Conservation prioritization for seahorses (Hippocampus spp.) at broad spatial scales considering socioeconomic costs

Biological Conservation 235 (2019) 79–88

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Conservation prioritization for seahorses (Hippocampus spp.) at broad spatial scales considering socioeconomic costs

T

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Xiong Zhang , Amanda C.J. Vincent Department of Zoology, The University of British Columbia, Canada Project Seahorse, Institute for the Oceans and Fisheries, The University of British Columbia, Canada

A R T I C LE I N FO

A B S T R A C T

Keywords: Marine protected area Conservation planning Marxan Rare species Socioeconomic cost Human impact

Identifying priority habitats at broad spatial scales is increasingly required for marine species, which generally have large geographic ranges. However, this is challenging due to the lack of techniques and data. Here we initiated prioritization studies for a genus of flagship species, seahorses (Hippocampus spp.), at a national scale (in China) and worldwide. Our target was to protect at least 2000 km2 area of occupancy (AOO) for each species at minimum costs. We first conducted a gap analysis to examine the coverage of existing marine protected areas (MPAs, both multiple-use MPAs and no-take MPAs) on species' AOO. We then used Marxan, a typical prioritization tool, to set priority habitats for species that didn't meet our target. We did this in different socioeconomic scenarios and overlaid their priority solutions to identify spatial convergence and divergence, representing final solutions for no-take reserves and multiple-use areas, respectively. We compared the utility of Marxan's outputs (best solution vs. selection frequency) in deriving better final solutions (more no-take areas, less patchy, lower cost). Our gap analysis indicated that species' AOOs were mainly covered by multiple-use MPAs, flagging the uncertain efficacy of existing MPAs in protecting seahorses. The two outputs of Marxan derived similar priority solutions, with the selection-frequency output tended to perform better than the bestsolution output. We identified new priority habitats for seahorses to inform MPA establishment in China and worldwide. Our study provides useful techniques to derive marine conservation priorities under different socioeconomic constraints at very broad spatial scales.

1. Introduction To prevent biodiversity loss, conservation prioritization plays a critical role in identifying new protected areas that satisfy a given target with minimum costs (Margules and Pressey, 2000). Usually, the conservation target is to protect a certain amount of distribution range for focal conservation feature (e.g., threatened species; Drummond et al., 2010). The concerned costs may include three components: (1) management costs from enforcing and maintaining the priority habitats (Balmford et al., 2004); (2) transaction costs resulting from negotiating protection (Naidoo et al., 2006); and (3) opportunity costs from foregone revenues (e.g., fisheries values; Cameron et al., 2008). Additionally, human impacts have also been used in prioritization studies (Ban and Klein, 2009). Human impacts can serve as an important dimension for conservation cost, since protecting highly-impacted populations may demand more resources and time to prevent further impacts and to restore the populations. Integrating these different costs into conservation prioritization is crucial for understanding

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stakeholders' interests and setting up feasible protected areas (Klein et al., 2008). Currently, there is an emergent need for marine conservation prioritization (MCP) at broad (e.g., multinational) scales to derive large networks of marine protected areas (MPAs) that may better protect marine species and ecosystems (Mazor et al., 2014). It is acknowledged that conservation initiatives should be based on a clear understanding of the scale at which they are working (Lourie and Vincent, 2004). Marine species usually have large geographic ranges (e.g., over 100,000 km2) and may travel thousands of miles to different habitats throughout their life history. As well, numerous anthropogenic activities (e.g., fishing) often occur at broad spatial scales (e.g., worldwide). Such ecological and social factors require broad-scale conservation planning to identify and protect all priority habitats across the species ranges (Agardy et al., 2011). Marine species covered by large networks of MPAs are also likely more resilient to broad-scale disturbances, which is vital for them to survive and adapt in a changing ocean environment (e.g., climate change; Agardy et al., 2011). Therefore,

Corresponding author at: 2202 Main Mall, Vancouver, BC V6M 1Z4, Canada. E-mail address: [email protected] (X. Zhang).

https://doi.org/10.1016/j.biocon.2019.04.008 Received 20 September 2018; Received in revised form 7 April 2019; Accepted 13 April 2019 0006-3207/ © 2019 Elsevier Ltd. All rights reserved.

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great challenges in practice as wild seahorses are caught and traded widely along China's coast (Zhang and Vincent, 2017). Such high human pressure and consumption demand call for urgent and feasible conservation actions to protect China's seahorses. Recent studies have already mapped distributions and cumulative human impacts for these seahorse populations (Zhang and Vincent, 2017; Zhang and Vincent, 2019). A nationwide conservation prioritization thus comes in time to guide seahorse habitat protections in China. Here we present the first prioritization study for seahorses in China and worldwide to shed light on broad-scale conservation planning for threatened marine species. We chose to do the prioritization first in China as a regional case study to examine our new prioritization approaches, and then apply the approaches further at the global scale. To do so, we compiled species distribution maps and human-pressure maps for seahorses (from our recent studies) and distribution maps for existing marine protected areas (MPAs). We identified seahorse habitats that are covered and uncovered by current MPAs. We then set habitat protection priorities for seahorses, which were not sufficiently covered compared with our target, discriminating different protection levels (limited-use vs. no-take). Our study is directed to inform 1) the management of MPAs which are likely covering seahorse habitats and 2) the establishment of new protected areas for seahorse populations in China and worldwide. By doing so, we expect to examine new approaches to integrating conservation costs in prioritization studies, providing new insights for marine conservation planning at broad spatial scales.

