Spatial conservation prioritization of biodiversity spanning the...

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ARTICLESPUBLISHED: 28 APRIL 2017 | VOLUME: 1 | ARTICLE NUMBER: 0151

© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

NATURE ECOLOGY & EVOLUTION 1, 0151 (2017) | DOI: 10.1038/s41559-017-0151 | www.nature.com/natecolevol

Spatial conservation prioritization of biodiversity spanning the evolutionary continuumSilvia B. Carvalho1, 2*, Guillermo Velo-Antón1, Pedro Tarroso1 , Ana Paula Portela1, Mafalda Barata1, Salvador Carranza3, Craig Moritz4 and Hugh P. Possingham2, 5

Accounting for evolutionary relationships between and within species is important for biodiversity conservation planning, but is rarely considered in practice. Here we introduce a novel framework to identify priority conservation areas accounting for phylogenetic and intraspecific diversity, integrating concepts from phylogeny, phylogeography, spatial statistics and spatial conservation prioritization. The framework allows planners to incorporate and combine different levels of evolutionary diver-sity and can be applied to any taxonomic group and to any region in the world. We illustrate our approach using amphibian and reptile species occurring in a biodiversity hotspot region, the Iberian Peninsula. We found that explicitly incorporating phyloge-netic and intraspecific diversity in systematic conservation planning provides advantages in terms of maximizing overall biodi-versity representation while enhancing its persistence and evolutionary potential. Our results emphasize the need to account for the evolutionary continuum in order to efficiently implement biodiversity conservation planning decisions.

Given accelerating declines in biodiversity, a major global objective is to improve the status of biodiversity by safe-guarding species and genetic diversity in networks of pro-

tected areas (strategic goal C, Convention on Biologic Diversity (CBD) Aichi Targets; http://www.cbd.int/sp/targets). One of the targets established by the CBD is to protect at least 17% of terres-trial land by 2020. Although the extension of protected land has increased over the past century, there are still substantial shortfalls around the world1.

Many different strategies have been developed to identify pri-ority conservation areas, including biodiversity hotspots2 and key biodiversity areas3. Alternatively, systematic conservation plan-ning4 uses complementarity and efficiency principles to maximize biodiversity representation and persistence5. Typically, species have been the fundamental units of diversity used in spatial conservation prioritization—even though the importance of accounting for evolutionary relationships between and within species has long been recognized6–9.

Preserving evolutionary relationships between species is impor-tant on the premise that different species represent different amounts of evolutionary history. Additionally, phylogenetic relationships are expected to be effective surrogates of underlying feature diversity (phenotypic, behavioural and/ or ecological) and, consequently, of functional diversity10. However, there are only a few studies in which evolutionary diversity has been explicitly accounted for in spatial prioritization11–14. This is probably related to a lack of comprehen-sive phylogenies, and to major uncertainties and missing taxa in those that are available15. It is also argued that conservation areas that are selected on the basis of species distributions will adequately represent phylogenetic diversity16; as such, species distributions can be effective surrogates for evolutionary diversity, though there is empirical evidence and theoretical arguments to the contrary11,17–19.

Intraspecific genetic diversity underpins evolutionary potential (the ability of a population or species to respond to future selection

pressures) in the sense that it promotes the generation of new spe-cies, the resilience to changing environmental conditions, and the ability of a species to undergo evolutionary adaptation20–22. The inference of phylogenetic trees at the intraspecific level allows detecting independently evolving sets of populations (hereafter called lineages). When linked to spatial models, it is possible to infer the spatial distribution of each lineage23,24. This spatial information is critical for conservation planning because it allows optimizing overall representation of diversity and preserving the potential for future nascent species25. Indeed, several studies have shown that intraspecific genetic diversity is spatially structured and that areas of higher genetic diversity are often coincident among several spe-cies26, resulting in either hotspots of genetic diversity or concentra-tions of phylogeographic endemism27. However, few studies have explicitly incorporated the intraspecific phylogeography of multiple species into spatial prioritization28–30. One reason for this is the lack of such information for multiple species in the same region; another is the absence of spatially explicit genetic data31.

