Multivariate assessment of seasonal patterns in benthic polychaete assemblages in Cheliff estuary (Algeria)

Avaliação multivariada de padrões sazonais em assembleias de poliquetas bentônicas no estuário de Cheliff (Argélia)

Abstract

Robust inference from ecological community data requires integrating multivariate diagnostics, effect sizes, and permutation-based tests. Here, we examined seasonal variation in polychaete assemblages across multiple sites in the Cheliff Estuary, Algeria, using abundance-based analyses. Species abundances were Hellinger-transformed, and Bray–Curtis dissimilarities were analyzed via non-metric multidimensional scaling (NMDS), while seasonal effects were evaluated using permutational multivariate analysis of variance (PERMANOVA) and complementary tests of multivariate dispersion. NMDS revealed high similarity among samples, reflected by a near-zero stress value and weak multivariate structure. PERMANOVA detected no significant differences between wet and dry seasons, with the seasonal factor accounting for only a small fraction of total community variance. Homogeneity of dispersion tests indicated no significant differences between seasons, suggesting that the PERMANOVA outcome reflects similarity in multivariate centroids rather than heterogeneity of variance. These results demonstrate that polychaete assemblages in the Cheliff Estuary are compositionally stable across seasons, and they highlight the value of assumption-aware, diagnostic-supported multivariate analyses for robust ecological inference in small to moderate datasets.

Keywords:
Bray-Curtis; community stability; ecological informatics; NMDS; PERMANOVA; polychaete assemblages

Resumo

A inferência robusta a partir de dados de comunidades ecológicas requer a integração de diagnósticos multivariados, tamanhos de efeito e testes baseados em permutação. Neste estudo, examinamos a variação sazonal em assembleias de poliquetas em múltiplos locais no estuário do Cheliff, na Argélia, utilizando análises baseadas em abundância. As abundâncias das espécies foram transformadas por Hellinger, e as dissimilaridades de Bray-Curtis foram analisadas por meio de escalonamento multidimensional não métrico (NMDS), enquanto os efeitos sazonais foram avaliados usando análise de variância multivariada permutacional (PERMANOVA) e testes complementares de dispersão multivariada. O NMDS revelou alta similaridade entre as amostras, refletida por um valor de estresse próximo de zero e uma estrutura multivariada fraca. A PERMANOVA não detectou diferenças significativas entre as estações chuvosa e seca, com o fator sazonal representando apenas uma pequena fração da variância total da comunidade. Os testes de homogeneidade de dispersão não indicaram diferenças significativas entre as estações, sugerindo que o resultado da PERMANOVA reflete similaridade nos centroides multivariados, em vez de heterogeneidade de variância. Esses resultados demonstram que as assembleias de poliquetas no estuário do Cheliff são composicionalmente estáveis ​​ao longo das estações do ano e destacam o valor de análises multivariadas que levam em consideração as premissas e são apoiadas por diagnósticos para inferências ecológicas robustas em conjuntos de dados de pequeno a médio porte.

Palavras-chave:
Bray-Curtis; estabilidade da comunidade; informática ecológica; NMDS; PERMANOVA; assembleias de poliquetas

1. Introduction

The Mediterranean Sea contains numerous estuaries that play a crucial role in ecological studies, particularly in assessing pollution resulting from human activities and its potential effects on marine fauna. Among the associated benthic organisms, Polychaeta are of particular importance, as they are widely used as indicators of water pollution, reflecting changes in both the water column and sediment composition (Kies et al., 2020).

Annelids are essential bioindicators of estuarine and coastal ecosystem health, as the presence or absence of specific species provides valuable information on water quality status (Sharifinia et al., 2025; Sajidevi et al., 2024). One of the Mediterranean estuaries subjected to intense anthropogenic pressure is the Cheliff Estuary in Algeria. Previous studies have highlighted the suitability of Polychaeta as bioindicators of water quality in this area, mainly due to the strong human influence from nearby urban settlements and industrial activities (Kaddeche et al., 2022; Benkaddour et al., 2019). A previous study on Polychaeta communities in Mediterranean estuaries identified four dominant taxa: (i) Nereis pelagica (Linnaeus, 1758), (ii) Nereis (Hediste) diversicolor (Müller, 1776), (iii) Nereis virens (Sars, 1835), and (iv) Nereis falsa (Quatrefages, 1866) (Kies et al., 2020). The aim of the present study is to characterize the community ecology of Polychaeta in the Cheliff Estuary and to determine seasonal variations in ecological structure.