ignoring the importance of broad-scale MCP could cause conservation failures (Agardy et al., 2011). Marine conservation prioritization (MCP) at broad spatial scales is challenging and understudied. Few prioritization tools provide exact priority solutions, as they were normally designed to produce multiple choices for local stakeholders' negotiations at relatively small scales (e.g., in a bay/strait; Ban and Klein, 2009; Pınarbaşı et al., 2017). At broader spatial scales (e.g., nationwide/worldwide), however, such mapping and negotiation can be much more formidable and timeconsuming than at local scales. By far, broad-scale MCP studies are rare and deriving multiple solutions is still a common approach (Ban and Klein, 2009; Mazor et al., 2014). An alternative approach is combining multiple interests into a composite cost layer for ease of the planning (Arafeh-Dalmau et al., 2017; Mazor et al., 2014). However, this requires an evaluation of each cost on monetary value and weighting each cost appropriately, which can be difficult and contentious (Cameron et al., 2008; Ban and Klein, 2009). For instance, a site with low composite cost (e.g., cumulative human impact) can have a high cost for a particular group of stakeholders (e.g., fishers) (Ban and Klein, 2009). Consequentially, strict protection of some priority areas may doom to fail. Without addressing the above issue, priority solutions identified at broad scales can be hardly feasible. Seahorses (Hippocampus spp.) are a genus of flagship fishes whose global conservation prioritization may benefit many other marine species. Given their charismatic appearance and threatened status, seahorses have been used as ‘flagships’ for promoting marine conservation around the word (Vincent et al., 2011). Fourteen seahorse species are known as being threatened (including two Endangered species; IUCN, 2017), calling for urgent conservation measures such as new marine reserves to protect them. Distributing around the world's shallow seas from the temperate to the tropic, seahorses are found in many biodiverse habitats such as seagrass beds, estuaries, mangroves, and coral reefs (Foster and Vincent, 2004). Such geographic and habitat traits imply that protecting seahorse habitats may benefit many other species therein, which indeed has been shown in previous studies (Shokri et al., 2009). Seahorses are traded around the world for traditional medicines, curios, and aquarium fishes (Vincent et al., 2011). As the first marine genus being fully listed on Appendix II of the Convention on International Trade in Endangered Species (CITES) (effective since 2004; Vincent et al., 2011), seahorse conservation planning may help CITES parties better fulfill their obligations (i.e., exports should not cause damages to local seahorse populations) and set an example for other listed species such as sharks and rays. Recent studies have accomplished in mapping global seahorse distributions and their cumulative human impacts (Zhang and Vincent, 2018; Zhang and Vincent, 2019), which provided essential information required for further conservation prioritization. China is one of the countries where seahorses are highly threatened and require immediate and feasible conservation measures (Vincent et al., 2011; Zhang and Vincent, 2017; Zhang and Vincent, 2019). At least six seahorse species are known present in China's coastal waters, and all were considered as threatened (‘Endangered’ or ‘Vulnerable’) by China Red List of Threatened Species (Wang and Xie, 2009). A recent study has also shown that Chinese seahorse populations are under the highest human pressures around the world (Zhang and Vincent, 2019). The heavy use of dried seahorses (~tens of millions per year) for Traditional Chinese Medicine (TCM) is also a big concern (Vincent et al., 2011; Lawson et al., 2017). Chinese government has long been aware of the conservation status of seahorses. One rarest species (i.e., H. kelloggi) has been on the List of Key Wildlife under National Protection (Second Class) since 1988 (MOF and MOA, 1989). Since all seahorses were listed on CITES Appendix II since 2002, Chinese government literally treated all seahorses as protected animals. More recently, China's Ministry of Agriculture has further confirmed that all seahorse species are nationally protected animals (Second Class) given their threatened status and listing on CITES (MOA, 2018). Implementing this protection list faces

2. Methods 2.1. Conservation prioritization framework at broad scales We adapted previous frameworks of systematic conservation planning to identify priorities for seahorse populations in China and worldwide (Fig. A1.1 in Appendix A). This framework contains three major steps: 1) setting conservation feature & target; 2) conservation gap analysis to of current MPA coverage; and 3) identifying priority areas based on analyses of multiple cost scenarios (Margules and Pressey, 2000; McIntosh et al., 2017; see detailed description in Appendix A). A major difference between our framework and the previous ones was that we discriminated the protection levels of MPAs (greaterprotection areas vs. lower-protection areas). By doing so, we expected to better estimate the role of existing MPAs, as illustrated in the following analyses. 2.2. National-scale study for Chinese seahorse populations 2.2.1. Setting conservation feature & target The conservation feature of our national-scale study was the six Chinese seahorse populations (H. histrix, H. kelloggi, H. kuda, H. mohnikei, H. spinosissimus, H. trimaculatus), whose areas of occupancy (AOO) were available. Here, the AOO is defined by the guidelines for using the IUCN Red List criteria (IUCN, 2006). The AOO map of the Spiny seahorse (H. histrix) was from a global-scale study (Zhang and Vincent, 2018), and the AOO maps of the other five species were from a national-scale study (Zhang and Vincent, 2017). Their AOO ranges from 2622 to 387,582 km2. All six species are threatened in China (Wang and Xie, 2009), and globally (IUCN, 2017). Our target was to protect at least 2000 km2 area of occupancy (AOO) for each of the six species. We chose 2000 km2 because this is the threshold of AOO (Criterion B2) below which the IUCN considers species to be threatened, starting with Vulnerable (IUCN, 2006). We treated this threshold as a ‘bottom-line’ target in the absence of detailed information to determine more reasonable targets for seahorse species. However, we acknowledged that meeting this target may be insufficient to protect many marine species (see detailed explanation in Appendix A). Future researchers should determine the target more wisely if related data of are available (e.g. population viability analysis). 80

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Table 1 Five cost scenarios for conservation and management planning for seahorses. Cost surrogates

Total boundary length Cumulative human impact (CHI) Artisanal fisheries (AF) Commercial fisheries (CF) Shipping (SP) Nutrient pollution (NP)

Conservation cost scenarios CHI-cost scenario

AF scenario

CF scenario

SP scenario

NP scenario

√ √

√

√

√

√

√ √ √ √

In each scenario, we ran Marxan with 500 replicates at 1 million or 10 million iterations for each repeat after initial trials of calibration (see guideline by Game and Grantham, 2008). This resulted in 500 solutions that satisfied our target and demonstrated how frequently the planning units contributed to the target. Marxan uses two weighting factors to balance the conservation target and total boundary length (here, management cost): species penalty factor (SPF) and boundary length modifier (BLM). We used an iterative process to determine the values for SPF and BLM to ensure our conservation target was always met in each replicate and priorities were compact and cost-effective (Ardron et al., 2010; Game and Grantham, 2008) (see detailed descriptions in Appendix A). We determined priority solutions for each scenario based on two different outputs of Marxan (i.e., best solution vs. selection frequency) in each scenario. The best solution is an ‘optimal’ solution that meets the target with the least cost among the 500 replicates in determining whether a planning unit should be selected. This ‘optimal’ solution does not necessarily ensure the best priority system and it may only marginally better than the other solutions (Game and Grantham, 2008). The selection frequency, on the other hand, records the number of times (0–500) of each planning unit being selected among the 500 replicates. Therefore, it requires users to determine the priority solution based on a frequency threshold. Here we applied an iterative process to identify such a threshold that ensured the conservation targets were met with the minimum number of planning units (see detailed explanation in Appendix A). By doing so, we converted the selection frequency into a binary map (‘priority’ and ‘non-priority’, 1 and 0), the same as the bestsolution map. We compared the five scenarios (Table 1) and the two solution approaches (selection frequency vs. best solution). We first compared the cumulative-cost (i.e., CHI-cost) scenario and each of the other four scenarios based on three measures: 1) Spearman's rank correlation on the selection frequency, 2) Cohen's kappa statistic (function kappa2 in R package irr, Gamer et al., 2017) on the priority solution derived from the selection frequency (Fielding and Bell, 1997), and 3) Cohen's kappa statistic on the best solution. Here the kappa statistic measures the spatial agreement of priority solutions between two scenarios, while correcting for random effects (Cutler et al., 2007). We also used this statistic to examine the extent of spatial agreement between the two priority solutions (selection frequency and best solution) in each scenario. We determined the final priority solution based on the solution approach (selection frequency vs. best solution) that derived more spatial convergences among all scenarios. Based on such a solution, we identified the planning units that were agreed by all scenarios and units that were not. The former represented units with the highest priorities considering our full set of human uses, and thus were considered as priorities for greater-protection areas: prohibiting demersal non-selective fishing, artisanal fishing, shipping, and nutrient pollution. In contrast, the latter represented conflict zones and was considered as priority sites for lower-protection areas (e.g., multiple-use zones). The permitted types of human uses could be determined by which socioeconomic scenario did not select the focal units as priority habitats.

