Figures
Abstract
Successfully enforced marine protected areas (MPAs) have been widely demonstrated to allow, within their boundaries, the recovery of exploited species and beyond their boundaries, the spillover of juvenile and adult fish. Little evidence is available about the so-called ‘recruitment subsidy’, the augmented production of propagules (i.e. eggs and larvae) due to the increased abundance of large-sized spawners hosted within effective MPAs. Once emitted, propagules can be locally retained and/or exported elsewhere. Patterns of propagule retention and/or export from MPAs have been little investigated, especially in the Mediterranean. This study investigated the potential for propagule production and retention/export from a Mediterranean MPA (Torre Guaceto, SW Adriatic Sea) using the white sea bream, Diplodus sargus sargus, as a model species. A multidisciplinary approach was used combining 1) spatial distribution patterns of individuals (post-settlers and adults) assessed through visual census within Torre Guaceto MPA and in northern and southern unprotected areas, 2) Lagrangian simulations of dispersal based on an oceanographic model of the region and data on early life-history traits of the species (spawning date, pelagic larval duration) and 3) a preliminary genetic study using microsatellite loci. Results show that the MPA hosts higher densities of larger-sized spawners than outside areas, potentially guaranteeing higher propagule production. Model simulations and field observation suggest that larval retention within and long-distance dispersal across MPA boundaries allow the replenishment of the MPA and of exploited populations up to 100 km down-current (southward) from the MPA. This pattern partially agrees with the high genetic homogeneity found in the entire study area (no differences in genetic composition and diversity indices), suggesting a high gene flow. By contributing to a better understanding of propagule dispersal patterns, these findings provide crucial information for the design of MPAs and MPA networks effective to replenish fish stocks and enhance fisheries in unprotected areas.
Citation: Di Franco A, Coppini G, Pujolar JM, De Leo GA, Gatto M, Lyubartsev V, et al. (2012) Assessing Dispersal Patterns of Fish Propagules from an Effective Mediterranean Marine Protected Area. PLoS ONE 7(12): e52108. https://doi.org/10.1371/journal.pone.0052108
Editor: Sebastian C. A. Ferse, Leibniz Center for Tropical Marine Ecology, Germany
Received: May 7, 2012; Accepted: November 12, 2012; Published: December 20, 2012
Copyright: © 2012 Di Franco et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research was funded by the Italian Ministero dell’Istruzione e dell’Università (PRIN Project: protocol no. 2008E7KBAE). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Many studies have highlighted the positive effects of successfully enforced marine protected areas (hereinafter MPAs) on populations of exploited coastal fishes in both tropical and temperate areas [1]–[3]. On a global scale, these studies have shown that density, biomass and size of exploited species tend to increase under protected conditions (see [4], [5], [6], [7] and references therein).
Due to a number of processes (e.g. density-dependent effects, ontogenetic migrations and diffusive movements), MPAs may also provide benefits to outer areas through spillover of juvenile and adult fish (see [8] for a review). There is an increasing body of scientific evidence suggesting that spillover of mobile adults from MPAs may replenish nearby exploited populations and therefore enhance fisheries adjacent to MPAs [9]. An additional process leading to population replenishment and fisheries enhancement in outer areas is the so-called ‘recruitment subsidy’ (following [8], see also references therein), i.e. the export of propagules (eggs and larvae) from MPAs.
MPAs hosting a high density of spawners (large-sized, mature individuals) have the potential to increase the occurrence of spawning aggregations (i.e. high-density groupings of conspecific fish gathered together for the purpose of reproducing) and, more in general, to generate a greater propagule production compared to fished areas [10], [11]. Therefore, besides the potential to enhance adjacent fisheries through spillover, some modeling studies suggest that MPAs can produce propagules that could be retained and/or exported outside their boundaries, even towards sites located at quite large distance [12], [13]. From this perspective, the use of MPAs as a way for improving ecosystem-based management has been widely advocated as crucial and complementary to large-scale spatial planning [14], [15].
While there is an abundant literature reporting cases of fish population recovery inside MPAs [16], a scarce body of evidence is available about the actual role of MPAs in sustaining fish stocks in fished areas beyond MPAs’ borders through propagule production and export [17]. Determining whether and how this process actually takes place is considered one of the major research gaps in MPA science [18]. A pre-condition for the increase in propagule production and the consequent retention/export from an effective MPA is the recovery of population abundance and structure within the MPA compared to outside. When this happens, the benefits of protection within the MPA have the potential to extend also well beyond its boundaries through larval dispersal and settlement support, highlighting the need to have well-enforced MPAs [19].
Empirical estimates of propagule retention/export are rare, due to the difficulties associated with tracking small-sized fish propagules from their natal origins through the pelagic environment to their possible settlement locations [20]–[24]. Direct methods for assessing propagule retention/export are also hindered by the dramatically low concentration of propagules in the open waters, while indirect approaches, such as microchemistry and genetic methods, have just begun to adequately describe spatial patterns of connectivity [20], [25]–[26]. Modeling approaches, conversely, have been widely used to investigate dispersal patterns and provide useful scenarios for management [27]–[32]. In particular, Lagrangian (i.e. individual-based) models allow the integration of physical hydrodynamic models with data on key biological traits. Biological information is typically represented by the timing of placement of eggs and/or larvae (i.e. spawning dates for fishes) and the time spent in the plankton (i.e. pelagic larval duration, hereinafter PLD), that, along with information about swimming speed and patterns of vertical migration, may help refine estimates of dispersal patterns [32], [33].
Most Lagrangian model applications are based on PLD values and spawning dates from single estimates (in space and in time) that have been extrapolated over larger scales. This is understandable due to the effort (time and resource consumption) and specific competences (e.g. ability to analyze otolith microstructure) required to get reliable information on PLD and spawning or birth dates. There is increasing evidence, however, that these biological traits significantly change in time and space [34]–[36], suggesting the need to use spatially and temporally proper (i.e. contextual) data to refine local predictions on dispersal patterns of propagules. In addition, studies aimed at assessing potential retention/export of propagules from MPAs on the basis of dispersal models usually lack supporting field-based evidence. Only very few studies, in fact, have investigated spatial and/or temporal patterns of settlement in supplying MPAs or nearby fished areas, strongly suggesting propagule export of mollusks and fishes [31], [37], [38]. Nevertheless, empirical measures of larval dispersal are still largely missing (but see [22]–[24]). While measuring propagule production and abundance is often difficult, settlement of newly metamorphosed larvae at appropriate habitats, which reflects abundance patterns of successfully settled larvae, can be quite easily investigated [39].
Information on patterns of spatial connectivity of fish populations can be obtained using a number of approaches (e.g. field data, modeling, genetic patterns; [20]) that can contribute to a better understanding of propagule retention and/or export from MPAs. This information is essential for the design of single MPAs and of MPA networks.
The aim of the present work is to investigate the potential propagule production and retention/export of a coastal fish from a Mediterranean MPA (Torre Guaceto MPA, SW Adriatic Sea). The white sea bream (Diplodus sargus sargus, Linnaeus 1758) was selected as a model species due to its ecological and economic importance [40], [41]. The originality of this study is the use of a multidisciplinary approach combining field observations, modeling of larval dispersal and molecular genetics to: 1) assess population recovery inside the MPA, 2) simulate patterns of retention and export of propagules from the MPA, 3) explore the effects of potential retention/export from the MPA on settlement patterns and on the genetic structure of the population(s) studied.
