DOI: 10.5061/dryad.nvx0k6f27
title: Interaction between climate change scenarios and biological invasion reveals complex cascading effects in freshwater ecosystems

description: Climate change often facilitates biological invasions, leading to
 potential interactive impacts of these global drivers on freshwater
 ecosystems. Although climatic mitigation efforts may reduce the magnitude
 of these interactive impacts, we are still missing experimental evidence
 for such effects under multiple climate change scenarios within a
 multi-trophic framework. To address this knowledge gap, we experimentally
 compared the independent and interactive effects of two climate change
 scenarios (mitigation and business-as-usual) and biological invasion on
 the biomass of major freshwater trophic groups (phytoplankton,
 zooplankton, periphyton, macroinvertebrates, and a native macrophyte) and
 the decomposition rate of allochthonous material. Among the independent
 effects, we found that the business-as-usual climate treatment resulted in
 lower native macrophyte biomass and higher periphyton biomass compared to
 the climatic baseline and mitigation treatments. This indicates the
 potential of climate change to alter the relative dominance of different
 freshwater producers and demonstrates that climate mitigation efforts can
 counteract these effects. Biological invasion alone increased the biomass
 of chironomids, a dominant macroinvertebrate group in tropical freshwater
 ecosystems, demonstrating a compensatory effect on climate change. Climate
 change and biological invasion interactively reduced the decomposition
 rate of allochthonous detritus, likely mediated by the feeding preference
 of abundant chironomids for periphytic algae associated with the presence
 of non-native macrophytes. We concluded that (i) climatic mitigation can
 maintain climate baseline conditions in freshwater ecosystems, and (ii)
 the interactive effects between future climate scenarios and biological
 invasion are related to complex cascading interactions among trophic
 groups on ecosystem processes.
description: We performed an outdoor mesocosm experiment at the Federal
 University of Rio de Janeiro (Latitude: -22.84, Longitude -43.23) over 35
 days between July and August 2021. We used 50 L plastic containers (65.8
 cm height and 31.0 cm diameter), each filled with a five cm-deep sediment:
 a bottom layer of organic substrate (Mega fértil – Box Reef, 1 cm deep)
 topped with thoroughly washed sand (4 cm deep), all commercially acquired.
 Each mesocosm was then filled with approximately 30 L of dechlorinated tap
 water, which was filtered through a 50 μm mesh plankton net. The mesocosms
 were submerged 20 cm below ground level to simulate shallow ponds or
 small-scale lagoon compartments. The native
 rooted-submerged macrophyte <em>Cabomba caroliniana</em> was
 established by planting four apical stem fragments (15 cm in length
 without lateral branches) into the substrate layer of each mesocosm,
 matching the shoot density range of submerged species in natural
 conditions (Li et al., 2015). <em>Cabomba caroliniana</em> is
 native to South America, including Brazilian freshwater environments, and
 co-occurs with several other native macrophyte species (Schooler et al.,
 2009). All fragments were cultivated under uniform conditions for ten days
 before introducing the non-native species <em>H.
 verticillata</em> as we aimed to simulate the arrival of a
 non-native species into an ecosystem with established native macrophyte
 (Petruzzella et al., 2020; Riis & Sand-Jensen, 2006). All
 macrophyte fragments were commercially acquired from Flora Aquática,
 Brazil. The freshwater trophic groups were established
 through a combination of free colonization and inoculation, depending on
 their identity. The zooplankton community was established by adding a
 subsample inoculum (1 L from a total of 500 L) into each mesocosm at the
 beginning of experiment manipulation. This subsample was collected from
 several locations within a macrophyte-rich wetland at the Guapiaçu
 Ecological Reserve (Latitude: -22.45, Longitude: -42.77) using a 50 μm
 mesh plankton net in the early July 2021. The phytoplankton, periphyton,
 and macroinvertebrates were established through free colonization,
 although these organisms were potentially present in the zooplankton
 inoculum. To evaluate periphyton growth, we attached a 100 cm² Vulcan
 plastic substrate to a floater in each mesocosm.
