Hydrodynamic patterns favouring sea urchin recruitment in coastal areas: A Mediterranean study case

Accepted Manuscript Hydrodynamic patterns favouring sea urchin recruitment in coastal areas: A Mediterranean study case S. Farina, G. Quattrocchi, I. Guala, A. Cucco PII:

S0141-1136(17)30779-1

DOI:

10.1016/j.marenvres.2018.05.013

Reference:

MERE 4527

To appear in:

Marine Environmental Research

Received Date: 18 December 2017 Revised Date:

4 May 2018

Accepted Date: 7 May 2018

Please cite this article as: Farina, S., Quattrocchi, G., Guala, I., Cucco, A., Hydrodynamic patterns favouring sea urchin recruitment in coastal areas: A Mediterranean study case, Marine Environmental Research (2018), doi: 10.1016/j.marenvres.2018.05.013. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Hydrodynamic patterns favouring sea urchin recruitment in coastal

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areas: a Mediterranean study case.

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S. Farina1*, G. Quattrocchi2, I. Guala1 and A. Cucco2

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Loc. Sa Mardini, Torre Grande, 09170 Oristano, Italy.

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IMC- International Marine Centre, Loc. Sa Mardini, Torre Grande, 09170 Oristano, Italy

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*[email protected]

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IAMC - Institute for Coastal Marine Environment, CNR - National Research Council of Italy,

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Abstract In invertebrate fisheries, sea urchin harvesting continues to grow with dramatic

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consequences for benthic ecosystems. The identification of areas with a marked natural

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recruitment and the mechanisms regulating it is crucial for the conservation of benthic

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communities and for planning the sustainable harvesting. This study evaluates the spatial

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distribution and density of recruits of the edible sea urchin Paracentrotus lividus along the Sinis

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+Peninsula (Sardinia) and explores its significant relationships with the local oceanographic

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features. Our results reveal that recruitment is favoured in areas with slow currents and high

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levels of confinement and trapping of the water masses. Analysis of the residual circulation

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indicates that the presence of local standing circulation structures promotes the sea urchin

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recruitment process. Our findings emphasize the importance of managing local sea urchin

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harvesting as a system of populations with their demographic influence mainly dependent on the

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most important ecological driver that is the recruitment.

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Key-words

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Coastal hydrodynamics, larval dispersal, Paracentrotus lividus, population dynamic, recruitment,

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sea urchin harvesting, ocean modelling

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1. Introduction

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In coastal areas, invertebrate catches rapidly increased over the past decades and artisanal

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fisheries and harvesting are the main factors determining imbalances in trophic interactions

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between fishes, invertebrates and macroalgae (Jackson et al., 2001). These effects are

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particularly intense when considering macrobenthic communities where some species of sea

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urchins with a functional role in the ecosystem are both the prey of commercially important fish

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species and the target of direct human exploitation (Andrew et al., 2002). Often, the uncontrolled proliferation of these herbivores due to the overfishing of the main

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predators leads to the creation of permanent barrens (Hereu et al., 2008; McClanahan and Sala,

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1998; Steneck et al., 2004, 2002). However, intensive sea urchin harvesting for the sale of the

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gonads in some temperate zones results in the rapid development of large brown algae and

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subsequent changes in the structure of the associated community (Andrew et al., 2002).

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In the Mediterranean Sea, the species Paracentrotus lividus (Lamarck 1816) is one of the

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most important functional herbivores of the shallow intertidal and sub-tidal rocky habitats

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(Boada et al., 2017; Hereu, 2004; McClanahan and Sala, 1997; Prado et al., 2007).At the same

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time, it is a widely appreciated fishing resource with many reported cases of overexploitation

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(Pais et al., 2007).

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The systematic removal of adult sea urchins due to harvesting can compromise the

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reproduction capacity of populations (Levitan and Sewell, 1998; Levitan et al., 1992; Loi et al.,

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2017; Pennington, 1985; Tegner and Dayton, 1977). As a consequence, larvae originating near a

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fishing area could be a critical requirement for avoiding the collapse of the local overexploited

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populations (Levitan et al., 1992; Pennington, 1985). Fertile size classes, i.e. individuals larger

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than 3 cm test diameter without spines (TD, hereafter) can produce more than one cohort of

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mature gametes in a single breeding season (Mita et al., 2007). Spawning occurs between

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January and June, with a continuous reproductive cycle characterized by one or two peaks, in

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February-March and in May-June (see reviews in Boudouresque and Verlaque, 2001; Ouréns et

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al., 2011).

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In general, sea urchin abundance varies widely from region to region, is mainly linked to

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larval supply and highly associated with oceanographic features (Fenaux et al., 1988; Prado et

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al., 2012). The planktonic life-stage ranges between 20 to 40 days. During this time the larvae,

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driven by the currents, can travel great distances until they find a favourable substrate to adhere

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to (Fenaux et al., 1988; López et al., 1998; Morgan et al., 2000; Prado et al., 2012; Treml et al.,

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2012). When larvae find suitable environmental conditions, they swim towards the bottom to

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undergo metamorphosis (Fenaux and Pedrotti, 1988). In this phase, the presence of adults, as

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well as the type of substrate (e.g. rugosity, presence of crustose algae, macrophyte canopy), are

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crucial to the settlement’s success (Boudouresque and Verlaque, 2001; Oliva et al., 2016).