2.2.2. Conservation gap analysis We quantified the ‘protected’ area of occupancy (AOO) for each species and compared it against the target value (2000 km2). To do so, we first gathered China's MPA and existing spatial planning data from local sources (see Table A1.1 in Appendix A). China's coastal provinces/ municipalities have initiated ‘marine functional zoning’ and ‘ecologicalredline’ planning in recent years (Lu et al., 2015). These plans aimed to protect important coastal habitats and have outlined restricted areas (including existing MPAs) for human activities. Based on this information, we categorized habitats with the fully no-take attribute as greater-protection areas and other partially-restricted areas as lowerprotection areas. The latter (i.e., lower-protection area) allows limited types of human uses (e.g., fisheries) but prohibit constructions (e.g., sea filling) and pollutions (Lu et al., 2015). We then estimated the proportion of each seahorse's AOO covered by greater-protection areas and lower-protection areas separately, as well as by all protected areas together (i.e., greater-protection areas plus lower-protection areas).

2.2.3. Setting conservation priorities For those species whose MPA coverage was smaller than the bottomline target, we conducted conservation planning in five scenarios (Table 1). The planning units were the AOO cells (resolution: 1 × 1 km2) of each species that were not protected by existing MPAs. The first scenario was composed of the management cost and the cumulative human impact (CHI; hereafter, cumulative cost). Four alternative scenarios replaced the cumulative cost with one of the socioeconomic surrogates (Table 1), respectively: (1) commercial (largescale) fisheries, (2) artisanal (small-scale) fisheries, (3) shipping, and (4) (land-based) nutrient pollution. Management cost, including establishment and maintenance costs, is positively correlated with the size of the MPA, although the relationship is usually nonlinear (Balmford et al., 2004; McCrea-Strub et al., 2011). We used the total boundary length of the selected planning units as the surrogate of management cost (Balmford et al., 2004; Ban and Klein, 2009; Possingham et al., 2000). We derived the boundary data with the ArcMarxan Toolbox based on the default setting in ArcMap. The cumulative-human-impact (CHI) dataset was derived from a global-scale analysis of CHI for seahorse species (Zhang and Vincent, 2019), and the other four types of opportunity costs were estimated based on the intensity of each human activity (Halpern et al., 2015). Among them, commercial fisheries were model estimates of the catch from demersal non-selective fisheries (e.g., bottom trawling and purse seine), which exerted the major pressures on seahorses that live close to the seafloor. We extracted above cost layers (resolution: 1 × 1 km2) by the planning units to derive cost values for each unit (see detailed descriptions in Appendix A). We used the Marxan software (version 2.3.4) to configure priorities that minimize conservation costs in the above five scenarios (Ball et al., 2009). Marxan is one of the most widely used prioritization tools that allow researchers to incorporate socioeconomic constraints in planning. Compared with other tools, Marxan has the capacity to handle large datasets (Leslie et al., 2003), as in our study. The simulated annealing algorithm and iterative improvement features were chosen in Marxan. 81

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2.3. Global-scale study for seahorse species

AOO (47% out of the 944 planning units) were selected as greaterprotection areas, and 219 km2 AOO as lower-protection areas in Hainan and Taiwan (Fig. 2).

We followed the same framework described above to conduct a global-scale analysis. Our focus was the 33 seahorse species whose area of occupancy (AOO) was available and ranges from 64 to 1,928,620 km2 (Zhang and Vincent, 2018). Among them, six rarest species have an AOO smaller than 2000 km2. The target was protecting all the AOO of the six rarest species, and at least 2000 km2 AOO for each of the other 27 species. We analyzed the conservation gaps for each species. To do so, we gathered global MPA data from the World Database on Protected Areas (UNEP-WCMC and IUCN, 2018), with China's MPA data from local sources as mentioned above. We then categorized them into two groups as we did in China: greater-protection areas and lesser-protection areas. The greater-protection areas (n = 3313) contained all MPAs with IUCN Categories I – IV and MPAs without IUCN Categories that were claimed to be entirely ‘no-take’. Protected areas with IUCN Categories I – IV are commonly considered as strict marine reserves (Jenkins and Van Houtan, 2016), although their protective efficacy may vary. The lowerprotection areas (n = 6520) contained all remaining MPAs, including those with IUCN Categories V and VI, which were created for sustainable multiple uses (e.g., recreation and tourism) (Dudley, 2008). The greater-protection-area and lower-protection-area maps from China were also added to the global categories respectively. We removed portions of MPA maps beyond marine boundaries, and combined overlapping polygons to avoid overestimates in the coverage analysis. We followed the approach described in 2.2.3 to identify priorities for species whose protected AOO fell short of the target value. The planning unit size was kept the same. Cost data were from the same sources.

