Estimation of species richness

cumulative species richness curve

We selected data collected from lakes Harris, Tohopekaliga, Tohopekaliga East and Minneola to perform the evaluation because of high numbers of samples collected in these lakes and years 30—90 samples.

This model structure also provides the flexibility to explicitly accommodate changes to sampling designs, such as sample sizes or methods. The first scenario was no change in species richness through time.

Furthermore, data sources such as museum collections or citizen volunteers may not provide the option of standardized samples when conducting biodiversity studies e.

Interpreting chao 1

We only evaluated a small subset of the estimators available for estimating species richness and, thus, recommend that similar evaluations take place for estimators excluded from this study. Formal evaluations of these methods are currently absent from the ecological literature. Sampling considerations[ edit ] Depending on the purposes of quantifying species richness, the individuals can be selected in different ways. Thus, manipulating k allowed us to mimic the sampling variation in real catch data. Introduction Describing the diversity of biological communities is essential for ecosystem management and conservation. For these situations, other means of investigating patterns in biodiversity will be necessary. Evaluating the accuracy and precision of different estimators for different assemblages is common in the ecological literature, but estimator performance is rarely measured in terms of research goals such as detecting patterns in diversity. The first modification was to simply repeat the experiment with zero species lost in the second year to determine the probability of detecting spurious changes in species richness when, in fact, no loss of species had occurred. In practice, people are usually interested in the species richness of areas so large that not all individuals in them can be observed and identified to species. When evaluating changes in species richness through space and time, there are two errors that can be made. This manipulation mimicked a reduction in evenness of the community, where the species assemblage becomes increasingly dominated by rare species with fewer abundant species. Trend Detection We evaluated the ability to detect monotonic trends in species richness over a temporal gradient as an example of a generic trend analysis. Bias and Precision We evaluated the bias and precision of the five richness estimators for comparison to other estimator evaluations in the literature. We simulated data sets as replicate samples from a hypothetical fish community with a known number of species, repeated across years.

Indeed, these metrics can provide direct measures of the ability of estimators to correctly detect changes in species assemblages, whereas simple measures of bias and precision cannot. For example, of the 52 lakes used to inform this simulation, 38 were sampled with annually varying numbers of replicates as part of the process of refining the monitoring design FWC unpublished data.

Secondly, we assessed a trend analysis. Once the set of individuals has been defined, its species richness can be exactly quantified, provided the species-level taxonomy of the organisms of interest is well enough known.

Sampling considerations[ edit ] Depending on the purposes of quantifying species richness, the individuals can be selected in different ways.

species accumulation graph

We determined this value with data from 20 randomly selected lakes to represent the average sampling variation of boat electrofishing for Florida lakes. Evaluating the accuracy and precision of different estimators for different assemblages is common in the ecological literature, but estimator performance is rarely measured in terms of research goals such as detecting patterns in diversity.

microbial richness

The precision of each estimator was calculated as the coefficient of variation CV of the richness estimates across simulation iterations. Trend Detection We evaluated the ability to detect monotonic trends in species richness over a temporal gradient as an example of a generic trend analysis.

Species richness graph

Indeed, these metrics can provide direct measures of the ability of estimators to correctly detect changes in species assemblages, whereas simple measures of bias and precision cannot. However, species richness is blind to the identity of the species. These quantities can reveal the inference consequences of the dependency of estimator bias and precision on community and sampling characteristics. When the variance of the estimate was undefined, we set the confidence interval to zero, as if there were no uncertainty in the estimate. Species richness is most often estimated from replicate samples of a community. We selected the species to remove from the community with relative probability inversely proportional to the expected abundance in the samples i. This indicates that there was no single estimator that universally performed best across community types or sampling designs. In practice, people are usually interested in the species richness of areas so large that not all individuals in them can be observed and identified to species. We evaluated the detection of a trend for simulated data sets to calculate the statistical power of the analysis as the proportion of simulations where the trend was detected. Applying different species delimitations will lead to different species richness values for the same set of individuals.

The number of replicate samples collected varied among lakes and years, ranging from six to 90 replicates per lake and year.

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Species richness