,...,.,.,,.,...,.,...,...,.,.,...

Areas of Endemism The definitions and criteria for areas of endemism are complex issues (Linder 2001; Morrone 1994b; Platnick 1991; Szumik et al. 2002; Viloria 2005). There are severa1 definitions of areas of endemism:

Identificatioii of Biotic Components 67,...,.,.,,.,...,.,...,...,.,.,... Fairly small areas that have a significant number of species that occur nowhere else; areas delirnited by the coincident distributions of taxa that occur nowhere else (Nelson and Platnick 1981:390,468). An area of endemism can be defined by the congruent distributional limits of two or more species (Platnick 1991:xi). Geographic region comprising the distributions of two or more monophyletic taxa that exhibit a phylogenetic and distributional congruence and have their respective relatives occurring in other such defined regions (Harold and Mooi 1994:262). Areas of nonrandom distributional congruence between different taxa (Morrone 1994b:438). Areas defined by the distributions of endemic taxa occurring in those areas (Humphries and Parenti 1999:6). Areas delimited by the congruent distribution of at least two species of restricted range (Linder 2001:893). Areas delimited by barriers, the appearance of which entails the formation of species restricted by these barriers (Hausdorf 2002:648). Areas that have many different groups found there and nowhere else (Szumik et al. 20022306). Some of these definitions entail extensive sympatry (Platnick 1991), although this congruence does not demand complete agreement on those limits at a11 possible scales of mapping (Hausdorf 2002; Linder 2001; Morrone 1994b; Morrone and Crisci 1995; Wiley 1981). Some definitions derive explicitly from a vicariance model (Harold and Mooi 1994; Hausdorf 2002), whereas others are neutra1 (Morrone 1994b; Szumik et al. 2002). Some refer to species (Hausdorf 2002; Linder 2001; Platnick 1991) and others to taxa (Harold and Mooi 1994; Humphries and Parenti 1999; Morrone 1994b). How do we recognize an area of endemism? Muller (1973) suggested a protoc01 for working out "dispersa1 centers," which has been applied to identify areas of endemism (Morrone 1994b; Morrone et al. 1994). It consists basically of plotting the ranges of species on a map and finding the areas of congruence between severa1 species. This approach assumes that the species' ranges are small compared with the region itself, that the limits of the ranges are known with certainty, and that the validity of the species is not in dispute. According to Linder (2001), areas of endemism should meet four criteria: They must have at least two endemic species; the ranges of the species endemic to them should be maximally congruent; they should be narrower than the ~7hole study area, so that severa1 areas are located; and they should be mutually exclusive. Severa1 issues concerning areas of endemism should be addressed. Crisp et al. (1995) suggested that the alternative procedures for identifying areas of endemism were controversial, especially questioning whether the hierarchical model of parsirnony analysis of endemicity (PAE) was adequate for that purpose. Humphries and Parenti (1999) argued that including species that are ecologically very dif-

68 Identification of Biotic Components ferent can argue for a historical rather than ecological explanation for the areas of endemism identified. Linder (2001) proposed three optimality criteria to help choose the best estimate of the areas of endemism: the number of areas identified, the proportion of the species restricted to the areas of endemism, and the congruence of the distributions of the species restricted to the areas of endemism. Roig- Juiíent et al. (2002) enumerated some problems with the identification of areas of endemism: lack of distributional data, bias toward locality data, and subjectivity in drawing the exact limits of the areas of endemism.

Parsimony Analysis of Endemicity PAE is also known as parsimony analysis of shared presences (Rosen and Smith 1988), sirnplicity analysis of endemicity (Crisci et al. 2000), parsimony analysis of distributions (Trejo-Torres and Ackerman 2001), parsimony analysis of species sets (Trejo-Torres 2003), cladistic analysis of distributions and endemism (Porzecanski and Cracraft 2005), and parsimony analysis of community assemblages (Ribichich 2005). It was formulated originally by Rosen (1985) and fully developed by Rosen (198813) and Rosen and Smith (1988). PAE (fig. 4.12) constructs cladograrns based on the parsimony analysis of a presence-absence data matrix of species and supraspecific taxa (Cecca 2002; Cracraft 1991; Escalante and Morrone 2003; Morrone 1994a, 1994b, 1998; Myers 1991; Nihei 2006; Porzecanski and Cracraft 2005; Posadas and Miranda-Esquivel1999; Rosen 1988b; Rosen and Smith 1988; Trejo-Torres 2003). PAE cladograms may allow one to infer the three biogeographic processes: Synapomorphies are interpreted as vicariance events, parallelisms as dispersa1 events, and reversals as extinction events. Crisci et al. (2000) distinguished three variants of PAE according to the units analyzed: localities, areas of endemism, and grid cells. There are severa1 other

