Evaluation of five taxa as surrogates for conservation prioritization in the Transmexican Volcanic Belt, Mexico

Journal Pre-proof Evaluation of five taxa as surrogates for conservation prioritization in the Transmexican Volcanic Belt, Mexico Tania Escalante, Ana M. Varela-Anaya, Elkin A. Noguera-Urbano, Leslie M. Elguea-Manrique, Leticia M. Ochoa-Ochoa, Ana L. ´ ´ ´ ´ Gutierrez-Vel azquez, Pedro Reyes-Castillo, Hector M. Hernandez, ´ Carlos Gomez-Hinostrosa, Adolfo G. Navarro-Siguenza, ¨ Oswaldo ´ ´ Clarita Rodr´ıguez-Soto Tellez-Vald es,

PII:

S1617-1381(19)30219-5

DOI:

https://doi.org/10.1016/j.jnc.2020.125800

Reference:

JNC 125800

To appear in:

Journal for Nature Conservation

Received Date:

12 June 2019

Revised Date:

22 January 2020

Accepted Date:

23 January 2020

Please cite this article as: Escalante T, Varela-Anaya AM, Noguera-Urbano EA, ´ ´ Elguea-Manrique LM, Ochoa-Ochoa LM, Gutierrez-Vel azquez AL, Reyes-Castillo P, ´ ´ ´ ´ O, Hernandez HM, Gomez-Hinostrosa C, Navarro-Siguenza ¨ AG, Tellez-Vald es Rodr´ıguez-Soto C, Evaluation of five taxa as surrogates for conservation prioritization in the Transmexican Volcanic Belt, Mexico, Journal for Nature Conservation (2020), doi: https://doi.org/10.1016/j.jnc.2020.125800

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Evaluation of five taxa as surrogates for conservation prioritization in the Transmexican Volcanic Belt, Mexico

Tania Escalante1*, Ana M. Varela-Anaya1, Elkin A. Noguera-Urbano2, Leslie M. ElgueaManrique1, Leticia M. Ochoa-Ochoa3, Ana L. Gutiérrez-Velázquez4, Pedro Reyes-

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Castillo5†, Héctor M. Hernández6, Carlos Gómez- Hinostrosa6, Adolfo G. Navarro-

Grupo de Biogeografía de la Conservación, Departamento de Biología Evolutiva, Facultad

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Sigüenza3, Oswaldo Téllez-Valdés7 and Clarita Rodríguez-Soto8

de Ciencias, Universidad Nacional Autónoma de México, Circuito Exterior s/n, Ciudad

Programa de Evaluación y Monitoreo de la Biodiversidad. Instituto de Investigación de

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Universitaria, Coyoacán, 04510 Mexico City, Mexico.

Recursos Biológicos, Alexander von Humboldt, Avenida Paseo Bolívar (Circunvalar) 16-20, Bogotá, D.C., Colombia.

Museo de Zoología “Alfonso L. Herrera”, Departamento Biología Evolutiva, Facultad de

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Ciencias, Universidad Nacional Autónoma de México, Circuito Exterior s/n, Ciudad Universitaria, Coyoacán, 04510 Mexico City, Mexico. Instituto de Ciencias Marinas y Pesquerías, Universidad Veracruzana, Xalapa, Mexico.

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Instituto de Ecología, A.C. Carretera antigua a Coatepec 351, El Haya, Xalapa 91070,

Veracruz, Mexico. 6

Departamento de Botánica, Instituto de Biología, Universidad Nacional Autónoma de

México, Ciudad Universitaria, Coyoacán, 04510 Mexico City, Mexico. 7

Laboratorio de Recursos Naturales, Unidad de Biología, Tecnología y Prototipos 1

(UBIPRO), Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Avenida de los Barrios 1, Los Reyes Iztacala, Tlalnepantla, 54090 Estado de México, Mexico. 8

Centro de Estudios e Investigación en Desarrollo Sustentable, Universidad Autónoma del

Estado de México. Matamoros 1007, Toluca 50130, México.

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Short title: Evaluation of surrogates in Mexico

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*Corresponding author: [email protected]

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Pedro Reyes-Castillo deceased March 20th, 2018.