Methods
Study Area and Species
The study was carried out at Torre Guaceto MPA (hereinafter TGMPA, 40°42′N; 17°47′E) and in surrounding unprotected areas up to about 100 km away from TGMPA borders. TGMPA is located in southeastern Italy, along the Apulian Adriatic coast (Mediterranean Sea; Fig. 1, Table 1). It was formally established in 1991, but enforcement became effective around 2000–2001 due to the previous shortage of MPA personnel and surveillance from maritime authorities (e.g. coast guard). The entire MPA covers 2227 ha, stretching along about 8 km of coastline, and is subdivided into three zones: (1) a no-take/no-access reserve (zone A, 179 ha); (2) a general reserve (zone B, 163 ha) and (3) a partial reserve (zone C, 1885 ha), where restrictions to human activities become progressively less severe. Access to zone A is restricted to scientists, reserve personnel and police authorities. In zone B only recreational bathing from the coast is allowed. In zone C, both professional and recreational fishing are allowed subject to permission of TGMPA management body, with the exception of spearfishing. Outside the TGMPA, fishing regulations are less restrictive compared to within the MPA (e.g. spearfishing is allowed, and recreational and professional fishing are regulated by national laws). The TGMPA is effectively enforced [19] and this is demonstrated by the clear ‘reserve effect’ observed at population and community levels [40].
Arrows indicate the sampling sites. Black arrows indicate sites within TGMPA, dark grey arrows indicate sites north of TGMPA borders and light grey arrows indicate sites south of TGMPA borders. See Table 1 for name and coordinates of the sampling sites.
The climatological circulation pattern of the Southern Adriatic Sea, where the study area is located, is characterized by well-known current and gyre structures [42]–[45], such as the cyclonic Southern Adriatic (SAd) gyre [42] located in the southern sub-basin. The SAd gyre is interconnected (with seasonally varying characteristics) with the Middle Adriatic (MAd) gyre (located in the central sub-basin) by two coastal currents: one flowing southward along the whole western coast from the Po delta to the Otranto Strait (Western Adriatic Coastal Current or WACC), the other flowing northward from the Otranto Strait along the eastern coast and reaching the central Adriatic sub-basin (Eastern Southern Adriatic Current or ESAC, [45]).
The white sea bream, Diplodus sargus sargus (Linnaeus 1758), usually inhabits the littoral zone in shallow waters down to about 50 m [46], [47]. It is targeted by many professional and recreational fisheries, and consequently shows a clear increase in density and size when protected from fishing [40], [48], [49]. Adults are relatively sedentary [50] and demersal, and produce eggs and larvae that develop in pelagic waters during a period ranging from 14 to 28 days [34], [35], [51]. Then post-larvae metamorphose and settle at about 1 cm total length (TL) in shallow (<2 m depth) coastal benthic habitats, mainly small bays with mixed sand and rocky bottom [47], [52]–[54].
Sampling Design, Data Collection and Statistical Analysis
Density, size distribution and biomass of adults (i.e. reproductive individuals >18 cm, [55]), and density of settlers (i.e. specimens <1.5 cm) were assessed at fourteen sites along approximately 200 km of the Apulian Coast in the South Adriatic Sea (Fig. 1, see Table 1 for names and coordinates of sampling sites). Two sites were placed inside the Torre Guaceto Marine Protected Area (TGMPA) and twelve sites were outside (six northwards and six southwards) in surrounding unprotected areas up to ∼100 km from the MPA.
Each site was defined as a stretch of coastline 10–100 m long. At each site, a total of 24 replicated visual censuses were carried out (n = 16 for settlers, n = 8 for adults) for a total of 336 replicates. Due to the high number of replicates, the sampling for settlers was carried out by two operators equally partitioning the number of replicates within each site.
Density and size of adults were estimated in July 2010 by scuba underwater visual census using strip transects of 25×5 m [56] at 6–10 m depth. In each transect, abundance and size of fish encountered were recorded. Fish size (total length, TL) was recorded within 2 cm size classes. Fish wet-weight was estimated from size data by means of a length–weight relationship available from the literature and referring to Mediterranean samples [57].
Density of settlers was evaluated during the settlement peak in June 2009 by means of snorkeling visual census along strip transects of 25×2 m [53], [56] at 0–2 m depth. For both adults and settlers each transect was explored in ∼10 min. Actual number of fish encountered was recorded up to 10 individuals, whereas larger groups were recorded using categories of abundance (i.e. 11–30, 31–50, 51–200, 201–500, >500 ind.; see [56]). Transects were parallel to the coastline, in small embayments (i.e. 200–400 m) with shallow rocky habitats alternated with sand patches, which represent the preferential habitat for settlers of D. sargus sargus [46], [47], [53].
Sampling sites were randomly selected out of a pool of possible sites having similar features in terms of habitat types’ coverage and exposition.
Concomitantly with density estimates, settlers of D. sargus sargus were collected for a preliminary genetic analysis. Settlers were collected in June 2009 at three separate sites within the study area: (i) one site (Punta Penna Grossa) within the TGMPA, (ii) one site (Torre Pozzella) located ∼20 km north of the MPA, and (iii) one site (Punta Penne) located ∼15 km south of the MPA. Genetic analyses were conducted on a sub-sample of 96 individuals, including 48 from the MPA, 24 from Torre Pozzella and 24 from Punta Penne. After collection both post-settlers and recruits were immersed in an ice slurry (<5°C) to minimize suffering and then stored in 95% ethanol. The experimental fishing activity was performed in strict accordance with the authorization protocol provided by the Italian Ministry of Agriculture, Foods and Forestry Politics (Permit Number: 0011267–2010).
To test for potential differences in fish densities along the study area, two univariate PERMANOVA analyses were carried out separately for adults and settlers. An asymmetrical sampling design was adopted that included the following factors: Geographic Area (GA; Fixed factor with three levels: North, MPA, South) and Site (Si, random factor, nested within GA, with 2–6 levels: 2 in TGMPA, 6 in South and North). Linear distance (Di) in km from TGMPA boundary (set to 0 for the two sites within TGMPA) was used as a covariate. Distances were measured using georeferenced satellite imagery from Google Earth (http://earth.google.com).
The same experimental design was adopted to test for potential spatial variation in biomass of adult specimens. Post-hoc pairwise tests were carried out, whenever appropriate, if PERMANOVA detected significant differences.
Putative differences in adult size among GAs were tested using 1-way PERMANOVA, which does not involve any assumption about the distribution of the variable. For each GA, individual fish size data were pooled and plotted as size–frequency distributions. Statistical analyses were run using Primer 6 PERMANOVA+software package (Plymouth Marine Laboratory).
Adriatic Forecasting System and Lagrangian Simulations
The study area is covered by the Adriatic Forecasting System (AFS) which provides daily oceanographic model outputs consisting of simulation (for the past time) and forecast for the next 10 days [45], [58]. The output products of the model are daily fields of ocean temperature, salinity and current on a regular three-dimensional grid. AFS products are produced and delivered by Istituto Nazionale di Geofisica and Vulcanologia (INGV, http://gnoo.bo.ingv.it/afs).
