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Fig. 4 in From forests to cattail: how does the riparian zone influence stream fish?

Fig. 4. Biplot resulting from Nonmetric Multidimensional Scaling Analysis (NMDS) with presence and absence data showing ordination of sample units that represent each stream group: preserved (PRE, open circles), intermediate (INT, dark circles), and degraded sites (DEG, triangles). NMDS biplot exhibited stress value of 0.15 in 2-dimension, indicating good to potential useful interpretation (Clarke & Warwick, 2001).

Fig. 5 in From forests to cattail: how does the riparian zone influence stream fish?

Fig. 5. Biplot of the Partial Redundancy Analysis (pRDA) on fish species composition (see abbreviations on Table 2) and abiotic variables relationships (arrows). Species with low abundance were not represented in the biplot following the option "orditorp r" in the package vegan of the software R 2.11.1.

Fig. 3 in From forests to cattail: how does the riparian zone influence stream fish?

Fig. 3. Sample-based rarefaction curve (Obs) and richness estimation curves (Chao 1) by 50 randomizations against cumulative samples of the preserved (PRE), intermediate (INT), and degraded (DEG) sites.

Fig. 2 in From forests to cattail: how does the riparian zone influence stream fish?

Fig. 2. Location of the preserved (PRE, open circles), intermediate (INT, dark circles), and degraded (DEG, triangles) sites in the northwestern portion of the state of São Paulo, Brazil.

Fig. 1 in From forests to cattail: how does the riparian zone influence stream fish?

Fig. 1. Characteristic stages of the degradation process of riparian zones in a stream. a) streams with preserved riparian forests (PRE); b) with riparian forests in intermediate stage of degradation (INT); c) without riparian forests and in advanced stage of degradation (DEG).

opencc-by-4.0Nov 2016View details →

Fig. 1 in Effects of changes in the riparian forest on the butterfly community (Insecta: Lepidoptera) in Cerrado areas

Fig. 1. Butterfly sampling sites at the Pindaíba River Basin, MT – Brazil; (CVS 1, CVS 2, CVS 3, CVS 4 = Caveira stream (1st to 4th order); MS 1, MS 2, MS 3 and MS 4 = Mata Stream (1st to 4th order).

opencc-by-4.0Mar 2024View details →

Urban Riparian Wetland Water Quality Dataset_Stormwater Capture in Beaver-mediated Wetlands along Walnut Creek, Raleigh, North Carolina, USA

This is the initial release of a water quality dataset pertaining to the riparian floodplain wetlands alongside Walnut Creek in Raleigh, North Carolina USA.  Walnut Creek is the main drainage channel in an urbanized watershed (HUC-12: 030202011101) in central North Carolina.  There are several riparian floodplain wetlands along the creek which are largely supplied by urban stormwater runoff including directed storm sewer flows and regular overbank flooding events. In many of these wetlands local water retention and residence time in the surface ponds is mediated by the damming activity of North American beavers (Castor canadensis).  This dataset contains data specific to the water quality values of Walnut Creek, its tributary Little Rock Creek, and the surface ponds and groundwater at the Walnut Creek Wetland Park which is actively influenced by resident beavers.  The period of this dataset is from January 5, 2023 through October 28, 2023.  This dataset includes a variety of common water quality parameters measured in situ by use of a YSI Pro water quality meter, as well as dissolved nutrient values determined by laboratory analysis of collected water samples.  YSI data was collected on a weekly basis and water samples were collected for laboratory analysis on a monthly basis. Additional measurements and collection took place during six large rainfall events to allow comparison between baseflow and stormflow conditions across the site.  This dataset aims to provide a comprehensive look at the water quality of Walnut Creek in comparison with the surface ponds and groundwater in the Walnut Creek Wetland Park, which are all ultimately sourced from urban stormwater runoff. This water quality dataset is intended to accompany the separate hydrology dataset published on Zenodo at URL: <a href="https://doi.org/10.5281/zenodo.10709630">https://doi.org/10.5281/zenodo.10709630</a>. Together, these datasets are meant to support an improved understanding of the water availability and water quality found in connection with beaver-mediated stormwater capture in an urbanized watershed in the North Carolina Piedmont.  This dataset resulted from research supported with a Graduate Student Research Grant awarded by the North Carolina Water Resources Research Institute (WRRI), under Project Number 23-10-W: "Stormwater Diversion, Storage, and Treatment by Beaver-enhanced Floodplain Wetlands in Piedmont Urban Watersheds".   This material is based upon work supported by the National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) under Grant No. (DGE 2137100). Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation. Special thanks to Raleigh Parks and Walnut Creek Wetland Park for making this work possible. Laboratory analysis support for evaluation of dissolved nutrients (nitrate+nitrite, TKN, total phosphorus, and total organic carbon) was provided by the NC State Environmental and Agricultural Testing Services (EATS) laboratory, Department of Crop and Soil Sciences.  Additional laboratory analysis support for evaluation of dissolved nutrients (TKN and total phosphorus) was provided by the NC State Environmental Analysis Laboratory (EAL), Department of Biological and Agricultural Engineering (BAE).  Usage of and technical support for the YSI Pro water quality meter used in this study was made possible by the Osburn Lab, Department of Marine, Earth and Atmospheric Sciences (MEAS), NC State University.