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However, as an extreme survival action, larvae are also able to metamorphose in pelagic

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environments (Fenaux and Pedrotti, 1988).

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Natural mortality (e.g. predation) and migration are then the habitat-specific regulation

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processes governing the benthic life stage across the seascape (Boada et al., 2018; Farina et al.,

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2017, 2016, 2014). Predation represents a bottleneck for urchin populations after the settlement

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and until individuals reach the refuge size of 5 cm TD (Farina et al., 2009; Guidetti et al., 2004;

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Hereu et al., 2005). Due to the limited mobility of species, migration regulates population density

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among patches of contiguous habitats, but it cannot be considered a connecting process between

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neighbouring populations like the recruitment process (Boada et al., 2018; Ceccherelli et al.,

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2009).

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The planktonic life stage of the sea urchin populations makes them demographically open.

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Coastal circulation patterns, that depend on coastal morphology, affect the spatial distribution of

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larvae supply (Morgan et al., 2000; Treml et al., 2012). Some studies have demonstrated how the

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stock-recruitment relationship showed strong benefits from Marine Protected Areas (MPA)

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species migration (e.g. Moffit et al., 2009). However, the persistence of sea urchin populations

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under harvesting pressure may also depend on the local hydrodynamics that affect the spatial

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distribution of larvae.

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On the island of Sardinia (Western Mediterranean Sea), sea urchin populations have suffered

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high human pressure for decades (Addis et al., 2014; Pais et al., 2012). In the central western

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coast of the region, despite the specific management measures adopted inside the local MPA -

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Peninsula of Sinis - Mal di Ventre Island (see methods), the abundance of sea urchin populations

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continues to show a dramatic decline (Pieraccini et al., 2016). Along the Sinis Peninsula,

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populations lacking fertile individuals greater than 5 cm TD are common both inside and outside

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the MPA (Loi et al., 2015; Pieraccini et al., 2016). Thus, the aim of this research work is to

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provide spatial information about specific hot spots of recruit density, and potential settlement,

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as well as about the physical processes regulating recruitment process along this stretch of coast

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(Puckett et al., 2014; Stenevik et al., 2012). This information and further monitoring will provide

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a solid data set for enhancing ecosystem-based decision-making strategies for conservational

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plans along the coast of the Sinis Peninsula (see Fig. 1).

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In the last years, the prominent role of hydrodynamics in driving dispersal and fluxes in

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planktonic larvae has been widely investigated through numerical modelling techniques.

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Individual-based biophysical models have been used to analyse the connectivity among marine

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populations of both fish and invertebrate species (Koeck et al., 2015; Ospina-Alvarezet al., 2015;

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Medel et al., 2018; Calò, et al., 2018). In the Mediterranean Sea, modelling applications based on

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the lagrangian approach were devoted to the investigation of the connectivity patterns between

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MPAs at basin and/or sub-basin levels (Andrello et al., 2013; Rossi et al., 2014; Dubois et al.,

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2016; Bray et al., 2017). In the specific case of the sea urchin, a recent work by Paterno et al.,

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2017 explored the connectivity patterns in populations between the Adriatic and the Ionian Sea.

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In all these studies, the Euclidean distances between the different marine provinces, between

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spawning and recruitment areas or between each target subpopulation was enough to consider

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them potentially separated. In fact, in many works, along with the individual-based model

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application, population genomics were used to validate the modelling results and confirm a

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possible hypothesis on connectivity (Calò et al., 2016; Paterno et al., 2017).

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In our study case, the investigated area was limited to a few tens of kilometres, where any

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possible genetic differentiation or physical separation between the local sea urchin population

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was a priori excluded. Therefore, the use of individual-based models to explore the local

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connectivity matrix was considered unnecessary. An alternative approach, based on the Eulerian

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frame of reference, was selected for investigating the local hydrodynamic patterns favouring the

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sea urchin recruitment process.

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Fig. 1. Geographical setting: coastline of the Sinis Peninsula (Central Western coast of Sardinia). This area includes the Marine Protected Area of the Peninsula del Sinis - Isola di Mal

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di Ventre and a high-pressure zone of sea urchin harvesting to the north of it. Dots indicate sampling sites: Funtana Meiga (1, 2), Seu (3-9), Sa Benda (10), Porto Suedda (11-15), Su Tingiosu (16-18), Putzu Idu (19-21), Su Pallosu (22, 23), Scal’e Sali (24, 25).

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larvae settlement and the indication of potential spatial variability of recruit density in relation to

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different levels of harvesting (inside and outside the MPA). Biological data were obtained from

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field work carried out along the whole coastal area over a ten-year period. The adopted dataset,

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that included the sea urchin density distribution of both adult individuals and recruits, was

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homogenized considering the type of habitat and the depth of the sampling sites.

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Moreover, potential relationships among recruit density, spatial variability and oceanographic

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features were investigated. A specific set of oceanographic variables, representative of the local

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hydrodynamics, was obtained by means of a numerical modelling application. A Generalized

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Linear Mixed Model (GLMM) was adopted to compare the spatial variability of the sea urchin

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density and oceanographic variables in order to detect significant correlations.

