• Corpus ID: 88523999

Empirical Bayes to assess ecological diversity and similarity with overdispersion in multivariate counts

@article{Divino2018EmpiricalBT,
  title={Empirical Bayes to assess ecological diversity and similarity with overdispersion in multivariate counts},
  author={Fabio Divino and Johanna Arje and Antti Penttinen and Kristian Meissner and Salme Karkkainen},
  journal={arXiv: Applications},
  year={2018}
}
The assessment of diversity and similarity is relevant in monitoring the status of ecosystems. The respective indicators are based on the taxonomic composition of biological communities of interest, currently estimated through the proportions computed from sampling multivariate counts. In this work we present a novel method able to work with only one sample to estimate the taxonomic composition when the data are affected by overdispersion. The presence of overdispersion in taxonomic counts may… 

References

SHOWING 1-10 OF 51 REFERENCES
Using the negative binomial distribution to model overdispersion in ecological count data.
TLDR
A parameterization of the negative binomial distribution is proposed, where two overdispersion parameters are introduced to allow for various quadratic mean-variance relationships, including the ones assumed in the most commonly used approaches.
Dealing with overdispersed count data in applied ecology
TLDR
The general simulation approach presented in this paper for identifying the most parsimonious model, as defined by information theory, should help to improve the understanding of the reliability of model selection when using AIC, and help the development of better selection rules.
Compositional analysis of overdispersed counts using generalized estimating equations
Multivariate abundance data are commonly collected in ecology, and used to explore questions of “community composition”—how relative abundance of different taxa changes with environmental conditions.
Some distribution properties of the sample species-diversity indices and their applications.
TLDR
If the diversities of the communities can be partially ordered through majorization as proposed by Solomon, and if the sample sizes remain the same, then the sample diversity indices can be stochastically ordered when the samples are selected at random from the communities either with or without replacement.
Estimating the effects of detection heterogeneity and overdispersion on trends estimated from avian point counts.
TLDR
This work uses a removal model of detection within an N-mixture approach to estimate abundance trends corrected for imperfect detection for 16 species using 15 years of monitoring data on three national forests in the western Great Lakes, USA.
Percent Model Affinity: A New Measure of Macroinvertebrate Community Composition
TLDR
A measure of macroinvertebrate community composition was developed using data from 46 shallow freshwater streams in New York State and found to be closely correlated with the Hilsenhoff Biotic Index (HBI) and the species richness of Ephemeroptera, Plecoptera, and Trichoptera and reflected water quality changes better than HBI did in instances of non-organic pollution.
A hierarchical zero‐inflated model for species compositional data—from individual taxon responses to community response
TLDR
A stressor-specific tolerance indicator is developed to separate truly sensitive EPT taxa from more tolerant taxa, and to compare the responses of different communities to the same stressor or responses of the same community in different regions.
Using observation-level random effects to model overdispersion in count data in ecology and evolution
TLDR
Simulations show that in cases where overdispersion is caused by random extra-Poisson noise, or aggregation in the count data, observation-level random effects yield more accurate parameter estimates compared to when overdisPersion is simply ignored, and that their ability to minimise bias is not uniform across all types of over Dispersion and must be applied judiciously.
General models for resource use or other compositional count data using the Dirichlet-multinomial distribution.
TLDR
The Dirichlet-multinomial distribution is proposed to accommodate overdispersed compositional count data, and the flexibility of the approach allows new hypotheses that have often not been considered in resource preference analysis, including that availability has no relation to use.
...
...