DOI: 10.5061/dryad.280gb5n12
title: Data from: Broad-scale meta-analysis of drivers mediating adverse impacts of flow regulation on riparian vegetation

description: Over two-thirds of global rivers are subjected to flow regulation.
 Although it is widely recognized that flow regulation can adversely affect
 riparian vegetation—a critical component of river ecosystems—the specific
 roles of various drivers remain poorly understood. To address this gap, we
 conducted a broad-scale meta-analysis, aiming to elucidate how different
 factors mediate the adverse impacts of flow regulation on riparian
 vegetation. This meta-analysis encompassed 59 papers, spanning 278 dams
 constructed on 146 rivers. We extracted data on four key indices of
 riparian vegetation: species richness and abundance of all riparian
 species, and those indices exclusively for non-native species. Indices
 were compared between regulated and free-flowing or pre-damming rivers to
 quantify the impact of flow regulation. Our meta-analysis revealed a
 moderate but significant reduction in the richness and abundance of all
 riparian species under flow regulation, coupled with a strong increase in
 the abundance of non-native species. Riparian vegetation in arid and
 continental climate regions experienced stronger negative impacts than
 those in tropical and temperate climates. Furthermore, the adverse effects
 on riparian vegetation were more pronounced downstream of dams than
 upstream. Considering climate region, study identity, and relative
 position to the dam as random variables, it became evident that years
 since flow regulation emerged as the most important factor influencing
 species richness. Over time, richness gradually recovered from initially
 low levels. However, this recovery was slowed by increasing flow
 regulation intensity (percentage of annual runoff stored). Additionally,
 the impact was more evident in larger rivers. To support regulated river
 management, we recommend prioritizing the protection of riparian
 vegetation in arid and continental climates, with emphasis on areas
 downstream of dams, limiting flow regulation intensity, particularly in
 larger rivers, and monitoring non-native species to prevent
 disproportionate spread.
description: ### **README for: Broad-scale meta-analysis of drivers mediating adverse
 impacts of flow regulation on riparian vegetation** **Dataset
 Description** This dataset accompanies the meta-analysis titled
 "**Broad-scale meta-analysis of drivers mediating adverse impacts of
 flow regulation on riparian vegetation**", providing comprehensive
 data from 59 peer reviewed studies across 278 dams and 146 rivers. This
 dataset serves as a resource for exploring the roles of geographical and
 hydrological factors mediating adverse impacts of flow regulation on
 riparian vegetation. #### **Structure and Content** The dataset is
 organized into four primary tables: **1. All species richness** Contains
 data on species richness for all riparian plants, comparing regulated and
 control conditions. **2. All species abundance** Contains data on species
 abundance for all riparian plants, comparing regulated and control
 conditions. **3. Non-native species richness** Contains data on species
 richness for non-native riparian plants, under regulated and control
 conditions. **4. Non-native species abundance** Contains data on species
 abundance for non-native riparian plants, under regulated and control
 conditions. **Glossary of Variables and Abbreviations** The variables and
 abbreviations used in the dataset are categorized as follows: **1. Study
 Information** **•** **Study**: Indicates the source of the study,
 referencing the authors and publication year. A single study may
 contribute multiple comparisons and observations. **2. Geographical
 Information** **•** **Country**: The country where the dam is located.
 **•** **Continent**: The continent where the dam is located. **•**
 **UpDownstreamDam**: Indicates whether the affected riparian zone is
 located upstream or downstream of the dam. **3. Climate Variables** **•**
 **Climate**: The climate zone where the dam is located, classified using
 the Köppen-Geiger climate classification: A: Tropical climate B: Arid
 climate C: Temperate climate D: Continental climate Bio_1: Annual Mean
 Temperature (℃). Bio_4: Temperature Seasonality, expressed as the standard
 deviation multiplied by 100. Bio_5: Maximum Temperature of the Warmest
 Month (℃). Bio_12: Annual Precipitation (mm). Bio_14: Precipitation of the
 Driest Month (mm). Bio_15: Precipitation Seasonality, expressed as the
 Coefficient of Variation. **4. Flow Regulation Metrics** **•**
 **DamNumber**: The number of dams involved in the study. To avoid double
 counting, some rows may leave this field blank. **•**
 **RegulatedRiverNumber**: The number of regulated rivers included in the
 study. Similar to **DamNumber**, some rows may leave this field blank.
 **•** **YearsSinceRegulation**: The duration (in years) of flow regulation
 by the dam, calculated as the time from the dam's commissioning to
 the year the study was conducted. **•** **DOR (Degree of Regulation)**:
 Quantifies how dam storage alters the natural flow regime of downstream
 river reaches and affects upstream reservoir water levels. **5.
