Endemism vs Richness: an example

Introduction

Biodiversity hotspots have a prominent role in conservation biology (Myers et al., 2000), but it remains controversial to what extent different types of hotspot are congruent (Bonn et al., 2002). Several authors states that the richness (species number per area) is equivalent to endemism area (Thomas & Mallorie, 1985; Soria-Auza & Kessler, 2008). However, Orme et al. (2005) disagrees with this statement. The most rich areas is not congruent with endemism centers. A simple form to estimate richness in an area is to calculate the species number per area. Several approaches and methodologies has been proposed to estimate the richness in an area. Among them, Chao (1984; 1987), Burnham & Overton (1978, 1979), Heltshe & Forrester (1983), Smith & van Belle (1984), and Raaijmakers (1987) – see DIVA-GIS manual - . An area of endemism is an area of nonrandom distributional congruence among taxa (Platnick, 1991). Several authors have developed techniques to identify areas of endemism. N.D.M. implements an optimality criterion based on the presence or absence of species in a given grid within an area (number of species that compose the area, species found nowhere else).

Methodology

Georeferenced records were collected from Bolívar and Miranda-Esquivel endemism analyses (2009). These data were organized in the DIVA-GIS v. 5.4 software. A richness analysis (species number per area) was conducted using the DIVA-GIS. The Chao 2 richness estimator (Chao, 1987) was used to estimate the species number per area. Chao 2 is based on the number of samples for an area. To create samples, DIVA-GIS divides each grid-cell into 4 or 9 sub-areas. The grid size used in the richness analyses was 1 per 1 and 0.5 per 0.5.
The endemism analyses were performed using the software N.D.M. v. 2.5 (Goloboff, 2006). The analyses were performed using a 0.5 per 0.5 grid size (n=2000; postchk; m=10; M=100) and 0.25 per 0.25 grid size (n=2000; postchk; m=10; M=15). Several searches were conducted in N.D.M. Using different parameters to identify changes in the resultanting endemic areas. The observation of endemic areas vs most richest areas were performed.

Results

The South-Western zone is the most richnest area in my study area (see Fig. 1). Medium richness level are found in the West and Central region. 90% of species number are in these zones (more that 3400 species). In the two richness analysis (1º x 1º and 0.5º x 0.5º), the rich species areas are partially different. However, the general pattern is similar between them, It showing significant richness levels in the Southern-Western regions.

The analyses using a grid size of 0.5º x 0.5º generates 53 endemic areas (Fig. 2). Among them, five endemic areas shows an maximum Endemicity Index (EI =/> 100). 43 endemic areas does spread the South-Western region from studied area. Likewise, others endemic areas is found along to Western and Central region. The second analysis shows identical endemic areas (Fig. 3). The South-Western region is the most endemic one. Further, the endemic areas with lower EI than the endemic areas in the South-Western region are found in the Western and Central region.

Endemic and richness pattern are similar on a resolution of 0.5º per 0.5º vs 1º per 1º grid size. The most richnest areas are congruent with the most endemic areas (EI => 100; Chao 2 =>3400). Further, endemic areas with EI medium (EI among 40.0 to 70.0) are placed in the same regions that the areas with a richness levels medium (Chao 2 among 1700 to 3400).







In the analyses using 0.25 per 0.25 vs 0.5 per 0.5, the endemism and richness pattern shows some incongruence. Although the general endemic/rich areas are recovered, the most endemic areas are not identified as the most richest areas (Fig. 2). Likewise, some endemic areas are not estimated as rich areas (see Apendix 1).

Discusion

In my results the fit between the endemic centers and richness is condicionated by the resolution of the used analyses. Likewise, the hierarchy resultanting from optimality criterion (Szumik & Goloboff, 2004; 2007) can be considered as equivalent to the degree of richness using the estimator Chao 2. Using several parameter, the results in N.D.M. are not affect the similarity between the analyses. So, the richness is equivalent to endemism but it similarity is subject to the resolution level of analysis.

My results that supports a rather weak relationship between richness/endemism indices agrees with the recent observation that patterns of avian species richness are determined by the distribution of widely distributed species, rather than restricted range species (Lennon et al., 2004). In Aves, the endemic species richness is thought to be a product of either refugia from past extinctions or of high rates of ecological and allopatric speciation.

This incongruence on different resolutions have important implications for understanding the ecological, evolutionary mechanisms that underlie the origin and maintenance of biodiversity (Orme et al., 2005).

Aditionally, the lack of congruence among approaches has implications for the use of areas or hotspots in conservation. If congruence among hotspots types are high then it may not matter which index of diversity was used to guide conservation policy, because any such index could act as an effective surrogate for other aspects of diversity (Orme et al., 2005).

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