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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.


The bin frequency distributions of weighted matrix characters generated by the different weighting techniques.Taxa restricted to a single cell were excluded as they are not effective for clustering. Total site taxon matrix character numbers are reported in Table 2.
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pone.0132538.g004: The bin frequency distributions of weighted matrix characters generated by the different weighting techniques.Taxa restricted to a single cell were excluded as they are not effective for clustering. Total site taxon matrix character numbers are reported in Table 2.

Mentions: The three weighting methods resulted in similar overall numbers of matrix characters (15 987 to 17 028), compared to the much fewer 4 478 for the unweighted analysis (Table 2) for the site by taxa (character) matrices. Bell weighting displayed the sharpest relative decrease in weighting associated with increasing taxon range size, followed by Inv and Int weighting (Fig 4); however, the latter two were very similar, although Inv was arguably preferable with fewer taxa in the final lowest weighted category (> 9 cells, Fig 4). Consistent with the above, Bell generated the highest numbers of effective characters in the smallest species range category, followed closely by Inv (Fig 4).


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

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

The bin frequency distributions of weighted matrix characters generated by the different weighting techniques.Taxa restricted to a single cell were excluded as they are not effective for clustering. Total site taxon matrix character numbers are reported in Table 2.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0132538.g004: The bin frequency distributions of weighted matrix characters generated by the different weighting techniques.Taxa restricted to a single cell were excluded as they are not effective for clustering. Total site taxon matrix character numbers are reported in Table 2.
Mentions: The three weighting methods resulted in similar overall numbers of matrix characters (15 987 to 17 028), compared to the much fewer 4 478 for the unweighted analysis (Table 2) for the site by taxa (character) matrices. Bell weighting displayed the sharpest relative decrease in weighting associated with increasing taxon range size, followed by Inv and Int weighting (Fig 4); however, the latter two were very similar, although Inv was arguably preferable with fewer taxa in the final lowest weighted category (> 9 cells, Fig 4). Consistent with the above, Bell generated the highest numbers of effective characters in the smallest species range category, followed closely by Inv (Fig 4).

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.