scholarly journals Attenuation of species abundance distributions by sampling

2015 ◽  
Vol 2 (4) ◽  
pp. 140219 ◽  
Author(s):  
Hideyasu Shimadzu ◽  
Ross Darnell
Keyword(s):  
Random Sampling ◽  
Species Abundance ◽  
Sampling Bias ◽  
Theoretical Studies ◽  
Limited Time ◽  
Species Abundance Distribution ◽  
Abundance Distribution ◽  
As Species ◽  
Species Abundance Distributions ◽  
Important Challenge

Quantifying biodiversity aspects such as species presence/ absence, richness and abundance is an important challenge to answer scientific and resource management questions. In practice, biodiversity can only be assessed from biological material taken by surveys, a difficult task given limited time and resources. A type of random sampling, or often called sub-sampling, is a commonly used technique to reduce the amount of time and effort for investigating large quantities of biological samples. However, it is not immediately clear how (sub-)sampling affects the estimate of biodiversity aspects from a quantitative perspective. This paper specifies the effect of (sub-)sampling as attenuation of the species abundance distribution (SAD), and articulates how the sampling bias is induced to the SAD by random sampling. The framework presented also reveals some confusion in previous theoretical studies.

scholarly journals Disentangling the effects of sampling scale and size on the shape of species abundance distributions

2020 ◽  
Author(s):  
Renato A. Ferreira de Lima ◽  
Paula Alves Condé ◽  
Cristina Banks-Leite ◽  
Renata C. Campos ◽  
Malva I. Medina Hernández ◽  
...  
Keyword(s):  
Sample Size ◽  
Species Abundance ◽  
Species Abundance Distribution ◽  
Abundance Distribution ◽  
Main Effect ◽  
Species Abundance Distributions ◽  
Sampling Scale ◽  
Main Factors ◽  
Statistical Approaches ◽  
Comparable Sample

AbstractMany authors have tried to explain the shape of the species abundance distribution (SAD). Some of them have suggested that sampling scale is an important factor shaping SADs. These suggestions, however, did not consider the indirect and well-known effect of sample size, which increases as samples are combined to generate SADs at larger scales. Here, we separate the effects of sample size and sampling scale on the shape of the SAD for three groups of organisms (trees, beetles and birds) sampled in the Brazilian Atlantic Forest. We compared the observed SADs at different sampling scales with simulated SADs having the same richness, relative abundances but comparable sample sizes, to show that the main effect shaping SADs is sample size and not sampling scale. The effect of scale was minor and deviations between observed and simulated SADs were present only for beetles. For trees, the match between observed and simulated SADs was improved at all scales when we accounted for conspecific aggregation, which was even more important than the sampling scale effect. We build on these results to propose a conceptual framework where observed SADs are shaped by three main factors, in decreasing order of importance: sample size, conspecific aggregation and beta diversity. Therefore, studies comparing SADs across sites or scales should use sampling and/or statistical approaches capable of disentangling these three effects on the shape of SADs.

Rarefaction and rarefiction—the use and abuse of a method in paleoecology

Paleobiology ◽  
1979 ◽  
Vol 5 (4) ◽  
pp. 423-434 ◽  
Author(s):  
John C. Tipper
Keyword(s):  
Species Richness ◽  
Species Abundance ◽  
Species Abundance Distribution ◽  
Abundance Distribution ◽  
Rarefaction Curve ◽  
Rarefaction Curves ◽  
Species Abundance Distributions

Rarefaction is a method for comparing community diversities that has consistently been abused by paleoecologists: here its assumptions are clarified and advice given on its application. Rarefaction should be restricted to comparison of collections from communities that are taxonomically similar and from similar habitats: the collections should have been obtained by using standardised procedures. The rarefaction curve is a graph of the estimated species richness of sub-samples drawn from a collection, plotted against the size of sub-sample: it is a deterministic transform of the collection's species-abundance distribution. Although rarefaction curves can be compared statistically, it may be more efficient to compare the species-abundance distributions directly. Both types of comparison are discussed in detail.