 Detritus biomass loss was assessed using leaves from four
 allochthonous tree species commonly found in Tijuca National Park, Rio de
 Janeiro, Brazil (Latitude: -22.95, Longitude: -43.27): <em>Ficus
 insipida, Artocarpus heterophyllus, Cecropia melaleuca</em>,
 and<em> Eugenia brasiliense. </em>Freshly senescent leaves
 were collected from the forest floor and dried at 60°C until they reached
 a stable dry weight. We used coarse mesh-size litter bags, which allow
 colonization of macroinvertebrates, and we filled them with a 2.02 ± 0.06
 g mix of dry leaves, equally composed of the four species. Although we
 used the detritus from the terrestrial matrix and aquatic microfauna
 typical of wetlands, both encompass the same Atlantic Forest
 biome. Experimental design We
 established two experimental climate change treatments based on the
 Representative Concentration Pathways (RCP) that provide future climate
 expectations according to different predicted greenhouse gas emissions
 (IPCC, 2023). We selected RCP 4.5 and RCP 8.5 scenarios for a 20-year
 time-series interval (2080-2099) as representative future climate
 scenarios. The RCP 4.5 scenario represents future conditions with moderate
 changes in temperature and rainfall regimes resulting from climate change
 mitigation policies (Thomson et al., 2011). The RCP 8.5 scenario, known as
 business-as-usual, represents future climatic conditions resulting from
 the continuation of current greenhouse gas emissions trends. The BAU
 scenario predicts comparatively strong changes in the temperature and
 rainfall regimes, along with an increasing frequency of extreme climatic
 events (Riahi et al., 2011). We also established a climatic control
 treatment to represent historical conditions, commonly described as the
 baseline observed between 1985 and 2004. Hereafter, the three experimental
 climate treatments are referred to as baseline, mitigation, and
 BAU. To establish the experimental climate treatments,
 we chose a representative year from the 20-year time series interval for
 both temperature and rainfall datasets. This selection process involved
 several steps. Firstly, we calculated the standard deviation (SD) of each
 year within the 20-year time interval concerning the mean value of the
 20-year interval for both temperature and rainfall values individually
 (eq. 1). Next, we summed the standard deviations of temperature and
 rainfall for each year within the 20-year interval, which represented the
 total standard deviation (eq. 2).
 SD<sub>y1</sub> = ∑ (|y|<sub>t1</sub> -
 x̅) + … + (|y|<sub>t960</sub>- x̅), in which:              
     (eq.1) SD<sub>total </sub>= ∑
 SD<sub>y1</sub> + … +
 SD<sub>y20.                                                                                </sub>(eq. 2) y = a year within the 20-year time series interval, |y| = temperature or rainfall absolute value for a year y, x̅ = 20-year time series interval mean temperature or rainfall, t = time series interval - every 3 hours for temperature and daily for rainfall. We selected the year with the lowest total standard deviation compared to the average of the 20-year interval for each scenario: <em>(i) </em>the year 1998 best represented the baseline climate conditions compared to the 1961-2005 time series, whereas <em>(ii)</em> the year 2097 best represented both the mitigation and BAU scenarios compared to the 2080-2100 time series (Figure 1). Selecting a year for each climate scenario ensured (1) the representation of the average climate conditions over the 20-year interval for each treatment, (2) the continuity and variability of temperature and precipitation, and (3) a robust comparison between the experimental climate treatments in this study. We used predicted data for each representative year of the three climate treatments for Central and South America based on the global circulation models described by the IPCC (Chou, 2014a, 2014b; Lyra, 2018). The modeled temperature and rainfall data were generated by the Brazilian Center of Weather Forecasting and Climate, affiliated with the National Institute for Space Research (CPTEC/INPE), and obtained from the PROJETA platform (https://projeta.cptec.inpe.br/; Chou, 2014a, 2014b; Lyra, 2018). We employed a high-resolution climate change model (0.5 