Corales, México. Lopéz-Pérez

Journal of

Applied Ichthyology J. Appl. Ichthyol. 29 (2013), 437–448 © 2012 Blackwell Verlag GmbH ISSN 0175–8659

Received: October 27, 2011 Accepted: February 15, 2012 doi: 10.1111/jai.12029

Species composition, habitat configuration and seasonal changes of coral reef fish assemblages in western Mexico

By R. A. Lo´pez-Pe´rez1, L. E. Calderon-Aguilera2, R. C. Zepeta-Vilchis1, I. Lo´pez Pe´rez Maldonado1 and A. M. Lo´pez Ortiz3 Instituto de Recursos, Universidad del Mar, Puerto A´ngel, Oaxaca, Me´xico; 2CICESE, Carretera Ensenada – Tijuana, Ensenada, Baja California, Me´xico; 3Programa de Postgrado en Ciencias Marinas y Costeras, Universidad Auto´noma de Baja California Sur, La Paz, B.C.S., Me´xico 1

Summary In spite of their ecological and economic importance, reef fishes from the coast of Oaxaca, Mexico are rarely studied, therefore precluding their management and conservation. In order to identify the set of habitat characteristics/environmental conditions that predict major shifts in fish assemblages in space and time, a stationary census (5′, u = 5 m) was conducted on a semi-monthly basis from 2006 to 2009 at patch reefs along the coast. Habitat configuration was gathered using 25 m long point-intersect transects (data every 25 cm), recording all underlying coral species and substrate characteristics (rocks, sand, algal mats, rubble or dead corals). Recorded were 65 452 fishes grouped in 11 orders, 36 families, 65 genera and 89 species. Labridae (nine species), Pomacentridae (eight species) and Serranidae (seven species) were the most frequent families. Abundance is severely skewed among species; four species Thalassoma lucasanum, Chromis atrilobata, Apogon pacificus and Stegastes acapulcoensis comprise nearly 59% of the fish abundance, 11 species contribute 30%, whereas most of the species (75) can be considered as rare since they contribute <1% each to the total.

Species richness and family-level assemblage composition are similar to those recorded elsewhere in the eastern Pacific.

Non-parametric multivariate analysis of variance demonstrated that changes of diversity metrics might be associated with environmental differences on the scale of hundreds of meters to kilometers, as well as coupled with major changes on oceanographic variables throughout time, exerting meaningful changes on reef-related fish assemblages.

Introduction Over the past two decades, coral reef studies in western Mexico have greatly improved the understanding of biodiversity, biogeography, community structure and dynamics, bioerosion, recruitment and symbioses (Reyes-Bonilla, 2003); as well as that of coral reef associated fauna such as crustaceans, mollusks, serpulids, echinoderms and sponges (Lo´pezPe´rez et al., 2010). Nonetheless, several invertebrate and vertebrate reef-associated groups remain rarely studied or completely unknown.

Amid myriad organisms, fishes are among the most abundant and conspicuous inhabitants of reefs and coral community systems in Oaxaca. Unfortunately, except for recent articles addressing biodiversity and community structure at specific sites or regions (e.g. Ramı´ rez-Ortiz et al., 2011), this U.S. Copyright Clearance Centre Code Statement:

group has remained mostly unstudied. So far, the distribution of the Mazunte-Bahı´ as de Huatulco reef-related fish fauna has been partially fulfilled (Lo´pez-Pe´rez et al., 2010), along with knowledge regarding the community structure in San Agustı´ n (Ramı´ rez-Gutie´rrez et al., 2007) and Isla Cacaluta (Lo´pez-Pe´rez et al., 2008). Nonetheless, reliable identification of some species (Hyporhamphus cf. H. rosae, Mycteroperca cf. M. rosacea, Parenques sp.) along with intensive and extensive samplings of cryptic crevice, bottom, and burrow dwellers (e.g. blennies, gobies) species is still pending (Lo´pez-Pe´rez et al., 2010); in addition, basic aspects of abundance, distribution and community structure and dynamics of reef related fish assemblages, among many others deserve further study.

In particular the absence of studies regarding distribution, abundance, community structure and dynamics of reefrelated fish assemblages precludes our understanding regarding biodiversity, conservation and resource management of the reefs itself, but also of small-scale fisheries and fishing communities from Oaxaca. Concurrently, the coast of Oaxaca possesses a rich number of coral reefs and communities (Glynn and Leyte-Morales, 1997) with distinct degrees of conservation and development that seasonally experience the influence of upwelling along the Gulf of Tehuantepec (Trasvin˜a et al., 2003). Considering the potentially broad set of habitat and environmental characteristics presented in coral communities and reefs in the coast of Oaxaca, it is predicted that such spatio-temporal changes must produce a complex array of fish assemblages, with major shifts among reefs and seasonal conditions. In this regard, the aim of this contribution was to analyze reef-related fish assemblages from Oaxaca, and to indentify a set of habitat characteristics/environmental conditions that predict major shifts in fish assemblages in space and time.

Materials and methods Study area

The study was conducted in the southwestern part of Mexico, from Puerto Escondido to Tangolunda (Fig. 1) at the coast of Oaxaca, from February 2006 to September 2009.