2. Material and Methods

2.1. Community data and preprocessing

Community composition analyses were based on abundance data of polychaete assemblages collected from multiple sites in the Cheliff Estuary under two seasonal conditions (wet and dry) (Kies et al., 2020; Figure 1; Table 1). Prior to analysis, species abundance matrices were carefully inspected and standardized. Column names were harmonized by removing accents, spaces, and special characters to ensure consistency across downstream analyses.

Figure 1
Map of studied site. See Kies et al. (2020).

Table 1
Geographical location and environmental parameters of studied site.

Site	Geographical location	Nature	Diameter (μm)	Fraction %
Salamandre	35°55’ N; 00°02’W	Sand (fine and coarse)	63 < d < 200	74
Sonactere	35°59’ N; 00°03’W	Silt	2 < d < 63	87
Chelif	36°05’ N; 00°07’W	Clay-mud	d < 63	89
Sokhara	36°07’ N; 00°09’W	Gravel	d<200	57
Hadjag	36°11’N; 00°17’W	Sand (fine and coarse)	63 < d < 200	68

• See: Kies et al. (2020).

2.2. All abundance values were converted to numeric format

Non-numeric entries were treated as missing values and subsequently replaced with zeros, assuming they represented true absences rather than measurement errors. From the resulting dataset, species richness, Shannon diversity index, Simpson diversity index, and Pielou’s evenness index were calculated following Magurran (2004).

To reduce the influence of highly abundant taxa and to meet the assumptions of Euclidean-based ordination methods, species abundance data were transformed using the Hellinger transformation (Legendre and Gallagher, 2001; Legendre and Legendre, 2012). This transformation preserves relative differences among samples while down-weighting dominant species, making it particularly suitable for ecological community datasets characterized by many zero values.

2.3. Dissimilarity measure

Patterns of community composition were quantified using Bray-Curtis dissimilarity, calculated from the Hellinger-transformed abundance matrix. The Bray-Curtis index is widely applied in benthic and community ecology because it is sensitive to species abundances while ignoring joint absences, making it appropriate for sparse ecological data (Clarke and Warwick, 2001).

2.4. Ordination analysis (NMDS)

Non-metric multidimensional scaling (NMDS) was used to visualize multivariate patterns in community composition among samples. NMDS was performed in two dimensions (k = 2) based on Bray-Curtis dissimilarities, with multiple random starts to ensure convergence on a stable solution. NMDS is a rank-based ordination technique that represents samples in a reduced-dimensional space while preserving the rank order of dissimilarities, making it robust to non-linear relationships commonly observed in ecological data (Kruskal, 1964; Clarke, 1993).

The goodness-of-fit of the ordination was evaluated using the stress value, which quantifies the mismatch between ordination distances and the original dissimilarities. Lower stress values indicate a better representation of the data in reduced-dimensional space (Clarke, 1993).

2.5. Hypothesis testing: PERMANOVA

Differences in community composition between seasonal conditions were tested using permutational multivariate analysis of variance (PERMANOVA), as implemented in the adonis2 function. PERMANOVA partitions variation in the multivariate dissimilarity matrix according to a specified explanatory factor (season) and assesses statistical significance through permutations rather than relying on distributional assumptions (Anderson, 2001).

Analyses were conducted using Bray-Curtis dissimilarities with 9,999 permutations under a reduced model. Effect sizes were quantified using the coefficient of determination (R²), representing the proportion of total multivariate variance explained by season.

2.6. Homogeneity of multivariate dispersion

Because PERMANOVA can be sensitive to differences in within-group dispersion, homogeneity of multivariate dispersion was assessed using the betadisper procedure. This method evaluates whether groups differ in their average distance to group centroids in multivariate space, providing a multivariate analogue to Levene’s test (Anderson, 2006). Statistical significance was evaluated using permutation tests.