km, continental, HADGEM2-ES) specifically for the municipality of Rio de Janeiro, Brazil (Latitude: -22.90, Longitude: -43.20), where our experiment was conducted. The PROJETA platform provided data on air temperature at 2 m height above the land surface in 3-hour intervals, along with daily precipitation data for each climate change scenario. Using these datasets, we established the temperature and rainfall manipulations for each treatment combination, as outlined in Figure 1. Overall, rainfall volume decreased from mitigation to BAU scenario compared to the baseline. Regarding warming, the mitigation and BAU scenario showed temperatures from 1.0 ± 2.0°C (mitigation, mean ± SD) to 2.6 ± 1.8°C (BAU, mean ± SD) higher than the baseline scenario mean temperature. The mean difference in temperature between mitigation and BAU scenarios was 1.7 ± 2.4°C (mean ± SD). We implemented a custom-made automated heating system (NAYAD Limno) to manipulate the temperature regime in the mesocosms. This system comprised a temperature sensor and an aquarium heater (80 W, Master, Rio de Janeiro, Brazil) positioned at the top of each mesocosm. These components were connected to a control system where the heater responded to the sensor, which monitored the difference between the current and predicted temperature for each climatic treatment in three-hour intervals. Remarkably, more than 90% of the predicted temperatures were accurately attained within a 3-hour timeframe for all experimental climatic treatments (Figure 1). We observed no significant differences between the monitored and modeled temperatures across any climate change treatment (Supplementary material: Table S1). Rainfall manipulation was carried out daily by adding dechlorinated tap water filtered through a 50 μm mesh size plankton net to the mesocosms to ensure no addition of new individuals. We tracked water volume variation throughout the experiment, noting that BAU treatment exhibited reduced water volume compared to baseline and mitigation treatments (Supplementary material: Figure S1). We also measured ortho-phosphate concentrations and related them to the water volume variation within the climatic treatments using generalized linear mixed-effects models (Supplementary material: Figure S2). We performed the ortho-phosphate analysis as described by Golterman (1978), and no additional nutrients were carried out during the experiment. The three climatic treatments (baseline, mitigation, and BAU) were factorially combined with the presence of biological invasion. We used the non-native submerged macrophyte <em>Hydrilla verticillata</em>, known for its successful colonization in shallow lakes either as a single dominant species or coexisting with others (Sousa, 2011). Originally native to Asia and Australia, <em>H. verticillata</em> has become cosmopolitan, establishing itself as an invasive species in numerous freshwater ecosystems, including those in Brazil (Langeland, 1996; Sousa, 2011). <em>Hydrilla verticillata </em>is associated with strong negative impacts on aquatic ecosystems, such as the replacement of native aquatic species and the reduction of multiple ecosystem services, which makes this species an ideal model organism for testing the impacts of invasive macrophytes (Calvo et al., 2019; Gentilin-Avanci et al., 2021; Silveira et al., 2018; Umetsu et al., 2012). We highlight that these impacts are especially strong when <em>Hydrilla</em> spreads its populations to new ecosystems and increases biomass over time (Sousa, 2011). In our experiment, <em>H. verticillata</em> was established by adding two apical stem fragments, each 15 cm in length without lateral branches, to the surface water of each mesocosm (<em>i.e., </em>macrophytes were not planted). Such density represents a medium propagule pressure (Li et al., 2015) in their colonization stage under natural conditions, as they can regenerate from fragments that float in the water columns for days or weeks before effectively colonizing the sediment layer (Umetsu et al., 2012). We thoroughly washed all fragments to prevent the accidental transfer of macroinvertebrates, zooplankton, phytoplankton, or periphyton into the mesocosms. Each treatment combination was replicated six times in a spatial block design, resulting in a total of 36 