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†

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Abstract Many biotic groups have been proposed as biodiversity surrogates; however, using different taxa could provide complementary information for choosing conservation priorities. There are two general ways to evaluate the performance of surrogates: (1) using cross-taxon congruency as an a priori test, before the application of some prioritization algorithm to select areas for conservation; and, (2) developing a system of priority areas as

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an a posteriori test. We have used both of these tests to evaluate five different taxa

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(Amphibia, Aves, Insecta, Mammalia and Magnoliopsida) as surrogates for each other; and likewise we evaluated three groupings of them (Invertebrates, Vertebrates and Plants). We

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also compared their performance in a priori and a posteriori tests based on their richness

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patterns and prioritization areas for conservation within the Transmexican Volcanic Belt. The prioritization was run in Zonation software. We evaluated whether the patterns are

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shared among taxa and groupings using two correlation analyses: Pearson and modified t test (to correct for spatial autocorrelation). Although we found some positive correlations

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between the richness patterns of taxa and groupings, the prioritized areas were uncorrelated. In general, the correlations were stronger for mammals and plants, and weaker for birds and insects. We found that patterns of richness and rarity (obtained through the prioritization) are not shared among taxa and groupings. Therefore, if

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conservation priorities are based on a single group and test, there is a high risk of leaving other groups unprotected. Keywords: cross-taxon congruence, conservation planning, a priori test, a posteriori test, vertebrates, invertebrates, plants.

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Introduction The use of surrogates in biological conservation is important because it is prohibitively expensive and difficult to measure, manage and conserve all elements of the biota in all ecosystems at all times (Lindenmayer et al., 2015). The study of surrogacy and its application is widespread, with more than 5000 scientific articles that include several approaches (Westgate et al., 2014, 2017) published to date on the topic. Many components

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within ecosystems, ranging from a single species to an entire biotic group, extending from

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viruses, bacteria, fungi, plants, arthropods to vertebrates, have been proposed as surrogates (Lindenmayer & Likens, 2011). Some authors have suggested the convenience of including

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more than one taxon in a single study because different taxa could provide complementary

functional traits (Westgate, 2015).

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information, especially if they use different ecological domains or have divergent

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Surrogates that have been more thoroughly evaluated are less likely to generate surprising and negative results (Westgate, 2015). There are two general ways to evaluate

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the performance of surrogates: (1) using the cross-taxon congruency as an a priori test; and, (2) developing a system of priority areas as an a posteriori test. Cross-taxon congruence means that there is coincidence between spatial patterns (like richness or endemism) among two or more taxa (Lamoreux et al., 2006; Beger, McKenna, &

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Possingham, 2007). However, there is controversy around the use of cross-taxon congruence results, for several reasons, including poor prediction by surrogates of other taxa, other spatio-temporal scales, ecosystem types, environmental circumstances, or taxonomic scales (Favreau et al., 2006; Caro, 2010). Therefore, cross-taxon congruence does not always predict the performance of a taxon as a surrogate (Beger, McKenna, & 4

Possingham, 2007). This test is performed before applying a prioritization algorithm to select areas for conservation, so we refer to it as the ‘a priori’ approach. Conversely, a taxon can be evaluated as a surrogate ‘a posteriori’ by constructing a simulated conservation area system based on that taxon and evaluating how well other taxa are captured in that simulated reserve system (Ferrier & Watson, 1999; Beger, McKenna, & Possingham, 2007). The assumption is that if the distribution of the surrogate or group of

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surrogates is representative of a larger set of taxa, the sites selected based on the surrogate

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will cover a large percentage of other taxa (Lawler, White, Sifneos, & Master, 2003). A

simple evaluation technique is to perform a species accumulation curve (Ferrier & Watson,

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1999), but complex algorithms can also be used. This a posteriori test may complement the

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a priori cross-taxon congruence test. Moreover, in the absence of cross-taxon patterns, the success of a surrogate depends on its ability to effectively sample a wide range of

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environmental conditions (Lawler, White, Sifneos, & Master, 2003). There are few comparisons showing the most optimal way to evaluate surrogates,

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so there is no consensus on which method is optimal or how to standardize methods for doing so (Favreau et al., 2006). Here, we implement both the a priori and a posteriori tests in order to compare the performance of surrogates and make the best decision about conservation policies.