 Hydrological Metrics** **•** **DIS**: Long-term (1971–2000) naturalized
 discharge, expressed in cubic meters per second (m³/s), simulated using
 the global hydrological model WaterGAP. **6. Vegetation Metrics** **•**
 **RegulatedMean**: Mean values of riparian vegetation indices in regulated
 zones. **•** **RegulatedSD**: The standard deviation of the vegetation
 indices in regulated zones. **•** **RegulatedN**: The number of replicates
 used to calculate the mean vegetation indices in regulated zones. **•**
 **ControlMea**n: Mean values of riparian vegetation indices in control
 zones. **•** **ControlSD**: The standard deviation of the vegetation
 indices in control zones. **•** **ControlN**: The number of replicates
 used to calculate the mean vegetation indices in control zones. **•**
 **HedgesG**: The standardized mean difference (*Hedge’s g*) used to
 measure the effect size of flow regulation on riparian vegetation. **•**
 **Vi_HedgesG:** Represents the variance of the effect size (Hedges'
 g) for the impacts of flow regulation on riparian vegetation, specifically
 reflecting the precision and uncertainty of the effect size estimates.
 **7. Other Information** **•** **NA**: Indicates missing data. ###
 **ReadMe for R code** #### **Code Description** This repository contains
 the code used for the meta-analysis titled "Broad-scale meta-analysis
 of drivers mediating adverse impacts of flow regulation on riparian
 vegetation." The code is organized into four main parts, each
 addressing different aspects of the study. **01-Code for Meta-analysis**
 **Section 1**: “Meta-analysis_All species richness” analyzes the effects
 of flow regulation on the richness of all riparian species. **Section 2**:
 “Meta-analysis_All species abundance” examines the effects of flow
 regulation on the abundance of all riparian species. **Section 3**:
 “Meta-analysis_Non-native species richness” investigates the effects of
 flow regulation on the richness of non-native riparian species. **Section
 4**: “Meta-analysis_Non-native species abundance” evaluates the effects of
 flow regulation on the abundance of non-native riparian species.
 **Analytical Aspects Covered in Each Script:** **Overall Effect Sizes of
 Flow Regulation (Figure 2):** Aggregates data from all studies to estimate
 the overall impact of flow regulation on the vegetation index. **Effect
 Sizes Across Different Climate Regions (Figure 3):** Groups studies by
 climate regions to compare the effects of flow regulation on the
 vegetation index among climate regions. **Effect Sizes Upstream vs.
 Downstream of Dams (Figure 4):** Compares the effects of flow regulation
 on the vegetation index in riparian zones located upstream and downstream
 of dams. **02-Code for Model Fitting** This section focuses on analyzing
 the effects of bioclimatic variables on the responses of four vegetation
 indices to flow regulation. Specifically, it covers: • The influence of
 bioclimatic variables such as annual precipitation, temperature
 seasonality, and precipitation seasonality (Table 2). • Analyzing the
 richness and abundance of non-native species in riparian vegetation in
 response to key drivers: Degree of Regulation (DOR) Years Since Regulation
 Discharge (DIS) **03-Code for Multilevel Mixed-effects Meta-regression**
 This section includes scripts for performing multilevel mixed-effects
 meta-regression models (Table 3). Specifically, these models analyze the
 effects of flow regulation metrics on all-species richness and all-species
 abundance by including key drivers such as: Degree of Regulation (DOR)
 Years Since Regulation Discharge (DIS) **Note:** This analysis was not
 applied to non-native species richness or abundance due to a limited
 number of data points for these indices. **04-Code for Supplementary
 Information** This section provides additional analyses and visualizations
 that supplement the main findings of the study. The following subsections
 are included: **Bioclimatic Variable Comparisons Across Climate Regions**
 **(Table S5):** Compares bioclimatic variables for four vegetation indices
 across climate regions. **Distribution of Flow Regulation Metrics (Figure
 S2):** Analyzes the distribution of degree of regulation (DOR), years
 since regulation, and discharge (DIS) across climate regions for all
 vegetation indices. **Spearman’s Rank Correlation Analysis (Table S7):**
 Examines correlations among key drivers: Degree of Regulation (DOR) Years
 Since Regulation Discharge (DIS) **Publication Bias Assessment** **(Figure
 S3):** Includes funnel plots and fail-safe N calculations to assess
 potential publication bias in the meta-analyses. ### **Data was derived
 from the following sources:** Aguiar, F. C., Fernandes, M. R., Martins, M.
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