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2. Study site

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The study was conducted along a stretch of coast of about 40 km on the western side of

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Sardinia, Italy (Sinis coastal area, see Fig. 1) between Cape Seu (39.9080° N, 8.3910° E) and Su

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Pallosu Bay (40.0379° N, 8.3938° E). The offshore ocean circulation is dominated by low-

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energetic anticyclonic gyres induced by the baroclinic instabilities of the Algerian current system

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(Millot, 1999; Olita et al., 2013; Schroeder et al., 2012).

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In the coastal area, the water current is mainly generated by the action of the frequent and

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intense wind events, mostly from the north-west (Mistral wind) and, to a lesser degree, from the

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south-west (Libeccio wind), characterized by a yearly mean speed of 7 m/s and a peak speed

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higher than 20 m/s (Zecchetto et al., 2016). Most of the coastline is exposed to the wind-waves

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generated on a wide fetch by the prevailing winds that, in the form of severe winter storms, can

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produce intense wave fields, with a Significant Wave Height of up to 5 m (Simeone et al., 2012a;

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Simeone et al., 2016). The tidal force is negligible, with very weak amplitudes of around 0.15 m,

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and doesn’t produce any remarkable current flows (Cucco et al., 2012a).

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This stretch of coast is characterized by highly variable geomorphology and hydrodynamics,

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with numerous types of substrates and habitat landscapes (Simeone et al., 2012b; De Falco et al.,

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2015). The middle and southern parts of the area are included in the Peninsula of Sinis - Mal di

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Ventre Island MPA, which covers a surface of twenty-five thousand hectares (see Fig. 1).

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However, the surface area that is fully protected is relatively small (529 ha, Guala et al., 2008a),

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while the remaining zones are intensively frequented by commercial and recreational fishermen

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(Pieraccini et al., 2016).

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In Sardinia, sea urchin harvesting is managed by a regional decree (RAS, Regione Autonoma

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della Sardegna decree no. 276 of March 3, 1994 and subsequent amendments). Currently, along

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the whole coast of Sardinia, 189 professional fishermen are authorized to collect sea urchins by

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scuba diving from November to April with each diver allowed to collect up to 2000 sea urchins

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per day. In the Peninsula of Sinis - Mal di Ventre Island MPA, the management board allows 55

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resident professional fishermen to harvest 500 to 1000 sea urchins per day for a shorter period of

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time, varying from year to year. Despite having more restrictive measures than the rest of

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Sardinia, in the MPA, the abundance of sea urchins of a commercial size showed a significant

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decrease after 2007 (Pieraccini et al., 2016). Thus, along the Sinis peninsula, inside and outside

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the MPA, a lack of fertile individuals with a TD > 5 cm is common among populations (Loi et

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al., 2017; Pieraccini et al., 2016). 3. Material and methods

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3.1 Sea urchin density distribution

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Abundance and size-frequency distribution of the sea urchin populations were estimated

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yearly from 2003 to 2012, both within the MPA borders and outside them. The portion outside

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was situated along the northernmost coastal area of the domain, which traditionally corresponds

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to a zone of high harvesting (Loi et al., 2017). Sampling was carried out in 25 sites at a depth of

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between 2 and 5 m on a calcareous substrate. This is the most common rocky habitat along this

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stretch of coast. The sampling sites were located inside the MPA (sites numbered from 1 to 15)

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and outside the MPA (sites numbered from 16 to 25) in order to observe the effects of the

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different regulations and different levels of harvesting pressure. For each site, sea urchin

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abundance was assessed in 5 m2 quadrats placed randomly for a total of three times. The sizes of

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the individuals (TD without spines) were measured with calipers to the closest mm.

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We define the recruits of P. lividus as those individuals that survive approximately one year

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after their settlement. The size of the one-year-old recruits is estimated using their growth rate

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(about 1 cm/y during their life stage, according to Ouréns et al., 2013). Therefore, we considered

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the individuals of P. lividus with TD < 1 cm as the recruits and their density (RD), expressed in

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terms of number of individuals per square meter (ind/m2), was estimated for each sampling site.

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Furthermore, since the aggregation of adult sea urchins, intended as individuals with TD > 3

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cm, provides a protected environment to recruits from both predation (Bonaviri et al., 2012;

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Oliva et al., 2016) and mechanical removal due to water currents and waves (e.g. Nishizaki and

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Ackerman, 2007), their densities, expressed as ind/m2, were also calculated for each sampling

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site. Adult density (AD), along with the oceanographic variables, was considered as one of the

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predictor factors that can potentially affect recruit distribution density.

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3.2 Oceanographic variables

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A three-dimensional, finite element hydrodynamic and wind wave model, SHYFEM-WWM

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(Umgiesser et al., 2004), was adopted to reproduce the hydrodynamic features of the investigated

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area. The model had already been used successfully for reproducing the wind-wave and the 3D

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water circulation along the Sinis Peninsula and in the adjacent Oristano Gulf (Cucco et al., 2006;

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De Falco et al., 2008; Cucco et al., 2016). The model uses an unstructured finite element grid for

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representing the computational domain. In Fig. 2, the grid geometry and the adopted bathymetric

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details are shown for the area of interest.

