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Fig. 5 in Ecomorphological patterns of the fish assemblage in a tropical floodplain: effects of trophic, spatial and phylogenetic structures

Fig. 5. Distribution of scores centroids of the 35 species grouped by habitat type on the first two axes of the Principal Components Analysis (PC 1 and PC 2), applied to the correlation matrix (Pearson) formed by 22 ecomorphological indices. Each polygon defines the morphological space occupied by the species that exploit the corresponding habitat type.

Fig. 3 in Ecomorphological patterns of the fish assemblage in a tropical floodplain: effects of trophic, spatial and phylogenetic structures

Fig. 3. Distribution of scores centroids of the 35 species on the first two axes of the Principal Components Analysis (PC 1 and PC 2), applied to the correlation matrix (Pearson) formed by 22 ecomorphological indices.

Fig. 2 in Ecomorphological patterns of the fish assemblage in a tropical floodplain: effects of trophic, spatial and phylogenetic structures

Fig. 2. Schematic representation of the linear morphometric measurements and the calculated areas: standard length (SL), maximum body height (MBH), body midline height (BMH), maximum body width (MBW), caudal peduncle length (CPdL), caudal peduncle height (CPdH), caudal peduncle width (CPdW), head length (HdL), head height (HdH), head width (HdW), length of snout with the mouth closed (LSC), length of snout with the mouth open (LSO), eye height (EH), mouth height (MH), mouth width (MW), dorsal fin length (DL), dorsal fin height (DH), caudal fin length (CL), caudal fin height (CH), anal fin length (AL), anal fin height (AH), pectoral fin length (PtL), pectoral fin height (PtH), pelvic fin length (PvL), pelvic fin height (PvH), eye area (EA), dorsal fin area (DA), caudal fin area (CA), anal fin area (AA), pectoral fin area (PtA), and pelvic fin area (PvA).

Fig. 1 in Ecomorphological patterns of the fish assemblage in a tropical floodplain: effects of trophic, spatial and phylogenetic structures

Fig. 1. Study area with sampling stations in the upper Paraná River floodplain: rivers: Paraná (1), Baía (2) and Ivinheima (3); channels: Cortado (4), Curutuba (5) and Ipoitã (6); connected lagoons: Garças (7), Guaraná (8) and Finado Raimundo (9); disconnected lagoons: Fechada (10), Ventura (11) and Zé do Paco (12).

Fig. 4 in Spatial and temporal distribution patterns of ichthyoplankton in a region affected by water regulation by dams

Fig. 4. Average abundance of fish eggs (a) and larvae (b) in the Ilha Grande National Park, from October 2001 to March 2005.

Fig. 2 in Spatial and temporal distribution patterns of ichthyoplankton in a region affected by water regulation by dams

Fig. 2. Average egg abundances (rectangles) and standard errors (bars) by period (a), month (b) and sampling area (c) in the Ilha Grande National Park, from October 2001 to March 2005.

Fig. 3 in Spatial pattern of a fish assemblage in a seasonal tropical wetland: effects of habitat, herbaceous plant biomass, water depth, and distance from species sources

Fig. 3. Partial regressions testing the effects of water depth (left) and distance from colonizing source (right) on fish species richness collected in 22 plots in Site of Long-Term Sampling (SLTS). Only statistically significant relationships are shown.

Fig. 1 in Spatial pattern of a fish assemblage in a seasonal tropical wetland: effects of habitat, herbaceous plant biomass, water depth, and distance from species sources

Fig. 1. Geographical location of the study area and the Site of Long-Term Sampling (in the area). The system is installed in the Pantanal, Brazil.

opencc-zeroSep 2021View details →

Data for: Using spatial patterns of seeds and saplings to assess the prevalence of heterospecific replacements among cloud forest canopy tree species

