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Prediction of avian species composition from assemblage structure

Prediction of avian species composition from assemblage structure
Prediction of avian species composition from assemblage structure
This research focuses on how capitalising on community pattern, a character of ecological communities, could improve the predictability of community models, thus facilitating research in conservation. Patterns of communities not only depict phenomena but are also useful for predicting potential changes in species composition when patterns are governed by specific mechanisms. Most conventional prediction models do not take community pattern into consideration, despite the fact that incorporating community patterns into conventional models for predicting species richness and composition may enhance predictability. In this thesis, I assessed if incorporating two community patterns, nestedness and species co-occurrence, into conventional prediction models could improve the model predictability. Nestedness is a non-random species distribution pattern in which species in depauperate sites are contained in species-rich sites. Co-occurrence networks categorise species assemblages that reflect differential habitat requirements. I demonstrate that capitalising on nestedness provides a novel approach for improving the predictive power of species accumulation curves for species richness in unsampled areas. Specifically, while species richness is usually overestimated when the data are inputted in random order (the conventional approach), species richness is underestimated when the data are inputted in nested order. Taking an average of projected species richness of these two inputting orders dramatically lowers the prediction error rate, indicating that using nestedness in addition to random orders can greatly improve the predictive power of species distribution curves. I also show that network analysis can improve the ability to correctly classify site groups, which is the basis for calculating the indicator species value, by accurately reflecting similar ecological requirements of co-occurred species. Indicator species identified by network modularity, comparing to conventionally based on the k-means clustering method, can more successfully assign unsampled sites to the correct species groups and recognise representative species for the groups. These methods were tested using both British and Taiwanese bird assemblages. Both case studies supported the above conclusions, suggesting that the methods developed in this thesis have real promise for conservation applications. However, further work is required to assess whether these two novel pattern-based approaches are similarly applicable in other geographic regions or taxas.
University of Southampton
Huang, Jing-Lun
3284f428-af19-4d3c-ad1c-73ec71a7d115
Huang, Jing-Lun
3284f428-af19-4d3c-ad1c-73ec71a7d115
Eigenbrod, Felix
43efc6ae-b129-45a2-8a34-e489b5f05827

Huang, Jing-Lun (2018) Prediction of avian species composition from assemblage structure. University of Southampton, Doctoral Thesis, 234pp.

Record type: Thesis (Doctoral)

Abstract

This research focuses on how capitalising on community pattern, a character of ecological communities, could improve the predictability of community models, thus facilitating research in conservation. Patterns of communities not only depict phenomena but are also useful for predicting potential changes in species composition when patterns are governed by specific mechanisms. Most conventional prediction models do not take community pattern into consideration, despite the fact that incorporating community patterns into conventional models for predicting species richness and composition may enhance predictability. In this thesis, I assessed if incorporating two community patterns, nestedness and species co-occurrence, into conventional prediction models could improve the model predictability. Nestedness is a non-random species distribution pattern in which species in depauperate sites are contained in species-rich sites. Co-occurrence networks categorise species assemblages that reflect differential habitat requirements. I demonstrate that capitalising on nestedness provides a novel approach for improving the predictive power of species accumulation curves for species richness in unsampled areas. Specifically, while species richness is usually overestimated when the data are inputted in random order (the conventional approach), species richness is underestimated when the data are inputted in nested order. Taking an average of projected species richness of these two inputting orders dramatically lowers the prediction error rate, indicating that using nestedness in addition to random orders can greatly improve the predictive power of species distribution curves. I also show that network analysis can improve the ability to correctly classify site groups, which is the basis for calculating the indicator species value, by accurately reflecting similar ecological requirements of co-occurred species. Indicator species identified by network modularity, comparing to conventionally based on the k-means clustering method, can more successfully assign unsampled sites to the correct species groups and recognise representative species for the groups. These methods were tested using both British and Taiwanese bird assemblages. Both case studies supported the above conclusions, suggesting that the methods developed in this thesis have real promise for conservation applications. However, further work is required to assess whether these two novel pattern-based approaches are similarly applicable in other geographic regions or taxas.

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Published date: January 2018

Identifiers

Local EPrints ID: 417922
URI: http://eprints.soton.ac.uk/id/eprint/417922
PURE UUID: cd7db07b-2b97-4429-a6a5-b5dc1fb837f7
ORCID for Felix Eigenbrod: ORCID iD orcid.org/0000-0001-8982-824X

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Date deposited: 16 Feb 2018 17:30
Last modified: 07 Jun 2019 00:32

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