Here we introduce and apply a framework to identify priority areas for the conservation of biodiversity spanning the evolutionary continuum, from inter- to intraspecific diversity. The framework consists of four main stages (Fig. 1). The first stage, mapping spe-cies distributions, involves collecting data on species distributions. The second, mapping interspecific phylogenetic diversity (PD), implicates collecting tissue samples of all species and extracting, amplifying and sequencing DNA. The DNA sequences will then be used to infer interspecific phylogenetic trees that, in turn, are used to calculate and then map branch lengths, and finally calculate PD across the study area. The third stage, mapping intraspecific lin-eage diversity (LD), consists of building a georeferenced database of DNA sequences, using literature data and/or new sequences. This database will then be used to infer an intraspecific phylogenetic tree for each species, and to identify and map the main phylogenetic lineages. Finally, stage four involves using the maps of interspecific

1CIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos da Universidade do Porto, R. Padre Armando Quintas, 4485-661 Vairão Portugal. 2The School of Biological Sciences, University of Queensland, St Lucia, Queensland 4072, Australia. 3Institute of Evolutionary Biology, CSIC-Universitat Pompeu Fabra, Barcelona E-08003, Spain. 4Research School of Biology and Centre for Biodiversity Analysis, The Australian National University, Acton, ACT 6201, Australia. 5Conservation Science, The Nature Conservancy, West End, Queensland 4101, Australia. *e-mail [email protected]

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http://orcid.org/0000-0002-2694-1170

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© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.


ARTICLES NATURE ECOLOGY & EVOLUTION

phylogenetic diversity and those of individual lineages to identify spatial conservation priorities.

We exemplify the framework using the amphibians and reptiles of the Iberian Peninsula (Supplementary Material 1 and 2). We identified priority conservation areas aimed at fulfilling different conservation objectives, using four scenarios (Fig. 1b; see Methods for details). In scenario ‘Sp’, we used individual species distributions as conservation features, and weighted all the species as equal. In the scenario ‘Br’, conservation features were the branches of the phylogenetic tree. The weight of each conservation feature was set as equal to the relative branch length of the respective branch. Scenario ‘SpLin’ was aimed at maximizing coverage of both spe-cies and lineages, regardless of the amount of PD embodied by each. Conservation features were composed by the distribution of intraspecific lineages (for species with available information) plus the individual distribution of remaining species. All species and lineages were weighted equally, thus assuring that all species and lineages are efficiently represented in the set of selected sites. In scenario ‘BrLin’, the aim was to maximize coverage of overall PD,

spanning the inter- and intraspecific levels. Conservation features were set as the branches of the interspecific phylogenetic trees (as in scenario Br), plus the distribution of all intraspecific lineages. To calculate the weight of each intraspecific lineage, we first calculated the relative PD of each lineage within the intraspecific tree and then scaled it to the interspecific tree by multiplying this value by the branch length of the terminal branch of the interspecific tree from which the lineages diverged. For species with intraspecific diversity, the weight of the terminal branch was set to zero.

We address the following issues: (a) how patterns of spe-cies richness, PD and LD are structured in space; (b) whether the direct use of PD and LD information uncovers conserva-tion priority areas not identified when using species distribu-tions; and (c) how efficiently priority areas identified using species distributions represent PD and LD. If species richness is spatially congruent with PD and LD, we expect to find congru-ent prioritization solutions among the three levels of diversity. This is expected if interspecific phylogenetic trees are symmetri-cally balanced and have long terminal branches18, if the ranges

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Identify main lineages(bGMYC, R)

Map lineage richness andindividual lineages

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Figure 1 | Methodological workflow and illustration of features’ weights. a, Methodological workflow, representing the four main steps of the framework: (1) map species distributions; (2) map interspecific phylogenetic diversity; (3) map intraspecific lineage diversity; and (4) spatial prioritization. For (1) and (2), the different sub steps are also listed. b, An example showing how different conservation features would be weighted in the four scenarios tested (Sp, Br, SpLin and BrLin). The top panel shows a hypothetical phylogenetic tree depicting interspecific phylogenetic relationships between species A–D (light grey) and intraspecific lineages LA1 and LA2 (dark grey). Red numbers represent relative branch lengths of the inter- or intraspecific tree. The bottom panels show the conservation features that would be included in each scenario and their weight.

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of lineages of the same species are homogenously distributed across the species range, and if there is no significant pattern of phylogeographic concordance among lineages of different species (particularly those that are range restricted). Conversely, species distributions will be a poor surrogate for PD and LD if interspe-cific phylogenies are asymmetric (for example, if smaller clades diverged from the remaining clades millions of years ago), or if several lineages ranges are very restricted and coincident among lineages of different species. If either or both are true, we expect that spatial prioritization solutions found when explicitly account-ing for PD and LD to be more efficient in representing those facets of biodiversity than when using species distributions alone. This higher efficiency would be of particular importance in the context of expanding current protected areas networks, in order to achieve the CBD target of protecting at least 17% of terrestrial land by 2020.

