DOI: 10.5061/dryad.jwstqjqgk
title: Data from: Combining mesocosms with models to unravel the effects of global warming and ocean acidification on a temperate marine ecosystem

description: Ocean warming and species exploitation have already caused large-scale
 reorganization of biological communities across the world. Accurate
 projections of future biodiversity change require a comprehensive
 understanding of how entire communities respond to global change. We
 combined a time-dynamic integrated food web modelling approach (Ecosim)
 with previous data from community-level mesocosm experiments to determine
 the independent and combined effects of ocean warming and acidification,
 and fisheries exploitation, on a well-managed temperate coastal ecosystem.
 The mesocosm parameters enabled important physiological and behavioural
 responses to climate stressors to be projected for trophic levels ranging
 from primary producers to top predators, including sharks. Through model
 simulations, we show that under sustainable rates of exploitation,
 near-future warming or ocean acidification in isolation could benefit
 species biomass at higher trophic levels (e.g., mammals, birds, and
 demersal finfish) in their current climate ranges, with the exception of
 small pelagic fish. However, under warming and acidification combined
 biomass-increases at higher trophic levels will be lower or absent, whilst
 in the longer term reduced productivity of prey species is unlikely to
 support the increased biomass at the top of the food web. We also show
 that increases in exploitation will suppress any positive effects of
 human-driven climate change, causing individual species biomass to
 decrease at higher trophic levels. Nevertheless, total future potential
 biomass of some fisheries species in temperate areas might remain high,
 particularly under acidification, because unharvested opportunistic
 species will likely benefit from decreased competition and show an
 increase in biomass. Ecological indicators of species composition such as
 the Shannon diversity index declined under all climate change scenarios,
 suggesting a trade-off between biomass gain and functional diversity. By
 coupling parameters from multi-level mesocosm food web experiments with
 dynamic food web models, we were able to simulate the generative
 mechanisms that drive complex responses of temperate marine ecosystems to
 global change. This approach, which blends theory with experimental data,
 provides new prospects for forecasting climate-driven biodiversity change
 and its effects on ecosystem processes.
description: Mesocosm Experiments We utilized our
 previously published mesocosm experiments to investigate the implications
 of future climate change on marine food webs. These experiments were
 specifically designed as multilevel, species-rich mesocosms (Ullah et al.,
 2018; Pistevos et al., 2015).   Model
 Parameterization For various
 functional groups, we extracted model parameters from our mesocosm study
 by Ullah et al. in 2018. The sole exception was for chondrichthyans, for
 which parameters were derived from external studies.   Consumer
 Composition in Mesocosms Our focus was
 primarily on highly mobile omnivorous and carnivorous consumers,
 consisting of juvenile forms of six different fish species and two shrimp
 species. Initial population density in each mesocosm comprised 55
 individuals, varying in size between 10-40 mm. These mesocosms were
 subjected to climate treatments for 4.5 months, allowing both trophic and
 competitive interactions to influence consumer composition. By the end of
 the experiment, an average of 25.1 ± 4.4 individuals per mesocosm
 remained.   Estimating Species
 Interaction Under Climate Change Using the data from
 our previous mesocosms, we estimated predator-prey interactions within
 food web models. We assessed the vulnerability of prey to predators, the
 predators' effective search rate, and biomass-dependent mortality for
 some groups.   Indirect Approach
 to Estimating Prey Vulnerability We employed an
 indirect approach, based on risk-tasking behavior in omnivorous and
 carnivorous fishes, to assess the vulnerability of prey groups at trophic
 levels ≥ 2 (Goldenberg et al., 2018). Three metrics were used: prey
 attraction, food search activity, and boldness, which were assessed
 through manual video tracking using Solomon Coder software.
