How Geographic Distance and Depth Drive Ecological Variability And

View metadata, citation and similar papers at core.ac.uk brought to you by CORE

provided by Portal do Conhecimento

Marine Ecology. ISSN 0173-9565

ORIGINAL ARTICLE How geographic distance and depth drive ecological variability and isolation of demersal ﬁsh communities in an archipelago system (Cape Verde, Eastern Atlantic Ocean) Anibal Medina1, Jean-Claude Breˆ thes1, Jean-Marie Se´ vigny2 & Bruno Zakardjian1

1 Institut des sciences de la mer de Rimouski, Rimouski, Que´ bec, Canada 2 Institut Maurice-Lamontagne, Peˆ ches et Oce´ ans Canada, Mont-Joli, Que´ bec, Canada

Keywords Abstract Archipelago system; bathymetry; biogeography; ecological variability; fish Cape Verde is a tropical oceanic ecosystem, highly fragmented and dispersed, community structure; geographic distance; with islands physically isolated by distance and depth. To understand how isolation factor; multivariate analysis; relative isolation affects the ecological variability in this archipelago, we conducted a dominance index. research project on the community structure of the 18 commercially most important demersal fishes. An index of ecological distance based on species rel- Correspondence ative dominance (D ) is developed from Catch Per Unit Effort, derived from Jean-Claude Breˆ thes, Institut des sciences de i la mer de Rimouski, 310 Alle´ e des Ursulines, an extensive database of artisanal fisheries. Two ecological measures of distance Rimouski, Que´ bec, G5L 3A1, Canada. between islands are calculated: at the species level, DDi, and at the community E-mail: [email protected] level, DD (sum of DDi). A physical isolation factor (Idb) combining distance (d) and bathymetry (b) is proposed. Covariance analysis shows that isolation Accepted: 15 April 2007 factor is positively correlated with both DDi and DD, suggesting that Idb can be considered as an ecological isolation factor. The effect of Idb varies with season doi:10.1111/j.1439-0485.2007.00163.x and species. This effect is stronger in summer (May to November), than in winter (December to April), which appears to be more unstable. Species react

differently to Idb, independently of season. A principal component analysis on the monthly (DDi) for the 12 islands and the 18 species, complemented by an agglomerative hierarchical clustering, shows a geographic pattern of island

organization, according to Idb. Results indicate that the ecological structure of demersal ﬁsh communities of Cape Verde archipelago, both in time and space, can be explained by a geographic isolation factor. The analytical approach used here is promising and could be tested in other archipelago systems.

with a large habitat area subdivided into habitat patches. Problem This has a major impact on population dynamics, com- The concept of geographic structure in marine ecosystems munity structure and ecosystem diversity. is fundamental for understanding population dynamics, Habitat fragmentation tends to reduce the area of hab- for ﬁsheries management, and for the implementation of itat patches and decreases the long-term survival probab- conservation measures. The geographic structure of popu- ility of populations and communities. In such habitats, lations generally results from spatial distribution patterns population sizes decrease and have a higher probability of that are related to historic or current barriers between going extinct because of environmental or demographic local communities, such as geographic distance, environ- stochasticity and an increasing vulnerability to external mental patchiness and environmental gradients that tend inﬂuences (Saunders et al. 1991). Several authors refer to to keep populations from mixing freely (Bailey 1997). empirical knowledge about the evolutionary and adaptive The geographic structure is often naturally fragmented, mechanisms underlying the response of populations and

Marine Ecology 28 (2007) 1–14 ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd 1 Ecological fish isolation in an archipelago system Medina, Breˆ thes, Se´ vigny & Zakardjian communities to fragmentation (Wiens et al. 1993; Diffen- anal fisheries, the present work analyses how geographic dorfer et al. 1995) and analyse the effects of such phe- distance and depth affect spatial and temporal structure nomena according to spatial scale (Andreassen et al. and variability of exploited demersal fish communities of 1998). On one hand, small-scale fragmentation is mainly Cape Verde archipelago. It also examines how physical related to patchy populations characterized by specific isolation explains the ecological variability among demer- life-history and behavioral characteristics like intrinsic sal fish communities in an archipelago system that has, growth rate, reproduction, food competition and social until now, received little scientific attention. organization. On the other hand, large-scale habitat fragmentation affects metapopulation dynamics through pro- Material and Methods cesses such as exchange rates between local populations, connectivity, speciation, resource turnover and environ- Site description mental pressures. The Cape Verde archipelago is one of the four oceanic Physical isolation by geographic distance has been pro- archipelagos belonging to the large biogeographic region posed to drive genetic isolation and population structure of Macaronesia, which also includes Madeira, the Azores (Wright 1943), not only in marine systems like coral reefs and the Canary Islands in the eastern Atlantic Ocean and archipelagos (De Wolf et al. 1998, 2000; Planes & (Fig. 1). With 4033 km2 of land, 1020 km of coastline and Fauvelot 2002; Duda & Rolań 2005), but also in terrestrial an exclusive economic zone of 734,265 km2 (Bravo de Lag- ecosystems for birds (Wiggins et al. 1998), mammals una 1985), this archipelago is made up of 10 volcanic (Bjørnstad et al. 1998), and plants and invertebrates islands and some islets. Seamounts also come close to the (Zschokke et al. 2000). Wright (1943) initially introduced sea surface (Noroeste, Nova Holanda, Bancona and Joaõ the concept of isolation by distance in an island popula- Valente). The insular shelf slope is steep until 200 m depth tion structure model where organisms’ dispersal ability is and then less pronounced down to 1000 m (Bravo de Lag- constrained by distance and where gene flow is most una 1985). There are a large variety of geographic distances likely to occur between neighboring populations (Planes and average depths between islands. The physical environ- & Fauvelot 2002). In parallel, the vertical dimension asso- ment is largely under the influence of dominant winds. ciated with bathymetry plays a varied and complex role A group of islands, named locally ‘Barlavento’ [Santo in ecosystem fragmentation through its interaction with Antaõ (SA), Saõ Vicente (SV), Santa Luzia (LZ), Ilheús hydrodynamic processes such as wind-driven circulation, Raso and Branco (IR), Saõ Nicolau (SN), Sal (SL) and turbulent mixing processes and buoyancy forces, as Boavista (BV)], are located windward of the northeasterly described by Werner et al. (1997), Jirka (2001) and John- trade winds. Another group, named ‘Sotavento’ [Maio ston & Merrifield (2003). Such mechanisms play major (MO), Santiago (ST), Fogo (FG), Brava (BR), Ilheús roles in population and community structure of archipe- Grande, de Cima and Luis Carneiro (IG); the island abbre- lago systems. These systems are usually characterized by a viations are used in Fig. 5], are leeward of those winds. rich endemic marine biodiversity due to their isolation Two main seasons are recognized during the year: a from continents. They often represent unique ecosystems warm one from May to November and a cold one from with clearly established spatial limits. Marine species may December to April. The warm season is divided into a respond to various aspects of the habitat’s fragmentation dry period between May and July, under the influence of that influence species richness, abundance, and distribu- Harmattan wind and dust from the Sahara, and a wet tion as well as biomass (O¨ hman & Rajasuriya 1998). period from August to September, which is influenced by In species with limited dispersal capacity, isolation by north-easterly Trade Winds from the Azores anti-cyclone distance can occur. and by south-westerly Trade Winds from the Santa He- The Cape Verde archipelago is a tropical oceanic eco- lena anti-cyclone. When rains occur on land, they are tor- system that is highly fragmented and dispersed, with rential, irregular and concentrated in the wet period islands and group of islands physically isolated by depths (90%) according to topography and altitude (De Brum often >3000 m. Geographic distance and bathymetry, Ferreira 1989). Sea-surface temperature follows the aver- along with other physical factors, likely induce various age air temperature, varying from 18 Cto27C during degrees of isolation between islands. Understanding how the year. isolation in such a naturally fragmented ecosystem affects The islands are also under the strong influence of the overall ecological variability is a key issue for a better large-scale oceanic circulation, being on the southern understanding of the Cape Verde marine population boundary of the North Atlantic’s northern anti-cyclonic dynamics, community structure and biodiversity. Such circulation and on the northern limit of the equatorial understanding is necessary for planning sustainable mar- counter-current. These two systems of oceanic currents ine exploitation. By using an extensive database of artis- present seasonal variations that affect the circulation of