The model used is the POM (Princeton Ocean Model). It is a three-dimensional finite difference, free surface, primitive equation numerical model, based on the Boussinesq and the hydrostatic approximation and a split mode time step. The AFS model domain encompasses the whole Adriatic basin and extends south of the Otranto channel into the northern Ionian Sea, where the only open boundary is located. The AREG (Adriatic REGional model) grid has a horizontal resolution of about 1/45° (about 2.2 km), on 31 σ-layers. The surface fluxes are interactively computed using sea surface temperature predicted by the model and realistic atmospheric data provided by the European Centre for Medium Range Weather Forecast (ECMWF) with a frequency of six hours and a resolution of 0.25°. AFS is nested into the Mediterranean Forecasting System, MFS [59], [60] that provides to AFS the initial and lateral boundary conditions for temperature, salinity and velocity. The lateral boundary conditions are taken from MFS on a daily basis. In MFS the use of the data assimilation [61] allows the reconstruction of the ocean dynamics in the past by merging model simulations with satellite and in situ observations.
The Lagrangian simulations used in this work were performed by using the User Visualization Tool (UVT), a software developed by INGV that ingests the AFS model products allowing the user to derive particle trajectories by simulation. Each Particle (P), representing a single propagule, is described in terms of its spatial position (Px,y,z) at each time step (t). The depth at which the particle is released at the beginning of the simulation (t = 0) was kept constant along the entire simulation as quantitative data is not available to support alternative assumption on larval behavior and vertical movements for the studied species.
UVT calculates current velocity components (ui,j,vi,j) for each particle location (Pi,j) at time t (spatial and temporal interpolation of AFS model output) using the information provided at each grid point by AFS products. UVT then calculates the movement of the particle ((ui,j,vi,j)×dt) where dt corresponds to the time step of integration. The new position of the particle at t+dt is then calculated. The horizontal resolution of the AFS model grid does not allow a precise simulation of hydrodynamic processes very close to the coast, and this should be taken into account when interpreting simulated dispersal patterns.
Results from otolith analysis taken from [34] provided information on the spawning schedule and PLD. Passive particles were released in UVT within TGMPA and tracked for 17 days (the average PLD estimated through otolith analysis; [34]) in order to assess dispersal patterns from TGMPA. Simulations were carried out at three different depths (1, 5 and 10 meters) along the water column. These depths were selected based on the available information on vertical distribution of white sea bream propagules, indicating that larvae are more frequently collected within 0–10 meters [62]. Twenty-four particles were released during each simulation. The number of particles was set up after preliminary analyses that did not highlight any detectable difference in trajectories increasing the number of particles to over 24 (Coppini and Lyubartsev, data not reported). The number of particles, therefore, was chosen in order to allow a clear visualization of trajectories of tracers.
The starting positions of the particles were defined over an equally spaced grid within a circle of 5 km around the actual sampling position. Simulations were started in coincidence of 4 spawning dates that were back-calculated from otolith analysis [34] - namely 9th, 11th, 13th, 15th of May, 2009– and were aimed at representing the oceanographic regime occurring at the time of, and in the 17 days following, the spawning events. A total of 12 simulations (arising from the combination of 4 spawning dates and 3 depths: 1 m, 5 m and 10 m) were performed. High consistency among repeated simulations (i.e. two simulations carried out under the same experimental setting) was observed during preliminary analyses.
Genetic Analyses
Minute sections of the caudal fin were digested in a lysis buffer containing 100 µl TE Buffer, 7 µl 1 M DTT (dithiothreitol) solution pH 5.2 (diluted in 0.08 M NaAC) and 2 µl proteinase K solution (20 mg/ml) for at least 8 hours at 56°C. After incubation at 96°C for 10 min, samples were centrifuged at 13,000 rpm for 11 min, and the supernatant was stored at −20°C.
Genotypes were examined at a total of 12 microsatellite loci originally developed for the gilthead sea bream Sparus aurata [63], [64] that positively amplified in D. sargus sargus. Microsatellites were grouped into two separate multiplexes in order to reduce PCR and genotyping costs (Table 2). PCR products were obtained in a GeneAmp PCR System 2700 Thermocycler (Applied Biosystems) using the QIAGEN Multiplex PCR Kit. PCR reactions consisted of 2 µl template DNA, 5 µl QIAGEN Multiplex PCR Master Mix, 0.2 µl 10 µM forward and reverse primers and water up to 10 µl. PCR conditions were as follows: 15 min at 95°C, 35 cycles of 30 sec at 94°C, 90 sec at 57°C and 1 min at 72°C and final elongation for 30 min at 60°C. PCR products were visualized on 1.8% agarose gels and screened for microsatellite polymorphism using an ABI 3130 AVANT automatic capillary sequencer (Applied Biosystems).
Within sample genetic diversity was assessed by observed (Ho) and expected (He) heterozygosities per locus using GENETIX version 4.05 [65] and allelic richness (AR) using FSTAT version 2.3.9.2 [66]. Diversity values across samples were compared with one-way ANOVA using STATISTICA version 10 (StatSoft). Deviations from Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium were tested using GENEPOP version 3.4 [67]. Significance levels for multiple comparisons were adjusted using the sequential Bonferroni technique [68]. Presence of null alleles was tested using MICROCHECKER version 2.2.3 [69].
Prior to the population structure analysis, the statistical power of the markers employed was assessed with POWSIM [70]. We tested a range of predefined levels of expected divergence (FST = 0.001, 0.005, 0.01, 0.05, 0.1) using an Ne value of 1,000. Differences in allele and genotype frequencies among samples were assessed using Fisher's exact test as implemented in GENEPOP. Significance levels for multiple simultaneous comparisons were adjusted using Bonferroni as described above. Population structure was explored by nonhierarchical AMOVA and by calculating pairwise FST between samples in ARLEQUIN 3.5.1.2 [71]. Population substructuring was also explored with the software STRUCTURE [72], a model-based clustering algorithm that infers the most likely number of groups in the data. The software organizes individuals into a predefined number of clusters (K) with a given likelihood, which might represent putative populations. The analysis was performed for 1< K <3, with five replicates per K and using the admixture model. A burn-in length of 104 iterations followed by 106 additional Markov Chain Monte Carlo (MCMC) iterations were performed. The most likely K was determined using the criterion of [73].
Results
Spatial Distribution Patterns of Adults and Settlers
Density of adult D. sargus sargus was significantly affected by distance from TGMPA, i.e. it decreased with distance from TGMPA borders. Within TGMPA density was significantly higher than that observed at both northern and southern GAs (122±51, 11±9 and 6±3 ind./ha respectively; Pairwise tests: TGMPA>North = South, Fig. 2). Density significantly varied among sites (Table 3).
Significant differences in average body size were found among GAs (p<0.001, Fig. 3). Pairwise tests highlighted that bigger fish sizes were observed within TGMPA compared to South and North areas (p<0.001 for both pairwise tests) while no significant difference was detected between South and North.
