Find research datasets worth reusing

opencc-by-4.0Dec 2021View details →

Figure 3 in Species richness and diversity of butterflies (Insecta: Lepidoptera) of Ganga Lake, Itanagar Wildlife Sanctuary, Arunachal Pradesh, India

Figure 3. Relative abundance of butterflies in Ganga Lake.

opencc-by-4.0Dec 2021View details →

Figure 1 in Species richness and diversity of butterflies (Insecta: Lepidoptera) of Ganga Lake, Itanagar Wildlife Sanctuary, Arunachal Pradesh, India

Figure 1. Map of the survey area (source: Google Map).

opencc-by-4.0Dec 2021View details →

Figure 1 in Altitudinal gradients and species richness: A study on diversity of orthoptera in Nilgiris Shola Forests and Grasslands

Figure 1. Dendrogram showing the similarity of sites in relation to Orthoptera species assemblages.

Figs. 31 in Aquatic Coleoptera Of Singapore: Species Richness, Ecology And Conservation #

Figs. 31. Zoogeography of Singaporean water beetles.

Fig. 4 in Aquatic Coleoptera Of Singapore: Species Richness, Ecology And Conservation #

Fig. 4: (Continued) for 3rd.

Fig. 1 in Aquatic Coleoptera Of Singapore: Species Richness, Ecology And Conservation #

Fig. 1: Map of Singapore, showing locations of sampling sites.

Fig. 2 in Aquatic Coleoptera Of Singapore: Species Richness, Ecology And Conservation #

Fig. 2: (Continued).

Fig. 4 in Aquatic Coleoptera Of Singapore: Species Richness, Ecology And Conservation #

Fig. 4: Key to genera of Hydrophilidae of Singapore.

Fig. 2 in Aquatic Coleoptera Of Singapore: Species Richness, Ecology And Conservation #

Fig. 2: Key to genera of Dytiscidae of Singapore.

Fig. 3 in Aquatic Coleoptera Of Singapore: Species Richness, Ecology And Conservation #

Fig. 3: Key toNoteridae and Gyrinidae of Singapore.

Fig. 34 in Aquatic Coleoptera Of Singapore: Species Richness, Ecology And Conservation #

Fig. 34. Distributionin temporary and/or permanent habitats.

Fig. 35 in Aquatic Coleoptera Of Singapore: Species Richness, Ecology And Conservation #

Fig. 35. Number of Water Beetle Species recorded in and/or outside of Singapore Nature Reserves.

Fig. 33 in Aquatic Coleoptera Of Singapore: Species Richness, Ecology And Conservation #

Fig. 33. Distributionin open and/or forested areas.

opencc-by-4.0Dec 2014View details →

Fig. 2. The Chao1 estimate with 95 in Estimating fossil ant species richness in Eocene Baltic amber

Fig. 2. The Chao1 estimate with 95% confidence intervals; asymptote value = 167.44.

opencc-by-4.0Aug 2024View details →

Dataset for plant species richness estimation in a wet grassland field using UAV data features

This dataset supports the estimation of plant species richness in a wet grassland field using features extracted from UAV (Unmanned Aerial Vehicle) data. It includes field and plot shapefiles, pre-processed input data, model performance metrics, spatial predictions (RASTER files).The dataset also contains geospatial imagery in the form of input and scaled GeoTIFF images, as well as two additional CSV files: <code>date.csv</code>, which records the cutting dates relevant to the study, and <code>merged_obs.csv</code>, which consolidates all the features with canopy height information extracted from Digital Elevation Model (DEM) data with field observed plant species richness. <ul> <li> Summary: <ul> <li>BIomass_Samples_Shapefiles: Contains shapefiles for field and plot-level data.</li> <li>Results: <ul> <li>ALLDATA: Pre-processed input data for RF and PLS models.</li> <li>MODELPERF: Performance metrics and variable importance for RF and PLS models.</li> <li>RASTER: Spatially-explicit predictions (maps) for plant species richness estimation.</li> <li>GLCM: Pre-processed Gray Level Co-occurrence Matrix (texture features).</li> <li>VI: Pre-processed Vegetation Indices.</li> </ul> </li> <li>TIF: Input and scaled geotiff images. <ul> <li>rescaled: Rescaled geotiff images.</li> <li>resampled: Resampled geotiff images.</li> </ul> </li> <li>date.csv: Contains cutting dates for the field.</li> <li>merged_obs.csv: Contains DEM and species richness data (number of species).</li> </ul> </li> </ul> This work was supported by the German Federal Ministry of Education and Research (BMBF) through the Digital Agriculture Knowledge and Information System (DAKIS) Project [Grant number 031B0729E].