2 Marine Ecology 28 (2007) 1–14 ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd Medina, Breˆ thes, Se´ vigny & Zakardjian Ecological ﬁsh isolation in an archipelago system

Fig. 1. Location of the Cape Verde archipelago in the biogeographic region of Macaronesia.

Pn the surface layer down to 200 m depth (Stramma & Sie- Ci dler 1988). f i CPUE ¼ i¼1 ð1Þ n Data source where C and f are respectively the catch (kg) and the fish- The work presented here is based on the artisanal fishery ing effort (h) of boat i, and n is the total number of database compiled by the Instituto Nacional de Desen- observations during a month. Artisanal fishing is mainly volvimento das Pescas (National Institute for Fisheries coastal, practised with hand lines on artisanal wooden Development from Cape Verde, INDP). That database and opened boats of standard size between 3 and 6 m, provides information about fishing boats, fishermen, fish- propelled by an outboard engine of 15 HP. Because of ing ground locations, trip date and duration, species technical limitations (storage capacity, communication identification and catches, from 1996 until the present. facilities, etc.) fishing units do not have autonomy at sea The database is supplied by a sampling plan which com- and thus fishing operations are carried out within a per- bines a stratified and a three-stage sampling design for iod of 24 h in a limited range area (c. 10 km) around units of unequal sizes as described in Medina (1995). islands. In such a tropical ecosystem, demersal fisheries We used data collected from January 1996 to December are multi-species; no particular commercial species is tar- 2002. Each landing site was visited six times a month, geted and the whole catch is landed at 85 artisanal fishing and all active boats were sampled at each visit providing ports, and sold on local markets. Because of these charac- data on time at sea (in hours), fishing location and catch teristics of the artisanal fishery, we do not expect vari- per fishing trip (in kg) for each species. Knowing the dis- ation in fishing power between boats, therefore, no tance between landing ports and fishing sites as well as standardization of CPUE was necessary. As original data the average boat speed, time at sea was corrected to pro- were expressed in weight, we used biomass of catches vide an estimate of the effective fishing time. Species rather than number of individuals. These procedure could abundance index was estimated as Catch Per Unit Effort lead to biases when estimating dominance of larger than (CPUE). For each island, monthly average CPUE for each average individuals. However, in the structuring of species was estimated boat-by-boat as: the communities, each individual has an ecological cost

Marine Ecology 28 (2007) 1–14 ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd 3 Ecological fish isolation in an archipelago system Medina, Breˆ thes, Se´ vigny & Zakardjian proportional to its biomass (i.e. the part of the resources important demersal fish. On one hand, such a choice that it takes) as well as an ecological value (i.e. its contri- warrants the availability of data for all species under bution to the stability of the system). Very often, catch study, and avoids missing values, which enhance the samples of demersal fish contain individuals of different robustness of statistical analyses. On the other hand, lengths and at different stages of maturity and so they dominant species determine the main biological charac- represent different ecological cost and value in the struc- teristics of demersal communities and the non-target turing of communities. Therefore, the use of biomass assemblages appear to remain relatively unchanged, units is recommended (Dickman 1968; Wilhm 1968; despite long-term intensive fishing activity (Colvocoresses Lyons 1981; Jin & Tang 1996; Legendre & Legendre & Musick 1984; Greenstreet & Hall 1996; Jin & Tang 1998). 1996). Doubtful and false identifications were excluded Winters & Wheeler (1985) present a general criticism in from the data set, and only data corresponding to the interpreting CPUE data as an abundance index without identification level of species were considered. prior information on population spatial distribution. However, in this research we worked at an island scale. Ecological indices Thus, despite these general criticisms and precaution, we assume that the spatial distribution of CPUE of 1354 stan- There is much ecological literature that focuses on the dard artisanal boats fishing all year round on the 260 fish- concept of species richness, seeking common approaches ing sites dispersed on a cumulative shelf area of 5394 km2 and frameworks for measuring population, community, (Bravo de Laguna 1985) over 7 years, actually reflects the and ecosystem variability. All methods aim at reducing spatial distribution of demersal species and is representa- the high dimensionality of species abundance data to tive of changes in the area occupied by demersal fish com- summarize community structure and each one is sensitive munities. As stated by Greenstreet & Hall (1996), to specific aspects of structural variation. No single meas- examining fisheries data from a multi-species perspective ure is therefore adequate to capture all community prop- is important because, for example, exploited species may erties. Thus, according to ecological aspects that one seeks be replaced by ecologically similar species differing only in to highlight at a species, community or ecosystem level, it their sensitivity to exploitation. So, for the purpose of this is often of interest to find the approach best adapted to work, such a procedure seems quite adequate according to the objectives of the research, able to provide the most the nature of data available and the peculiarities of the robust conclusions. Ecological distance measures allow fishery and ecosystem being studied. subsequent ordination methods, statistical approaches widely used in ecology to depict community structure and respective variation in space and time (Legendre & Species selection Legendre 1998; McCune & Grace 2002). For the purpose of this study, the 18 most abundant spe- For the purposes of the present study, we considered cies were selected (Table 1) from about 40 commercially each island as a finite community with several ecological