mesocosms (Supplementary material: Figure S6 represents the variation in the field air temperature within each block). Within each spatial block, there was one replicate of all treatment combinations. Sample processing We determined the biomass of all freshwater trophic groups at the end of the experiment. Phytoplankton biomass (μg L<sup>-1</sup>) was estimated using chlorophyll-<em>a</em> concentration assessed in the pelagic zone. Chlorophyll-<em>a</em> concentration was determined by filtering 200 ml of water through Whatman GF/F 0.7 pm filters and subsequently spectrophotometrically using hot (60–70 °C) ethanol (Jespersen & Christoffersen, 1987). Periphyton biomass (DW, g) was determined by vacuum filtering the material detached from the 100 cm² Vulcan plastic substrate. We used pre-burned and pre-weighed Whatman GF 6 filters to determine periphyton ash-free dry mass. The filters were dried at 60 °C for 24 hours to determine the dry weight and then incinerated in a muffle at 500°C for 1 hour to determine the ash-free content. Zooplankton community biomass (DW m<sup>-3</sup>, μg) was assessed at the end of the experiment by filtering the total mesocosm volume (Mean volume, Baseline: 22.6 L; Mitigation: 21.1 L; BAU: 11.5 L) using a 50 μm plankton net. The filtered material was concentrated in 100 mL aliquots and fixed with formaldehyde. Enumeration of the zooplankton community was conducted in a Sedgewick-Rafter chamber under an Olympus BX50 microscope for rotifers and cladocerans. Young nauplii and copepodites were quantified separately in open chambers under an Olympus SZ-40 stereomicroscope. Adult copepods were quantified under a microscope and identified using a stereoscopic microscope for detailed observation of identifying traits. Samples were thoroughly evaluated under a stereoscopic microscope in an open chamber to identify rare species. All organisms were identified to the lowest taxonomic level possible. The individual dry weight of rotifers was calculated from the biovolume, where 30 individuals of each species were measured (Ruttner-Kolisco, 1977). To calculate the individual dry weight of cladocerans and copepods, including juvenile forms, a total of 30 individuals of each species and taxonomic category were measured. In cases where samples contained less than 30 individuals, all individuals found were measured. The dry weight of cladocerans and copepods was calculated using the biovolume measurements and published weight-length regressions (Azevedo & Verdade, 2012; Bottrell & Newsome, 1976; Dumont et al., 1975). The total dry biomass of each group was calculated by multiplying the density (ind m<sup>-3</sup>) by the mean individual dry weight (DW, μg). Macroinvertebrate biomass (DW, g) was determined by filtering the total mesocosm volume, including the material from washed macrophyte fragments and detritus leaf litter, sorting the sediment through a set of sieves (diameter of 1 mm and 0.5 mm). For the sediment, we sampled an area of 98 cm². All macroinvertebrates were sorted in transilluminated trays and fixed with 70% ethanol for identification to the lowest possible taxonomic level, usually to the genus level. We used the biomass of genus <em>Chironomus </em>(DW, g) as a representative of the macroinvertebrate trophic group due to its very high abundance (encompassing 91.53% of the total macroinvertebrate biomass) and its presence across all treatment combinations. We dried <em>Chironomus</em> individuals at 60 °C for 72 hours and then weighed all individuals in a Mettler UMT2 high precise balance (readability: 0.1 µg). Native and non-native macrophyte biomass (DW, g) was determined by drying their fragments to a constant mass at 60 °C for 48 hours and individually weighing them using a Shimadzu AUW220D precise balance. Detritus biomass loss (DW, g) was accessed by drying all leaves and leaf fragments from the litterbags individually to a constant mass at 60 °C for 48 hours and weighing them in a laboratory balance. We calculated the leaf biomass loss as the difference in the dry leaf mass between the beginning and end of the experiment. We also performed ammonium and orthophosphate analysis for all mesocosms, following the methods described by Holm-Hansen (1980) for ammonium and Golterman (1978) for orthophosphate.
