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41 results for “estimated species richness”
Figure. Observed (S obs) and estimated species richness for Chao 2, Jackknife 2, and Bootstrap, calculated for Lumbricidae in East Serbia. Vertical dashed lines represent 50%, 75%, and 100% of the sampling effort, respectively. in A nonparametric approach in quantifying species richness of Lumbricidae in East Serbia, Balkan Peninsula
Figure. Observed (S obs) and estimated species richness for Chao 2, Jackknife 2, and Bootstrap, calculated for Lumbricidae in East Serbia. Vertical dashed lines represent 50%, 75%, and 100% of the sampling effort, respectively.
Fig. 4 in Quantifying zooplankton species: use of richness estimators
Fig. 4. Species accumulation curves, uniques and duplicates for the BA1 station of Furnas reservoir, state of Minas Gerais, Brazil, from March 2011 to February 2012.
Fig. 6 in Quantifying zooplankton species: use of richness estimators
Fig. 6. Species accumulation curves, uniques and duplicates for the BA3 station of Furnas reservoir, state of Minas Gerais from March 2011 to February 2012.
Fig 1 in Quantifying zooplankton species: use of richness estimators
Fig 1. Sampling stations in the Hydroelectric Power Plant of Furnas reservoir, state of Minas Gerais, Brazil (A, Barranco Alto region; B, junction of rivers Verde and Sapucai - VSJ).
Fig. 5 in Quantifying zooplankton species: use of richness estimators
Fig. 5. Species accumulation curves, uniques and duplicates for the BA2 station of Furnas reservoir, state of Minas Gerais, Brazil from March 2011 to February 2012.
Fig. 3 in Quantifying zooplankton species: use of richness estimators
Fig. 3. Species accumulation curves, uniques and duplicates for the VSJ station in Furnas reservoir, state of Minas Gerais, Brazil, collected with vertical hauls.
Fig. 1. The Chao 1 in Estimating fossil ant species richness in Eocene Baltic amber
Fig. 1. The Chao 1 (top line) and ACE (bottom line) richness estimates computed using Colwell (2013); note the slightly lower ACE.
Fig. 5. Estimated species richness E in Spatial and temporal variation of benthic fish assemblages during the extreme drought of 1997-98 (El Niño) in the middle rio Negro, Amazonia, Brazil
Fig. 5. Estimated species richness E(Sn) by strata at rio Negro (a-Sep, b-Nov 1997 and c-Feb 1998) and rio Branco (d-Sep
Fig. 3. Estimated species richness E in Spatial and temporal variation of benthic fish assemblages during the extreme drought of 1997-98 (El Niño) in the middle rio Negro, Amazonia, Brazil
Fig. 3. Estimated species richness E(Sn) by months of collection for (a) rio Negro and (b) rio Branco.
FIGURE 3 in Inferring global species richness from megatransect data and undetected species estimates
FIGURE 3 Latitudinal distribution of currently valid species of Pholcidae. Numbers of Pholci- dae species (x-axis) known from different latitudes (y-axis; N, north; S, south), with the land mass distribution shown in grey (from www.ecoclimax.com; excluding Antarctica). Pholcidae species richness is slightly shifted towards the north, possibly as a result of the unbalanced land masses and/or taxonomists' biases, but most diversity is in tropical regions.
FIGURE 2 in Inferring global species richness from megatransect data and undetected species estimates
FIGURE 2 Cumulative percentages of new species (y-axis) as a function of cumulative field days (x-axis) (left) and cumulative number of total (upper/blue line) and new (lower/red line) species (y-axis) as a function of culumative field days (x-axis) (right), for the three major tropical megatransects shown on the map. Each green dot represents a sampling locality. For raw data of all geographic regions, see supplementary tables S1–S5.
FIGURE 1 in Inferring global species richness from megatransect data and undetected species estimates
FIGURE 1 Cumulative curve of currently valid species of Pholcidae (y-axis) as a function of time (x-axis). The curve suggests that we are far from approaching a complete taxonomic knowledge of the family.
Figure 2. Bird species accumulation curve and estimated richness curve obtained from the Chao 1 in Avifauna of the region of the Volta Grande Hydroelectric Power Plant in Southeast Brazil
Figure 2. Bird species accumulation curve and estimated richness curve obtained from the Chao 1 index for the study area located throughout the reservoir of the Volta Grande Hydroelectric Power Plant in Southeast Brazil. Vertical bars represent the standard deviation of the estimate.
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.
