When time affects space: Dispersal ability and extreme weather events determine metacommunity organization in marine sediments

Accepted Manuscript When time affects space: Dispersal ability and extreme weather events determine metacommunity organization in marine sediments Guilherme Nascimento Corte, Thiago Goncalves-Souza, Helio H. Checon, Eduardo Siegle, Ross A. Coleman, A. Cecília Z. Amaral PII:

S0141-1136(17)30721-3

DOI:

10.1016/j.marenvres.2018.02.009

Reference:

MERE 4458

To appear in:

Marine Environmental Research

Received Date: 24 November 2017 Revised Date:

3 February 2018

Accepted Date: 11 February 2018

Please cite this article as: Corte, G.N., Goncalves-Souza, T., Checon, H.H., Siegle, E., Coleman, R.A., Amaral, A.Cecí.Z., When time affects space: Dispersal ability and extreme weather events determine metacommunity organization in marine sediments, Marine Environmental Research (2018), doi: 10.1016/j.marenvres.2018.02.009. 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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When time affects space: dispersal ability and extreme weather events determine

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metacommunity organization in marine sediments

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Running page head: Metacommunity organization in marine sediments

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Guilherme Nascimento Corte

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Ross A. Coleman , A. Cecília Z. Amaral

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Departamento de Biologia Animal, Instituto de Biologia, Universidade Estadual de Campinas

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[email protected]

(UNICAMP), Campinas, São Paulo, Brasil. CEP 13083-862.

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Tel: +55 19 999417566

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*Corresponding author

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(USP), São Paulo, SP, Brasil.

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Ecology, Federal Rural University of Pernambuco, Recife, Pernambuco, Brazil.

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Universidade de São Paulo (USP), São Paulo, SP, Brasil.

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University of Sydney, Sydney, New South Wales, Australia.

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Departamento de Oceanografia Biológica, Instituto Oceanográfico, Universidade de São Paulo

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Phylogenetic and Functional Ecology Lab (ECOFFUN), Departament of Biology, Area of

Departamento de Oceanografia Física, Química e Geológica, Instituto Oceanográfico,

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Coastal and Marine Ecosystems Group, School of Life & Environmental Sciences, The

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, Thiago Goncalves-Souza , Helio H. Checon , Eduardo Siegle ,

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ACCEPTED MANUSCRIPT Abstract

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Community ecology has traditionally assumed that the distribution of species is mainly

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influenced by environmental processes. There is, however, growing evidence that

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environmental (habitat characteristics and biotic interactions) and spatial processes (factors that

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affect a local assemblage regardless of environmental conditions - typically related to dispersal

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and movement of species) interactively shape biological assemblages. A metacommunity,

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which is a set of local assemblages connected by dispersal of individuals, is spatial in nature

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and can be used as a straightforward approach for investigating the interactive and independent

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effects of both environmental and spatial processes. Here, we examined (i) how environmental

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and spatial processes affect the metacommunity organization of marine macroinvertebrates

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inhabiting the intertidal sediments of a biodiverse coastal ecosystem; (ii) whether the influence

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of these processes is constant through time or is affected by extreme weather events (storms);

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and (iii) whether the relative importance of these processes depends on the dispersal abilities of

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organisms. We found that macrobenthic assemblages are influenced by each of environmental

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and spatial variables; however, spatial processes exerted a stronger role. We also found that

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this influence changes through time and is modified by storms. Moreover, we observed that the

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influence of environmental and spatial processes varies according to the dispersal capabilities

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of organisms. More effective dispersers (i.e., species with planktonic larvae) are more affected

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by spatial processes whereas environmental variables had a stronger effect on weaker

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dispersers (i.e. species with low motility in larval and adult stages). These findings highlight that

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accounting for spatial processes and differences in species life histories is essential to improve

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our understanding of species distribution and coexistence patterns in intertidal soft-sediments.

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Furthermore, it shows that storms modify the structure of coastal assemblages. Given that the

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influence of spatial and environmental processes is not consistent through time, it is of utmost

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importance that future studies replicate sampling over different periods so the influence of

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temporal and stochastic factors on macrobenthic metacommunities can be better understood.

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Key-words: macrobenthos; soft-sediments; biological assemblages; storms; larval dispersion;

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variation partitioning

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1. Introduction Community ecology has historically focused on a single spatial scale assuming that biological assemblages are mainly structured by local interactions (Paine 1969, Connell 1980,

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1983) and environmental features (Janzen 1967, Pianka 1981, Logue et al. 2011), a niche-

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based perspective. Over the past few decades, however, an increasing body of work has been

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showing that spatial processes such as dispersal and movement also determine the

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organization of local assemblages (e.g., Underwood 1994, Leibold et al. 2004, Goncalves-

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Souza et al. 2014, Leibold and Loeuille 2015).

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An effective conceptual tool to investigate how environmental and spatial processes

drive the distribution of species is the metacommunity ecology (Cottenie 2005, Holyoak et al.

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2005). A metacommunity is defined as a set of local assemblages that are linked by dispersal

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and is regulated by niche differentiation and species competitive and dispersal abilities (Wilson

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1992, Leibold et al. 2004). In theory, low dispersal rates turn neighboring assemblages more

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similar, reflecting in a spatially structured metacommunity. On the other hand, higher dispersal

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rates allow species to reach optimal patches and enhance the importance of local species

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interactions and environmental characteristics (i.e., niche-based or species-sorting processes)

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(Leibold et al. 2004, Cottenie 2005). Furthermore, recent studies argue that excessively high

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dispersal generates a mass-effect and allows species to colonize suboptimal patches, thereby

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reducing the environmental control and favoring spatial processes (Heino and Gronroos 2013;

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Penha et al. 2017).

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Although the metacommunity ecology is a straightforward approach to evaluate how environmental and spatial processes affect biological communities, there are few broad

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generalizations because most investigations around metacommunity organization were done in

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freshwater habitats, with few or no tests for other systems (Logue et al. 2011, , Heino et al.

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2015, Checon and Amaral 2017, Rodil et al. 2017a). Importantly, most studies are based on

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single samplings (snapshot approach), thereby considering distributional patterns as static

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properties and likely misrepresenting the relative importance of processes through time

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(Fernandes et al. 2014, Heino et al. 2015).

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Given that dispersal rates and environmental variables may change over time due to

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seasonality or stochastic factors such as disturbance events or random supply of individuals

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(Vanschoenwinkel et al. 2013, Datry et al. 2015, Gerwing et al. 2016), strong changes in

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metacommunities dynamics are expected. For instance, Reed et al. (2000) found significant

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temporal variation in distributional patterns of marine assemblages due to large disturbances

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events, whereas Fernandes et al. (2014) showed that the role of spatial and environmental

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processes in structuring floodplain-fish metacommunities changed through time as a result of

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modifications in environmental heterogeneity and landscape connectivity. Therefore,

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investigating temporal variation of metacommunities favors a holistic understanding of their

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dynamic and improves the predictive ability of extinction and colonization rate. Indeed, including

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time in metacommunty ecology is an urgent task for researchers and essential information in

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conservation and management efforts (Azeria and Kolasa 2008, Fernandes et al. 2014). Metacommunity ecology can be especially useful in coastal marine systems (Heino et

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al. 2015), where (1) all the patches are characterized as open systems, and thus they are

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virtually linked to each other via dispersal (Gray and Elliott 2009), and (2) species have different

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dispersal capabilities. Macrobenthic invertebrates (i.e., individuals larger than 0.5 mm and

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generally in the size range from 1 mg to 2 g dry tissue - mainly polychaetes, molluscs and

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crustaceans - McLachlan and Brown 2006) inhabiting marine sediments, for example, disperse

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across all life-history stages, ranging from species with reduced dispersal rates (absence of

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pelagic larval stages and adults with reduced mobility) to species that can reach great distances

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from their parents (with long-lived planktonic larval stages and high motile adults) (Thorson

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1950, Whitlatch et al. 1998, Valanko et al. 2010, Pilditch et al. 2015). Furthermore, dispersal

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rates in coastal marine systems are highly dependent on hydrodynamics features, such as the

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action of waves and currents (Norkko et al. 2001,Valanko et al. 2010, Reed, 2000), which may

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change over time due to the influence of extreme weather events such as storms and cold

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fronts.