3.2. Global-scale conservation gaps and priorities for seahorses We found that although all 33 species had some parts (10–128,299 km2, or 2–85%) of their AOO covered by existing MPAs, this was largely shaped by lower-protection areas (B.4 in Appendix B). Generally, significantly more AOO were covered by lower-protection areas than by greater-protection areas (paired Wilcoxon test, P < 0.001; Fig. B.3a). Indeed, greater-protection areas only accounted for 35% of the overall protected AOO of seahorses. The protected AOO largely consisted of small patches (area < 10 km2), itself a conservation concern. We found that threatened species had significantly more protected AOO (absolute value per species) but lower percentage of protected AOO (relative value per species) than did non-threatened species (Wilcoxon tests, P < 0.05). Further tests revealed that AOO protected by greater-protection areas was nearly significantly different between the two groups (threatened vs. non-threatened, Wilcoxon test, P = 0.06; Fig. B.3b), and AOO protected by lower-protection areas significantly varied between the two (Wilcoxon test, P < 0.05; Fig. B.3c). Interestingly, however, the ratio of the protected AOO to all AOO (per species) for threatened species was in fact significantly lower than that for non-threatened species (Wilcoxon test, P < 0.05). We identified nine species whose protected AOO was lower than the target value (2000 km2; Table B.4). Among the nine species, the six rarest species were not included in habitat prioritization with Marxan, given each of them had an AOO already smaller than 2000 km2 (Table B.4). We added all unprotected AOOs of the six rarest species into the map of global conservation priorities. That left only three species for habitat prioritization analyses in Marxan: one Southeast Asian species (H. barbouri) and two sympatric Australian species (H. abdominalis and H. minotaur). We conducted the prioritization for H. barbouri alone as it does not share habitats with the other two species, whose prioritization was done together. In the end, we derived a total of 20 priority solutions (2 regions × 5 scenarios × 2 priority-selection approaches). Our comparison results showed that the agreement level between solutions of the cumulative-cost scenario and those of alternative scenarios varied in different cases (Table 2). In the case of H. barbouri, selection frequency of the cumulative-cost scenario was generally similar to that of artisanal-fisheries, commercial-fisheries, shipping, and nutrient-pollution scenarios, in decreasing order of similarity (r = 0.86–0.60; see Fig. B.4 in Appendix B). In the case of two Australian seahorses (H. abdominalis and H. minotaur), selection frequency of the cumulative-cost scenario was generally different from that of artisanal-fisheries, nutrient-pollution, commercial-fisheries, and shipping scenarios, in decreasing order of similarity (r = 0.63–0.25; see Fig. B.5 in Appendix B). The priority solutions (i.e., selected priority units) based on the selection frequency and the best solution were generally similar, but each derived a better solution in one of the two cases. Compared with the best solution, the selection frequency derived generally higher convergences (i.e., higher Cohen's kappa statistics) between cumulative-cost scenario and alternative scenarios (Kappa-f vs. Kappa-b in Table 2). For H. barbouri, the selection frequency derived more priority units agreed by all scenarios than did the best solution (Table B.2, also see Fig. B.6 vs. B.7 in Appendix B). The priority solution based on the selection frequency also contained lower cumulative cost, shorter total boundary length, and fewer patches than did the best solution (Table B.2). For the two Australian species, no priorities were agreed by all five scenarios based on the selection frequency or the best solution (Table B.2, also see Fig. B.8 vs. Fig. B.9 in Appendix B). The priority solution based on the selection frequency contained a slightly lower cumulative human impact than the best solution (Table B.2). However, the former

3. Results 3.1. National-scale conservation gaps and priorities for Chinese seahorses We found that the bottom-line target was met by current MPAs in China for all six species, except the rarest one, H. histrix (protected area of occupancy < 2000 km2, Table B.1). For all species, the area of occupancy (AOO) covered by greater-protection areas (10,380 km2) only accounted for 16% of the overall protected AOO (here, shared areas of greater-protection areas or AOO between/among species were only counted once). The unprotected AOO units of H. histrix were located in coastal waters of Hainan and Taiwan Provinces, where we further set priorities. For H. histrix, the cumulative-cost scenario (Fig. 1a and f) had high correlations with all alternative scenarios in terms of selection frequency (Table 2), with the highest correlation with the artisanal-fisheries scenario (r = 0.95, Fig. 1b and g), followed by the nutrient-pollution (r = 0.81, Fig. 1c and h), shipping (r = 0.77, Fig. 1d and i), and commercial-fisheries scenarios (r = 0.76; Fig. 1e and j). The selection-frequency approach performed better than the bestsolution approach in determining priority solutions. Compared with the best solution, the selection frequency derived priorities with higher convergence between cumulative-cost and alternative scenarios (Kappa-f > Kappa-b in Table 2). Consequentially, more priorities were selected as greater-protection areas based on the selection frequency than based on the best solution (Table B.2, Figs. B.1 and B.2 in Appendix B). The priority areas based on the selection frequency also contained lower cumulative human impacts, shorter total boundary length, and fewer patches than based on the best solution (Table B.2). Overall, there were medium to high extents of consistency between the two types of priority solutions (selection frequency vs. best solution) in each scenario (Cohen's kappa = 0.645–0.806, all P < 0.001, Table B.3 in Appendix B). Given the above comparison, we chose the priorities derived based on the selection-frequency approach for H. histrix. A total of 447 km2 82

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Fig. 1. Selection frequency of planning units for the Chinese seahorse population of Hippocampus histrix in Hainan (a – e) and Taiwan (f – j) for five cost scenarios: cumulative-human-impact cost (CHI-cost scenario, (a) and (f)), artisanal fisheries (AF scenario, (b) and (g)), nutrient pollution (NP scenario, (c) and (h)), shipping (SP scenario, (d) and (i)), and commercial fisheries (CF scenario, (e) and (j)). Selection frequency was classified into four categories based on the quantiles in the distribution of the selection frequency.

(i.e., 53% out of the 3156 planning units) in the Philippines and northeast Malaysia were selected as priorities by the cumulative-cost scenario (Fig. 3). Within these priorities, 828 km2 area was also selected by all four socioeconomic scenarios (i.e., spatial convergence) and thus were considered as priorities for greater-protection areas (Fig. 3). The remaining 844 km2 habitats selected by the cumulative-cost scenario were not consistently selected by the four alternative scenarios, and thus were considered as priorities for lower-protection areas (Fig. 3 and Fig. B.7 in Appendix B). For H. abdominalis and H. minotaur, a total of

contained longer total boundary length and more patches than the latter for the Australian species (Table B.2). Similar to the nationalscale result, we found medium to high extents of consistency between the two types of priority solutions (selection frequency vs. best solution) within different scenarios (Cohen's kappa = 0.622–0.927, all P < 0.001; Table B.3). Based on the above comparison, we chose the selection-frequency approach to set priorities for H. barbouri, and the best-solution approach for the two Australian species. For H. barbouri, a total of 1672 km2 area