opencc-by-4.0Apr 2024View details →

Data and R code used in Hennecke et al. "Plant species richness and the root economics space drive soil fungal communities"

To investigate how plant diversity and root traits relate to soil fungal communities, in 2021 we collected trait data from plots in the Jena Experiment (https://the-jena-experiment.de; funded by the DFG FOR 5000) and characterized fungal communities by sequencing, respiration and lipid fatty acid quantification.

opencc-zeroDec 2017View details →

Data from: Plant community responses to long-term fertilization: changes in functional group abundance drive changes in species richness

Declines in species richness due to fertilization are typically rapid and associated with increases in aboveground production. However, in a long-term experiment examining the impacts of fertilization in an early successional community, we found it took 14 years for plant species richness to significantly decline in fertilized plots, despite fertilization causing a rapid increase in aboveground production. To determine what accounted for this lag in the species richness response, we examined several potential mechanisms. We found evidence suggesting the abundance of one functional group—tall species with long-distance (runner) clonality—drove changes in species richness, and we found little support for other mechanisms. Tall runner species initially increased in abundance due to fertilization, then declined dramatically and were not abundant again until later in the experiment, when species richness and the combined biomass of all other functional groups (non-tall runner) declined. Over 86 % of the species found throughout the course of our study are non-tall runner, and there is a strong negative relationship between non-tall runner and tall runner biomass. We therefore suggest that declines in species richness in the fertilized treatment are due to high tall runner abundance that decreases the abundance and richness of non-tall runner species. By identifying the functional group that drives declines in richness due to fertilization, our results help to elucidate how fertilization decreases plant richness and also suggest that declines in richness due to fertilization can be lessened by controlling the abundance of species with a tall runner growth form.

opencc-zeroDec 2017View details →

Data from: The biogeographical patterns of species richness and abundance distribution in stream diatoms are driven by climate and water chemistry

In this inter-continental study of stream diatoms, we asked three important but still unresolved ecological questions: 1) What factors drive the biogeography of species richness and species abundance distribution (SAD); 2) Are climate-related hypotheses, which have dominated the research on the latitudinal and altitudinal diversity gradients, adequate in explaining spatial biotic variability; and 3) Is the SAD response to the environment independent of richness? We tested a number of climatic theories and hypotheses (i.e., the species-energy and the metabolic theory; and the energy variability and the climatic tolerance hypothesis) but found no support for any of these concepts as the relationships of richness with explanatory variables were non-existent, weak or unexpected. Instead, we demonstrated that diatom richness and SAD evenness generally increased with temperature seasonality and at mid- to high total phosphorus concentrations. The spatial patterns of diatom richness and the SAD—mainly longitudinal in the US, but latitudinal in Finland—were defined primarily by the covariance of climate and water chemistry with space. The SAD was not entirely controlled by richness, emphasizing its utility for ecological research. Thus, we found support for the operation of both climate and water chemistry mechanisms in structuring diatom communities, which underscores their complex response to the environment and the necessity for novel predictive frameworks.

opencc-zeroJun 2021View details →

The species richness-productivity relationship varies among regions and productivity estimates, but not with spatial resolution

The relationship between species richness and productivity (SRPR) has been a long-studied and hotly debated topic in ecology. Different studies have reported different results with variable shapes (i.e. unimodal, linear) and directions (i.e. positive, negative) of SRPRs depending on spatial grain (i.e. size of sampling unit for species richness), productivity estimates, and study extent. In this study, we quantified the effect of multiple estimates of productivity (aboveground, belowground and total biomass, and various measures of soil fertility) on species richness across three spatial grains (0.04 m2, 1 m2, and 25 m2) across temperate grasslands from two regions in Central Europe. We analyzed SRPR in each of the two regional datasets separately, as well as the two datasets pooled together. Our results have revealed that differences caused by spatial grain were unexpectedly small, and the direction of the SRPR was consistent within each productivity estimate, but differed between regions. Productivity estimates (across all spatial scales) had different, sometimes contrasting effects on SRPR (together with predictive power) within a region, and this pattern was more pronounced when compared between regions. The combination of different datasets led to very different results than when these were analyzed separately. We did not find any evidence for a unimodal response. This study points to the necessity of careful assessing when combining datasets from different regions, even if the plant communities belong to the same vegetation type. The dataset combination may blur the role of different drivers, which likely determine the shape and strength of SRPR. We suggest that data and study comparability may be enhanced by consistently using the same productivity estimates, which would allow for more robust interpretation of possible ecological drivers underlying the SRPR.