In plant populations alleles often deviate from a random distribution and reveal positive autocorrelation at short distances. In species with both clonal and sexual reproduction, such clustering may be because ramets of the same genet were sampled at nearby locations. Alternatively, clustering may be the result of limited gene¯ow through pollen or seeds (isolation-by-distance). Here, we modify a conventional spatial autocorrelation analysis using the join-count statistic in order to dierentiate between these two causes of genetic structure. We examined the distribution of seven microsatellite loci representing 37 alleles in a 20´80 m plot of a perennial population of eelgrass Zostera marina L. In analysing join-counts between all like genotypes we found signi®cant genetic autocorrelation among ramets at distances between 1 and 7 m (P < 0.001). We then excluded joins between clonemates which were identi®ed from the expected likelihood of their seven-locus genotypes. Without joins within genets, no autocorrelation was evident, indicating that most of the signi®cant genetic clustering was caused by clonal spread. At distances up to 27 m, alleles were distributed at random, indicating a panmictic population at this spatial scale. These results illustrate the need for an a priori estimation of genet±ramet structure in clonally reproducing plants in order to avoid erroneous inferences about putative gene¯ow at various spatial scales.Keywords: clonal reproduction, genetic structure, join-count, microsatellites, spatial autocorrelation, Zostera marina. IntroductionSpatial autocorrelation techniques are a powerful tool for detecting the nature and scale of genetic dierentiation over a range of spatial scales (e.g. Sokal & Oden, 1978a,b; Cli & Ord, 1981). Simulations have demonstrated that these methods are able to distinguish between several causes of genetic structure, i.e. directional migration, selection or restricted gene¯ow (isolation-by-distance, Sokal et al., 1997). In contrast to statistics based on allele frequencies such as F ST , autocorrelation techniques make few assumptions regarding the underlying population genetic model (Heywood, 1991). No information is lost by pooling of individuals into arbitrary sampling areas for subsequent comparison of gene frequencies among subpopulations. Instead, in autocorrelation analyses, the explicit information of each spatial location of individuals relative to one another can be utilized (reviewed in Heywood, 1991; Epperson, 1993). Therefore, autocorrelation techniques may have a superior power over methods based on nested samples of gene frequencies for detecting spatial patterns (Epperson & Li, 1996; Epperson, 1997).The numerical value of an autocorrelation statistic is often examined as a function of the Euclidean distance among pairs of plants, a correlogram. The shape of the correlogram allows inferences about the direction and magnitude of the evolutionary processes at work (Sokal et al., 1997). Isolation-by-distance typically results in a clustered distribution...

14In the northern hemisphere, Zostera marina is the most important and widespread seagrass 15 species. Despite its ecological importance, baseline data on eelgrass distribution and 16 abundance are mostly absent, particularly in subtidal areas with relatively turbid waters. 17Here we report a combined approach on vegetation mapping in the Baltic Sea coupled to a 18 species distribution model (SDM). Eelgrass cover was mapped continuously in 2010/11 with 19 an underwater tow-camera along ~400 km of seafloor. Eelgrass populated 80 % of the study 20 region and occurred at water depths between 0.6 and 7.6 m at sheltered to moderately 21 exposed coasts. Mean patch length was 128.6 m, but was higher at sheltered locations, with

For many coastal areas including the Baltic Sea, ambitious nutrient abatement goals have been set to curb eutrophication, but benefits of such measures were normally not studied in light of anticipated climate change. To project the likely responses of nutrient abatement on eelgrass (Zostera marina), we coupled a species distribution model with a biogeochemical model, obtaining future water turbidity, and a wave model for predicting the future hydrodynamics in the coastal area. Using this, eelgrass distribution was modeled for different combinations of nutrient scenarios and future wind fields. We are the first to demonstrate that while under a business as usual scenario overall eelgrass area will not recover, nutrient reductions that fulfill the Helsinki Commission's Baltic Sea Action Plan (BSAP) are likely to lead to a substantial areal expansion of eelgrass coverage, primarily at the current distribution's lower depth limits, thereby overcompensating losses in shallow areas caused by a stormier climate.

