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Effects of multiple stressors on the dimensionality of ecological stability


Polazzo, Francesco; Rico, Andreu (2021), Effects of multiple stressors on the dimensionality of ecological stability, Dryad, Dataset,


Ecological stability is a multidimensional construct. Investigating multiple stability dimensions is key to understand how ecosystems respond to disturbance. Here, we evaluated the single and combined effects of common agricultural stressors (insecticide, herbicide and nutrients) on four dimensions of stability (resistance, resilience, recovery and invariability) and on the overall dimensionality of stability (DS) using the results of a freshwater mesocosm experiment. Functional recovery and resilience to pesticides were enhanced in nutrient-enriched systems, whereas compositional recovery was generally not achieved. Pesticides did not affect compositional DS, whereas functional DS was significantly increased by the insecticide only in nutrient-enriched systems. Stressor interactions acted non-additively on single stability dimensions as well as on functional DS. Moreover, we demonstrate that pesticides can modify the correlation between functional and compositional aspects of stability. Our study shows that different disturbance types, and their interactions, require specific management actions to promote ecosystem stability.


The data contained in this dataset come from a freshwater mesocosm experiment performed between April and July 2019 in Central Spain. During the experiment, phytoplankton, zooplankton and macroinvertebrates were sampled several time. 

Usage Notes

The data contained in this dataset come from a freshwater mesocosm experiment performed in central Spain between April and July 2019. During the experiment phytoplankton, zooplankton and macroinvertebrates were sampled several times. The Excel file containing the data is composed of six sheets. The first one contains the zooplankton density data; the second one macroinvertebrates’ abundance data and all the remaining sheets contain phytoplankton abundance data.

Each Excel sheet has the same structure. The first three columns report the sampling day relative to the application of the pesticides (start of the treatments), the number of the mesocosm and the treatments, respectively. The treatments are indicated through a code. Control systems are indicated as “control”. Mesocosms undergoing nutrients enrichment are indicated as “N”. The insecticide treatment is indicated as “I”. The herbicide treatment is indicated as “H”. When two or more treatments were applied together, we used the symbol “x” to indicate the interaction. Therefore, the combined application of the insecticide and the herbicide is indicated as “IxH”. All the other columns in each sheet are composed of the species names.

Phytoplankton and zooplankton densities reported in the Excel file were calculated as individuals/mL and individual/L, respectively.  

The dataset has no missing values. When a species identified in a previous sampling day was not found in the following sampling day, it was not removed from the sheet, but it appears with an abundance7density of 0.


Phytoplankton were sampled on days -5, 7, 15, 50 relative to the pesticides’ application. Depth- integrated water samples were taken with a PVC tube (six sub-samples per mesocosm mixed in a bucket). Next, 250 mL of this sample was introduced into glass amber bottles and 10% Lugol’s iodine was added for preservation. Counting and identification were done to the lowest taxonomic resolution possible with an inverted microscope (Motic AE31) coupled to a Motic Plan x 100/1.25 Oil ∞/0.17 objective.


 Zooplankton samples were taken on days -5, 7, 14, 30, and 50 relative to the application of pesticides. Six depth-integrated samples of approximately 1 L were collected from each mesocosm using a PVC tube and mixed together. Next, 5 L of the composed water sample were passed through a zooplankton net (55 μm) and concentrated to an approximate volume of 100 mL. The concentrated samples were fixed with Lugol’s iodine solution and stored in dark conditions at room temperature until identification and counting. Cladocera, Ostracoda and Copepoda were identified and counted in the entire zooplankton sample using an Olympus SZx2-TR30 stereomicroscope with a magnification of 20×. As for Rotifera and Copepoda nauplii, a subsample of 1 mL was taken and analysed using a binocular Olympus UCTR30-2 microscope with a magnification of 100×. Cladocera and Rotifera were identified up to the genus or species level. For Cyclopoida, a distinction was made between the adult stages (as the sum of true adults and copepodite stages) and the nauplius stages during counting and for further statistical analyses. Ostracoda appeared in relatively low abundance and were not further identified.


Macroinvertebrates were sampled on days -32, 15, 30, and 50 relative to the application of the pesticides. In order to collect pelagic and benthic individuals, three sampling methods were used. First, a net (mesh size: 0.5 mm) was passed twice through the side of the mesocosms (in both directions) to catch the animals that were swimming or resting on the mesocosm’s wall. Second, two pebble baskets positioned over the sediment surface were collected, and third, two traps filled with macrophyte shots (Elodea sp.), Populus sp. leaves and stones were collected from the sediment’s surface using a net. The invertebrates sampled from each mesocosm with the three sampling methods were pooled together, identified, and counted alive. Afterward, the invertebrates were returned to their original mesocosms together with the colonizing pebble baskets and traps. The macroinvertebrate taxonomic identification was performed to the lowest practical resolution level making use of freshwater biology guides.


H2020-MSCA-ITN ECORISK2050, Award: 813124

Ramón y Cajal Grant, Award: RYC2019-028132-I

H2020-MSCA-ITN ECORISK2050, Award: 813124

Ramón y Cajal Grant, Award: RYC2019-028132-I