Selecting important areas for bryophyte conservation: Is the higher taxa approach an effective method?

Journal for Nature Conservation 29 (2016) 105–113

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Selecting important areas for bryophyte conservation: Is the higher taxa approach an effective method? C. Alves a,∗ , C. Vieira b , C. Sérgio c , C. Garcia c , S. Stow d , H. Hespanhol b Faculdade de Ciências da Universidade do Porto, Rua do Campo Alegre, Edifício FC4, s/n◦ , 4169-007 Porto, Portugal CIBIO/InBio, Centro de Investigac¸ão em Biodiversidade e Recursos Genéticos da Universidade do Porto, Rua do Campo Alegre, Edifício FC4, s/n◦ , 4169-007 Porto, Portugal c Centre for Ecology, Evolution and Environmental Changes (cE3c), Lisbon, Portugal d The Durrell Institute of Conservation and Ecology, School of Anthropology and Conservation, Marlowe Building, University of Kent, Canterbury, Kent CT2 7NR, UK a

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a r t i c l e

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Article history: Received 9 September 2015 Received in revised form 4 December 2015 Accepted 16 December 2015 Keywords: Bryophytes richness Surrogates Genera Complementarity Important plant areas (IPA) Scoring

a b s t r a c t Surrogates have been used as a support for conservation practices, since they are easier to assess and less time consuming than collecting species-level data. One of these surrogates is the “higher taxa approach”, i.e., the use of data with coarser taxonomic resolution than the species level, such as genus and family levels, as a surrogate for total species richness. The aim of this work was to test if higher taxa (Genera) could be used in the selection of important areas for bryophyte conservation, using three different methodological approaches: Scoring, Important Plant Areas and Complementarity-based approach. We tested these approaches in a protected area, the Peneda-Gerês National Park, one of the best studied areas in Portugal for bryophytes and one of the first areas in the country with bryophyte collections. The knowledge of bryophyte distribution in this National Park has been increasing and distribution maps and detailed species lists were recently published, so we thought it would be a good area to test if the higher taxa approach is an effective method for selecting important areas for bryophyte conservation. Our results showed that localities were ranked in a similar way using species or genera data, regardless of the methodology used. The Complementarity-based approach in comparison with other methodologies protected a higher percentage of bryophyte species. In general, the three approaches identified the same areas as important areas for bryophyte conservation. Therefore, for the studied area and independently of the approach used, genera could be used in the selection of important areas for bryophyte conservation. © 2015 Elsevier GmbH. All rights reserved.

1. Introduction One of the challenges in conservation practice today consists of the lack of complete datasets with information on distribution of species, data that could be used for planning and management (Mandelik, Dayan, Chikatunov, & Kravchenko, 2007). In recent years, surrogates (i.e., habitat, environmental, taxonomic surrogates) have been used as support for conservation practices. Recently, the higher taxa-approach (i.e., the use of data at a coarser taxonomic resolution than the species level, such as of genus- and family-levels, as a surrogate for species richness) has been widely studied in terrestrial ecosystems (Balmford,

∗ Corresponding author. E-mail address: [email protected] (C. Alves). http://dx.doi.org/10.1016/j.jnc.2015.12.004 1617-1381/© 2015 Elsevier GmbH. All rights reserved.

Jayasuriya, & Green, 1996; Bergamini et al., 2005; Mandelik et al., 2007). The advantages of using these surrogates in biodiversity inventories are: (1) higher taxa (i.e., genera and families) are more easily identified than species; (2) time and cost associated with sampling and taxa identification is reduced when adopting the higher taxa approach; (3) more localities can be surveyed when using higher taxa because it is less time-consuming (Gladstone & Alexander, 2005). For the purposes of conservation and reserve selection and design, surrogates have been tested in different habitats, for different taxonomic groups of flora and fauna and at different spatial scales (Balmford, Lyon, & Lang, 2000; Cardoso, Silva, de Oliveira, & Serrano, 2004a; Gladstone & Alexander, 2005; Guareschi et al., 2012; Larsen & Rahbek, 2005; Mazaris, Kallimanis, Sgardelis, & Pantis, 2008; McMullan-Fisher, Kirkpatrick, May, & Pharo, 2010). Surrogate data at the finest possible geographical resolution are of