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In coastal marine sediments, considerable research has shown the strong influence of

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environmental variables such as sediment type, depth, and wave action on the distribution of

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benthic macrofauna (e.g., Lercari and Defeo 1999, Gray 2002, Thrush et al. 2005a, Corte et al.

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2017a). Consequently, environmental control is expected to have a significant role in structuring

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marine soft-sediments assemblages (Defeo and Mclachlan 2005, Rodil et al. 2017a). Over the

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past decade, however, studies have demonstrated that coastal benthic assemblages are also

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significantly influenced by the dispersal ability of individuals and distance between assemblages

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(e.g., Moritz et al. 2013, Quillien et al. 2015, Gerwing et al. 2016, Rodil et al. 2017a, 2017b).

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Additionally, researchers have been suggesting that high dispersal rates observed at marine

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coastal systems may homogenize assemblages irrespective of their environmental conditions

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(i.e., generate a mass effect: Moritz et al. 2013, Heino et al. 2015), thereby favoring spatial

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processes.

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Here, we examined the importance of environmental and spatial processes on marine

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macrobenthic invertebrates inhabiting the sediments of a biodiverse coastal ecosystem in four

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periods with different hydrodynamic conditions (i.e., before or after storms of different

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magnitude). Specifically, we tested three predictions:

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1.

Since there is a strong influence of environmental variables on species performance of

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marine macrobenthic fauna, we expect that environmental processes should exert a major

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control in the metacommunity organization of marine sediment assemblages.

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2.

Given that increased waves and currents associated with storms are expected to strongly

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increase dispersal in coastal ecosystems (Gunter 1992, Corte et al. 2017b), possibly

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generating a mass effect, we expect the influence of spatial processes to be higher after

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storms.

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Lastly, as observed for sandy beach macrobenthic fauna (Rodil et al. 2017a, 2017b), we expect that the relative importance of environmental and spatial processes depends on the

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species dispersal abilities, and that the role of spatial processes should decrease from

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stronger (species with planktonic larvae and motile adults) to weaker dispersers (species

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with nonplanktonic larvae and sessile or discretely motile adults).

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2. Methods

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2.1. Study area and sampling

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This work was done at Araçá Bay (23º 49’S, 45º 24’W), located in the Marine Protected Area of the Northern coast of São Paulo State, Southeast Brazil (Figure 1). Araçá Bay has a

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susbtantial intertidal area (approximately 300 m wide) and is characterized as a heterogeneous

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and biodiverse rich environment (Amaral et al. 2016, Checon et al. 2017). These features

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provide an ideal test system to investigate the relative contribution of spatial and environmental

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processes to community variation.

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Fig. 1. Map showing the location of the study area and the spatial distribution of sampling sites in the intertidal area of Araçá Bay. Figure published in Corte et al. 2017b under a CC BY 4.0 open access license.

Araçá Bay is one of the few tide-dominated environments along the southeastern coast

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of Brazil (Dottori et al. 2015; Siegle et al. 2017), and its hydrographic properties are subject to

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physical forcing by frontal systems, when current speeds can increase eightfold (Fo 1990). At

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the region, the highest storm waves are associated to cold fronts and reaching offshore

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significant wave heights of 6.4m (Pianca et al.2010).

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Field work was done following procedures described in Corte et al. 2017b. Briefly, we sampled the tidal flat during spring tide on four times at ca. three month intervals, from

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September 2011 to July 2012 (25 September 2011, 5 February 2012, 7 May 2012, and 29 July

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2012). The last two sampling events occurred on the first spring tide after strong storms hit the

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study area (one-day lag in May and 11 days in July) (Fig 2). Sampling was done early in the

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morning of two consecutive days.

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Fig. 2 Wave height (a) and wave power (b) during the study period (sampling events are shown by dots. Red dots correspond to storm sampling events). Figure published in Corte et al. 2017b under a CC BY 4.0 open access license.

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During each sampling event, fauna were collected from 34 sites attempting to cover the

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greatest diversity of habitats (i.e. different sediment types in different depth zones of the tidal

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flat); the same locations were sampled at each period (the position of each sampling site was

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recorded with a handheld GPS (Garmin eTrex Legend, datum WGS84). At each sampling site,

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three sediment samples were collected using a 20 cm diameter core of 20 cm depth for

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biological data. An additional sediment sample was collected using a 3 cm diameter core x 20

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cm deep for sediment analyses.

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2.2. Biological and environmental data Each sample was placed in a separate plastic bag and all were immediately transported

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to the laboratory, where they were washed on the same day of collection through stacked

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aluminum sieves (1000µm and 300µm). Although macrofauna is defined as being retained by

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0.5mm mesh, we chose to use a smaller size to improve the collection of recently settled

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juveniles (Schlacher & Wooldridge 1996). The fauna retained were sorted in taxonomic groups

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and fixed in 70% ethanol. Subsequently, all individuals were further identified to the lowest

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taxonomic level possible.

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Sediment granulometric analysis was performed with standard dry sieving described by Suguio (1973). Sediment statistics were calculated with SysGran software (Camargo 2006)

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using the parameters of Folk and Ward (1957). Organic matter content was determined by the

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weight differences between samples that were dried at 60°C for 24 h and then incinerated at

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550°C for 6 h. Calcium carbonate content was obtain ed by HCl 10% attack. Interstitial water

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salinity and temperature were measured in situ at all sampling sites with a refractometer and a

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digital thermometer, respectively. Wave height and period for the region were obtained for

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24.5 S and 45.5 W from the global wave generation model WaveWatch III (NCEP/NOAA). Wave

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power (Pw) was calculated as: Pw= ρg H T / 32π, where ρ is water density (1,027 kg/m ), g the

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acceleration due to gravity (9.81 m/s ), H the wave height (m), and T the wave period (s)

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(Herbich 2000). We considered wave height and power for the three days before each sampling

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because this time lag has the strongest correlation between wave height/power and changes in

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macrobenthic species in the area (Turra et al. 2016, Corte et al. 2017b). Depth of each

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sampling site was based on a high resolution bathymetric survey conducted with a Personal

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Watercraft and topographic surveys of intertidal areas. Bottom wave orbital velocity, a measure

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of the interaction between surface water with the marine bottom, was estimated by modelling

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the relationship between wave height and period with sediment depth (Wiberg and Sherwood,

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2008). Briefly, waves have been propagated across the bay through the application of a

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numerical model (Delft 3D) based on wave data measured through an ADCP moored at the

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entrance of the bay (Dottori et al., 2015; Siegle et al. 2017). Then, wave orbital velocities for

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each sampling point were estimated based on the interaction between wave characteristics and

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local depth.

