1. Seamounts hotspots of pelagic biodiversity in the open ocean Telmo Morato, S. Hoyle, V. Allain, S. Nicol I.S.R Portugal, IMAR-Açores Portugal, Secretariat of the Pacific Community New Caledonia

2. It has been defined many times ( 1-6 ) but there is no “generally accepted” definition; Before ( 1,5 ): undersea mountains that rise more than 1000m from the sea floor to below sea level; SBN definition ( 6,7 ): “any geographically isolated topographic feature on the seafloor taller than 100 m, including ones whose summit regions may temporarily emerge above sea level” 1- Menard, 1964; 2- Wessel, 2001; 3- Schmidt and Schmincke, 2000; 4- Pitcher et al., 2007; 5- International Hydrographic Organization, 2008; 6- Wessel et al., 2010; 7- Staudigel et al. 2010 Seamounts – Definition

3. Sandwell et al., 2002 Seamounts – How many are out there? “the surfaces of Mars, Venus, and the Moon are much better mapped than Earth’s ocean floors”

4. Pitcher et al., 2007 Seamounts – How many are out there?

5. Locations for 14,287 seamounts. Yellow indicates seamounts within EEZs (59% of total), blue indicates seamounts in international waters (41%). Data by Kitchingman et al., 2007 Our estimates for the whole world Seamounts – How many are out there?

6. Height-frequency distributions of seamounts. There could be approximately 125,000 seamounts taller than 1 km, and as many as 25 million taller than 100 m, but the uncertainties are considerable. The white star (3 million) is an adjusted prediction by hillier and Watts (2007). Wessel, 2010 Seamounts – How many are out there?

7. There has been considerable debate about the status and sustainability of pelagic fisheries around the world ( 1–4 ) It has been demonstrated that many species may be protected by identifying biodiversity hotspots and managing them for conservation ( 5 ). This approach is well established for terrestrial systems and marine tropical reefs ( 6, 7 ), but less so for the pelagic ecosystems of the open ocean ( 5 ). 1- Baum et al., 2003; 2- Myers and Worm, 2003; 3- Hampton et al., 2005; 4- Hilborn, 2006; 5- Worm et al., 2003; 6- Myers et al., 2000; 7- Roberts et al. 2002 Rationale– Biodiversity hotspots

8. Hotspots that have been identified in open ocean areas have been typically associated with particular environmental factors and mesoscale oceanographic features such as fronts or eddies ( 1, 2 ); To address this issue, dynamic marine reserves that move with the wildlife have been suggested ( 3 ), but such approaches may not be workable ( 4 ). 1- Worm et al., 2005; 2- Etnoyer et al., 2004; 3- Norse, 2006; 4- Malakoff, 2004 Rationale– Biodiversity hotspots

9. We examined if seamounts aggregate pelagic biodiversity by applying ocean basin scale generalized linear models (GLMs) to location-specific fisheries catch data. In addition, we analyzed catch per unit of effort (CPUE) in relation to distance to seamounts to identify those pelagic species that are significantly associated with seamounts. Morato et al. 2010 Objectives– Seamounts are hotspots? Science, 27 May 2010

10. Morato et al. 2010 Methods– Seamount Database Seamounts included in the present study (n=1145)

11. Methods– Observer Database Morato et al. 2010 Location of the 24338 longline sets monitored by the observer program (1980 -2007)

12. Methods– Observer Database Morato et al. 2010 Total longline fishing effort (number of hooks; 1980-2007.) Each cell has 50x50 km.

13. Methods– Observer Database Morato et al. 2010 Mean catch per unit of effort (CPUE in number of individuals per 100 hooks) of by-catch species in the tuna longline (1980-2007). Albacore, bigeye, skipjack and yellowfin tuna species are not included in this figure

14. The expected number of species (Ŝ40), standardized to 1000 hooks per longline set, was rarefied for subsamples of 40 individuals The effects of habitat type and distance to habitat feature were analyzed for the estimated rarefied richness. GLM techniques were used to standardize rarefied richness and to evaluate whether the presence of habitat features and the distance to the feature were significant explanatory variables. The explanatory variables included in the model were year, moon phase, geographical area, fleet type, distance to the closest feature, and fishing effort. The Akaike's Information Criterion (AIC) was used to compare the model fits Methods– Statistical analyses Pelagic Biodiversity analyses Morato et al. 2010

