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Optimising Regionalisation Techniques: Identifying Centres of Endemism in the Extraordinarily Endemic-Rich Cape Floristic Region.

Bradshaw PL, Colville JF, Linder HP - PLoS ONE (2015)

Bottom Line: We show that weighted data (down-weighting widespread species), similarity calculated using Kulczinsky's second measure, and clustering using UPGMA resulted in the optimal classification.Post-clustering GIS manipulation substantially enhanced the endemic composition and geographic size of candidate CoEs.Although there was broad spatial congruence with previous phytogeographic studies, our techniques allowed for the recovery of additional phytogeographic detail not previously described for the CFR.

View Article: PubMed Central - PubMed

Affiliation: Park Planning and Development Unit, South African National Parks, Port Elizabeth, South Africa; Department of Botany, Nelson Mandela Metropolitan University, Port Elizabeth, South Africa.

ABSTRACT
We used a very large dataset (>40% of all species) from the endemic-rich Cape Floristic Region (CFR) to explore the impact of different weighting techniques, coefficients to calculate similarity among the cells, and clustering approaches on biogeographical regionalisation. The results were used to revise the biogeographical subdivision of the CFR. We show that weighted data (down-weighting widespread species), similarity calculated using Kulczinsky's second measure, and clustering using UPGMA resulted in the optimal classification. This maximized the number of endemic species, the number of centres recognized, and operational geographic units assigned to centres of endemism (CoEs). We developed a dendrogram branch order cut-off (BOC) method to locate the optimal cut-off points on the dendrogram to define candidate clusters. Kulczinsky's second measure dendrograms were combined using consensus, identifying areas of conflict which could be due to biotic element overlap or transitional areas. Post-clustering GIS manipulation substantially enhanced the endemic composition and geographic size of candidate CoEs. Although there was broad spatial congruence with previous phytogeographic studies, our techniques allowed for the recovery of additional phytogeographic detail not previously described for the CFR.

No MeSH data available.


Our phytogeographic areas and those of earlier studies.
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pone.0132538.g010: Our phytogeographic areas and those of earlier studies.

Mentions: Hierarchical clustering using Jaccard and Kulczinsky2 indices produced broadly similar relationships between core CFR CoEs, indicating fairly robust patterns (Fig 9a & 9b); however, Jaccard retrieved greater internal dendrogram structure (Fig 9b). The western CoEs grouped into a larger Western area (cluster 9 in Fig 9a & 9b), while only Jaccard recorded hierarchical dendrogram structure in the east (Fig 9b). Both Kulczinsky2 and Jaccard depicted the northern Northwest BR (1) as terminal to the rest of the CFR (Fig 9a & 9b). Generally, the CoEs with lower levels of endemism and richness had the least consistent dendrogram placement, e.g. the northern Southeast CoE (22) and the Swartruggens (33), which may indicate ambiguous placement due to low taxon numbers, or being a transitional floristic area. The Agulhas Plains BR (5) and Witteberg BR (3) formed distinct BRs in both analyses. In the Kulczinsky2 analysis, the west Langeberg-Waboomsberg (16) and the Langeberg BR (3) formed a discrete cluster (Fig 9a), while in the Jaccard analysis they were sister to the Karoo Mountain BR (4) and Southeast BR cluster (5) (Fig 9b). The Saldanha Peninsula was basal to (Jaccard) or less associated with (Kulczinsky2) the Southwest BR (Figs 9a, 9b and 10).


Optimising Regionalisation Techniques: Identifying Centres of Endemism in the Extraordinarily Endemic-Rich Cape Floristic Region.

Bradshaw PL, Colville JF, Linder HP - PLoS ONE (2015)

Our phytogeographic areas and those of earlier studies.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493007&req=5

pone.0132538.g010: Our phytogeographic areas and those of earlier studies.
Mentions: Hierarchical clustering using Jaccard and Kulczinsky2 indices produced broadly similar relationships between core CFR CoEs, indicating fairly robust patterns (Fig 9a & 9b); however, Jaccard retrieved greater internal dendrogram structure (Fig 9b). The western CoEs grouped into a larger Western area (cluster 9 in Fig 9a & 9b), while only Jaccard recorded hierarchical dendrogram structure in the east (Fig 9b). Both Kulczinsky2 and Jaccard depicted the northern Northwest BR (1) as terminal to the rest of the CFR (Fig 9a & 9b). Generally, the CoEs with lower levels of endemism and richness had the least consistent dendrogram placement, e.g. the northern Southeast CoE (22) and the Swartruggens (33), which may indicate ambiguous placement due to low taxon numbers, or being a transitional floristic area. The Agulhas Plains BR (5) and Witteberg BR (3) formed distinct BRs in both analyses. In the Kulczinsky2 analysis, the west Langeberg-Waboomsberg (16) and the Langeberg BR (3) formed a discrete cluster (Fig 9a), while in the Jaccard analysis they were sister to the Karoo Mountain BR (4) and Southeast BR cluster (5) (Fig 9b). The Saldanha Peninsula was basal to (Jaccard) or less associated with (Kulczinsky2) the Southwest BR (Figs 9a, 9b and 10).

Bottom Line: We show that weighted data (down-weighting widespread species), similarity calculated using Kulczinsky's second measure, and clustering using UPGMA resulted in the optimal classification.Post-clustering GIS manipulation substantially enhanced the endemic composition and geographic size of candidate CoEs.Although there was broad spatial congruence with previous phytogeographic studies, our techniques allowed for the recovery of additional phytogeographic detail not previously described for the CFR.

View Article: PubMed Central - PubMed

Affiliation: Park Planning and Development Unit, South African National Parks, Port Elizabeth, South Africa; Department of Botany, Nelson Mandela Metropolitan University, Port Elizabeth, South Africa.

ABSTRACT
We used a very large dataset (>40% of all species) from the endemic-rich Cape Floristic Region (CFR) to explore the impact of different weighting techniques, coefficients to calculate similarity among the cells, and clustering approaches on biogeographical regionalisation. The results were used to revise the biogeographical subdivision of the CFR. We show that weighted data (down-weighting widespread species), similarity calculated using Kulczinsky's second measure, and clustering using UPGMA resulted in the optimal classification. This maximized the number of endemic species, the number of centres recognized, and operational geographic units assigned to centres of endemism (CoEs). We developed a dendrogram branch order cut-off (BOC) method to locate the optimal cut-off points on the dendrogram to define candidate clusters. Kulczinsky's second measure dendrograms were combined using consensus, identifying areas of conflict which could be due to biotic element overlap or transitional areas. Post-clustering GIS manipulation substantially enhanced the endemic composition and geographic size of candidate CoEs. Although there was broad spatial congruence with previous phytogeographic studies, our techniques allowed for the recovery of additional phytogeographic detail not previously described for the CFR.

No MeSH data available.