ResultsInter- and intraspecific phylogenies. We inferred a consensus interspecific phylogenetic ultrametric tree for the amphibian species and another for the reptile species (Supplementary Material 6), and

intraspecific phylogenies for 14 amphibian and 19 reptile species or species complex (Supplementary Material 7). The number of intra-specific lineages inferred using bGMYC varied between 2–13 for amphibians and 2–8 for reptiles (Supplementary Materials 2 and 7).

Spatial patterns of diversity. Spatial patterns of species richness (SR) varied considerably across the Iberian Peninsula. For the amphibians, higher SR was found mostly in northern and western Iberia (with particularly high richness in the La Coruña, Cadiz and Huelva provinces, along the Central System and on the Portuguese southwest coast), as well as in the northeastern region (across the Barcelona and Gerona provinces). For the reptiles, the highest SR was found mostly in southern Iberia (Huelva and Cadiz provinces), along the eastern and northeastern coast (particularly in the Barcelona and Gerona provinces) and along the Central System.

Spatial patterns of amphibian PD were similar to those for amphib-ian species richness (Fig. 2a,b). Nonetheless, some areas showed a significantly higher PD than expected based on the null model (areas with significantly higher standardized effect size). These were scattered across central and northeastern Iberia (Supplementary

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planners to incorporate and combine different levels of evolutionary diversity, consistent with CBD principles, and provides advantages relative to other existing methods (such as key biodiversity areas or biodiversity hotspots) that, while capable of incorporating evolu-tionary relationships to some extent32,33, do not account for comple-mentarity and efficiency principles, and do not allow combining evolutionary relationships both between and within species.

The different scenarios demonstrate how different levels of bio-diversity (species, phylogenetic diversity and lineage diversity) can be used individually or together. The weighting scheme used exem-plifies one way of combining the different levels, but many other schemes are also possible. For example, we could have used a com-bined index of evolutionary distinctness and extinction risk—in a similar fashion to the EDGE prioritization logic34—or prioritization protocols that balance evolutionary distinctness and species num-bers35. Managers can also decide if they prefer to weight all lineages equally or differently; for example, lineages could be weighted pro-portionally to their overall genetic diversity.

It is worth noting that spatial conservation prioritization does not necessarily mean establishing new conservation areas. While some areas of the world are still below the CBD 17% target for land protection, other parts of the world are already above that target, and it is unlikely that additional protected areas will be designated. However, spatial prioritization can be also used to prioritize man-agement actions in space, such as restoring natural habitat that sup-ports multiple unique genetic lineages, managing invasive species pressure over multiple threatened prey populations, or restoring connectivity between populations to avoid inbreeding depression.

The application of the framework to the case study of Iberian amphibians and reptiles, with the described weighting scheme, revealed that priority conservation areas selected when using spe-cies distributions can differ significantly from those selected when using a finer-scale measure of diversity (PD, LD or both), and are generally less efficient in representing these types of diversity than when using PD and/or lineage distributions. However, the dimin-ished efficiency varied substantially with the amount of landscape preserved, the efficiency metric (relative or target), and the scenario tested (Br, SpLin or BrLin). This shortfall of species-based analyses was strongest when most (> 65%) of the land was unavailable for

Material 8). The analysis of spatial patterns of regional phylogenetic endemism (RPE) for the amphibians highlighted mostly the north-ern and northeastern fringe of the Peninsula (Fig. 2c).

Spatial patterns of reptile PD also largely matched reptile spe-cies richness (Fig. 2e,f) with only a small fraction of areas show-ing significantly higher PD than expected based on the null model, particularly concentrated in southwestern Iberia and along the northeastern coast (Supplementary Material 8). Interestingly, the southern coastal fringe of the Peninsula was also the area with high-est levels of RPE (Fig. 2g), which contrasts with the northern areas found to have high RPE for the amphibians.