Biomass of adult white sea bream was related to distance from TGMPA (with density decreasing at increasing distance from TGMPA borders) and it was significantly higher at TGMPA than at northern and southern areas (58.5±2.3, 6±2.4 and 10.9±2.2 Kg/ha; pairwise tests: MPA>North = South, Fig. 2). Adult biomass did not vary significantly at the spatial scale of sites (Table 3).
Density of settlers was not related to distance from TGMPA and was significantly affected by GA. The highest density of settlers was found within the TGMPA (14431.25±4205 ind./ha, mean±S.E.), followed by that observed at the southern GA (9808.33±1438.75 ind./ha), while the lowest values were found at the northern GA (2202.08±624.65 ind./ha). Mean density of settlers at both TGMPA and southern GA were significantly greater than the mean values in northern GA (pairwise tests: TGMPA = South>North, Fig. 4). Density of settlers significantly differed among sites (Table 4).
Simulated Dispersal Trajectories and Travel Distances
The twelve simulations (4 release dates×3 depths) generated with the UVT showed that the particles released from TGMPA stayed in part within TGMPA and in part flowed southward, with travel distances ranging between few kilometers and about 180 kilometers (Fig. 5, 6, 7).
Simulations started respectively a) on 9th, b) 11th, c) 13th, d) 15th of May. All simulations lasted 17 days.
Simulations started respectively a) on 9th, b) 11th, c) 13th, d) 15th of May. All simulations lasted 17 days.
Simulations started respectively a) on 9th, b) 11th, c) 13th, d) 15th of May. All simulations lasted 17 days.
Simulations carried out at three different depths (1 m, 5 m and 10 m) were in agreement in terms of overall southwards direction for all release dates considered, but differed in terms of dispersal distance. Particles released at the shallowest depth (1 m, Fig. 5) travelled at distances between few kilometers (<2–5 km) and about 100 km of coastline, while those released and travelling at 5 m (Fig. 6) and 10 m (Fig. 7) depth travelled from few kilometers (<2–5 km) to about 180 km of coastline.
Genetic Patterns
Values of genetic diversity in all settler samples are detailed in Table 5. Similar values were observed for all diversity indices including observed (Ho = 0.617–0.692) and expected (He = 0.677–0.721) heterozygosity and allelic richness (AR = 7.78–9.77). All comparisons across samples using one-way ANOVA were statistically not significant (p>0.5). Only four out of 34 tests departed from HWE, but none were significant after Bonferroni correction. The software MICROCHECKER showed no evidence for scoring errors due to stuttering or large allele dropout. No linkage disequilibrium was observed between any pair of loci after Bonferroni correction. Simulations using our empirical microsatellite data in POWSIM, taking into account the sample size of each population and a wide range of predefined FST values, showed that our markers have enough statistical power to detect FST values ranging from 0.001 to 0.1 (p = 1.000).
Comparison of allele frequencies among samples showed no significant differences in settlers at any locus or across all loci (p = 0.221). AMOVA analysis suggested no genetic sub-structuring (FST = 0.001), with no significant differences within (FIS = 0.071) or among sites (FIT = 0.072). All pairwise comparisons were not significant (p>0.05). The software STRUCTURE inferred one single population as most likely (K = 1; ln likelihood = −3864.8) in comparison with K = 2 (ln likelihood = −3990.1) or K = 3 (ln likelihood = −4098.6).
Discussion
This study provides a strong indication that TGMPA is effective in protecting adults (i.e. spawners) of the white sea bream D. sargus sargus, accordingly with previous results [40] and the patterns observed for other Mediterranean MPAs [19], [48], [74]. In fact, TGMPA hosts the highest density and biomass of larger sized spawners across the 200 km of the sampling area along the southern Apulian Adriatic coast. Therefore, TGMPA can be considered as one of the potentially most effective sources of propagules (eggs and larvae) of white sea bream in the SW Adriatic Sea.
This hypothesis matches the findings related to a congeneric species (Diplodus vulgaris) that was demonstrated to have a potential egg production approx. 15 times higher within TGMPA than in outer fished areas [75].
These findings allow to hypothesize an effectively enhanced production of propagules and subsequent retention/export from TGMPA, with potential effects on population recovery/replenishment within the TGMPA and in the surrounding areas. High densities of large-sized spawners within MPAs are well known to enhance breeding stock biomass [76], favor the occurrence of spawning aggregations [77] and, consequently, generate far greater production of propagules compared to fished areas [10], [11], [78].
Even though it is reasonable to say that local populations within effective MPAs could self-sustain via propagule retention and/or seed unprotected areas via propagule export [1], empirical evidence is scarce and sometimes controversial [24], [38], [79]. Retention and/or export from a source area are mechanisms mostly driven by dispersal at the larval stage. Marine currents and long pelagic larval phases imply a high potential for long-distance dispersal in marine organisms [80]–[81]. Literature examples indicate a wide range of larval dispersal distances for fish, which may vary from a few meters to hundreds of kilometers (with an average of ∼100 km; [82]–[83]). In the case of D. sargus sargus, a larval dispersal of ∼100–200 km has been recently reported using otolith chemistry [84], a spatial scale which agrees with the results of the Lagrangian simulations presented here.
Although larvae may have the potential to disperse over large distances, fractions of those produced locally may be retained near the spawning areas [85]–[89]. This is generally attributed to local oceanographic structures favoring retention along with larval behavior and swimming ability, which allow larvae to swim towards or stay at the (suitable) habitats that already host the adults of the same species, sometimes contrasting sea currents [90]. Fish larvae can reach (or be retained in) suitable sites for settlement thanks to powerful sensory cues (i.e. olfactory, hearing and celestial) that are used for orientation in the pelagic environment ([91], [92] and see [93] for a review). In the present study, even though simulated particles were considered as passive, the Lagrangian simulations supported the hypothesis of propagule retention within TGMPA thanks to the presence of coastal eddies preventing a fraction of larvae to disperse far beyond the TGMPA boundaries.
It is well know, however, that larval behavior and particularly vertical migration [93] can affect dispersal pattern [94]. Therefore, the incorporation of behavioral information in Lagrangian models can deeply influence the predicted output of the models [94]–[96]. Reliable information on the pattern of vertical migration in larvae is nonetheless scarce and most Lagrangian simulation exercises considered particles as passive (see [96]). Looking at the scanty literature available on the subject, a higher rate of retention is observed whenever larval behavior is accounted for (i.e. [95], [96]). It is thus possible that our model might have underestimated larval retention at TGMPA. Further modeling and field studies will be necessary in the future to thoroughly investigate pattern, magnitude and geographical range of larval dispersal in this geographical area.
The evidence arising from the three approaches adopted in the present study (visual census, Lagrangian modeling and genetics) suggests a high degree of connectivity between the MPA and adjacent areas at the spatial scale investigated, indicative of transport of propagules well beyond the MPA boundaries. All simulations showed a general southward export, while dispersal distances varied depending on the depth of simulation. Similarly, using dispersal models in large estuarine systems of North America, other authors found a significant variability in spatial patterns of dispersed particles related to depth [97].
