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137 results for “macrophytes”
The percent cover of aquatic macrophytes in China's large lakes during 1980-2017
<p>In this dataset, we provide the percent cover of aquatic macrophytes in China's large lakes during 1980-2017 at yearly time scales based on the remote sensing imagery interpretation.</p> <p>There are five fields in the dataset provided here:</p> <p>1.'LAKE_NAME', which provide the name of lakes.</p> <p>2. 'LON', longitude at the center of each lake.</p> <p>3. 'LAT', latitude at the center of each lake.</p> <p>4. 'YEAR', the resolution of our time series is yearly.</p> <p>5. 'The percent cover of lake macrophytes', which provide the percent cover of lake macrophytes of each lake.</p>
Data of physiology, biomechanics and hydrodynamics of a freshwater macrophyte
<p>This data package provides post-processed data used in the manuscript “Linking plant stress and hydrodynamics: evidence from freshwater macrophytes” submitted to Water Resources Research. The data package contains data obtained from direct measurements of maximum quantum yield of photosystem II, morphological characteristics and flexural rigidity of the freshwater macrophyte <em>Potamogeton natans</em>. Further, data of flow velocity, drag force, plant deflected height and plant centroid height obtained from flume experiments carried out with samples of <em>P. natans</em> are reported.</p>
Effects of nutrient enrichment on freshwater macrophyte and invertebrate abundance: A meta-analysis
<p>The zip-file contains the data and code accompanying the paper 'Effects of nutrient enrichment on freshwater macrophyte and invertebrate abundance: A meta-analysis'. Together, these files should allow for the replication of the results.</p> <p>The 'raw_data' folder contains the 'MA_database.csv' file, which contains the extracted data from all primary studies that are used in the analysis. Furthermore, this folder contains the file 'MA_database_description.txt', which gives a description of each data column in the database.</p> <p>The 'derived_data' folder contains the files that are produced by the R-scripts in this study and used for data analysis. The 'MA_database_processed.csv' and 'MA_database_processed.RData' files contain the converted raw database that is suitable for analysis. The 'DB_IA_subsets.RData' file contains the 'Individual Abundance' (IA) data subsets based on taxonomic group (invertebrates/macrophytes) and inclusion criteria. The 'DB_IA_VCV_matrices.RData' contains for all IA data subsets the variance-covariance (VCV) matrices. The 'DB_AM_subsets.RData' file contains the 'Total Abundance' (TA) and 'Mean Abundance' (MA) data subsets based on taxonomic group (invertebrates/macrophytes) and inclusion criteria.</p> <p>The 'output_data' folder contains maps with the output data for each data subset (i.e. for each metric, taxonomic group and set of inclusion criteria). For each data subset, the map contains random effects selection results ('Results1_REsel_<subset>.csv'), the fixed effects selection results ('Results2_FEsel_<subset>.csv'), the random variance components and R^2 values for the best models subset ('Results3_BestModels_<subset>.csv'), the parameter value estimations for the fixed effects ('Results4_Parameters_<subset>.csv'), the standard errors for the estimated parameter values ('Results5_SE_<subset>.csv'), and the consensus model parameter values ('Results6_ConsensusModel_<subset>.csv'). Furthermore, each map contains a file with the best-selected random effects model structure ('BestRanEf_<subset>.RData'), the model with the best-selected random effects structure without moderators (only for IA) ('BestRanEfModel_<subset>.RData'), and a file with the consensus model ('ConsensusModel_<subset>.RData').</p> <p>The 'scripts' folder contains all R-scripts that we used for this study. The 'PrepareData.R' script takes the database as input and adjusts the file so that it can be used for data analysis. The 'PrepareDataIA.R' and 'PrepareDataAM.R' scripts make subsets of the data and prepare the data for the meta-regression analysis and mixed-effects regression analysis, respectively. The regression analyses are performed in the 'SelectModelsIA.R' and 'SelectModelsAM.R' scripts to calculate the regression model results for the IA metric and MA/TA metrics, respectively. These scripts require the 'RandomAndFixedEffects.R' script, containing the random and fixed effects parameter combinations, as well as the 'Functions.R' script. The 'CreateMap.R' script creates a global map with the location of