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The Transmexican Volcanic Belt (TVB) is a biogeographic province located in

central Mexico (Morrone et al., 2017). Several authors have postulated that the TVB is a geologically and biologically complex area, resulting in many geographically restricted species and areas of endemism (see Luna, Morrone, & Espinosa, 2007; Gámez et al., 2012). For these reasons, the biota of the TVB has been considered of high importance for 5

conservation actions. Endemic species are likely to be particularly vulnerable to extinction due to their range restriction, rareness and specialization to particular habitats in the TVB. Furthermore, high rates of deforestation and land use and land cover change in Mexico have been recorded in the TVB (Aguilar et al., 2000; Aguilar-Tomasini et al., unpublished data; Mendoza-Ponce et al., 2018). In this study, we aimed to evaluate five different taxa (Amphibia, Aves, Insecta,

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Mammalia and Magnoliopsida) as biodiversity surrogates for each other, as well as three

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groupings of them (Invertebrates, Vertebrates and Plants), in order to compare their

performance in a priori and a posteriori tests in the prioritization of areas for conservation

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in the TVB.

Material and methods

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Data - We defined our study area based on a polygon (shapefile format) of the Transmexican Volcanic Belt (TVB), (Morrone et al., 2017;

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mexicanmap.atlasbiogeografico.com/) and a 6.3 km-wide buffer around it (equivalent to the narrowest latitudinal distance within the polygon) to include species whose distribution may not exactly match the TVB. We compiled a database of five taxonomic groups for use as surrogates: Amphibia

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(Flores-Villela, Canseco-Márquez, & Ochoa-Ochoa, 2010); Aves (Navarro-Sigüenza, Peterson, & Gordillo-Martínez, 2003; Peterson, Navarro-Sigüenza, & Gordillo-Martínez, 2016); Insecta (Reyes- Castillo & Morón-Ríos, 2005; SNIB-Conabio, 2017; GBIF, 2017), Mammalia (Escalante, 2015); and, Plants (Magnoliopsida) (Escalante, 2015; Hernández & Gómez-Hinostrosa, 2011, 2015; Téllez, 2017). We obtained 3,477 data points of 6

occurrence for 167 species primarily distributed in the TVB (at least 50% of their presence records fell within the TVB polygon and surrounding buffer). This comprised 25 species of amphibians (570 data points), one bird (18), 89 insects (647), 15 mammals (1,208) and 37 plants (1,034). We overlapped the data points with the TVB study area. The list of species and number of data points for each of them is provided as Supplementary Material (S1). To ensure that each cell could have only one data point for a species, reduce the

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spatial autocorrelation, and avoid issues associated with geographical sampling bias,

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occurrence data was thinned with a 10 km radius rule using the R package spThin (AielloLammens et al., 2015). These geographic processes were performed in QGIS v. 2.18.4

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(QGIS Development Team, 2016).

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Species distribution models - We generated species distribution models for species with more than six data points using Maxent 3.4.0 (Phillips et al., 2017) in order to identify

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suitable habitats for the selected species. Maxent was run using the ‘dismo’ package (Hijmans, Phillips, Leathwick, & Elith, 2017; R version 3.4.2, R Core Team 2017).

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Logistic output format was used to describe the probability of presence (Phillips & Dudík, 2008), which is a continuous habitat suitability range between 0 (unsuitable) and 1 (the most suitable). Details of the modelling protocol can be consulted in the Supplementary Material (S2).

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Because rare species are often the most vulnerable, it is crucial to understand the degree to which they are included in selected sites if surrogates are used as a conservation tool (Lawler, White, Sifneos, & Master, 2003). Rare species, which had five or less data points, could not be modeled, so their area of distribution was estimated by generating a 2 km buffer around each data point. The buffer areas were then rasterized in order to be 7

integrated in the analyses. Species with poor performance in the models were also rasterized in this way (Supplementary Material S2). All species distribution models and rasterized distributions were considered as potential geographic distributional areas (DA) of the species in the TVB. Evaluation of surrogates - We compared the performance of four taxa (amphibians, insects, mammals and plants) as biodiversity surrogates. We decided not to

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evaluate the single bird because this under-represents the Class Aves and it could be

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limiting the interpretations related to this group. We decided to include this single species because it is endemic, and it was analyzed only when it was possible to put it together

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with other groups of vertebrates.