Lagrangian simulations and settler density observed in the field (considered as a proxy of larval production) agreed in suggesting both propagule retention and export mechanisms directed southwards. Density of settlers, moreover, did not vary significantly with distance from TGMPA, suggesting a fairly homogenous propagule dispersal in open waters over a scale of ∼100 km. On the other hand, density of settlers varied among sites, which could reflect a different suitability for settlement of coastal sites as a function of habitat type.
A significant export of larvae beyond TGMPA boundaries, as predicted by model simulations, is in agreement with the observed homogeneity found in our preliminary genetic study. Comparison of samples obtained in TGMPA and neighboring areas showed no differences in genetic variability or genetic composition, which indicates that the TGMPA is not isolated and that there is genetic connectivity among sites at the scale of approximately 50 km. The pattern of dispersion suggested by the oceanographic model could explain the genetic similarity between the TGMPA and southern sites, with down-current sites effectively replenished by TGMPA. However, the genetic homogeneity between TGMPA and northern sites cannot be explained by larval dispersal from TGMPA under the simulated oceanographic regime, as all particles originating from TGMPA either stayed within the protected area or moved southward. As a consequence, the observed genetic homogeneity could be due to gene flow from northern sites towards TGMPA and even more southern sites. In addition, a gene flow between TGMPA and adjacent northern areas could be ascribed also to the exchange of juvenile and adult individuals between TGMPA and northern sites. Evidence of connectivity among TGMPA and the adjacent northern areas has been documented only for juveniles [84]. Alternatively, as evidenced by [98]–[100], under particularly strong Sirocco (i.e. the wind blowing from the southeast) conditions the dominant southward WACC may reverse in direction resulting in a northward dispersal of larvae potentially able to homogenize the allele frequencies between TGMPA and the northern sites. From this perspective at the spatial scale investigated in the present study TGMPA does not seem to act as a genetic diversity reservoir. However this statement should be taken cautiously because the sampling scheme we adopted is not fully suitable to investigate this noteworthy point that would deserve further investigation. Additional genetic work including a large number of sampling sites and an higher number of samples in each site should be conducted to confirm the results from this preliminary study.
Gotelli [101] coined the term ‘propagule rain’ to describe the possible spatial decoupling between propagule production at spawning sites and juvenile replenishment occurring at settlement sites, due to dispersal at larval stage. Conceptually, propagule rain does not involve a priori any directionality in dispersal (i.e. sensu [17]). A number of field studies showed that abundance of fish larvae was indeed higher near MPAs and decreased, with no specific directionality, with distance from MPA boundaries [38], [102], [103]. Similar patterns were found also for mollusk settlers (used as a proxy of larval production [31], [37]). A mechanism similar to Gotelli’s propagule rain but characterized by a strong directionality might instead occur in the coastal region investigated in the present study: the results of the oceanographic simulations support the hypothesis that the specific ‘directional’ pattern observed in settlement in the SW Adriatic can be ascribed to the specific hydrodynamic conditions (e.g. current regimes) and topography of this region.
This picture does not match completely with the ‘directional’ patterns we have found in the SW Adriatic, which could be due to differences in early life-history traits of species belonging to different phyla (mollusks vs fishes), or differences in hydrodynamic conditions (e.g. current regimes) and topography (e.g. continuous coastline vs islands) among the different study areas.
Pelc et al. [17] discussed the conditions under which dispersal can be more easily detected: 1) short distance of larval dispersal from the source (e.g. a MPA), 2) directional dispersal and 3) appropriate sampling schemes. Particularly in case of a 10 km MPA with a 15-fold higher production compared to outside (as recorded for a congeneric fish at TGMPA, [75]), spatial pattern at settlement for long-dispersing species (>50 km) did not show any decline across the MPA’s boundary and over tens of kilometers. On the other hand, a large decline was modeled for short-dispersing species (i.e. <10 km) [17]. From this perspective the absence of difference among MPA and downcurrent area (i.e. South) in density of settlers could be explained by an effective dispersal of white seabream larvae, estimated to be >100 km in the study area [84]. Conversely, the difference between TGMPA and up-current area (i.e. North) in terms of settlement magnitude could be due to a reduced larval dispersal northwards as suggested by Lagrangian simulations. However, in the absence of further evidence (e.g. arising from empirical measures of larval dispersal, [22]–[24]) such an hypothesis has to be taken cautiously.
Fishes are generally characterized by relatively high larval dispersal [82] compared to mollusks. The white sea bream displays a fairly wide larval dispersal of at least 200 km along the SW Apulian coast [84]. The directional dispersal observed in the present study, the spatially replicated design used here, along with the use of multiple approaches, allowed us to obtain converging evidence about the dispersal patterns of D. sargus sargus around TGMPA in SW Apulia. These findings support the idea that TGMPA could produce benefits that go well beyond its boundaries.
The lack of knowledge on whether and how MPAs can benefit fisheries through propagule export remains a major roadblock to successfully designing and implementing MPAs worldwide [17], [18]. Properly assessing patterns of propagule dispersal from MPAs is crucial and the results of the present study offer useful information to build up guidelines to design both single MPAs and networks of MPAs that may satisfy both conservation and management needs. Our study also highlights the importance of integrating different approaches (field sampling, modeling and genetics; see also [104]) to investigate propagule dispersal from MPAs and get reliable convergent evidence on patterns of fish propagule dispersal.
In conclusion, the present study provides some elements suggesting that, in the case of D. sargus sargus in the SW Adriatic, a network of effective and self-sustaining MPAs (as it seems to be the case for TGMPA) placed at a distance of 100–200 kilometers could provide benefits for both conservation and fishery output thanks to the replenishment of fished areas through larval export. Carrying on similar studies on a number of representative species and in other areas, and possibly also taking into account propagule inflow in MPAs (e.g. using Lagrangian back-tracking techniques), will make it possible to draw more general conclusions that may help design effective regional networks of MPAs.
Acknowledgments
Authors wish to thank Christian Vaglio, Manfredi Di Lorenzo (University of Salento), Commander Ugo Adorante (Nucleo Subacqueo Carabinieri, Bari, Italy) and his team (Pietro Di Pinto, Gianfranco Simonini, Carlo Del Console, Fabrizio Pichierri, Gianni Sgariglia, Roberto Ciccacci, Mauro Bellini) for assistance during field work. Thanks also to Dr. Sandro Ciccolella (Director of Torre Guaceto MPA) and his staff for helpful assistance. Many thanks are due to Rita Sahyoun (CNRS-EPHE, France) for invaluable assistance in preparation of genetic samples, to Prof. K. Robert Clarke (Plymouth Marine Laboratory, Plymouth, UK), Prof. Marti J. Anderson (Massey University, NZ) and Prof. Antonio Terlizzi (University of Salento) for their invaluable suggestions about experimental design.
Authors wish to thank the editor Dr Sebastian C. A. Ferse and the two anonymous reviewers for their useful comments which have helped us greatly improve the manuscript.
The experimental fishing activity was performed in strict accordance with the authorization protocol provided by Italian Minister of Agriculture, Foods and Forestry Politics (Permit Number: 0011267–2010).