Table 2 Comparisons on prioritization results between the cumulative-cost (i.e., cumulative-human-impact-cost or CHI-cost) scenario and other four scenarios: artisanal fisheries, commercial fisheries, nutrient pollution, and shipping. The prioritization was done in three regions: China (for H. histrix), Southeast Asia (for Hippocampus barbouri), and Australia (for H. abdominalis and H. minotaur). r, Spearman rank correlation coefficient of the pairwise selection frequencies; Kappa-f, Cohen's kappa statistic of the pairwise binary solutions based on the selection frequency data; Kappa-b, Cohen's kappa statistic of the pairwise binary solutions based on the best solution data. Alternative scenarios

Artisanal fisheries Commercial fisheries Nutrient pollution Shipping ⁎

CHI-cost scenario (China)

CHI-cost scenario (Southeast Asia)

CHI-cost scenario (Australia)

r

Kappa-f

Kappa-b

r

Kappa-f

Kappa-b

r

Kappa-f

Kappa-b

0.950 0.760 0.810 0.770

0.860 0.800 0.600 0.710

0.715 0.639 0.515 0.559

0.606 0.446 0.426 0.526

0.630 0.280 0.520 0.250

0.433 0.611 0.491 −0.027

0.307 −0.009⁎ 0.346 −0.026

0.724 0.553 0.625 0.365

0.484 0.372 0.443 0.458

NOT statistically significant (P > 0.05). 83

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Fig. 2. Conservation priorities for Chinese seahorses including habitats covered by current marine protected areas, i.e., greater-protection areas (GPAs) and lowerprotection areas (LPAs), and selected priorities for GPAs and LPAs based on the selection frequency in Marxan.

1415 km2 (13% out of the 10,685 planning units) in southeast Australia were selected by the cumulative-cost scenario but not agreed by alternative scenarios (Fig. 3 and Fig. B.8). These habitats could only be considered as priorities for lower-protection areas. The global priority map for the 33 seahorse species showed that more protected areas are required in the Indo-Pacific region (Fig. 4, also see details from Figs. B.10 - B.35 in Appendix B). In addition to the priorities mentioned above, the map also included unprotected AOO of the six rarest species in Hawaii (US), George to Plettenberg Bay (South Africa), Shark Bay (Western Australia), Mandurah to Perth (Western Australia), the Coral Triangle region, and Shizuoka (Japan) (see detailed maps in Figs. B.10 – B.35).

converting the ‘selection frequency’ into a binary priority solution (Mazor et al., 2014; Solovyev et al., 2017), we applied an iterative approach to determine a threshold that could ensure the derived solution meet the conservation target at a minimum cost. This approach produced similar and even better solutions than Marxan's ‘best solution’, highlighting its robustness. Furthermore, we used overlap analyses to address the difficulty of deriving final solutions from multiple socioeconomic scenarios with Marxan. Such spatial analyses helped us to identify specific priorities for both no-take reserves and multiple-use MPAs. These novel approaches may facilitate the application of Marxan at broad spatial scales, which is understudied in the literature (Ban and Klein, 2009; Arafeh-Dalmau et al., 2017).

4. Discussion

4.1. Conservation priorities and implications for seahorses

We derived novel knowledge for seahorse and marine conservation, and shed some encouraging light in setting conservation priorities for threatened marine species at very broad spatial scales. Our study indicated that the rarest species (in terms of area of occupancy) were the least covered by current MPAs, in concert with a recent study on scleractinian corals and labrid fishes (Mouillot et al., 2016). This suggests that there is a significant conservation gap for protecting rare and evolutionarily distinctive organisms in the ocean. We also highlighted that seahorse populations covered by current MPAs were mainly located in the lower-protection areas (i.e., multiple-use zones), flagging the effectiveness uncertainty of existing MPA networks (Gill et al., 2017). In line with previous studies, we showed that Marxan can be a powerful tool to derive conservation priorities at broad scales (Mazor et al., 2014). Importantly, our study has made several advancements. First, unlike previous studies that used arbitrary thresholds for

Our results suggest that existing MPAs are likely playing a very limited role in protecting global seahorse biodiversity (Gill et al., 2017). Given that ~ 45% seahorse species are threatened primarily by nonselective bottom fishing including destructive ones and pollutions (IUCN, 2017; Zhang and Vincent, 2019), existing MPAs that prevent these threats may play a vital role in safeguarding seahorses. However, we showed that this might not be the case as current greater-protection areas covered much smaller seahorse habitats than did lower-protection areas. What's worse is that threatened seahorse species have lower percentages of protected habitats than their non-threatened congeners. Hitherto the usefulness of different MPAs in protecting seahorses has been understudied. One rare case in Port Stephens (NSW, Australia) indicated that seahorses were more abundant in habitat-protection zones (destructive fishing banned) than in no-take reserves (Harasti et al., 2014). This finding might partly result from that no-take reserves 84

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Fig. 3. Conservation priorities for the seahorse species in (a) the Philippines and (b) southeastern Australia, including habitats covered by existing lower-protection areas (LPAs) and greater-protection areas (GPAs), habitats not yet in MPAs for six rarest species (here in (a), H. denise), and identified priorities for LPAs and GPAs in Marxan.

Fig. 4. Global conservation priorities for 33 seahorse species, including habitats covered by lower-protection areas (LPAs) and greater-protection areas (GPAs), habitats falling outside MPAs for six rarest species, supplementary priorities for LPAs and GPAs. Note that conservation priorities in China are included in the map, and the sizes of the red and purple areas were manually increased to make them more outstanding. See more smaller scale maps from Figs. B.10 - B.35 in Appendix B, or view our identified priorities in figshare database under the project folder “Global Priorities for Seahorse Species”. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 85