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In order to evaluate the performance of the ‘all vertebrates’ together (including Amphibia, Aves and Mammalia) as a single group, we developed other comparison

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between Invertebrates (Insecta), Vertebrates and Plants (Magnoliopsida). A priori evaluation - First, we generated richness maps for each surrogate (except for

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Aves, which has only one species), adding the DA for each taxon and taxonomic grouping; the resulting maps are hereafter referred to as surrogate maps. Then, we calculated the bivariate Pearson’s correlation coefficient between each pair of surrogate maps for the all individual taxa and for all groupings. Pearson’s r-correlation test is usually used to indicate

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how collinear two variables are (Dormann et al., 2013); therefore, we consider that two surrogate maps show similar richness patterns if they have a correlation coefficient (r) of at least 0.7 (r > 0.7). All tests were performed in R 3.5.2 (2017). In addition, p-value was corrected for spatial autocorrelation by performing a modified t test to assess the correlation between two spatial variables using Dutilleul’s method to adjust for spatial autocorrelation 8

(Dutilleul, 1993). The model was performed in R using the package SpatialPack v. 0.3 (Venables & Ripley, 2002; Vallejos, Osorio, & Bevilacqua, 2018). A posteriori evaluation - We used the software Zonation v. 4 (Moilanen et al., 2014) to prioritize the conservation areas based on each surrogate taxon and taxonomic grouping. We ran Zonation using the core-area algorithm (CAZ), which prioritizes rarity, minimizing biological loss by picking a pixel that has the smallest occurrence for the most

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valuable species over all taxa in the pixel (Moilanen et al., 2005; Lehtomäki & Moilanen,

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2013). The last pixels to be removed would be those having high priority species (high

weight) (Moilanen et al., 2005). Because all of the species within the surrogate taxa are

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geographically restricted to the TVB, they were all given a weight of ‘1’. The order of priority of the pixels was compared for each taxon and grouping. In order to evaluate the

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performance of the surrogates with one another, we calculated the bivariate Pearson’s

Results

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evaluation.

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correlation coefficient and the modified t test (Dutilleul’s method), as in the a priori

A priori evaluation - The results of the Pearson’s r-correlation test using richness patterns for all four taxa are shown in the Table 1 and for all groupings in the Table 2 (maps of the

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richness paterns for the four taxa and three groupings are in the Supplementary Material S3). All correlations of species richness within taxa were positive, though they varied in magnitude, with some correlation coefficients (r) below 0.7, showing only moderate correlation. The highest value was between mammals and plants (r= 0.95, p-value= 0), and the lowest value (0.0.81, p-value= 0) was between amphibians and insects. Results were 9

similar for the groupings with high correlation in all comparisons. Results of the modified t test are shown in Table 3 for the four taxa and in Table 4 for the three groupings. In all cases, there were positive correlations, with the strongest correlation between plants and mammals (0.79, p-value= 0.03). Although insects and plants had a correlation of 0.88, the p-value was not significant. On the other hand, the groupings were highly correlated, with a correlation value of ‘1’ and high significance (p ≈ 0).

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A posteriori evaluation - The maps for all prioritized cells using Zonation for all

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five taxa and three groupings are shown in Figures 1 to 6. The results of Pearson’s rcorrelation test for the prioritized pixels using all taxa (Table 5) showed low or no

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correlation. Particularly, there was no correlation between Amphibians and Insects (r close

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to ‘0’), and the mammals were moderately correlated with plants (r=0.50). The correlation test for the prioritization cells considering the groupings showed a similar pattern, with no

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correlation between Invertebrates and Vertebrates (r= 0.10) and weak correlations among the remaining groups (Table 6).

