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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.


Related in: MedlinePlus

A histogram of the number of taxa in each of the taxon area sizes.The 4 304 taxa analysed were divided into bins dependent on the number of cells they occupied, and the frequency in each bin was calculated. A regression line was then plotted between the bin size and its taxon number. The regression line, graph equation (used for integration), and the goodness of fit value (R2) are indicated on the graph.
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pone.0132538.g002: A histogram of the number of taxa in each of the taxon area sizes.The 4 304 taxa analysed were divided into bins dependent on the number of cells they occupied, and the frequency in each bin was calculated. A regression line was then plotted between the bin size and its taxon number. The regression line, graph equation (used for integration), and the goodness of fit value (R2) are indicated on the graph.

Mentions: A novel weighting technique, Integration weighting (“Int”), which exploits the relationship between species range and the frequency of species in these ranges was developed here, and compared to Bell and Inv Weighting. The relationship between species area (range; x-axis) and the frequencies of species of that particular area (y-axis) was used to plot a line of best fit (y = 4773.5x-1.802; R2 = 0.928), which is specific for the dataset analysed (Fig 2). The area under the curve was then divided into nine equal area portions (the nine weighted area categories), beginning at taxon range size of two cells (the minimum range size contributing to clustering) and ending at the most widespread taxon with a range of 169 cells. The nine equal area portions were given descending weighting along the x-axis (Fig 2). Species with ranges in these nine weighting area categories were weighted accordingly. This weighting technique adapts to individual datasets (whether entire floras, samples of floras, or taxonomic groups) a priori, without having to adjust the two variables (a and p) of Bell weighting for optimisation. Int takes into account the relative distributional sizes of species as a proportion of the entire clade or dataset, as well as the relative frequencies of the species in each distribution category, by integrating the relationship between these two properties.


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 histogram of the number of taxa in each of the taxon area sizes.The 4 304 taxa analysed were divided into bins dependent on the number of cells they occupied, and the frequency in each bin was calculated. A regression line was then plotted between the bin size and its taxon number. The regression line, graph equation (used for integration), and the goodness of fit value (R2) are indicated on the graph.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0132538.g002: A histogram of the number of taxa in each of the taxon area sizes.The 4 304 taxa analysed were divided into bins dependent on the number of cells they occupied, and the frequency in each bin was calculated. A regression line was then plotted between the bin size and its taxon number. The regression line, graph equation (used for integration), and the goodness of fit value (R2) are indicated on the graph.
Mentions: A novel weighting technique, Integration weighting (“Int”), which exploits the relationship between species range and the frequency of species in these ranges was developed here, and compared to Bell and Inv Weighting. The relationship between species area (range; x-axis) and the frequencies of species of that particular area (y-axis) was used to plot a line of best fit (y = 4773.5x-1.802; R2 = 0.928), which is specific for the dataset analysed (Fig 2). The area under the curve was then divided into nine equal area portions (the nine weighted area categories), beginning at taxon range size of two cells (the minimum range size contributing to clustering) and ending at the most widespread taxon with a range of 169 cells. The nine equal area portions were given descending weighting along the x-axis (Fig 2). Species with ranges in these nine weighting area categories were weighted accordingly. This weighting technique adapts to individual datasets (whether entire floras, samples of floras, or taxonomic groups) a priori, without having to adjust the two variables (a and p) of Bell weighting for optimisation. Int takes into account the relative distributional sizes of species as a proportion of the entire clade or dataset, as well as the relative frequencies of the species in each distribution category, by integrating the relationship between these two properties.

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.


Related in: MedlinePlus