scholarly journals On the proportional abundance of species: Integrating population genetics and community ecology

2017 ◽  
Author(s):  
Pablo A. Marquet ◽  
Guillermo Espinoza ◽  
Sebastian R. Abades ◽  
Angela Ganz ◽  
Rolando Rebolledo
Keyword(s):  
Stochastic Model ◽  
Good Description ◽  
Species Abundance ◽  
Structured Populations ◽  
Abundance Distribution ◽  
Gene Frequencies ◽  
Conceptual Integration ◽  
Neutral Models ◽  
Number Of Individuals ◽  
Integration Of Theories

ABSTRACTThe frequency of genes in interconnected populations and of species in interconnected communities are affected by similar processes, such as birth, death and immigration. The equilibrium distribution of gene frequencies in structured populations is known since the 1930s, under Wright’s metapopulation model known as the island model. The equivalent distribution for the species frequency (i.e. the species proportional abundance distribution (SPAD), at the metacommunity level, however, is unknown. In this contribution, we develop a stochastic model to analytically account for this distribution (SPAD). We show that the same as for genes SPAD follows a beta distribution, which provides a good description of empirical data and applies across a continuum of scales. This stochastic model, based upon a diffusion approximation, provides an alternative to neutral models for the species abundance distribution (SAD), which focus on number of individuals instead of proportions, and demonstrate that the relative frequency of genes in local populations and of species within communities follow the same probability law. We hope our contribution will help stimulate the mathematical and conceptual integration of theories in genetics and ecology.

scholarly journals On the Sensitivity of the Parameters of the Intensity-Based Stochastic Model for Terrestrial Laser Scanner. Case Study: B-Spline Approximation

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2964 ◽  
Author(s):  
Gaël Kermarrec ◽  
Hamza Alkhatib ◽  
Ingo Neumann
Keyword(s):  
Stochastic Model ◽  
Good Description ◽  
Spline Approximation ◽  
Laser Scanner ◽  
Terrestrial Laser Scanner ◽  
Case Scenario ◽  
Constant Variance ◽  
Power Law Function ◽  
Stochastic Properties

For a trustworthy least-squares (LS) solution, a good description of the stochastic properties of the measurements is indispensable. For a terrestrial laser scanner (TLS), the range variance can be described by a power law function with respect to the intensity of the reflected signal. The power and scaling factors depend on the laser scanner under consideration, and could be accurately determined by means of calibrations in 1d mode or residual analysis of LS adjustment. However, such procedures complicate significantly the use of empirical intensity models (IM). The extent to which a point-wise weighting is suitable when the derived variance covariance matrix (VCM) is further used in a LS adjustment remains moreover questionable. Thanks to closed loop simulations, where both the true geometry and stochastic model are under control, we investigate how variations of the parameters of the IM affect the results of a LS adjustment. As a case study, we consider the determination of the Cartesian coordinates of the control points (CP) from a B-splines curve. We show that a constant variance can be assessed to all the points of an object having homogeneous properties, without affecting the a posteriori variance factor or the loss of efficiency of the LS solution. The results from a real case scenario highlight that the conclusions of the simulations stay valid even for more challenging geometries. A procedure to determine the range variance is proposed to simplify the computation of the VCM.

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.

scholarly journals Inferring True Species Richness and Complete Abundance Distribution in Six Reef-fish Communities from Red-sea, Using the Numerical Extrapolation of Incomplete Samplings

2019 ◽  
pp. 1-21
Author(s):  
Jean Beguinot
Keyword(s):  
Reef Fish ◽  
Red Sea ◽  
Field Data ◽  
Full Range ◽  
Species Abundance ◽  
Fish Communities ◽  
Ecological Communities ◽  
Abundance Distribution ◽  
Internal Organization ◽  
Unrecorded Species

Even when ecological communities are incompletely sampled (which is most frequent in practice, at least for species-rich assemblages including many rare species), it remains possible to retrieve much more information than could be expected first, by applying numerical extrapolation to incomplete field data. Indeed, recently developed procedures of numerical extrapolation of partial samplings now allow to estimate, with fair accuracy, not only the number of the still unrecorded species but, moreover, the distribution of abundances of each of these unrecorded species, thereby making available the full range of the Species Abundance Distribution, despite dealing with incomplete data only. In turn, this allows to address a series of descriptive and functional aspects of the internal organization of species assemblages, which otherwise would have required disposing of truly exhaustive samplings. This approach is applied, here, to the previously reported partial samplings of six neighboring reef-fish communities from Tiran Island, Red Sea, with the goal of better understanding their internal organization in relation to their respective environments. In practice, the numerical completion contributes to avoid erroneous interpretations that would likely stem from considering only the incomplete field data. This point is especially relevant when studying reef-associated communities because accurate understanding of their organization will help guiding and refining at best the protective measures required by these particularly vulnerable communities.

scholarly journals Analysing species abundance distribution patterns across sampling scales in three natural forests in Northeastern China

2020 ◽  
Vol 13 (6) ◽  
pp. 482-489
Author(s):  
L Tan ◽  
P Zhang ◽  
X Zhao ◽  
C Fan ◽  
C Zhang ◽  
...  
Keyword(s):  
Species Abundance ◽  
Distribution Patterns ◽  
Northeastern China ◽  
Species Abundance Distribution ◽  
Natural Forests ◽  
Abundance Distribution

scholarly journals Measurement of Biodiversity (MoB): A method to separate the scale‐dependent effects of species abundance distribution, density, and aggregation on diversity change

2018 ◽  
Vol 10 (2) ◽  
pp. 258-269 ◽  
Author(s):  
Daniel J. McGlinn ◽  
Xiao Xiao ◽  
Felix May ◽  
Nicholas J. Gotelli ◽  
Thore Engel ◽  
...  
Keyword(s):  
Distribution Density ◽  
Species Abundance ◽  
Species Abundance Distribution ◽  
Abundance Distribution

Using a Macroecological Approach to Study Geographic Range, Abundance and Body Size in the Fossil Record

2010 ◽  
Vol 16 ◽  
pp. 117-141 ◽  
Author(s):  
S. Kathleen Lyons ◽  
Felisa A. Smith
Keyword(s):  
Body Size ◽  
Fossil Record ◽  
Species Abundance ◽  
Geographic Range ◽  
Species Abundance Distribution ◽  
Abundance Distribution ◽  
Geographic Ranges ◽  
Spatial And Temporal Scales ◽  
Geographic Range Size ◽  
Fossil Data

Macroecology is a rapidly growing sub-discipline within ecology that is concerned with characterizing statistical patterns of species' abundance, distribution and diversity at spatial and temporal scales typically ignored by traditional ecology. Both macroecology and paleoecology are concerned with answering similar questions (e.g., understanding the factors that influence geographic ranges, or the way that species assemble into communities). As such, macroecological methods easily lend themselves to many paleoecological questions. Moreover, it is possible to estimate the variables of interest to macroecologists (e.g., body size, geographic range size, abundance, diversity) using fossil data. Here we describe the measurement and estimation of the variables used in macroecological studies and potential biases introduced by using fossil data. Next we describe the methods used to analyze macroecological patterns and briefly discuss the current understanding of these patterns. This chapter is by no means an exhaustive review of macroecology and its methods. Instead, it is an introduction to macroecology that we hope will spur innovation in the application of macroecology to the study of the fossil record.

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