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A simulation run was performed accounting for both oceanic and meteorological seasonal

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variability. Large-scale atmospheric and oceanographic data provided by already operational

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ocean and atmospheric models for the biennium 2009 and 2010 were adopted as forcing for the

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coastal application of SHYFEM-WWM. The 2009-2010 biennium was selected for its

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meteorological conditions which are highly representative of the local climate (see Appendix

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A1).

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The ocean data used as model boundary conditions, including water levels, current speeds,

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salinity and temperature fields, were provided by the WMED ocean forecasting system (Sorgente

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et al., 2016). The atmospheric data, needed for model upper boundaries and including wind

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speeds, atmospheric pressure and thermal fluxes were provided by SKIRON meteorological

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forecasting system (Papadopoulos et al., 2001). We refer to Cucco et al. (2012a, 2012b) for a

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detailed description of the model parameters and the treatment of boundary conditions

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Fig. 2. Finite element grid and bathymetric data adopted for the numerical simulations: a) the external domain and b) the close-up of the Sinis coastal area with details of the c) bathymetry and sampling site arrangement

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The hydrodynamic variables describing the main physical processes influencing the local

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recruitment of the sea urchin juveniles were defined a priori. The Current Speed (CS, expressed

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in cm/s), the Significant Wave Height (SWH, expressed in m), the Sea Surface Temperature

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(SST, expressed in °C) and the Eulerian Ritention Index (ERI, an adimensional variable

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describing the spatial distribution of the average concentration of a passive tracer exposed to the

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water circulation patterns), were selected as predictor variables as they are generally considered

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the main oceanographic factors affecting the larvae fluxes, settlement and the recruitment

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processes of benthic organisms (e.g. Tracey et al., 2012).

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From simulation results, the CS, SST and SWH were obtained once an hour for all the

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elements of the model domain and for the whole simulation period. CS and SST were computed

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for the surface waters represented by the first 10 m of the water column. This was necessary to

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include the depths of the sampling sites and to avoid any aliasing errors generated by the model

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spatial discretization.

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For each element of the domain, the SST and SWH were averaged over the whole simulated

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period whereas the CS values were averaged only considering the results obtained between

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January and June, when spawning occurs (see Introduction), for each simulated year.

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A further variable, the ERI, characterized by a higher order of complexity with respect to the

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previous ones, was selected as predictor factor. We simplified and approximated the sea urchin

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spawning process by modelling the current-induced transport of a dissolved passive tracer

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released along the whole coastal area. The retention of this tracer, quantified by the time

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evolution of its local average concentration, was selected as a proxy for planktonic organism-

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aggregation capacity, a feature favouring larvae settlement and the recruitment.

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Numerically, the ERI was estimated by adding, daily, a unitary concentration of a dissolved

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passive tracer to all the elements of the model domain characterized by water depths ranging

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between 0 and 20 m. This corresponds to the habitats and the bathymetric range where sea

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urchins are most abundant (Boudouresque and Verlaque, 2001). The transport of the tracer

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induced by the water circulation was simulated for the whole spawning period, from January to

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June. Its concentration was computed hourly for all the elements of the model domain. The

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concentration values were then averaged in time and normalized between their minimum and

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maximum values, obtaining the spatial distribution of an adimensional variable defined as the

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Eulerian Retention Index. Previous studies on hydrodynamic connectivity between

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Mediterranean sub-regions (Rossi et al., 2014; Dubois et al., 2016) indicated that the inner shelf

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of the Sardinian island is relatively isolated from the outer-ocean circulation. This justified the

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choice of seeding the tracer concentration along the coastal waters only. From a methodological

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standpoint, ERI is similar to the concept of water residence time (Cucco and Umgiesser; 2015).

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However, unlike this concept, ERI cannot provide any quantitative information about the time

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scales of the transport processes. In fact, ERI supplies only relative indications about the

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different capacities of the local current patterns to promote the trapping of the water masses

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along the investigated stretch of coast. It does not give information about the lifetimes of the

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urchins or duration of the simulated larvae or eggs.

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The different timing of the two averaging procedures reflects the impacts that the selected

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physical factors were supposed to generate on the sea urchin recruitment. SST and SWH were

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expected to mostly influence the annual reproductive cycle, the growth and the fixing ability of

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recruits (Bulleri et al., 2015; Ouréns et al., 2011). Therefore, while these variables influence the

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sea urchin population during the entire year, CS and ERI were more related to the transport and

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settlement of the sea urchin larvae, which generally occur between late January and early June.

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3.3 Data analysis

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A Shapiro-Wilk and Pearson test was applied to the recruit and adult density, ascertaining

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that the two datasets were assessed as being non-normally distributed. Therefore, the variance of

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the sea urchin density of both recruits and adults, were analysed considering the “Protection”

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effect as a fixed factor and the “Site” effect as a random factor nested in the previous one. For

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this analysis, a preliminary Generalized Linear Mixed Model (GLMM) was chosen as the best

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tool for analysing datasets characterized by non-normal distribution and which involve random

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effects (Bolker et al., 2009). GLMM was run to model the relationship between density variability for the recruits and the

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predictors consisting of the oceanographic variables (CS, SWH, SST and ERI) and the adult

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density (AD). In order to exclude the statistical effects induced by different sampling depths (2

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or 5 m), the “depth” was set as a further predictor characterized by a random distribution and

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independent from the response variable RD. Moreover, the whole dataset was scaled to follow

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Poisson distribution, which is a necessary condition before applying the GLMM based analysis

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(Bolker et al., 2009). According to the protocol provided by Zuur et al. (2010), before testing the

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model and providing the final solution, a data exploration of all the predictor variables was

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carried out by analysing the covariates among all the different datasets to detect possible

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collinearity. Finally, we used a Multi-model Averaging method for the Best Model (BM)

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selection, which is a necessary method for providing a robust mean to infer the relative

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importance of the different predictive variables (Grueber et al., 2011).