description: # Interaction between climate change scenarios and biological invasion
 reveals complex cascading effects in freshwater ecosystems
 [https://doi.org/10.5061/dryad.nvx0k6f27](https://doi.org/10.5061/dryad.nvx0k6f27) ## Description of the data and file structure ### Files and variables #### File: trophic\_groups\_biomass.csv **Description:** The dataset on the biomass of the different trophic groups. Empty cells in the dataset, indicated as "NA," represent samples that could not be collected during the study due to logistical constraints. ##### Variables * Block: A spatial block design  * Treatment: Treatment identity (i.e., B, B * I*, *RCP 4.5 * I*, RCP4.5*I, RCP8.5, RCP8.5 * I) * CC: Climate change treatments (*i.e.,* baseline, mitigation, and BAU (business-as-usual)) * IN: Biological invasion treatments (presence (yes) and absence (no) of the non-native macrophyte *Hydrilla verticillata*) * Macrophyte: Native macrophyte total biomass (*Cabomba carolinana* fragments, DW, g) * Periphyton: Periphyton biomass attached to a 100 cm² Vulcan plastic substrate (DW, g) * Phytoplankton: Phytoplankton biomass (chlorophyll-a concentration μg L-1) * Zooplankton: Zooplankton community biomass (DW m-3, μg)  * ChironomusTotal: *Chironomus* total biomass (DW, g) * Chironomus: *Chironomus* mean biomass (DW, g) * Biomassloss: Allochthonous detritus biomass losss (DW, g) * Invasive: Non-native invasive macrophyte total biomass (*Hydrilla verticillata*, DW, g) #### Files: Temperature_final_data_baseline.csv, Temperature_final_data_mitigation.csv, and Temperature_final_data_BAU.csv **Description:** The dataset of modelled and predicted temperature (ºC) of each climate change scenario. ##### Variables * Date/Time: Date and time of the experimental day * Day: Experimental day * Baseline (modelled): Modelled temperature (ºC) value obtained from the PROJETA platform * sys1temp1 sys1temp2 sys2temp1 sys2temp2 sys3temp1 sys3temp2 sys4temp1 sys4temp2 sys5temp1 sys5temp2 sys6temp1 sys6temp2: Temperature (ºC) monitored by sensors * Mean_sensors: Mean temperature (ºC) of all sensors * sys1temp1_D sys1temp2_D sys2temp1_D sys2temp2_D sys3temp1_D sys3temp2_D sys3temp3_D sys3temp4_D sys5temp1_D sys5temp2_D sys6temp1_D sys6temp2_D: Deviation of each sensor from the modelled temperature (ºC) * Mean_D: Mean deviation of each sensor from the modelled temperature (ºC) * sys1temp1_A sys1temp2_A sys2temp1_A sys2temp2_A sys3temp1_A sys3temp2_A sys4temp1_A sys4temp2_A sys5temp1_A sys5temp2_A sys6temp1_A sys6temp2_A: Model accuracy of each sensor (%) * Mean_A: Mean accuracy of all sensors (%) #### File: Temperature rainfall PROJETA plataform.xlsx **Description:** The dataset of temperature (ºC) and rainfall (ml) obtained from the PROJETA platform and calculation of the representative year of each climate change scenario.  **Sheet**: Temperature dataset and SD **Variables:** * Latitude/Longitude: Latitude and longitude of the experimental site * Day: Experimental day * Month: Experimental Month * 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004: Temperature  (ºC) data of the 20-year interval of baseline treatment * Mean baseline: Mean temperature of the 20-year interval of baseline treatment * 2080_M 2081_M 2082_M 2083_M 2084_M 2085_M 2086_M 2087_M 2088_M 2089_M 2090_M 2091_M 2092_M 2093_M 2094_M 2095_M 2096_M 2097_M 2098_M 2099_M:  Temperature (ºC) data of the 20-year interval of mitigation treatment * Mean mitigation: Mean temperature (ºC) of the 20-year interval of mitigation treatment * 2080_BAU 2081_BAU 2082_BAU 2083_BAU 2084_BAU 2085_BAU 2086_BAU 