Author Contributions
Conceived and designed the experiments: ADF PG LZ JMP MG PM GDL. Performed the experiments: ADF PG LZ JPM MG PM GDL GC VL. Analyzed the data: ADF PG LZ JPM MG PM GDL GC VL. Contributed reagents/materials/analysis tools: ADF PG LZ JPM MG PM GDL GC VL. Wrote the paper: ADF PG LZ JPM MG PM GDL GC VL.
References
- 1. Halpern BS, Warner RR (2002) Marine reserves have rapid and lasting effects. Ecology Letters 5: 361–366.
- 2.
PISCO, Partnership for Interdisciplinary Studies of Coastal Oceans (2007) The Science of Marine Reserves (2nd Edition, International Version). www.piscoweb.org. 22 pages.
- 3.
PISCO, Partnership for Interdisciplinary Studies of Coastal Oceans (2011) The Science of Marine Reserves (2nd Edition, Europe). www.piscoweb.org. 22 pages.
- 4. Halpern BS (2003) The impact of marine reserves: Do reserves work and does reserve size matter? Ecological Applications 13: S117–S137.
- 5. Fenberg PB, Caselle J, Claudet J, Clemence M, Gaines SD, et al. (2012) The science of European marine reserves: Status, efficacy, and future needs. Marine Policy 36: 1012–1021.
- 6. Babcock RC, Shears NT, Alcala AC, Barrett NS, Edgar GJ, et al. (2010) Decadal trends in marine reserves reveal differential rates of change in direct and indirect effects. Proceedings of National Academy of Science USA 107: 18256–18261.
- 7. Lester SE, Halpern BS, Grorud-Colvert K, Lubchenco J, Ruttenber BI, et al. (2009) Biological effects within no-take marine reserves: a global synthesis. Marine Ecology Progress Series 384: 33–46.
- 8. Grüss A, Kaplan DM, Guénette S, Roberts CM, Botsford LW (2011) Consequences of adult and juvenile movement for marine protected areas. Biological Conservation 144: 692–702.
- 9. Halpern BS, Lester SH, Kellner JB (2009) Spillover from marine reserves and the replenishment of fished stocks. Environmental Conservation 36: 268–276.
- 10. Branch GM, Odendaal F (2003) The effects of marine protected areas on the population dynamics of a South African limpet, Cymbula oculus, relative to the influence of wave action. Biological Conservation 114: 255–269.
- 11. Evans RD, Russ GR, Kritzer JP (2008) Batch fecundity of Lutjanus carponotatus (Lutjanidae) and implications of no-take marine reserves on the Great Barrier Reef, Australia. Coral Reefs 27: 179–189.
- 12. Gell FR, Roberts CM (2003) Benefits beyond boundaries: the fishery effects of marine reserves. Trends in Ecology & Evolution 18: 448–455.
- 13. Halpern BS, Warner RR (2003) Matching marine reserve design to reserve objectives. Proceedings of Royal Society of London B 270: 1871–1878.
- 14. Le Quesne WJF, Codling EA (2009) Managing mobile species with MPAs: the effects of mobility, larval dispersal, and fishing mortality on closure size. ICES Journal of Marine Science 66: 122–131.
- 15. Agardy T, Notarbartolo di Sciara G, Christie P (2011) Mind the gap: Addressing the shortcomings of marine protected areas through large scale marine spatial planning. Marine Policy 35: 226–232.
- 16.
Claudet J, Guidetti P, Mouillot D, Shears NT, Micheli F (2011) Effects of marine protected areas. Ecology: ecological effects of marine protected areas: conservation, restoration, and functioning. In: Marine Protected Areas. A multidisciplinary approach (J Claudet, ed.), Cambridge University Press: 37–71.
- 17. Pelc RA, Warner RR, Gaines S, Paris CB (2010) Detecting larval export from marine reserves. PNAS 107(43): 18266–18271.
- 18. Sale PF, Cowen RK, Danilowicz BS, Jones GP, Kritzer JP, et al. (2005) Critical science gaps impede use of no-take fishery reserves. Trends in Ecology and Evolution 20: 74–80.
- 19. Guidetti P, Milazzo M, Bussotti S, Molinari A, Murenu M, et al. (2008) Italian marine reserve effectiveness: Does enforcement matter? Biological Conservation 141: 699–709.
- 20. Jones GP, Almany GR, Russ GR, Sale PF, Steneck RS, et al. (2009) Larval retention and connectivity among populations of corals and reef fishes: history, advances and challenges. Coral Reefs 28: 307–325.
- 21. Williamson DH, Jones GP, Thorrold SR (2009) An experimental evaluation of transgenerational isotope labeling in a coral reef grouper. Marine Biology 156: 2517–2525.
- 22. Planes S, Jones GP, Thorrold SR (2009) Larval dispersal connects fish populations in a network of marine protected areas. Proceedings of National Academy of Science USA 106(14): 5693–5697.
- 23. Christie MR, Tissot BN, Albins MA, Beets JP, Jia Y, et al. (2010) Larval Connectivity in an Effective Network of Marine Protected Areas. PLoS ONE 5(12): e15715 .
- 24. Harrison HB, Williamson DH, Evans RD, Almany GR, Thorrold SR, et al. (2012) Larval Export from Marine Reserves and the Recruitment Benefit for Fish and Fisheries. Current Biology 22(11): 1023–1028 .
- 25. Almany GR, Connolly SR, Heath DD, Hogan JD, Jones GP, et al. (2009) Connectivity, biodiversity conservation, and the design of marine reserve networks for coral reefs. Coral Reefs 28: 339–351.
- 26. Botsford LW, White JW, Coffroth MA, Paris CB, Planes S, et al. (2009) Connectivity and resilience of coral reef metapopulations in marine protected areas: matching empirical efforts to predictive needs. Coral Reefs 28: 327–337.
- 27. Paris C, Cowen RK (2004) Direct evidence of a biophysical retention mechanism for coral reef fish larvae. Limnology and Oceanography 49: 1964–1979.
- 28. Paris C, Cowen RK, Claro R, Lindeman K (2005) Larval transport pathways from Cuban snapper (Lutjanidae) spawning aggregations based on biophysical modeling. Marine Ecology Progress Series 296: 93–106.
- 29. Cowen RK, Paris C, Srinivasan A (2006) Scaling of connectivity in marine populations. Science 311: 522–527.
- 30. Treml EA, Halpin PN, Urban DL, Pratson LF (2008) Modeling population connectivity by ocean currents, a graph-theoretic approach for marine conservation. Landscape Ecology 23: 19–36.
- 31. Cudney-Bueno R, Lavin MF, Marinone S, Raimondi PT, Shaw WW (2009) Rapid effects of marine reserves via larval dispersal. PLoS One 4: e4140.
- 32. Watson JR, Mitarai S, Siegel DA, Caselle JE, Dong C, et al. (2010) Realized and potential larval connectivity in the Southern California Bight. Marine Ecology Progress Series 401: 31–48.
- 33. Planes S, Galzin R, Garcia Rubies A, Goni R, Harmelin JG, et al. (2000) Effects of marine protected areas on recruitment processes with special reference to Mediterranean littoral ecosystems. Environmental Conservation 27(2): 126–143.