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All the described procedures and analyses were performed using the Nortest, lme4 and MultiModel Inference packages (Barton, 2012) in R ( R Development Core Team 2010).

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4. Results

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4.1 Sea urchin density distribution

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The preliminary GLMM analysis revealed that the “Protection” factor strongly influenced the

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variability of distribution for recruits (P-value =7.58x10-13) but not for adults (see Table 1). The

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spatial distribution of the recruits (RD) was highly variable and ranged between 0 and 11 ind/m2

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between sampling sites. RD values were significantly higher in sampling sites located outside the

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MPA (sites from 16 to 25) than the ones inside (sites from 1 to 15), with 4±0.8 and 0.8±0.2

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ind/m2 respectively (see Fig. 3). The highest values were observed at Su Tingiosu and Su Pallosu

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(sampling sites number 18 and 22) with 6.3±0.9 and 7.2±1.8 ind/m2 respectively (Fig.3).

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Fixed effect - Protection SE

Z-value

AD

RD

AD

RD

AD

RD

AD

18.066

-0.049

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0.102

7.169

-0.480

7.58x10-13

0.630

σ2

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AD

0.008

0.0041

SD

RD

AD

RD

AD

RD

AD

0.089

0.064

0.101

0.014

0.318

0.119

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RD

σ2

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Random effect - Site

Table 1. Generalized Linear Mixed Models (GLMM) for both recruit (RD) and adult densities (AD) in relation to the level of protection (fixed factor) and sites (random factor nested to protection). Coefficient estimates (Estimate), standard errors (SE), z-values, and significance level (P-value) are provided for fixed effects, while estimates of the variance (σ2) and standard deviations (SD) are reported for random effect.

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P-value

RD

Random effect – Protection: Site

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Conversely, inside the MPA where harvesting was more controlled, lower RD densities were

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found at Cape Seu and Porto Suedda (sampling sites number 4 and 11) with values 0.2±0.1 and

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0.2±0.2 ind/m2, and two sites (5 and 15) with an absence of recruits (Fig. 3).

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Fig. 3. Spatial variability of Paracentrotus lividus recruits across the coast (density for m2). Sampling sites from 1 to 15 are placed inside the Marine Protected Area and are indicated in Fig.1. Details of significant differences are reported in Table 1.

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Adult density (AD: >3cm TD) varied significantly between sites (1 and 27 ind/m2), but no

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significant differences were found inside and outside the MPA (8.5 ±1.6 ind/m2 and 7.3 ±1.4

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ind/m2 respectively, see Table 1, Fig 4). In fact, both the highest and the lowest density values

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were found inside the MPA, near Cape Seu, at sampling sites 4 (20.9 ±2.7 ind/m2) and 8 (5.2

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±1.3 ind/m2), respectively.

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Fig. 4. Density of adult individuals for m2 (TD>3 cm) along the coast inside and outside the Marine Protected Area (grey area). Significant differences between sites are reported in Table 1.

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4.2 Oceanographic variables

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In figure 5 the spatial distribution of the four diagnostics, CS, ERI, SST and WHS, used as

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predictors and obtained by the oceanographic model results, are reported for the area of interest.

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Fig. 5. Spatial patterns of the oceanographic variables used as predictors. Spatial distribution of the semi-annual mean of the surface a) Current Speed (CS) expressed in cm/s, b) normalized tracer concentration (ERI) and of the annual c) Sea Surface Temperature (SST) expressed in degree Celsius and d) Significant Wave Height (SWH) expressed in m.

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The CS, (see Fig. 5a) varied from a few cm/s up to 28 cm/s with higher values found in the

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offshore areas and lower values in proximity of the coastline. Exceptions were found near the

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main capes, Mannu, Seu and Sa Sturaggia (see Fig. 1), where the compression of the flow field

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promotes the intensification of the CS. Along the coasts, the CS pattern can be clearly divided

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into three areas: the Su Pallosu and Putzu Idu Bay, located to the north and south of Cape

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Mannu, characterized by lower CS values (less than 10 cm/s), and the remaining part of the

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domain, from Sa Sturaggia to Seu Cape, characterized by higher CS values (less than 20 cm/s)

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with peaks in proximity of the two headlands.

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As reported in appendix A2, the averaging procedure adopted to compute the CS, was proven

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to be statistically confident. This allowed us to deepen the analysis of the surface hydrodynamics

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throughout the computation of the Residual Circulation (RC). The RC was obtained by averaging

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the hourly vector fields of the surface currents over time (see Cucco et al., 2006; 2016) obtained

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by the model results. The RC differs from the average current speed (CS) by providing essential

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information about the direction of the average circulation.