In general, spatial patterns of lineage richness (Fig. 2d,h) coin-cided with spatial patterns of species richness, which suggests that the taxa sampled for intraspecific genetic diversity are good repre-sentatives of the broader diversity. Major concentrations of both amphibian and reptile lineage richness were found in the area north from the Gulf of Cádiz (and the Gibraltar region for reptiles) and along the Central System. However, when accounting for species richness, the highest diversity of lineage richness was found in northwest Iberia (for amphibians) and around the Gulf of Cadiz (for both amphibians and reptiles) (Supplementary Material 9). The number of amphibian lineages in eastern Iberia was relatively low, except in the northeastern region (Cataluña province).

Spatial prioritization. Spatial solutions found when using species-level information only partially matched those found when explic-itly targeting PD, LD or both. When analysing the top 17% of ranked sites (1,352 grid cells), 280 (20.7%) cells were selected consistently in all four scenarios (Sp, Br, SpLin and BrLin) (Fig. 3a). However, areas selected both in SP and one of the alternative scenarios var-ied between 714 (52.81%) grid cells for the SpLin scenario and 1,251 (92.52%) for the BrLin scenario. The number of exclusively selected grid cells in the alternative scenarios was 576 (42.60%), 636 (47.04%) and 99 (7.32%) in scenarios Br, SpLin and BrLin, respec-tively (Fig. 3b–d).

Most of the areas with higher values of regional phylogenetic endemism were selected in the Sp scenario. These areas include the northern fringe of Iberia for amphibians and the southern coast for reptiles. However, surprisingly, the alternative scenarios (par-ticular Br) tended to be less inclusive of these areas. For instance, the southern strip of Iberia, which has high PE for reptiles, was poorly represented in the Br solution for those taxa. The areas with highest lineage richness also tended to be mainly included in solutions found in the Sp scenario, but mostly excluded from the Br scenario.

The effectiveness of Zonation spatial conservation solutions in representing inter- and/or intraspecific diversity was typically lower when using species distributions than when using the alternative data set (Br, BrLin or SpLin) (Fig. 4). However, solutions found using species distributions were more efficient than random solutions in all scenarios. The difference in efficiencies between the Sp and the alternative scenarios varied with the proportion of landscape lost, but were always much more pronounced for target efficiencies (with maximum values ranging between 17.9 and 30.2) than for relative efficiencies (with maximum values ranging between 3.8 and 9.7). For target efficiencies, the maximum differences between Sp and the alternative scenario occurred when more than 75% of the land was lost.

Similarly, the Marxan best solutions using the alternative scenar-ios (Br, BrLin and SpLin) always had higher performances than the Sp scenario; they were also more pronounced in target Efficiency than in relative efficiency (Fig. 4).

DiscussionWe have developed a framework to explicitly incorporate PD, LD or both into spatial conservation prioritization. The framework allows

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Figure 3 | Comparison of Zonation solutions (areas included in the highest 17% rank) between the Sp scenario and the alternatives (Br, SpLin and BrLin) for both amphibians and reptiles. a, Areas included in all four scenarios. b–d, Matches and mismatches between spatial solutions found for the Sp scenario and each of the alternative scenarios.

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conservation—precisely where spatial prioritization is most impor-tant, since preserving more that 65% of land is unlikely given socio-economic constraints.

The partial spatial congruence between solutions found for the Sp and alternative scenarios was expected given that spatial patterns of PD and LD generally matched species richness patterns (Fig. 2). However, since both phylogenetic trees for amphibians and reptiles are unbalanced and contain several clades with recent radiations, we also expected to find a considerable amount of non-coincident spatial solutions between the Sp and Br scenarios. Indeed, only 57% of the grid cells selected in the Br scenario were coincident with cells selected in the Sp scenario. However, there was a clear tendency for Sp scenario solutions to include most of the areas with higher values of regional phylogenetic endemism, which suggests that species endemicity is tightly nested with regional phylogenetic endemicity. Areas selected in the Br scenario but not in Sp tended to match areas with a higher mean PD or higher standardized effect size, but this relation was not always straightforward. This suggests that achieving site complementarity for phylogenetic diversity (using Zonation) is not the same as selecting priority areas based on richness patterns.

Similarly, only 53% of the grid cells selected in the SpLin scenario were coincident with cells selected in the Sp scenario. This result was also expected since the phylogeography of many species is highly spatially structured, several species have many intraspecific lineages (some occupying very small ranges), and previous studies revealed high phylogeographic concordance for Iberian amphibian and reptiles36. However, the mismatched areas did not always correspond to areas of higher lineage richness, which suggests that some lineages may be geographically restricted to areas with low lineage richness.