Seagrass meadows have a disproportionally high organic carbon (Corg) storage potential within their sediments and thus can play a role in climate change mitigation via their conservation and restoration. However, high spatial heterogeneity is observed in Corg, with wide differences seen globally, regionally, and even locally (within a seagrass meadow). Consequently, it is difficult to determine their contributions to the national remaining carbon dioxide (CO2) budget without introducing a large degree of uncertainty. To address this spatial heterogeneity, we sampled 20 locations across the German Baltic Sea to quantify Corg stocks and sources in Zostera marina seagrass-vegetated and adjacent unvegetated sediments. To predict and integrate the Corg inventory in space, we measured the physical (seawater depth, sediment grain size, current velocity at the seafloor, anthropogenic inputs) and biological (seagrass complexity) environments to determine regional and local drivers of Corg variation. Here, we show that seagrass meadows in Germany constitute a significant Corg stock, storing on average 7,785 g C/m2, 13 times greater than meadows from other parts of the Baltic Sea, and fourfold richer than adjacent unvegetated sediments. Stocks were highly heterogenous; they differed widely between (by 10-fold) and even within (by 3- to 55-fold) sites. Regionally, Corg was controlled by seagrass complexity, fine sediment fraction, and seawater depth. Autochthonous material contributed to 78% of the total Corg in seagrass-vegetated sediments, and the remaining 22% originated from allochthonous sources (phytoplankton and macroalgae). However, relic terrestrial peatland material, deposited approximately 6,000 years BP during the last deglaciation, was an unexpected and significant source of Corg. Collectively, German seagrasses in the Baltic Sea are preventing 8.14 Mt of future CO2 emissions. Because Corg is mostly produced on-site and not imported from outside the meadow boundaries, the richness of this pool may be contingent on seagrass habitat health. Disturbance of this Corg stock could act as a source of CO2 emissions. However, the high spatial heterogeneity warrants site-specific investigations to obtain accurate estimates of blue carbon and a need to consider millennial timescale deposits of Corg beneath seagrass meadows in Germany and potentially other parts of the southwestern Baltic Sea.

Seagrass meadows have a disproportionally high organic carbon (Corg) storage potential ('blue carbon') within their sediments and thus can play an important role in climate change mitigation via their conservation and restoration. However, high spatial heterogeneity is observed in Corg, with wide differences seen globally (i.e. tropical vs temperate), regionally, and even locally (within a seagrass meadow). Consequently, it is difficult to determine their contributions to the national remaining carbon dioxide (CO2) budget without introducing a large degree of uncertainty. In order to address this spatial heterogeneity, we sampled 20 locations across the Baltic Sea coast of Germany to quantify carbon stocks and sources in Zostera marina seagrass-vegetated and adjacent unvegetated sediments. To predict and integrate the Corg inventory in space, we measured the physical (seawater depth, sediment grain size, current velocity at the seafloor, anthropogenic inputs) and biological (seagrass complexity) environment to determine regional (between sites) and local (within site) drivers of Corg variation. Here we show that seagrass meadows in the German Baltic Sea constitute a significant Corg stock, storing on average 7,785 + 679 g C/m2, 13 times greater than meadows from other parts of the Baltic Sea (outside of Germany), and four-fold richer than adjacent unvegetated sediments. Stocks were highly heterogenous; they differed widely between (by 10-fold) and even within (by 3 to 55-fold) sites. At a regional scale (350 km), Corg was controlled by seagrass complexity, fine sediment fraction, and seawater depth. Autochthonous material (seagrass-derived and large infauna) contributed to 78% of the total Corg in vegetated sediments and the remaining 22% originated from allochthonous sources (phytoplankton, drift algae Pilayella littoralis, and other macroalgae). However, relic terrestrial peatland material, deposited during the last deglaciation 5,806 and 5,095 years BP, was an unexpected and significant source of Corg. Collectively, German seagrass meadows in the Baltic Sea are preventing 8.14 Mt of future CO2 emissions. Because Corg is mostly produced on site, and not imported from outside the boundaries of the meadow, the richness of this pool may be contingent on seagrass habitat health. Disturbance of this Corg stock could act as a source of CO2 emissions. However, the high spatial heterogeneity seen across the region warrant site-specific investigations to obtain accurate estimates of blue carbon, and a need to consider millennial timescale deposits of Corg beneath seagrass meadows in Germany and potentially other parts of the southwestern Baltic Sea.