2087_BAU 2088_BAU 2089_BAU 2090_BAU 2091_BAU 2092_BAU 2093_BAU 2094_BAU 2095_BAU 2096_BAU 2097_BAU 2098_BAU 2099_BAU: Temperature data of the 20-year interval of BAU treatment * Mean BAU: Mean temperature (ºC) of the 20-year interval of BAUtreatment * 1985_SD 1986_SD 1987_SD 1988_SD 1989_SD 1990_SD 1991_SD 1992_SD 1993_SD 1994_SD 1995_SD 1996_SD 1997_SD 1998_SD 1999_SD 2000_SD 2001_SD 2002_SD 2003_SD 2004_SD: Standard deviation from the mean temperature (ºC) of the 20-year interval of baseline treatment * 2080_M_SD 2081_M_SD 2082_M_SD 2083_M_SD 2084_M_SD 2085_M_SD 2086_M_SD 2087_M_SD 2088_M_SD 2089_M_SD 2090_M_SD 2091_M_SD 2092_M_SD 2093_M_SD 2094_M_SD 2095_M_SD 2096_M_SD 2097_M_SD 2098_M_SD 2099_M_SD: Standard deviation from the mean temperature (ºC) of the 20-year interval of mitigation treatment * 2080_BAU_SD 2081_BAU_SD 2082_BAU_SD 2083_BAU_SD 2084_BAU_SD 2085_BAU_SD 2086_BAU_SD 2087_BAU_SD 2088_BAU_SD 2089_BAU_SD 2090_BAU_SD 2091_BAU_SD 2092_BAU_SD 2093_BAU_SD 2094_BAU_SD 2095_BAU_SD 2096_BAU_SD 2097_BAU_SD 2098_BAU_SD 2099_BAU_SD: Standard deviation from the mean temperature (ºC) of the 20-year interval of BAUtreatment **Sheet:** Rainfall dataset and SD **Variables:** * Latitude/Longitude: Latitude and longitude of the experimental site * Day: Experimental day * Month: Experimental Month * 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004: Rainfall (mm) data of the 20-year interval of baseline treatment * Mean baseline: Mean rainfall (mm) of the 20-year interval of baseline treatment * 2080_M 2081_M 2082_M 2083_M 2084_M 2085_M 2086_M 2087_M 2088_M 2089_M 2090_M 2091_M 2092_M 2093_M 2094_M 2095_M 2096_M 2097_M 2098_M 2099_M:  Rainfall (mm) data of the 20-year interval of mitigation treatment * Mean mitigation: Mean rainfall (mm) of the 20-year interval of mitigation treatment * 2080_BAU 2081_BAU 2082_BAU 2083_BAU 2084_BAU 2085_BAU 2086_BAU 2087_BAU 2088_BAU 2089_BAU 2090_BAU 2091_BAU 2092_BAU 2093_BAU 2094_BAU 2095_BAU 2096_BAU 2097_BAU 2098_BAU 2099_BAU: Rainfall (mm) data of the 20-year interval of BAU treatment * Mean BAU: Mean rainfall (mm) of the 20-year interval of BAU treatment * 1985_SD 1986_SD 1987_SD 1988_SD 1989_SD 1990_SD 1991_SD 1992_SD 1993_SD 1994_SD 1995_SD 1996_SD 1997_SD 1998_SD 1999_SD 2000_SD 2001_SD 2002_SD 2003_SD 2004_SD: Standard deviation from the mean rainfall  (mm) of the 20-year interval of baseline treatment * 2080_M_SD 2081_M_SD 2082_M_SD 2083_M_SD 2084_M_SD 2085_M_SD 2086_M_SD 2087_M_SD 2088_M_SD 2089_M_SD 2090_M_SD 2091_M_SD 2092_M_SD 2093_M_SD 2094_M_SD 2095_M_SD 2096_M_SD 2097_M_SD 2098_M_SD 2099_M_SD: Standard deviation from the mean rainfall (mm) of the 20-year interval of mitigation treatment * 2080_BAU_SD 2081_BAU_SD 2082_BAU_SD 2083_BAU_SD 2084_BAU_SD 2085_BAU_SD 2086_BAU_SD 2087_BAU_SD 2088_BAU_SD 2089_BAU_SD 2090_BAU_SD 2091_BAU_SD 2092_BAU_SD 2093_BAU_SD 2094_BAU_SD 2095_BAU_SD 2096_BAU_SD 2097_BAU_SD 2098_BAU_SD 2099_BAU_SD: Standard deviation from the mean rainfall (mm) of the 20-year interval of BAUtreatment **Sheet:** Representative year **Variables** * Baseline timeserie temperature: Each year of the 20-year interval  * Baseline summed SD temperature: Summed standard deviation of temperature (ºC) from the 20-year interval  * Mitigation timeserie temperature: Each year of the 20-year interval  * Mitigation summed SD temperature: Summed standard deviation of temperature (ºC) from the 20-year interval  * BAU timeserie temperature: Each year of the 20-year interval  * BAU summed SD temperature: Summed standard deviation of temperature (ºC) from the 20-year interval * SD