Questions: To gain insights into the role of species-by-species replacements in cloud forest community structuring, we asked: (1) What are the effects of the spatial distribution of standing individuals on the seed rain, soil seed bank, and sapling density and survival in this cloud forest? and (2) What is the prevalence of conspecific vs. heterospecific replacements in the regeneration of this forest? Location: Santo Tomás Teipan, Oaxaca State, southern Mexico. Methods: In a 1-ha cloud forest plot we assessed seed rain, seed bank, and sapling density and survival of four canopy tree species (Chiranthodendron pentadactylon, Cornus disciflora, Quercus laurina, Oreopanax xalapensis). All standing individuals of these and other tree species (dbh ≥ 2.5 cm) were mapped. We used neighbourhood models to examine the spatial patterns of the three life cycle stages relative to the spatial distribution of adults. The neighbourhood effect was assessed through the Neighbourhood Index, which integrates information on size (dbh) and distance to adults. Data analysis was based on maximum likelihood and model selection procedures. Results: We found large between-species differences regarding the spatial patterns of seeds and saplings. Three species showed evidence for the Janzen-Connell effect operating at the seed (C. pentadactylon and Q. laurina) or sapling (O. xalapensis) stage. We also found support for a critical role of specific microsite factors (i.e., niche differentiation) in the regeneration of two species (C. pentadactylon and C. disciflora). Conclusions: Seed and sapling distribution patterns suggest the prevalence of heterospecific replacements, and that both Janzen-Connell and niche differentiation effects contribute to this pattern. Our results largely support the notion that the prevalence of heterospecific replacements among canopy species promotes species coexistence in cloud forest.

opencc-by-4.0Jan 2023View details →

Dataset for: 'Patterns in the Plankton – Spatial distribution and long-term variability of copepods on the Agulhas Bank'

This dataset contains environmental data (in situ temperature and chlorophyll a) and integrated biomass (mg C m-2) data for a number of copepod taxa, as well as total copepod biomass and abundance, on the Agulhas Bank, South Africa, as predicted by a Generalized Additive Model (GAM), during late austral spring (October-December) from 1988 to 2011. Mean environmental and copepod biomass parameters for each area and year are also provided. Relevant information on sampling and statistical analysis of spatial distributions has been extracted from the paper. Please see paper for full details and figures, including supplementary data; <a href="https://doi.org/10.1016/j.dsr2.2023.105265">https://doi.org/10.1016/j.dsr2.2023.105265</a>. Please see the Word document Huggett_et_al_2023_README.docx for a list of the data files and descriptions of the contents.

opencc-zeroMar 2023View details →

Data from: The spatial patterns of community composition, their environmental drivers and their spatial scale dependence vary markedly between fungal ecological guilds

Aim How community composition varies in space and what governs the variation has been extensively investigated in macroorganisms. However, we have only limited knowledge for microorganisms, especially fungi, despite their ecological and economic significance. Based on previous research, we define and test a series of hypotheses regarding the composition of fungal communities, its most influential drivers and their spatial scale dependence. Location Czech Republic. Time period Present. Taxa studied Fungi. Methods We analyzed the distance decay relationships, community composition and its drivers (physical distance, litter and soil chemistry, tree composition, climate) in fungi, using multivariate analyses. We compared the results across three fungal ecological guilds (ectomycorrhizal fungi, saprotrophs and yeasts), two forest microhabitats (litter and bulk soil) and six spatial scales (from 5 m to 80 km) that comprehensively cover the Czech Republic. Results We found that, similar to macroorganisms, the ectomycorrhizal fungi and saprotrophs showed marked distance-decay relationships, and their community composition was driven mainly by vegetation and dispersal at local scales, but at regional scales, by environmental effects. In contrast, the third fungal guild, the unicellular yeasts, showed little distance decay, suggesting extraordinary spatial homogeneity, as often seen in microorganisms, such as bacteria. Main conclusions Our results underscore the remarkable variation in the community ecology of fungi, which seems to range well-known patterns both from the macro- and the microworld. Knowledge of these patterns advances our understanding of the ecology of fungi, rather understudied organisms of significant ecological and economic importance, which our findings identify as a potentially suitable model for bridging the gaps between the biogeography of micro- and macroorganisms.

opencc-by-4.0Apr 2023View details →

Climate-driven spatial and temporal patterns in peatland pool biogeochemistry

This archive entry contains the original datasets used in the article "Climate-driven spatial and temporal patterns in peatland pool biogeochemistry" as CSV files. The Global_dataset.csv file is a synthesis of the morphological, biogeochemical and climate properties of peatland pools from eastern Canada, southern Patagonia, and the United Kingdom and comprises a total of 240 observations. The GPB_full_dataset.csv file includes the morphological and biogeochemical properties of nine pools of a peatland of eastern Canada that have been sampled regularly over the 2016 to 2021 summers. The GPB_aggregated_dataset.csv file shows the average pool biogeochemical and climate properties of the same peatland of eastern Canada, for 50-day windows between day of year 150 to 300. Statistical analyses shown in the "Climate-driven spatial and temporal patterns in peatland pool biogeochemistry" article are based on the GPB_aggregated_dataset.csv dataset.

opencc-by-4.0Jun 2023View details →

Multiscale Spatial Patterns in Giant Dike Swarms Identified through Objective Feature Extraction Datasets