78 Identification of Biotic Components d e Figure 4.12 Parsimony analysis of endemicity. (a-c) Distributional maps of three species, with grid cells superimposed; (d) data matrix of grid cells x species; (e) cladogram of grid cells; (f) area of endemism obtained. - f units, such as hydrological basins (Aguilar-Aguilar et al. 2003), real and virtual islands (Luna-Vega et al. 1999, 2001; Maldonado and Uriz 1995; Trejo-Torres and Ackerman 2001)) transects (García-Trejo and Navarro 2004; León-Paniagua et al. 2004; Navarro et al. 2004; Trejo-Torres and Ackerman 2002), communities (Ribichich 2005)) and political entities (Cué-Bar et al. 2006). García-Barros (2003) proposed a more appropriate classification based on the objectives of the analysis: to der historical relationships between areas, to identify areas of endemism, and to classify areas (as a phenetic association method). In order to root the PAE cladograms, a hypothetical area with all "O" is added to the matrix. However, some authors (Cano and Gurrea 2003; Ribichich 2005) have used an area coded with a11 "1." This alternative rooting groups areas according to shared absences, which would imply depletion through time starting from a cosmopolitan biota (Cecca 2002). PAE may be used for panbiogeographic analyses, where the clades obtained are considered generalized tracks (Craw et al. 1999; Luna-Vega et al. 2000; Morrone and Márquez 2001). With the aim that the "1" appears only once and does not revert to "O" (as in a compatibility analysis), Luna-Vega et al. (1999) undertook the parsimony analysis with PAUP 4.0.1 (Swofford 1999), setting Goloboff concavity k = O. Luna-Vega et al. (2000) and García-Barros et al. (2002) proposed that when the most parsimonious cladograms have been obtained, it is possible to remove or exclude the taxa supporting the different clades and analyze the reduced matrix to search for alternative clades supported by other taxa. This procedure has been named parsimony analysis of endemicity with progressive character elimination (PAE-PCE) (García-Barros 2003; García-Barros et al. 2002).

Identification of Biotic Components 79 Equal (nondifferential) weighting is usually used for PAE. However, Linder (2001) suggested a protocol to weight species inversely to their distribution areas so that widespread species do not obscure the analyses by introducing homoplasy into the data. It consists of four steps: 1. Weight each species in each grid cell by the inverse of its distribution range so that a species restricted to a grid cell would be scored as 1, a species restricted to two grid cells as 0.5, three grid cells as 0.33, and so forth. 2. Transform the values to a scale of 0-20, multiplying each by 20 and rounding the product. This results in a matrix with values of 20 (single-grid endemics), 10 (two-grid endemics), 7 (three-grid endemics), and so forth. 3. Simplify the matrix by changing the 20s and 10s to 9 (because single-grid species carry no grouping information in a parsimony analysis) so that each species will be represented by a single-digit value in each grid cell score. 4. Input the matrix into a parsiinony analysis, treating the characters' states (O to 9) as additive. PAE has received some criticism. Linder and Mann (1998) criticized Morrone's (1994b) approach for identifying areas of endemism with PAE because grid cells can be used only as presence-absence data, and undercollecting may result in grid cells being omitted. Some authors suggested that PAE is not a valid historical method because it does not take into account the phylogenetic relationships of the taxa analyzed (García-Barros et al. 2002; Humphries 1989,2000; Santos 2005). According to Rosen (1988b; see also Nihei 2006; Trejo-Torres 2003; Trejo-Torres and Ackerman 2002), there are two possible interpretations for PAE cladograms: static and dynamic. The former assumes that cladograms constitute an alternative to phenetic classification methods, whereas according to the latter, cladograms are hypotheses on the historical or ecological relationships of the areas analyzed. If we interpret the external area with a11 "O" as an area lacking suitable conditions for the taxa to survive therein (ecological interpretation), relationships will indicate ecological affinities. If we interpret the external area as a geologicaíly ancient area, where none of the taxa has yet evolved (historical interpretation), relationships will indicate biotic interchanges or vicariance events. Most of the authors who have used PAE explored historical interpretations of the detected patterns, usually from a vicariance viewpoint; for ecological interpretations, see Trejo-Torres and Ackerman (2002), Trejo-Torres (2003), and Ribichich (2005). Enghoff (2000) considered PAE an extreme "assumption 0" approach because only the widespread taxa provide evidence of area relationships. Morrone and Márquez (2001) and Brooks (2005) considered PAE an incomplete implementation of Brooks parsimony analysis (BPA). Szumik et al. (2002) criticized the use of PAE for identifying areas of endemism because an explicit optimality criterion is used a posteriori to select areas of endemism found by what they considered less appropriate means. Brooks and Van Veller (2003) criticized the use of PAE as a cladistic biogeographic method, which is erroneous because it has a different objective.