   Effective Search
 Rates for Sharks The search
 efficiency of Port Jackson sharks was quantified by measuring the time
 required to locate hidden prey in the sand. Data were sourced from
 Pistevos et al. (2015).   Assessment of
 Primary Producers Biomass data for
 primary producer groups, including phytoplankton, phytobenthos,
 mat-forming algae, and macrophytes, were also collected from
 mesocosms.   PPB Food Web
 Modeling The PPB food web
 model was built using the Ecopath approach, providing a static snapshot
 for 1990 and extending it to a dynamic model up to 2015 (Christensen et
 al., 2008). The model contains 53 functional groups and requires four
 primary input variables.   Climate Change
 Effect Sizes and Forcing Functions We calculated the
 mean effect sizes relative to present-day controls for multiple variables,
 including prey vulnerability and search activity. These were then used to
 derive model-forcing functions for various climate change
 scenarios.   Climate Change
 Impact Assessment The ecological
 implications of climate change were analyzed by comparing observed
 biomasses and ecological indicators under a no-change (NC) scenario to
 those under climate change scenarios.
description: # Data from: Combining mesocosms with models to unravel the effects of
 global warming and ocean acidification on a temperate marine ecosystem
 This README file was generated on 2024-02-24 by Hadayet Ullah. --- GENERAL
 INFORMATION 1. Title of Dataset: Combining Mesocosms and Models to Unravel
 Global Warming and Ocean Acidification Effects on a Temperate Marine
 Ecosystem. The dataset is organized into Excel files. Each file serves a
 distinct role in supporting the article's content. 2. Author
 Information: A. First Author Contact information: Hadayet Ullah Southern
 Seas Ecology Laboratories, School of Biological Sciences, University of
 Adelaide, Adelaide, Australia B. Corresponding Author Contact information:
 Ivan Nagelkerken Southern Seas Ecology Laboratories, School of Biological
 Sciences, University of Adelaide, Adelaide, Australia 1. Date of data
 collection (approximate date): 2017-2018; Data type: Both primary
 (mesocosm experiment) and secondary 2. Geographic location of primary data
 collection: South Australian Research & Development Institute (SARDI),
 St Vincent and Spencer gulf, South Australia 3. Information about funding
 sources that supported the collection of the data: Australian Research
 Council Future Fellowship Grant to I.N. (FT120100183) and International
 Postgraduate Research Scholarship (IPRS) to Ullah, H. SHARING/ACCESS
 INFORMATION 1. Licenses/restrictions placed on the data: CC0 1.0 Universal
 (CC0 1.0) Public Domain 2. Links to publications that cite or use the
 data: Ullah, H., Fordham, D. A., Goldenberg, S.U., Nagelkerken, I. (2024).
 Combining mesocosms with models reveals effect of global warming and ocean
 acidification on a temperate marine ecosystem. Ecological Applications. 1.
 Links to other publicly accessible locations of the data: None 2.
 Links/relationships to ancillary data sets: None 3. Recommended citation
 for this dataset: Ullah, H., Fordham, D. A., Goldenberg, S.U.,
 Nagelkerken, I. (2024). Data from: Combining Mesocosms and Models to
 Unravel Global Warming and Ocean Acidification Effects on a Temperate
 Marine Ecosystem. Dryad Digital Repository. DOI:10.5061/dryad.jwstqjqgk
 6.Supplemental files included in this manuscript is compatible with the
 CC0 license waiver required by Dryad for publication and are not included
 within the related manuscript. --- DATA & FILE OVERVIEW File Naming
 Conventions The dataset files are named descriptively to facilitate easy
 identification by readers. Specific naming conventions have been followed
 in accordance with the guidelines provided by the journal Ecological
 Applications. An initial "M" indicates data supporting the main
 text, while an "A" is used for data supporting appendix files.
 Excel Files for Main Article Four Excel files, each starting with an
 initial "M," contain the data used to create the figures in the
 main body of the article. Each file includes a legend that clarifies the
 content and specifies the figure generated using that particular dataset.
 Supplementary Data Files Additional Excel files, starting with an initial
 "A," are provided to support the figures and tables in the
 article's supplementary materials. These files contain data that have
 been both generated and analyzed to produce the supplementary figures and
 tables. Reviewer File An additional Excel file, named "Diet_PPB
 Model," has been included for review purposes. This file contains
 unique data essential for the review process 1. File List: A. Ecosystem
 model skill assesment_TestData A. Effect sizes and direction of impacts on
 biomass A. Global sensitivity analysis_OAT A. Higher TL biomass change
 under climate and effort A. Higher TL biomass change under climate change
 A. Historical trends of fishing events A. Lower TL biomass change under
 climate and effort A. Lower TL biomass change under climate change A.
