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Macrofauna play a key role in the functioning of mangrove ecosystems. Nevertheless, our understanding of the diversity and functional structure of macrofaunal communities across different habitats in the mangrove forests of the Persian Gulf is limited. In this study, we investigated species diversity and biological trait patterns of macrofauna in different mangrove-associated habitats, i.e., encompassing actual mangrove forests, and adjacent Beaches and Creeks, which exhibit different levels of habitat heterogeneity. Samples were collected from the different habitats in five different locations, over four seasons. A total of 122 macrofauna taxa were identified. The diversity of species was higher in summer than in winter. In the Beach habitats, species diversity showed an increasing trend from land toward the mangrove, whereas in Creek habitats diversity decreased from the Creek toward the mangrove. Multivariate community analysis showed differences in the distribution of abundant species and biological traits across all habitats. Deposit-feeding, crawlers, medium-size, and free-living were the dominant trait modalities in all habitats. The similarities within habitats over the four seasons had the same specific pattern of species and biological trait abundance in the Beach and the Creek, increasing from the non-covered habitat into the mangrove trees. Although many species shared similar traits, the abundance-driven differences in trait expression between habitats showed the importance of habitat filtering. The results of this study will be useful in the conservation of mangrove forests and they give a deeper understanding of the ecological patterns and functions of benthic macrofaunal communities in the Persian Gulf.

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The growth pattern and reproductive biology of Acanthopagrus latus were studied to derive information required for their management in the south part of Iran, Persian Gulf. Samples collected monthly from October 2014 to September 2015. Parameter values of the Von Bertalanffy growth function fit to length frequency data (males and females combined) were estimated as k = 0.23 per year, L∞ =50.4 cm (LF), t o = −0.7 years. Total, natural and fishing mortality (males and females combined) calculated as Z=0.87 per year, M=0.57 per year and F=0.3 per year, the exploitation rate were estimated for this species as E=0.45 per year. According to collected length data, A. latus exploited below the mean size at which females achieved first sexual maturity (24.4 cm LF). The length-weight relationships of males and females together estimated as, W=0.0939*LF 2.57 and exhibited negative allometric growth. Gonado-somatic index estimation indicated February as a spawning time for this species, with the main spawning period enduring to Jun. In general, parameters values obtained, characterized this species of relatively slow growing and long lived, which should be taken into consideration for sustainable management and exploitation for preventing overexploitation.

We examined long-term variability in the abundance of German Bight soft bottom macro-zoobenthos together with major environmental factors (sea surface temperature, winter NAO index, salinity, phosphate, nitrate and silicate) using one of the most comprehensive ecological long-term data sets in the North Sea . Two techniques, Min/Max Autocorrelation Factor Analysis (MAFA) and Dynamic Factor Analysis (DFA) were used to identify underlying common trends in the macrofaunal time series and the relationships between this series and environmental variables. These methods are particularly suitable for relatively short (N15-25 years), non-stationary multivariate data series. Both MAFA and DFA identify a common trend in German Bight macrofaunal abundance i.e. a slight decrease (1981-mid-1990s) followed by a sharp trough in the late 1990s. Subsequently, scores increased again towards 2011. Our analysis indicates that winter temperature and North Atlantic Oscillation were the predominant environmental drivers of temporal variation in German Bight macrofaunal abundance. The techniques applied here are suitable tools to describe temporal fluctuations in complex and noisy multiple time series data and can detect distinct shifts and trends within such time series.