A Point-Process Model for Variance-Occupancy-Abundance Relationships

The relationship between species abundance, the variance of the number of individuals, and species occupancy is a fundamental ecological characteristic of a community. Moreover, this relationship varies across scales, and any model for the variance-occupancy-abundance (VOA) relationship has to addre...

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Bibliographic Details
Published in:The American naturalist 2011-09, Vol.178 (3), p.383-396
Main Authors: Picard, Nicolas, Favier, Charly
Format: Article
Language:English
Subjects:
Animal and plant ecology
Animal, plant and microbial ecology
Biological and medical sciences
Datasets
Ecology - methods
Ecosystem
Fundamental and applied biological sciences. Psychology
General aspects
Grain size
Mathematics
Modeling
Models, Biological
Models, Statistical
Nonnative species
Parametric models
Plant populations
Power laws
Predictive modeling
Rainforests
Spatial models
Species
Trees
Tropical Climate
Tropical rain forests
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Summary:The relationship between species abundance, the variance of the number of individuals, and species occupancy is a fundamental ecological characteristic of a community. Moreover, this relationship varies across scales, and any model for the variance-occupancy-abundance (VOA) relationship has to address its scale dependency in a consistent way. In this study, point-process theory was used to define a multiscale model that jointly predicts the VOA relationship across scales in a consistent way. This provides a tool to jointly analyze data sets collected at different scales and to give insights into the biological processes underlying the VOA relationship. This model can also account for different types of individual spatial pattern (clustered, random, or regular). Three stand-mapping data sets of tree species in tropical rain forests were used to assess the relevance of this model. When compared with four existing models, the model based on point-process theory provided the best fit to the data and was the most often ranked as the model with the best predictive performance.
ISSN:0003-0147
1537-5323
DOI:10.1086/661249