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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.


Flow chart summary of the change in CoE properties from clustering to GIS interrogation.CoE properties (endemic taxa, richness, number and size of CoEs / Sub-CoEs) of the best clustering technique results (Inv:K2, Bell:K2 & Int:K2), showing the effects of applying majority rule and strict consensus on CoE/Sub-CoE properties are depicted (the legend indicates what each number row refers to in each block). Also displayed are the improvements of CoE / Sub-CoE properties with GIS interrogation (the values in brackets indicate additional taxa or cells added to CoEs). Finally, the minimal changes required in CoE properties are reported when converting CoEs to BRs.
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pone.0132538.g008: Flow chart summary of the change in CoE properties from clustering to GIS interrogation.CoE properties (endemic taxa, richness, number and size of CoEs / Sub-CoEs) of the best clustering technique results (Inv:K2, Bell:K2 & Int:K2), showing the effects of applying majority rule and strict consensus on CoE/Sub-CoE properties are depicted (the legend indicates what each number row refers to in each block). Also displayed are the improvements of CoE / Sub-CoE properties with GIS interrogation (the values in brackets indicate additional taxa or cells added to CoEs). Finally, the minimal changes required in CoE properties are reported when converting CoEs to BRs.

Mentions: The spatial results of the strict and majority rule consensus trees of the Kulczinsky2 similarity coefficient clustering and GIS modifications (on Bell, Inv and Int weighting) are displayed in Fig 7. This indicates pattern robustness, where the clusters (CoEs) retained after strict consensus are most robust, followed by those retained in majority rule consensus, while CoEs retrieved from post clustering GIS interrogation are arguably the least robust. The taxonomic and spatial properties of CoEs during these intermediate steps are summarised in Fig 8. In areas with high numbers of narrow endemics, such as the southwest CFR (CoE 1, 2 & 3), CoEs were smaller and there was less conflict with phytogeographic boundaries, with most phytogeographic patterns retained in the strict consensus, indicating their robustness (Fig 7). To the east where there are fewer narrow endemics, CoEs were generally larger (CoE 4 & 5) with more variability in CoE boundaries; thus relatively fewer floristic units were retained in the strict consensus as compared to the majority rule consensus. Many of the inland central and northern CoEs of the CFR (CoE 11, 14, 16, 21, 23 & 25) showed some conflict in phytogeographic patterns, probably because they are small with lower levels of endemism and richness. Very little of this area was retrieved in the strict consensus analysis. Overall, the marginal increase in the numbers of endemics and cells in CoEs when using majority rule consensus was due to the merging of CoEs from the three individual level analyses (Fig 8), thus increasing CoE size but reducing CoE numbers.


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

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

Flow chart summary of the change in CoE properties from clustering to GIS interrogation.CoE properties (endemic taxa, richness, number and size of CoEs / Sub-CoEs) of the best clustering technique results (Inv:K2, Bell:K2 & Int:K2), showing the effects of applying majority rule and strict consensus on CoE/Sub-CoE properties are depicted (the legend indicates what each number row refers to in each block). Also displayed are the improvements of CoE / Sub-CoE properties with GIS interrogation (the values in brackets indicate additional taxa or cells added to CoEs). Finally, the minimal changes required in CoE properties are reported when converting CoEs to BRs.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0132538.g008: Flow chart summary of the change in CoE properties from clustering to GIS interrogation.CoE properties (endemic taxa, richness, number and size of CoEs / Sub-CoEs) of the best clustering technique results (Inv:K2, Bell:K2 & Int:K2), showing the effects of applying majority rule and strict consensus on CoE/Sub-CoE properties are depicted (the legend indicates what each number row refers to in each block). Also displayed are the improvements of CoE / Sub-CoE properties with GIS interrogation (the values in brackets indicate additional taxa or cells added to CoEs). Finally, the minimal changes required in CoE properties are reported when converting CoEs to BRs.
Mentions: The spatial results of the strict and majority rule consensus trees of the Kulczinsky2 similarity coefficient clustering and GIS modifications (on Bell, Inv and Int weighting) are displayed in Fig 7. This indicates pattern robustness, where the clusters (CoEs) retained after strict consensus are most robust, followed by those retained in majority rule consensus, while CoEs retrieved from post clustering GIS interrogation are arguably the least robust. The taxonomic and spatial properties of CoEs during these intermediate steps are summarised in Fig 8. In areas with high numbers of narrow endemics, such as the southwest CFR (CoE 1, 2 & 3), CoEs were smaller and there was less conflict with phytogeographic boundaries, with most phytogeographic patterns retained in the strict consensus, indicating their robustness (Fig 7). To the east where there are fewer narrow endemics, CoEs were generally larger (CoE 4 & 5) with more variability in CoE boundaries; thus relatively fewer floristic units were retained in the strict consensus as compared to the majority rule consensus. Many of the inland central and northern CoEs of the CFR (CoE 11, 14, 16, 21, 23 & 25) showed some conflict in phytogeographic patterns, probably because they are small with lower levels of endemism and richness. Very little of this area was retrieved in the strict consensus analysis. Overall, the marginal increase in the numbers of endemics and cells in CoEs when using majority rule consensus was due to the merging of CoEs from the three individual level analyses (Fig 8), thus increasing CoE size but reducing CoE numbers.

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.