* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Estimating dispersal in marine fish to improve resilience to
Attribution of recent climate change wikipedia , lookup
Citizens' Climate Lobby wikipedia , lookup
Solar radiation management wikipedia , lookup
Climate governance wikipedia , lookup
Climate change and agriculture wikipedia , lookup
Climate change in Tuvalu wikipedia , lookup
Media coverage of global warming wikipedia , lookup
Climate resilience wikipedia , lookup
Climate change adaptation wikipedia , lookup
Scientific opinion on climate change wikipedia , lookup
Effects of global warming on humans wikipedia , lookup
IPCC Fourth Assessment Report wikipedia , lookup
Climate change and poverty wikipedia , lookup
Public opinion on global warming wikipedia , lookup
Effects of global warming on Australia wikipedia , lookup
Climate change in Saskatchewan wikipedia , lookup
Years of Living Dangerously wikipedia , lookup
Surveys of scientists' views on climate change wikipedia , lookup
Estimating dispersal in marine fish to improve resilience to climate change Malin L. Pinsky, [email protected], Stanford University, Pacific Grove, CA, USA. Humberto R. Montes, Jr., Visayas State University, Leyte, Philippines Malin Pinsky Maintaining the movement of organisms between populations (connectivity) is one of the most commonly advocated conservation strategies in response to climate change (Opdam & Wascher 2004). However, robust estimates of dispersal are required to design effective connectivity conservation, and there is substantial uncertainty about whether marine species have short or long-distance dispersal. In the ocean, marine currents and long pelagic larval stages for most organisms create a high potential for long-distance dispersal (Roberts 1997). However, recent studies have found populations that are largely replenished by local offspring, suggesting local dispersal (Swearer et al. 1999; Almany et al. 2007). To address this problem and improve our ability to plan for the impacts of climate change, we developed an approach using population genetics to measure the distances traveled by marine larvae (Pinsky et al. 2010). Calculating organisms dispersal distances in marine Figure 1: Isolation-by-distance genetic patterns for A. clarkii along two islands (Cebu and Leyte) in the central Philippines. The y-axis is a Population genetics provides a useful tool for studying measure of genetic distance. dispersal because it uses the natural tags present in every organism’s DNA. In particular, when the genetic distance between populations increases with the geographic distance between them (Figure 1), the slope of the relationship can be used to calculate the typical distance traveled by an organism (Rousset 1997). This pattern is called “isolation-by-distance.” We demonstrated how to use this method with Amphiprion clarkii, a common clownfish on tropical coral reefs. As a fish with a short larval duration (7 to 11 days) and therefore a species likely to have lower dispersal abilities, adaptation strategies sufficient to maintain connectivity for this species will likely maintain connectivity for a wide range of fishes. To apply isolation-by-distance methods, we sampled 17-34 fish at 18 sites across 450km of coastline on two islands in the central Philippines. Samples were collected from both adults and juveniles to allow us to estimate density from the strength of genetic drift (see Pinsky et al. 2010 for explanation of these methods). We also estimated adult density from visual transects. This sampling strategy is simple to apply, and therefore should be applicable to a wide range of species. We then measured genetic distance between each sampled population with 13 microsatellite loci, and we regressed this against geographic distance (Figure 1). Finally, we used the slope of the regression and our estimate of density to estimate that typical dispersal distances in A. clarkii are 11 km (4-27 km) (see Pinsky et al. 2010 for details). Interestingly, this distance is two orders of magnitude larger than has recently been suggested (Shanks 2009), likely because some previous methods have difficulty detecting long-distance dispersers. The dispersal distance we calculated can then be used to design conservation measures and climate adaptation strategies for clownfish and other marine fish. Using dispersal knowledge to improve resilience Figure 2. Map of a marine protected area (MPA) to climate change network on the island of Cebu, Philippines. The red rectangles are MPAs. The arrow indicates a When attempting to improve climate change gap in the network that may impede connectivity. resilience in marine fish, there are two primary Map courtesy of the Coastal Conservation and considerations: improving the resilience of Education Foundation (CCEF, Cebu, populations in situ, and allowing populations to move Philippines). in search of more favorable conditions. Reducing exposure to non-climate-related stressors can strengthen population resilience, and marine protected areas (MPAs) have been one especially effective approach (Lester et al. 2009). MPAs are areas of the ocean where fishing, habitat destruction, and other extractive activities are limited. To sustain a viable population, however, a substantial fraction of the dispersing offspring must stay within the MPA. An isolated MPA for A. clarkii would have to be about 20km wide, or twice the typical dispersal distance (Lockwood et al. 2002). An alternative is to design networks of much smaller MPAs, each connected to the other by dispersing larvae (Figure 2). MPA networks also have a number of benefits for climate adaptation. In many species, some populations are already adapted to high temperatures and other conditions predicted under climate change (Balanyá et al. 2006). When designed in networks, MPAs create stepping-stones that allow beneficial genes to flow to those populations experiencing climatic extremes for the first time. This process would aid local adaptation to climate change. Without a network of MPAs, habitat destruction and overfishing could create large gaps that would impede beneficial gene flow. MPA networks can also allow populations to move to more favorable conditions by providing the necessary stepping-stones. If local conditions become too extreme, this may be the only survival strategy. Because clownfish, and most coastal marine species, are site-attached as adults, migration will take place primarily during the larval stage. Given what we now know about dispersal in A. clarkii, spacing between MPAs would need to be about 10-20 km to facilitate this movement (Figure 2). In conclusion, estimates of dispersal are important for designing effective conservation strategies that improve the climate resilience of marine populations. While dispersal distances in marine organisms have been highly uncertain, we have shown how dispersal can be estimated with a genetic method that will be applicable to a wide range of species. References Almany G.R., Berumen M.L., Thorrold S.R., Planes S. & Jones G.P. (2007). Local replenishment of coral reef fish populations in a marine reserve. Science, 316, 742-744. Balanyá J., Oller J.M., Huey R.B., Gilchrist G.W. & Serra L. (2006). Global genetic change tracks global climate warming in Drosophila subobscura. Science, 313, 1773-5. Lester S.E., Halpern B.S., Grorud-Colvert K., Lubchenco J., Ruttenberg B.I., Gaines S.D., Airamé S. & Warner R.R. (2009). Biological effects within no-take marine reserves: a global synthesis. Mar. Ecol. Prog. Ser., 384, 33-46. Lockwood D.R., Hastings A. & Botsford L.W. (2002). The effects of dispersal patterns on marine reserves: does the tail wag the dog? Theor. Popul. Biol., 61, 297-309. Opdam P. & Wascher D. (2004). Climate change meets habitat fragmentation: linking landscape and biogeographical scale levels in research and conservation. Biol. Conserv., 117, 285-297. Pinsky M., Montes H.R., Jr. & Palumbi S.R. (2010). Using isolation by distance and effective density to estimate dispersal scales in anemonefish. Evolution, 64, 2688-2700. doi:10.1111/j.1558-5646.2010.01003.x Roberts C.M. (1997). Connectivity and management of Caribbean coral reefs. Science, 278, 1454-1457. Rousset F. (1997). Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics, 145, 1219-1228. Shanks A.L. (2009). Pelagic larval duration and dispersal distance revisited. Biological Bulletin, 216, 373-385. Swearer S.E., Caselle J.E., Lea D.W. & Warner R.R. (1999). Larval retention and recruitment in an island population of a coral-reef fish. Nature, 402, 799-802.