S1 - Linked dike clusters for the Columbia River Flood Basalt group including the four identified subswarms: Chief Joseph, Monument, Ice Harbor, and Steens as compiled in Morriss et al., 2020. This dataset uses the a UTM Zone 11N projection (EPSG:26911). S2 - Linked dike clusters for the Deccan Traps including the four identified subswarms: Saurashtra, Narmada-Tapi, Central and Coastal. Due to their overlap Central and Coastal Swarms have been combined in this dataset into the Central Swarm. This dataset uses the a WGS 84 projection (EPSG:3857).  S3 - Dike segment data for Spanish Peaks and Dike Mountain located in the Rio Grande Rift of Colorado. This dataset was digitized using QGIS based on the map by Johnson (1961). This dataset uses the a UTM Zone13N projection (EPSG:32613). The file includes the start, end points, and midpoints of the dikes; segment length; calculated $\rho$ and $\theta$ for the Hough Transform; the origin used for the Hough Transform which is different for each subswarm (xc,yc); dike rock type if known; and a unique identification calculated based on the start and endpoints. This dataset has been preprocessed to remove curving dikes and is the data set used to produce later products (Data set S4).  S4 - Linked dike clusters for the Spanish Peaks and Dike Mountain. This dataset was produced using the Agglomerative Clustering algorithms using the parameters set in Table 1. This dataset uses the a UTM Zone 13N projection (EPSG:32613).      These datasets were produced using the Agglomerative Clustering algorithms using the parameters set in Table 1. The datasets are in the format of a CSV file but can be read into GIS programs using Well Known Text (WKT) linestring. TThe file includes the start and end points of the average line in the cluster and it's mid points, cluster length and width (Xstart, Xend, Xmid, Ymid, in meters and UTM coordinates, Dike Cluster Width  or R\_Width, Dike Cluster Length or R\_Length all in meters); calculated average $\rho$ and $\theta$ for the Hough Transform $\rho$ units measured in meters, $\theta$ units measured in degrees, unless otherwise stated); the origin used for the Hough Transform which is different for each subswarm ($xc$,$yc$, meters in UTM coordinates); average slope and intercept (AvgSlope, AvgIntercept meters); range and standard deviation for $\rho$ and $\theta$ for all objects in the cluster ($\rho$ units measured in meters, $\theta$ units measured in degrees); cluster size (Size); sum of segment lengths in a cluster (SegmentLSum, meters); whether the cluster crosses between negative and positive values (ClusterCrossesZero, boolean); overlap as calculated in the main text where the length of overlap is normalized by the sum of segment lengths in a cluster; maximum number of overlapping segments (nOverlapingSegments); twist angle which is the difference in angle betweeen the average cluster line and the average line formed by cluster midpoints (EnEchelonAngleDiff, degrees); the p-value for the midpoint line fit of the segments where $p<0.05$ is considered to be a significant fit (EEPValue); the maximum, median, and minimum segment nearest neighbors distances in the cluster which is calculated using the cartesian midpoints of each segment and normalized by the Cluster Length (MaxSegNNDist, MedianSegNNDist, MinSegNNDist); characterization of each cluster as filtered or not, filtered clusters are of size greater than $3$ and have a MaxSegNNDist of less than $0.5$ (TrustFilter, boolean); the date edited (Date\_Changed), and the clustering parameters used for each cluster (Rho\_Threshold in meters, Theta\_Threshold in degrees) and a unique identification calculated based on the start and endpoints (ClusterHash).

opencc-zeroJun 2023View details →

Niche suitability and spatial distribution patterns of anurans in a unique Ecoregion mosaic of Northern Pakistan

The lack of information regarding biodiversity states hampers designing and implementation conservation strategies and future targets. Northern Pakistan consists of a unique ecoregion mosaic which supports a myriad of environmental niches for anuran diversity to flourish in comparison to the deserts and xeric shrublands throughout the rest of the country. In order to study the niche suitability, overlap and distribution patterns in Pakistan, we collected observational data for nine amphibian species across several distinct ecoregions by surveying 87 randomly selected locations from 2016 to 2018 in District Rawalpindi and Islamabad Capital Territory. Our model showed that the precipitation of the warmest and coldest quarter, distance to rivers and vegetation were the greatest drivers of anuran distribution, expectedly indicating that the presence of humid forests and proximity to waterways greatly influences the habitable range of anurans in Pakistan. Sympatric overlap between species occurred at significantly higher density in tropical and subtropical coniferous forests than in other ecoregion types. We found species such as Minervarya spp., Hoplobatrachus tigerinus and Euphlyctis spp. showed preference for the lowlands in proximal, central and southern parts of the study area proximal to urban settlements, little vegetation and higher average temperatures. The toads Duttaphrynus bengalensis and D. stomaticus had scattered distributions throughout the study area with no clear preference for elevation. Sphaerotheca pashchima showed a patchy distribution in the midwestern extent of the study area as well as the foothills to the north. Microhyla nilphamariensis also showed a wide distribution throughout the study area with a preference for both lowlands and montane terrain. Endemic frogs (Nanorana vicina and Allopaa hazarensis) were observed only in locations with higher elevations, higher density of streams and lower average temperatures as compared to the other seven species sampled.

