide. We then analysed the
 isotopic niche in SIBER. For <strong>stomach
 content analysis </strong>the entire gut was extracted from each
 fish. Using a stereomicroscope, for <strong>count</strong> and
 biomass, all prey items in the stomach were counted first. For each prey
 category, well-preserved individuals were photographed and their mass was
 calculated based on length and width. The average mass per individual for
 each category was then multiplied by the count to determine total prey
 <strong>biomass</strong>. For the
 <strong>volumetric</strong> method, the volume contribution of
 each prey category relative to the total stomach content was visually
 estimated (algae were accounted for). Digested items that were not
 identifiable were excluded from the analysis. Each stomach content dataset
 was reduced to two dimensions with non-metric multidimensional scaling
 (nMDS) to be then analysed in SIBER. To assess habitat
 choice, visual surveys were conducted on SCUBA, to record the microhabitat
 type and orientation occupied by F<em>orsterygion
 lapillum</em><em>, Notoclinops yaldwyni and
 </em><em>Notoclinops segmentatus. </em>The resulting
 dataset comprised a total of 17 distinct combinations of habitat types and
 surface orientations. The dataset was simplified to two dimensions using
 correspondence analysis (CA) for subsequent SIBER analysis.
 Fish <strong>behaviour</strong> was assessed using
 GoPro cameras both in situ and during controlled aquarium experiments. In
 the field, recordings lasted 30 minutes across 4 days, with analysis
 conducted using VLC. Initial acclimation and periodic intervals (10
 minutes every 5 minutes) were excluded from analysis. In controlled
 <strong>aquarium</strong> settings, individuals of
 <em>Forsterygion lapillum</em> and <em>Notoclinops
 yaldwyni </em>were observed both in isolation and paired. Their
 habitat preference, surface orientation, and activity levels were recorded
 for 10 minutes to assess behaviour independent of external influences.
 Both datasets were dimensionally reduced for analysis in SIBER: non-metric
 multidimensional scaling (nMDS) was applied to the in situ behavioral
 data, while principal component analysis (PCA) was used for the aquarium
 experiments. <strong>Unified analysis of niche
 overlap </strong> We quantified the local
 realised niche space for each fish species at control and vent along the
 four niche classes, adapting the data as follows: 
 isotopes (continuous data): raw data.
 stomach content (continuous data): reduced dimension from the
 volumetric measure of the previous step. habitat
 association (elective score): habitat and orientation preference linked
 to <em>Manly’s Alpha </em>association matrix.
 behaviour (continuous data): raw data.
description: # Can niche plasticity mediate species persistence under ocean
 acidification?
 [https://doi.org/10.5061/dryad.x0k6djhtq](https://doi.org/10.5061/dryad.x0k6djhtq) This dataset originates from a study investigating the impact of ocean acidification on a temperate rocky reef fish assemblage using natural CO2 vents as analogues. The dataset covers various niche dimensions, including trophic, habitat, and behavioural niches. The study focused on how fish niches are modified in response to ocean acidification, assessing changes in breadth, shift, and overlap with other species between the acidified site and the control site. ## Description of the data and file structure #### Raw\_single\_niche\_data The “*Raw_single_niche_data*” dataset consists of seven spreadsheets, each sharing two essential columns: 'group' and 'community'. These columns are crucial for subsequent analysis using the SIBER framework. **group** = species * Common = common triplefin, *Forsterygion lapillum* * Yaldwyn = Yaldwyn’s triplefin, *Notoclinops yaldwyni* * Blue_eyed = blue-eyed triplefin, *Notoclinops segmentatus* * Blenny = crested blenny, *Parablennius laticlavius* **community** = treatment * C = control * V = CO2 vents **Description of the seven spreadsheets:** 1. **Isotopes -** the dataset includes ratios of 13C/12C and 15N/14N expressed in the conventional δ notation as parts per thousand deviation from international standards. Stable isotopes were derived from a total of 251 fishes collected across three years of sampling.  iso1= δ13C  iso2= δ15N 2. **Stomach volumetric** - The dataset includes estimated volumetric measures of stomach contents, where the volume contribution of each prey category relative to the total stomach content (100%) was visually estimated. Data were collected between 2018 and 2019. The stomach content was analysed with this method for common triplefin, Yaldwyn's triplefin, blue eyed triplefin and crested blenny. There are 19 prey categories. 