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Results of the modified t test are shown in Tables 7 and 8. For the prioritized pixels of all five taxa, all correlations were lower than 0.7, and in some cases close to ‘0’ (Table 7). For example, mammals had a low correlation with plants (0.5) with a p-value= 0.01; therefore, their prioritized pixels are not similar. In contrast, amphibians and insects had the

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worst correlation (r= 0.01) with a p-value of 0.92. Finally, for the groupings, all correlation values were similar to the individual taxa

(Table 8), with some r close to ‘0’. The strongest correlation was between Vertebrates (amphibians, birds and mammals) and plants (r= 0.55, p ≈0). Nevertheless, this value of correlation does not necessarily imply a similar trend for priority sites. 10

Discussion Lentini & Wintle (2015) found that spatial priorities in conservation are very sensitive to the taxonomic groups used as biodiversity surrogates. In our analyses, this proved to be true for the five taxa analyzed in the TVB. In addition, using different tests for analyzing different patterns affected the results. For both tests, the number of species

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included in each surrogate analyzed was absolutely decisive; therefore, we did not

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compare the ‘richness’ of the Class Aves because it is under-represented. In the a priori test, the richness patterns of the four taxa were highly similar. When the bird was

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grouped with the other Vertebrates, the pattern was highly correlated with Invertebrates

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and plants; although the addition of the bird data to Vertebrates, probably made no difference. However, strategies of conservation planning using a multi-taxa approach

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are more adequate, as was reported by other authors (see Westgate, 2015). Although richness maps showed a trend toward high richness at the center and

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eastern zones of the TVB for all taxa and groupings, it is evident that the pattern of rarity is not shared by them, which is shown by the Zonation’s prioritizations. For example, while the richest areas for insects are in the eastern portion of the TVB, there are many areas in the southern part of the TVB that are considered high priority for conservation because

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they have rare species. For mammals, the pattern is similar: the richness is concentrated towards the center and the eastern portions, but areas that are important due to the presence of rare species are in the western TVB. All correlations performed poorly in the a posteriori test, even for the mammals and plants or Vertebrates and plants. Irwin et al. (2014) have reported that variation between 11

richness of taxonomic groups may be reflected by other groups. Nevertheless, the lack of cross-taxon congruency indicated that different groups of species could not share similar patterns in their habitat (Ricketts et al. 2002). Some authors have suggested that one taxon can be a reasonable surrogate of another only if it shows at least 60% (Leal et al., 2010), 75% (Lovell et al., 2007) or 80% (Fleishman et al., 2005) similarity in their distribution patterns. Therefore, the low relation observed in our analyses indicated that richness

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patterns are poor predictors for each other. The lack of congruency supports the idea that

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there are no good general candidate surrogates in the taxa included, based on the prioritized areas by rarity.

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Positive correlations like the geographical richness patterns arise between taxa

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because they respond to similar processes, particularly at large spatial and temporal scales; understanding the circumstances where congruence is expected is therefore informative in

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conservation decision making (Westgate, 2015). However, richness and rarity patterns are not necessarily similar, and may not match other biogeographical patterns like endemism.

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Lamoreux et al. (2006) provide evidence that “endemism”, and not richness alone, is a useful surrogate for the conservation of all terrestrial Vertebrates. Endemism could be incorporated into the a posteriori test, selecting the exclusive taxa of an area, and using an algorithm that prioritizes the rarity or micro-areality. When we used the CAZ algorithm of

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Zonation, the results of the correlations dramatically changed for both individual taxa and groupings. Although we found strong correlation between endemic mammal and plant richness, this correlation was lost when Zonation was applied. This means that the patterns of richness and rarity (and micro-areality) do not coincide; therefore, if conservation priorities are based on one group and one geographic pattern, the risk of leaving species 12

unprotected is high. Lawler, White, Sifneos, & Master (2003) proposed that rarity is also related to species at risk, and that rare species are less likely to be protected than widespread species. Therefore, it is necessary take into account the rarity of species, particularly the insects, which are under-represented in our analyses. Many authors have proposed the complex origin of the biota of the TVB due to multiple environmental, geological and evolutionary processes (see Halffter & Morrone,

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2017). The TVB belongs to the Mexican Transition Zone (Morrone et al., 2017), where