Mitigation + BAU temperature: Summed standard deviation of temperature (ºC) from the 20-year interval for mitigation and BAU treatments * Baseline timeserie rainfall: Each year of the 20-year interval  * Baseline summed SD rainfall: Summed standard deviation of rainfall (mm) from the 20-year interval  * Mitigation timeserie rainfall: Each year of rainfall (mm) from the 20-year interval  * Mitigation summed SD rainfall: Summed standard deviation of rainfall (mm) from the 20-year interval  * BAU timeserie rainfall: Each year of the 20-year interval  * BAU summed SD rainfall: Summed standard deviation of rainfall (mm) from the 20-year interval * SD Mitigation + BAU rainfall: Summed standard deviation of rainfall (mm) from the 20-year interval for mitigation and BAU treatments * Baseline_Lowest SD Temperature + Rainfall: Classification by the lowest standard deviation of both temperature (ºC) and rainfall (mm) of the 20-year interval on the baseline treatment * Mitigation_Lowest SD Temperature + Rainfall: Classification by the lowest standard deviation of both temperature (ºC) and rainfall (mm) of the 20-year interval on the mitigation treatment * BAU_Lowest SD Temperature + Rainfall: Classification by the lowest standard deviation of both temperature (ºC) and rainfall (mm) of the 20-year interval on the BAU treatment #### File: Water column variation.csv **Description:** The dataset of water column variation (height and volume) to each treatment during the experiment. Empty cells in the dataset, indicated as "NA," represent samples that could not be collected during the study due to logistical constraints. ##### Variables * Day: Experimental day * Date: Date of the experimental day  * Block: A spatial block design * Treatment: Treatment identity (i.e., B, B * I*, *RCP 4.5 * I*, RCP4.5*I, RCP8.5, RCP8.5 * I) * Water column (cm): Water column height (cm) * Volume (L): Water volume related to the water column height (cm) #### File: Ortho-phosphate concentrarion.csv **Description:** The dataset of ortho-phosphate concentration to each treatment during the experiment. Empty cells in the dataset, indicated as "NA," represent samples that were lost during the data analysis process due to technical issues.  ##### Variables * Sampling: Sampling identity  * Block: A spatial block design * Treatment: Treatment identity (i.e., B, B * I*, *RCP 4.5 * I*, RCP4.5*I, RCP8.5, RCP8.5 * I) * PO₄³⁻ (µmol/L): Ortho-phosphate concentrarion #### File: Interaction classification.xlsx **Description:** The dataset on the calculation of the climate change scenarios deviation from baseline and the predicted additive effect.  **Variables:**  * Block: A spatial block design * Response: trophic group under evaluation * mean_baseline: Mean biomass (g) of the response under the treatment * mean_baseline_inv: Mean biomass (g) of the response under the treatment * %baseline_*inv: Observed interactive effect under baseline* * mean_4.5: Mean biomass (g) of the response under the treatment  * mean_inv_4.5: Mean biomass (g) of the response under the treatment  * %4.5_inv: *Observed interactive effect under mitigation *treatment * %predicted4.5: Predicted additive effect under mitigation * %deviation4.5: Deviation from predicted additive effect under mitigation * mean_8.5: Mean biomass of the response under the treatment  * mean_inv_8.5: Mean biomass (g) of the response under the treatment  * %8.5_inv: *Observed interactive effect under BAU *treatment * %predicted8.5: Predicted additive effect under BAU * %deviation8.5: Deviation from predicted additive effect under BAU