Some 31 patch reefs and coral reef communities have been previously documented (Reyes-Bonilla et al., 2005) in this area. Patches are mainly concentrated in the vicinity of Puerto Escondido, Puerto Angel and Huatulco; in particular, Bahı´ as de Huatulco represents the southernmost reef system in the Mexican Pacific and because of its high richness and

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Fig. 1. Location of sampling sites, Oaxaca, southern Mexican Pacific coast

biogeographic value, a portion of the system along the adjacent continental area was declared as a Natural Protected Area by the Mexican government in 1998 (Glynn and LeyteMorales, 1997). From Puerto Escondido to Tangolunda the coral reefs and coral communities display various degrees of conservation and structural development, depending on the natural history, environmental disturbances, coastal development and resource exploitation (Lirman et al., 2001; Lo´pezPe´rez and Herna´ndez-Ballesteros, 2004).

Most of the year the area is influenced by tropical, shallow waters (high temperature, low salinity), except in severe winter conditions when the California Current (low temperature, low salinity) reaches the Gulf of Tehuantepec. The most important source of interannual oceanographic variability along the coast of Guerrero is the development of the El Nin˜o- Southern Oscillation (ENSO) phenomenon every 4–5 years (Fiedler and Talley, 2006), characterized by a deepening of the thermocline and nutricline, which negatively impacts primary productivity. When ENSO changes to the cold phase (La Nin˜a), the conditions reverse, resulting in a reduced depth of the thermocline. Consequently, nutrientrich waters can come close to the ocean surface, enhancing regional productivity.

but not regularly at all sites due to meteorological conditions and other limitations. In Fig. 2, the number of samples at each site is indicated as well as the number of censuses conducted each month.

Habitat configuration of each patch reef was gathered using 25 m long point-intersect transects (data every 25 cm).

Figure 3 indicates number of samples at each site. Along each transect, all underlying coral species at each intersectpoint were recorded. Additionally, substrate characteristics such as rocks, sand, algal mat, rubble or cemented coral cover (dead corals), were recorded. All survey transects were randomly placed in the reefs, running parallel to the coastline at random depths but inside the bathymetric interval where reefs occur. The number and periodicity of habitat transect did not necessarily coincide with fish stationary censuses.

When available, monthly sea surface temperature (Aqua MODIS; 4 km resolution; °C), particulate inorganic carbon (Aqua MODIS; 4 km resolution; mol.m3), chlorophyll-a (Aqua MODIS; 4 km resolution; mg.m3) and dissolved organic matter (Aqua MODIS; 4 km resolution) were gathered for each sampled locality/time. Since not all locality/ time satellite images were available, time-pooled data was used to analyze possible relationships among fish abundance and temporal environmental variations.

Data source

Fish assemblages were sampled by stationary point count method (SPC) following Bohnsack and Bannerot (1986).

Basically, the method consists of counting all fish that move across observation cylinders within a 5-m radius (79 m2), in a 5-min period. Sampling sites are depicted in Fig. 1; surveys were conducted during daytime (10.00 to 16.00 hours) from February 2006 to September 2009 on a semi-monthly basis

Data analysis

Stationary censuses were considered as replicates inside each locality and time. Data were used to estimate per census (mean ± 95% confidence interval) species richness (S), fish abundance (N), diversity (Shannon H’), evenness (Pielou J’) and dominance (Simpson k). Since diversity estimates did

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(a)

(f)

(b)

(g)

(c)

(h)

(d)

(i)

(e)

(j)

Fig. 2. Box plots of ecological descriptors [species richness (a, f), abundance (b, g), diversity (Shannon H′; c, h), evenness (Pielou J′; d, i), and dominance (Simpson k; e, j)] per site (a–e) and month (f–j). Boxes depict standard deviation of the mean (filled circles); lines depict 95% confidence interval. Number above site name and month = sample size

not fulfill the normality or the variance homogeneity criteria, estimates were compared across locations and times, through nonparametric Kruskal-Wallis one-way analysis of variance.

The effect of site and time on the development of reefrelated fish assemblages in Oaxaca was tested by two-way crossed permutation-based analysis of variance (PERMANOVA).

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Fig. 3. Substrate characteristics (% of cover) at each sampling site, Oaxaca, southern Mexican Pacific coast. Black = coral, light grey = algae, dark grey = sand, white = rock, horizontal lines = dead coral, vertical lines = rubble. Within parentheses = number of surveys conducted at each site

Since logistics, patch size and weather precluded balanced sampling intensity across locations and times (see Figs 2 and 3), unbalanced PERMANOVA was performed. Both locality and time were considered as fixed factors. PERMANOVA was conducted on Bray-Curtis similarity matrices of square-root transformed fish data and 9999 permutations were performed in all cases. Further, an ordination procedure was used to graphically determine if sites and times could be meaningfully grouped based on their overall fish assemblages. Nonmetric multidimensional scaling (MDS) was selected because this technique plots the similarity among samples based on their distance from each other in multivariate space. MDS was conducted on Bray-Curtis similarity matrices of squareroot transformed fish data (Clarke and Warwick, 2001). The interpretation of an MDS is straightforward: points that are close together represent samples that are very similar in community composition; points that are far apart correspond to very different values of the variable set. PERMANOVA and MDS were computed using Primer 6 (Clarke and Gorley, 2006).