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2.3. Defining groups differing in dispersal ability

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To investigate if the influence of environmental and spatial variables on macrobenthic

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assemblages depend on their dispersal abilities we divided the species in four dispersal ability

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(1) species with planktonic larvae and motile adults (PLMA), (2) planktonic larvae and sedentary

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adults (e.g. tube builders, PLSA), (3) non-planktonic larvae and motile adults (NLMA), and non-

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planktonic larvae and sedentary adults (NLSA) (Appendix 1). Sedentary organisms include

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species classified as discretely motile and sessile (sensu Fauchald and Jumars 1979). We

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considered species with planktonic larvae and adults with reduced mobility (PLSA) as more

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effective dispersers than species with nonplanktonic larvae and motile adults (NLMA). Whitlatch

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et al. (1998) have showed that movement of juvenile and/or adult life-stages across the seabed

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usually occurs at smaller scales than before settlement. Therefore, we expected the importance

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of the spatial variables to increase from PLMA to NLSA (i.e., NLSA > NLMA > PLSA > PLMA <

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PLSA < NLMA < NLSA). It is relevant to highlight, however, that the role of larval and adult

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dispersal in marine sediments has been subject of a constant debate and some authors argue

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that the post-settlement dispersal can be even more important than pre-settlement dispersal

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(see Pilditch et al. (2015) and references therein). Within each DAGs, there is probably much

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among-species variation and characterizing dispersal rates more directly could likely be a better

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approach. In fact, lecitotrophic and planktotrophic larvae were both considered planktonic even

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though they may differ in time spent in water column, and there are obvious differences in the

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locomotion way of several species considered motile. Data on the dispersal of marine species

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is, however, extremely scant and limited to a few species (Heino et al. 2015), hence analyzing

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group of species based on dispersal traits has been pointed as a promising alternative

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(Landeiro et al. 2014, Heino et al. 2015).

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Information about the development mode and mobility of species was thus gathered

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from expert knowledge, peer-reviewed literature and publicly available databases. When the

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information about some species were not available, we relied on information from higher

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taxonomic levels (Appendix 1).

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2.4. Statistical Analyses

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We used sediment characteristics (percentages of silt and clay, fine sand (very fine and fine sands), coarse sand (very coarse and coarse sands), and pebble), organic matter and

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CaCO3 content, temperature, depth, wave orbital velocity and interstitial water salinity of each

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location as environmental variables. Prior the analysis, we evaluated variables correlation to

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keep the variation inflation factor lower than 3 (Zuur et al. 2010). Whereas coarse sand and fine

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sandy content were negatively correlated (r = -0.84), depth and orbital were positively correlated

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(r = 0.75), Therefore, we excluded coarse sand and orbital velocity from the analysis. All

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variables were standardized to mean of zero and unit variance (z-transformation) to account for

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their different scales of measurement that can affect some statistical analysis (Legendre &

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Legendre 2012).

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We used distance-based Moran eigenvector maps (dbMEMs) calculated for the sites coordinates matrix to derive spatial variables (proxy for spatial processes). dbMEMs provide

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orthogonal vectors that maximize the spatial autocorrelation (Dray et al. 2006). Large-range

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spatial correlation is modelled by the initial dbMEMs, while the last dbMEMs correspond to fine-

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scale spatial correlation (Borcard et al. 2011). We used the longest distance connecting two

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neighboring sites as a threshold to truncate the distance matrix. Distances larger than the

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threshold value were replaced by an arbitrarily large value equal to four times the threshold and

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were disconnected in a neighbor matrix (i.e., truncated matrix) (Borcard and Legendre 2002).

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After we generate the spatial variables, we assessed the importance of spatial and

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environmental variables on metacommunity structure by applying permutation tests (10000

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permutations) on redundancy analysis (RDA) models (alpha = 0.05). We used a variation

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partitioning approach (Peres-Neto et al. 2006) applied to the redundancy analysis (partial RDA)

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to disentangle species response to environmental and spatial processes (Legendre and

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Legendre 2012). Redundancy Analysis is a powerful tool for the analysis of community

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composition data tables and has been shown to provide unbiased estimates when used with

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variation partitioning approaches (Peres-Neto et al. 2006, Borcard et al. 2011). Here, the total

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percentage of variation in the species data is decomposed into pure (independent) and shared

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(interactive) contributions of two sets of predictors (i.e., environmental and spatial variables) and

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can be attributed to different fractions based on adjusted fractions of variation (Radj ): total

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explained variation [a + b + c], environmental variation [a + b], spatial variation [b + c],

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environmental variation without the spatial fraction [a], spatial variation without the

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environmental fraction [c], the common fraction of variation shared by environmental (E) and

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spatial predictors (S) [b], and the residual fraction of variation not explained by E and S [d]

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(Peres-Neto et al. 2006).

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The partial RDAs were run for each dispersal mode group and for the whole community

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(pooled groups) for the four sampling periods. We checked for spatial autocorrelation on

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residuals using direct multiscale ordination (MSO) (Wagner 2004) (Appendix 2). To minimize

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random effects by dominant taxa and make data more appropriate to be analyzed by linear

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ordination methods (Peres-Neto et al. 2006), we transformed the total counts of species using

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the Hellinger transformation (Legendre and Legendre 2012). As a form of sensitivity analyses to

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account for differences in number of species in each DAG, we tested the effect of environmental

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and spatial processes in two ways: 1) by considering the full species data for each period, and

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2) by excluding rare taxa (i.e., those represented by at less than three individuals and present in

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less than three sites at each sampling period). Comparing these alternative classifications

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showed congruent results (Appendix 3), and we felt that the analyses without rare taxa provided

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an appropriate resolution to present the influence of environmental and spatial processes on the

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metacommunity organization across the studied area. All analyses were undertaken in the R

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environment using vegan (Oksanen et al. 2013) and spacemakeR (Dray et al. 2013) packages.

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3. Results

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3.1. Environmental characterization Seawater temperature varied seasonally, with warmer waters during summer (February

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2012) and cooler waters in winter (July 2012) (Appendix 4). Salinity was greater in September

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2011 and lower in July 2012. No great variation in organic matter content was recorded.

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Sediment features varied throughout the study period, and the content of silt and clay and fine

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sand in the sediment increased from September 2011 to July 2012, whereas the coarser

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fractions of sediment decreased. These changes are likely related to storm effects, given that

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increased rainfall enhance freshwater input and reduce salinity. Furthermore, Alcántara-Carrió

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et al. (2017) showed that increases of fine sediments in Araçá Bay are associated with the input

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of terrigenous sediment after intense rains and with resuspension of sediments on the adjacent

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shelf of the São Sebastião Channel by storm waves, in addition to further transport by wind-

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driven currents during cold front events.

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3.2. Biotic characterization

One hundred and twenty six macrobenthic species were recorded in this study

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(Appendix 1). Polychaetes, molluscs and crustaceans made up 94% of the total number of

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species (polychaetes: 67 species, molluscs: 34 species, crustaceans: 18 species). Species with

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planktonic larvae and sedentary adults (PLSA) were the most representative group (n = 55),

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followed by species with planktonic larvae and motile adults (PLMA; n = 46), species with non-

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planktonic larvae and motile adults (NLMA, n = 11), and species with non-planktonic larvae and

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sedentary adults (NLSA, n = 11). Three species were not included in the DAGs due to lack of

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information.

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3.3. Relative importance of environmental and spatial processes on metacommunity structure

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Both environmental and spatial processes significantly affected the macrobenthic

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metacommunity structure of the whole community. Nevertheless, both sets of predictors

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explained only a third of the total variation. A stronger spatial pattern was observed in three of

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the four periods analyzed (Table 1). Sediment features and depth were the environmental

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variables that most influenced species composition. The number of spatial variables retained for

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the partial RDA model ranged from 1 in September 2011 to 5 in the other periods (Table 2).

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Table 1. Variation partitioning (%) and associated P values (p ≤ 0.05 in italics) for the influence of environmental and spatial factors on the distribution of macrobenthic community in the four periods analysed. September/11 2 p Radj [E + S]

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February/12 2 p Radj 31

May/12 2

Radj 33

July/12 p

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Radj

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<0.01

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0.13

[S]

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<0.01

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0.002

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<0.001

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<0.001

[E ∩ S]

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Residuals

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Significant variables [E] Peb*, Dep*, CaCO3* [S] 2*** (dbMEM)

1*, 3*, 4*, 8**, 28*

1**, 2**, 3**, 5**, 8**

1**, 2**, 3**, 5**, 8**

[E + S] = total explained variation by all sources, [E] = pure environmental variation, [S] = pure spatial variation, [E ∩ S] = variation shared by [E] and [S]. Peb peebles percentage, Dep depth, CaCO3 calcium carbonate content, dbMEM are spatial predictors extracted from sampling sites with distance-based Morans eigenvector maps. Asterisks correspond to statistically significant values: *P<0.05; **P<0.01; ***P<0.001.

