Linking surface hydrodynamics to planktonic ecosystem: the case study of the ichthyoplanktonic assemblages in the Central Mediterranean Sea

Abstract

Oceanographic processes play a key role in influencing the structure of the marine planktonic ecosystems. Taking advantage of the quasi-simultaneous collection of a large ichthyoplanktonic dataset in different regions of the Central Mediterranean Sea (Italian/Maltese, Tunisian and Libyan waters), this study aimed at the identification of the main environmental drivers that control the structure of the larval fish assemblages. Spatial distribution and taxa composition were related to physical forcings (geostrophic currents and wind stress) and environmental conditions (bottom depth, temperature, salinity, chlorophyll-a concentration). ANOSIM and SIMPER identified contribution of fish taxa to the average Bray-Curtis dissimilarity among regions. In Italian and Libyan waters, two assemblages (neritic and oceanic) were identified, while a mixed assemblage characterized only some stations. Two neritic and one oceanic assemblages were discriminated in Tunisian waters. Random Forest classification model highlighted the essential role of the bathymetry, while Lagrangian simulations evidenced the action of the hydrodynamics in mixing neritic and oceanic assemblages in the Italian/Maltese and partially in Libyan waters. These findings highlighted the importance of the multidisciplinary approach and shed light on the potential value of the ichthyoplanktonic surveys for the assessment of the state of the marine ecosystem and the conservation of the fishery resources.

Unveiling the Relationship Between Sea Surface Hydrographic Patterns and Tuna Larval Distribution in the Central Mediterranean Sea

“…Larvae originating from the spawning activity in the southeastern sector of the study area accumulate with larvae advected by the Atlantic waters from the offshore western area, causing the higher concentrations observed in the retention area for the three species investigated. In the same area, this advection mechanism has also been verified for other species in previous studies (García Lafuente et al, 2002;Cuttitta et al, 2018;Patti et al, 2018). So, definitively the dynamics of this frontal area seems to play a key role for larval retention in the eastern zone.…”

“…In general, tuna larvae are found in oligotrophic areas, probably as a strategy to encounter fewer predators (Bakun, 2013). Despite the eastern area being less productive than the western area, local enrichment processes such as cold filaments from the upwelling zone together with the thermohaline front and eddies could concentrate prey, improving feeding opportunities (Cuttitta et al, 2016(Cuttitta et al, , 2018Torri et al, 2018). These oceanographic processes could create patches of higher food abundance that, together with scarcity of predators, could increase the chances of larval survival.…”

Seascape connectivity of European anchovy in the Central Mediterranean Sea revealed by weighted Lagrangian backtracking and bio-energetic modelling

“…Ad hoc investigations in this particular field may enable managers to design and apply specific strategies, beyond geographical borders, also promoting a more effective management of fishery resources 6 , 7 . In this context, dynamic oceanographic structures have a crucial role 8 – 12 . The effect of transport efficiency on the early life stage of fishes is, indeed, at the base of the understanding distributions of small pelagic species showing short life span, high fecundity, and a vulnerable planktonic stage among their life-history traits 13 .…”

Dolphin whistles can be useful tools in identifying units of conservation

“…In order to deal with a dataset that violates the a priori assumption of normality and homoscedasticity, we used the Random Forest (RF) machine learning method [78]. This methodological approach has been successfully implemented on structurally similar data in recent ecological studies [79][80][81][82], in which the high heterogeneity of the ecological data encourages the use of approaches not based on a priori assumptions regarding the distribution of input data. We implemented six RF models with three species (bottlenose dolphin, short-beaked common dolphin, striped dolphin) and two response variables (genetic units, geographical groups).…”