Since there was no one-to-one numeric or temporal coincidence between fish censuses and habitat transects, pooled data was used to characterize fish assemblages and habitat configuration for each locality. It was tested if habitat configuration could predict fish assemblages on coral reef through two suites of analysis. First, fish and habitat configuration data were standardized by column total such that the influence of sampling intensity differences was downplayed among localities (Clarke and Warwick, 2001). Further, two Bray-Curtis similarity matrices of square-root transformed fish and habitat configuration data were constructed. The strength of the association between these two matrices was determined via RELATE procedure in Primer. Basically, the analysis proceeded by determining the Spearman rank correlation between these two matrices followed by a permutation test to determine the probability that the measured correlation arose by chance. Secondly, which local habitat variables were most important were determined to explain the pattern in the fish assemblage observed at each locality using the BIO-ENV procedure in Primer. The analysis determined the Spearman rank correlation between the fish community and all possible combinations of habitat variables, and the ten models with highest values were identified; further, the significance of the highest correlation was tested by a permutation test to determine the probability that the observed highest correlation arose by chance. Finally, Pearson correlations were performed in order to test the association between

species abundance and community estimates against habitat characteristics and environmental variables across time.

Results Recorded were 65 452 fishes grouped in 11 orders, 36 families, 65 genera and 89 species. Labridae (nine species), Pomacentridae (eight species) and Serranidae (seven species) were the most frequent families. Fifteen of the 65 genera possess up to two species; of these, Haemulon and Halichoeres are the richest genera represented by five species each, the remainder are represented each by one species in the area. In general, abundance is severely skewed among species; just four species [Thalassoma lucasanum (Gill), Chromis atrilobata Gill, Apogon pacificus (Herre) and Stegastes acapulcoensis (Fowler)] make up nearly 59% of the fish abundance, 11 species contribute the next 30%, whereas most of the species (75) can be considered as rare since they each contribute <1% to the total fish abundance.

In general, per census species richness was relatively higher at Puerto Angel (>11 species) than at Puerto Escondido or Bahı´ as de Huatulco (<8 species) (Kruskal-Wallis H (16, 618) = 117.08, P < 0.001; Fig. 2a); mean abundance was particularly high at Mazunte and La Mina, whereas at Jicaral it was relatively low (N, Kruskal–Wallis H(16,618) = 101.38, P < 0.001); in addition, diversity (H′; Kruskal-Wallis H(16,618) = 36, P < 0.01), evenness (J′; Kruskal–Wallis H(16,618) = 40.94, P < 0.001) and dominance (k; Kruskal-Wallis H(16,618) = 29.61, P < 0.05) also showed significant differences across locations.

Unfortunately, nonparametric multiple comparisons failed to detect differences among localities, probably related to the low sample size and high variation of the estimates in the Puerto Escondido and Puerto Angel regions (Fig. 2a–e). A more comprehensive picture can be drawn from the variation of diversity estimates across time (Fig. 2f–j). Per census species richness descend from a maximum in January to a minimum during May, and then rise to reach a second maximum during November (Kruskal–Wallis H(9,618) = 59.29, P < 0.001); in particular, from September to February species richness is relatively similar in the studied area (multiple comparison test, P > 0.05), whereas most of the differences occurred from March to June, compared to the time between September to November (multiple comparison test, P < 0.05; Fig. 2f). Abundance was high during February and March (Kruskal–Wallis H(9,618) = 59.03, P < 0.001), with significant differences among them and April, May and June; and between the last two in comparison with September (multiple

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comparison test, P < 0.05; Fig. 2g). Although slightly out of phase, diversity (H′) approximately mimics the observed temporal trend for species richness (high values from June to January; Kruskal-Wallis H(9,618) = 35.72, P < 0.001), with meaningful differences between January and April, April vs June and November, and between May and November (multiple comparison test, P < 0.05; Fig. 2h). Evenness (J′) possesses relatively low values during February and March (Kruskal–Wallis H(9,618) = 35.34, P < 0.001), with meaningful differences between February and June, between the former and November, and also during March and June (multiple comparison test, P < 0.05; Fig. 2h). Dominance (k) values increased from January to May, then decreased toward November (Kruskal–Wallis H(9,618) = 21.94, P < 0.01) in almost an opposite trend to species richness, diversity and evenness; differences occurred among January and May and between the latter and November (multiple comparison test, P < 0.05; Fig. 2j). Summarizing, assemblage species richness varies systematically among regions (i.e. Puerto Angel > Puerto Escondido/Bahı´ as de Huatulco), whereas the large variability observed in the remainder of diversity estimates (H′, J′, k) precludes identifying any spatial trend; to the contrary, diversity estimates of fish assemblages oscillate in a more comprehensive way over time.

Non-parametric analysis of variance PERMANOVA of the fish data showed that there were significant differences in fish assemblages across locations and from time to time, along with significant interaction between locality and time (Table 1). Pair-wise comparisons among localities showed that except for Isla Montosa and Boquilla (t = 1.3486, P = 0.05), Isla Montosa and Violin (t = 1.1545, P = 0.18), Isla Cacaluta and Violin (t = 1.3334, P = 0.05), Isla Cacaluta and Riscalillo (t = 1.3153, P = 0.05), Tijera and Boquilla (t = 1.0233, P = 0.40), Boquilla and Estacahuite (t = 1.2239, P = 0.08) and Maguey and Violin (t = 1.3607, P = 0.05), the remaining comparisons were highly meaningful. In general, the effect of locality on fish assemblages showed a slight regional pattern according to the MDS (Fig. 4a). Puerto Escondido localities appeared on the left side of the graph (Zapatito, Faro and Carrizalillo), whereas Puerto AngelBahı´ as de Huatulco locations are intermixed and located on the central and right side of the MDS; in particular, nonsignificant differences occurred within (Puerto Angel region) and between (Puerto Angel vs Bahı´ as de Huatulco region) localities of this group (Fig. 4a). Regarding time, pair-wise comparison demonstrated that there were no assemblage differences among May and June (t = 1.3325, P = 0.06), October and December (t = 1.0279, P = 0.36), October and January (t = 1.3859, P = 0.05) and December and January

Table 1 Two-way crossed non-parametric permutation-based analysis of variance of 89 fish species abundance data Source

Df

SS

MS

F

P

Unique perms

Locality Time Loc x time Residual Total

15 8 36 555 615

1.7831E5 71162 1.2797E5 1.325E6 1.7309E6

11887 8895.2 3554.7 2387.5

4.9791 3.7258 1.4889

0.0001 0.0001 0.0001

9762 9802 9641

P-values were obtained using 9999 permutations of given permutable units for each term.