 Monte Carlo simulations food web groups A. Predicted vs observed biomass
 fit_PPB A. Transferability and model skill assessment Diet_PPB Model M.
 Biomass change under fishing and climate scenarios M. Biomass changes
 under climate scenarios in 2100 M. Ecological indicators of change in
 community M. Mesocosm transferability and model skill assessment 2.
 Relationship between files, if important: None 3. Are there multiple
 versions of the dataset? No A. If yes, name of file(s) that was updated:
 NA i. Why was the file updated? NA ii. When was the file updated? NA 4.
 The presence of empty cells in certain Excel sheets indicates that data
 for those specific entries were either not reported for that year or were
 unavailable --- DATA-SPECIFIC INFORMATION FOR: A. Ecosystem model skill
 assesment_TestData 1. Number of variables: 5 2. Number of cases/rows: 77
 3. Variable List: Species (Name of the species); Year (Data for the year);
 predictedPPB (model predicted biomass for PPB); predictedMESO observed
 (model predicted biomass for mesocosm) 4. Specialized formats or other
 abbreviations used: None 5: Figure/Table associated with the manuscript :
 Figure S7 --- DATA-SPECIFIC INFORMATION FOR: A. Effect sizes and direction
 of impacts on biomass 1. Number of variables: 3 2. Number of cases/rows:
 30 3. Variable List: Communitygroups (Community groups are broader groups
 within the food web that are composed of associated/similar
 species/groups); Hedgesg (Hedges’ g is a measure of effect size); Scenario
 ( Different climate change scenario tested in the model) 4. Specialized
 formats or other abbreviations used: None 5: Figure/Table associated with
 the manuscript : Figure S2 --- DATA-SPECIFIC INFORMATION FOR: A. Global
 sensitivity analysis_OAT 1. Number of variables: 6 2. Number of
 cases/rows: 5200 3. Variable List: Trial (Number of simulation Ecosim
 model simulation); B_OAT (Biomass under combined ocean acidification and
 warming); PB_OAT (Production biomass ratio under combined ocean
 acidification and warming); QB_OAT (consumption biomass ratio under
 combined ocean acidification and warming); EE_OAT (Ecotrophic efficiency
 under combined ocean acidification and warming); FutureB_OAT (Modeled
 future biomass under combined ocean acidification and warming) 4.
 Specialized formats or other abbreviations used: None 5: Figure/Table
 associated with the manuscript : Table S7 5. Missing data code: NA ---
 DATA-SPECIFIC INFORMATION FOR: A. Higher TL biomass change under climate
 and effort 1. Number of variables: 6 2. Number of cases/rows: 420 3.
 Variable List: CommunityGroup (Community froup); Functionalgroup (food web
 functional group); BiomChangePercent (Change in biomass); FutureScenerio
 (Climate change scenario); Effort (Level of fishing effort) 4. Specialized
 formats or other abbreviations used: None 5: Figure/Table associated with
 the manuscript: Figure S5 --- DATA-SPECIFIC INFORMATION FOR: A. Higher TL
 biomass change under climate change 1. Number of variables: 4 2. Number of
 cases/rows: 105 3. Variable List: Functionalgroup (food web functional
 group); BiomChangePercent (Change in biomass); FutureScenerio (Climate
 change scenario); 4. Specialized formats or other abbreviations used: None
 5: Figure/Table associated with the manuscript : Figure S3 ---
 DATA-SPECIFIC INFORMATION FOR: A. Historical trends of fishing events 1.
 Number of variables: 2 2. Number of cases/rows: 27 3. Variable List:
 Fishing events 4. Specialized formats or other abbreviations used: None 5:
 Figure/Table associated with the manuscript: Figure S8 --- DATA-SPECIFIC
 INFORMATION FOR: A. Lower TL biomass change under climate and effort 1.