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In Figure 6, the residual surface current field, computed for the first six months of both

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simulated years, is reported for the interested area. As can be noted, the residual flow is not

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uniform. Several cyclonic and anticyclonic circulation cells can be detected in the coastal areas

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of the northern part of the domain. In contrast, a constant north-south residual flow is found in

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the southern part in both the offshore and coastal areas. The described flow pattern represents the

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average driver of the passive tracer used to compute the ERI.

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Fig. 6. Residual sea-surface circulation computed from simulation results obtained for the first half of the year between January and June. Dots refer to the sea urchin sampling sites

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The spatial distribution of ERI (Fig. 5b) is generally characterized by a cross-shore gradient

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with lower values found in the offshore areas and higher values found in proximity of the coast.

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As for the CS, the ERI pattern can be summarily divided into three areas: the two small bays

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(Putzu Idu and Su Pallosu bays) located at the north and south of Cape Mannu, characterized by

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higher ERI values (around 0.9), and the remnant part of the coast, south of Sa Sturaggia Cape,

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where the values of ERI were generally lower (around 0.4).

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The SST spatial distribution (see Fig. 5c) is quite homogeneous, as we expected, due to the

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small extent of the area of interest. The annual average values ranged between 17.5° C and to 19°

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C with colder open waters and warmer coastal waters. Similarly, to previous cases, the spatial

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pattern of the SST in the coastal waters (including the two bays in proximity to Cape Mannu),

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was characterized by a northern part, which had a higher SST average, and by a southern part

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with colder waters.

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Finally, the spatial distribution of SWH (see Fig. 5d) ranged between 0.3 and 2 m with the

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peak values found offshore, in correspondence to Cape Mannu. The lowest values were found in

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the inner part of the Putzu Idu and Su Pallosu bays, in correspondence with the shallow areas

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which are sheltered from Mistral and Libeccio main wave regimes, respectively. Along the

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remaining part of the coast, the SWH is quite homogeneous with values close to 1.5 m as this

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area is exposed to both the two main wave regimes.

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4.3 Recruitment model results

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A GLMM was carried out to relate the sea urchin recruit density to the local hydrodynamic

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features and to the local abundance of adult sea urchins. A preliminary covariate analysis

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revealed that the SST was characterized by a high value of the Variance Inflation Factor

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(VIF>3), indicating a strong collinearity with the CS and with the ERI and, consequently, it was

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excluded from the set of model predictors. Accordingly, the GLMM was conducted using CS, ERI, SWH and AD as predictor variables

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and RD as the response variable. For the whole dataset, the GLMM results, based on the

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Akaike's Information Criterion (AIC) and the likelihood ratio tests (Zuur et al., 2009), revealed

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that only few covariates influenced the RD variability along the selected coastal area. In fact,

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from the collection of covariates, the CS and the ERI were the only predictors to significantly

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influence the abundance of recruits (AIC 821, see Appendix A.3).

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Finally, a multi-model media selection, taking into account the uncertainty of the model, was

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developed in order to guarantee the robustness of the GLMM results (Grueber et al., 2011). This

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technique resulted in a set of the five best models with AIC ranging from 821 for the first model

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(weight 0.25), up to 823 for the fifth model (weight 0.07) and where, the Current Speed (CS) and

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the Eulerian Retention Index (ERI) were the most significant explanatory variables with a

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relative importance of 1 and 0.93, respectively (Table 2).

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Estimate coefficient

Unconditional SE

P-value

Relative importance

Current Speed

-1.22

0.12337

< 2e-16

1

Eulerian Retention Index

0.13452

0.05142

0.00564

0.93

Significant Wave Height

0.09853

0.05772

0.08781

0.56

Adult density

0.06531

0.10433

0.20265

0.42

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Covariate

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394 395 396 397

Table 2. Relative importance of local hydrodynamic factors in determining sea urchin recruitment along the Sinis coastline, using a multi-model averaging technique. The estimates reflect the weighted average of covariates derived from the six best models identified with a setting of Delta AIC<4; Significant P-values are given in bold (see also Table A3b).

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398 CS intensity negatively correlated with recruit densities (negative estimate coefficient), ERI

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is positively associated with them (positive estimate coefficient). Fig. 7 shows the clear opposite

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trends of recruit density with CS and ERI along the sampling sites.

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Fig. 7. Significant correlations between response variable of recruit density (ind/m2) and a) CS (P-value=6.58e-10) and b) ERI (P-value=<2e-16).

416 5. Discussion

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The recruit density displays a marked spatial heterogeneity in the shallow-sea rocky habitat

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of the Peninsula of Sinis. In particular, two areas with significant differences can be identified: a

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low-density area located inside the MPA and a high-density area outside the MPA which

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corresponds to the non-protected, highly-fished areas in the North of Sinis Peninsula.

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Conversely, the adult density distribution was found to be similar inside and outside the

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protected area.

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The GLMM analysis highlighted that the hydrodynamic variables Current Speed (CS) and

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Eulerian Retention Index (ERI), which represent different aspects of the sea surface transport

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processes, were the most important predictors of the recruit density distribution in the

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investigated area. In particular, the results revealed that in areas where the average current speeds

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were low and the waters were more confined or trapped, corresponding to high ERI values,

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recruitment (of sea urchins <1 cm) could be favoured, since a significant correlation with recruit

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density was found. These features are typically associated with the presence of eddies or

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standing circulation structures that force the water masses to recirculate locally. This increases

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their residence times and favours the sinking of suspended elements, in the case of sediments or

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biotic aggregates, or settling, in the case of larvae (Monsen et al., 2002; Canu et al., 2012; Cucco

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et al., 2015).