15. Mean expected species diversity (±95% confidence limits) rarefied from 40 individuals (Ŝ 40 ) as a function of: (A) the main habitat (seamount -SM-, shore and oceanic) where all means are significantly different at  =0.01 (ANOVA and Tukey's Honestly Significant Difference Test), (B) distance to seamount summit where the fitted logarithmic regression is also shown (grey line), and (C) 5 degrees latitude Morato et al. 2010 Results – Rarefied Diversity

16. Expected species diversity rarefied from 40 (Ŝ40) individuals as a function of 1x1 degree cells. Stars denote locations of seamounts with longline sets close to their summits. Morato et al. 2010 Results – Rarefied Diversity

17. Summary statistics for the GLM single variable elimination analyses relating species diversity with habitat and other variables. AIC is the Akaike's Information Criterion. Results – Rarefied Diversity The relationship for describing species diversity was complex with distance to features, number of hooks and latitude the strongest predictors of species diversity Morato et al. 2010 55,394

18. The effect of the variables distance*feature on species diversity rarefied from 40 individuals (Ŝ 40 ). One variable was predicted at a time from the results of the GLM by fixing the other variables. Results – Rarefied Diversity When all variables except distance to feature were kept constant, seamounts were found to have higher rarefied diversity within 30-40 km of the summit. Morato et al. 2010

19. There was sufficient data to analyze 37 taxa seamount aggregation effects were detected for 41% of the taxa (15 taxa of shark, billfish and pelagic teleost fish) the opposite were detected for only three taxa Effect of including the term for distance to seamount on the AIC (ΔAIC), the parameter estimate for the relationship with log(distance to seamount), and whether the effect represents a significantly higher or lower catch rate close to seamounts (SM). Only those taxa with statistically significant trends are shown here. Results – By-catch species Morato et al. 2010

20. For the shark taxa the probability of catching the species / or average number caught increased closer to seamounts for: porbeagle shark (Lamna nasus) short-finned mako shark (Isurus oxyrinchus) silky shark (Carcharhinus falciformis) silky sharks blue shark (Prionace glauca) decreased for pelagic: stingray (Pteroplatytrygon violacea) Results – By-catch species Analyses of the GLMs for each highly migratory pelagic species Morato et al. 2010 Visitors to a seamount – Blue Planet A Natural History of the Oceans - BBC

21. For the billfishes and tunas taxa the probability of catching the species / or average number caught increased closer to seamounts for: yellowfin tuna (Thunnus albacares) blue marlin (Makaira nigricans) swordfish (Xiphias gladius) and decreased for: albacore (Thunnus alalunga) shortbill spearfish (Tetrapturus angustirostris) Results – By-catch species Analyses of the GLMs for each highly migratory pelagic species Morato et al. 2010 Visitors to a seamount – Blue Planet A Natural History of the Oceans - BBC

22. For the other pelagic teleost fish taxa the probability of catching the species / or average number caught increased closer to seamounts for: ribbon fish (Trachipterus trachypterus) butterfly kingfish (Gasterochisma melampus) big-scaled pomfret (Taractichthys longipinnis) Atlantic pomfret (Brama brama) long-snouted lancetfish (Alepisaurus ferox) short-snouted lancetfish (Alepisaurus brevirostris) moonfish (Lampris guttatus) Results – By-catch species Analyses of the GLMs for each highly migratory pelagic species Morato et al. 2010 Visitors to a seamount – Blue Planet A Natural History of the Oceans - BBC

23. 1.Our analyses suggest that seamounts are hotspots of pelagic biodiversity, since they show consistently higher species richness than do shore or oceanic areas. 2.Moreover, our study indicates that higher species diversity is likely to occur within 30 to 40km of seamount summits. 3.This study also demonstrates that many marine predators and other visitors are associated with seamounts. 4.Conserving biodiversity hotspots has been demonstrated to yield significant conservation benefits 5.Therefore, our analyses support the utility of seamounts as potential locations for offshore marine reserves. 6.Seamount habitats are easier to conserve than ephemeral areas since they are easier to map, survey and enforce. 7.The establishment of a network of marine reserves on seamounts may help to conserve pelagic biodiversity and achieve sustainability of marine predator species Conclusions – Pelagic biodiversity analyses Morato et al. 2010

24. Thanks We wish to acknowledge the SPC member countries for the collection and provision of observer data. Nick Davies and Michael Manning for help with the modeling; Emmanuel Schneiter, Colin Millar and Peter Williams for helping with the databases