Table 1. List of the main demersal fish INDP database code scientific name English name local name species reported in the INDP artisanal fisheries 253 Bodianus scrofa Barred hogfish Bedja database and selected for the present 259 Lethrinus atlanticus Atlantic emperor Bica de rocha research. 260 Epinephelus marginatus Dusky grouper Mero 268 Apsilus fuscus African forktail snapper Fatcho de fundo 270 Lutjanus agennes Gorean snapper Goraz 322 Pseudopeneus prayensis West African goatfish Salmonete 369 Galeoides decadactylus Lesser African threadfin Barbo 380 Parapristipoma humile Guinean grunt Papagaio 385 Pomadasys incisus Bastard grunt Besugo 394 Priacanthus arenatus Atlantic bigeye Façola 402 Scarus hoefleri Guinean parrotfish Bidia˜ o 452 Scorpaena scrofa Largescaled scorpionfish Fanhama 457 Cephalopholis taeniops African hind Garoupa 465 Mycteroperca rubra Mottled grouper Badejo 470 Serranus atricauda Blacktail Comber Manelinho 528 Virididentex acromegalus Bulldog dentex Bentelha 606 Acanthurus monroviae Monrovia doctorfish Barbeiro 699 Seriola dumerili Greater amberjack Esmoregal

4 Marine Ecology 28 (2007) 1–14 ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd Medina, Breˆ thes, Se´ vigny & Zakardjian Ecological fish isolation in an archipelago system niches characterized by a particular species richness, equitability and community structure. Therefore, based on the nature of the data available (fishery commercial data), we introduced an ecological index Di based on species relative dominance between pairs of islands. Domi- nance is recognized as an important parameter of community structure that integrates the majority of the mathematical formulations of diversity indices and ecological distance measures (Frontier 1983; Legendre & Legendre 1998; Mouillot & Lepretre 2000). To assess the ecological distance between two islands 1 and 2, we defined a relative dominance (Di) of each species (i) in each island as the proportion of its biomass Bi at each island related to the total abundance of all species in the two islands:

Bi Di ¼ ð2Þ B1 þ B2 where B1 and B2 represent the total biomass of all species in each island. As stated by Legendre & Legendre (1998) species are basic units in the theory of the ecological niche, each representing a genetic pool that shares ecosystem suitability and, in doing so, they are structured in communities. We assumed then that the value of Di over space and time might be an indicator of variation in ecosystem suitability as perceived by a particular species and is an imprint of the community structure. The contribution of each species to the ecological distance between communities from two islands is the absolute value DDi: Bi;1 Bi;2 DDi ¼ ¼ Di;1 Di;2 ð3Þ B1 þ B2 Then we deﬁne an index of ecological distance between two islands, DD as the sum of DDi for the n species recorded on the two islands:

Xn Fig. 2. Conceptual model to explain the effect of geographic dis- DD ¼ DDi ð4Þ tance (– – –) and bathymetry (—–) on species relative dominance (Di) i¼1 and community structure variation among islands in an archipelago system. For a potentially dominant species, Di between islands 1 and DD became a particular expression of Steinhaus’s coeffi- 2 may be affected by positive covariance (A and B), negative covari- cient (Legendre & Legendre 1998). It varies between zero ance (C and D), partial positive correlation (E and F) or partial negative and unity respectively from identical to completely differ- correlation (G and H). ent community structure. In other cases, geographic distance may counteract the bathymetric effect (negative covariance) either by redu- Effect of distance and bathymetry cing isolation and ecological variability between ecosys- In order to better understand how geographic distance tems (Fig. 2C) or by increasing isolation (Fig. 2D). and bathymetry can be integrated to explain ecological Furthermore, particular situations exist where one of variability among islands, we propose a conceptual co- those variables masks the positive or negative effect of the variation model for the most probable scenarios (Fig. 2). other. This is the typical situation of suppressor variables, In some cases, geographic distance may strengthen the first introduced by Horst (1941) and widely reported in bathymetric effect (positive covariance) by inflating isola- the scientific literature (Lancaster 1999; Mackinnon et al. tion and variability between ecosystems (Fig. 2A and B). 2000; Lynn 2003). Despite considerations about the

Marine Ecology 28 (2007) 1–14 ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd 5 Ecological fish isolation in an archipelago system Medina, Breˆ thes, Se´ vigny & Zakardjian deleterious effect of colinearity between independent varia- Secondly, we investigated ecological variability between bles and the recommended statistical procedures to exclude islands at two levels of organization (species and commu- one of them from computations (Zar 1984), the compu- nities) with a series of analyses of covariance (ANCOVA). tation of a first-order partial correlation has been recom- In a preliminary step, we carried out an ANCOVA mended (Jolicoeur 1997; Legendre & Legendre 1998) in between DDi as the dependent variable and Idb as covari- order to better describe the relationships between variables. ates for two factors (18 species levels – S, and 12 months It is then possible to distinguish between a positive (Fig. 2E levels – M) as independent qualitative variables. That ana- and F) or negative (Fig. 2G and H) effect. lysis allowed us to define species groups and seasons.

In the present work, the shortest geographic distances Next, we computed an ANCOVA between DD and Idb for between pairs of islands (d), expressed in kilometers those species groups (C: two levels) and for seasons (P: (km), were obtained by direct measurement on the Cape two levels). Verde archipelago map published by the United Kingdom Multivariate analyses were used to illustrate the spatial Hydrographic Ofﬁce (1989). Distances (d) varied from organization of the demersal ﬁsh community in the archi-