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the utmost importance for the selection of important areas, in order to provide guidance for the identification of actual reserves in the field (Larsen & Rahbek, 2005). Additionally, different underlying criteria, such as hotspots, complementarity of species or rarity (Fox & Beckley 2005; Margules, Nicholls, & Pressey, 1998; Vane-Wright, Humphries, & Williams, 1991) and irreplaceability (Carwardine et al., 2007; Ferrier, Pressey, & Barrett, 2000) have been applied to identify a set of sites which maximize the diversity of what is conserved. Species richness is one of many measures of diversity, and is used to evaluate the biodiversity of a site. Through species richness we can study the dynamics, spatial scale and temporal distribution of biodiversity. This biological component has been widely used in the selection of important areas for conservation and for reserve network design (Mazaris et al., 2008), but, to our knowledge, bryophyte genera richness have never been used to select areas for bryophyte conservation. The most common approaches used in prioritization of areas important for conservation are scoring and Complementaritybased approaches (Marignani & Blasi, 2012). Scoring procedures establish one or several criteria (such as species richness, rarity or vulnerability) to rank sites in order of value or priority (Abellán, Sánchez-Fernández, Velasco, & Millán, 2005). Some studies have tested this approach in terrestrial ecosystems with spiders (Cardoso et al., 2004a), wasps (Vieira, Seneca, & Sérgio, 2012), and vertebrates (Mazaris et al., 2008). Complementarity-based approaches also allow the selection of sites that represent all targeted biodiversity features together (Rodrigues & Brooks, 2007). This approach minimizes the number of selected sites necessary to represent the maximum number of species (Beger, Jones, & Munday, 2003). The reason for success of this approach is the fact that sites complement one another biologically (Shokri & Gladstone, 2009). Furthermore, this approach has been widely studied across aquatic ecosystems (Beger et al., 2003; Shokri & Gladstone, 2009), and terrestrial ecosystems (Cardoso et al., 2004a,b; Vieira, Oliveira, Brewster, & Gayubo, 2012). Globally, another approach commonly identified as Important Plant Area (IPA) has been developed by Plantlife International, with the purpose of identification and protection of a network of the best sites for plant conservation worldwide (Anderson, 2002). This approach consists of three basic principles for selecting IPAs: (1) the site needs to harbor significant populations of one or more species whose conservation is of global or European interest; (2) the site has an exceptionally rich flora in the European context in relation to its biogeographical zone; and (3) the site is an outstanding example of a habitat of interest for plant conservation, and of botanical importance at the global or European level (Anderson, 2002). This approach has been previously applied to bryophytes (GarcíaFernández, Draper, & Ros, 2010; Sérgio et al., 2012). Other studies have been developed with bryophytes in Portugal using different approaches and methodologies for prioritization of important sites for conservation. For instance, Sergio, Araujo, and Draper (2000) proposed a first approach for selecting a network of reserves in Portugal using gap analysis. On the other hand, Draper, RossellóGraell, Garcia, Tauleigne Gomes, and Sérgio (2003) tested the selection of protected areas for conservation according to the habitat suitability of endangered bryophyte species. Bryophytes can be used as structural organisms at the microhabitat-level because they establish ecological relationships with other small organisms such as arthropods or earthworms (Draper et al., 2003). Bryophytes usually go unnoticed in conservation planning because of their small size, difficulty of identification and unrecognized levels of local diversity. Furthermore, it is usually difficult or impractical to undertake bryophyte surveys in some seasons due to their ephemeral cycles. However, their role in ecosystems, contribution to overall biodiversity and potential as

Fig. 1. Mountain areas of the Peneda-Gerês National Park (PNPG): C—Castro Laboreiro plateau; P—Peneda mountain; S—Soajo mountain; A—Amarela mountain; G—Gerês mountain; M—Mourela plateau.

biological resources highlight the need for their inclusion in conservation planning (McMullan-Fisher et al., 2010). The aim of this study was to test if a higher taxa approach (at genus-level) could be used in the selection of areas for bryophyte conservation in the Peneda-Gerês National Park, using three common approaches for reserve selection: Scoring approach, Important Plant Areas, and Complementarity-based approach. The PenedaGerês National Park (PNPG) is the only National Park in Portugal and one of the best studied sites for bryophytes in Portugal, making this area a good case study to test the ongoing hypothesis. 2. Material and methods 2.1. Study area The PNPG has a total area of approximately 70,000 ha, with altitudes ranging from 50 to 1500 m. The main geomorphological units in PNPG (Fig. 1) are: Castro Laboreiro plateau (C), Peneda mountain (P); Soajo mountain (S); Amarela mountain (A); Gerês mountain (G); Mourela plateau (M). Despite the overall Atlantic climate PNPG has peculiar climatic conditions, from Rio Homem valley with thermophytic and humid conditions, to the high mountains and interior with warm and heavy rainfall conditions. Geologically, PNPG is dominated mainly by granites (Sérgio et al., 2012). 2.2. Data source A georeferenced bibliography-based dataset was prepared based on pre-existent bryophyte data from the University of Lisbon (LISU) and Oporto (PO) herbaria resulting from sporadic surveys, projects (Sérgio et al., 2012) and Ph.D. studies (Garcia, 2006; Hespanhol, Séneca, Figueira, & Sérgio, 2011; Vieira et al., 2012). For each taxon a threat category was assigned, according to the Portuguese Red Data Book (Sérgio et al., 2013): critically endangered (CR); endangered (EN); vulnerable (VU); near threatened (NT); low concern species which require special attention (LC-Att); species with insufficient data (DD and DD-n) and species of low con-

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cern (LC). Species in the categories CR, EN and VU are considered to be threatened. All datasets (herbarium and bibliographic) were georeferenced at a 1 × 1 Km scale (in MGRS coordinates). For those records with insufficient information a cross- reference was made with herbarium specimens, and records without a precise indication of locality were not included. Accumulation curves for both species and genera were performed with PRIMER v6 (Clarke & Gorley, 2006) with 999 randomizations using all 1 km UTM squares, in order to summarize overall completeness of the sampling effort.

2.3. Data analysis Fig. 2. Frequency of bryophyte species per genus.

2.3.1. Scoring approach We ranked UTM squares based on richness, from lowest to highest species and genera richness, respectively. This method corresponds to a ‘scoring approach’ (Cardoso et al., 2004a) or, alternatively, ‘richness approach’ (Vieira et al., 2012). We used IBM SPSS v.21 (IBM, 2012) to calculate Spearman rank correlation coefficients in order to test the reliability of surrogacy between species and genera richness.

2.3.2. Important plant areas (IPA) This approach was based on the methodology applied in the Program of Plantlife International (Anderson, 2002) and for the area of Murcia (García-Fernández et al., 2010). Some changes were made, such as not including habitat quality because the variations of habitat in PNPG are very high. In this study three criteria were used: Criterion 1 (C1): based on the total number of species in each 1 Km UTM square, a richness class was attributed to each square: (1) 1–10 taxa: poor; (2) 11–50 taxa: moderately rich; (3) 51–100 taxa: rich; (4) more than 100 taxa: particularly rich. Criterion 2 (C2): based on the number of threatened bryophytes (CR, EN and VU): a value of 1 is assigned to each threatened bryophyte species present in each 1 Km UTM square. Criterion 3 (C3): based on the presence of important species in each 1 Km UTM square: species of national and international importance are assigned a value of 1; Habitat Directive species are assigned a value of 3 and LC-Att or NT species a value of 1. For each 1 Km UTM square an Area Importance Index was calculated. This was calculated by summing the values of the three criteria (C1 + C2 + C3). Following Sérgio et al. (2012), all UTM squares with an index value equal to or greater than 9 were considered to be areas of importance for bryophytes. This index was calculated for both bryophyte species and genera. In the case of the latter, C1 was based on genera richness, while the values compiled for C2 and C3 used species level information.

2.3.3. Complementarity-based approach Using this more iterative approach, we first selected the UTM square with the highest species and genera richness, respectively, and then in stepwise manner selected UTM squares according to the highest number of new species (i.e., the species that were not present in any of the previously selected UTM squares). This procedure was based on the algorithm described by Rebelo (1994) and executed in DIVA-GIS v7.5 software (Hijmans, Guarino, & Mathur, 2012). Finally, we used species and genera accumulation curves to determine the percentage of total bryophyte species that can be accounted for using the minimum number of UTM squares that protects all genera.

Fig. 3. Species and genera richness accumulation curves.

3. Results 3.1. Genera and species richness In the PNPG dataset 366 species, belonging to 155 genera, were found. Approximately 44.9% of the genera were represented by only one species in the dataset. The genera with the most species were Bryum, and Racomitrium, with 13 species, Grimmia and Sphagnum, with 11 species, and Jungermania and Frullania, with 6 species (Fig. 2). In PNPG, the average number of species per UTM square is 19.1, with a standard deviation of 23.7, ranging from 1 to 170. In turn, genera average 14.7, with a standard deviation of 16.1, ranging between 1 and 111. Species and genera accumulation curves showing the increase in the number of taxa observed with sampling effort, exhibited different patterns, since the genera curve reached an asymptote much earlier than the species curve. In the case of genera 155 UTM squares were obtained, and in the case of species 248 UTM squares were obtained (Fig. 3). 3.2. Reserve selection approaches The scoring approach ranked UTM squares from highest to lowest based on richness (i.e., raw number of genera/species). Species-level data and genus-level data were ranked in a very similar way, since a significant and highly positive correlation (0.990) was found between species and genera richness (Fig. 4; Appendix A). Using the Important Plant Area (IPA) methodology with specieslevel data, 24 UTM squares were identified as important areas

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Fig. 4. Correlation between species and genus richness ranks.

Fig. 6. Distribution of selected UTM squares using the Complementarity-based approach. All but one (in the northwest of PNPG) of the selected genera squares overlap with the selected species squares.

be able to protect 91.8% of the total bryophyte species (Fig. 7). In addition, when using the number of UTM squares that protects all genera, 85.2% of threatened bryophyte species would be included in those areas. 4. Discussion

Fig. 5. Distribution of selected UTM squares using the Important Plant Area (IPA > 9) method. All but one (in the south of PNPG) of the selected genera squares overlap with the selected species squares.

for bryophyte conservation, while using genus-level data 23 UTM squares were identified (Fig. 5; Appendix B). The IPAs identified using genera data coincide with those areas selected using species data and are mainly located in Gerês mountain, particularly in Rio Homem valley, Caldas do Gerês, Peneda mountain, and Mourela plateau. When using genera information to select IPAs, 74.3% of the total bryophyte species would be included in those areas. The Complementarity-based approach selected 42 UTM squares as important areas for bryophyte conservation using data at species-level, and 17 UTM squares with the genus-level data. Generally, the areas identified based on genera coincide with those areas selected using species data and are mainly located in Gerês mountain, particularly in Rio Homem valley, Caldas de Gerês, Peneda mountain and Mourela plateau (Fig. 6; Appendix B). Species and genera accumulation curves revealed that the seventeen areas selected using genera data in the richness-based approach would

Our three analyses suggest that higher taxa at genus-level could be used as a surrogate of bryophyte species richness, in the studied area and within the level of information of the dataset available. This surrogate could therefore be applied in the prioritization of sites for bryophyte conservation. This was in concordance with some other studies that tested this assumption in conservation biology using other taxonomic groups such as spiders, intertidal molluscs, rocky reef fishes and wasps (Cardoso et al., 2004a; Gladstone & Alexander, 2005; Vieira et al., 2012). In addition, cumulative richness curves of species and genera indicated that there was a significant reduction in the sampling effort required for genus in relation to species assessments. Likewise, Bergamini et al. (2005) had similar results. Surprisingly, in other studies, sampling effort was approximately the same for species and genera, mainly due to a high percentage of species-poor genera (Mandelik et al., 2007). Further, the available data suggest that bryophyte collection data was more complete for genera as opposed to species and therefore, at the species-level, additional sampling effort would be necessary to improve species distribution data. In general, regardless of the approach used, important areas selected in PNPG for bryophyte conservation are located mainly in Gerês mountain, Peneda mountain and Mourela plateau. These areas were already pointed out as vulnerable areas for bryophytes in an earlier study performed in PNPG (Sérgio et al., 2012). In our research, the scoring approach showed that UTM squares are ranked in much the same way for both species and genera richness. Other studies showed similar results for other taxonomic groups such as spiders and wasps (Cardoso et al., 2004a; Vieira et al., 2012). This scoring approach has some advantages such as being easy to perform, data needed is not difficult to obtain and

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Fig. 7. Taxa accumulation curves at the species and genus levels, using the complementarity prioritization of UTM squares.

no specific software is needed (Abellán et al., 2005). However, this methodology has some disadvantages, such as subjectivity (i.e., results may vary highly with scores given to specific site attributes, or chosen thresholds), lack of accountability (i.e., not taking into account the complementarity, or lack thereof, of attributes between sites), and transparency (i.e., not stating clearly the options taken). Also, this approach is greatly affected by sampling bias (Abellán et al., 2005; Pressey & Nicholls, 1989). In this study we did not use a threshold in scoring approach, as the aim was only to realize if this approach would rank all UTM squares in the same way for species and genera. When using the IPA approach, the UTM squares were ranked in a similar way whether using species or genera data. According to Sérgio et al. (2012), who used the IPA methodology with species data only, this method can be used to protect a high number of bryophyte species, whilst also including the sensitive ones such those that are threatened. The Complementarity-based approach is considered the most efficient method for finding the largest number of species that can be preserved when the number of sites allowed for protection is restricted (Abellán et al., 2005). Other studies have tested this methodology with the higher taxa approach with encouraging results showing that using the genus-level data also protects a high number of species in other taxonomic groups such as macrofungi, fishes, invertebrates, plants and wasps (Balmford et al., 2000; Vanderklift, Ward, & Phillips, 1998; Vieira et al., 2012). On the other hand, van Jaarsveld et al. (1998) in South Africa, using plant and animal data, found that using the higher taxonomic levels in the selection of important areas for conservation was inefficient and this approach did not protect most of the species. Our results showed that the Complementarity-based approach protects a higher amount of bryophyte species when compared to other methodologies such as IPA. Additionally, the prioritization of sites with the Complementarity-based approach was effective in protecting threatened bryophyte species. Therefore, it seems that the Complementarity-based approach could be more efficient than other approaches when selecting areas important for conservation, as already pointed out by Abellán et al. (2005). A study comparing IPA and Complementarity-based approaches using only species and habitat richness was undertaken in Italy at a national scale (Marignani & Blasi, 2012). Their results supported that both IPA and Complementarity-based approaches should be combined in order to select areas important for conservation, since it would optimize the results and locate areas of highest importance for conservation. They also advocated that the focus of conservation

efforts should be on several small reserves with high habitat quality, rather than on few large ones (Marignani & Blasi, 2012). Bergamini et al. (2005) debated the problem of taxonomic changes in the case of lichen genera. This could be a problem for the applicability of genera as a surrogate of species richness, and it could hinder the effectiveness of genera as a surrogate for bryophyte species. As suggested by Bergamini et al. (2005), after major taxonomic changes, the relationship between genera and species needs to be reevaluated. Additionally, one must be aware that, for conservation issues, species-level identifications are inevitable and genera datasets are of limited use for further analyses. Moreover, for this study, all data was verified by experts in order to control for duplications related to synonyms and validate the taxonomic hierarchy. Therefore, the quality of future studies should always be guaranteed and validated by taxonomic experts. In conclusion, our results indicate that genus surrogacy could be a useful method to define a conservation priority site network for bryophytes in PNPG, whether we apply a simple scoring approach, the IPA methodology or a much more efficient iterative approach such as the Complementarity-based approach to the problem of site ranking. Additionally, genus surrogacy can be a valuable method for conservation decision-making, especially when there are time and financial constraints. Nevertheless, more studies are needed in different regions and ecosystems and also at larger scales to test the effectiveness of genus surrogacy in the selection of important areas for bryophyte conservation. Acknowledgements The authors are grateful for financial support provided by the project BrioAtlas—Fundo EDP para a Biodiversidade. H.H. is funded by the Fundac¸ão para a Ciência e Tecnologia (FCT) under a Postdoctoral fellowship (SFRH/BPD/64665/2009) and C.V. is funded by the Fundac¸ão para a Ciência e Tecnologia (FCT) under a Postdoctoral fellowship (SFRH/BPD/63741/2009), co-funded by the Programa Operacional Ciência e Inovac¸ão—2010 and Fundo Social Europeu. Appendix A. Table A1 Appendix B. Selected UTM squares (those with an index value of 9 or greater) for both species and genera according to the IPA method.

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Table A1 Taxa richness of the sampled UTM squares and respective ranking according to the scoring approach. UTM

NG6919 NG6920 NG7227 NG7127 NG6616 NG7022 NG7024 NG8030 NG7219 NG5743 NG6446 NG6517 NG9228 NG7023 NG7217 NG9129 NG7228 NG5944 NG7018 NG5737 NG7923 NG7021 NG7124 NG7319 NG7025 NG6518 NG6619 NG7020 NG7528 NG6635 NG7428 NG6519 NG8732 NG7518 NG7929 NG9328 NG6520 NG7122 NG8825 NG6146 NG6133 NG7627 NG6139 NG7129 NG7328 NG7417 NG5543 NG7323 NG9236 NG5638 NG6148 NG9229 NG5843 NG6447 NG7019 NG6034 NG6136 NG6536 NG9139 NG5141 NG6125 NG6134 NG6820 NG6950 NG6952 NG7622 NG5243 NG5544 NG6535 NG6732 NG7628 NG8633 NG6438

Richness

Table A1 (Continued) UTM

Rank

Species

Genera

Species

Genus

170 156 119 98 93 92 91 90 89 82 81 79 69 67 66 66 59 55 55 54 54 50 49 49 48 47 44 41 41 40 39 38 36 33 33 33 32 32 32 31 30 30 29 29 29 29 28 28 28 26 26 26 25 25 25 24 24 24 24 23 23 23 23 23 23 23 22 22 22 22 22 22 21

111 99 79 65 67 66 55 68 54 52 54 58 50 55 49 45 41 32 39 41 34 37 37 37 32 41 32 33 26 33 32 34 28 21 21 27 29 29 27 27 25 24 22 21 25 28 21 17 26 21 17 20 22 22 23 19 19 16 21 16 17 22 18 19 20 18 16 17 21 15 18 16 17

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 15 17 18 18 20 20 22 23 23 25 26 27 28 28 30 31 32 33 34 34 34 37 37 37 40 41 41 43 43 43 43 47 47 47 50 50 50 53 53 53 56 56 56 56 60 60 60 60 60 60 60 67 67 67 67 67 67 73

1 2 3 7 5 6 9 4 11 13 11 8 14 9 15 16 17 28 20 17 24 21 21 21 28 17 28 26 39 26 28 24 34 49 49 36 32 32 36 36 41 43 45 49 41 34 49 69 39 49 69 56 45 45 44 58 58 76 49 76 69 45 61 58 56 61 76 69 49 84 61 76 69

NG6729 NG6848 NG9141 NG6126 NG6144 NG8631 NG8632 NG6234 NG6249 NG6714 NG6922 NG7925 NG5242 NG5945 NG6347 NG6825 NG6849 NG6917 NG6925 NG7125 NG7823 NG8525 NG6346 NG6350 NG6654 NG6730 NG6949 NG7318 NG8220 NG5830 NG5931 NG6615 NG6655 NG6724 NG6926 NG7415 NG6035 NG6027 NG6617 NG6744 NG6747 NG6954 NG7930 NG9036 NG6149 NG6242 NG6435 NG6550 NG6653 NG7027 NG7054 NG7515 NG8023 NG8731 NG6243 NG6814 NG6948 NG6953 NG6957 NG7128 NG7454 NG8024 NG5730 NG5930 NG6043 NG6437 NG6921 NG7053 NG7055 NG7226 NG7817 NG8018 NG8826 NG9140 NG6226

Richness

Rank

Species

Genera

Species

Genus

21 21 21 20 20 20 20 19 19 19 19 19 18 18 18 18 18 18 18 18 18 18 17 17 17 17 17 17 17 16 16 16 16 16 16 16 15 14 14 14 14 14 14 14 13 13 13 13 13 13 13 13 13 13 12 12 12 12 12 12 12 12 11 11 11 11 11 11 11 11 11 11 11 11 10

14 15 17 18 15 18 18 14 16 18 14 13 16 16 15 14 13 15 17 18 12 12 16 12 13 13 14 15 13 11 11 15 15 12 12 13 14 12 13 10 13 13 9 12 11 12 13 12 12 13 10 11 10 9 10 10 12 11 8 11 9 11 11 11 10 8 9 8 7 10 9 10 10 10 9

73 73 73 77 77 77 77 81 81 81 81 81 86 86 86 86 86 86 86 86 86 86 96 96 96 96 96 96 96 103 103 103 103 103 103 103 110 111 111 111 111 111 111 111 118 118 118 118 118 118 118 118 118 118 128 128 128 128 128 128 128 128 136 136 136 136 136 136 136 136 136 136 136 136 148

92 84 69 61 84 61 61 92 76 61 92 98 76 76 84 92 98 84 69 61 109 109 76 109 98 98 92 84 98 120 120 84 84 109 109 98 92 109 98 129 98 98 139 109 120 109 98 109 109 98 129 120 129 139 129 129 109 120 151 120 139 120 120 120 129 151 139 151 163 129 139 129 129 129 139

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Table A1 (Continued)

Table A1 (Continued) UTM

Richness

Rank

Species

Genera

Species

Genus

NG7317 NG8226 NG9238 NG5041 NG5729 NG5836 NG6030 NG6240 NG6434 NG6452 NG6646 NG6652 NG7453 NG7525 NG7924 NG8727 NG5244 NG6037 NG6516 NG6523 NG6718 NG6735 NG6817 NG7026 NG7126 NG7827 NG8330 NG5442 NG5828 NG6250 NG6334 NG6723 NG6835 NG7828 NG9030 NG9230 NG5142 NG5736 NG6150 NG6554 NG6629 NG6756 NG7017 NG8019 NG5630 NG6241 NG6345 NG6623 NG6757 NG7029 NG7358 NG7418 NG7516 NG7728 NG7824 NG9234 NG5343 NG5542 NG5928 NG6038 NG6140 NG6229 NG6336 NG6822 NG7028 NG7056 NG7223 NG7928 NG8119 NG8325 NG9029 NG5241 NG5344 NG5731 NG5742 NG5849

10 10 10 9 9 9 9 9 9 9 9 9 9 9 9 9 8 8 8 8 8 8 8 8 8 8 8 7 7 7 7 7 7 7 7 7 6 6 6 6 6 6 6 6 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3

8 9 7 9 8 8 8 6 8 6 9 9 6 9 6 9 7 7 8 7 6 7 8 6 8 6 8 7 4 7 7 6 7 6 7 7 6 4 6 2 6 6 6 6 5 5 3 5 4 5 4 4 5 5 3 5 3 3 4 3 4 4 4 3 4 4 3 3 4 4 3 3 3 3 2 3

148 148 148 152 152 152 152 152 152 152 152 152 152 152 152 152 165 165 165 165 165 165 165 165 165 165 165 176 176 176 176 176 176 176 176 176 185 185 185 185 185 185 185 185 193 193 193 193 193 193 193 193 193 193 193 193 205 205 205 205 205 205 205 205 205 205 205 205 205 205 205 220 220 220 220 220

151 139 163 139 151 151 151 175 151 175 139 139 175 139 175 139 163 163 151 163 175 163 151 175 151 175 151 163 197 163 163 175 163 175 163 163 175 197 175 228 175 175 175 175 190 190 210 190 197 190 197 197 190 190 210 190 210 210 197 210 197 197 197 210 197 197 210 210 197 197 210 210 210 210 228 210

UTM

NG6719 NG6918 NG7051 NG7220 NG9035 NG5342 NG5826 NG5844 NG5942 NG6244 NG6445 NG6755 NG7218 NG7324 NG5042 NG5545 NG5845 NG5847 NG5927 NG5932 NG6049 NG6147 NG6436 NG6525 NG6622 NG6713 NG6715 NG7119 NG7120 NG7315 NG7325 NG7327 NG7517 NG7830 NG7926 NG8227 NG8725 NG8932 NG9028

Richness

Rank

Species

Genera

Species

Genus

3 3 3 3 3 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

3 3 3 3 3 2 2 2 1 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

220 220 220 220 220 230 230 230 230 230 230 230 230 230 239 239 239 239 239 239 239 239 239 239 239 239 239 239 239 239 239 239 239 239 239 239 239 239 239

210 210 210 210 210 228 228 228 238 228 228 228 228 228 238 238 238 238 238 238 238 238 238 238 238 238 238 238 238 238 238 238 238 238 238 238 238 238 238

C1—(1) 1–10 taxa: poor; (2) 11–50 taxa: moderately rich; (3) 51–100 taxa: rich; (4) more than 100 taxa: especially rich; C2—number of threatened bryophytes (CR, EN and VU) in each 1 Km grid square, for each bryophyte species the value 1 is assigned; C3—species of national and international importance (value of 1 is assigned), Habitat directive species (value of 3 is assigned), and LC-Att or NT species (value 1 is assigned) in each 1 Km grid square. IPA—sum of the values of the three criteria (C1 + C2 + C3).

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UTM

Species

C1

C2

C3

IPA

UTM

Sequence (Species)

Classes (Species)

Additional classes (Species)

NG6920 NG6919 NG7227 NG6616 NG6517 NG7024 NG7127 NG7022 NG9228 NG5743 NG6446 NG7023 NG7228 NG8030 NG9129 NG7018 NG7219 NG5737 NG7923 NG5944 NG6518 NG6619 NG7217 NG7319

156 170 119 93 79 91 98 92 69 82 81 67 59 90 66 55 89 54 54 55 47 44 66 49

4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 2

17 15 12 8 7 9 7 8 4 3 4 3 6 7 3 7 2 2 5 3 3 1 4 3

11 10 10 12 11 7 9 7 8 8 7 8 5 4 8 3 8 6 3 4 5 6 2 4

32 29 26 23 21 19 19 18 15 14 14 14 14 14 14 13 13 11 11 10 10 9 9 9

UTM

Genera

C1

C2

C3

IPA

NG6920 NG6919 NG7227 NG6616 NG6517 NG7024 NG7127 NG7022 NG5743 NG7023 NG8030 NG9228 NG6446 NG7219 NG7228 NG9129 NG7018 NG5737 NG6518 NG7923 NG5944 NG6619 NG7319

99 111 79 67 58 55 65 66 52 55 68 50 54 54 41 45 39 41 41 34 32 32 37

3 4 3 3 3 3 3 3 3 3 3 2 2 3 2 2 2 2 2 2 2 2 2

17 15 12 8 7 9 7 8 3 3 7 4 4 2 6 3 7 2 3 5 3 1 3

11 10 10 12 11 7 9 7 8 8 4 8 7 8 5 8 3 6 5 3 4 6 4

31 29 25 23 21 19 19 18 14 14 14 14 13 13 13 13 12 10 10 10 9 9 9

NG6919 NG6920 NG7219 NG7024 NG6517 NG9129 NG8030 NG5944 NG7627 NG5743 NG5737 NG7127 NG8018 NG8330 NG7923 NG6616 NG5638 NG9030 NG9228 NG7227 NG7025 NG8825 NG7022 NG6952 NG6550 NG6149 NG6148 NG6446 NG6744 NG5141 NG9140 NG5442 NG6437 NG8732 NG7055 NG6226 NG7124 NG6520 NG7220 NG6518 NG7018 NG6814

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

170 156 89 91 79 66 90 55 30 82 54 98 11 8 54 93 26 7 69 119 48 32 92 23 13 13 26 81 14 23 11 7 11 36 11 10 49 32 3 47 55 12

170 50 24 17 14 12 10 6 5 4 4 4 4 3 3 3 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

UTM

Sequence (Genera)

Classes (Genera)

Additional classes (Genera)

NG6919 NG6920 NG7127 NG5737 NG8030 NG7923 NG8330 NG8018 NG6517 NG5442 NG5843 NG9129 NG6226 NG8825 NG7124 NG7219 NG6616

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

111 99 65 41 68 34 8 10 58 7 22 45 9 27 37 54 67

111 13 5 4 4 4 3 2 2 1 1 1 1 1 1 1 1

Appendix C.

References

Taxa richness of the sampled UTM squares and respective ranking according to the Complementarity-based approach. Sequence—indicates the order in which the grid squares were selected. Classes—indicates how many different classes (species or genera) are in each selected UTM. Additional classes—indicates how many new classes (species or genera) are in each UTM. These are defined as species not present in any of the previously selected grid squares.

Abellán, P., Sánchez-Fernández, D., Velasco, J., & Millán, A. (2005). Conservation of freshwater biodiversity: a comparison of different area selection methods. Biodiversity & Conservation, 14, 3457–3474. Anderson, S. (2002). Identifying important plant areas: a site selection manual for Europe, and a basis for developing guidelines for other regions of the world. London: Plantlife International. Balmford, A., Jayasuriya, A. H. M., & Green, M. J. B. (1996). Using higher-taxon richness as a surrogate for species richness. 2. Local applications. Proceedings of the Royal Society B-Biological Sciences, 263, 1571–1575. Balmford, A., Lyon, A. J. E., & Lang, R. M. (2000). Testing the higher-taxon approach to conservation planning in a megadiverse group: the macrofungi. Biological Conservation, 93, 209–217.

C. Alves et al. / Journal for Nature Conservation 29 (2016) 105–113 Beger, M., Jones, G. P., & Munday, P. L. (2003). Conservation of coral reef biodiversity: a comparison of reserve selection procedures for corals and fishes. Biological Conservation, 111, 53–62. Bergamini, A., Scheidegger, C., Stofer, S., Carvalho, P., Davey, S., Dietrich, M., et al. (2005). Performance of macrolichens and lichen genera as indicators of lichen species richness and composition. Conservation Biology, 19, 1051–1062. Cardoso, P., Silva, I., de Oliveira, N. G., & Serrano, A. R. M. (2004a). Higher taxa surrogates of spider (Araneae) diversity and their efficiency in conservation. Biological Conservation, 117, 453–459. Cardoso, P., Silva, I., de Oliveira, N. G., & Serrano, A. R. M. (2004b). Indicator taxa of spider (Araneae) diversity and their efficiency in conservation. Biological Conservation, 120, 517–524. Carwardine, J., Rochester, W., Richardson, K., Williams, K. J., Pressey, R., & Possingham, H. (2007). Conservation planning with irreplaceability: does the method matter? Biodiversity and Conservation, 16, 245–258. Draper, D. A., Rosselló-Graell, C., Garcia, C., Tauleigne, G., & Sérgio, C. L. (2003). Application of GIS in plant conservation programmes in Portugal. Biological Conservation, 113, 337–349. Ferrier, S., Pressey, R. L., & Barrett, T. W. (2000). A new predictor of the irreplaceability of areas for achieving a conservation goal, its application to real-world planning, and a research agenda for further refinement. Biological Conservation, 93, 303–325. Fox, N. J., & Beckley, L. E. (2005). Priority areas for conservation of Western Australia n coastal fishes: a comparison of hotspot, biogeographical and complementarity approaches. Biological Conservation, 125, 399–410. Garcia, C. (2006). Briófitos epífiticos de ecossiatemas florestais em Portugal.Biodiversidade e conservac¸ão. Tese de Doutoramento. Faculdade de Ciências da Universidade de Lisboa. García-Fernández, M. E., Draper, I., & Ros, R. M. (2010). Contribución a la conservación de la biodiversidad en la región de Murcia: áreas importantes nola de Briología, 34, 37–60. para briófitos. Boletín de la Sociedad Espa˜ Gladstone, W., & Alexander, T. (2005). A test of the higher-taxon approach in the identification of candidate sites for marine reserves. Biodiversity & Conservation, 14, 3151–3168. Guareschi, S., Gutierrez-Canovas, C., Picazo, F., Sanchez-Fernandez, D., Abellan, P., Velasco, J., et al. (2012). Aquatic macroinvertebrate biodiversity: patterns and surrogates in mountainous Spanish national parks. Aquatic Conservation-Marine and Freshwater Ecosystems, 22, 598–615. Hespanhol, H., Séneca, A., Figueira, R., & Sérgio, C. (2011). Microhabitat effects on bryophyte species richness and community distribution on exposed rock outcrops in Portugal. Plant Ecology & Diversity, 4, 251–264. Hijmans, R. J., Guarino, L., and Mathur, P. (2012). DIVA-GIS v7.5. A geographic information system for the analysis of species distribution data. (manual available at: http://www.diva-gis.org). IBM. (2012). IBM SPSS statistics for windows, version 21.0. Armonk, NY: IBM Corp. Larsen, F. W., & Rahbek, C. (2005). The influence of spatial grain size on the suitability of the higher-taxon approach in continental priority-setting. Animal Conservation, 8, 389–396. Mandelik, Y., Dayan, T., Chikatunov, V., & Kravchenko, V. (2007). Reliability of a higher-taxon approach to richness, rarity, and composition assessments at the local scale. Conservation Biology, 21, 1506–1515.