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also protected more seahorse predators (e.g., octopus) from fishing, as shown by the researchers. It might suggest that seahorses may benefit more from MPAs with habitat-protection objectives than from no-take reserves. However, this interpretation drawn from a single study remains uncertain, given seahorses are good at camouflage and their natural enemies are believed rare (Vincent et al., 2011). Currently, the management effectiveness of existing MPAs (especially multiple-use MPAs) also remains uncertain and possibly low (Gill et al., 2017). We recommend researchers conduct further evaluations of habitat-protection effectiveness of existing MPAs covering seahorse habitats. Additionally, assessing threats management outside MPAs (e.g., water pollution control) is also vital to understand the true protection capacity of MPAs (Wenger et al., 2016). We identified important conservation priorities for protecting seahorse species at broad spatial scales. The identified priorities for the Spiny seahorse (H. histrix) in Hainan and Taiwan suggest that local governments have not included some priority habitats in their marine conservation plans, and thus they should be addressed in future projects (e.g., eco-redline in China; Lu et al., 2015). At the global scale, we should pay more attention to the six less-protected and rarest species. They contain the ‘Endangered’ Knysna seahorse (H. capensis) and five potentially threatened species (IUCN, 2017; Zhang and Vincent, 2018). Conservation measures are demanded to mitigate human pressures in both protected and unprotected habitats of these six species. For instance, demersal destructive fishing and pollutions should be eliminated near the gorganian coral habitats that the Denise's pygmy seahorse (H. denise) is highly specialized to (Lourie and Randall, 2003). Otherwise, protecting permanent artificial habitats (e.g., swimming nets, Reno mattresses) may also benefit some species such as H. subelongatus (Clynick, 2008) and H. capensis (Claassens et al., 2018). Meanwhile, we indicated that new feasible MPAs (including no-take reserves) for H. barbouri are required in some coastal waters of the Malaysian state of Sabah and the Philippines, where seahorse species are rich (Zhang and Vincent, 2018). For the two Australian species, the absence of spatial agreements among socioeconomic scenarios is in line with the reported intensive conflicts between conservation and human activities (e.g., fishing) in the region (e.g., New South Wales) (Gladstone, 2014). Local managers may then need to set up habitatprotection areas while allowing some levels of multiple uses in these priorities as shown in our results.

4.3. Novel approaches to deriving feasible priority solutions from Marxan at broad-spatial scales We provide a novel approach to determining an exact priority solution from Marxan, filling the technique gap in setting feasible conservation priorities at broad spatial scales (Ban and Klein, 2009; Mazor et al., 2014). First, to derive priority solutions from the ‘selection frequency’, previous studies often chose arbitrary thresholds (e.g., frequency > 50%) (Mazor et al., 2014; Solovyev et al., 2017), which could not ensure all conservation targets are met or the conservation costs are minimized. We are among the first to address this issue accordingly by choosing thresholds that could meet the target while minimizing the cost with an iterative process. Second, previous studies have determined priorities from the selection frequency and/or from the best solution (Ardron et al., 2010; Solovyev et al., 2017), and rarely compared the two. We are among the first to use quantitative statistics to compare the two outputs (selection frequency vs. best solution) and demonstrated that they could derive moderately to highly similar results. It has been highlighted that using the single ‘best solution’ from multiple runs can be risky (Game and Grantham, 2008; Ardron et al., 2010). Another set of runs may derive a different version of best solution since the commonly-used algorithm of Marxan (i.e., simulated annealing) is finding near-optimal rather than absolutely the best solution (Possingham et al., 2000). Using ‘selection frequency’ is perhaps more meaningful when Marxan is often finding near-optimal solutions in multiple runs, which requires an appropriate setting of parameters as we did in our study (e.g., high SPF value; Ardron et al., 2010). These fundamental differences may explain why the ‘selection frequency’ derived more cost-effective priorities than the ‘best solution’ in two of the total three prioritization cases. We recommend future researchers compare both outputs as we did and identify a better option for specific cases. 4.4. Advancements in marine conservation prioritization at broad spatial scales We advanced previous conservation-prioritization frameworks by setting priorities for both strict management (i.e., greater protection) and multiple uses (i.e., lower protection) at broad spatial scales (Klein et al., 2010). Usually, marine conservation needs both no-take reserves to protect the entire ecosystem therein (Costello and Ballantine, 2015), and partially protected zones that allow resource uses and safeguard some focal species (Hilborn, 2016). By setting priorities for both areas, planners can establish a network of protected areas that work for both the species and local citizens that rely on marine resources. The Marxan with Zones (a.k.a., MarZone) can do comparable work (Watts et al., 2009). However, MarZone requires planners to define specific targets for socioeconomic interests, which is beyond our study's scope and could be data-demanding in other cases. Our approach is thus more applicable than MarZone when socioeconomic targets cannot be determined at broad spatial scales. We address the technique gap in identifying broad-scale conservation priorities by using spatial overlap analyses on multiple socioeconomic scenarios (Ban and Klein, 2009; Arafeh-Dalmau et al., 2017). To set conservation priorities, it will work best if planners present an ideal conservation scenario against different socioeconomic scenarios (Cameron et al., 2008; Ban and Klein, 2009; Mazor et al., 2014), as we did in our study. This enables planners to explore their commonalities and differences with stakeholders. Previous studies at smaller spatial scales have also used multiple-scenario approaches to help stakeholders better understand and reconcile each other's interest, with an expectation of finding feasible priority solutions (Ban and Klein, 2009). Unlike these local-scale studies, we identified priority solutions based on spatial convergences and divergences among different cost scenarios, which might provide a practical and clear comparison among different stakeholder's interests at large spatial scales. The map of

4.2. Caveats and recommendations for future studies Although we initiated conservation prioritization for seahorses at broad spatial scales, there are several caveats that future studies are encouraged to address. First, the conservation target we used in the study is merely a baseline value based on IUCN Red List criterion B2 (IUCN, 2006). More meaningful conservation targets should be determined through in-depth analyses (e.g., population viability analyses) when related population data become available (Carroll et al., 2003). Second, due to data paucity, our study could not integrate some important factors in spatial conservation planning, such as population connectivity and climate change impacts (Agardy et al., 2011; Magris et al., 2014). Such factors usually interact with large-scale processes (e.g., population dispersion) and thus should be considered in marine conservation planning at broad spatial scales. It requires future ecological studies in quantifying these processes (e.g., pelagic-stage length) for many data-poor species, and technique advances in integrating the related elements in conservation targets of prioritization tools (Magris et al., 2014). Third, our maps of species' area of occupancy and socioeconomic costs are from model estimates and thus require field observations to validate and improve them. Mapping distributions of marine species and socioeconomic costs are vital for broad-scale marine conservation planning, and these are very active research fields (Levin et al., 2014; Mazor et al., 2014). 86

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convergence and divergences based on this comparison demonstrated clear agreements and conflicts between conservation and human uses in the sea. In other conservation circumstances, planners might prefer to combine multiple costs into one cost for ease of the planning. However, such a combination is very challenging, because weighting each cost appropriately is often difficult or contentious (Cameron et al., 2008; Ban and Klein, 2009). Using our overlap analyses could avoid such problems.