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dependent on the hydrodynamic conditions (Fig. 3) and dispersal ability of each group (Fig. 4).

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Whereas the influence of environmental processes on species composition decreased after

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periods of higher wave power (mean% R = 9.5 before and 1 after the storm), the influence of

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spatial factors increased (mean% R = 11 before and 20 after). The environmental variables

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had a stronger effect on the composition of weaker dispersers (i.e. species with low motility in

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larval and adult stages); however, this pattern was clearly observed only before the study area

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was hit by strong storms (Fig. 4).

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The relative importance of environmental and spatial processes, however, was highly

Fig. 3. Variance explained (%) by environmental (left) and spatial processes (right) during four sampling events associated with significant variation in wave energy (number inside parentheses) preceding each sampling event.

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Fig. 4. Variance explained (%) by environmental and spatial processes in each dispersal ability group during four sampling events associated with significant variation in wave energy preceding each event. Asterisks correspond to statistically significant (p <0.05) values.

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4. Discussion

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Each of environmental and spatial processes influenced the metacommunity structure

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of soft sediment macrobenthic assemblages; however, spatial variables were better predictors

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(i.e., they explained higher variance) of metacommunity organization. Importantly, we also

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found that the role of environmental and spatial processes changed through time, being

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modified by disturbance events (storms), and was dependent on the dispersal ability of

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macrobenthic organisms.

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Contrary to our prediction, environmental processes were not the main structuring factor

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of macrobenthic assemblages of Araçá Bay. Metacommunity theory assumes that the relative

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role of environmental and spatial processes in the assembly of local communities depends on

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the spatial scale, and that fine-scale (e.g., within a bay) spatial distributions of species are

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primarily determined by environmental processes (Cottenie 2005, Meutter et al. 2007, Árva et

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al. 2015). This would be expected for assemblages inhabiting the soft-sediments of Araçá Bay

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due to the relatively small spatial scale of the study area (less than 1 Km ), and also because

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several studies have shown that macrobenthic invertebrates are strongly influenced by physical

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and biotic characteristics of the environment (e.g., McLachlan 1990, Defeo and McLachlan

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2005, de Juan and Hewitt 2014). Our results, however, show that spatial processes were the

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main factor structuring the metacommunity organization of macrobenthic fauna at Araçá Bay.

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This result corroborates the findings of recent studies (e.g., Quillien et al. 2015, Gerwing et al.

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2016, Checon and Amaral 2017, Rodil et al. 2017a,b) and reinforces the importance of

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dispersal and movement of organisms in structuring coastal ecosystems.

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One main achievement of our study was to show that the relative importance of spatial

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and environmental processes on metacommunity dynamic of soft sediments assemblages

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changed through time and was strongly affected by storms. According to our expectation, the

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percentage of variation explained by spatial processes increased in the last two periods, likely

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due to increased hydrodynamics related to extreme weather events. In tidal systems, dispersion

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of individuals is promoted by regular tidal-mediated currents and by wave-driven resuspension

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events (Valanko et al. 2010). Extreme currents under stormy weather conditions can suspend a

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high load of intertidal macrofauna in the water column and disperse the organisms into distant

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areas (Günther 1992). For example, Dobbs and Vozarik (1983) found substantial increases in

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the number of infaunal species and individuals in the water column after a strong storm hit the

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coast of Connecticut, USA, whereas Reineck et al. (1968) (apud Günther 1992) observed that

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mudsnails Peringia ulvae were resuspended and transported far away from the tidal flats of the

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Wadden Sea after storms. Similarly, Gerwing et al. (2015) found that physical disturbance of

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sediments affected the structure of infaunal communities on mudflats in the Bay of Fundy,

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Atlantic Canada. At Araçá Bay, Alcántara-Carrió et al. (2017) showed that storm waves and

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wind-driven currents are responsible for sediment resuspension and transport. Therefore, it is

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reasonable to infer that the storms that affected the study area increased dispersal rates in

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macrobenthic assemblages. This increased dispersal, in turn, likely generated a mass-effect

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and changed spatial patterns of abundance, thereby modifying the metacommunity organization

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of macrobenthic species.

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Temporal changes in metacommunity organisation, however, were not restricted to

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storm effects. Similar wave conditions were registered in September 2011 and February 2012,

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but the role of spatial and environmental processes differed between periods. Specifically, the

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influence of spatial effects increased from September 2011 to July 2012, whereas the

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environmental effect decreased. Besides disturbances and changes in environmental

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conditions, several other factors may influence the metacommunity organization of coastal

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ecosystems. Strong peaks of reproduction and recruitment, for example, may change the

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increasing the abundance of species with aggregate distribution, as most macrobenthic

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organisms inhabiting marine soft sediments (Defeo and McLachlan 2005, McLachlan and

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Brown 2006). At Araçá Bay, many abundant species, such as the tanaidacea

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Monokalliapseudes schubarti, the clam Anomalocardia brasiliana, and the polychaete Capitella

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nonatoi, show clustered dispersion and increased reproduction and recruitment during summer

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and beginning of autumn (Leite et al. 2013, Corte et al. 2014, Corte 2015). Furthermore, Corte

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et al. (2017b) showed a high increase in the number of individuals in February 2012, suggesting

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high recruitment at this period. The significative influence of a dbMEM corresponding to fine-

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scale spatial correlation in February (i.e., dbMEM 28) shows that neighboring assemblages

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were more similar, and reinforces the hypothesis of high recruitment leading to a stronger

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influence of spatial processes. Nevertheless, this is still a much-overlooked topic in coastal, and

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also general, ecology which needs to be investigated in future studies.

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Besides showing that the role of environmental and spatial processes on macrobenthic

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assemblages changed over time, our results also demonstrated that their influence depends on

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the species dispersal abilities. Previous studies have shown that dispersal capabilities

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determine the relative importance of environmental and spatial processes in different aquatic

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systems such as fens (Hájek et al. 2011), lakes (Heino 2013), and streams (Grönroos et al.

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2013). Overall, these studies found that the importance of environmental processes decreases

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from stronger to weaker dispersers. Nevertheless, the opposite pattern was reported for marine

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soft-sediment assemblages. Investigating sandy beach assemblages in Europe, Rodil et al.

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(2017a, b) found that taxa with planktonic larval development, categorized as high dispersive

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species, were more influenced by spatial process whereas low dispersive species were

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predicted by a combination of environmental and spatial processes. Similarly, our results show

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that stronger disperses responded strongly to spatial variables, whereas weaker disperses were

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more influenced by environmental variables.

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It seems that relative influences of environmental and spatial processes (e.g.,

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environmental control, dispersal limitation, and mass effects) are divergent for a given group

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(e.g., benthic invertebrates) in different types of aquatic system (Heino et al., 2015). In fact,

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dispersal between sites in coastal marine systems differs in several ways from that in other

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aquatic systems (Heino et al. 2015). For instance, dispersal rates are very low in communities of

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temporary ponds, which are expected to be mainly structured by dispersal limitation. In streams,

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dispersal is still low, but enough to favor an environmental control of local assemblages. On the

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other hand, dispersal rates can be extraordinarily high in coastal soft-sediment habitats due to

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increased waves and currents, and species are not attached to the substratum (i.e., they can

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disperse across all life-history stages) (Valanko et al. 2010). As a consequence, coastal marine

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systems seem to be less controlled by dispersal, and under high influence of mass effects

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processes. In particular, species with planktonic larvae may disperse at high rates and spread

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over an extended area, thereby reducing the environmental control and homogenizing

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neighboring assemblages..