 Number of variables: 6 2. Number of cases/rows: 204 3. Variable List:
 CommunityGroup (Community froup); Functionalgroup (food web functional
 group); BiomChangePercent (Change in biomass); FutureScenerio (Climate
 change scenario); Effort (Level of fishing effort) 4. Specialized formats
 or other abbreviations used: None 5: Figure/Table associated with the
 manuscript : Figure S6 --- DATA-SPECIFIC INFORMATION FOR: A. Lower TL
 biomass change under climate change 1. Number of variables: 4 2. Number of
 cases/rows: 51 3. Variable List: Functionalgroup (food web functional
 group); BiomChangePercent (Change in biomass); FutureScenerio (Climate
 change scenario); 4. Specialized formats or other abbreviations used: None
 5: Figure/Table associated with the manuscript : Figure S4 ---
 DATA-SPECIFIC INFORMATION FOR: A. Monte Carlo simulations food web groups
 1. Number of variables: 94 2. Number of cases/rows: 138529 3. Variable
 List: Communitygroup (Community groups are broader groups within the food
 web that are composed of associated/similar species/groups); Model
 (Different climate change scenario tested in the model including the no
 change scenario); Year; Trail 1 to trial 91 (Number of valid Monte Carlo
 simulations run) 4. Specialized formats or other abbreviations used: None
 5: Figure/Table associated with the manuscript : Figure S10 ---
 DATA-SPECIFIC INFORMATION FOR: A. Predicted vs observed biomass fit_PPB 1.
 Number of variables: 4 2. Number of cases/rows: 104 3. Variable List:
 Year; Observed (fisheries biomass or catch data for Port Philip Bay);
 Predicted (model predicted biomass or catch data); Species (selected
 species for model comparison) 4. Specialized formats or other
 abbreviations used: None 5: Figure/Table associated with the manuscript :
 Figure 9 --- DATA-SPECIFIC INFORMATION FOR: A. Transferability and model
 skill assessment 1. Number of variables: 5 2. Number of cases/rows: 38 3.
 Variable List: Year; Predicted (model predicated biomass or catch);
 Species; Model (Mesocosm or Port Philip Bay food web model); Observed
 (fisheries biomass or catch data for Port Philip Bay) 4. Specialized
 formats or other abbreviations used: None 5: Figure/Table associated with
 the manuscript : Figure S7 --- DATA-SPECIFIC INFORMATION FOR: Diet_PPB
 Model 1. Number of variables: 2 varaibles and 47 column with different
 species or functional groups 2. Number of cases/rows: 55 3. Variable List:
 Functional group or species 4. Specialized formats or other abbreviations
 used: Matrix (Diet data for food web model) 5: Figure/Table associated
 with the manuscript : Figure S3 --- DATA-SPECIFIC INFORMATION FOR: M.
 Biomass change under fishing and climate scenarios 1. Number of variables:
 5 2. Number of cases/rows: 12000 3. Variable List: CommunityGroup
 (Community froup); Functionalgroup (food web functional group);
 BiomChangePercent (Change in biomass); FutureScenerio (Climate change
 scenario); Effort (Level of fishing effort) 4. Specialized formats or
 other abbreviations used: None 5: Figure/Table associated with the
 manuscript : Figure 3 --- DATA-SPECIFIC INFORMATION FOR: M. Biomass
 changes under climate scenarios in 2100 1. Number of variables: 5 2.
 Number of cases/rows: 3000 3. Variable List: Communitygroups (Community
 groups are broader groups within the food web that are composed of
 associated/similar species/groups; Trial (Number of valid Monte Carlo
 simulations run) BiomChangePercent (Change in biomass); FutureScenerio
 (Climate change scenario); TL= Trophic level 4. Specialized formats or
 other abbreviations used: None 5: Figure/Table associated with the
 manuscript : Figure 2 --- DATA-SPECIFIC INFORMATION FOR: M. Ecological
 indicators of change in community 1. Number of variables: 98 2. Number of
 cases/rows: 42623 3. Variable List: Year; Effort (level of fishing
 effort); Scenario (Climate change scenario); Indicator (Model derived
 biodiversity indicator) 4. Specialized formats or other abbreviations
 used: None 5: Figure/Table associated with the manuscript : Figure 4 ---
 DATA-SPECIFIC INFORMATION FOR: A. Higher TL biomass change under climate
 and skill assessment 1. Number of variables: 5 2. Number of cases/rows: 76
 3. Variable List: Year; Predicted (model predicated biomass or catch);
 Species; Model (Mesocosm or Port Philip Bay food web model); Observed
 (fisheries biomass or catch data for Port Philip Bay) 4. Specialized
 formats or other abbreviations used: None 5: Figure/Table associated with
 the manuscript : Figure 5