80 Identification of Biotic Components Parenti and Humphries (2004) suggested that PAE adopts protocols directly from phylogenetic systematics and violates some of the basic assumptions of cladistic biogeography. Nihei (2006) presented a revision of PAE, including a discussion of its history and applications. He suggested that most of the criticisms dealt with its method rather than its theory and that they usually resulted from the conf~ision between the dynamic and static approaches. Nihei warned biogeographers applying PAE to be aware of the problems and limitations of both dynamic and static PAE and to evaluate new variations of PAE. Algorithm PAE-PCE consists of the following steps (Craw 1989b; Crisci et al. 2000; Grehan 2001c; Lomolino et al. 2006; Morrone 1994b, 2004b; Posadas and Miranda- Esquive1 1999; Vargas 2002): 1. Construct an r x c matrix, where r (rows) represents the units analyzed (e.g., localities, distributional areas, grid cells) and c (columns) represents the taxa. Each entry is "1" or "0," depending o11 whether the taxon is present or absent in the locality. A hypothetical area coded with a11 "O" is added to the matrix in order to root the resulting cladograms. 2. Analyze the matrix with a parsimony algorithm. If more than one cladogram is found, calculate the strict consensus cladogram. 3. Connect on a map the area relationships supported by tn70 or more-taxa as generalized tracks or areas of endemism. 4. Remove the taxa supporting the previous generalized tracks or areas of endemism. 5. Repeat steps 24 until no more taxa support any clade. Software Hennig86 (Farris 1988), PHYLIP (Felsenstein 1993), NONA (Goloboff 1998), PAUP (Swofford 2003), Pee-Wee (Goloboff, available at http:/ /wt\7w.zmuc. dk/public/phylogeny/nona-peewee/), and TNT (Goloboff et al., retrieved May 25,2008, from http://ww~w.zmuc.dk/public/phylogeny/tnt/). For reading and editing data files and cladograms: Winclada (Nixon 1999), compatible with NONA, Pee-Wee, and Hennig86; and MacClade (Maddison and Maddison, retrieved May 25,2008, from http://macclade.org/macclade.html), compatible with PAUP.

Endemicity Ana lysis Szumik et al. (2002) proposed a method that takes into consideration the spatial position of the species in order to identify the set of grid cells that represent an optimal area of endemism according to a score based on the number of species endemic to it (Szumik and Roig-Jufient 2005). In order to assign the values of endemicity to the sets of grid cells evaluated, Szumik et al. (2002) suggested four criteria (fig. 4.15a4.15d): 1. First criterion (fig. 4.15a): The distribution of a species must adjust perfectly to the area to contribute to the score, so it must be present in a11 the grid cells of the set. 2. Second criterion (fig. 4.15b): A species can contribute to the score if it is present in some grid cells outside the area as long as the cell is adjacent to the area. This criterion does not require that a11 species contributing to the score have identical distributions. 3. Third criterion (fig. 4.15~): It is not required that a11 the grid cells of the set have identical species composition, but only species occurring in each one of the cells contribute to the score. 4. Fourth criterion (fig. 4.15d): A species may be absent from a given cell but still contribute to the score. Only a species that is evenly distributed in the area satisfies this criterion. Szumik and Goloboff (2004) developed an endemicity value that gives weight to each species, considering its adjustment to the evaluated area. The degree of adjustrnent between the distributional area of each species and the area of endemism

Identification of Biotic Components 87 Figirre 4.15 Assignrnent of scores under different criteria in endemicity analysis. (a) Area with score 3 under the first criterion (contributed by the three species); (b) area with score 2 under the second criterion (in addition to species "circle," species "square" contributes to the score); (c) area with score 2 under the third criterion (species"triangle" does not contribute to the score because it is found only in some cells); (d) area with score 4 under the fourth criterion (the four species contribute to the score, although none of them is found in a11 the cells). under evaluation depends on the relationship between the number of grid cells where it is found and the total number of grid cells. Additionally, the endemicity value increases with the number of grid cells where the presence of the species is assumed or inferred and decreases with the number of grid cells outside the area of endemism where the species is observed or assumed to be present (Szumik and Goloboff 2004). Algorithm It consists of the following steps (Szumik et al. 2002,2006; Szumik and Goloboff 2004; Szumik and Roig-Juííent 2005): 1. Plot species localities on a map with a grid. 2. Assign values of endemicity to a11 possible sets of grid cells, counting the species that may be considered endemic to them according to the four criteria defined by Szumik et al. (2002). 3. Choose the sets of grid cells with the highest endemicity scores. 4. Draw the sets of grid cells on a map as areas of endemism. Software NDM and VNDM (Goloboff 2004; Szumik et al. 2006).