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Study limitations and avenues for future studies Several authors have pointed out that the variation accounted by spatial processes may

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arise from two main sources: it can be attributed either to some spatially structured unmeasured

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environmental variables or pure spatial processes related to dispersion (e.g., Legendre and

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Legendre 2012, Landeiro et al. 2014, Provete et al. 2014). Although we included most

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environmental variables that are commonly found to affect the species distribution of soft

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sediment macrobenthic assemblages (i.e., sediment, waves, depth, and salinity), many others

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were not considered. Variables such as oxygen and nitrogen content and microphytobenthic

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pigments in the sediment, for example, are known to exert a strong influence on marine

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invertebrates (Riedel et al. 2012, Corte et al. 2017c) and were not considered here. Thus, it is

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possible that this issue can be underestimating the interpretation of the environmental control

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observed here, and is important that future studies on macrobenthic assemblages attempt to

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include a broader range of environmental variables.

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Despite the inclusion of different environmental variables or not, our results clearly show that storms (disturbance events) altered the role of spatial and environmental processes on

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metacommunity organization of macrobenthic assemblages. Given that changes in storm

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frequency and intensity are predicted to increase over the 21st century (IPCC 2013), it is

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essential that future works investigate how these extreme events may affect the organization of

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coastal biodiversity (Harris et al., 2011, Machado et al. 2017).

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Finally, although development mode and adult mobility seem to be crucial to dispersal capabilities of macrobenthic species, other traits may also exert a strong influence. Number and

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size of eggs or larvae may affect the distance traveled and potential of recolonization (Lundquist

445

et al. 2004). For instance, species which produce a large number of larvae are expected to

446

reach higher distances due to random processes. Similarly, species with small larvae (or eggs)

447

are expected to have reduced fall velocity and stay more time within the water column, thus

448

being transported long distances to colonize distant habitats. Larval behavior may also have

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exerted strong importance on the structure of marine macrobenthic metacommunities, as

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studies are accumulating that demonstrate that many larvae are capable of swimming to orient

451

themselves (Metaxas 2001, Kingsford et al. 2002, Shanks 2009). It can be speculated that

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these larvae/species can actively search for suitable habitats, thus favoring environmental

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processes, or staying close to the bottom where currents are much slower, therefore favoring

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spatial processes (Shanks 2009). Unfortunately, information on how larval dispersal influence

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species distributions between soft-sediment assemblages remain a poorly explored field of

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research (Defeo and McLachlan 2005, Rodil et al. 2017b) and this topic should be addressed in

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future studies.

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5. Conclusions Our results show that macrobenthic assemblages may be primarily influenced by spatial processes, and that metacommunity organization is not consistent through time and is affected

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by storms. Therefore, it is evident that broader consideration of the roles of spatial processes

462

should enhance our understanding about the ways macrobenthic assemblages are structured

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(Thrush et al. 2005a, 2005b, Zajac et al. 2013). This can be especially important for coastal

464

ecosystems, where alongshore currents with reduced velocities can keep the larvae closer to

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shore (Shanks 2009) or extreme currents under stormy weather conditions can disperse the

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organisms to distant areas. By comparing groups of species varying in dispersal ability, we

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recognized that the relative influence of environmental and spatial variables is dependent on

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species dispersal capabilities. In this regard, accounting for differences in species life histories

469

is essential to understand species distribution and coexistence patterns in intertidal soft-

470

sediments better. It is important to note, however, that the environmental and spatial variables

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used in this study explained only a third of the total variation when the whole assemblage was

472

analyzed. Thus, some unmeasured factors (e.g., biotic interactions) significantly contributed to

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the metacommunity organization (Rodil et al. 2017b, Van Allen et al. 2017) and additional

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variables should be included in future studies. Overall, our results bring important contributions

475

to achieve a better understanding of the organization of coastal ecosystems, as well as to

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improve the current knowledge about metacommunity ecology. We suggest that future studies

477

expand our findings by replicating sampling in time and under different environmental conditions

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so the influence of seasonal and stochastic factors on metacommunities can be better

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understood.

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6. Acknowledgements

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We are indebted to Yasmina Shah Esmaeili, Aline Martinez, Paulo Paiva, Maikon di Domenico, André Garraffoni and Ronaldo Christofoletti for providing helpful comments on early

483

versions. Many friends and colleagues helped in field and taxonomic work. Their assistance is

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greatly appreciated. This paper was supported by fellowships from São Paulo Research

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Foundation (FAPESP - 2011/10130-3; 2016/10810-8; 2017/17071-9) and Brazilian National

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Council of Technological and Scientific Development (CNPQ 141429/2011-9) to GNC. This

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work was also supported by FAPESP under Project “Biodiversidade e funcionamento de um

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ecossistema costeiro subtropical: subsídios para gestão integrada” (2011/50317-5) and CNPQ

489

through a productivity grant to ACZA (306558/2010) and to ES (310028/2015). We also thank

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the Institute of Biology of the University of Campinas (IB/UNICAMP) and the Center for Marine

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Biology of University of São Paulo (CEBIMar/USP) for logistic support, and Coordination for the

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Improvement of Higher Education Personnel (CAPES - BEX 14796/13-9) and the University of

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Sydney for a visiting scholarship to Sydney.

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7. References

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The Importance of Environmental and Spatial Factors in the Metacommunity Dynamics of Exposed Sandy Beach Benthic Invertebrates. Estuaries and Coasts. https://doi.org/10.1007/s12237-017-0263-9 Schlacher T.A. & Wooldridge T.H. 1996. How sieve mesh size affects sample estimates of estuarine benthic macrofauna. Journal of Experimental Marine Biology and Ecology 201, 159-71. Shanks, A.L. 2009. Pelagic larval duration and dispersal distance revisited. Biological Bulletin 216: 373385. Siegle, E., Dottori, M., and B.C. Villamarin. 2017. Hydrodynamics of a subtropical tidal flat: Araçá Bay, Brazil. Ocean & Coastal Management. https://doi.org/10.1016/j.ocecoaman.2017.11.003 Thorson, G. 1950. Reproductive and larval ecology of marine bottom invertebrates. Biological Reviews 25:1-45. Thrush, S. F., J. E. Hewitt, P. M. J. Herman, and T. Ysebaert. 2005a. Multi-scale analysis of speciesenvironment relationships. Marine Ecology Progress Series 302:13-26. Thrush, S. F., C. J. Lundquist, and J. E. Hewitt. 2005b. Spatial and temporal scales of disturbance to the seafloor: A generalized framework for active habitat management. Pages 639-649 in B. W. Barnes and J. P. Thomas, editors. Benthic Habitats and the Effects of Fishing. Turra A, Pombo M, Petracco M, Siegle E, Fonseca M, Denadai MR (2016) Frequency, Magnitude, and Possible Causes of Stranding and Mass-Mortality Events of the Beach Clam Tivela mactroides (Bivalvia: Veneridae). PLoS ONE 11(1): e0146323. doi:10.1371/journal.pone.0146323. Underwood, A. J. 1994. Indeterminism, time, space and the need for long-term ecological studies. Bulletin of the Ecological Society of America 75:235-235. Valanko, S., A. Norkko, and J. Norkko. 2010. Strategies of post-larval dispersal in non-tidal soft-sediment communities. Journal of Experimental Marine Biology and Ecology 384:51-60. Van Allen, B. G., Rasmussen, N. L., Dibble, C. J., Clay, P. A. and Rudolf, V. H. W. 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RI PT

689 690 691 692 693 694 695 696 697 698 699 700 701 702

AC C

EP

TE D

M AN U

SC

703

20

ACCEPTED MANUSCRIPT Appendix 1

704 705

Species found in this study and their Dispersal ability group (DAG).

Species

Adult Motility

Larval Motility

DAG

Reference

sedentary

planktonic

PLSA

Brusca et al. (2003).