all studies included in the analysis (figure 1 in the paper). The 'CreateForestPlots.R' script creates plots showing the IA data distribution for both taxonomic groups (figure 2 in the paper). The 'CreateHeatMaps.R' script creates heat maps for all metrics and taxonomic groups (figure 3 in the paper, figures S11.1 and S11.2 in the appendix). The 'CalculateStatistics.R' script calculates the descriptive statistics that are reported throughout the paper, and creates the figures that describe the dataset characteristics (figures S3.1 to S3.5 in the appendix). The 'CreateFunnelPlots.R' script creates the funnel plots for both taxonomic groups (figures S6.1 and S6.2 in the appendix) and performs Egger's tests. The 'CreateControlGraphs.R' script creates graphs showing the dependency of the nutrient response to control concentrations for all metrics and taxonomic groups (figures S10.1 and S10.2 in the appendix).</p> <p>The 'figures' folder contains all figures that are included in this study.</p>
Quantifying the ecological impacts of alien aquatic macrophytes: A global meta‐analysis of effects on fish, macroinvertebrate and macrophyte assemblages
<p>Biological invasions constitute a pervasive and growing threat to the biodiversity and functioning of freshwater ecosystems. Macrophytes are key primary producers and ecosystem engineers in freshwaters, meaning that alien macrophyte invasions have the capacity to alter the structure and function of recipient aquatic ecosystems profoundly. Although prevailing wisdom holds that alien macrophyte invasions tend to compromise freshwater ecosystem structure and function, the ecological impacts of alien macrophyte invasion have not been quantitatively reviewed to date.</p> <p>Here we present a global meta-analysis of 202 cases from 53 research articles, exploring the impacts of alien macrophyte invasion on the abundance and diversity of three ubiquitous and ecologically important focal groups, which together comprise the bulk of non-microbial freshwater biodiversity: resident macrophytes, macroinvertebrates and fish. Our synthesis includes data from all continents except Antarctica and Asia, covering 25 alien macrophyte species, but reveals considerable taxonomic and geographical biases in knowledge.</p> <p>Meta-analysis results reveal that invasion by alien macrophytes has an overall negative impact on taxonomic diversity of the three focal groups, but no consistent effect on abundance. At a finer resolution, we detect a strong negative effect of alien macrophyte invasion on resident macrophyte abundance and diversity, and a significant but smaller positive effect of submerged alien macrophyte invasion on macroinvertebrates. Effects on fish appear inconsistent.</p> <p>Our findings emphasise the importance of context- and taxon-specific ecological research in informing appropriate and proportionate management of alien macrophyte invasions, since alien macrophyte impacts are not consistently negative. We also identify significant geographical and taxonomic limitations in existing studies, quantitative data being lacking for many alien taxa.</p>
Figure 2 in Variation in glyphosate effects and accumulation in emergent macrophytes
Figure 2. Concentration-response relationships for Phragmites australis (top graph), Typha latifolia (middle graph) and Typha × glauca (bottom graph) based on the proportion of dead shoots observed 27 days post-exposure to 0–8% glyphosate (Roundup WeatherMAX® formulation). Each treatment was replicated seven times per taxa, except T. latifolia at 5% (n = 6). Measured concentrations were used for modelling (Table S2). Grey points represent the replicates and are darker when points overlap; grey shading illustrates the 95% confidence bounds of the concentration-response model; solid red dots represent LC50 estimates; LC50 estimates on top left corner of each graph are given with lower and upper 95% confidence limits.
Figure 3 in Variation in glyphosate effects and accumulation in emergent macrophytes
Figure 3. Average residues of glyphosate (left) and aminomethylphosphonic acid (AMPA; right) detected in above-ground plant tissues of the three macrophytes Phragmites australis (PA), Typha latifolia (TL) and Typha × glauca (TXG) 27 days post-exposure to 8% and 5% glyphosate solutions (Roundup WeatherMAX® formulation). Four replicates per taxon and treatment, except for TL at 5% (n = 3); error bars represent standard errors of the means; letters a-b (left plot) and c-d (right plot) indicate significant differences in residues among taxa. Note y-axes have different scales.