6 km, between Brava island and Secos islets, to 270 km, pelago. The matrix of monthly average DDi, with the 12 between Santo Antaõ and Maio islands. Average isobaths, islands as observations and the 18 species as descriptors, expressed in meters (m), were obtained from Bravo de was analysed with a principal component analysis (PCA) Laguna (1985) and data issued by IOC, IHO & BODC based on the Pearson correlation coefficient. To comple- (2003). The mean depth between pairs of islands (b) was ment this analysis, we performed an agglomerative hierar- then calculated and it varies from 55 m between Brava chical clustering (AHC) for the same data matrix. island and Secos islets to 3990 m between Saõ Vicente Although single linkage is widely used as an agglomerative and Fogo islands. method to complement PCA (Legendre & Legendre We investigated the relationship between species relat- 1998), we used a complete linkage method because we ive dominance (Di), the geographic distance (d) and the were mainly interested in a better contrast between average bathymetry (b). Islands were first compared two islands or island groups. Finally, a complementary analy- by two. A time series of Di was calculated for each island, sis of islands and species in the space of the first two over the entire period and for all species. The two series PCA axes enabled us to study the variability between of Di were compared by a Pearson correlation analysis. islands and the correlations between species in addition A pair of islands was then characterized by (i) the corre- to the relationships between islands and species. For all lation coefficient between Di, (ii) the geographic distance statistical tests the null hypothesis was analysed for an a (d), and (iii) the average bathymetry (b) between those error of 0.05. All statistical calculations were made with islands. For the entire system, those parameters are repre- the XLSTAT-Pro v.7.5.3 software. sented in three square matrices that were compared by a series of Mantel tests (Dietz 1983; Smouse et al. 1986; Results Oden & Sokal 1992), which provided total and partial correlations between those three matrices. In order to Relationships between Di, d and b meet conditions of normality, Di values were log2 trans- Linear correlations were not significant between Di and d formed. Missing values and double zeros were excluded and between Di and b but, highly significant between d from computations. and b (Mantel tests, Table 2). Partial correlation was

Finally, to integrate mutual inﬂuence of distance and highly signiﬁcant as well between Di and d as between depth, we computed a physical isolation factor (Idb) as: Di and b. It suggests that d and b interact as suppressor

Idb ¼ d b ð5Þ

Table 2. Linear and ﬁrst-order partial correlations (in bold) from a 2 Computed values of Idb varied from 0.33 km between Mantel test computed between three symmetrical matrices (r correla- 2 Brava island and Secos islets to 846 km between Sal and tion between log2Di), (geographic distance d) and (average depth b). Brava islands. In order to prevent scale measures effects, Tests were performed for an exhaustive number of permutations. for statistical analysis Idb data were normalized. matrix linear correlation partial correlation P

log D · b 0.156 – 0.11 Spatial and temporal variability 2 i log2Di · d 0.077 – 0.441 b · d 0.945 – <0.0001 First, we tested main effects on DDi of year (Y, seven levels), month (M, 12 levels) and species (S, 18 levels) log2Di · d| b – 0.779 <0.0001 log D · b| d – )0.698 <0.0001 with a three ﬁxed-factor ANOVA without replication. 2 i

6 Marine Ecology 28 (2007) 1–14 ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd Medina, Breˆ thes, Se´ vigny & Zakardjian Ecological ﬁsh isolation in an archipelago system

variables, masking a larger and significant correlation groups (C1: 253, 259, 270, 322, 369, 402, 606; C2: 260, between each one of them and Di. 268, 380, 385, 394, 452, 457, 465, 470, 528, 699; see spe- Positive partial correlation between Di and d, on one cies’ identifications in Table 1). hand, and negative partial correlation between Di and b, Then, DD was computed between islands for the two on the other hand, show that, for potentially dominant defined seasons (two levels – P1 and P2) and species species, distance and depth do not affect community groups (two levels – C1 and C2), and its spatial and tem- structure in the same way (Table 2). Increasing the geo- poral variability was studied by further ANCOVA graphic distance while average depth remains constant (Table 3, lower panel). Results indicate a significant cova- will increase Di (positive partial correlation); reciprocally, riance between DD and Idb, which is different between increasing the average depth while geographic distance seasons and between species groups. It is particularly remains constant will decrease Di (negative partial corre- interesting to note that using seasons (P) and species lation). groups (C) instead of month (M) and species (S) does not cause any significant deviation from the general co- variation model, which shows the robustness of our Relation between DD , DD and I i db approach. On one hand, a main seasonal effect and the

Results indicate that DDi is highly related to species, year absence of interaction between P and Idb reveal an and month (ANOVA, S: F17,7845 ¼ 473.23, P< 0.001; Y: important inﬂuence of Idb on the ecological distance DD F7,7845 ¼ 37.181, P< 0.001; M: F11,7845 ¼ 233.626; between islands, not varying from one season to another. P< 0.001). Monthly patterns are consistent between years On the other hand, a signiﬁcant interaction between spe-

(interactions between Y and M: F66,7845 ¼ 0.917, P ¼ cies groups and Idb suggests that those groups do not fol- 0.484 n.s.). Despite a year effect, no particular trend was low the same pattern. Species seem to behave differently observed in the time series. However, S and Y show signi- in the same season.

ﬁcant interaction (F187,7845 ¼ 7.295, P ¼ 0.000) illustra- The whole statistical analysis led us to a general covari- ting the existence of a strong monthly pattern in DDi, ance model illustrating the inﬂuence of Idb on DD common to all species. between islands. The ecological distance between islands Following these results, to assess relationships between varies between two seasons, being higher during the warm

DDi and Idb, further analyses were carried out on an than the cold season (Fig. 3A, Table 4). Within each sea- ‘average year’, calculated from monthly averaged DDi.We son one group of species (C1) was less sensitive to the observe a signiﬁcant effect of month and species on the isolation factor (Idb) than the other one (C2) (Fig. 3B and covariance between DDi and Idb (Table 3, upper panel). C, and Table 4). The seasonal pattern is illustrated by the By evaluating the signiﬁcant amount of information variation of the ecological distance index DD averaged by brought by each variable and parameter to the model, we month from January to December (Fig. 4); the index is distinguish two seasons (P1: January, February, March, lower during the cold season. According to the standard April, and December; P2: May, June, July, August, Sep- deviation, DD seems to be more stable during the warm tember, October, and November) and two discrete species season.

Table 3. Results from the ANCOVA between DDi (species level) and DD (community level) as dependent variables and Idb as covariates. The upper panel of the table shows months (M) and species (S) effects. The lower panel shows season (P) and species group (C) effects. dependent variable source df sum of squares mean square Fisher’s F P

2 DDi (r ¼ 0.619) Idb 1 163.988 166.351 5626.995 <0.0001 M 11 43.046 3.272 134.279 <0.0001 S 17 34.009 2.282 68.644 <0.0001 M · S 187 3.737 0.019 0.686 0.999

M · Idb 11 0.409 0.040 1.276 0.231

S · Idb 17 0.331 0.019 0.668 0.837 Error 4742 138.196 0.029 2 DD(r ¼ 0.806) Idb 1 2.224 2.224 265.634 <0.0001 P 1 1.711 1.711 204.324 <0.0001 C 1 0.530 0.530 63.356 <0.0001 P · C 1 0.019 0.019 2.267 0.135