- 34. Di Franco A, Guidetti P (2011) Patterns of variability in early-life traits of fishes depend on spatial scale of analysis. Biology Letters 7: 454–456.
- 35. Di Franco A, De Benedetto G, De Rinaldis G, Raventos N, Sahyoun R, et al. (2011) Large scale variability in otolith microstructure and microchemistry: the case study of Diplodus sargus sargus (Pisces: Sparidae) in the Mediterranean Sea. Italian Journal of Zoology 78(2): 182–192.
- 36. Kingsford MJ, Smith FJA, Flood MJ (2011) Growth and pelagic larval duration of presettlement and newly settled neon damselfish, Pomacentrus coelestis, at multiple spatial scales. Coral Reefs 30: 203–214.
- 37. Pelc RA, Baskett ML, Tanci T, Gaines SD, Warner RR (2009) Quantifying larval export from South African marine reserves. Marine Ecology Progress Series 394: 65–78.
- 38. Crechriou R, Alemany F, Roussel E, Chassanite A, Marinaro JY, et al. (2010) Fisheries replenishment of early life taxa: potential export of fish eggs and larvae from a temperate marine protected area. Fisheries Oceanography 19: 135–150.
- 39. Gaines S, Brown S, Roughgarden J (1985) Spatial variation in larval concentrations as a cause of spatial variation in settlement for the barnacle, Balanus glandula. Oecologia 67(2): 267–272.
- 40. Guidetti P (2006) Marine reserves re-establish lost predatory interactions and cause community-wide changes in rocky reefs. Ecological Application 16: 963–976.
- 41. Lloret J, Zaragoza N, Caballero D, Riera V (2008) Biological and socioeconomic implications of recreational boat fishing for the management of fishery resources in the marine reserve of Cap de Creus (NW Mediterranean). Fishery Research 91: 252–259.
- 42. Artegiani A, Bregant D, Paschini E, Pinardi N, Raicich F, et al. (1997) The Adriatic Sea general circulation. Part II: Baroclinic Circulation Structure. Journal of Physical Oceanography 27: 1515–1532.
- 43. Poulain PM (2001) Adriatic Sea surface circulation as derived from drifter data between 1990 and 1999. Journal of Marine Systems 29: 3–32.
- 44. Zavatarelli M, Pinardi N (2003) The Adriatic Sea modelling system: a nested approach. Annales Geophysicae 21: 345–364.
- 45. Oddo P, Pinardi N, Zavatarelli M (2005) A numerical study of the interannual variability of the Adriatic Sea (1999–2002). Science of Total Environment 353: 39–56.
- 46. Tortonese E (1965) Biologie comparée de trois espèces méditerranéennes de Diplodus (Pisces, Sparidae). Rapp. Commission International pour l’Exploration Scientifique de la Mer Méditérranée 18: 189–192.
- 47. Harmelin-Vivien ML, Harmelin JG, Leboulleux V (1995) Microhabitat requirements for settlement of juvenile sparid fishes on Mediterranean rocky shores. Hydrobiologia 300–301: 309–320.
- 48. Guidetti P, Sala E (2007) Community-wide effects of marine reserves in the Mediterranean Sea. Marine Ecology Progress Series 335: 43–56.
- 49. Di Franco A, Bussotti S, Navone A, Panzalis P, Guidetti P (2009) Evaluating effects of total and partial restrictions to fishing on Mediterranean rocky-reef fish assemblages. Marine Ecology Progress Series 387: 275–285.
- 50. D’Anna G, Giacalone VM, Pipitone C, Badalamenti F (2011) Movement pattern of white seabream, Diplodus sargus (L., 1758) acoustically tracked in an artificial reef area. Italian Journal of Zoology 78(2): 255–263.
- 51.
Vigliola L (1998) Contrôle et régulation du recruitment des sparidae (Poissons, Téléostéens) en Méditerranée: importance des processus pré- et post-installation benthique. PhD thesis, University Aix-Marseille II.
- 52. Macpherson E (1998) Ontogenetic shifts in habitat use and aggregation in juvenile sparid fishes. Journal of Experimental Marine Biology and Ecology 220: 127–150.
- 53. Bussotti S, Guidetti P (2011) Timing and habitat preferences for settlement of juvenile fishes in the Marine Protected Area of Torre Guaceto (south-eastern Italy, Adriatic Sea). Italian Journal of Zoology 78: 243–254.
- 54. Cheminee A, Francour P, Harmelin-Vivien M (2011) Assessment of Diplodus spp. (Sparidae) nursery grounds along the rocky shore of Marseilles (France, NW Mediterranean). Scientia Marina 75(1): 181–188.
- 55. Mouine N, Francour P, Ktari M, Chakroun-Marzouk N (2007) The reproductive biology of Diplodus sargus sargus in the Gulf of Tunis (central Mediterranean). Scientia Marina 71(3): 461–469.
- 56. Harmelin-Vivien ML, Harmelin JG, Chauvet C, Duval C, Galzin R, et al. (1985) Evaluation des peuplements et populations de poissons. Méthodes et problèmes. Rev Ecol Terre Vie 40: 467–539.
- 57.
Bayle-Sempere JT, Valle C, Verdù A (2002) ECOCEN v1.00.00. Application for managing fish visual counts. Universitat d’Alacant.
- 58.
Guarnieri A, Oddo P, Pastore M, Pinardi N, Ravaioli M (2010) “The Adriatic Basin Forecasting System: new model and system development”. Coastal to Global Operational Oceanography: Achievements and Challenges. Proceeding of 5th EuroGOOS Conference, Exeter, 20–22 May 2008, 184–190. Edited by Dahlin H., Fleming N.C., and Petersson S.E.
- 59. Tonani M, Pinardi N, Dobricic S, Pujol I, Fratianni C (2008) A High Resolution Free Surface Model on the Mediterranean Sea. Ocean Science 4: 1–14.
- 60. Pinardi N, Coppini G (2010) Operational oceanography in the Mediterranean Sea: the second stage of development. Ocean Science Special Issue, Mediterranean Ocean Forecasting System: toward environmental predictions – the results. Ocean Science 6: 263–267.
- 61. Dobricic S, Pinardi N (2008) An oceanographic three-dimensional assimilation scheme. Ocean Modelling 22: 89–105.
- 62. Olivar MP, Sabates A (1997) Vertical distribution of fish larvae in the north-west Mediterranean Sea in spring. Marine Biology 129: 289–300.
- 63. Franch R, Louro B, Tsalavuota M, Chatziplis D, Tsigenopoulos CS, et al. (2006) A genetic linkage map of the hermaphrodite teleost fish Sparus aurata L. Genetics. 174: 851–861.
- 64. Massault C, Franch R, de Koning DJ, Bovenhuis H, Pellizzari C, et al. (2011) Quantitative trait loci for resistance to fish pasteurellosis in gilthead sea bream Sparus aurata. Animal Genetics 41: 191–203.
- 65.
Belkhir K, Borsa P, Goudet J, Bonhomme F (2005) GENETIX 4.05: Logiciel sous Windows pour la genetique des populations. Laboratoire Genome and Population, CNRS-UPR/Universite de Montpellier II, Montpellier, France.
- 66.