opencc-zeroAug 2023View details →

Spatial patterns and drivers of native plant diversity in Hainan, China

The Hainan Island has the most extensive and well-preserved tropical forests in China. With rapid economic development of Hainan, biodiversity is increasingly at risk. Determining the spatial patterns of plant diversity in Hainan and explaining the drivers behind plant diversity are important considerations in assessing and maximizing the effectiveness of national parks, such as the newly designated Hainan Rainforest National Park. We assessed phylogenetic diversity patterns, and species richness using 106,252 georeferenced specimen records and a molecular phylogeny of 3,792 native plant species. Based on phylogenetic range-weighted turnover metrics, we divided the Hainan flora into four major floristic units. The Grade of Membership model was used to further verify the four units and to understand their boundaries and the internal structure of each floristic unit. Finally, the best combination model was used to explore the driving mechanisms underlying the division. Our results reveal that central Hainan is the most important hotspot for plant endemism and diversity, followed by the southern. Environmental energy is the main factor determining the spatial patterns of native plant diversity on the island, and accessibility has the greatest impact on native plant diversity among social factors. We explore patterns of spatial phylogenetics and biogeography to identify potential priorities for management and conservation drivers of plant diversity patterns across Hainan, to provide the basis for the effective protection of native plant diversity and the improvement of national parks of Hainan Island.

opencc-by-4.0Jun 2023View details →

Associated Dataset for Genome-wide DNA methylation patterns in bumble bee (Bombus vosnesenskii) populations from spatial-environmental range extremes

The dataset contains the final methylation call set (n=14,627,533), variant calling file for population genomics analyses, analysis codes/scripts, and other associated files related to the research (Constitutive and variable patterns of genome-wide DNA methylation in populations from spatial-environmental range extremes of the bumble bee Bombus vosnesenskii). Raw WGBS reads generated in this study have been deposited and are currently available at the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under NCBI BioProject PRJNA956115.

opencc-by-4.0Jul 2023View details →

Dissecting glial scar formation by spatial point pattern and topological data analysis