-

-

-

mobile

planktonic

PLMA

sedentary

planktonic

PLSA

Bulla striata

mobile

nonplanktonic

NLMA

Cerithium atratum

mobile

planktonic

Chione cancellata

sedentary

planktonic

Chione subrostrata

sedentary

planktonic

Corbula caribaea

sedentary

Corbula sp.1

sedentary

Cyclinella tenuis

sedentary

Diplodonta patagonica

sedentary

Diplodonta punctata

sedentary

Edwardsia migottoi Nemertea Nemertea sp. 1

Anomalocardia brasiliana

Donax gemmula

mobile

(1)

Mouëza et al. (1999) Berrill (1931)

PLMA

Houbrick (1971)

PLSA

Morsan and Kroeck (2005)

PLSA

Morsan and Kroeck (2005)

M AN U

Abra sp.1

-

SC

Mollusca

RI PT

Cnidaria

plankfeed

PLSA

(1)

plankfeed

PLSA

(1)

planktonic

PLSA

Morsan and Kroeck (2005)

planktonic

PLSA

Raven (2013)

planktonic

PLSA

Raven (2013)

plankfeed

PLMA

Carstensen et al. (2010)

planktonic

PLSA

Raven (2013)

sedentary

planktonic

PLSA

Webb (1986)

sedentary

planktonic

PLSA

Morsan and Kroeck (2005)

sedentary

plankfeed

PLSA

Webb (1986)

sedentary

planktonic

PLSA

Webb (1986); Carstensen et al. (2010)

mobile

planktonic

PLMA

Scheltema (1965)

mobile

planktonic

PLMA

Crandall (1999)

sedentary

planktonic

PLSA

(1)

mobile

planktonic

PLMA

Edwards (1968)

sedentary

planktonic

PLSA

Raven (2013)

Phacoides pectinata

sedentary

planktonic

PLSA

Raven (2013)

Pitar fulminatus

sedentary

planktonic

PLSA

Morsan and Kroeck (2005)

Protothaca pectorina

sedentary

planktonic

PLSA

Morsan and Kroeck (2005)

Semele sp. 1

sedentary

planktonic

PLSA

Raven (2013)

Solen tehuelchus

sedentary

planktonic

PLSA

Raven (2013)

sedentary

Eurytellina lineata

Iphigenia brasiliana Macoma sp. Phrontis vibex Neritina virginea Nucula semiornata Olivella minuta

AC C

Periploma ovata

EP

Gouldia cerina

TE D

Ervilia nitens

Sphenia antillensis

sedentary

planktonic

PLSA

(1)

Strigilla pisiformis

sedentary

planktonic

PLSA

Webb (1986)

Tagelus divisus

sedentary

planktonic

PLSA

Morsan and Kroeck (2005)

Tagelus plebeius

sedentary

planktonic

PLSA

Morsan and Kroeck (2005)

Tellina sp. 1

sedentary

planktonic

PLSA

Webb (1986)

Tellina sp. 2

sedentary

planktonic

PLSA

Webb (1986)

21

ACCEPTED MANUSCRIPT Tivela mactroides

sedentary

planktonic

PLSA

Morsan and Kroeck (2005)

Ancistrosyllis jonesi

mobile

planktonic

PLMA

Blake (1975)

Aricidea (Allia) albatrossae

mobile

planktonic

PLMA

(2)

Aricidea (Aricidea) fragilis

mobile

planktonic

PLMA

(2)

Aricidea cf. wassi

mobile

planktonic

PLMA

(2)

Armandia agilis

mobile

planktonic

PLMA

(1)

Armandia hossfeldi

mobile

planktonic

PLMA

(1)

Armandia polyophtalama

mobile

planktonic

PLMA

Boccardia polybranchia

sedentary

planktonic

PLSA

Boccardiella ligerica

sedentary

planktonic

PLSA

Capitella nonatoi

mobile

planktonic

PLMA

Cirriformia filigera

sedentary

planktonic

NLSA

(1)

Cirriformia punctata

sedentary

planktonic

NLSA

(1)

Cirriformia tentaculata

sedentary

planktonic

NLSA

(1)

Clymenella dalesi

sedentary

Diopatra aciculata

sedentary

Diopatra dexiognatha

sedentary

Dispio remanei

sedentary

Dispio uncinata

sedentary

Eteone alba

mobile

Glycinde multidens

(1) (1)

Rouse and Pleijel (2001)

SC

M AN U

mobile

(1)

planktonic

PLSA

(1)

nonplanktonic

NLSA

Rouse and Pleijel (2001) ; (3)

nonplanktonic

NLSA

Rouse and Pleijel (2001) ; (3)

planktonic

PLSA

(1)

planktonic

PLSA

(1)

planktonic

PLMA

(1)

planktonic

PLMA

(1)

TE D

Dorvillea sp.

RI PT

Polychaeta

mobile

planktonic

PLMA

(1)

mobile

planktonic

PLMA

(1)

mobile

planktonic

PLMA

Giese and Pearse (2012)

mobile

planktonic

PLMA

(3)

mobile

planktonic

PLMA

Jumars et al. (2015)

mobile

planktonic

PLMA

(1)

sedentary

planktonic

PLSA

Hernández-Alcántara and Solís-Weiss (2009)

mobile

planktonic

PLMA

Mazurkiewicz (1975)

Loimia medusa

sedentary

nonplanktonic

NLSA

Rouse and Pleijel (2001); (3)

Magelona californica

sedentary

planktonic

PLSA

(1)

Magelona nonatoi

sedentary

planktonic

PLSA

(1)

Magelona papilicornis

sedentary

planktonic

PLSA

(1)

Magelona variolamellata

sedentary

planktonic

PLSA

(1)

Marphysa sebastiana

sedentary

planktonic

PLSA

(1)

mobile

planktonic

PLMA

(3)

sedentary

planktonic

NLSA

Rouse and Pleijel (2001); (3)

Naineris setosa

mobile

planktonic

PLMA

Giangrande and Petraroli (1991)

Nematonereis hebes

mobile

nonplanktonic

NLMA

Rouse and Pleijel (2001); (3)

Goniada litorea Haploscoloplos sp. 1

Hermundura tricuspis Heteromastus filiformis Isolda pulchella

AC C

Laeonereis culveri

EP

Hemipodia simplex

Mediomastus californiensis Mooreonuphis lineata

22

ACCEPTED MANUSCRIPT Nicolea uspiana

nonplanktonic

NLSA

(3)

Rashgua hemipodus

mobile

planktonic

PLMA

(1)

Rashgua lobatus

mobile

planktonic

PLMA

(1)

Onuphis eremita oculata

sedentary

nonplanktonic

NLSA

(3)

Owenia brasiliensis

sedentary

planktonic

PLSA

Brusca et al. (2003)

Owenia fusiformis

sedentary

planktonic

PLSA

Brusca et al. (2003)

Paraprionospio pinnata

sedentary

planktonic

PLSA

(1)

mobile

planktonic

PLMA

Brusca et al. (2003)

Poecilochaetus australis

sedentary

planktonic

PLSA

Poecilochaetus perequensis

sedentary

planktonic

PLSA

Poecilochaetus sp. 1

sedentary

planktonic

PLSA

Polydora nuchalis

sedentary

planktonic

PLSA

Polydora sp.1

sedentary

planktonic

PLSA

Polydora sp.2

sedentary

planktonic

Polydora websteri

sedentary

planktonic

Prionospio steenstrupi

sedentary

planktonic

Protoaricia sp. 1

mobile

Scolelepis sp.1

sedentary

Scolelepis squamata

sedentary

Scolelepis texana

sedentary

(1) (1)

(1) (1)

SC

(1)

PLSA

(1)

PLSA

(1)

PLSA

(1)