P · Idb 1 0.007 0.007 0.795 0.374

C · Idb 1 0.033 0.033 3.962 0.049 Error 122 1.022 0.008

Marine Ecology 28 (2007) 1–14 ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd 7 Ecological ﬁsh isolation in an archipelago system Medina, Breˆ thes, Se´ vigny & Zakardjian

Fig. 4. Monthly variation of ecological distance (DD). Two main seasons are illustrated: cold, from December to April, more variable, with lower level of DD and a negative trend; warm, from May to Novem- ber, with a higher level of DD and apparently more stable.

ing to degree of physical isolation and ecological similar- ities (Fig. 5) and species are assembled according to their contribution to the ecological distance between islands. Moreover, a superposition of the two factorial spaces (islands and species) in the PCA led us to recognize a significant association between islands and species (Fig. 6). At a high similarity level (‡0.80), a group is formed by Brava and the Secos islets, in the southern part of the archipelago, and another one by Saõ Vicente, Santa Luzia and Raso islet in the northern part. Those two island groups are well correlated but, in an opposite way, Fig. 3. Covariance models between community structure variation explaining 42.2% of the total variance. Such a correlation (DD) and physical isolation Idb among Cape Verde islands according to seems to be related to two different species assemblages. temporal patterns with two different seasons: cold from December to While the northern group appears to be more associated April and warm May to November (A) and species groups showing two levels of sensitivity (B and C). with Apsilus fuscus – 268, Lutjanus agennes – 270, Pseu- dupeneus payensis – 322, Scarus hoefleri – 402 and Scorp- aena scrofa – 452 (assemblage 1), the southern group is better characterized by Serranus atricauda – 470, Pomada- Island and community structure sys incisus – 385, Priacanthus arenatus – 394 and Seriola Results of the PCA and of the AHC, illustrated by a dumerili – 699 (assemblage 3). In the east, Sal, Boavista Wroclaw diagram, show a clear geographic pattern of and Maio form another group at 0.69 similarity, but monthly average species’ DDi. Islands are grouped accord- Maio and Boavista are more strongly linked, with a

Table 4. Covariance model parameters and adjusted r2 for different levels of variability between seasons and species’ sensitivity to ecological isolation.

general model more sensitive species (C2) less sensitive species (C1) parameters cold season (P1) warm season (P2) cold season (P1) warm season (P2) cold season (P1) warm season (P2) elevation 0.114 0.265 0.110 0.184 0.101 0.088 slope 0.326 0.395 0.386 0.476 0.205 0.222 adjusted r2 0.794 0.891 0.771 0.703 0.731 0.849

8 Marine Ecology 28 (2007) 1–14 ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd Medina, Breˆ thes, Se´ vigny & Zakardjian Ecological ﬁsh isolation in an archipelago system

variance. In almost complete contrast to assemblage 3, species from assemblage 1 (Parapristipoma humile – 380, Galeoides decadactylus – 369, Virididentex acromegalus – 528 and Acanthurus monroviae – 606) can be considered intermediate between assemblage 2 and 4 in structuring the northern and eastern island groups. At lower similar- ities, Santo Anta˜o and Sa˜o Nicolau tend to be linked with the northern group, while Santiago and Fogo islands, remain isolated. The species Mycteroperca rubra (465) is strongly and negatively correlated with assemblage 2, but does not belong to any assemblage.

Discussion A large variety of ecological indices are used to assess ecological variability and community structures. In the present research, we used an ecological index based on species relative dominance that also measures species contribution to community structure variation between two islands. Such contribution is measured by the relative dominance of each species on the total species biomass of the two communities. This approach was the most appropriate given the nature of the available data. Interpreting community structure based on commercial fisheries data can be distorted because these data are neither random nor exhaustive and fished communities cannot be assumed to function naturally. In fact, commercial fisheries result from a complex interaction among several factors related to fishers’ strategy (Salthaug & Godø 2001). Even in the situation of a mixed fishery (Ketchen 1964; Chadwick & O’Boyle 1990), the data do not represent all species from exploited communities. However, in the system under study, artisanal fishermen sample the fish density around islands in a time invariant process without definite fleet tactics. In such a case where fishing process does not change, CPUE data can be used to construct a Fig. 5. Spatial patterns among Cape Verde islands as determined time series of comparable abundance indices (Petitgas from a PCA and HAC computed with a Pearson correlation matrix 1998). The monthly and annual variation patterns of based on the DDi from 18 demersal fish species. A: Island structure these indices on the main fishing grounds can be indicat- according to the first two axes, explaining 74.3% of total variance, ive of a population’s complexity and allow the assessment and a Wroclaw diagram drawn for different levels of similarity (see text for island identifications). B: Species correlation according to the of spatial and temporal variations in abundance (Begg & pffiffi two first axes. The circle (radius ¼ 2;d¼ species number) represents Waldman 1999). Thus the use of classical approaches, d the balanced contribution of species to the two axes (thick dashed either synthetic measurements like the Shannon–Weaver, line). Numbers in bold represent fish assemblages, other numbers rep- Simpson, Hill and McIntosh indices (Frontier 1983; resent species codes (see Table 1). Legendre & Legendre 1998) or analytical procedures like rank-frequency diagrams (Frontier 1983; Mouillot & Lepretre 2000), are not very useful in such a situation higher similarity (0.75). This group appears to be associ- because of their sensitivity to species richness. The ated with Bodianus scrofa – 253, Lethrinus atlanticus – 259 approach we propose in the present study to assess com- and Epinephelus marginatus – 260 (assemblage 4). Cepha- munity structure variation has the advantage of being lopholis taeniops – 457 is weakly linked to this assemblage mainly sensitive to species abundance distribution rather and seems well correlated with Sal island. This latter than to species richness and so is useful in a context island species association explains 32.1% of the total where only fisheries data are available.

Marine Ecology 28 (2007) 1–14 ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd 9 Ecological ﬁsh isolation in an archipelago system Medina, Breˆ thes, Se´ vigny & Zakardjian

Fig. 6. Map of ecological similarity derived from PCA analysis among demersal ﬁsh populations from Cape Verde Islands showing pattern of ecological variability, driven by a physical isolation factor (geographic distance and average depth). For illustration purpose, only the most discriminating species are represented (see Fig. 5 and Table 1).