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In our study case, the presence of such dynamic features was confirmed by the analysis of the

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residual circulation (see figure 6). In particular, residual circulation cells were found in both Su

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Pallosu and Putzu Idu bays, outside the MPA, where CS values are lower and ERI values higher,

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respectively. On the contrary, along the rest of the coastal area, where CS values are higher and

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ERI values lower, the residual flow is uniform, reducing the probability of aggregation or

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trapping of the suspended particles, including planktonic larvae.

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The spatial distributions of ERI and CS are in line with the results obtained from studies that

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adopt particle-tracking models to investigate the retention features of the Mediterranean waters.

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Dubois et al., 2016 found that the retention of numerical particles used to simulate planktonic

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larvae and eggs was generally favoured by weak current conditions and complex bathymetry. In

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coastal areas, these give rise to convoluted patterns of circulation which promote the trapping of

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water masses (Paris and Cowen, 2004; Treml et al., 2012). In particular, along the Western

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Sardinian shelf, the absence of both energetic large-scale coastal currents and mesoscale

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structures travelling offshore, reduces the connectivity between the shelf and the open waters

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(Dubois et al., 2016). This increases the retention of planktonic aggregates in the coastal waters.

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Within this context, in those stretches of coast, where ERI and CS were high and low

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respectively, the local circulation further promotes the retention of planktonic organisms such as

452

sea urchin eggs and larvae.

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Although, the SST was excluded from the GLMM analysis due to collinearity with CS and

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ERI, its values were generally higher (up to 1 °C more) in those zones characterized by the

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presence of recirculation cells than in the rest of the coast (see Fig. 3c). This supports the

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hypothesis that the trapping processes occur in correspondence to the recirculation cells, and

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indicates that these specific areas can also be more suitable for the reproduction success of

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populations than the rest of the coast, due to the higher local SST (Ouréns et al., 2013).

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The results of the GLMM also exclude wind waves as a potential factor influencing the

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variability in recruit densities, suggesting that the local sea urchin population is capable of

461

surviving in energetic environments. In fact, the distribution of the average SWH, that define

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areas with a high or low average energy, in terms of wave impact, highlights that the whole

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stretch of coast is affected by intense storms throughout the year. Implicitly, this confirms that

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almost everywhere along the western Sardinian coast, the local sea urchin population is subjected

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to intense wind wave events shaping the population dynamics (see Fig. 3d) thanks to the

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presence of the Mistral and Libeccio wind regimes.

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Regarding the importance of the biotic variables, the adult sea urchins are known to protect

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newly settled individuals from predators under the spine canopy, thus favouring the aggregation

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of juveniles (Oliva et al., 2016; Ourèns et al., 2014). However, according to the similar densities

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of adults observed inside and outside the MPA (Fig. 2), results of the GLMM exclude AD as an

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explanatory variable of difference in recruit density variation.

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Generally, inside MPAs, sea urchin populations suffer a high natural mortality of recruits and

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middle-sized classes as a consequence of the local fish abundance and sizes of fish present which

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are generally larger as a direct effect of the protection rules (Guidetti, 2006; Hereu et al., 2012,

475

2005; Sala et al., 1997). However, in the investigated MPA Peninsula of Sinis - Mal di Ventre

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Island, the effects of protection are not appreciable due to the high-fishing pressure (Casola et al.,

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2014; Guala et al., 2008b) that compromises the effectiveness of the reserve (Marra et al., 2016;

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Pieraccini et al., 2016). The abundance of the main specialist predators of small sea urchins (0-

479

1cm TD), the wrasses Coris julis and Thalassoma pavo, was observed to be similar in the rocky

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habitat substrate inside and outside the MPA (Marra et al., 2016). Accordingly, differences in

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recruit densities (0-1 cm TD) inside and outside the MPA cannot depend on predation. The

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higher density outside the MPA seems to be primarily caused by the peculiar circulation pattern

483

detected in Su Pallosu and Putzu Idu bays. In general, populations are inclined to a high reproductive capacity in zones where

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oceanographic forcing is weak, like for example low wave exposure that positively influences

486

the number of gametes released in the environment (Bulleri et al., 2015; Gianguzza et al., 2013;

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Guettaf et al., 2000; Lozano et al., 1995; Meidel and Scheibling, 1998; Sellem and Guillou,

488

2007). However, local water circulation also makes larval supply available to a large amount of

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coastal populations (Fenaux et al., 1988; López et al., 1998; Prado et al., 2012). In this sense,

490

larval dispersion in the offshore and coastal areas and their transport induced by the local current

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field assume a crucial role for population survival (Largier, 2003).

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As concerns the coastlines of the Peninsula of Sinis, it is feasible that both larvae produced

493

locally and coming from the nearby areas are mainly trapped in the Su Pallosu and Putzu Idu

494

bays, while they only partially travel down the coast. Then, the higher SST in the recirculation

495

cells and the favourable environmental constraints of the area (e.g. substrate rugosity), could

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increase the chances of larvae survival and their in situ settling (Boudouresque and Verlaque,

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2001; Oliva et al., 2016).