2.7. Cluster analysis

To further explore similarity patterns among samples, hierarchical cluster analysis was performed using the unweighted pair-group method with arithmetic mean (UPGMA) based on Bray-Curtis dissimilarities. The resulting dendrogram was used as an exploratory tool to identify groups of samples with similar species composition and to complement the ordination results.

2.8. Software

All statistical analyses were conducted in R, using packages from the R ecosystem, particularly vegan for multivariate community analyses and ggplot2 for graphical visualization (R Core Team, 2024; Oksanen et al., 2024; Wickham, 2016).

3. Results

3.1. Alpha diversity

Alpha diversity indices were calculated for each site and season (wet vs. dry) (Table 2). Observed species richness was consistently four taxa in most samples, except at the Cheliff site during the dry season, where richness decreased to three taxa. Shannon diversity values ranged from 1.01 to 1.38, with the lowest value recorded at Cheliff in the dry season. Pielou’s evenness remained high across samples (0.92-0.99), indicating relatively even species distributions among site-season combinations (Table 3).

Table 2
Abundance by species for studied and season obtained in the present study.

Season: Wet	Chelif	Hadjaj	Salamandre	Sokhara	Sonactere
Nereis diversicolor	3.33 ± 1.63	3.67 ± 1.63	4.67 ± 2.88	2.17 ± 1.17	2.00 ± 1.41
Nereis falsa	3.50 ± 1.41	3.17 ± 2.48	2.83 ± 1.72	2.67 ± 0.82	2.67 ± 0.75
Nereis pelagica	2.33 ± 1.75	4.33 ± 3.01	2.50 ± 0.84	1.50 ± 1.22	2.00 ± 1.41
Nereis virens	3.00 ± 1.41	4.33 ± 2.34	5.00 ± 3.63	3.33 ± 1.75	1.17 ± 0.75
Season: Dry	Chelif	Hadjaj	Salamandre	Sokhara	Sonactere
Nereis diversicolor	0.17 ± 0.41	0.50 ± 0.55	0.50 ± 0.55	0.17 ± 0.41	0.17 ± 0.41
Nereis falsa	0.17 ± 0.41	0.33 ± 0.52	0.55 ± 2.84	0.33 ± 0.52	0.17 ± 0.41
Nereis pelagica	0.17 ± 0.41	0.17 ± 0.41	0.17 ± 0.41	0.17 ± 0.41	0.33 ± 0.52
Nereis virens	0.17 ± 0.41	0.50 ± 0.55	0.50 ± 0.55	0.17 ± 0.41	0.17 ± 0.41

Table 3
Results of diversity index observed for sited included in the present study.

Site	Season	Species richness	Shannon index	Simpson index	Pielou index
Salamandre	Wet	4	1.375	0.744	0.991
Sonactere	Wet	4	1.378	0.746	0.994
Chelif	Wet	4	1.342	0.728	0.968
Sokhara	Wet	4	1.346	0.730	0.971
Hadjag	Wet	4	1.347	0.731	0.972
Salamandre	Dry	4	1.337	0.722	0.964
Sonactere	Dry	4	1.280	0.695	0.923
Chelif	Dry	3	1.013	0.612	0.922
Sokhara	Dry	4	1.337	0.722	0.964
Hadjag	Dry	4	1.337	0.722	0.964

3.2. Ordination (NMDS)

Non-metric multidimensional scaling (NMDS) based on Bray-Curtis dissimilarities of the Hellinger-transformed community matrix resulted in an extremely low stress value (stress = 7.95 × 10⁻⁵). Although low stress values generally indicate an excellent fit, the near-zero stress suggests limited variation in the dataset, likely due to the small number of taxa and high similarity among samples. Consequently, the ordination should be interpreted with caution, as the apparent “perfect” configuration may reflect low ecological complexity rather than strong underlying gradients.