113

Margules, C. R., Nicholls, A. O., & Pressey, R. L. (1988). Selecting networks of reserves to maximise biological diversity. Biological Conservation, 43, 63–76. Marignani, M., & Blasi, C. (2012). Looking for important plant areas: selection based on criteria, complementarity, or both? Biodiversity and Conservation, 21, 1853–1864. Mazaris, A. D., Kallimanis, A. S., Sgardelis, S. P., & Pantis, J. D. (2008). Does higher taxon diversity reflect richness of conservation interest species? The case for birds, mammals, amphibians, and reptiles in Greek protected areas. Ecological Indicators, 8, 664–671. McMullan- Fisher, S. J. M., Kirkpatrick, J. B., May, T. W., & Pharo, E. J. (2010). Surrogates for macrofungi and mosses in reservation planning. Sustitutos de Macrohongos y Musgos en la Planificación de Reservas. Conservation Biology, 24, 730–736. Pressey, R. L., & Nicholls, A. O. (1989). Efficiency in conservation evaluation: scoring versus iterative approaches. Biological Conservation, 50, 199–218. Rebelo, A. G. (1994). Iterative selection procedures: centres of endemism and optimal placement of reserves. Strelitzia, 1, 231–257. Rodrigues, A. S. L., & Brooks, T. M. (2007). Shortcuts for biodiversity conservation planning: the effectiveness of surrogates. Annual Review of Ecology, Evolution, and Systematics, 38, 713–737. Sergio, C., Araujo, M., & Draper, D. (2000). Portuguese bryophyte diversity and priority areas for conservation. Lindbergia, 25, 116–123. Sérgio, C., Garcia, C., Hespanhol, H., Vieira, C., Stow, S., & Long, D. (2012). Diversidad de briófitos en el Parque Nacional Peneda-Gerês (Portugal): seleccionando áreas importantes de plantas important plant areas (IPA) basado en nuevos datos y anteriores referencias. Botanica Complutensis, 36, 39–50. Sérgio, C., Garcia, C. A., Sim-Sim, M., Vieira, C., Hespanhol, H., and Stow, S. (2013). Atlas e livro vermelho dos briófitos ameac¸ados de Portugal. Shokri, M. R., & Gladstone, W. (2009). Higher taxa are effective surrogates for species in the selection of conservation reserves in estuaries. Aquatic Conservation: Marine and Freshwater Ecosystems, 19, 626–636. van Jaarsveld, A. S., Freitag, S., Chown, S. L., Muller, C., Koch, S., Hull, H., et al. (1998). Biodiversity assessment and conservation strategies. Science (New York, N. Y.), 279, 2106–2108. Vanderklift, M. A., Ward, T. J., & Phillips, J. C. (1998). Use of assemblages derived from different taxonomic levels to select areas for conserving marine biodiversity. Biological Conservation, 86, 307–315. Vane- Wright, R. I., Humphries, C. J., & Williams, P. H. (1991). What to protect—systematics and the agony of choice. Biological Conservation, 55, 235–254. Vieira, C., Seneca, A., & Sérgio, C. (2012). Floristic and ecological survey of bryophytes from Portuguese watercourses. Cryptogamie, Bryologie, 33, 113–134. Vieira, L. C., Oliveira, N. G., Brewster, C. C., & Gayubo, S. F. (2012). Using higher taxa as surrogates of species-level data in three Portuguese protected areas: a case study on Spheciformes (Hymenoptera). Biodiversity and Conservation, 21, 3467–3486.