The relationship among species relative dominance, near-equilibrium population. However the present study geographic distance and average depth allow us to high- considers two spatial scales: (i) at a island scale, niches light an important ecological feature of the species–hab- seem to be continuous and non-overlapping and (ii) at a itat dynamics in the Cape Verde archipelago that may be archipelago scale, niches are obviously discrete. applicable to similar systems. Depth and distance are par- The rate of expansion and contraction of a population tially correlated with species relative dominance but in within a distribution area is unlikely to be uniform as opposite ways, which led us to propose a niche effect habitat conditions will be less favorable at the extremes associated with variation in shelf area and thus, in space than at the centre (Winters & Wheeler 1985). In terms of availability. On one hand, increasing geographic distance the amount and arrangement of favorable habitats, the with a constant average depth means more space available spatial correlation between geographic distance and depth and thus improves general suitability of the habitat for is particularly important in determining the overall suit- potentially dominant species in a particular area. Conse- ability of the environment (Hiebeler 2000). In a heteroge- quently Di will increase. On the other hand, an increase neous and naturally fragmented landscape like the Cape in the average depth for the same geographic distance Verde archipelago, this is particularly important for theo- would decrease the space available and reduce general retical studies on the dynamics of population abundance habitat suitability. As a result, Di will decrease because a and density (MacCall 1990; Petitgas 1998; Hiebeler 2000). species has less space available to express its dominance. The computation of a physical isolation factor based From the ecological point of view, since space availability on geographic distance and average depth between islands is the physical support of biotic and abiotic conditions allows the integration of their effects and mutual interac- within which the species can exist, such aspects seem to tions into a single variable whose covariance was tested be important for the dynamics of species’ use of space against community structure variation indices. The rejec- and are related to ecosystem resource partitioning. Hence, tion of the null hypothesis with such an approach has it seems to be an important factor structuring demersal suggested that, for the Cape Verde archipelago, physical ﬁsh assemblages. This rationale corresponds to the Mac- isolation is an important factor that drives ecological Arthur (1957) hypothesis about environment structure isolation among islands. Such ecological isolation varies and relative abundance of birds in an equilibrium or through the year according to two distinct seasons that

10 Marine Ecology 28 (2007) 1–14 ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd Medina, Breˆ thes, Se´ vigny & Zakardjian Ecological fish isolation in an archipelago system are separated by different wind and temperature regimes. and dynamic systems like coral reefs (Dornelas et al. The shorter cold season from December to April seems to 2006) or archipelagos such as Cape Verde. However, be more unstable and shows lower ecological variability future research on this matter can contribute understand- than the warmer one, from May to November, which ing of ecological variability in the context of functional appears to be more stable. This observation leads us to ecology and the design of appropriate conservation and formulate the hypothesis that, in the Cape Verde archi- management strategies. pelago, ecological isolation between islands is affected by Community structure shows a geographic pattern different hydrodynamic regimes between cold and warm according to the physical isolation factor, which suggests seasons. This dynamic process would be regulated, inter that demersal communities are affected by geographic dis- alia, by the correlation between the average depth and the tance and depth. Similar results have been found recently distance between islands. Although this assumption will for the genetic structure of gastropod populations (Conus have to be validated by knowledge of oceanic circulation sp.) in the eastern Cape Verde islands (Duda & Rolań around Cape Verde islands, such information does not 2005). These authors found that species from Boavista yet exist. However, it is well known that wind and tem- and Maio islands are genetically more similar to each perature variations favor turbulent mixing processes in other than they are to species from Sal islands. In fact, the water column as well as near the bottom (Spall 2000; the migration of species between Boavista and Maio has Jirka 2001; Johnston & Merrifield 2003) and in archipe- apparently been more common than between Sal and lago systems, these process are frequently related to jet Boavista. This phenomenon seems to be facilitated by current flows between islands (Xie et al. 2001). stronger ecological isolation (Idb) between the two latter For the species considered, physical isolation does not islands. Despite the importance of oceanographic influ- affect their relative dominance with the same intensity ences on larval dispersal and their consequences for during the same season. We could distinguish between population connectivity (Werner et al. 1997; Cowen more sensitive species, dominated by Serranidae (C. taeni- 2002), we believe that the long-term persistence of such ops, S. atricauda, M. rubra and E. marginatus) and Hae- physical isolation between islands might be at the origin mulidae (P. humile and P. incisus), followed by six other of important speciation processes, resulting in high mar- families, each one represented by a single species, and less ine biodiversity at the genetic, specific, taxonomic, eco- sensitive species, represented by seven families with one logical and functional levels. species each. These differences in behavior can be related Because there has been no other scientific research on to interactions between spatial and temporal features the population structure among the Cape Verde islands affecting their distribution. Species with coastal and shal- related to how fishing affects genetic resources and biodi- low distributions throughout the year are expected to be versity, we suggest that, for conservation and manage- less sensitive that those with a broader and deeper distri- ment purposes, populations from spatially distinct islands bution. However, these demersal fishes are mostly seden- or islands groups, as identified in the present study, tary and reef-associated, colonizing rocky or sandy should be considered as discrete management units. bottoms on the coastal zones around islands. Thus, at As stated by Stephenson (1999), aside from the debate on least at the juvenile stage, such different behaviors with the basic premise behind the modern concept of a stock respect to ecological isolation would be complex to (Booke 1999; Swain & Foote 1999), this is particularly understand. However, species dispersal capability can vary important as part of a precautionary approach to fisheries with development stage. Thus, we hypothesize that the management (FAO 1995) and to topical discussions of main significant interactions between physical and hydro- the potential value of Marine Protected Areas. dynamic features that affect species dispersal and distri- Several physical and geographic features are common bution probably occur during spawning or early life among archipelagos from the large biogeographic region stages. For the Cape Verde archipelago, no information of Macaronesia (Cape Verde, Madeira, Azores and the exists about flows of energy, materials, or organisms Canary Islands). It would be interesting to test the across island boundaries. This is a major aspect of the approach we propose here in a broader context in order theory of island biogeography (MacArthur & Wilson to study ecological variability and demersal population 1967), and its generalization in neutral theory (Hubbell structure in the context of archipelago systems. However, 1997, 2001). Aside from the recent debate on these the- the spatial and temporal patterns of the hydrodynamic ories, both supportive (Volkov et al. 2003; Hubbell 2006) features that are expected to influence ecological isolation and critical (McGill 2003; Wootton 2005), the main must be included in such a research program. assumption of ecological equivalence in communities Among significant scientific questions to be addressed constrained by dispersal and recruitment seems to limit related to geographic distance, depth and ecological isola- the applicability of such theories in very heterogeneous tion in the study area, it will be of great interest to

