Species traits shape the relationship between local and regional species abundance distributions

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.

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

10.32942/osf.io/qfpa9 ◽

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.

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

10.1101/131284 ◽

2017 ◽

Cited By ~ 5

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.

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

10.7287/peerj.preprints.745 ◽

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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Linking species abundance distributions in numerical abundance and biomass through simple assumptions about community structure

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2009.2189 ◽

2010 ◽

Vol 277 (1687) ◽

pp. 1561-1570 ◽

Cited By ~ 25

Author(s):

Peter A. Henderson ◽

Anne E. Magurran

Keyword(s):

Community Structure ◽

Species Abundance ◽

Numerical Abundance ◽

Species Abundance Distributions

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Tree Species Traits Determine the Success of LiDAR-Based Crown Mapping in a Mixed Temperate Forest

Remote Sensing ◽

10.3390/rs12020309 ◽

2020 ◽

Vol 12 (2) ◽

pp. 309 ◽

Cited By ~ 8

Author(s):

Jack H. Hastings ◽

Scott V. Ollinger ◽

Andrew P. Ouimette ◽

Rebecca Sanders-DeMott ◽

Michael W. Palace ◽

...

Keyword(s):

Temperate Forest ◽

Spectral Characteristics ◽

Species Abundance ◽

Remote Sensing Data ◽

Parameter Tuning ◽

Species Traits ◽

Tree Level ◽

Species Evenness ◽

Individual Tree ◽

Crown Delineation

The ability to automatically delineate individual tree crowns using remote sensing data opens the possibility to collect detailed tree information over large geographic regions. While individual tree crown delineation (ITCD) methods have proven successful in conifer-dominated forests using Light Detection and Ranging (LiDAR) data, it remains unclear how well these methods can be applied in deciduous broadleaf-dominated forests. We applied five automated LiDAR-based ITCD methods across fifteen plots ranging from conifer- to broadleaf-dominated forest stands at Harvard Forest in Petersham, MA, USA, and assessed accuracy against manual delineation of crowns from unmanned aerial vehicle (UAV) imagery. We then identified tree- and plot-level factors influencing the success of automated delineation techniques. There was relatively little difference in accuracy between automated crown delineation methods (51–59% aggregated plot accuracy) and, despite parameter tuning, none of the methods produced high accuracy across all plots (27—90% range in plot-level accuracy). The accuracy of all methods was significantly higher with increased plot conifer fraction, and individual conifer trees were identified with higher accuracy (mean 64%) than broadleaf trees (42%) across methods. Further, while tree-level factors (e.g., diameter at breast height, height and crown area) strongly influenced the success of crown delineations, the influence of plot-level factors varied. The most important plot-level factor was species evenness, a metric of relative species abundance that is related to both conifer fraction and the degree to which trees can fill canopy space. As species evenness decreased (e.g., high conifer fraction and less efficient filling of canopy space), the probability of successful delineation increased. Overall, our work suggests that the tested LiDAR-based ITCD methods perform equally well in a mixed temperate forest, but that delineation success is driven by forest characteristics like functional group, tree size, diversity, and crown architecture. While LiDAR-based ITCD methods are well suited for stands with distinct canopy structure, we suggest that future work explore the integration of phenology and spectral characteristics with existing LiDAR as an approach to improve crown delineation in broadleaf-dominated stands.

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