Dryad
Published June 20, 2021 | Version v1
Dataset Open

Positive species interactions strengthen in a high-CO2 ocean

Creators

  • 1. University of Adelaide

Description

Negative interactions among species are a major force shaping natural communities and are predicted to strengthen as climate change intensifies. Similarly, positive interactions are anticipated to intensify, and could buffer the consequences of climate-driven disturbances. We used in situ experiments at volcanic CO2 vents within a temperate rocky reef to show that ocean acidification can drive community reorganization through indirect and direct positive pathways. A keystone species, the algal-farming damselfish Parma alboscapularis, enhanced primary productivity through its weeding of algae whose productivity was also boosted by elevated CO2. The accelerated primary productivity translated into increased densities of primary consumers (herbivorous invertebrates), which indirectly supported increased secondary consumers densities (predatory fish) (i.e. strengthening of bottom-up fuelling). However, this keystone species also reduced predatory fish densities through behavioural interference, releasing invertebrate prey from predation pressure and enabling a further boost in prey densities (i.e. weakening of top-down control). We uncover a novel mechanism where a keystone herbivore mediates bottom-up and top-down processes simultaneously to boost populations of a co-existing herbivore, resulting in altered food web interactions and predator populations under future ocean acidification.

Notes

Data was blinded analyzed. Samples labels were randomly assigned in the field. Sample label and local (vent or control site), as well as the photo-quadrat ID, were noted underwater in an underwater paper sheet. These notes were only revised and added to sample labels after all laboratory analyses (productivity measurements) and count been performed (prey and predator abundances).

We were unable to collect data on productivity for the year 2017 (missing values = 20) and to use the farming exclusion plot photos to access predator density (missing values = 36; year 2016b).  

Data files contain all data used in the manuscript preparation and are divided as:

  1. Main_data_set sheet contains data used to construct Figures 1 and 2, as well as to run the analyses shown in Tables S1-S3. Productivity (missing values = 20 samples; year 2017); Predator density (missing values = 36 samples; year 2016b). Column descriptions: (A) sample (sample ID); (B) date (data collection year); (C) treatment (Control vs Vent); (D) Cage.Ex (whether data were collected from a experiment, logical valus, Yes/No);  (E) Caged (if samples were from a experiment logical values of Open and Closed were assing to open plots and caged plots); (F) farmer (position of the sample in relation to the area within the fish farm, logical values of inside or border); (G) prey (invertebrate snail abundance); (H) d.prey (invertebrate snail density; individual/cm2); (I) predator (triplefin density; individual/m2); (J) algal.biomass (total turf biomass represented in grams); and (K) productivity (turf algae productivity expressed in mg O2.g). 
  2. Predator_visual_x_photo sheet was used to draw Figure S3 and perform the analysis which the result is shown in Table S6. Column descriptions: (A) sample (sample ID); (B) obs (visual census method; visual or photo); (C) date (date collection year); (D) treatment (Control vs Vent); and (E) density (triplefin density; individual/m2).
  3. Cage_effect sheet was used to draw Figure S1 and construct the analysis which result is shown in Table S5. It is important to note that Procedural Control samples (column = farming; 5 at vent and 5 at control sites) were only used to test the cage effect and were excluded from all other analyses. (A) sample (sample ID); (B) treatment (Control vs Vent); (C) farming (whether data was a Procedural control or Exclusion cage); and (D) p.mgO2.g (turf algae productivity expressed in mg O2.g).

Funding provided by: Australian Research Council Future Fellowships*
Crossref Funder Registry ID:
Award Number: FT120100183

Funding provided by: Australian Research Council Future Fellowships*
Crossref Funder Registry ID:
Award Number: FT0991953

Funding provided by: ARC Discovery*
Crossref Funder Registry ID:
Award Number: DP150104263

Funding provided by: Ciência sem Fronteiras
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100017564
Award Number: 13058134

Funding provided by: Australian Research Council Future Fellowships
Crossref Funder Registry ID:
Award Number: FT120100183

Funding provided by: ARC Discovery
Crossref Funder Registry ID:
Award Number: DP150104263

Files

Files (55.8 kB)

Name Size Download all
md5:1a290ac37233669e831687d98526b978
55.8 kB Download