Marine Ecology 28 (2007) 1–14 ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd 11 Ecological fish isolation in an archipelago system Medina, Breˆ thes, Se´ vigny & Zakardjian investigate the population spatial dynamics of the main Cowen R. K. (2002) Oceanographic Influences on Larval Disper- demersal species on the insular shelf and how they can be sal and Retention and their Consequences for Population related to seasonal and annual variations in population Connectivity. Coral Reef Fishes. Academic Press, London, abundance. In order to prevent and control the unknown New York: 149–170. effect of fishing, it would be desirable to combine com- De Brum Ferreira D. (1989) Le climat de l’Atlantique oriental mercial fisheries and survey data (Fox & Starr 1996; Petit- des Açores auxˆ ıles du Cap Vert. Contribution a` l’e´tude du ´ gas et al. 2003; Verdoit et al. 2003). Furthermore, to syste`me oceán-atmosphe`re. The`se de Doctorat d’Etat, address how such ecological isolation affects population Universite´ de Paris-Sorbonne, 1989 (ISSN: 0294-1767). genetic structure and dynamics, more data are needed on De Wolf H., Backeljau T., Van Dongen S., Verhagen R. (1998) Large-scale patterns of shell variation in Littorina striata, a the population genetics of marine species in the archipel- planktonic developing periwinkle from Macaronesia (Mol- ago. Although recent research has begun to address this lusca: Prosobranchia). Marine Biology, 131, 309–317. issue (De Wolf et al. 1998, 2000; Duda & Rolań 2005), De Wolf H., Verhagen R., Backeljau T. (2000) Large scale pop- these studies were mainly concerned with molluscs. ulation structure and gene flow in the planktonic developing Because of the ecological complexity of such tropical periwinkle, Littorina striata, in Macaronesia (Mollusca: oceanic systems, it will be advantageous to direct research Gastropoda). Journal of Experimental Marine Biology and efforts toward a greater diversity of taxa. Ecology, 246, 69–83. Dickman M. (1968) Some indices of diversity. Ecology, 49, Acknowledgements 1191–1193. Dietz E.J. (1983) Permutation tests for association between We thank the INDP of Cape Verde and all its staff who two distance matrices. Systematic Zoology, 32, 21–26. assisted in the compilation of the fisheries data used in Diffendorfer J.E., Gaines M.S., Holt, R.D. (1995) Habitat frag- this paper. The first author benefited from a fellowship mentation and movements of three small mammals (Sigmo- from the Programme Canadien des Bourses de la Fran- don, Microtus and Peromyscus). Ecology, 76, 827–839. cophonie (PCBF). We also thank three anonymous Dornelas M., Connolly S.R., Hughes T.P. (2006) Coral reef reviewers for their helpful comments in the earlier version diversity refutes the neutral theory of biodiversity. Nature, of this manuscript. 440, 35–36. Duda T.F. Jr., Rolań E. (2005) Explosive radiation of Cape Verde Conus, a marine species flock. Molecular Ecology, 14, References 267–272. Andreassen H.P., Hertzberg K., Ims R.A. (1998) Space-use FAO (1995) Code of Conduct for Responsible Fisheries. Food responses to habitat fragmentation and connectivity in the and Agricultural Organization of the United Nations, Rome. root vole Microtus oeconomus. Ecology, 79, 1223–1235. Fox D.S., Starr R.M. (1996) Comparison of commercial fishery Bailey K.M. (1997) Structural dynamics and ecology of flatfish and research catch data. Canadian Journal of Fish and Aqua- populations. Journal of Sea Research, 37, 269–280. tic Science, 53, 2681–2694. Begg G.A., Waldman J.R. (1999) An holistic approach to fish Frontier S. (1983) Strate´gies d’ećhantillonnage en´ ecologie. stock identification. Fisheries Research, 43, 35–44. Les presses de l’Universite´ Laval, Que´bec, Masson. Bjørnstad O.N., Andreassen H.P., Ims R.A. (1998) Effects of Greenstreet S.P.R., Hall S.J. (1996) Fishing and the ground-fish habitat patchiness and connectivity on the spatial ecology of assemblage structure in the north-western North Sea: an the root vole Microtus oeconomus. Journal of Animal Ecology, analysis of long-term and spatial trends. Journal of Animal 67, 127–140. Ecology, 65, 577–598. Booke H.E. (1999) The stock concept revisited: perspective on Hiebeler D. (2000) Populations on fragmented landscapes with its history in fisheries. Fisheries Research, 43, 9–11. spatially structured heterogeneities: landscape generation Bravo de Laguna J. (1985) Plateaux insulaires et Zone Ećonomi- and local dispersal. Ecology, 81, 1629–1641. ques Exclusive de la Re´publique du Cap-Vert. Rapport Horst P. (Ed.) (1941) The role of prediction variables which technique no 6, PNUD/FAO – Project CVI/82/003, Praia, are independent of the criterion. In: The Prediction of Perso- Cap-Vert. nal Adjustment. Social Science Research Council, New York: Chadwick M., O’Boyle R.N. (1990) L’analyse des donneés de 431–436. capture et d’effort. In: Breˆthes J.-C. & O’Boyle R.N. (Eds) Hubbell S.P. (1997) A unified theory of biogeography and Me´thodes d’e´valuation des stocks halieutiques. Projet CIEO- relative species abundance and application to tropical rain 860060, Centre international d’exploitation des oceáns, Hali- forests and coral reefs. Coral Reefs, 16, S9–S21. fax (Nouvelle-E´ cosse, Canada): 77–101. Hubbell S.P. (2001) The Unified Theory of Biodiversity and Bio- Colvocoresses J. A., Musick J.A. (1984) Species associations geography. Princeton University Press, Princeton, NJ, USA. and community composition of middle Atlantic Bight Con- Hubbell S.P. (2006) Neutral theory and the evolution of eco- tinental Shelf demersal fishes. Fishery Bulletin, 82, 295–313. logical equivalence. Ecology, 87, 1387–1398.

12 Marine Ecology 28 (2007) 1–14 ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd Medina, Breˆ thes, Se´ vigny & Zakardjian Ecological ﬁsh isolation in an archipelago system