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several distributional patterns coincide, corresponding to the temporal integration of cenocrons, that is a set of taxa that dispersed and integrated into a biota (Halffter &

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Morrone, 2017). Therefore, it is possible that some patterns are not shared by all taxa. As

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such, in situ evolution should also be considered; for example, the rabbit Romerolagus diazi is endemic to the discontinuous patches on central volcanoes of the TVB (AMCELA,

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2008), and it is cataloged as Endangered by the IUCN (AMCELA, 2008; consulted January, 2019). Another species, the cactus Mammillaria backebergiana is distributed at

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the southern boundary of the TVB in only two localities (Hernández & Gómez-Hinostrosa, 2015) and is cataloged as Data Deficient by the IUCN (Terrazas, Arias, & Arreola, 2017; consulted January, 2019). Both species are vulnerable and rare, and conservation priorities should consider these features, which are missed when only the richness pattern is used.

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There are few evaluations of surrogates in Mexico. Monroy-Gamboa, BrionesSalas, Sarkar, & Sánchez-Cordero (2019) evaluated the performance of Vertebrates (amphibians, birds, mammals and reptiles) in Oaxaca, Mexico, in an a posteriori test using ConsNet software with a rarity-based approach. They found that in general, single taxa were not good surrogates, rather, the inclusion of multiple terrestrial vertebrate groups 13

performed better than single taxa. Other studies have developed prioritizations in central Mexico, but without evaluating surrogates (Fuller et al., 2006; Suárez-Mota & TéllezValdés, 2014). In order to develop an effective conservation plan in central Mexico, we consider necessary to include other distributional patterns, such as “areas of endemism”, which relate the co-occurrence and exclusivity to an area. In addition, the incorporation of the

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dynamics of land use and land cover change could allow us to reach better conservation

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policy solutions.

This study has demonstrated that choosing conservation priorities is very sensitive

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to the methods used for selecting surrogates. The five different taxa evaluated (Amphibia,

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Aves, Insecta, Mammalia and Magnoliopsida) indicated that a multi-group reserve network is necessary to ensure their conservation. The treeline zones found at the highest elevations

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of the mountains are considered islands, which require connectivity among wild communities to maintain their interactions. We did not consider deforestation or

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connectivity valuation in the TVB; the next step in this proposal of TVB conservation priorities could be the analysis of these two factors in sites nominated for conservation. It is also necessary to consider that the TVB could be affected by climatic changes that could promote the invasion of species in the future, which would modify the distributional

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patterns of endemic species. Furthermore, it is necessary to consider that effective and equitable management of conservation area networks requires ecologically representative and well-connected systems of protected areas integrated into the wider landscapes (Aichi Biodiversity Targets – target 11; Woodley et al., 2012).

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Conclusions The decision about which surrogate should be employed in conservation policies is transcendental. The use of a single taxon or a single approach could produce inadequate decisions. A protocol that incorporates two or more evaluations, as was employed here, allows the exploration of the performance of different patterns in order to select the best surrogate. For the endemic species of the Transmexican Volcanic Belt, no taxon or

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grouping could be considered a good surrogate. Although the richness pattern was in

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general shared among the all taxa analyzed and their groupings, when rarity was

prioritized, none of the taxa or groupings coincided in the most important cells for their

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conservation. Therefore, all taxa here analyzed, and different patterns should be

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considered when making decisions in Systematic Conservation Planning in central

Acknowledgements

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Mexico.

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This work was supported by the Program UNAM-DGAPA-PAPIIT, Project IN217717. Itzel Arenas Hidalgo, César Miguel-Talonia and Gonzalo Pinilla-Buitrago help us with the databases. We would like to especially thank Pedro Reyes-Castillo† who, while no longer

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with us, continues to inspire students and colleagues.

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Tables

Table 1. Pearson correlation coefficients (r) from the a priori test: cross-taxon congruence of species richness for the four taxa. Values higher than 0.7 are in bold. P-values are shown

Amphibians

Insects 0.81 (0) -

Insects

Plants 0.89 (0) 0.92 (0) 0.95 (0) -

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Mammals

Mammals 0.91 (0) 0.84 (0) -

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Amphibians -

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in parentheses.