These data were generated by the Laboratory of Neurovascular Interactions (https://elalilab.com/) at University Laval (Quebec, Canada), and reported in "Dissecting glial scar formation by spatial point pattern and topological data analysis".  Please refer to the Open Science Framework (OSF) repository (https://osf.io/3vg8j/) or GitHub (https://github.com/elalilab/GlialScar_PPA-TDA_2022) to see the processing pipeline. AUTHORS Manrique-Castano, Daniel; Bhaskar, Dhananjay; ElAli, Ayman KEYWORDS Stroke, cerebral ischemia, brain injury, glial scar, reactive astrocytes, reactive microglia,  1. STUDY DESCRIPTION                                                              This research provides a quantitative analysis of reactive glia and glial scar formation in a mouse model of cerebral ischemia. The dataset in this repository consists of raw widefield microscopy images from healthy and ischemic animals.     2. EXPERIMENTAL CONDITIONS Six-month-old C57BL/6 mice were subjected to 30 minutes of cerebral ischemia by middle cerebral artery occlusion (MCAO). Brains were harvested at 5, 15, and 30 days post-ischemia (DPI) (see 10.5281/zenodo.3559570). 5 sham animals were included as controls. The full protocol for brain harvesting is available at 10.17504/protocols.io.4r3l27q5pg1y/v1. Brain sections were stained with NeuN, Gfap, and Iba1 antibodies to detect neurons and reactive glia after injury. Full protocol available at 10.17504/protocols.io.yxmvmk94og3p/v1                           3. FILE DESCRIPTION - GT5X_Gfap_Iba1_NeuN.rar: Contain widefield (5x magnification) .tif images grouped by animals (5-7 images per animal; see research article for further details). The images were taken with the following parameters. Objective:        Fluar 5x/0.25 M27 Scaling per pixel:    1.300 x 1.300 µm Bit depth:        16 bit     Stainings: Neun            Channel    AF647; Excitation 653; Emission    668; Exposure 3 s IBA1            Channel    AFCy3; Excitation 458; Emission    561; Exposure 4 s GFAP            Channel    AF488; Excitation 493; Emission    517; Exposure 1 s DAPI            Channel    AF405; Excitation 353; Emission    465; Exposure 50 ms We used a FIJI script to pre-process the original .czi files. The script is shared in the GitHub repository under the name GT_Exp2_5x_GenerateTiffs.jim. - GT10X_Gfap_Iba1_NeuN.rar: Contain a single widefield (10x magnification) .tif image per animal at the level of the MCA territory (see research article for further details). The images were taken with the following parameters. Objective:        ECM paln-NeoFluar 10x/0.30 M27 Scaling per pixel:    0.45 x 0.45 µm Bit depth:        16 bit     Stainings: Neun            Channel    AF647; Excitation 653; Emission    668; Exposure 200 ms IBA1            Channel    AFCy3; Excitation 458; Emission    561; Exposure 250 ms GFAP            Channel    AF488; Excitation 493; Emission    517; Exposure 100 ms DAPI            Channel    AF405; Excitation 353; Emission    465; Exposure 10 ms We used a FIJI script to pre-process the original .czi files. The script is shared in the GitHub repository under the name GT_Exp2_10x_GenerateTiffs.jim.      For 5x and 10x images, the following naming strings apply: GT5x:         Research project identifier indicating the magnification M01(n):     Animal ID 5D(n):      Days post-ischemia. 0D refers to healthy (naive) animals.  Scene1(n):     Bregma level. Scene 1 corresponds to the most anterior area sampled, while Scene 6 or 7 is the most posterior. - PointPatterns_10x.rds: 2D point patterns of GFAP, IBA1, and NeuN generated by the r-package spatstat. The observation window comprises a horizontal ROI from the ventricular area to the outer border of the dorsolateral cerebral cortex. The point patterns were generated from the files and coordinates contained in the QupathProjects_10x.rar file in this repository. To reproduce the generation of point patterns please refer to the associated GitHub repository (https://github.com/elalilab/Stroke_GlialScar_PPA-TDA).  - PointPatterns_5x.rds: 2D point patterns of GFAP, IBA1, and NeuN generated by the r-package spatstat. The observation window comprises the ischemic hemisphere. The point patterns were generated from the files and coordinates contained in the QupathProjects_5x.rar file in this repository. To reproduce the generation of point patterns please refer to the associated GitHub repository (https://github.com/elalilab/Stroke_GlialScar_PPA-TDA).  - QupathProjects_5x.rar: QuPath project folder for 5x images (GT5X_Gfap_Iba1_NeuN.rar). Each subfolder (per animal) contains the necessary files to import annotations (alignment to the Allen Brain Atlas) generated by ABBA (https://biop.github.io/ijp-imagetoatlas/). Please see the research article for further details.  **NOTE** Gfap, Iba1, and NeuN folders contain raw .tsv data originated by QuPath (cell counting). These folders are read in the R processing pipeline to extract the coordinates of each cell. Please make sure the whole folder is in the R working directory. The file "project.qpproj" in each folder opens the QuPath project in QuPath and reads the classifiers and data folders. Each folder also contains "_Alignement.json" and "_Registration_json" files generated during the alignment and annotation procedures in ABBA. However, when the route of the source images is changed, the plugin does not allow rerouting, and the files are of no practical use. The issue has been reported to the ABBA Github repository.    - QupathProjects_10x.rar: QuPath project folder for 10x images (GT5X_Gfap_Iba1_NeuN.rar). The folder contains the necessary files to import annotations (Alignment to the Allen Brain Atlas) generated by ABBA (https://biop.github.io/ijp-imagetoatlas/). Please see the research article for further details.  **NOTE** Gfap, Iba1, NeuN, and DAPI folders contain raw .tsv data originated by QuPath (cell counting). These folders are read in the R processing pipeline to extract the coordinates of each cell. Please make sure the whole folder is in the R working directory. The file "project.qpproj" opens the QuPath project in QuPath and reads the classifiers and data folders.

publicSep 2021View details →

Data and analysis scripts for: Co-occurrence patterns at four spatial scales implicate reproductive processes in shaping community assembly in clovers

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publicOct 2020View details →

Level and spatial pattern of overstory retention impose tradeoffs for regenerating and retained trees

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