This dataset stores the data used to obtain the results (Table 1) and the scripts developed in R for each result. The table presented brings together the categories created from the articles retrieved in the search carried out for the systematic review. For more information read the methodology in the original paper.

This dataset stores the data used to obtain the results (Table 1) and the scripts developed in R for each result. The table presented brings together the categories created from the articles retrieved in the search carried out for the systematic review. For more information read the methodology in the original paper.

In order to synthesize the main predictive models applied to benthic macroinvertebrates, we conducted a systematic literature review. The reporting of this systematic literature review was guided by the standards of the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Statement. PRISMA is a guideline that helps researchers to report more clearly the methodology applied in systematic reviews (i.e., information source, eligibility criteria, selection of included studies, data extraction and analysis) in order to make it more transparent and reliable (Moher et al., 2009; Sarkis-Onofre et al., 2021). The main steps in the classification of studies for this systematic review are depicted in Figure 1. Studies were identified in the Web of Science database in December 2020 applying the limit date from 1945 to 2020, using the following search terms: ((model* OR simula*) AND (benth* OR macrozoobenth* OR macrobenth OR macroinvertebrates OR invertebrates) NOT (Benth OR Bentham OR Bentheim OR Bentheimer OR BENTHEM OR BENTHIOCARB OR benthamii OR benthamiana)). The terms included after the ‘NOT’ boolean operator were used to avoid records not related to the benthic fauna.

In order to synthesize the main predictive models applied to benthic macroinvertebrates, we conducted a systematic literature review. The reporting of this systematic literature review was guided by the standards of the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Statement. PRISMA is a guideline that helps researchers to report more clearly the methodology applied in systematic reviews (i.e., information source, eligibility criteria, selection of included studies, data extraction and analysis) in order to make it more transparent and reliable (Moher et al., 2009; Sarkis-Onofre et al., 2021). The main steps in the classification of studies for this systematic review are depicted in Figure 1. Studies were identified in the Web of Science database in December 2020 applying the limit date from 1945 to 2020, using the following search terms: ((model* OR simula*) AND (benth* OR macrozoobenth* OR macrobenth OR macroinvertebrates OR invertebrates) NOT (Benth OR Bentham OR Bentheim OR Bentheimer OR BENTHEM OR BENTHIOCARB OR benthamii OR benthamiana)). The terms included after the ‘NOT’ boolean operator were used to avoid records not related to the benthic fauna.

1. R code (R Markdown file)Complete simulation for the taxon Cirratulidae2. Occurrence dataThis file contains data indicating where sediment samples containing benthic organisms were sampled. The sampling campaign is indicated by the site name and the location of the collection point (latitude and longitude). Finally, data on taxa (name and abundance) collected in the Jaguaripe River estuary between 2006 and 2019 are presented.3. Environmental layers The environmental Layers were obtained from the data collected in situ. Then they were interpolated using IDW (Inverse Distance Weighted) in ArcGIS and converted from raster format to text (.asc) format.The layers used in this simulation are salinity and different sediment fractions. The salinity data (minimum and maximum) from the Jaguaripe River estuary were recorded using a multiparameter probe (Horiba) and data-logger sensors (HOBO). The sea-level rise scenarios were proposed through the quantitative synthesis from a systematic review of numerical models that simulated saline intrusion as a result of sea-level rise in estuaries (Costa et al., “Trends in the effects of sea-level rise in estuaries: A qualitative and quantitative synthesis towards a simple general model to estimate future saline intrusion in estuaries”, unpublished). The predict function in R software (R Development Core Team, 2016) was used in the GLM (i.e., multiple regression model) to obtain the saline intrusion values for each scenario. The sediment classes used as environmental layers were pebble, granule, very coarse sand (vcsand), coarse sand (csand), medium sand (msand), fine sand (fsand), very fine sand (vfsand) and mud (silt and clay fractions).

1. R code (R Markdown file)Complete simulation for the taxon Cirratulidae2. Occurrence dataThis file contains data indicating where sediment samples containing benthic organisms were sampled. The sampling campaign is indicated by the site name and the location of the collection point (latitude and longitude). Finally, data on taxa (name and abundance) collected in the Jaguaripe River estuary between 2006 and 2019 are presented.3. Environmental layers The environmental Layers were obtained from the data collected in situ. Then they were interpolated using IDW (Inverse Distance Weighted) in ArcGIS and converted from raster format to text (.asc) format.The layers used in this simulation are salinity and different sediment fractions. The salinity data (minimum and maximum) from the Jaguaripe River estuary were recorded using a multiparameter probe (Horiba) and data-logger sensors (HOBO). The sea-level rise scenarios were proposed through the quantitative synthesis from a systematic review of numerical models that simulated saline intrusion as a result of sea-level rise in estuaries (Costa et al., “Trends in the effects of sea-level rise in estuaries: A qualitative and quantitative synthesis towards a simple general model to estimate future saline intrusion in estuaries”, unpublished). The predict function in R software (R Development Core Team, 2016) was used in the GLM (i.e., multiple regression model) to obtain the saline intrusion values for each scenario. The sediment classes used as environmental layers were pebble, granule, very coarse sand (vcsand), coarse sand (csand), medium sand (msand), fine sand (fsand), very fine sand (vfsand) and mud (silt and clay fractions).

1. R code (R Markdown file)Complete simulation for the taxon Cirratulidae2. Occurrence dataThis file contains data indicating where sediment samples containing benthic organisms were sampled. The sampling campaign is indicated by the site name and the location of the collection point (latitude and longitude). Finally, data on taxa (name and abundance) collected in the Jaguaripe River estuary between 2006 and 2019 are presented.3. Environmental layers The environmental Layers were obtained from the data collected in situ. Then they were interpolated using IDW (Inverse Distance Weighted) in ArcGIS and converted from raster format to text (.asc) format.The layers used in this simulation are salinity and different sediment fractions. The salinity data (minimum and maximum) from the Jaguaripe River estuary were recorded using a multiparameter probe (Horiba) and data-logger sensors (HOBO). The sea-level rise scenarios were proposed through the quantitative synthesis from a systematic review of numerical models that simulated saline intrusion as a result of sea-level rise in estuaries (Costa et al., “Trends in the effects of sea-level rise in estuaries: A qualitative and quantitative synthesis towards a simple general model to estimate future saline intrusion in estuaries”, unpublished). The predict function in R software (R Development Core Team, 2016) was used in the GLM (i.e., multiple regression model) to obtain the saline intrusion values for each scenario. The sediment classes used as environmental layers were pebble, granule, very coarse sand (vcsand), coarse sand (csand), medium sand (msand), fine sand (fsand), very fine sand (vfsand) and mud (silt and clay fractions).