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Ichthyoplanktonic dataset
Spatial distribution of anchovy early-life stage was estimated from ichthyoplanktonic surveys in the GSA 16 from 2009 to 2012. During each survey, the same systematic sampling was carried out following a regular grid of stations (1/10° × 1/10° on the continental shelf and 1/5° × 1/5° further offshore). Mesozooplanktonic samples were collected in the study area using a bongo net (40-cm opening) towed obliquely from the surface to a 100-m depth, equipped with a 200-µm mesh size net. In each tow, the volume of filtered water was estimated using mechanical flowmeters (General Oceanics Inc., FL, USA). Over the period included in the analyses for anchovy eggs data, the average number of sampling stations was 154.
Samples were immediately fixed after collection and preserved in a 70% alcohol solution for further analysis in laboratory by stereomicroscopy. Anchovy larvae were identified in a land-based laboratory and otoliths were extracted for the age determination. Both sagittae were extracted using insect pins and fixed on a slide using a mounting medium for microscopy. Daily rings were counted at 1000× magnification (oil immersion) starting from the spawning mark31; each otolith was read blind (i.e., with no size or collection location information available) by a single reader43. When all otoliths were completed, the process was repeated. If the difference in age between the 2 readings was {rho }_{min}^{Chl}), where ({rho }_{min}^{Chl}=frac{1}{2}K) and (K) = 0.09 mg m−3, i.e., half of the saturation value for anchovy larvae feeding50.
This idea is justified by the relationship between the pelagic growth rate and the Chlorophyll concentration: only those individuals that travel within Chlorophyll-rich marine waters have a significant survival probability. In this way, we have a criterion to rule out all the final positions of the simulated backward trajectories, which have poor or null Chlorophyll content.
Bioenergetic modeling
DEB represents a reliable and powerful tool to mechanistically describe the whole life cycle of an organism individual performance and to make predictions of life-history traits27, 40,50,51,52. In particular, DEB is a complex mechanistic model that relies on several differential equations that are solved to obtain the final amount of energy allocated to vital functions, such as metabolic maintenance and to growth and reproduction53. Here we adapted this model to the European anchovy and we adopted the standard version of the DEB model, which considers one reserve, one structure compartment and isomorphic growth54.
The energy gathered through feeding processes is stored in a reserve pool, from which it is allocated according to the κ-rule: part of the energy (κ) sustains the somatic tissues and the growth of structures, while the rest (1 − κ) maintains the maturity level and maturation or gamete production in adults. Temperature controls the rates of all energetic flows and it follows the Arrhenius rules within the thermal-tolerance range26,54,55. The Type II functional response56 instead models the relationship between food density and ingestion rate54. DEB theory therefore allows, through the explicit modeling of energy and mass fluxes through organisms, to derivate individual performance in terms of the most important life-history traits of a species such as, for instance, the total reproductive output (TRO) and maximum length.
In the present study, we followed a well-tested spatially explicit contextualized approach already successfully adopted in several companion studies27,40,57. This approach consists in running DEB models in each spatial pixel of the study area using organismal body temperature (which is assumed to be similar to the Sea Surface Temperature, as extracted through satellite imagery for every single pixel) and environmental food availability. Food availability was expressed as density (wet mass mg m−3), which for anchovy primarily comprise zooplankton, and obtained as a spatially continuous dataset on the distribution of food throughout the study area40. Nevertheless, to run DEB model in each pixel with local temperature and food density is a long computational process. Thus, due to the large number of pixels of the study area, we moved the standard DEB model from the original Kooijman’s Matlab code (https://www.bio.vu.nl/thb/)—once adapted it using anchovy DEB parameters40—to an R code. In doing so, we were able to automatize and speed up the process since R coding improve the computational effort and allows running DEB models at larger spatial and temporal scales27,40,58. The coding re-arrangement was performed by the Ecology Lab of the University of Palermo, which is one among the DEB node of the DEB world net (https://www.debtheory.org/wiki/index.php?title=DEBnet).
Our simulations were restricted to the continental shelf, based on depth (from 0 to 200 m below sea level). A vector polygon grid feature class of 346 square cells (having a size of 0.11° × 0.11° [~ 150 km2]) covering the study area was used. Food availability is an important forcing of the model and, for anchovy, primarily comprises zooplankton52. Since locally collected data for zooplankton were spatially and temporally fragmented due to sampling effort, we followed the recent approach proposed by Strömberg et al.59 and applied by Mangano et al.28,40 to obtain a continuous (space and time wise) dataset on the distribution of food throughout the study area and across time (i.e. weekly Net Primary Productivity was transformed into wet mass of zooplankton). Due to the short life span of the anchovy (~ 4 years), we extracted daily sea surface temperatures (SST; 1 km resolution) from JPL MUR SST data (2010) (https://podaac.jpl.nasa.gov/Multi-scale_Ultra-high_Resolution_MUR-SST) over a time range of 4 years (2009–2012) for each cell (see the recent proof of concept by Mangano et al.40 for a detailed presentation of the model and its validation).
TRO values are validated by using in situ data, collected during ad-hoc oceanographic surveys40. This approach assumes stationarity in biological parameters (i.e. DEB parameter values estimated for populations in one location/time are valid for populations elsewhere). We adopted the DEB parameters designed for the Mediterranean anchovy by Pethybridge et al.50, the degree of uncertainty of our simulations was low (see Fig. 2 in Mangano et al.40) and sufficiently robust to allow reliable predictions of anchovy life-history traits. We are aware that phenotypic plasticity and/or local adaptation have the potential to increase the degree of uncertainty of modeling outcomes and we suggest the use of DEB parameters values that, to the extent possible, realistically match those of local populations rather than global (species specific) parameters. More

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