Supplemental Information Challenges and Priorities in Shark and Ray Conservation

Transcription

1 Supplemental Information Challenges and Priorities in Shark and Ray Conservation Nicholas K. Dulvy, Colin A. Simpfendorfer, Lindsay N. K. Davidson, Sonja V. Fordham, Amie Bräutigam, Glenn Sant, and David J. Welch 1

2 5 4 Extinction risk (mean IUCN Red List Status) Geographic Range (number of EEZs) Figure S1. Geographic range size and extinction risk. Geographic range size is indexed as the number of Exclusive Economic Zones spanned. For each EEZ bin the mean IUCN Red List Status of species was calculated. Extinction risk is indexed as the mean of the IUCN Red List Categories, where Data Deficient =1, Least Concern = 2, Near Threatened = 3, Vulnerable = 4 and Endangered = 5. The fitted line is a loess smooth curve with a span of 0.95, with 95% confidence intervals. 2

3 False Alarm = 13% True positive Sustainable & managed Sustainable & unmanaged True negative Miss = 17% Rebuilding 8 Unsustainable CR EN VU NT LC DD Figure S2. Harmony of global IUCN Red List status and local fisheries stock status. The fisheries status of 44 populations of 33 species from [S4] were aligned with the global IUCN Red List status of the species. Harmony between fisheries and IUCN criteria are classified as hits (green) where populations are either sustainable and IUCN Red List category is LC or NT (positive hit) or unsustainable/rebuilding and IUCN Red List category is VU, EN or CR (negative hit). False alarms (red) are where the species is IUCN Red Listed EN or CR but the population is sustainably fished. Misses are (orange) where species are IUCN Red Listed as NT or LC but populations are unsustainably fished or rebuilding. Bubble size is proportional to the number of stocks and inset number is the rounded percent. 3

4 Table S1. Fisheries country classification thresholds. Scoring of fisheries priorities based on quantiles of, (1) globally reported FAO shark catch (with countries not reporting, or mis-reporting to FAO given the highest score) , (2) the quantity of shark fin exported to Hong Kong in 2008, (3) catch percentage comprised of the top 20 shark species [unless percent in the nei - not elsewhere identified >90% then given maximum score since species composition is unknown] (Table S2), and (4) the proportion of the catch reported to FAO that was listed as nei. Score Percentile ranges Average FAO catch (tonnes) Shark fin trade (kg, 2008) Share of shark catch comprised of top 20 priority shark species (%) Catch share nei (%) 3 >80th >17,512 >184,409 > th 7,088-17,512 86, , th 4,646-7,088 39,370-86, th 2,335-4,646 7,052-39, <20th <2335 <7, <

5 Table S2. Fisheries species reported catch volumes. The top 50 species/species groupings recorded in global FAO landings where taxonomic identification is to Family level at least (this excludes ~50 % of the total reported landings). Catch and catch % of the total are based on the average catch for the period nei refers to aggregated data not elsewhere indicated. Species Scientific name Average catch (t) % catch Blue shark Prionace glauca Requiem sharks nei Carcharhinidae Stingrays, butterfly rays nei Dasyatidae Raja rays nei Raja spp Picked dogfish Squalus acanthias Thresher sharks nei Alopias spp Dogfish sharks nei Squalidae Smooth-hounds nei Mustelus spp Shortfin mako Isurus oxyrinchus Narrownose smooth-hound Mustelus schmitti Small-spotted catshark Scyliorhinus canicula Silky shark Carcharhinus falciformis Thornback ray Raja clavata Eagle rays nei Myliobatidae Tope shark Galeorhinus galeus

6 Whitespotted wedgefish Rhynchobatus australiae Little skate Raja erinacea Argentine angelshark Squatina argentina Hammerhead sharks, etc. nei Sphyrnidae Cuckoo ray Raja naevus Mantas, devil rays nei Mobulidae Plownose chimaera Callorhinchus callorynchus Gummy shark Mustelus antarcticus Guitarfishes, etc. nei Rhinobatidae Dark ghost shark Hydrolagus novaezealandiae Blonde ray Raja brachyura New Zealand rough skate Zearaja nasuta Spotted ray Raja montagui Ghost shark Callorhinchus milii Milk shark Rhizoprionodon acutus Starry ray Raja radiata Pelagic thresher Alopias pelagicus Spotted estuary smooth-hound Mustelus lenticulatus Lusitanian cownose ray Rhinoptera marginata Southern stingray Dasyatis americana