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It is worth noting that, while larval availability is influenced by large or medium scale factors

499

(e.g. currents, temperature), settlement and early post-settlement events are affected by local

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scale features such as substrate rugosity and micropredation by small invertebrates (Bonaviri et

501

al., 2012; Jennings and Hunt, 2011; Oliva et al., 2016). This work did not explore either the

502

abundance of settlers, nor the micropredation which entails post-settlement mortality and later

503

demographic stages. However, considering predation and habitat features as constant and similar

504

inside and outside MPA, it is reasonable to suppose that the correlation between local

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hydrodynamic patterns and post-settlement recruit density could be extended to larval supply and

506

settlers as intermediate phases (Prado et al., 2012). Moreover, our results highlight the possible

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existence of hot-spots of P. lividus recruitment and their placement outside the local MPA

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borders, in correspondence to the unprotected northernmost part of the domain.

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According to this hypothesis, this study could explain how the sea urchin populations in this

510

area can sustain the strong human pressure which is present. However, the persistence of the

511

harvesting has led to a deficit of the large adults and the potential reproductive contribution of

512

the population is dependent on the youngest of the species (Loi et al., 2017).

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Our findings allow us to infer that the success of the recruitment favoured by the water

514

circulation pattern would lead to a faster recovery of the local population in terms of total

515

abundance, thus ensuring the availability of new juveniles for the future stock of local fishing.

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Understanding the effects of demographic drivers of population processes and their spatial

517

and temporal variability is necessary in order to accurately evaluate the long-term population

518

viability and to devise effective management practices. In this sense, an interesting perspective

519

for realizing a systematic conservational plan of sustainable sea urchin harvesting could be based

520

on metapopulation dynamics and their connection through a source-sink model (Hanski and

521

Simberloff, 1997; Hastings and Harrison, 1994; Ilkka, 1999). These concepts emphasize the

522

importance of connectivity in generating a system of discrete local metapopulations: each one

523

determines its own internal dynamics to a large extent, but has a degree of identifiable

524

demographic influence from other populations which are connected via dispersion processes

525

(Knight and Landres, 2002). For instance, the populations of Su Pallosu and Putzu Idu bays are

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characterized by high densities of recruits and middle-sized classes of sea urchins (Loi et al.,

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2017). Due to the high harvesting pressure, death rates should exceed birth rates and, as a

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consequence, these metapopulations should be classified as “sink”. Conversely, metapopulations

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whose birth rates will exceed death rates should be referred to as “source”. The phenomenon

530

where “source” populations supplement “sink” populations via dispersing individuals is a rescue

531

effect that should be considered crucial for planning protection plans and sustainable fisheries

532

(Paterno et al. 2017).

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Paradoxically sea urchin harvesting inside MPAs, as an extra-predation pressure, can

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dramatically compromise the persistence of metapopulations more than in overfished areas.

535

However, in the case of the investigated MPA where there is no significant reserve effect (Marra

536

et al., 2016), the balance between death rate and birth rate along the coast is almost exclusively

537

dependent on the larval supply. Thus, the knowledge of the main routes of the seasonal dispersal

538

of sea urchin larvae through the MPA Peninsula of Sinis - Mal di Ventre Island and the

539

surrounding areas becomes a key factor for the successful management of the resource.

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Moreover, the presence of residual circulation favouring the trapping of planktonic organisms

541

has a major impact on ecosystem conservation issues. In fact, when the circulation pattern tends

542

to reduce the dispersion of marine organisms, a local increase of the biomasses is found (e.g.

543

Melià et al., 2016). This supports trophic interactions among predators, preys, competitors and

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macrophytes.

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Our findings point out the necessity of evaluating ecological processes which drive sea urchin

546

population dynamics, such as recruitment, to differentiate the harvesting in different sectors.

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However, we also believe that our study could contribute positively to an efficient management

548

plan for biodiversity conservation. In particular, further investigation which extended the study

549

site to the whole western Mediterranean region and, in this case, used an individually based

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modelling approach would improve the understanding of the potential role of larvae dispersal

551

processes in the management of this important resource.

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552 Acknowledgements

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This research was carried out thanks to the cooperation between IMC and IAMC-CNR (Oristano,

555

Sardinia). We thank Maura Baroli, Fabio Ledda, Clara Diago, Stefania Coppa, Alessia Iannuzzi,

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Serena Como, Serena Donadi, Francesco Wrachien, Giorgio Massaro who have collaborated in

557

the field data collection. Finally, we would like to thank Katie Duff for her patient revision of the

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manuscript.

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559 Supplementary data

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Supplementary data related to this article can be found at

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ACCEPTED MANUSCRIPT Highlights 1. In Sardinia sustainable fishing of the sea urchin Paracentrotus lividus have become a needs 2. Sea urchin abundances are driven by the recruitment processes which dependent on coastal transport

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3. specific oceanographic variables are used to investigate the spatial variability of recruits 4. Recruitment is favored by low current speeds and by high confinement and trapping of the water masses

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5. Results contribute to the development of sustainable management strategies of artisanal

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fishing