Goudet J. (2002) FSTAT version 2.9.3.2. Available: http://www2.unil.ch/popgen/softwares/fstat.htm.
- 67. Raymond M, Rousset F (1995) GENEPOP (version 1.2): a population genetics software for exact tests and ecumenicism. Journal of Heredity 86: 248–249.
- 68. Rice WR (1989) Analyzing tables and statistical tests. Evolution 43: 223–225.
- 69. Van Oosterhout CV, Hutchinson WF, Wills DPM, Shipley P (2004) Micro-checker: software for identifying and correcting genotyping errors in microsatellite data. Molecular Ecology 4: 535–538.
- 70. Ryman N, Palm S (2006) POWSIM: a computer program for assessing statistical power when testing genetic differentiation. Molecular Ecology Notes 6: 600–602.
- 71. Excoffier L, Laval G, Schneider S (2005) Arlequin ver. 3.0: an integrated software package for population genetics data analysis. Evolutionary Bionformatics Online 1: 47–50.
- 72. Pritchard JK, Stephens M, Donelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155: 945–959.
- 73. Evanno G, Regnaut S, Houdet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular Ecology 14: 2611–2620.
- 74. Claudet J, Osenberg CW, Benedetti-Cecchi L, Domenici P, García-Charton JA, et al. (2008) Marine reserves: Size and age do matter. Ecology Letters 11(5): 481–489.
- 75.
Guidetti P, Planes S, Bussotti S, Di Franco A, Di Lorenzo M, et al.. (2012) Designing networks of marine protected areas (MPAs) in the Mediterranean context. Total Foundation, final report: 87 pp.
- 76. Roberts CM, Polunin NVC (1991) Are marine reserves effective in management of reef fisheries. Reviews in Fish Biology and Fisheries 1: 65–91.
- 77. Marinaro J, Roussel E, Lawson J, Crechriou R, Planes S (2005) Premier signalement d’une reproduction effective du mérou brun, Epinephelus marginatus, dans la Réserve marine de Cerbère-Banyuls (France). Cybium 29(2): 198–200.
- 78. Diaz D, Mallol S, Parma AM, Goni R (2011) Decadal trend in lobster reproductive output from a temperate marine protected area. Marine Ecology Progress Series 433: 149–157.
- 79. Valles H, Sponaugle S, Oxenford HA (2001) Larval supply to a marine reserve and adjacent fished area in the Soufriere Marine Management Area, St Lucia, West Indies. Journal of Fish Biology 59: 152–177.
- 80. Mora C, Sale PF (2002) Are populations of coral reef fishes open or closed? Trends in Ecology and Evolution 17: 422–428.
- 81. Pinsky ML, Montes HR, Palumbi SR (2010) Using isolation by distance and effective density to estimate dispersal scale in anemonefish. Evolution 64: 2688–2700.
- 82. Kinlan BP, Gaines SD (2003) Propagule dispersal in marine and terrestrial environments: a community perspective. Ecology 84: 2007–2020.
- 83. Shanks AL, Grantham BA, Carr MH (2003) Propagule dispersal distance and the size and spacing of marine reserves. Ecological Applications 13: 159–169.
- 84. Di Franco A, Gillanders BM, De Benedetto G, Pennetta A, De Leo GA, et al. (2012) Dispersal Patterns of Coastal Fish: Implications for Designing Networks of Marine Protected Areas. PLoS ONE 7(2): e31681 .
- 85. Swearer SE, Caselle JE, Lea DW, Warner RR (1999) Larval retention and recruitment in an island population of a coral-reef fish. Nature 402: 799–802.
- 86. Taylor MS, Hellberg ME (2003) Genetic evidence for local retention of pelagic larvae in a Caribbean reef fish. Science 299: 107–109.
- 87. Palumbi SR, Warner RR (2003) Why gobies are like hobbits. Science 299: 51–52.
- 88. Jones GP, Planes S, Thorrold SR (2005) Coral reef fish larvae settle close to home. Current Biology 15: 1314–1318.
- 89. Almany GR, Berumen ML, Thorrold SR, Planes S, Jones GP (2007) Local replenishment of coral reef fish populations in a marine reserve. Science 316: 742–744.
- 90. Fisher R, Leis JM, Clark DL, Wilson SK (2005) Critical swimming speeds of late-stage coral reef fish larvae: variation within species, among species and between locations. Marine Biology 147: 1201–1212.
- 91. Simpson SD, Meekan M, Montgomery J, MacCauley R, Jeffs A (2005) Homeward Sound. Science 308: 221.
- 92. Gerlach G, Atema J, Kingsford MJ, Black KP, Miller-Sims V (2007) Smelling home can prevent dispersal of reef fish larvae. PNAS 104: 858–863.
- 93.
Leis JM, Siebeck U, Dixson DL (2011) How Nemo Finds Home: The Neuroecology of Dispersal and of Population Connectivity in Larvae of Marine Fishes. Integrative and Comparative Biology: 1–18. doi:10.1093/icb/icr004.
- 94. Butler MJ IV, Paris CB, Goldstein JS, Matsuda H, Cowen RK (2011) Behavior constrains the dispersal of long-lived spiny lobster larvae. Marine Ecology Progress Series 422: 223–237.
- 95. Paris CB, Cherubin LM, Cowen RK (2007) Surfing, diving or spinning: effects on population connectivity. Marine Ecology Progress Series 347: 285–300.
- 96. Sundelöf A, Jonsson PR (2012) Larval dispersal and vertical migration behaviour – a simulation study for short dispersal times. Marine Ecology 33: 183–193.
- 97. Engie K, Klinger T (2007) Modeling Passive Dispersal through a Large Estuarine System to Evaluate Marine Reserve Network Connections. Estuaries and Coasts 30: 201–213.
- 98. Poulain PM, Mauri E, Ursella L (2004) Unusual upwelling event and current reversal off the Italian Adriatic coast in summer 2003. Geophysical Research Letters 31: L05303 .
- 99.
Guarnieri G, Pinardi N, Oddo P, Bortoluzzi G, Ravaioli M (2012) Impact of tides in a baroclinic circulation model of the Adriatic Sea. Journal of Geophysical Research: in press.
- 100. Orlic M, Kuzmic M, Pasaric Z (1994) Response of the Adriatic Sea to the bora and sirocco forcing. Continental Shelf Research 14(1): 91–116.
- 101. Gotelli NJ (1991) Metapopulation models: the rescue effect, the propagule rain, and the core-satellite hypothesis. American Naturalist 138: 768–776.
- 102. López-Sanz A, Vert N, Zabala M, Sabatés A (2009) Small-scale distribution of fish larvae around the Medes Islands marine protected area (NW Mediterranean). Journal of Plankton Research 31: 763–775.
- 103. López-Sanz A, Stelzenmüller V, Maynou F, Sabatés A (2011) The influence of environmental characteristics on fish larvae spatial patterns related to a marine protected area: The Medes islands (NW Mediterranean). Estuarine, Coastal and Shelf Science 92: 521–533.
- 104. Bradbury IR, Campana SE, Bentzen P (2008) Estimating contemporary early life-history dispersal in an estuarine fish: integrating molecular and otolith elemental approaches. Molecular Ecology 17: 1438–1450.