Solutions found for the BrLin scenario were those with the highest percentage of coincident sites selected in the Sp scenario. This probably reflects the low weights attributed to the lineages, which were scaled to the phylogenetic diversity of the terminal branch in the interspecific tree.

Results for relative and target efficiencies indicate that solutions found for the Sp scenario are usually more efficient than random solutions in maximizing overall representation of PD and LD. However, the efficiency varied considerably with the amount of landscape preserved. The difference in target efficiencies achieved between the Sp and alternative scenarios tended towards zero when

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Figure 4 | Relative and target efficiencies. a,b, Relative efficiency (a) and target efficiency (b) for both amphibians and reptiles, obtained by solutions found when aiming to protect species (Sp) and each of the alternative features—phylogenetic diversity (Br), lineages (SpLin) and both (BrLin)—for each proportion of landscape lost. Bottom plots in each row highlight the difference in achieved efficiencies (Dif.) between Sp and the alternative features. Circles indicate efficiencies achieved by Marxan best solutions and the respective proportions of land required to achieve them. Grey lines represent efficiencies achieved by 1,000 random solutions, with the thickness of the line representing standard deviations.

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Inference of interspecific phylogenies. We gathered tissue samples for all amphibian and reptile species included in the study (Fig. 1a, step 2.1), and up to six genes were PCR amplified and sequenced in both directions (Fig. 1a, step 2.2). For amphibians, we sequenced three mitochondrial genes—12S rRNA (12S), 16S rRNA (16S) and cytochrome b (CYTB)—and one nuclear gene, recombination-activating gene 1 (RAG1). For reptiles, we sequenced the same three mitochondrial regions plus three nuclear genes—the oocyte maturation factor Mos (CMOS), melanocortin receptor 1 (MC1R) and recombination-activating gene 1 (RAG1) (Supplementary Material 3). We complemented the data set by downloading sequences from GenBank (Supplementary Material 4).

We obtained ultrametric phylogenetic trees, one for amphibians and another for reptiles, with the Bayesian inference (BI) method implemented in the software BEAST v.1.8.047 (Fig. 1a, step 2.3) (Supplementary Material 3).

Inference of intraspecific phylogenies. We reviewed the literature containing phylogeographic data for each amphibian and reptile species occurring in the Iberian Peninsula (Fig. 1a, step 3.1) (Supplementary Material 5). From the species studied, we identified those with high intraspecific diversity in Iberia and downloaded their respective sequences from GenBank. When available, geographical locations of sample collection sites were assembled in a GIS. Otherwise, we requested the information from the authors, or searched for the geographic description on Google Earth v. 7.1.5.1557, and compared the information obtained from figures presented in the publications to avoid errors.

We complemented published data with new sequences obtained from tissue samples collected in the field or provided by colleagues (Fig. 1a, step 3.2). Sampling locations selected for each species were aimed at filling major gaps within each species range, particularly at the edges. We sequenced different mtDNA fragments for different species in order to complement existing data (Supplementary Materials 3 and 5). We overlaid the locations of available sequences with its range and discarded from subsequent analysis those species with only a small fraction of the range sampled (labelled as ‘not enough data’ in Supplementary Material 5).

We inferred intraspecific phylogenetic trees for species (or species complexes) which had phylogeographic information (Fig. 1a, step 3.3). We assessed phylogenetic relationships within each species using Bayesian inference in BEAST v1.8.047. Species for which it was not possible to infer a robust phylogenetic tree were also discarded from subsequent analysis.

Spatial patterns of diversity. We inferred spatial patterns of species richness (SR), interspecific phylogenetic diversity (PD), regional phylogenetic endemism (RPE) and lineage richness (LR). Spatial patterns of SR were inferred by summing the total number of species occurring in each grid cell. The interspecific phylogenetic trees inferred for the amphibians and reptiles were combined with the individual species distributions to identify spatial patterns of PD and RPE. To measure PD in each grid cell, we calculated the total branch length of the phylogenetic tree represented by the occurring taxa6, (Fig. 1a, step 1.4). RPE was calculated following a previously published method33, but considering only the phylogeny of Iberian species. For that purpose, 1,000 trees were randomly sampled from the posterior distribution (for the amphibian and reptile trees separately). For each tree, PD in each grid cell was computed using the ‘picante’ package for R48 while RPE was computed using customized scripts in R49. Mean PD and mean RPE of the 1,000 trees were mapped for the amphibians and reptiles.