no-take zones led to cuts in New South Wales' (Australia) system of marine protected areas. Aquat. Conserv. Mar. Freshwat. Ecosyst. 24, 287–296. Halpern, B.S., Frazier, M., Potapenko, J., Casey, K.S., Koenig, K., Longo, C., Lowndes, J.S., Rockwood, R.C., Selig, E.R., Selkoe, K.A., 2015. Spatial and temporal changes in cumulative human impacts on the world's ocean. Nat. Commun. 6, 7615. Harasti, D., Martin-Smith, K., Gladstone, W., 2014. Does a no-take marine protected area benefit seahorses? PLoS One 9, e105462. Hilborn, R., 2016. Marine biodiversity needs more than protection. Nature 535, 224. IUCN, 2006. Guidelines for using the IUCN RedList categories and criteria. Version 6.2. Prepared by the standards and petitions working group of the IUCN SSC biodiversity assessments sub-committee in December 2006. Downloadable from. http://app.iucn. org/webfiles/doc/SSC/RedList/RedListGuidelines.pdf. IUCN, 2017. The IUCN Red List of Threatened Species. Version 2017–3. http://www. iucnredlist.org (Downloaded on 05 December 2017). Jenkins, C.N., Van Houtan, K.S., 2016. Global and regional priorities for marine biodiversity protection. Biol. Conserv. 204, 333–339. https://doi.org/10.1016/j.biocon. 2016.10.005. Klein, C.J., Chan, A., Kircher, L., Cundiff, A.J., Gardner, N., Hrovat, Y., Scholz, A., Kendall, B.E., Airame, S., 2008. Striking a balance between biodiversity conservation and socioeconomic viability in the design of marine protected areas. Conserv. Biol. 22, 691–700. Klein, C.J., Steinback, C., Watts, M., Scholz, A.J., Possingham, H.P., 2010. Spatial marine zoning for fisheries and conservation. Front. Ecol. Environ. 8, 349–353. https://doi. org/10.1890/090047. Lawson, J.M., Foster, S.J., Vincent, A.C., 2017. Low bycatch rates add up to big numbers for a genus of small fishes. Fisheries 42, 19–33. Leslie, H., Ruckelshaus, M., Ball, I.R., Andelman, S., Possingham, H.P., 2003. Using siting algorithms in the design of marine reserve networks. Ecol. Appl. 13, 185–198. Levin, N., Coll, M., Fraschetti, S., Gal, G., Giakoumi, S., Göke, C., Heymans, J.J., Katsanevakis, S., Mazor, T., Öztürk, B., 2014. Biodiversity data requirements for systematic conservation planning in the Mediterranean Sea. Mar. Ecol. Prog. Ser. 508, 261–281. Lourie, S.A., Randall, J.E., 2003. A new pygmy seahorse, Hippocampus denise (Teleostei Syngnathidae) from the Indo-Pacific. Zool. Stud. 42, 284–291. Lourie, S.A., Vincent, A.C., 2004. A marine fish follows Wallace's line: the phylogeography of the three-spot seahorse (Hippocampus trimaculatus, Syngnathidae, Teleostei) in Southeast Asia. J. Biogeogr. 31, 1975–1985. Lu, W.-H., Liu, J., Xiang, X.-Q., Song, W.-L., McIlgorm, A., 2015. A comparison of marine spatial planning approaches in China: marine functional zoning and the marine ecological red line. Mar. Policy 62, 94–101. Magris, R.A., Pressey, R.L., Weeks, R., Ban, N.C., 2014. Integrating connectivity and climate change into marine conservation planning. Biol. Conserv. 170, 207–221. Margules, C.R., Pressey, R.L., 2000. Systematic conservation planning. Nature 405, 243–253. https://doi.org/10.1038/35012251. Mazor, T., Giakoumi, S., Kark, S., Possingham, H.P., 2014. Large-scale conservation planning in a multinational marine environment: cost matters. Ecol. Appl. 24, 1115–1130. McCrea-Strub, A., Zeller, D., Sumaila, U.R., Nelson, J., Balmford, A., Pauly, D., 2011. Understanding the cost of establishing marine protected areas. Mar. Policy 35, 1–9. McIntosh, E.J., Pressey, R.L., Lloyd, S., Smith, R.J., Grenyer, R., 2017. The impact of systematic conservation planning. Annu. Rev. Environ. Resour. 42. MOA, 2018. Circular about the List of Aquatic Species on the Appendices of the Convention on International Trade in Endangered Species. Issued by the. Ministry of Agriculture and Rural Affairs of the People's Republic of China (MOA) (Accessed on 8 Dec. 2018). MOF, MOA, 1989. List of Key Wildlife under National Protection. Order issued by the Ministry of Forestry (MOF), and the Ministry of Agriculture (MOA), P.R.C. http:// www.forestry.gov.cn/main/3954/content-1063883.html. Mouillot, D., Parravicini, V., Bellwood, D.R., Leprieur, F., Huang, D., Cowman, P.F., Albouy, C., Hughes, T.P., Thuiller, W., Guilhaumon, F., 2016. Global marine protected areas do not secure the evolutionary history of tropical corals and fishes. Nat. Commun. 7, 10359. Naidoo, R., Balmford, A., Ferraro, P.J., Polasky, S., Ricketts, T.H., Rouget, M., 2006. Integrating economic costs into conservation planning. Trends Ecol. Evol. 21, 681–687. Pınarbaşı, K., Galparsoro, I., Borja, Á., Stelzenmüller, V., Ehler, C.N., Gimpel, A., 2017. Decision support tools in marine spatial planning: present applications, gaps and future perspectives. Mar. Policy 83, 83–91. Possingham, H., Ball, I., Andelman, S., 2000. Mathematical methods for identifying representative reserve networks. In: Quantitative Methods for Conservation Biology. Springer, pp. 291–306. Shokri, M.R., Gladstone, W., Jelbart, J., 2009. The effectiveness of seahorses and pipefish (Pisces: Syngnathidae) as a flagship group to evaluate the conservation value of estuarine seagrass beds. Aquat. Conserv. Mar. Freshwat. Ecosyst. 19, 588–595. Solovyev, B., Spiridonov, V., Onufrenya, I., Belikov, S., Chernova, N., Dobrynin, D., Gavrilo, M., Glazov, D., Krasnov, Y., Mukharamova, S., 2017. Identifying a network of priority areas for conservation in the Arctic seas: practical lessons from Russia. Aquat. Conserv. Mar. Freshwat. Ecosyst. 27, 30–51. UNEP-WCMC, IUCN, 2018. Protected Planet: [insert name of component database; The World Database on Protected Areas (WDPA)/The Global Database on Protected Areas Management Effectiveness (GD-PAME)] [On-line], [Insert Month/Year of the Version Downloaded]. UNEP-WCMC and IUCN, Cambridge, UK Available at. www. protectedplanet.net. Vincent, A.C.J., Foster, S.J., Koldewey, H.J., 2011. Conservation and management of seahorses and other Syngnathidae. J. Fish Biol. 78, 1681–1724. https://doi.org/10. 1111/j.1095-8649.2011.03003.x.