(a)

(b)

Fig. 4. Reef-associated fish assemblages, Oaxaca, southern Mexican Pacific. (a) Non-metric multidimensional scaling (MDS) ordination based on locality, pairwise tests Bray-Curtis similarity matrix, (b) MDS ordination based on time, pairwise tests Bray-Curtis similarity matrix

(t = 1.1559, P = 0.19), but that there are significant differences among the remaining months. The effect of time on fish assemblages was smaller from November through January and during May to June, as shown by a relatively small separation of the points on the MDS (Fig. 4b); in addition, the apparent time grouping appears to coincide with the environmental conditions given on the Oaxaca coast.

Regarding locality and time interaction, pair-wise comparisons across localities within levels of the time factor showed consistent locality differences particularly during January, February, September, November and December (Appendix 1). On the other hand, pair-wise comparisons across times within levels of factor locality demonstrated consistent differences among times at Isla Cacaluta and Jicaral; in addition, significant differences consistently occurred among times located in opposite groups (i.e. November–January, February–March, June–October, September) depicted on the MDS (Fig. 4b; Appendix 2).

Results of the RELATE routine demonstrated a small but meaningful relationship between habitat (Fig. 3) and fish assemblage similarity matrices (q = 0.25); 12 out of 999 random permutations resulted in correlation values equal or larger than observed, indicating that the correlation was significant (P < 0.01). Concurrently, the BIO-ENV routine showed a strong and significant association between fish assemblages and habitat characteristics matrices (weighted Spearman rank = 0.721, P < 0.001); in particular, the

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optimal configuration of habitat parameters, necessary to give a very similar result to the ordination based on fish assemblages, includes the percentage of dead coral and coral rubble. It is quite remarkable that in all of the ten best models analyzed, characteristics associated with reef deterioration (dead coral, coral rubble, algae) are the most common, whereas live coral coverage appeared on the two models with the lowest correlation explaining the fish assemblages.

Similar trends to those addressed above can be observed on the abundance of specific fish species with respect to substrate characteristics. Thirty-three of 89 reef-related fish species showed significant correlations (positive, negative or both) with habitat characteristics (Table 2). Notably, Aetobatus narinari, Diodon hystrix, Lutjanus argentiventris, Pseudobalistes naufragium and Thalassoma lucasanum were more abundant in localities with larger areas of live coral cover, whereas Halichoeres chierchiae become scarce in the same places.

On the other hand, 13 out of 89 reef-related fish species showed a significant correlation with environmental variables across time (Table 3). Diodon hystrix, Haemulon sexfasciatum, Melichthys niger and Scorpaena mystes were more abundant when dissolved organic matter increased. Remaining correlations are presented in Table 3.

Discussion Species richness and family level assemblage composition recorded at the Oaxaca reef track is closely similar to those

recorded elsewhere in the eastern Pacific. Plankton feeding pomacentrids, along with labrids and serranids that feed on mobile invertebrates and fishes, were the most frequent families, whereas the omnivorous Thalassoma lucasanum and Stegastes acapulcoensis, and the planktivorous Chromis atriloba and Apogon pacificus contribute 60% of the fish abundance in reefs and coral community systems in Oaxaca, as has been similarly described for rocky and coral reefs in the Gulf of California (Pe´rez-Espan˜a et al., 1996; AburtoOropeza and Balart, 2001; Alvarez-Filip et al., 2006; Villegas-Sa´nchez et al., 2009), Mexican Pacific (Ramı´ rezGutie´rrez et al., 2007; Lo´pez-Pe´rez et al., 2008, 2010; Galva´n-Villa et al., 2010) and Central America (DominiciArosemena and Wolff, 2006; Benfield et al., 2008).

Notwithstanding the apparent homogeneity of rocky and coral reef fish assemblages throughout the entire eastern Pacific, the coast of Oaxaca experiences significant changes in fish assemblages from locality to locality and from time to time, not just mean species richness (S) and abundance (N), but also diversity (H′), evenness (J′) and dominance (k).

Although not particularly appropriate for assessing multivariate variation for several species simultaneously, changes in diversity metrics in Oaxaca suggest that environmental differences on the scale of hundreds of meters to kilometers coupled with major changes in oceanographic variables throughout time, may exert meaningful changes in reefrelated fish assemblages. On the coast of Oaxaca, for instance, mean fish abundance was observed to be larger at localities where rock cover is important, whereas diversity

Table 2 Correlation of reef-related fish abundance and habitat characteristics Species Acanthurus xanthopterus Aetobatus narinari Alphestes immaculatus Aluterus scriptus Apogon retrosella Arothron hispidus Canthigaster punctatissima Cephalopholis panamensis Chaetodon humeralis Diodon holocanthus Diodon hystrix Echidna nebulosa Epinephelus itajara Haemulon maculicauda Halichoeres chierchiae Halichoeres nicholsi Lutjanus argentiventris Mugil curema Muraena lentiginosa Ophioblennius steindachneri Pseudobalistes naufragium Rypticus bicolor Scarus rubroviolaceus Selar crumenophthalmus Serranus psittacinus Stegastes acapulcoensis Stegastes rectifraenum Thalassoma grammaticum Thalassoma lucasanum Trachinotus rhodopus Tylosurus pacificus Urobatis halleri Zanclus cornutus

Corals

Algae

0.20 0.52 0.22 0.37 0.26 0.38 0.24 0.07 0.09 0.20 0.58 0.25 0.41 0.24 0.50 0.36 0.62 0.22 0.14 0.23 0.51 0.28 0.32 0.42 0.01 0.11 0.04 0.39 0.55 0.28 0.38 0.45 0.30

0.75 0.04 0.10 0.21 0.78 0.10 0.01 0.66 0.51 0.51 0.30 0.29 0.21 0.31 0.07 0.33 0.20 0.77 0.34 0.32 0.30 0.21 0.02 0.02 0.67 0.09 0.27 0.21 0.05 0.18 0.22 0.24 0.34

Sand 0.11 0.20 0.31 0.17 0.08 0.17 0.27 0.10 0.47 0.21 0.22 0.63 0.51 0.64 0.18 0.36 0.05 0.07 0.56 0.17 0.10 0.01 0.28 0.27 0.10 0.45 0.66 0.04 0.22 0.20 0.19 0.09 0.62

Rock 0.33 0.53 0.15 0.54 0.25 0.63 0.54 0.37 0.17 0.56 0.47 0.12 0.35 0.10 0.14 0.17 0.74 0.27 0.10 0.30 0.58 0.02 0.11 0.51 0.45 0.06 0.01 0.09 0.64 0.50 0.56 0.50 0.14

Dead coral 0.35 0.33 0.56 0.34 0.32 0.43 0.38 0.16 0.23 0.49 0.04 0.29 0.09 0.32 0.51 0.68 0.37 0.31 0.12 0.20 0.37 0.53 0.64 0.31 0.42 0.50 0.31 0.53 0.69 0.31 0.35 0.33 0.30

Coral rubble 0.13 0.20 0.80 0.14 0.08 0.19 0.22 0.33 0.41 0.07 0.24 0.13 0.29 0.20 0.97 0.72 0.41 0.12 0.19 0.64 0.19 0.78 0.91 0.16 0.20 0.70 0.27 0.88 0.32 0.11 0.14 0.17 0.11

Only fish species with significant correlation were included. Bold values represent significant relationships (P < 0.05). For all cases n = 16.

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Table 3 Correlation of reef-related fish abundance and satellite-derived environmental variables across time Species Bodianus diplotaenia Diodon hystrix Haemulon flaviguttatum Haemulon sexfasciatum Kyphosus elegans Melichthys niger Microspathodon bairdii Microspathodon dorsalis Rypticus bicolor Scarus ghobban Scorpaena mystes Synodus lacertinus Thalassoma lucasanum

DIM 0.52 0.63 0.17 0.65 0.48 0.73 0.53 0.49 0.52 0.48 0.71 0.02 0.17

PIC (mol.m 3) 0.19 0.25 0.65 0.10 0.63 0.10 0.13 0.07 0.66 0.42 0.08 0.69 0.31

T (°C) 0.71 0.64 0.02 0.69 0.35 0.33 0.65 0.81 0.25 0.44 0.36 0.29 0.68

Chl a (mg m 3) 0.27 0.65 0.31 0.38 0.58 0.69 0.30 0.33 0.49 0.81 0.69 0.30 0.39

DIM, dissolved inorganic mater; PIC, particulate inorganic carbon (mol m 3); T, sea surface temperature (°C); Chl a, Chlorophyll a (mg m 3).

Only fish species with significant correlation were included. Bold values represent significant relationships (P < 0.05). For all cases n = 10.

rose at localities where coral rubble increased. There is abundant evidence in the eastern Pacific and elsewhere to suggest that the structure of the habitat has important effects on spatial reef-associated fish populations, both in tropical coral reefs and in temperate rocky reef systems (McClanahan and Arthur, 2001; Anderson and Millar, 2004; Dominici-Arosemena and Wolff, 2006; Aguilar and Appeldoorn, 2008; Benfield et al., 2008; Villegas-Sa´nchez et al., 2009; Galva´n-Villa et al., 2010), but there is no agreement on the direction and intensity of the relationship (Dominici-Arosemena and Wolff, 2006; Benfield et al., 2008; Villegas-Sa´nchez et al., 2009; Galva´n-Villa et al., 2010). In this sense the evidence is not conclusive and further studies must clearly disentangle the effect or synergy of rock cover and water movement on coral and rocky reef-related fish abundance.

In the same way, non-parametric multivariate analysis of variance demonstrated that as a whole, reef-related fish assemblages experience significant differences across localities and times. There is evidence that these changes did not occur randomly; rather, both RELATE and BIO-ENV routines suggest that habitat characteristics can, to some extent, predict reef-associated fish assemblages at the coast of Oaxaca.

A combination of rock, dead coral, rubble, sand, algae and live coral cover consistently correlated with the pattern of ordination observed on reef-associated fish assemblages from Oaxaca. In particular, the percentage of dead coral and coral rubble represented the optimal configuration of habitat parameters necessary to give a very similar result to the ordination based on fish assemblages. Results corroborate the previously observed influence of habitat in tropical and temperate eastern Pacific reefs and rocky reef-associated fish assemblages (Aburto-Oropeza and Balart, 2001; AlvarezFilip et al., 2006; Dominici-Arosemena and Wolff, 2006; Benfield et al., 2008; Villegas-Sa´nchez et al., 2009; Galva´nVilla et al., 2010; and references therein). More importantly, however, the results suggest that reef-related fish assemblages on the coast of Oaxaca are mainly modulated by habitat characteristics associated with reef deterioration. Evidence suggests that coral reefs appear to be particularly susceptible to a range of natural and anthropogenic disturbances

(Hoegh-Guldberg, 1999); in addition, recent work also indicates that coral reef fish assemblages often exhibit dramatic changes in structure and loss of biodiversity in relation to declining coral cover (Halford et al., 2004; Jones et al., 2004; Graham et al., 2006; Wilson et al., 2006, 2009), probably related to the effect of habitat degradation on the fish settlement (Feary et al., 2007).

Results suggest that reef deterioration in Oaxaca may have been produced by assemblages dominated by small-bodied species of damselfishes (Pomacentridae), wrasses (Labridae) and groupers (Serranidae); nonetheless, such assemblages may also result from selective over-fishing of medium and largebodied fishes of the families Lutjanidae (snappers), Serranidae (groupers) and Carangidae (jacks), as have been seen in Cuba (Aguilar et al., 2004), Jamaica (Hughes, 1994) and the IndoPacific (Harmelin-Vivien, 1992). Studies regarding reef deterioration at the coast of Oaxaca are still pending; nonetheless, published information suggests that reefs and coral communities in the three areas have experienced natural (Lirman et al., 2001; Reyes-Bonilla et al., 2002; Benı´ tez-Villalobos et al., 2009) and long-standing anthropogenic perturbations (Lo´pezPe´rez and Herna´ndez-Ballesteros, 2004), ranging from mild to severe – depending on the proximity to large human population centers (i.e. Puerto Escondido, Puerto Angel and Bahı´ as de Huatulco development) and the scale, severity and frequency of the disturbance. On the other side, there is no published information on the impacts of human activities on coastal fish populations of Oaxaca, especially around large population centers, although there is a long standing tradition on fishing large-bodied snappers, groupers and jacks around Puerto Escondido, Puerto Angel and Huatulco (C. AlejoPlata, unpub. data).

As can be seen, reef-associated fish assemblages in Oaxaca closely correlate with habitat characteristics; such correlations, however, can also be noted on the species level. The effect of habitat is clear in the present study; nonetheless, several other aspects of habitat, acting in solo or simultaneously, may also be involved in structuring assemblages (Anderson and Millar, 2004; Dominici-Arosemena and Wolff, 2006; Aburto-Oropeza et al., 2007; Benfield et al., 2008; Robertson and Allen, 2008; Villegas-Sa´nchez et al., 2009; Galva´n-Villa et al., 2010; and references therein). In this regard, although valuable, the observed correlations should be considered with caution and more detailed speciesspecific studies must be conducted in order to establish unambiguous cause-effect relationships.

Although not all localities were systematically sampled throughout time or over several years, and inferences concerning inter-annual variation in general could not be viewed as very precise, several lines of evidence concur on suggesting that reef-related fish assemblages on the coast of Oaxaca have experienced significant changes over time, probably associated with the major seasonal oceanographic changes that occur in the zone. Upwelling from November to February (Trasvin˜a et al., 2003), coupled with a dry season from November to April and a rainy season from May to October (Fiedler and Talley, 2006) produce a productive-cold-dry and a warm-wet season along with transition periods between major climatic conditions. Such conditions are partially reflected in diversity estimate changes, fish assemblage ordination, significant differences and homogeneities according to PERMANOVA, and meaningful correlations. Most studies on reef fishes, when both spatial and temporal scales are combined, have highlighted the larger persistence of spatial

444

patterns over time (Galzin, 1987; Fowler, 1990; Alvarez-Filip et al., 2006; Graham et al., 2007; Letourneur et al., 2008; Villegas-Sa´nchez et al., 2009); nonetheless, the pattern may be associated to the extent of the climatic modification experience in the studied area. Particularly in Oaxaca, it is hypothesized that large temporal variation in fish assemblages may occur under usual circumstances on reefs that experience large climatic modifications (i.e. upwelling areas) as opposed to those developing on stable to mildly changing conditions (i.e. non-upwelling). Such a hypothesis, however, deserves further testing in coming years.

It has been demonstrated that reef-associated fish assemblages in Oaxaca experience significant changes in space and time, associated with habitat characteristics and climatic conditions; in the coming years, however, intensive and extensive sampling and hierarchical studies over various spatial and temporal scales would render a more comprehensive understanding of the reef-related fish fauna of the southern Mexican Pacific.

Acknowledgements We are indebted to all of our colleagues who participated in the fieldwork over the years. The fieldwork was funded by CONACYT (37527-B), SEMARNAT-CONACYT (0605, 23390), PROMEP (103.5/07/2597) and CONABIO (HJ029).

Comments from two anonymous reviewers enhanced previous versions of this work.

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Author’s address: Luis E. Calderon-Aguilera, CICESE, Carretera Ensenada - Tijuana 3918, Ensenada, Baja California 22860, Me´xico.

E-mail: leca@cicese.mx

1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,

2 3 4 5 6 7 8 9 10 11 12 13 14 3 4 5 6 7 8 9 10 11 12 13 14 4 5 6 7 8 9 10 11 12 13 14 5 6 7 8 9 10 11 12 13 14

0.0004 0.0043 0.0007 0.0003 0.0149

0.0001 0.0749 0.0432 0.0001

0.0005 0.001 0.0075

0.0296 0.0002

2.4325 1.2946 1.404 2.3455

2.038 2.1051 1.6234

1.3853 1.9111

P

2.0273 1.9135 1.847 2.168 1.6911

t

Jan

0.005

0.0001 0.0106 0.0005 0.0001 0.0001

2.1913 1.5976 1.8325 2.0444 2.6773

0.0001 0.0071 0.0009 0.0002 0.0001

2.4159 1.7278 1.8339 2.3331 2.6091

1.5976

0.102 0.0364

0.0001 0.0009 0.0002 0.0001 0.0001

2.5163 1.8378 2.0386 2.3714 2.8587

1.2894 1.4504

0.0152 0.7029 0.0023

0.0001 0.0205 0.0107 0.0001 0.0001

2.2793 1.4431 1.5248 2.158 2.5167

1.5979 0.85448 1.786

0.0038 0.2912 0.0707 0.0717

P

1.7011 1.086 1.3257 1.2887

t

Feb

1.5134 2.1275

1.9856 1.7149 1.2652

1.1803 1.2143 1.0118 1.3152

1.1059 1.0713 1.794 1.758 0.93143

t

Mar

0.019 0.0001

0.0001 0.0054 0.1374

0.1703 0.1304 0.413 0.0551

0.2396 0.344 0.0007 0.0011 0.5501

P

1.1071 1.7065

1.8824 1.7279 1.3636

1.5473 1.0911 1.046 1.503

1.1537 1.2975 1.2971 1.3799 1.345

t

May

0.2863 0.0093

0.0018 0.0066 0.0594

0.0205 0.2964 0.3477 0.0308

0.2047 0.0973 0.1127 0.0629 0.0595

P

1.5985 1.7116

1.4482 1.2848 1.3728

1.0984 1.2524 0.94748 1.599

1.3082 1.014 1.5706 1.3784 1.3052

t

Jun

0.0101 0.0032

0.0121 0.0832 0.0155

0.2662 0.1176 0.5166 0.0021

0.0919 0.4162 0.0172 0.0738 0.1088

P

1.9306 1.9153 1.9667

1.5267 1.4304 1.4059 1.4563 0.73171

1.4938 1.7859 1.7302 1.8411 1.8571 1.8026

1.4075 1.2028 1.6488 1.916 1.9265 1.7309 1.7025

1.448 1.6258 1.4927 1.3536 1.3115

t

Sep

0.0013 0.0004 0.0003

0.0056 0.0211 0.0263 0.0292 0.8482

0.0244 0.004 0.0035 0.0006 0.0001 0.0002

0.0216 0.1313 0.0016 0.0002 0.0003 0.0027 0.0037

0.0229 0.0011 0.0139 0.039 0.0577

P

1.1396

1.1597 0.65355

t

Oct

0.2136

0.1995 0.9122

P

1.6494 1.715

1.4565 1.5575 1.516

1.5731 1.52 1.2963 1.8331

1.5119 1.0053 1.3349 1.5877 1.5363

t

Nov

0.0214 0.0077

0.0351 0.0324 0.0328

0.0081 0.0111 0.0735 0.0006

0.0127 0.379 0.0779 0.0238 0.0383

P

Appendix 1 Results of pair-wise comparisons across localities within levels of factor time. Missing cells = no-sampling times/localities

0.0225 0.0525

0.0003 0.1611 0.0523

0.0764 0.0251 0.2376 0.0604

0.0186 0.0395 0.0021 0.0956 0.174

P

(continued)

1.4969 1.3243

1.8332 1.2304 1.3811

1.419 1.5538 1.1386 1.4053

1.5464 1.4351 1.8603 1.2975 1.2028

t

Dec

446 R. A. Lo´pez-Pe´rez et al.

1.861

0.0043

P

0.0001 0.0171 0.1144 0.0001 0.0001

0.4058 0.0005 0.0002 0.0003 0.0445 0.079 0.0144 0.0017 0.0001 0.0006

1.0233 1.932 1.7308 2.7481 1.4474 1.2239 1.9509 1.9177 1.9604 2.6177

P

2.1435 1.5021 1.2769 2.2014 2.5544

t

Feb

1.7368

t

Mar

0.0025

P 1.4528

t

May

0.0361

P 1.5514

t

Jun

0.0115

P

1.3607 1.5871 1.5678

1.8657 1.8642 1.7782 1.5908 1.6559 1.6863

1.5171

t

Sep

0.0528 0.0056 0.003

0.001 0.0006 0.0017 0.0113 0.0028 0.003

0.0163

P t

Oct P 1.2944

t

Nov

0.1214

P

1.1545

t

Dec

0.2032

P

1 = Isla Montosa, 2 = La Entrega, 3 = Isla Cacaluta, 4 = Jicaral, 5 = San Agustin, 6 = Dos Hermanas, 7 = Tijera, 8 = Boquilla, 9 = La Mina, 10 = Estacahuite, 11 = Mazunte, 12 = Maguey, 13 = Violin, 14 = Riscalillo.

P-values obtained by 9999 permutations of appropriate units. Bold values represent signiďŹ cant relationships (P < 0.05).

5, 6 5, 7 5, 8 5, 9 5, 10 5, 11 5, 12 5, 13 5, 14 6, 12 6, 13 6, 14 7, 8 7, 9 7, 10 7, 11 8, 9 8, 10 8, 11 9, 10 9, 11 10, 11 12, 13 12, 14 13, 14

t

Jan

Appendix 1 (continued)

Coral reef ďŹ sh assemblages in western Mexico 447

1.2134 1.0622 1.0086 1.2501 1.6177 1.2273 1.3164 1.2144 1.3907 1.1704 1.1898 1.5691 1.286 1.4965 1.3453 1.3381 0.86319 1.7488 1.0952 1.1939 1.0845 1.2451 1.6491 1.1069 1.1246 1.0407 2.0767 1.2295 1.9044 1.4672 1.4366 1.4185 1.2112 1.4441 1.258 0.95806

0.1507 0.3249 0.4182 0.1392 0.0032 0.1261 0.0791 0.1521 0.0557 0.1873 0.1854 0.0033 0.0783 0.0247 0.0666 0.083 0.6268 0.0006 0.2715 0.1572 0.278 0.1562 0.0034 0.2501 0.2412 0.343 0.0002 0.1094 0.0006 0.0233 0.0535 0.052 0.1465 0.0516 0.1185 0.4737

P 1.4917 1.0173 0.8561 1.0803 1.3914 1.4217 1.3655 1.4236 1.4839 1.1698 1.0952 1.0953 1.2701 1.2389 1.1837 1.5012 1.0721 1.4206 1.0971 1.2719 1.197 1.4157 1.363 0.86278 1.2631 1.1379 1.3456 1.2981 1.2833 1.335 1.223 0.90168 1.121 0.87585 0.99841 1.2856

t

La Entrega

0.016 0.4104 0.6918 0.3142 0.0495 0.0313 0.0326 0.0549 0.0191 0.1841 0.2693 0.2796 0.1078 0.1026 0.1713 0.0174 0.3134 0.0381 0.2763 0.0929 0.1846 0.035 0.0622 0.6851 0.1081 0.2231 0.0665 0.0907 0.1006 0.0809 0.128 0.6156 0.2442 0.6884 0.4212 0.1177

P 1.8424 1.5579 1.2146 1.3129 1.8394 1.3029 1.1974 1.564 1.2954 1.2139 1.3767 1.6236 1.1446 1.0869 1.0412 1.7293 1.0429 1.8382 1.385 1.2094 1.5006 1.9396 1.6131 1.1645 1.1675 1.5145 1.4425 1.2349 1.5016 1.231 1.2024 1.0926 0.97285 1.4332 1.0357 1.1071

t

Isla Cacaluta

0.0011 0.0085 0.1547 0.0818 0.0005 0.0893 0.1623 0.0118 0.0991 0.1613 0.0288 0.0032 0.2124 0.3015 0.3856 0.0065 0.3728 0.0004 0.0678 0.1606 0.0352 0.0002 0.0024 0.1759 0.1563 0.0125 0.0277 0.1178 0.0251 0.1035 0.162 0.262 0.4504 0.0696 0.353 0.276

P 0.0055 0.1976 0.4157 0.2028 0.3687 0.0616 0.0389 0.0088 0.0034 0.0008 0.0715 0.0018 0.1145 0.0005 0.4675 0.019 0.0475 0.0082 0.0014 0.0023 0.0671 0.0015 0.0039 0.0203 0.0753

0.0588 0.0016 0.1125

1.3583 1.3502 1.6688 1.7279 1.8249 1.3419 2.1528 1.2611 1.7342 0.992 1.5224 1.4267 1.5681 1.6358 1.6628 1.3754 1.6455 1.6391 1.6202 1.2981

1.3612 1.9865 1.2436

P

1.5068 1.1655 1.022 1.1531 1.0305

t

Jicaral

1.0464 0.90092 0.93195

1.4683 1.0955

1.1421 1.0275 1.481

1.2133 1.3586 1.5482 1.2884

1.4097 1.1697 1.5948 1.1962 1.4692

1.9322 1.68 1.8054 0.99483 0.89236 1.3564

2.1586 1.7368 1.6703 1.3296 2.1356

t

San Agustı´ n

0.3282 0.5278 0.4959

0.0375 0.2675

0.2136 0.3522 0.0329

0.1734 0.0811 0.0299 0.0961

0.074 0.1997 0.0236 0.1522 0.0366

0.0009 0.0056 0.0141 0.3933 0.5908 0.0759

0.0003 0.0025 0.0036 0.0752 0.0001

P

1.1017 1.094 1.2339

1.3988 0.91236

1.5344 1.0754 1.6713

0.93557 1.0311 1.7499 1.0076

1.7433 1.4593 1.2184 1.2004 1.1298

1.6944 1.1645 1.4539 1.6191

t

Dos Hermanas

0.3004 0.2979 0.1378

0.0612 0.5981

0.0132 0.3111 0.0055

0.5387 0.3777 0.002 0.4197

0.0037 0.0302 0.1448 0.1622 0.2235

0.0035 0.212 0.0257 0.0055

P

1 = January, 2 = February, 3 = March, 5 = May, 6 = June, 9 = September, 10 = October, 11 = November, 12 = December P-values obtained by 9999 permutations of appropriate units.

Bold values represent significant relationships (P < 0.05).

2, 1 2, 3 2, 5 2, 6 2, 9 2, 10 2, 11 2, 12 3, 1 3, 5 3, 6 3, 9 3, 10 3, 11 3, 12 5, 1 5, 6 5, 9 5, 10 5, 11 5, 12 6, 1 6, 9 6, 10 6, 11 6, 12 9, 1 9, 10 9, 11 9, 12 10, 1 10, 11 10, 12 11, 1 11, 12 12, 1

t

Isla Montosa

Appendix 2 Pair-wise comparisons among times within levels of factor locality. Missing cells = no-sampling times/localities

448 R. A. Lo´pez-Pe´rez et al.