We used a null model to test the hypothesis that the observed phylogenetic diversity in each grid cell is significantly higher (α = 0.05; one-tailed test) than the phylogenetic diversity expected based on a random evolutionary relationship between species. The null model consisted of randomizing the phylogenetic distances between species by shuffling the species names in the tips of the consensus tree 999 times, using the ‘phylotools’ R package50. The topology and the branch lengths of the trees were fixed as well as species distributions. The null models were then used to calculate the standardized effect size (SES) and P values in each grid cell (that is, the probability that the observed phylogenetic diversity in each grid cell is significantly higher than that expected based on the null model). If the P value is higher than 0.95, we reject the null hypothesis.

We mapped the occurrence of each intraspecific evolutionary lineage. We first identified the number of lineages within each intraspecific phylogeny and classified the different haplotypes into one of the lineages identified (Fig. 1a, step 3.4) using the Bayesian General Mixed Yule-Coalescent model (GMYC), implemented in the R package ‘bgmyc’51 (Supplementary Material 3). Next, we identified the spatial distribution of each identified lineage within the area of occurrence of the species (Fig. 1a, step 3.5) using the R package ‘phylin’23 (Supplementary Material 3). The probability of occurrence of each lineage in each grid cell was converted to zero if it was lower than 0.4 of the maximum probability of occurrence of the lineage, and to 1 if it was higher. Lineage richness was calculated as the total number of lineages occurring in each grid cell. We used ordinary least regression (OLS) to identify areas of higher LR than expected based on SR, and mapped the residuals (Supplementary Material 9).

Spatial prioritization. We identified priority areas for conservation of amphibians and reptiles in the Iberian Peninsula using Zonation v.4.052, which is a commonly used tool to maximize the representation of conservation features (such as habitats,

most (> 50%) of the landscape was preserved, and peaked when > 80% of the landscape was lost, with alternative scenarios being up to 30% more efficient. Since the CBD recommendations are to preserve 17% of the area of each member party, this segment of pro-portion of landscape lost is the one with more implications for con-servation. Results found when using Marxan corroborate Zonation results, with differences in efficiency being greater for target effi-ciency, particularly for the BrLin scenario.

Ours results are in line with previous studies by showing that pri-ority conservation solutions using explicit phylogenetic or intraspe-cific information are partially spatially congruent with those using species distributions29,37, and that species-based solutions are more efficient in representing those diversity levels than random solu-tions38,39. However, some diversity features may be missed if not explicitly accounted for in spatial conservation prioritization, partic-ularly if a specific representation target is to be met11,28,29. This could have happened in our case study, since five amphibian and seven rep-tile species had missing phylogeographic data, or a well-supported phylogenetic tree could not be inferred from the available genetic data. Thus, when PD and LD data are available, the best strategy would be to use those data directly to prioritize conservation areas.

Despite the current boom in the availability of available macro-scale phylogenetic and phylogeographic data, there are still several data constraints that limit the direct use of PD and LD in conserva-tion planning, particularly in a regional, multi-taxa context15,31. In the absence of phylogenetic and phylogeographic data, the use of species distributions seem to be a suitable surrogate. Nonetheless, evolutionary data, even if available for only some of the occurring taxa, provides valuable information for conservation planning. For instance, in our case study, we had phylogeographic information for 14 (out of 27) amphibian species and 19 (out of 50) reptile species. Our framework is flexible enough to include in the same data set species with and without phylogeographic information. Still, it is important that a significant fraction of species ranges have been sampled for phylogeographic analysis, in order to assure accuracy in lineages delimitation. When available phylogenies do not include all the species occurring in the study area, it is possible to estimate the potential position and branch length of a species in the tree by its taxonomy33. Undeniably, detailed genetic information is available for many species, particularly for those under higher extinction risk, but there seems to be a lack of dialogue between conservation genet-icists and phylogeographers, conservation planners and decision- makers on how best to use these data40,41.

There are many other possibilities to further incorporate other levels of diversity into the framework. We believe that the key to success is that whatever the level of phylogeny in the analysis, diversity has to be mapped and weighted according to predefined rules. For example, it has been demonstrated how different facets of population genetics can be incorporated into spatial prioritization, including allelic richness, genetic distinctness, genetic sub-regions, and recent rates of gene flow42. Additionally, recent progress in the genomic technologies promises to exponentially increase the available data for conservation at reasonable costs43, with some of the major advantages being the possibility of detecting and map-ping adaptive genetic diversity44. The major challenges ahead are to understand which facets of evolutionary diversity are most important to protect, and to set decision rules, as choosing different objectives leads to very different solutions.

