A meta-analysis of species-abundance distributions

Oikos ◽  
2010 ◽  
Vol 119 (7) ◽  
pp. 1149-1155 ◽  
Author(s):  
Werner Ulrich ◽  
Marcin Ollik ◽  
Karl Inne Ugland
Keyword(s):  
Meta Analysis ◽  
Species Abundance ◽  
Species Abundance Distributions

scholarly journals A lognormal distribution of the lengths of terminal twigs on self-similar branches of elm trees

2017 ◽  
Vol 284 (1846) ◽  
pp. 20162395 ◽  
Author(s):  
Kohei Koyama ◽  
Ken Yamamoto ◽  
Masayuki Ushio
Keyword(s):  
Lognormal Distribution ◽  
Species Abundance ◽  
Self Similarity ◽  
Lognormal Distributions ◽  
Multiplicative Effect ◽  
Wide Range ◽  
Error Distributions ◽  
Species Abundance Distributions ◽  
Self Similar ◽  
Ulmus Davidiana

Lognormal distributions and self-similarity are characteristics associated with a wide range of biological systems. The sequential breakage model has established a link between lognormal distributions and self-similarity and has been used to explain species abundance distributions. To date, however, there has been no similar evidence in studies of multicellular organismal forms. We tested the hypotheses that the distribution of the lengths of terminal stems of Japanese elm trees ( Ulmus davidiana ), the end products of a self-similar branching process, approaches a lognormal distribution. We measured the length of the stem segments of three elm branches and obtained the following results: (i) each occurrence of branching caused variations or errors in the lengths of the child stems relative to their parent stems; (ii) the branches showed statistical self-similarity; the observed error distributions were similar at all scales within each branch and (iii) the multiplicative effect of these errors generated variations of the lengths of terminal twigs that were well approximated by a lognormal distribution, although some statistically significant deviations from strict lognormality were observed for one branch. Our results provide the first empirical evidence that statistical self-similarity of an organismal form generates a lognormal distribution of organ sizes.

scholarly journals The commonness of rarity: Global and future distribution of rarity across land plants

2019 ◽  
Author(s):  
Brian Joseph Enquist ◽  
Xiao Feng ◽  
Bradley Boyle ◽  
Brian Maitner ◽  
Erica A. Newman ◽  
...  
Keyword(s):  
Plant Species ◽  
Rare Species ◽  
Large Fraction ◽  
Neutral Theory ◽  
Species Abundance ◽  
Land Plant ◽  
Observation Data ◽  
Heavy Tailed Distributions ◽  
Species Abundance Distributions ◽  
Heavy Tailed

A key feature of life’s diversity is that some species are common but many more are rare. Nonetheless, at global scales, we do not know what fraction of biodiversity consists of rare species. Here, we present the largest compilation of global plant species observation data in order to quantify the fraction of Earth’s extant land plant biodiversity that is common versus rare. Tests of different hypotheses for the origin of species commonness and rarity indicates that sampling biases and prominent models such as niche theory and neutral theory cannot account for the observed prevalence of rare species. Instead, the distribution of commonness is best approximated by heavy-tailed distributions like the Pareto or Poisson-lognormal distributions. As a result, a large fraction, ~36.5% of an estimated ~435k total plant species, are exceedingly rare. We also show that rare species tend to cluster in a small number of ‘hotspots’ mainly characterized by being in tropical and subtropical mountains and areas that have experienced greater climate stability. Our results indicate that (i) non-neutral processes, likely associated with reduced risk of extinction, have maintained a large fraction of Earth’s plant species but that (ii) climate change and human impact appear to now and will disproportionately impact rare species. Together, these results point to a large fraction of Earth’s plant species are faced with increased chances of extinction. Our results indicate that global species abundance distributions have important implications for conservation planning in this era of rapid global change.

scholarly journals Estimation accuracy of species abundance based on environmental DNA with relation to its production source, state, and assay methodology suggested by meta-analyses

2021 ◽  
Author(s):  
Toshiaki Jo ◽  
Hiroki Yamanaka
Keyword(s):  
Meta Analysis ◽  
Empirical Studies ◽  
Species Abundance ◽  
Environmental Dna ◽  
Efficient Estimation ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Abundance Estimation ◽  
Natural Environments ◽  
Promising Tool

Environmental DNA (eDNA) analysis is a promising tool for non-disruptive and cost-efficient estimation of species abundance. However, its practical applicability in natural environments is limited because it is unclear whether eDNA concentrations actually represent species abundance in the field. Although the importance of accounting for eDNA dynamics, such as transport and degradation, has been discussed, the influences of eDNA characteristics, including production source and state, and methodology, including collection and quantification strategy and abundance metrics, on the accuracy of eDNA-based abundance estimation were entirely overlooked. We conducted a meta-analysis using 56 previous eDNA literature and investigated the relationships between the accuracy (R2) of eDNA-based abundance estimation and eDNA characteristics and methodology. Our meta-regression analysis found that R2 values were significantly lower for crustaceans than fish, suggesting that less frequent eDNA production owing to their external morphology and physiology may impede accurate estimation of their abundance via eDNA. Moreover, R2 values were positively associated with filter pore size, indicating that selective collection of larger-sized eDNA, which is typically fresher, could improve the estimation accuracy of species abundance. Furthermore, R2 values were significantly lower for natural than laboratory conditions, while there was no difference in the estimation accuracy among natural environments. Our findings shed a new light on the importance of what characteristics of eDNA should be targeted for more accurate estimation of species abundance. Further empirical studies are required to validate our findings and fully elucidate the relationship between eDNA characteristics and eDNA-based abundance estimation.

scholarly journals How, when, and where relic DNA biases estimates of microbial diversity

2017 ◽  
Author(s):  
JT Lennon ◽  
ME Muscarella ◽  
SA Muscarella ◽  
BK Lehmkuhl
Keyword(s):  
Microbial Diversity ◽  
Dna Sequences ◽  
Species Abundance ◽  
Rrna Gene ◽  
Bacterial Dna ◽  
Soil Sediment ◽  
Species Abundance Distributions ◽  
Abundance And Diversity ◽  
Dna Pool ◽  
Biased Estimates

Extracellular or “relic” DNA is one of the largest pools of nucleic acids in the mbiosphere1,2. Relic DNA can influence a number of important ecological and evolutionary processes, but it may also bias estimates of microbial abundance and diversity, which has implications for understanding environmental, engineered, and host-associated ecosystems. We developed models capturing the fundamental processes that regulate the size and composition of the relic DNA pools to identify scenarios leading to biased estimates of biodiversity. Our models predict that bias increases with relic DNA pool size, but only when the species abundance distributions (SAD) of relic and intact DNA are distinct from one another. We evaluated our model predictions by quantifying relic DNA and assessing its contribution to bacterial diversity using 16S rRNA gene sequences collected from different ecosystem types, including soil, sediment, water, and the mammalian gut. On average, relic DNA made up 33 % of the total bacterial DNA pool, but exceeded 80 % in some samples. Despite its abundance, relic DNA had no effect on estimates of taxonomic and phylogenetic diversity, even in ecosystems where processes such as the physical protection of relic DNA are common and predicted by our models to generate bias. Rather, our findings are consistent with the expectation that relic DNA sequences degrade in proportion to their abundance and therefore may contribute minimally to estimates of microbial diversity.

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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