scholarly journals A general pattern of the species abundance distribution

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5928
Author(s):  
Qiang Su
Keyword(s):  
General Pattern ◽  
Species Abundance ◽  
Zipf’S Law ◽  
Species Abundance Distribution ◽  
Abundance Distribution ◽  
Zipf's Law ◽  
Frequency Distributions ◽  
Essential Questions ◽  
Species Abundance Distributions ◽  
Taxonomic Groups

Since the 1970s, species abundance distributions (SADs) have been one of the most fundamental issues in ecology and have frequently been investigated and reviewed. However, there was surprisingly little consensus. This study focuses on three essential questions. (1) Is there a general pattern of SAD that no community can violate it? (2) If it exists, what does it look like? (3) Why is it like this? The frequency distributions of 19,833 SADs from eight datasets (including eleven taxonomic groups from terrestrial, aquatic, and marine ecosystems) suggest that a general pattern of SAD might exist. According to two hypotheses (the finiteness of the total energy and the causality from the entropy to the diversity), this study assumes that the general pattern of SAD is approximately consistent with Zipf’s law, which means that Zipf’s law might be more easily to observe when one investigates any SAD. In the future, this conjecture not only needs to be tested (or supported) by more and more datasets, but also depends on how well it is explained from different angles of theories.

scholarly journals Efficient algorithms for sampling feasible sets of macroecological patterns

2013 ◽  
Author(s):  
Kenneth J. Locey ◽  
Daniel J. McGlinn
Keyword(s):  
Species Abundance ◽  
Previous Method ◽  
Species Abundance Distribution ◽  
Abundance Distribution ◽  
Feasible Set ◽  
As Species ◽  
Random Samples ◽  
Feasible Sets ◽  
Sampling Algorithms ◽  
New Algorithms

Ecological variables such as species richness (S) and total abundance (N) can strongly influence the forms of macroecological patterns. For example, the majority of variation in the species abundance distribution (SAD) can often be explained by the majority of possible forms having the same N and S, i.e. the feasible set. The feasible set reveals how variables such as N and S determine observable variation and whether empirical patterns are exceptional to the majority of possible forms. However, this approach has currently only been applied to the SAD using relatively inefficient random sampling algorithms. We extend the use of the feasible set approach by developing new algorithms to efficiently generate random samples of the feasible set for the SAD and the intraspecific spatial abundance distribution (SSAD). These algorithms are often several orders of magnitude faster than a previous method, which greatly increases the size and diversity of communities that can be examined.

scholarly journals A new method to analyze species abundances in space using generalized dimensions

2015 ◽  
Author(s):  
Leonardo A Saravia
Keyword(s):  
Species Abundance ◽  
Abundance Distribution ◽  
Community Patterns ◽  
Information Dimension ◽  
Generalized Dimension ◽  
Species Abundances ◽  
Species Abundance Distributions ◽  
Species Area ◽  
Generalized Dimensions ◽  
Relationship Of

Species-area relationships (SAR) and species abundance distributions (SAD) are among the most studied patterns in ecology, due to their application to both theoretical and conservation issues. One problem with these general patterns is that different theories can generate the same predictions, and for this reason they cannot be used to detect different mechanisms of community assembly. A solution is to search for more sensitive patterns, for example by extending the SAR to the whole species abundance distribution. A generalized dimension ($D_q$) approach has been proposed to study the scaling of SAD, but to date there has been no evaluation of the ability of this pattern to detect different mechanisms. An equivalent way to express SAD is the rank abundance distribution (RAD). Here I introduce a new way to study SAD scaling using a spatial version of RAD: the species-rank surface (SRS), which can be analyzed using $D_q$. Thus there is an old $D_q$ based on SAR ($D_q^{SAD}$), and a new one based on SRS ($D_q^{SRS}$). I perform spatial simulations to examine the relationship of $D_q$ with SAD, spatial patterns and number of species. Finally I compare the power of both $D_q$, SAD, SAR exponent, and the fractal information dimension to detect different community patterns using a continuum of hierarchical and neutral spatially explicit models. The SAD, $D_q^{SAD}$ and $D_q^{SRS}$ all had good performance in detecting models with contrasting mechanisms. $D_q^{SRS}$, however, had a better fit to data and allowed comparisons between hierarchical communities where the other methods failed. The SAR exponent and information dimension had low power and should not be used. SRS and $D_q^{SRS}$ could be interesting methods to study community or macroecological patterns.

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