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Plants

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Table 2. Pearson correlation coefficients (r) from the a priori test using groupings: crosstaxon congruence of species richness for the three groups. Values higher than 0.7 are in

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bold. P-values are shown in parentheses.

Invertebrates

Invertebrates Vertebrates

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Plants

-

Vertebrates 0.86 (0) -

Plants 0.92 (0) 0.95 (0) -

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Table 3. Modified t test for the a priori test: cross-taxon congruence of species richness for the four taxa. P-values are in parenthesis.

Insects

-

Insects 0.59 (0.04) -

Mammals

-

-

Mammals 0.68 (0.00) 0.69 (0.07) -

Plants

-

-

-

Plants 0.63 (0.03) 0.88 (0.06) 0.79 (0.03) -

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Amphibians

Amphibians -

Table 4. Modified t test for the a priori test: cross-taxon congruence of species richness for

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the three groupings. P-values are shown in parentheses.

Vertebrates

-

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-

Plants 1 (0) 1 (0) -

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Plants

Vertebrates 1 (0) -

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Invertebrates

Invertebrates -

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Table 5. Pearson correlation coefficients (r) for a posteriori test: prioritized cells using Zonation for the five taxa. P-values are shown in parentheses. Amphibians Amphibians

Birds

-

0.35 (0) -

Birds

Mammals

0.01 (0) 0.35 (0) -

0.42 (0) 0.16 (0) 0.20 (0) -

Plants 0.32 (0) 0.41 (0) 0.29 (0) 0.50 (0) -

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Insects

Insects

Mammals

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Plants

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Table 6. Pearson correlation coefficients (r) from the a posteriori test: cells were prioritized

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using Zonation for the three groupings. P-values are shown in parentheses. Invertebrates

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Invertebrates Vertebrates

0.10 (0) -

Plants 0.29 (0) 0.55 (0) -

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Plants

-

Vertebrates

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Table 7. Modified t test from the a posteriori test: cells were prioritized using Zonation for the five taxa. P-values are shown in parentheses.

Birds

-

Birds 0.35 (0.02) -

Insects

-

-

Insects 0.01 (0.92) 0.36 (0) -

Mammals

-

-

-

Mammals 0.42 (0.00) 0.16 (0.24) 0.21 (0.02) -

Plants

-

-

-

-

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Amphibians

Plants 0.32 (0.00) 0.41 (0.00) 0.39 (0.00) 0.50 (0.01) -

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Amphibians -

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Table 8. Modified t test from the a posteriori test: cells were prioritized using Zonation for

Plants

-

Plants 0.10 (0.22) -

-

-

Vertebrates 0.39 (0.00) 0.55 (0.00) -

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Vertebrates

Invertebrates -

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Invertebrates

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the three groupings. P-values are shown in parentheses.

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Figures Fig. 1. Map of all prioritized cells using Zonation for 25 species of amphibians endemic to the Transmexican Volcanic Belt. Higher values (near 1) represent areas that are more important to the conservation of the taxon. Fig. 2. Map of all prioritized cells using Zonation for a bird species endemic to the

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Transmexican Volcanic Belt. Higher values (near 1) represent areas that are more important to the conservation of the taxon.

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Fig. 3. Map of all prioritized cells using Zonation for 89 species of insects endemic to the

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Transmexican Volcanic Belt. This map is the same for the grouping of Invertebrates Higher values (near 1) represent areas that are more important to the conservation of the

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taxon.

Fig. 4. Map of all prioritized cells using Zonation for 15 species of mammals endemic to

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the Transmexican Volcanic Belt. Higher values (near 1) represent areas that are more important to the conservation of the taxon.

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Fig. 5. Map of all prioritized cells using Zonation for 37 species of plants endemic to the Transmexican Volcanic Belt. This map is the same for the grouping Plants. Higher values (near 1) represent areas that are more important to the conservation of the taxon.

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Fig. 6. Map of all prioritized cells using Zonation, for 41 species of Vertebrates endemic to the Transmexican Volcanic Belt. Higher values (near 1) represent areas that are more important to the conservation of the taxon.

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