MethodsSpecies distributions data. We compiled observed distribution records of 27 amphibian and 50 reptile species (Fig. 1a, step 1) occurring in the study area from the Portuguese and Spanish atlases of amphibians and reptiles45,46 and the database of the Spanish Herpetological Society (http://siare.herpetologica.es/). Occurrence records were referenced to the UTM (Universal Transverse Mercator) grid of 10 × 10 km containing 7,955 grid cells and systematized in a Geographic Information System (GIS) (see Supplementary Materials 2 and 3 for details).

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7




species, phylogenetic diversity, intraspecific lineages, and so on) given a limited cost (maximal coverage problem). Zonation produces a conservation rank of sites in a given region based on the representation of conservation features, their weights and the cost of protecting a site. It starts by assuming that the entire landscape is protected and then iteratively removes a site at each turn (the one with the least conservation benefit). As such, it produces a solution (a set of priority grid cells for conservation) for each fraction of the landscape protected, ranging from 100% (including all grid cells in the solution) to 0% (no grid cells included in the solution) of landscape protected. Different weights can be attributed to different conservation features in order to set different representation priorities. For instance, higher weights can be set for endangered or rare species. Priority areas were identified using Zonation and four scenarios, differing in the type of conservation features used and in the weighting scheme (Fig. 1a (step 4) and b). The cost of each site was set equal to one in all scenarios to simplify comparisons between scenarios. All scenarios were produced with the data set inclusive of both amphibian and reptile species, as detailed below.

In the first scenario (Sp), we used individual species distributions (of both amphibian and reptile species) as conservation features, and weighted all the species as equal (weight equal to one). The solution found under this scenario was considered a baseline solution to which we could compare alternative prioritization solutions aimed at efficiently representing PD (scenario Br), LD (SpLin), or both (BrLin).

The second scenario (Br) was aimed at efficiently representing PD. Conservation features were the branches of the phylogenetic tree. The spatial distribution of the terminal branches, equals the distribution of individual species, while the spatial distribution of internal branches, equals the lumped distribution of all species descending from the respective branch. In this scenario, the weight of each conservation feature was set as equal to the relative length of that respective branch. This approach implies that the amount of evolutionary history shared among two co-occurring species is only accounted once when evaluating the contribution of a given site to the overall set of selected sites. It also assures phylogenetic diversity complementarity by guaranteeing that all tree branches are efficiently represented in the set of selected sites (see refs 11,53 for detailed methodological explanations). To produce the conservation feature data set inclusive of both amphibians and reptiles, we first produced an individual data set for each group based on the respective phylogenetic trees, and subsequently concatenated them.

The third scenario (SpLin) was aimed at maximizing coverage of both species and lineages. Conservation features were composed of the distribution of intraspecific lineages identified for 14 amphibian species (total of 58 lineages) and for 19 reptile species (total of 68 lineages), plus the individual distribution of the remaining species. All species and lineages were weighted equally (weight equal to one), thus assuring that all species and lineages are efficiently represented in the set of selected sites. This scenario represents a conservation goal aiming at maximizing evolutionary potential by representing all independent evolutionary lineages. A data set was produced inclusive of all amphibian and reptile species or lineage distributions.

In the fourth scenario (BrLin), the aim was to maximize coverage of overall PD, spanning the inter- and intraspecific levels. Conservation features were set as the branches of the interspecific phylogenetic trees (as in scenario BR), plus the distribution of all intraspecific lineages. To calculate the weight of each intraspecific lineage, we first calculated the relative PD of each lineage within the intraspecific tree and then scaled it to the interspecific tree by multiplying this value by the branch length of the terminal branch of the interspecific tree from which the lineages diverged. For species with intraspecific lineages, the weight of the terminal branch was set to zero. Similarly to previous scenarios, the features’ data set was a concatenation of the data sets obtained for amphibian and reptile species separately.

We used the basic core-area zonation cell removal rule, and repeated the analysis 1,000 times for all the scenarios using the random removal algorithm, which removes cells in a random order54.