opencc-by-4.0Dec 2018View details →

Fig. 1 in Changes in soil moisture and riparian forest structure after a dam construction

Fig. 1. Satellite image of a riparian forest on southern Brazil. Study area image with square showing plots locations. A = Spillway and the beginning of Reduced Outflow Stretch, A' = end of Reduced Outflow Stretch, B = hydroeletric dam, B' = end of hydroelectric dam, C = artificial lake created by dam, D = river patch returns to normal flow. The square ilustrates the study area.

opencc-by-4.0Dec 2018View details →

Fig. 3 in Changes in soil moisture and riparian forest structure after a dam construction

Fig. 3. Major changes that drives the community changes. Before river diversion, the sectors near the river had greater basal areas because they had many thick trees while distant sectors had thin trees (the density was statistically similar). After four years of river diversion, there were many trunks of still alive trees and dead trees in the sector closer to the river. Even with high growth, the basal area in this sector was severely reduced and became similar to the distant sector (which already has small basal area).

opencc-by-4.0Dec 2018View details →

Fig. 2 in Changes in soil moisture and riparian forest structure after a dam construction

Fig. 2. Soil moisture changes that occurred due to construction of the dams. A and C represent soil moisture in dry forests before damming, and B and D represent soil moisture after damming construction. The continuous line represents soil surface; vertical black bars represent soil sampling sites; blue bars represent soil moisture and their thickness illustrates soil moisture; and thicker bars represent more moisture. After dam influence, soil moisture increased mainly in the dry season and mainly near the lakeshore.

Riparian buffers maintain aquatic trophic structure in agricultural landscapes

Supporting data and R code for the publication entitled "Riparian buffers maintain aquatic trophic structure in agricultural landscapes".

mDRONES4rivers-project: Portfolios of classification results, UAV and gyrocopter data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany

Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project &bdquo;Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices.  Within the project period (2019-2022) data was collected at different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘.  In this dataset, the following portfolios of classifications, UAS and gyrocopter data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River are available for download: •             Multispectral orthophotos produced with the aid of UAS (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: MS_ORTHO) •             RGB-orthophotos and digital surface models produced with the aid of UAS (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: PH_SR_ORTHO_DSM) •             Multispectral orthophotos and Digital Surface Models produced with the aid of a gyrocopter (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: PANX_ORTHO_DSM) •             Classification results based on UAV- and a gyrocopter data (PDF, Detailed description of processing procedure for different classification levels; abbreviation: CLASSIF_PROD) •             German translated version of all above mentioned product portfolios (PDF, abbreviation: product_portfolio_collection_ger)

mDRONES4rivers-project: Classification results based on UAV data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany

Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project &bdquo;Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices.  Within the project period (2019-2022) an object oriented image classification was conducted based on UAV and gyrocopter data for different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘.  In this dataset, the following classification results and metadata of the project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany is available for download: •             Basic & Vegetation Classification (ESRI Shapefile; abbreviation: lvl2_vegetation_units) •             Classification of dominant stands (ESRI Shapefile; abbreviation: lvl4_dominant_stands ) •             Classification of substrat types (ESRI Shapefile; abbreviation: lvl4_substrate_types) •             associated reports (PDF; statistical and additional information on the classifiaction results and workflow) The above-mentioned files are provided for download as dataset stored in one directory per projekt site and season (e.g. mDRONES4rivers_Niederwerth_2019_03_Summer_Classification.zip = projectname_projectsite_year_no.season_name.season_product). To provide an overview of all files and general background information plus data preview the following files are additionally provided:  •             Portfolios (PDF, Detailed description of classification products and classification workflow, 1x for basic surface types, 1x for classification of vegetation units, 1x for classification of dominant stands,  1x for classification of substrate types) •            Color Coding table for the visualization of the classifiaction units (.xlsx)

opencc-by-4.0Jun 2021View details →

Fig. 3 in Diversity And Structure Of Nesting Birds In The Coastal Riparian Zones Of Great Kabylia In Algeria

Fig. 3. Principal Components Analysis (PCA) Showing the Avifauna Organization According to the Study Sites and the Environmental Variables.

opencc-by-4.0Mar 2022View details →

Sapling regeneration within canopy gaps in a temperate montane riparian forest.

This is a dataset of sapling regeneration within canopy gaps in a temperate montane riparian forest. The followings are details of each file. GapSeedlings_v1.0.0.csv <ul> <li><code>Plot</code>    Integer. The ID of plots, some plots include more than one gap.</li> <li><code>Gap</code>    Factor. The ID of gaps.</li> <li><code>Quadrat</code>    Integer. The ID of quadrats within a gap.</li> <li><code>stemID</code>    Character. The ID of stems.</li> <li><code>Sp.</code>    Factor. The species names.</li> <li><code>Family</code>    Factor. The family name of the species.</li> <li><code>Substrate</code>    Factor. Established substrates. NA means that it was not recorded.</li> <li><code>Heightyyyy</code>    Numeric. Vertical heights of trees (cm) in yyyy. The individuals with <code>CensusIn2020</code> = 0, their <code>Height2020</code> is NA because they had not been censused in 2020.</li> <li><code>Lengthyyyy</code>    Numeric. Length of trees (cm) in yyyy. The individuals with <code>CensusIn2020</code> = 0, their <code>Length2020</code> is NA because they had not been censused in 2020.</li> <li><code>DBH1_yyyy</code>, <code>DBH2_yyyy</code>    Numeric. Diameter at breast height (mm) in yyyy. DBH1 and DBH2 were measured to cross at right angles.  The individuals with <code>CensusIn2020</code> = 0, their <code>DBH2020_1</code> and <code>DBH2020_2</code> are NA because they had not been censused in 2020.</li> <li><code>Cmtyyyy</code>    Character. Comments in yyyy.</li> <li><code>CensusIn2020</code>    Factor. 1 means that the plot was censused in 2020, 0 does not.  </li> </ul> Map_Gaps.pdf <code>p. 1</code>: The overall picture of the positional relations between each gap. <code>pp. 2-19</code>: The details of gaps.   Metadata_GapSeedlings.txt Metadata of "GapSeedlings_v0.1.0.csv". It is the same as this description.

opencc-by-4.0Mar 2022View details →

Seedling recruitment and sapling bank dynamics on fluvial deposits in a temperate montane riparian forest.