Figure 1 in Variation in glyphosate effects and accumulation in emergent macrophytes
Figure 1. Representative replicates of the control treatment (tap water) and glyphosate (Roundup WeatherMAX® formulation) concentrations of 8% (43.2 g L-1) and 5% (27.0 g L-1) on day 4, 11 and 27 post-exposure. Each replicate (blue bucket) contained one pot of Typha latifolia (in the back), one pot of Typha × glauca (in the front), and one pot of Phragmites australis (on the left), and was sprayed with 50 mL of the respective treatment solution evenly covering aboveground plant tissues. Photos by Verena Sesin.
Figure 4 in Tomorrow Never Dies: biodegradation and subsequent viability of invasive macrophytes following exposure to aquatic disinfectants
Figure 4. Mean (± SE) count of new shoots for macrophyte fragmentary propagules at 28 days post exposure to aquatic disinfectants, for 0% (0 g L-1), 2% (20 g L-1) and 4% (40 g L-1) solutions of selected aquatic disinfectants. Fragments were submerged for five, fifteen or thirty minutes (n = 3 per treatment). Cont. = Control; Virk = Virkon® Aquatic; Vira = Virasure® Aquatic.
Figure 1 in Tomorrow Never Dies: biodegradation and subsequent viability of invasive macrophytes following exposure to aquatic disinfectants
Figure 1. Median degradation score depicting visual biodegradation stages and/or resumption of growth for four different species of macrophyte fragmentary propagules at 28 days post exposure to aquatic disinfectants, for 0% (0 g L-1), 2% (20 g L-1) and 4% (40 g L-1) solutions of selected aquatic disinfectants. Fragments were submerged for five, fifteen or thirty minutes (n = 3 per treatment). Bars signify minimum and maximum scores attained. The dashed line highlights a score of 5, which indicates no meaningful deterioration of the plant tissues or resumption of growth has occurred. Scores of 0–4 portray incremental levels of degradation, while noting the presence of sustained viability. Scores of 6–10 denote plant tissue degradation stages that lack viability in relation to the resumption of new growth. See Table 3 for description of the score categories. Cont. = Control; Virk = Virkon® Aquatic; Vira = Virasure® Aquatic.
Figure 3 in Tomorrow Never Dies: biodegradation and subsequent viability of invasive macrophytes following exposure to aquatic disinfectants
Figure 3. Mean (± SE) count of new roots for macrophyte fragmentary propagules at 28 days post exposure to aquatic disinfectants, for 0% (0 g L-1), 2% (20 g L-1) and 4% (40 g L-1) solutions of selected aquatic disinfectants. Fragments were submerged for five, fifteen or thirty minutes (n = 3 per treatment). Cont. = Control; Virk = Virkon® Aquatic; Vira = Virasure® Aquatic.
Figure 2 in Tomorrow Never Dies: biodegradation and subsequent viability of invasive macrophytes following exposure to aquatic disinfectants
Figure 2. Median degradation score depicting visual biodegradation stages and/or resumption of growth for fragmentary propagules of Hydrocotyle ranunculoides at 21 days post exposure to aquatic disinfectants, for 0% (0 g L-1), 2% (20 g L-1) and 4% (40 g L-1) solutions of selected aquatic disinfectants. Fragments were submerged for five, fifteen, thirty or sixty minutes (n = 3 per treatment). Bars signify minimum and maximum scores attained. The dashed line highlights a score of 5, whereby no meaningful deterioration of the plant tissues or resumption of growth has occurred. Scores of 0–4 portray incremental levels of degradation, while noting the presence of sustained viability. Scores of 6–10 denote plant tissue degradation stages which lack of viability in relation to the resumption of new growth. See Table 3 for description of the score categories. Cont. = Control; Virk = Virkon® Aquatic; Vira = Virasure® Aquatic.
Figure 5 in Tomorrow Never Dies: biodegradation and subsequent viability of invasive macrophytes following exposure to aquatic disinfectants
Figure 5. Mean (± SE) relative growth rate for new shoot growth produced by macrophyte fragmentary propagules at 28 days post exposure to aquatic disinfectants, for 0% (0 g L-1), 2% (20 g L-1) and 4% (40 g L-1) solutions of selected aquatic disinfectants. Fragments were submerged for five, fifteen or thirty minutes (n = 3 per treatment). Cont. = Control; Virk = Virkon® Aquatic; Vira = Virasure® Aquatic.