Given the CBD goal of 17% of land allocated for biodiversity conservation, we identified the 17% highest ranked sites in Zonation (1,306 grid cells). We compared sites identified in the Sp scenario with those found in the Br, SpLin and BrLin scenarios. Finally, we evaluated the effectiveness of spatial conservation solutions found when using species distributions and when using random removal in representing PD, LD and both. To that purpose we used two metrics: RE and TE. The relative efficiency (RE) consists of a standardized weighted average of the percentage of the range remaining of all features:

=∑

∈ ...×

=×

×( ){ } i n nRE

max , {1, , }

100(1)

in R W

T

R WT

1i i

i

i ii

The target efficiency (TE) is the percentage of features with a remaining range higher than 17% of the overall range:

=

∑≥ . ⇒ ×

< . ⇒

∑×

=

=

if W

if

WTE

0 17 1

0 17 0100

(2)in

RT i

RT

in

i

1

1

ii

ii

where Ri is the range of feature i remaining in the landscape, Ti is the overall range of feature i, Wi is the weight attributed to feature i, n is the total number of features and

×∈ ...

R WT

i nmax , {1, , }i i

i

is the maximum value obtained within the range of features. Both RE and TE vary between 0 and 100, with 100 representing maximum efficiency.

We also asked how efficient a solution aimed at finding the near-optimal minimum set of planning units required to achieve 17% of each species distribution would be in representing phylogenetic diversity, intraspecific diversity and both (minimum set problem). We used the software Marxan55 and the same scenarios (Sp, Br, SpLin and BrLin), setting as the representation target 17% of the distribution of all conservation features present in each scenario. The cost of each grid cell was set equal to 100. We calculated the RE and TE (as in equations (1) and (2), respectively) of the best solutions found in each scenario (that is, the ones that achieved lower scores of the Marxan objective function), and compared the efficiency and costs of the best solution found in the SP scenario in representing PD, LD, and both. For each scenario, we ran Marxan 1,000 times with the following parameters: algorithm—simulated annealing; penalty cost for not achieving the target—10,000; iterations per simulation—10,000,000; temperature decreases per simulation—10,000; initial temperature and cooling factor—adaptive.

Data availability. The data sets generated and/or analysed during the current study are available from the corresponding author on reasonable request. DNA sequences generated were deposited in Genbank (https://www.ncbi.nlm.nih.gov/genbank) with the accession numbers indicated in Supplementary Materials 4 and 5.

Received 22 September 2016; accepted 22 March 2017; published 28 April 2017

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AcknowledgementsThis work was developed under the project PTDC/BIABIC/118624/2010, funded by Fundação para a Ciência e Tecnologia (FCT) through the North of Portugal Regional Operational Programme 2007/2013 (ON.2O Novo Norte), the National Strategic Reference Framework (NSRF), and the European Regional Development Fund (ERDF); and through project PTDC/BIA-BIC/3545/2014, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). P.T. and S.B.C. were funded by FCT postdoctoral grants (SFRH/BPD/93473/2013 and SFRH/BPD/74423/2010, respectively) and G.V.-A. was supported by an IF contract (IF/01425/2014), attributed by FCT. S.C. was funded by project CGL2015-70390-P (MINECO/FEDER). H.P.P. was supported by the Australian Research Council. We thank all the colleagues who kindly provided samples and molecular data: A. Perera, A. Miraldo, A. Kaliontzopoulou, B. Carvalho, C. Rato, D. Guicking, F. F. Martínez, H. Gonçalves, I. Martínez-Solano, J. Harris, M. Fonseca, M. Carretero, N. Sillero, R. Godinho, R. Cunha, U. Fritz and X. Santos.

Author contributionsS.B.C., S.C., C.M. and H.P.P. designed the study. S.B.C., P.T. and A.P.P. compiled the data. M.B., G.V.-A. and S.C. collected field samples. M.B. carried out the laboratory work. G.V.A. and S.C. performed the molecular analysis. P.T. and S.B.C. performed the lineage’s occurrence analysis. S.B.C. and A.P.P. performed the spatial prioritization analysis. S.B.C. led the writing, to which all authors contributed.

Additional informationSupplementary information is available for this paper.

Reprints and permissions information is available at www.nature.com/reprints.

Correspondence and requests for materials should be addressed to S.B.C.

How to cite this article: Carvalho, S. B. et al. Spatial conservation prioritization of biodiversity spanning the evolutionary continuum Nat. Ecol. Evol. 1, 0151 (2017).

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Competing interestsThe authors declare no competing financial interests.

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