7 Yellownose skate Dipturus chilensis Catsharks, etc. nei Scyliorhinidae Ratfishes nei Hydrolagus spp Pacific angelshark Squatina californica Dogfishes and hounds nei Squalidae, Scyliorhinidae Catsharks, nursehounds nei Scyliorhinus spp Smooth-hound Mustelus mustelus Dusky smooth-hound Mustelus canis Cape elephantfish Callorhinchus capensis Mackerel sharks,porbeagles nei Lamnidae Nursehound Scyliorhinus stellaris Oceanic whitetip shark Carcharhinus longimanus New Zealand smooth skate Dipturus innominatus Lowfin gulper shark Centrophorus lusitanicus

8 Table S3. Fisheries country priorities. The top 50 countries reporting shark landings to FAO based on the average catches for the period , showing the average annual catch and the % of the total reported landings (FAO, 2014). Country Average Share of global shark catch (%) Indonesia Spain India United States of America Argentina Mexico Taiwan (Province of China) Malaysia Brazil Nigeria New Zealand France Portugal Iran Japan Pakistan Korea, Republic of

9 Yemen Peru Ecuador Venezuela Tanzania Libya Oman Australia Senegal Thailand Sri Lanka Ghana Madagascar Philippines Costa Rica United Kingdom Bangladesh Morocco Russian Federation Canada

10 Sierra Leone Mauritania Chile South Africa Egypt Uruguay Cuba China Namibia Panama Tunisia Belgium Guinea

11 Supplemental Methods and Materials Evaluating the harmony of fisheries stock assessments and IUCN Red List Assessment We caution that the IUCN does not recommend that Red List Categories be used, in isolation, for priority setting. IUCN Red List Assessments span the global range of species, and -- for wide-ranging species -- they incorporate considerable variation in pressures and local status. IUCN recommends that other information also be taken into account. Here, we rely on the increasing evidence for alignment and coherence of fisheries and IUCN Red List assessments to differentiate between shark species in need of urgent conservation action and those for which conservation attention is less immediate or not necessary. Most comparisons of fisheries stock status and IUCN Red List status focus on bony (teleost fishes) [S1-4]. These tend to compare stock assessment status in given year and calculate the IUCN A criterion rate of decline over the greater of 10 years or three generation span. The gold standard comparison is of geographically matched assessments done independently by fisheries advisory bodies and IUCN Red List Assessors across the same time period [S5]. While this approach again reveals strong coherence between regional fisheries stock assessments and IUCN regional Red List Assessments, it is mainly limited to bony (teleost fishes). Here, we extract the most recent IUCN Red List Assessments for the sharks (including rays and chimaeras) that have also been recently evaluated through fisheries stock assessments [S6]. We caution that there is mismatch in both geographic and temporal scales between these data. Geographically, the stock assessments are conducted at subnational population scale, whereas IUCN Red List assessments are conducted across the global scale of the species. For example, within Europe, the (IUCN Red List and fisheries stock assessment) status of many species is worse in the Mediterranean Sea compared to the better-managed Northeast Atlantic seas [S5, S7]. Similarly, many Indo-Pacific species found in Australia and New Zealand may be Least Concern there, but are threatened elsewhere in their range [S8]. The IUCN Red List Assessments are on average 4 years older (mean year of assessment was 2009) than the fisheries stock assessments (mean date of publication was 2013) [S6]. Despite the differences in geographic scale and temporal mismatch, there is surprising harmony between global IUCN species status and local fisheries population assessments. The performance results in an Area Under the Curve of 0.77, where a coin toss would yield an AUC of 0.5 and a perfect alignment of assessments would result in an AUC of 1 [S3, S9, S4]. There is a high probability of the criteria aligning with a 68% chance of a hit (combined rate of true and false positives, Figure S2). Hits comprised true positives (sustainable and either LC or NT; 42%) and true negatives (rebuilding or unsustainable; 24%). The false alarms (or false positives) comprised three skates in NW Atlantic for which there is increasing evidence of rebuilding (Barndoor Skate Dipturus laevis, Smooth Skate Malacoraja senta, and Winter Skate Leucoraja ocellata). Hence, these false alarms might arise from the lag of IUCN Red List Assessment behind the more recently updated fisheries assessments resulting from improved reporting and management [S10]. The misses arise from the unknown status of three New Zealand species (Dark Ghost Shark Hydrolagus novaezealandiae, Rig Mustelus lenticulatus, and Rough Skate Zearaja nasuta) and the Australian assessment of Australian Blacktip Shark (Carcharhinus tilstoni). Species Red List Status This analysis considered the 1,088 chondrichthyan species assessed for the IUCN Red List (as of February 2015; Table 1). Species were categorized using the IUCN 3.1 Categories and Criteria version 3.1 [S11] at 17 workshops involving more than 300 experts who incorporated all available information on distribution, catch, abundance, population trends, habitat use, life histories, threats, and conservation measures [S12]. Species were separated into four target groups based on their habitat, because fisheries exploitation patterns tend to follow these habitat groupings (dataset can be found at, 11

12 Fisheries species prioritization A two-step process was used to identify priority species or species groups for management. We note that most of these species require management. Yet, some species, such as the US and Canadian skates (Rajidae), are already under some form of quota management [S6]. Firstly, species-specific data on average catches reported to Food and Agriculture Organization of the United Nations (FAO) for were combined with indicators of whether these species had been identified as having declined substantially (IUCN Red List Category and listing on the appendices of Convention on International Trade in Endangered Species [CITES] and Convention of Migratory Species [CMS]) were used [S12]. Given the incompleteness of the species-specific data held by FAO, the initial prioritization was conducted by our expert group (CAS, DJW, SVF, with input and review from other coauthors) which had had extensive experience (minimum of 20 years each) in shark fisheries assessment, management, and conservation. Fisheries country prioritization Four factors were used to rank countries as to where fisheries management improvements were needed based on volume of catch and trade and presence of priority sharks (Table S1) and lack of species differentiation of catches. For each factor countries were assigned values between zero and three (1) the proportion of globally reported shark catch (those countries not reporting, or misreporting to FAO given the highest score). This demonstrates the size of the shark catch, but overlooks discards, (2) the volume (kg) of shark fin exported to Hong Kong in This indicates the importance of trade in shark products, (3) the shark catch percentage that is comprised of the top 20 priority shark species (unless percent in the nei - not elsewhere identified >90% then given maximum score since species composition is unknown; Table S1). This indicated the share of the catch comprised of those shark species groups for which management is a priority, and (4) the proportion of the catch reported to FAO that was listed as nei. This indicates the degree of taxonomic aggregation of species which is a leading indicator of propensity for sustainable management, following the proverb what gets measured, gets managed [S13, S14]. For each factor the values were assigned scores based on five percentile ranges (>80% = % = 2.5, 40-59% = 2, 20-39% =1.5, and <20% = 1) of the relevant values (Table S1). While these nations make up many of the priorities identified, several countries that do not report (e.g. Papua New Guinea, Uruguay) or misreport (e.g. China) to FAO were also included [S15]. Where data were not available the maximum score was assigned. Scores were multiplied together to give an aggregated value and countries ranked from highest (1) to lowest (54) in terms of priority for improved fisheries management. Calculation of Conservation likelihood We used a modified conservation likelihood framework to broadly contextualize the types of intervention needed for the different countries. For example, implementation of spatial management or fisheries stock assessment might be possible within a 5-year timeframe in those countries with highest conservation likelihood. By contrast, several decades of transformation of governance through international aid relief may be needed in those countries with lowest conservation likelihood. The development of the conservation likelihood index was inspired by [S16-18] and is laid out in detail here [S19]. This index 12