Fig. 2 in Macrophyte Vegetation Assessment In Streams Of The Venta River Basin District
Fig. 2. Percentage share of growth forms of aquatic plants in different types of rivers (R3- medium-sized rhitral rivers, R4- medium-sized potamal rivers, R5- large rhitral rivers, R6- large potamal rivers).
Figure 2 in Effect of untreated and pretreated sugarcane molasses on growth performance of Haematococcus pluvialis microalgae in inorganic fertilizer and macrophyte extract culture media
Figure 2. Cell density and growth rate of Haematococcus pluvialis in two different culture media NPK and ME, in mixotrophic cultivation untreated (UN) and pretreated (PR) sugarcane molasses. Error bars express standard mean deviations.
Figure 3 in Effect of untreated and pretreated sugarcane molasses on growth performance of Haematococcus pluvialis microalgae in inorganic fertilizer and macrophyte extract culture media
Figure 3. Protein (P), lipids (L), carbon (C) and nitrogen (N) (% biomass dry weight) of Haematococcus pluvialis growth in two different culture media (NPK and ME) in mixotrophic cultivation untreated (UN) and pretreated (PR) sugarcane molasses.
Fig. 4 in Forecasting the impact of an invasive macrophyte species in the littoral zone through aquatic insect species composition
Fig. 4. Comparison among Bray-Curtis dissimilarity indices of aquatic insect assemblages associated with white ginger lily banks and native vegetation profile in the littoral zone of a tropical reservoir in the Brazilian Savanna (Group 1, white ginger lily; Group 2, invaded forest; Group 3, native macrophyte; Group 4, riparian vegetation).
Fig. 2 in Forecasting the impact of an invasive macrophyte species in the littoral zone through aquatic insect species composition
Fig. 2. Comparison between ecological variables of aquatic insect assemblages associated with invasive white ginger lily bank and other native vegetation banks in the littoral zone of a tropical reservoir in the Brazilian Savanna (A, abundance; B, richness; C, Simpson diversity; IM, invasive macrophyte; IF, invaded forest; NM, native macrophyte; RV, riparian vegetation).
Fig. 1 in Forecasting the impact of an invasive macrophyte species in the littoral zone through aquatic insect species composition
Fig. 1. Location and characterization of vegetation profile banks of the Fazzari reservoir in the Brazilian Savanna (Cerrado Biome, Brazil).
Fig. 3 in Forecasting the impact of an invasive macrophyte species in the littoral zone through aquatic insect species composition
Fig. 3. Analyses of non-metric MDS of aquatic insect assemblages associated with white ginger lilY banks and native vegetation profiles in the littoral zone of a tropical reservoir in the Brazilian Savanna (●, white ginger lilY; ○, invaded forest; ∆, native macrohYte; ▲, riparian vegetation).
FIGURE 5 in Morphological divergences as drivers of diet segregation between two sympatric species of Serrapinnus (Characidae: Cheirodontinae) in macrophyte stands in a neotropical floodplain lake
FIGURE 5 | Graph of Spearman's correlation coefficient calculated between the morphological traits indicated by CVA and main food items consumed by Serrapinnus notomelas and Serrapinnus sp.1 in a lake in the upper Paraná River floodplain, Brazil. Values of r and p indicate the correlation and statistical significance, respectively. Positive correlations are represented by blue color and negative correlations by red color. ALG – algae; ZOO – zooplankton; DI – Depression index; CI – Compression index; RLPd – Relative lenght of caudal peduncule; RHPd – Relative height of caudal peduncule; RWPd – Relative width of caudal peduncule; RAD – Relative area of dorsal fin; ARC – Aspect ratio of caudal fin; ARA – Aspect ratio of anal fin; ARPt – Aspect ratio of pectoral fin; ARPv – Aspect ratio of pelvic fin; RLHd – Relative length of head; RHHd – Relative height of head; RWHd – Relative width of head; RHM – Relative height of mouth; RWM – Relative width of mouth; EP – Relative position of eye; MT – multicuspid teeth; PT – pentacuspid teeth; ICO – Intestinal coefficient; GRL – Gill raker length.
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International Brain Laboratory public data
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OpenNeuro
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