IOC, IHO, BODC (2003) Centenary Edition of the GEBCO community diversity. Environmental Monitoring and Digital Atlas, published on CD-ROM on behalf of the Inter- Assessment, 63, 279–295. governmental Oceanographic Commission and the Interna- Oden N.L., Sokal R.R. (1992) An investigation of three-matrix tional Hydrographic Organization as part of the General permutation tests. Journal of Classification, 9, 275–290. Bathymetric Chart of the Oceans. British Oceanographic Data O¨ hman M.C., Rajasuriya A. (1998) Relationships between hab- Centre, Liverpool, UK. itat structure and fish communities on coral and sandstone Jin X., Tang Q. (1996) Changes in fish species diversity and reefs. Environmental Biology of Fishes, 53, 19–31. dominant species composition in the Yellow Sea. Fisheries Petitgas P. (1998) Biomass-dependent dynamics of fish spatial Research, 26, 337–352. distributions characterized by geostatistical aggregation Jirka G.H. (2001) Large scale flow structures and mixing pro- curves. ICES Journal of Marine Science, 55, 443–453. cesses in shallow flows. Journal of Hydraulic Research, 39, Petitgas P., Poulard J.C., Biseau A. (2003) Comparing com- 567–573. mercial and research survey catch per unit of effort: megrim Johnston T.M.S., Merrifield M.A. (2003) Internal tide scatter- in the Celtic Sea. ICES Journal of Marine Science, 60, 66–76. ing at seamounts, ridges and islands. Journal of Geophysical Planes S., Fauvelot C. (2002) Isolation by distance and vicari- Research, 108, II-1–II-17. ance drive genetic structure of a coral reef fish in the Pacific Jolicoeur P. (1997) Introduction a` la Biome´trie, 3nd edn. Deća- Ocean. Evolution, 56, 378–399. rie, Montreál, Que´bec, Canada. Salthaug A., Godø O.R. (2001) Standardisation of commercial Ketchen K.S. (1964) Measures of abundance from fisheries for CPUE. Fisheries Research, 49, 271–281. more than one species. Rapport et Proce`s Verbal, Reúnion Saunders D.A., Hobbs, R.G., Margules C.R. (1991) Biological du Conseil International pour l’Exploration de la Mer, 155, consequences of ecosystem fragmentation – a review. Con- 113–116. servation Biology, 5, 18–32. Lancaster B.P. (1999) Defining and interpreting suppressor Smouse P.E., Long J.C., Sokal R.R. (1986) Multiple regression effects: advantages and limitations. Education Resources and correlation extension of the Mantel test of matrix Information Center. Annual meeting of the Southwest correspondence. Systematic Zoology, 35, 627–632. Educational Research Association, San Antonio, TX. ED Spall M.A. (2000) Buoyancy-forced circulations around islands 426 097. and ridges. Journal of Marine Research, 58, 957–982. Legendre L., Legendre P. (1998) Numerical Ecology, 2nd English Stephenson R.L. (1999) Stock complexity in fisheries manage- edn (Developments in Environmental Modelling, 20). Elsevier ment: a perspective of emerging issues related to population Scientific Publication, Amsterdam. sub-units. Fisheries Research, 43, 247–249. Lynn H.S. (2003) Suppression and confounding in action. Stramma L., Siedler G. (1988) Seasonal changes in the North The American Statistician, 57, 58–61. Atlantic Subtropical Gyres. Journal of Geophysical Research, Lyons N.I. (1981) Comparing diversity indices based on count 93, 8111–8118. weighted by biomass or other important values. American Swain D.P., Foote C.J. (1999) Stocks and chameleons: the use Naturalist, 118, 438–442. of phenotypic variation in stock identification. Fisheries MacArthur R.H. (1957) On the relative abundance of bird spe- Research, 43, 113–128. cies. Proceedings of the National Academy of Science of the United Kingdom Hydrographic Office (1989) Archipe´lago de United States of America, 43, 293–295. Cabo Verde. North Atlantic Ocean, chart 366. Taunton, Som- MacArthur R.H., Wilson E.O. (1967) The Theory of Island Bio- erset TA1 2DN, UK. geography. Princeton University Press, Princeton, NJ, USA. Verdoit M., Pelletier D., Bellail R. (2003) Are commercial log- MacCall A.D. (1990) Dynamic Geography of Marine Popula- book and scientific CPUE data useful for characterizing the tions. Washington Sea Grant Press, Seattle, WA, USA. spatial and seasonal distribution of exploited populations? Mackinnon D.P., Krull J.L., Lockwood C.M. (2000) Equival- The case of the Celtic Sea whiting. Aquatic Living Resources, ence of the mediation, confounding and suppression effects. 16, 467–485. Prevention Science, 1, 173–181. Volkov I., Banavar J.R., Hubbell S.P., Maritan A. (2003) Neut- McCune B., Grace J.B. (2002) Analysis of Ecological Communi- ral theory and relative species abundance in ecology. Nature, ties. MJM software design. Gleneden Beach, OR, USA. 424, 1035–1037. McGill B.J. (2003) A test of the unified neutral theory of bio- Werner F.E., Quinlan J.A., Blanton B.O., Luettich R.A. Jr diversity. Nature, 422, 881–888. (1997) The role of hydrodynamics in explaining variability Medina A.D. (1995) Les peˆcheries artisanales dans l’archipel du in fish populations. Journal of Sea Research, 37, 195–212. Cap-Vert. Typologie des ports et estimation des de´barquements Wiens J.A., Stenseth N.C., Van Horne B., Ims R.A. (1993) quotidiens. MSc dissertation, Universite´ du Que´bec a` Ecological mechanisms and landscape ecology. Oikos, 66, Rimouski, Que´bec, M 383. 369–380. Mouillot D., Lepretre A. (2000) Introduction of relative Wiggins D.A., Møller A.P., Sorensen M.F.L., Brand L.A. (1998) abundance distribution (RAD) indices, estimated from the Island biogeography and reproductive ecology of great tits rank-frequency diagrams (RFD) to assess changes in Parus major. Oecologia, 115, 478–482.

Marine Ecology 28 (2007) 1–14 ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd 13 Ecological ﬁsh isolation in an archipelago system Medina, Breˆ thes, Se´ vigny & Zakardjian

Wilhm J.L. (1968) Use of biomass units in Shannon’s formula. Zar H.J. (1984) Biostatistical analysis, 2nd edn. Prentice-Hall Ecology, 49, 153–156. Inc., Englewood Cliffs, NJ, USA. Winters G.H., Wheeler J.P. (1985) Interaction between stock Zschokke S., Dolt C., Rusterholz H-P., Oggier P., Braschler B., area, stock abundance and catchability coefﬁcient. Canadian Thommen G.H., Lu¨din E., Erhardt A., Baur B. (2000) Journal of Fisheries and Aquatic Science, 42, 989–998. Short-term response of plants and invertebrates to experi- Wootton J.T. (2005) Field parameterization and experimental mental small-scale grassland fragmentation. Oecologia, 125, test of the neutral theory of biodiversity. Nature, 433, 559–572. 309–312. Wright S. (1943) Isolation by distance. Genetics, 28, 114–138. Xie S.P., Liu W.T., Lui Q., Nonaka M. (2001) Far-reaching effects of the Hawaiian Islands on the Paciﬁc Ocean-Atmo- sphere. Science, 292, 2057–2060.

14 Marine Ecology 28 (2007) 1–14 ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd