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Estimating species abundance from occurrence
The number of individuals, or the abundance, of a species
in an area is a fundamental ecological parameter and a
critical consideration when making management and conservation decisions (Andrewartha and Birch 1954; Krebs
1978; Gaston 1994; Caughley and Gunn 1996). However,
unless the scale is very fine or localized (e.g., in a measurable habitat or a forest stand), abundance is not readily determined. At coarse or regional scales for many species, information on commonness and rarity is, at best, limited to a map of their presence or absence from recording units in a specified time frame. Various species data at large scales are increasingly documented in this presence/absence forma
Species abundance dynamics under neutral assumptions: a Bayesian approach to the controversy
1. Hubbell's 'Unified Neutral Theory of Biodiversity and Biogeography' (UNTB) has generated much controversy about both the realism of its assumptions and how well it describes the species abundance dynamics in real communities. 2. We fit a discrete-time version of Hubbell's neutral model to long-term macro-moth (Lepidoptera) community data from the Rothamsted Insect Survey (RIS) light-traps network in the United Kingdom. 3. We relax the assumption of constant community size and use a hierarchical Bayesian approach to show that the model does not fit the data well as it would need parameter values that are impossible. 4. This is because the ecological communities fluctuate more than expected under neutrality. 5. The model, as presented here, can be extended to include environmental stochasticity, density-dependence, or changes in population sizes that are correlated between different species
Attenuation of species abundance distributions by sampling
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.Publisher PDFPeer reviewe
Species Abundance Patterns in Complex Evolutionary Dynamics
An analytic theory of species abundance patterns (SAPs) in biological
networks is presented. The theory is based on multispecies replicator dynamics
equivalent to the Lotka-Volterra equation, with diverse interspecies
interactions. Various SAPs observed in nature are derived from a single
parameter. The abundance distribution is formed like a widely observed
left-skewed lognormal distribution. As the model has a general form, the result
can be applied to similar patterns in other complex biological networks, e.g.
gene expression.Comment: 4 pages, 3 figures. Physical Review Letters, in pres
Neutral Theory and Relative Species Abundance in Ecology
The theory of island biogeography[1] asserts that an island or a local
community approaches an equilibrium species richness as a result of the
interplay between the immigration of species from the much larger metacommunity
source area and local extinction of species on the island (local community).
Hubbell[2] generalized this neutral theory to explore the expected steady-state
distribution of relative species abundance (RSA) in the local community under
restricted immigration. Here we present a theoretical framework for the unified
neutral theory of biodiversity[2] and an analytical solution for the
distribution of the RSA both in the metacommunity (Fisher's logseries) and in
the local community, where there are fewer rare species. Rare species are more
extinction-prone, and once they go locally extinct, they take longer to
re-immigrate than do common species. Contrary to recent assertions[3], we show
that the analytical solution provides a better fit, with fewer free parameters,
to the RSA distribution of tree species on Barro Colorado Island (BCI)[4] than
the lognormal distribution[5,6].Comment: 19 pages, 1 figur
Species abundance information improves sequence taxonomy classification accuracy.
Popular naive Bayes taxonomic classifiers for amplicon sequences assume that all species in the reference database are equally likely to be observed. We demonstrate that classification accuracy degrades linearly with the degree to which that assumption is violated, and in practice it is always violated. By incorporating environment-specific taxonomic abundance information, we demonstrate a significant increase in the species-level classification accuracy across common sample types. At the species level, overall average error rates decline from 25% to 14%, which is favourably comparable to the error rates that existing classifiers achieve at the genus level (16%). Our findings indicate that for most practical purposes, the assumption that reference species are equally likely to be observed is untenable. q2-clawback provides a straightforward alternative for samples from common environments
Modeling large scale species abundance with latent spatial processes
Modeling species abundance patterns using local environmental features is an
important, current problem in ecology. The Cape Floristic Region (CFR) in South
Africa is a global hot spot of diversity and endemism, and provides a rich
class of species abundance data for such modeling. Here, we propose a
multi-stage Bayesian hierarchical model for explaining species abundance over
this region. Our model is specified at areal level, where the CFR is divided
into roughly one minute grid cells; species abundance is observed at
some locations within some cells. The abundance values are ordinally
categorized. Environmental and soil-type factors, likely to influence the
abundance pattern, are included in the model. We formulate the empirical
abundance pattern as a degraded version of the potential pattern, with the
degradation effect accomplished in two stages. First, we adjust for land use
transformation and then we adjust for measurement error, hence
misclassification error, to yield the observed abundance classifications. An
important point in this analysis is that only of the grid cells have been
sampled and that, for sampled grid cells, the number of sampled locations
ranges from one to more than one hundred. Still, we are able to develop
potential and transformed abundance surfaces over the entire region. In the
hierarchical framework, categorical abundance classifications are induced by
continuous latent surfaces. The degradation model above is built on the latent
scale. On this scale, an areal level spatial regression model was used for
modeling the dependence of species abundance on the environmental factors.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS335 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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