Dataset for plant species richness estimation in a wet grassland field using UAV data features
<p>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.</p> <ul> <li> <p><strong>Summary:</strong></p> <ul> <li><strong>BIomass_Samples_Shapefiles:</strong> Contains shapefiles for field and plot-level data.</li> <li><strong>Results:</strong> <ul> <li><strong>ALLDATA:</strong> Pre-processed input data for RF and PLS models.</li> <li><strong>MODELPERF:</strong> Performance metrics and variable importance for RF and PLS models.</li> <li><strong>RASTER:</strong> Spatially-explicit predictions (maps) for plant species richness estimation.</li> <li><strong>GLCM:</strong> Pre-processed Gray Level Co-occurrence Matrix (texture features).</li> <li><strong>VI:</strong> Pre-processed Vegetation Indices.</li> </ul> </li> <li><strong>TIF:</strong> Input and scaled geotiff images. <ul> <li><strong>rescaled:</strong> Rescaled geotiff images.</li> <li><strong>resampled:</strong> Resampled geotiff images.</li> </ul> </li> <li><strong>date.csv:</strong> Contains cutting dates for the field.</li> <li><strong>merged_obs.csv:</strong> Contains DEM and species richness data (number of species).</li> </ul> </li> </ul> <p>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]. </p>
The species richness-productivity relationship varies among regions and productivity estimates, but not with spatial resolution
<p>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 m<sup>2</sup>, 1 m<sup>2</sup>, and 25 m<sup>2</sup>) 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.</p>
Estimating total species richness: fitting rarefaction by asymptotic approximation
<p class="MsoNormal"><span>Estimating the number of species in a community is important for assessments of biodiversity. Previous species richness estimators are mainly based on non-parametric approaches. Although parametric asymptotic models have been applied, they received limited attention due to specific limitations. Here, we introduce parametric models fitting the probability-based rarefied species richness curve that allow us to estimate the 'Total Expected Species' (TES) in a community based on species' abundance data. We develop two approaches to calculate TES (termed 'TESa' and 'TESb'), based on two slightly different mathematical assumptions regarding Expected Species (ES) models. We provide R functions to calculate both these estimation approaches and their standard deviation. The function also enables users to visualize the estimation. We test the performance of TESa, TESb and their average (TESab) across simulated and empirical data, and compare their bias, precision and accuracy with other, commonly used, non-parametric species richness estimators; the bias-corrected (bc-)Chao1 and the Abundance-based Coverage Estimator (ACE). Simulation reveals that in small samples, TESa shows a tendency to over-estimate and TESb to under-estimate overall species richness. TESab performs well in bias, precision and accuracy when compared to (bc-)Chao1 and ACE estimators. Results from empirical data shows that the variance generated from TES estimates is comparable to that for (bc-)Chao1 and ACE. Our study demonstrates that rarefaction theory in combination with parametric approximation models provides a valuable new approach to estimate the species richness of incompletely sampled communities. <a name="_Hlk114347649"></a>Robust estimates are likely to be obtained where the observed number of species is greater than half of the TES estimation. When the ratio of TESa to the observed richness is >> 2, we suggest the use of TESb or TESab. Although more comprehensive comparisons with other estimators are suggested, we encourage researchers to consider the TES approach in their biodiversity studies as a complement to current existing estimators.</span></p>
Data from: Acoustic indices estimate breeding bird species richness with daily and seasonally variable effectiveness in lowland temperate Białowieża forest
<p><span>Biodiversity monitoring is important to follow temporal changes of the environment. We examined whether acoustic indices can be used as a rapid and easy-to-apply tool for bird biodiversity estimation in one of the least changed European lowland forests – the Białowieża Forest.</span></p> <p><span>We collected soundscape recordings in early and late spring at 84 randomly chosen recording points. At each recording point, we analysed 72 1-min sound samples to evaluate how well acoustic indices predict bird species richness from the perspective of a single sound sample, single survey, and recording point, and how they follow the daily pattern of singing activity. For each 1-min sound sample, we prepared a list of vocalizing bird species and calculated three acoustic indices: Bioacoustic Index (BI), Acoustic Complexity Index (ACI), and Acoustic Diversity Index (ADI)</span>.</p> <p><span>We found that from the perspective of a single 1-min sound sample, BI best predicts the bird species richness, independently of time in the season but variably across the day, while ACI and ADI showed weaker and seasonally and daily variable dependency. The correlation between each index and the number of bird species was stronger in the early survey than in the late survey. All acoustic indices followed daily bird activity patterns, yet they provided greater values before the peak of the species richness estimated by manual spectrogram scanning and listening to recordings.</span></p> <p><span>We showed that acoustic indices correlate moderately to strongly with the bird species richness obtained by manual spectrogram scanning and listening to recordings by humans. Therefore, acoustic indices can be used as a tool for rapid estimation of bird biodiversity in temperate forests. However, daily and seasonal variation in effectiveness of acoustic indices should be taken into account in the analysis.</span></p>
Estimating total species richness: fitting rarefaction by asymptotic approximation
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The species richness-productivity relationship varies among regions and productivity estimates, but not with spatial resolution
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