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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.


A diagrammatic example of the modified third order branch cut-off (BOC3m).The cut-offs are displayed as bold vertical lines (green), and the resulting candidate CoE clusters are indicated with red blocks around the cells.
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pone.0132538.g003: A diagrammatic example of the modified third order branch cut-off (BOC3m).The cut-offs are displayed as bold vertical lines (green), and the resulting candidate CoE clusters are indicated with red blocks around the cells.

Mentions: For the identification of CoEs in the CFR, standard second order Strahler branches were still too terminal (CoE too small and disjunct), and standard third order Strahler branches were often too basal / deep to allow for meaningful cut-offs (i.e. CoE too large, thus losing phytogeographic detail). Therefore, the technique applied here for CoE identification deviated from a pure Strahler approach in that a third order branch could be formed where either a first or second order branch joined another second order branch, and was thus referred to as modified third order branch cut-off (BOC3m). This change was applied to allow the formation of a more intermediately placed cut-off, optimising the number as well as the size of CoEs. The rest of the branch order assignment was identical to that of Strahler’s. Dendrogram terminals are first order (Fig 3, blue branches), which link together to form second order branches (Fig 3, orange branches); two second orders or a second and a first order join together to form a third order grouping (Fig 3, green branches), with the cut-off being placed on the most basal parts of the third order branches—forming a nested candidate CoE cluster (Fig 3, green block) —before they become a fourth order branch (Fig 3, yellow branches). This approach was applied to all 16 dendrograms (Fig 1: Step 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)

A diagrammatic example of the modified third order branch cut-off (BOC3m).The cut-offs are displayed as bold vertical lines (green), and the resulting candidate CoE clusters are indicated with red blocks around the cells.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0132538.g003: A diagrammatic example of the modified third order branch cut-off (BOC3m).The cut-offs are displayed as bold vertical lines (green), and the resulting candidate CoE clusters are indicated with red blocks around the cells.
Mentions: For the identification of CoEs in the CFR, standard second order Strahler branches were still too terminal (CoE too small and disjunct), and standard third order Strahler branches were often too basal / deep to allow for meaningful cut-offs (i.e. CoE too large, thus losing phytogeographic detail). Therefore, the technique applied here for CoE identification deviated from a pure Strahler approach in that a third order branch could be formed where either a first or second order branch joined another second order branch, and was thus referred to as modified third order branch cut-off (BOC3m). This change was applied to allow the formation of a more intermediately placed cut-off, optimising the number as well as the size of CoEs. The rest of the branch order assignment was identical to that of Strahler’s. Dendrogram terminals are first order (Fig 3, blue branches), which link together to form second order branches (Fig 3, orange branches); two second orders or a second and a first order join together to form a third order grouping (Fig 3, green branches), with the cut-off being placed on the most basal parts of the third order branches—forming a nested candidate CoE cluster (Fig 3, green block) —before they become a fourth order branch (Fig 3, yellow branches). This approach was applied to all 16 dendrograms (Fig 1: Step 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.