3.3. Community composition (PERMANOVA)

PERMANOVA revealed no significant differences in community composition between wet and dry seasons (adonis2: F = 0.3428, R² = 0.0411, p = 0.7908; 9,999 permutations). Season explained only 4.1% of the variation in Bray-Curtis dissimilarities, indicating that polychaete assemblages were largely similar between seasons.

3.4. Homogeneity of multivariate dispersion

The analysis of multivariate dispersion (betadisper) showed no significant differences in within-group dispersion between seasons at α = 0.05 (F = 2.895, p = 0.0938). Although the result was marginal at the 0.10 level, it supports the PERMANOVA outcome, suggesting that the lack of seasonal differences in community composition is unlikely to be driven by heterogeneity of dispersion.

3.5. Cluster analysis (UPGMA)

Hierarchical cluster analysis (UPGMA) based on Bray-Curtis dissimilarities illustrated similarity patterns among samples (Figure 2). In agreement with the weak seasonal signal detected by PERMANOVA and the limited variation indicated by the NMDS results, clustering revealed no clear separation between wet and dry seasons, instead reflecting overall similarity in species composition among samples.

Figure 2
Non-metric multidimensional scaling (NMDS) ordination of polychaete assemblages based on Bray-Curtis dissimilarities. Points represent individual samples (site × season), with colors and symbols indicating wet and dry seasons. Ellipses denote 95% confidence intervals around seasonal groups. The low stress value indicates an excellent fit of the ordination.

4. Discussion

Multivariate analyses consistently indicated that polychaete community composition remained largely invariant between wet and dry seasons across the studied sites. Both ordination and permutation-based hypothesis testing converged on this result, suggesting weak seasonal forcing on assemblage structure in the Cheliff Estuary. This consistency emphasizes the importance of evaluating not only statistical outcomes but also data structure and information content when interpreting community-level patterns in ecological studies.

The NMDS ordination based on Bray-Curtis dissimilarities yielded an extremely low stress value, indicating an excellent fit in reduced-dimensional space. However, such near-zero stress values can also arise in datasets characterized by low taxonomic richness and limited compositional variability, as observed in the present study. Under these conditions, ordination results primarily reflect high similarity among samples rather than strong ecological gradients, underscoring the need for cautious interpretation of apparently “perfect” ordination configurations in small or homogeneous community datasets (Oksanen et al., 2024; Zuur et al., 2007).

Consistent with the ordination results, PERMANOVA detected no significant seasonal differences in community composition, with season explaining only a small proportion of the total multivariate variance. Importantly, the absence of statistical significance should not be interpreted as a lack of ecological signal, but rather as evidence that any seasonal effects are weak relative to within-group variability and the limited dimensionality of the dataset. Recent methodological syntheses highlight that PERMANOVA is most informative when effect sizes, dispersion patterns, and data structure are interpreted jointly, rather than relying solely on p-values (Anderson and Walsh, 2013; Warton et al., 2015).

The assessment of homogeneity of multivariate dispersion revealed no significant differences between seasons, supporting the robustness of the PERMANOVA findings. This result suggests that the observed non-significant seasonal effect reflects similarity in group centroids rather than heterogeneity in within-group dispersion, a distinction increasingly recognized as critical for reliable interpretation of multivariate ecological analyses (Anderson et al., 2017; Ramette, 2007).

From an ecological informatics perspective, these findings illustrate how multivariate, data-driven analytical workflows can robustly identify cases in which community structure is relatively stable and weakly responsive to categorical predictors. In such contexts, increasing temporal replication, incorporating continuous environmental variables, or integrating functional and trait-based approaches may yield greater explanatory power than additional hypothesis testing applied to the same taxonomic matrix (Mouillot et al., 2011; Green et al., 2022).

Overall, the present study demonstrates that polychaete assemblages in the Cheliff Estuary exhibit high compositional similarity across seasons and highlights the value of transparent, assumption-aware multivariate analysis pipelines for ecological inference in small to moderate community datasets.

Acknowledgements

The present study was financed by project MECESUP UCT 0804, and University Abd El Hamid Ibn Badis.

Data Availability Statement

Data are available from request to the main and correspondence author.

References

• ANDERSON, M.J. and WALSH, D.C., 2013. Permanova, anosim, and the Mantel test in the face of heterogeneous dispersions. Ecological Monographs, vol. 83, no. 4, pp. 557-574. https://doi.org/10.1890/12-2010.1
  » https://doi.org/10.1890/12-2010.1
• ANDERSON, M.J., 2001. A new method for non‐parametric multivariate analysis of variance. Austral Ecology, vol. 26, no. 1, pp. 32-46. https://doi.org/10.1111/j.1442-9993.2001.01070.pp.x
  » https://doi.org/10.1111/j.1442-9993.2001.01070.pp.x
• ANDERSON, M.J., 2006. Distance-based tests for homogeneity of multivariate dispersions. Biometrics, vol. 62, no. 1, pp. 245-253. https://doi.org/10.1111/j.1541-0420.2005.00440.x PMid:16542252.
  » https://doi.org/10.1111/j.1541-0420.2005.00440.x
• ANDERSON, M.J., WALSH, D.C.I., ROBERT CLARKE, K., GORLEY, R.N. and GUERRA-CASTRO, E., 2017. Some solutions to the multivariate Behrens-Fisher problem for dissimilarity-based analyses. Australian & New Zealand Journal of Statistics, vol. 59, no. 1, pp. 57-79. https://doi.org/10.1111/anzs.12176
  » https://doi.org/10.1111/anzs.12176
• BENKADDOUR, B., ABDELMALEK, F., ADDOU, A., NOGUER, T., AUBERT, D. and VOUVE, F., 2019. Assessment of anthropogenic and natural factors on Cheliff River Waters (North-West of Algeria) at two contrasted climatic seasons. International Journal of Environmental Research, vol. 13, no. 6, pp. 925-941. https://doi.org/10.1007/s41742-019-00223-7
  » https://doi.org/10.1007/s41742-019-00223-7
• CLARKE, K.R. and WARWICK, R.M., 2001. Change in Marine Communities: an approach to statistical analysis and interpretation 2nd ed. Plymouth: Plymouth Marine Laboratory.
• CLARKE, K.R., 1993. Non‐parametric multivariate analyses of changes in community structure. Australian Journal of Ecology, vol. 18, no. 1, pp. 117-143. https://doi.org/10.1111/j.1442-9993.1993.tb00438.x
  » https://doi.org/10.1111/j.1442-9993.1993.tb00438.x
• GREEN, S.J., BROOKSON, C.B., HARDY, N.A. and CROWDER, L.B., 2022. Trait-based approaches to global change ecology: moving from description to prediction.Proceedings of the Royal Society B: Biological Sciences, vol. 289, no. 1971, pp. 20220071. https://doi.org/10.1098/rspb.2022.0071
  » https://doi.org/10.1098/rspb.2022.0071
• KADDECHE, H., NOUNE, F., DZIZI, S., CHAIB, N., BOUDJELLAB, Z.E. and BLANCO, S., 2022. Determinant factors of diatom assemblage’s distribution along the Coastal Central Constantine (Northeastern Algeria). Aquatic Ecology, vol. 56, no. 4, pp. 1245-1269. https://doi.org/10.1007/s10452-022-09980-8
  » https://doi.org/10.1007/s10452-022-09980-8
• KIES, F., KERFOUF, A., ELEGBEDE, I., MATEMILOLA, S., DE LOS RIOS ESCALANTE, P., KHORCHANI, A. and SAVARI, S., 2020. Assessment of the coastal and estuarine environment quality of western Algeria using the bioindicator Polychaeta; the genus Nereis. Journal of Materials & Environmental Sciences, vol. 11, no. 9, pp. 1472-1481.
• KRUSKAL, J.B., 1964. Nonmetric multidimensional scaling: A numerical method. Psychometrika, vol. 29, no. 2, pp. 115-129. https://doi.org/10.1007/BF02289694
  » https://doi.org/10.1007/BF02289694
• LEGENDRE, P. and GALLAGHER, E.D., 2001. Ecologically meaningful transformations for ordination of species data. Oecologia, vol. 129, no. 2, pp. 271-280. https://doi.org/10.1007/s004420100716 PMid:28547606.
  » https://doi.org/10.1007/s004420100716
• LEGENDRE, P. and LEGENDRE, L., 2012. Numerical ecology (3rd ed.). Amsterdam: Elsevier.
• MAGURRAN, A.E., 2004. Measuring biological diversity. Oxford: Blackwell Publishing.
• MOUILLOT, D., VILLÉGER, S., SCHERER-LORENZEN, M. and MASON, N.W., 2011. Functional structure of biological communities predicts ecosystem multifunctionality. PLoS One, vol. 6, no. 3, e17476. https://doi.org/10.1371/journal.pone.0017476 PMid:21423747.
  » https://doi.org/10.1371/journal.pone.0017476
• OKSANEN, J., SIMPSON, G., BLANCHET, F., KINDT, R., LEGENDRE, P., MINCHIN, P., O'HARA, R., SOLYMOS, P., STEVENS, M., SZOECS, E., WAGNER, H., BARBOUR, M., BEDWARD, M., BOLKER, B., BORCARD, D., BORMAN, T., CARVALHO, G., CHIRICO, M., DE CACERES, M., DURAND, S., EVANGELISTA, H., FITZJOHN, R., FRIENDLY, M., FURNEAUX, B., HANNIGAN, G., HILL, M., LAHTI, L., MARTINO, C., MCGLINN, D., OUELLETTE, M., RIBEIRO CUNHA, E., SMITH, T., STIER, A., TER BRAAK, C. and WEEDON, J. 2024 [accessed 5 January 2026]. vegan: Community Ecology Package. R package version 2.8-0 [software]. Available from: https://github.com/vegandevs/vegan
  » https://github.com/vegandevs/vegan
• R CORE TEAM, 2024. R: a language and environment for statistical computing Vienna: R Foundation for Statistical Computing.
• RAMETTE, A., 2007. Multivariate analyses in microbial ecology: pitfalls and promises. FEMS Microbiology Ecology, vol. 62, no. 2, pp. 142-160. https://doi.org/10.1111/j.1574-6941.2007.00375.x PMid:17892477.
  » https://doi.org/10.1111/j.1574-6941.2007.00375.x
• SAJIDEVI, A.M., ABRAHAM, B. and BENCHAMIN, D., 2024. Environmental drivers of mesozooplankton dynamics in the Munroe Island, adjacent to Ashtamudi Estuary, India. Environmental Monitoring and Assessment, vol. 196, no. 10, pp. 963. https://doi.org/10.1007/s10661-024-13129-5 PMid:39304582.
  » https://doi.org/10.1007/s10661-024-13129-5
• SHARIFINIA, M., TAHERIZADEH, M., NAMIN, J.I. and KAMRANI, E., 2025. How do macrobenthic-based indices respond to anthropogenic pressures? Insights from estuaries of the Persian Gulf and Gulf of Oman. Deep-sea Research. Part II, Topical Studies in Oceanography, vol. 221, pp. 105481. https://doi.org/10.1016/j.dsr2.2025.105481
  » https://doi.org/10.1016/j.dsr2.2025.105481
• WARTON, D.I., BLANCHET, F.G., O’HARA, R.B., OVASKAINEN, O., TASKINEN, S., WALKER, S.C. and HUI, F.K., 2015. So many variables: joint modeling in community ecology. Trends in Ecology & Evolution, vol. 30, no. 12, pp. 766-779. https://doi.org/10.1016/j.tree.2015.09.007 PMid:26519235.
  » https://doi.org/10.1016/j.tree.2015.09.007
• WICKHAM, H. 2016. ggplot2: Elegant Graphics for Data Analysis New York: Springer.
• ZUUR, A.F., IENO, E.N. and SMITH, G.M., 2007. Analyzing Ecological Data New York: Springer. https://doi.org/10.1007/978-0-387-45972-1
  » https://doi.org/10.1007/978-0-387-45972-1

Edited by

• Editor:
  Takako Matsumura Tundisi

Publication Dates

• Publication in this collection
  20 July 2026
• Date of issue
  2026

History

• Received
  05 Jan 2026
• Accepted
  17 Feb 2026