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Potential Global-Local Inconsistency in Species-Area Relationships Fitting
Xubin Pan1, Xiuling Zhang2, Feng Wang3
1Institute of Plant Quarantine, Chinese Academy of Inspection and Quarantine Beijing, China.
Frontiers in Plant Science
|September 15, 2016
Summary
The Species-Area Relationship (SAR) shows global-local inconsistency. Standard power and logarithm models do not accurately represent species diversity at larger scales, potentially leading to misestimations.
Area of Science:
- Ecology
- Biodiversity Science
- Conservation Biology
Background:
- The Species-Area Relationship (SAR) is crucial for understanding species diversity and extinction.
- Existing SAR models have been debated due to inconsistencies between field observations and simulations.
- Large-area sampling limitations have historically hindered accurate SAR model fitting.
Purpose of the Study:
- To investigate the global-local inconsistency in Species-Area Relationship (SAR) fitting.
- To evaluate the accuracy of power and logarithm SAR models across different sampling ranges.
- To identify potential biases in species diversity and extinction predictions due to SAR model limitations.
Main Methods:
- Simulated species abundance data using log-normal and negative-binomial distributions.
- Fitted power and logarithm SAR models to simulated data across various sampling area ranges.
- Analyzed the linearity and convexity of SAR curves in log-log and semi-log plots.
Main Results:
- Power and logarithm SAR models fit well within small to intermediate sampling ranges.
- Logarithm SAR fitting revealed a sigmoid curve in the log10 Area-Number of Species plane.
- Power SAR fitting showed a convex curve, deviating from the assumed linearity in linear regression.
- Neither model remained linear at large sampling ranges, irrespective of species abundance distribution.
Conclusions:
- A global-local inconsistency exists in SAR fitting, particularly at larger scales.
- Standard SAR models may misestimate total species richness and extinction rates when extrapolated beyond the fitted range.
- Accurate SAR modeling requires accounting for scale-dependent relationships and potential non-linearities.
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