M AN U

Phyllodoce mucosa

RI PT

sedentary

nonplanktonic

NLMA

Rouse and Pleijel (2001); (3)

planktonic

PLMA

(1)

planktonic

PLMA

(1)

planktonic

PLMA

(1)

planktonic

PLMA

Ghodrati Shojaei et al. (2015)

nonplanktonic

PLMA

(1)

planktonic

PLMA

Achari (1975)

mobile

Scoloplos (leodamas) sp. 1

mobile

Sigambra grubii

mobile

Sigambra tentaculata

mobile

planktonic

PLMA

Achari (1975)

mobile

planktonic

PLMA

(3)

mobile

nonplanktonic

NLMA

Rouse and Pleijel (2001); (3)

sedentary

nonplanktonic

NLSA

Rouse and Pleijel (2001); (3)

sedentary

planktonic

PLSA

Brusca et al. (2003)

mobile

planktonic

PLMA

Brusca et al. (2003)

sedentary

nonplanktonic

NLSA

Lopes and Masunari (2004); Leite (1996)

Callinectes danae

mobile

planktonic

PLMA

Branco and Masunari (2000)

Caridae sp.1

mobile

planktonic

PLMA

Brusca et al. (2003)

Clibanarius antillensis

mobile

planktonic

PLMA

Clibanarius vittatus

mobile

planktonic

PLMA

Dendobranchiata

mobile

planktonic

PLMA

Rupert et al. (2004)

sedentary

nonplanktonic

NLSA

Pennafirme and Soares-Gomes (2009); Leite

TE D

Scoletoma tetraura

Sternaspis capilata Syllis sp.1 Terebellides anguicomus

Sipuncula sp. 1 Crustacea Alpheus nuttingi

AC C

Amphipoda sp.1

EP

Sipuncula

Monokalliapseudes schubarti

Varadarajan and Subramoniam (1982); Sant'Anna et al. (2009)

Varadarajan and Subramoniam (1982); Sant'Anna et al. (2009)

23

ACCEPTED MANUSCRIPT et al. (2003) Ocypodidae

mobile

-

-

-

Ogyrides alphaerostris

mobile

planktonic

PLMA

Packer (1985)

Pagurus criniticornis

mobile

planktonic

PLMA

Negreiros-Fransozo and Hebling (1987)

Panopeus occidentalis

mobile

planktonic

PLMA

Harvey and Epifanio (1997)

Pinnixa chaetopterana

sedentary

planktonic

Processa bermudensis

mobile

planktonic

PLMA

Uca leptodactyla

mobile

planktonic

PLMA

Upogebia brasiliensis

sedentary

planktonic

PLSA

Upogebia paraffins

sedentary

planktonic

PLSA

Upogebia vasquezi

sedentary

planktonic

PLSA

Holothuroidea sp. 1

-

-

-

-

Ophiuroidea sp. 1

-

-

-

-

-

-

-

-

Cephalochordata sp. 1

706 707 708

(1) (2) (3)

RI PT

Yamaguchi (2001)

De Oliveira et al. (2014) De Oliveira et al. (2014) De Oliveira et al. (2014)

www.genustraithandbook.org.uk http://www.sealifebase.org http://polychaetes.lifewatchgreece.eu/

709 References

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Achari, G. 1975. Studies on new or little known polychaetes from the Indian seas 4. On a new record of Sigambra tentaculata (Treadwell)(Pilargidae), from the southwest coast of India along with observations on its early larval stages. Journal of the Marine Biological Association of India 17:238-241. Berrill, N. 1931. The natural history of Bulla hydatis Linn. Journal of the Marine Biological Association of the United Kingdom (New Series) 17:567-571. Blake, J. A. 1975. The larval development of Polychaeta from the Northern California Coast. III Eighteen species of Errantia. Ophelia 14:23-84. Branco, J. O., and S. Masunari. 2000. Reproductive ecology of the blue crab, Callinectes danae Smith, 1869 in the Conceicao Lagoon system, Santa Catarina Isle, Brazil. Rev Bras Biol 60:17-27. Brusca, R. C., G. J. Brusca, and N. Haver. 2003. Invertebrates. Sunderland, Massachusetts. Sinauer Associates, Inc. Carstensen, D., J. Laudien, W. Sielfeld, M. E. Oliva, and W. E. Arntz. 2010. Early larval development of Donax obesulus: response to El Nino temperature and salinity conditions. Journal of Shellfish Research 29:361-368. Crandall, E. D. 1999. Early life history aspects of amphidromous neritid snails in Moorea, French Polynesia. Berkeley Scientific 3. De Oliveira, D. B., J. M. Martinelli-Lemos, and F. A. Abrunhosa. 2014. The complete larval development of the mud shrimp Upogebia vasquezi (Gebiidea: Upogebiidae) reared in the laboratory. Zootaxa 3826:517-543. Edwards, D. C. 1968. Reproduction in Olivella biplicata. Veliger 10:297-304. Ghodrati Shojaei, M., L. Gutow, J. Dannheim, H. Pehlke, and T. Brey. 2015. Functional Diversity and Traits Assembly Patterns of Benthic Macrofaunal Communities in the Southern North Sea. Pages 183195 in G. Lohmann, H. Meggers, V. Unnithan, D. Wolf-Gladrow, J. Notholt, and A. Bracher, editors. Towards an Interdisciplinary Approach in Earth System Science. Springer International Publishing. Giangrande, A., and A. Petraroli. 1991. Reproduction, larval development and post-larval growth ofNaineris laevigata (Polychaeta, Orbiniidae) in the Mediterranean Sea. Marine Biology 111:129137. Giese, C., and J. S. Pearse. 2012. Annelids and Echiurans. 1st Edition edition. Elsevier. Harvey, E., and C. Epifanio. 1997. Prey selection by larvae of the common mud crab Panopeus herbstii Milne-Edwards. Journal of Experimental Marine Biology and Ecology 217:79-91.

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710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742

M AN U

Chordata

(2013)