Acknowledgements This is a contribution from Project Seahorse. We thank Dr. William Cheung, Dr. Christopher Harley, and Dr. Sarah Gergel for their constructive comments on the primitive manuscript. Many thanks to Alessia Kockel for her advice of using Marxan software. XZ was supported by a Chinese Doctoral Scholarship from the China Scholarship Council. Project Seahorse is supported by Guylian Chocolates (Belgium) in a partnership for marine conservation. Appendix A. Supplementary data for Methods and Results Supplementary data to this article can be found online at https:// doi.org/10.1016/j.biocon.2019.04.008. References Agardy, T., di Sciara, G.N., Christie, P., 2011. Mind the gap: addressing the shortcomings of marine protected areas through large scale marine spatial planning. Mar. Policy 35, 226–232. Arafeh-Dalmau, N., Torres-Moye, G., Seingier, G., Montaño-Moctezuma, G., Micheli, F., 2017. Marine spatial planning in a transboundary context: linking Baja California with California's network of marine protected areas. Front. Mar. Sci. 4, 150. Ardron, J., Possingham, H.P., Klein, C., 2010. Marxan Good Practices Handbook, Version 2. Pacific Marine Analysis and Research Association. Ball, I.R., Possingham, H.P., Watts, M., 2009. Marxan and relatives: Software for spatial conservation prioritisation. In: Spat. Conserv. Prioritisation Quant. Methods Comput. Tools, pp. 185–195. Balmford, A., Gravestock, P., Hockley, N., McClean, C.J., Roberts, C.M., 2004. The worldwide costs of marine protected areas. Proc. Natl. Acad. Sci. 101, 9694–9697. Ban, N.C., Klein, C.J., 2009. Spatial socioeconomic data as a cost in systematic marine conservation planning. Conserv. Lett. 2, 206–215. Cameron, S.E., Williams, K.J., Mitchell, D.K., 2008. Efficiency and concordance of alternative methods for minimizing opportunity costs in conservation planning. Conserv. Biol. 22, 886–896. Carroll, C., Noss, R.F., Paquet, P.C., Schumaker, N.H., 2003. Use of population viability analysis and reserve selection algorithms in regional conservation plans. Ecol. Appl. 13, 1773–1789. Claassens, L., Booth, A.J., Hodgson, A.N., 2018. An endangered seahorse selectively chooses an artificial structure. Environ. Biol. Fish 1–11. Clynick, B., 2008. Harbour swimming nets: a novel habitat for seahorses. Aquat. Conserv. Mar. Freshwat. Ecosyst. 18, 483–492. Costello, M.J., Ballantine, B., 2015. Biodiversity conservation should focus on no-take Marine Reserves: 94% of Marine Protected Areas allow fishing. Trends Ecol. Evol. 30, 507–509. Cutler, D.R., Edwards, T.C., Beard, K.H., Cutler, A., Hess, K.T., Gibson, J., Lawler, J.J., 2007. Random forests for classification in ecology. Ecology 88, 2783–2792. https:// doi.org/10.1890/07-0539.1. Drummond, S.P., Wilson, K.A., Meijaard, E., Watts, M., Dennis, R., Christy, L., Possingham, H.P., 2010. Influence of a threatened-species focus on conservation planning. Conserv. Biol. 24, 441–449. Dudley, N., 2008. Guidelines for Applying Protected Area Management Categories. Gland, Switzerland: IUCN. X + 86pp. https://doi.org/10.1007/s10666-005-9001-y. Fielding, A.H., Bell, J.F., 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24, 38–49. Foster, S.J., Vincent, A.C.J., 2004. Life history and ecology of seahorses: implications for conservation and management. J. Fish Biol. 65, 1–61. https://doi.org/10.1111/j. 1095-8649.2004.00429.x. Game, E.T., Grantham, H.S., 2008. Marxan User Manual: For Marxan Version 1.8. 10. Aust. Univ. Queensland, Queensland (St. Lucia). Gamer, M., Lemon, J., Singh, I., 2017. Irr: Various Coefficients of Interrater Reliability and Agreement. 2012. R Package Version 0.84. Gill, D.A., Mascia, M.B., Ahmadia, G.N., Glew, L., Lester, S.E., Barnes, M., Craigie, I., Darling, E.S., Free, C.M., Geldmann, J., Holst, S., Jensen, O.P., White, A.T., Basurto, X., Coad, L., Gates, R.D., Guannel, G., Mumby, P.J., Thomas, H., Whitmee, S., Woodley, S., Fox, H.E., 2017. Capacity shortfalls hinder the performance of marine protected areas globally. Nature 543, 665–682. https://doi.org/10.1038/ nature21708. Gladstone, W., 2014. Criticisms of science, social impacts, opinion leaders, and targets for

87

Biological Conservation 235 (2019) 79–88

X. Zhang and A.C.J. Vincent

Zhang, X., Vincent, A.C.J., 2017. Integrating multiple datasets with species distribution models to inform conservation of the poorly-recorded Chinese seahorses. Biol. Conserv. 211, 161–171. Zhang, X., Vincent, A.C.J., 2018. Predicting distributions, habitat preferences and associated conservation implications for a genus of rare fishes, seahorses (Hippocampus spp.). Divers. Distrib. 24, 1005–1017. Zhang, X., Vincent, A.C.J., 2019. Using cumulative-human-impact models to reveal global threat patterns for seahorses. Conserv. Biol. https://doi.org/10.1111/cobi.13325.

Wang, S., Xie, Y., 2009. China species red list. In: Vertebrates. vol. 2 Higher Education Press. Watts, M.E., Ball, I.R., Stewart, R.S., Klein, C.J., Wilson, K., Steinback, C., Lourival, R., Kircher, L., Possingham, H.P., 2009. Marxan with zones: software for optimal conservation based land- and sea-use zoning. Environ. Model. Softw. 24, 1513–1521. https://doi.org/10.1016/j.envsoft.2009.06.005. Wenger, A.S., Williamson, D.H., da Silva, E.T., Ceccarelli, D.M., Browne, N.K., Petus, C., Devlin, M.J., 2016. Effects of reduced water quality on coral reefs in and out of notake marine reserves. Conserv. Biol. 30, 142–153.

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