13 differs slightly from that reported originally which considered three classes of information: governance, economics and welfare, and human pressure (indexed by human population growth, coastal human population size, and the Sea Around Us Project catch reconstructions). Here, we have derived the conservation likelihood score solely using the governance plus the economic and welfare metrics. Governance included political stability, government effectiveness, control of corruption and regulatory quality; economics and welfare included gross domestic product, purchasing power parity and human development index; human pressure included annual human population growth, and human population 100 km from the coast [S13, S19]. We used 2014 measurements unless none were available, in which case we used the most recent year (no later than 2011). We focused prioritization of those endangered species IUCN Critically Endangered or Endangered), most threatened families, Data Deficient, and freshwater species (Table 1; Figure 4). Supplemental References S1. Dulvy, N.K., Jennings, S.J., Goodwin, N.B., Grant, A., and Reynolds, J.D. (2005). Comparison of threat and exploitation status in Northeast Atlantic marine populations. Journal of Applied Ecology 42, S2. Davies, T.D., and Baum, J.K. (2012). Extinction risk and overfishing: reconciling conservation and fisheries perspectives on the status of marine fishes. Sci Rep 2, 561. S3. d Eon-Eggertson, F., Dulvy, N.K., and Peterman, R.M. (2015). Reliable Identification of Declining Populations in an Uncertain World. Conservation Letters 8, S4. Porszt, E., Peterman, R.M., Dulvy, N.K., Cooper, A.B., and Irvine, J.R. (2012). Reliability of indicators of declines in abundance. Conservation Biology 26, S5. Fernandes, P.G., Ralph, G.M., Nieto, A., García Criado, M., Vasilakopoulos, P., Maravelias, C.D., Cook, R., Pollom, R.A., Kovačić, M., Pollard, D., et al. (Acccepted). Coherent assessments of Europe s marine fishes show regional divergence and megafauna loss. Nature Ecology and Evolution. S6. Simpfendorfer, C.A., and Dulvy, N.K. (2017). Bright spots of sustainable shark fishing. Current Biology 27, R97-R98. S7. Nieto, A., Ralph, G.M., Comeros-Raynal, M.T., Kemp, J., García Criado, M., Allen, D.J., Dulvy, N.K., Walls, R.H.L., Russell, B., Pollard, D., et al. (2015). European Red List of Marine Fishes. (European Union), p. 90. S8. White, W.T., and Kyne, P.M. (2010). The status of chondrichthyan conservation in the Indo- Australasian region. Journal Of Fish Biology 76, S9. Connors, B.M., Cooper, A.B., Peterman, R.M., and Dulvy, N.K. (2014). The false classification of extinction risk in noisy environments. Proceedings of the Royal Society B: Biological Sciences 281, S10. Curtis, T.H., and Sosebee, K.A. (2015). Landings Composition of the Northeast U.S. Skate, Rajidae, Wing Fishery and the Effectiveness of Prohibited Species Regulations. Marine Fisheries Review 77, 1-8. S11. IUCN (2016). Guidelines for using the IUCN Red List Categories and Criteria. (Gland, Switzerland: IUCN Species Survival Commission), p. 87. S12. Dulvy, N.K., Fowler, S.L., Musick, J.A., Cavanagh, R.D., Kyne, P.M., Harrison, L.R., Carlson, J.K., Davidson, L.N.K., Fordham, S., Francis, M.P., et al. (2014). Extinction risk and conservation of the world's sharks and rays. elife 3, e S13. Davidson, L.N.K., Krawchuk, M.A., and Dulvy, N.K. (2016). Why have global shark and ray landings declined: improved management or overfishing? Fish and Fisheries 17, S14. Melnychuk, M.C., Peterson, E., Elliott, M., and Hilborn, R. (2017). Fisheries management impacts on target species status. Proceedings of the National Academy of Sciences 114, S15. Lam, V.Y.Y., and Sadovy de Mitcheson, Y. (2011). The sharks of South East Asia - unknown, unmonitored and unmanaged. Fish and Fisheries 12,

14 S16. Dickman, A.J., Hinks, A.E., Macdonald, E.A., Burnham, D., and Macdonald, D.W. (2015). Priorities for global felid conservation. Conservation Biology 29, S17. McClanahan, T.R., Cinner, J.E., Maina, J., Graham, N.A.J., Daw, T.M., Stead, S.M., Wamukota, A., Brown, K., Ateweberhan, M., Venus, V., et al. (2008). Conservation action in a changing climate. Conservation Letters 1, S18. Allison, E.H., Perry, A.L., Adger, W.N., Badjeck, M.-C., Brown, K., Conway, D., Halls, A., Pilling, G.M., Reynolds, J.D., and Dulvy, N.K. (2009). Vulnerability of national economies to the impacts of climate change on fisheries. Fish and Fisheries 10, S19. Davidson, L.N.K., and Dulvy, N.K. (2017). Global marine protected areas to prevent extinctions. Nature Ecology & Evolution 1,