SC

Echinodermata

Martínez-Mayén and Román-Contreras

24

ACCEPTED MANUSCRIPT

794 795

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M AN U

SC

RI PT

Hernández-Alcántara, P., and V. Solís-Weiss. 2009. Ampharetidae Malmgren, 1866. Poliquetos (Annelida: Polychaeta) de México y América Tropical 1:57-75. Houbrick, J. R. 1971. Some aspects of the anatomy, reproduction, and early development of Cerithium nodulosum (Bruguière)(Gastropoda, Prosobranchia). Jumars, P. A., K. M. Dorgan, and S. M. Lindsay. 2015. Diet of worms emended: an update of Polychaete feeding guilds. Marine Science 7. Leite, F. P. P. 1996. Growth and reproduction of Hyale media Dana (Amphipoda, Gammaridae, Hyalidae) associated to Sargassum cymosum C. Agardh. Revista Brasileira De Zoologia 13:597-606. Leite, F. P. P., A. Turra, and E. C. F. Souza. 2003. Population biology and distribution of the tanaid Kalliapseudes schubarti Mane-Garzon, 1949, in an intertidal flat in southeastern Brazil. Brazilian Journal of Biology 63:469-479. Lopes, O. L., and S. Masunari. 2004. Reproductive biology of Talitroides topitotum (Burt)(Crustacea, Amphipoda, Talitridae) from Serra do Mar, Guaratuba, Paraná, Brazil. Revista Brasileira De Zoologia 21:755-759. Martínez-Mayén, M., and R. Román-Contreras. 2013. Data on reproduction and fecundity of Processa bermudensis (Rankin, 1900)(Caridea, Processidae) from the southern coast of Quintana Roo, Mexico. Crustaceana 86:84-97. Mazurkiewicz, M. 1975. Larval Development and Habits of Laeonereis culveri (Webster) (Polychaeta: Nereidae). Biological Bulletin 149:186-204. Morsan, E. M., and M. A. Kroeck. 2005. Reproductive cycle of purple clam, Amiantis purpurata (Bivalvia: Veneridae) in northern Patagonia (Argentina). Journal of the Marine Biological Association of the United Kingdom 85:367-373. Mouëza, M., O. Gros, and L. Frenkiel. 1999. Embryonic, larval and postlarval development of the tropical clam, Anomalocardia brasiliana (Bivalvia, Veneridae). Journal of Molluscan Studies 65:73-88. Negreiros-Fransozo, M. L., and N. J. Hebling. 1987. Desenvolvimento pós-embrionário de Pagurus brevidactylus (Stimpson, 1858)(Decapoda, Paguridae), em laboratório. Revista Brasileira De Zoologia 4:181-194. Packer, H. A. 1985. A guide to the larvae of New Zealand shallow water Caridea (Crustacea, Decapoda, Natantia). Department of Zoology, Victoria University of Wellington. Pennafirme, S., and A. Soares-Gomes. 2009. [Population Biology and Reproduction of Kalliapseudes Schubartii Mañé-Garzón, 1949 (Peracarida, Tanaidacea) in a Tropical Coastal Lagoon, Itaipu, Southeastern Brazil, Population Biology and Reproduction of Kalliapseudes Schubartii MañéGarzón, 1949 (Peracarida, Tanaidacea) in a Tropical Coastal Lagoon, Itaipu, Southeastern Brazil]. Crustaceana 82:1509-1526. Raven, C. P. 2013. Morphogenesis: the analysis of molluscan development. Elsevier. Rouse, G., and F. Pleijel. 2001. Polychaetes. Oxford university press. Rupert, E., R. Fox, and R. Barnes. 2004. Invertebrate zoology: a functional evolutionary approach. Brooks/Cole, Belmont, CA. Sant'Anna, B. S., A. L. D. Reigada, and M. A. A. Pinheiro. 2009. Population biology and reproduction of the hermit crab Clibanarius vittatus (Decapoda: Anomura) in an estuarine region of southern Brazil. Marine Biological Association of the United Kingdom. Journal of the Marine Biological Association of the United Kingdom 89:761. Scheltema, R. S. 1965. The relationship of salinity to larval survival and development in Nassarius obsoletus (Gastropoda). The Biological Bulletin 129:340-354. Varadarajan, S., and T. Subramoniam. 1982. Reproduction of the continuously breeding tropical hermit crab Clibanarius clibanarius. Marine ecology progress series. Oldendorf 8:197-201. Webb, C. 1986. Post-larval development of the tellinacean bivalves Abra alba, Tellina fabula and Donax vittatus (Mollusca: Bivalvia), with reference to the late larva. Journal of the Marine Biological Association of the United Kingdom 66:749-762. Yamaguchi, T. 2001. Incubation of eggs and embryonic development of the fiddler crab, Uca lactea (Decapoda, Brachyura, Ocypodidae). Crustaceana 74:449-458.

AC C

743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793

25

ACCEPTED MANUSCRIPT 796 Appendix 2

797

Fig.1. Plot of the MSO of a RDA of the Hellinger-transformed macrobenthic data explained by the environmental variables (left), and by the environmental variables controlling for spatial structure (right). Residual variances within the boundaries of confidence interval indicate no spatial autocorrelation.

EP

800 801 802 803

AC C

799

TE D

M AN U

SC

RI PT

798

26

ACCEPTED MANUSCRIPT 804 Appendix 3

805 806

Table1. Number of species and percentage of variation attributable to different fractions in each dispersal ability group excluding rare taxa. PLSA

NLMA

17 7 15 21 57

6 4 0 34 62

7 20 4 2 74

3 18 6 15 61

13 11 8 26 55

7 8 5 13 74

5 13 0 8 79

3 13 0 18 69

11 0 13 21 66

5 9 4 10 77

6 6 2 14 78

2 0 13 31 66

5 2 21 30 47

2 10 0 74 16

816

SC

TE D

Environment: pure environmental variation; Shared: spatially-structured environmental variation; Space: pure spatial variation; Unexplained: variation not explained by any set of explanatory variables. (PLMA) species with planktonic larvae and motile adults, (PLSA) planktonic larvae and sedentary adults, (NLMA) nonplanktonic larvae and motile adults, and (NLSA) nonplanktonic larvae and sedentary adults. Bold numbers correspond to statistically significant (P<0.05) values. Species included in each DAG per sampling period are listed in Supplementary material.

EP

815

5 5 18 12 65

AC C

809 810 811 812 813 814

15 3 9 19 69

NLSA

RI PT

PLMA September Species Environment Shared Space Unexplained February Species Environment Shared Space Unexplained May Species Environment Shared Space Unexplained July Species Environment Shared Space Unexplained

M AN U

807 808

27

ACCEPTED MANUSCRIPT Table 2. Number of species and percentage of variation attributable to different fractions in each dispersal ability group considering the full species data. PLSA

NLMA

NLSA

39 7 13 19 61

32 2 2 19 67

4 20 4 2 74

8 15 8 19 58

33 8 9 15 68

31 6 5 10 79

5 6 6 0 88

10 11 0 12 77

33 0 11 20 69

27 7 3 9 81

4 5 3 12 80

3 0 13 31 66

29 3 9 19 69

27 4 7 12 77

4 4 18 13 47

7 5 0 50 16

SC

TE D EP

824

Environment: pure environmental variation; Shared: spatially-structured environmental variation; Space: pure spatial variation; Unexplained: variation not explained by any set of explanatory variables. (PLMA) species with planktonic larvae and motile adults, (PLSA) planktonic larvae and sedentary adults, (NLMA) nonplanktonic larvae and motile adults, and (NLSA) nonplanktonic larvae and sedentary adults. Species included in each DAG per sampling period are listed in Supplementary material.

AC C

819 820 821 822 823

RI PT

PLMA September Species Environment Shared Space Unexplained February Species Environment Shared Space Unexplained May Species Environment Shared Space Unexplained July Species Environment Shared Space Unexplained

M AN U

817 818

28

ACCEPTED MANUSCRIPT Appendix 4

825

Table 1. Environmental parameters recorded during the study. September/11

February/12

May/12

July/12

mean (se)

mean (se)

mean (se)

21.9 (0.2)

27.4 (0.2)

25.0 (0.2)

20.4 (0.1)

Salinity

32.3 (0.3)

31.7 (0.9)

30.6 (0.7)

29.9 (0.6)

Mean grain size (phi)

2.5 (0.7)

2.7 (0.7)

2.7 (0.5)

2.8(0.64)

o

RI PT

mean (se) Temperature ( C)

Silt and clay (%)

4.2 (0.6)

4.7 (0.6)

4.8 (0.6)

5.7 (0.9)

Fine sand (%)

68.4 (3.2)

73.5 (3.2)

74.1 (3.8)

74.7 (3.1)

Coarse sand (%)

10.7 (1.6)

9.5 (1.7)

7.9 (1.3)

7.3 (1.6)

Pebbles (%)

6.2 (1.4)

3.7 (1.1)

3.2 (0.9)

3.2 (1.0)

Organic matter (%)

1.6 (0.1)

1.7 (0.2)

1.7 (0.2)

1.9 (0.2)

CaCO3 (%)

4.9 (0.4)

4.4 (0.4)

3.8 (0.5)

3.5(0.3)

Height of waves (m) 4

Power of waves (10 W/s)

1.5 (0.06)

1.6 (0.04)

20.1 (1.7)

18.1 (7.3)

2.1 (0.11)

1.7 (0.04)

42.8 (5.3)

30.4 (3.4)

AC C

EP

TE D

M AN U

827 828

SC

826

29

ACCEPTED MANUSCRIPT We examined the metacommunity organization of marine benthic assemblages Spatial processes exerted an important role in structuring benthic assemblages But the organization changed through time and was strongly modified by storms Importantly, it varied according to the dispersal capabilities of organisms

AC C

EP

TE D

M AN U

SC

RI PT

Metacommunity studies should consider temporal and life history variation