This is a dataset of Seedling recruitment and sapling bank dynamics on fluvial deposits in a temperate montane riparian forest. The followings are details of each file. Saplings_inFluvialDepositsv1.0.0.csv <ul> <li><code>Plot_x</code>    Factor. X coordinates of plots.</li> <li><code>Plot_y</code>    Factor. Y coordinates of plots.</li> <li><code>x</code>    Integer. X coordinates in plots.</li> <li><code>y</code>    Integer. Y coordinates in plots.</li> <li><code>Substrate</code>    Factor. Established substrates. NA means that it was not recorded.</li> <li><code>stemID</code>    Character. The ID of individual trees.</li> <li><code>Sp.</code>    Factor. The species names.</li> <li><code>Family</code>    Factor. The family name of the species.</li> <li><code>Heightyyyy</code>    Numeric. Vertical heights of trees (cm) in yyyy. Height2007ad is the heights after disturbance in 2007.</li> <li><code>Lengthyyyy</code>    Numeric. Length of trees (cm) in yyyy. Length2007ad is the length after disturbance in 2007.</li> <li><code>DBH1_yyyy</code>, <code>DBH2_yyyy</code>    Numeric. Diameter at breast height (mm) in yyyy. DBH1 and DBH2 were measured to cross at right angles.</li> <li><code>Noteyyyy</code>    Character. Comments in yyyy.</li> </ul>   Seedlings_inFluvialDepositsv1.0.0.csv <ul> <li><code>Plot</code>    Factor. The plot ID.</li> <li><code>ID</code>    Character. The individual ID.</li> <li><code>Sp.</code>    Factor. The species names.</li> <li><code>Family</code>    Factor. The family names of species.</li> <li><code>Hyyyy</code>    Numeric. Vertical height of trees (cm) in yyyy.</li> <li><code>Ageyyyy</code>    Numeric. The years of trees (cm) in yyyy.</li> <li><code>noteyyyy</code>    Character. Comment in yyyy.</li> </ul>   Map_FluvialDeposits.pdf <ul> <li><code>p. 1</code>: The overall picture of the positional relations between each gap.</li> <li><code>p. 2</code>: The details of seedling quadrats.</li> </ul>   Metadata_Saplings_inFluvialDeposits.txt Metadata of "Saplings_inFluvialDepositsv1.0.0.csv". It is the same as this description.   Seedlings_inFluvialDepositsv1.0.0.csv Metadata of "Seedlings_inFluvialDepositsv1.0.0.csv". It is the same as this description.

opencc-by-4.0Apr 2022View details →

Data from: Riparian reforestation on the landscape scale – Navigating trade-offs among agricultural production, ecosystem functioning and biodiversity

Short description This repository contains the relevant data and code used for the analyses of the scientific publication: "Riparian reforestation on the landscape scale – Navigating trade-offs among agricultural production, ecosystem functioning and biodiversity", published in the Journal of Applied Ecology. For further details please see the original article and its supplementary materials.   Organization of the data The repository contains two main folders:    1. Target indicators & spatial analysis ‘target indicators.csv’: Measured variables that have been quantified at the CROSSLINK field sampling campaign in the Zwalm catchment (EPT taxa richness, diatoms functional evenness, cotton-strip assay). ‘bio-suitability segments.csv’: Biophysical suitability for food production of the arable land for each riparian segment of the Zwalm. ‘spatial analysis.xlsx’: Results of the Zwalm spatial analyses addressing land-use and physiographic properties of the (1) local riparian corridors; (2) full riparian corridors within in the upstream catchments and (3) total upstream catchment areas for each sampling site. ‘Summary model development Zwalm.pptx’: Additional information on the models that have been used in the CoMOLA optimization framework.    2. CoMOLA input & parameterisation The files in this folder can be used for the parameterisation of the Python tool CoMOLA (Strauch et al., 2019). Source for CoMOLA, including user manual: https://github.com/michstrauch/CoMOLA ‘config.ini’: Basic configuration file of CoMOLA (needs to be adjusted to local settings) ‘input’ folder: Includes the CoMOLA input files that have been used in our study. See CoMOLA manual for more details on each file. ‘models’ folder: Includes the Python code of the models that are used for the calculation of all target indicators within the optimization framework (‘Zwalm_4_Models_v1_utf8.py’). The sub-folders ‘GIS_temp_files’ and ‘Input’ contain all files that are needed and have been used to run the Python code.

opencc-by-4.0May 2020View details →

Fig. 1 in The Diversity Of Cuculiform And Piciform Species In Partly Transformed Riparian Zambezi Forest

Fig. 1. Location of the study area. Explanations: a — grassland (flooded area); b — Zambezi riparian forest; c — Colophospermum mopane forest; d — Kalahari Woodland; e — arable land; f — urbanized built-up areas; g — rural areas; h — Zambezi River; i — border of the study area.

Multi-temporal Structure from Motion ponit clouds of riparian vegetation

the dataset consists of three pointclouds and two NIR orthomosaics generated through a Structure from Motion standard workflow of the same forested area. The study area is typical riparian habitat vegetation. The data were acquired in different phenological stages: the first acquisition was realised in leaves-off conditions (march 2020) The second acquisition was realised in June 2020 the third acquisition was realised in July 2020. Reference system: WGS84/32N [EPGS: 32632] For further information regarding the data processing please refer to https://doi.org/10.3390/rs13091756