3. **Stomach count** - All prey items were counted in 10 prey categories: copepods, ostracods, polychaetes, amphipods, gastropods, bivalves, tanaids, mites, isopods , and others. Digested items that were not identifiable were excluded from the analysis. The stomach content was analysed with this method for common triplefin, Yaldwyn's triplefin and blue eyed triplefin.  4. **Stomach biomass -** The dataset includes calculated biomass derived from the mass of prey subsamples within each category, multiplied by their count.  5. **Habitat** - The microhabitat occupied and habitat orientation (horizontal, angled and vertical) was recorded using free roaming visual surveys on SCUBA (February 2018). *Microhabitat types:* t. = turf algae &lt;10 cm in height ca. = erect calcareous algae cca. = crustose coralline algae b. = bare rocky substratum sp. = encrusting fleshy green algae cobble. = cobbles (~0.5–2 cm in diameter) *Type of surface orientation:* hor = horizontal angle = angled vert = vertical 6. **Behaviour** - Behavioural variables quantified from underwater footage and expressed as rates per minute. The behaviours are:  swimming, jumping, feeding, attacking and fleeing from an attack. 7. **Aquarium**: Data from an aquarium experiment involving *Forsterygion lapillum and Notoclinops yaldwyni*, showing the proportion of time spent in available habitat types to assess habitat preference in controlled conditions. Time in each habitat type and spent in activity was derived from video recordings of 10 minutes and expressed as a proportion of total observation time. Common = common triplefin, *Forsterygion lapillum* Yaldwyn = Yaldwyn’s triplefin, *Notoclinops yaldwyni* Common.c = common triplefin in presence of Yaldwyn’s triplefin Yaldwyn.c = Yaldwyn’s triplefin in presence of common triplefin turf.horizontal = time spent on horizontal turf substratum bare.horizontal = time spent on horizontal bare substratum turf.vertical = time spent on vertical turf substratum bottom = time spent on the bottom of the tank swimming = time spent swimming aquarium.wall = time spent on the walls of the tank switches = numbers of changes between habitats #### Unified\_overlap\_dataset The *“Unified_overlap_dataset”* consists of ten spreadsheets, each sharing “id”, “year”, “location” and “species “column (with few exceptions detailed). These first columns need to be factors for analysis using the Unified overlap framework. We used the R scripts provided in the original study ([Geange et al, 2011](https://doi.org/10.1111/j.2041-210X.2010.00070.x)), as detailed in the manuscript. Data for control and vents are in separate data sheets, with C = control and V = vent. **Id**: sample number **Year:** year the data were collected **Location:** North (n) or South (s), site location **Species**: fish species * Common = common triplefin, *Forsterygion lapillum* * Yaldwyn = Yaldwyn’s triplefin, *Notoclinops yaldwyni* * Blue_eyed = blue-eyed triplefin, *Notoclinops segmentatus* * Blenny = crested blenny, *Parablennius laticlavius* We used the same data as per previous section. **Isotopes C and Isotopes V:** * iso1= δ13C  * iso2= δ15N **Diet V and Diet C:** For **stomach content**: we used only volumetric stomach content data as inclusive of all species of interest. It is not raw data, but we used the reduced dimension obtained from nonmetric multidimensional scaling (nMDS), thus the 2 columns resulting from this analysis are vol1 and vol2. Raw data are in the datasheet **Stomach volumetric** in the “*Raw_single_niche_data*” dataset. **Habitat association C and Habitat association V** / **Habitat - C and Habitat - V** For **Habitat association**, the columns are id, species, habitat and position. The habitat association for each species is categorical based on habitat occupied and position (e.g., turf - vertical). Information for Crested blenny were extracted from the behavioural video recordings (with each video being a replicate). The dataset is then linked to **Habitat cover** in both control (C) and vent (V) sites to determine the choice of the habitat based on habitat availability. Therefore, the habitat cover only presents the percentage cover of each habitat type at control and vent.  *Habitat:* turf = turf algae &lt;10 cm in height ca = erect calcareous algae cca = crustose coralline algae barren = bare rocky substratum sp = encrusting fleshy green algae cobble = cobbles (~0.5–2 cm in diameter) sand = sand *Position:* hor = horizontal angle = angled vert = vertical **Behaviour C and Behaviour V**: Behavioural variables quantified from underwater footage and expressed as rates per minute. The behaviours are:  swimming, jumping, feeding, attacking and fleeing from an attack. Reference: Geange, S. W., Pledger, S., Burns, K. C., & Shima, J. S. (2011). A unified analysis of niche overlap incorporating data of different types. *Methods in Ecology and Evolution*, 2(2), 175-184. [https://doi.org/10.1111/j.2041-210X.2010.00070.x](https://doi.org/10.1111/j.2041-210X.2010.00070.x)