Abstract

The noble pen shell Pinna nobilis L. is the largest, endemic, critically endangered, and protected bivalve of the Mediterranean Sea. Effective conservation and management strategies for this species highly depend on understanding how environmental change and anthropogenic pressures impact its physiology and thereby ecological function, population persistence, and survival. Dynamic Energy Budget (DEB) theory offers a valuable mechanistic modelling framework for capturing how an organism acquires and utilizes available energy for growth, maturation, development and reproduction throughout its life cycle, while accounting for environmental conditions. In this study we parameterized and compared two types of DEB models using limited literature data: a standard model that accounts for morphological metamorphosis only, and a model that through metabolic acceleration between birth and metamorphosis captures physiological changes occurring in the larval life stage. The model with metabolic acceleration performed better, successfully simulating life history traits, growth, and reproduction of P. nobilis. We used the model to predict how food availability implemented through functional response affects growth, maturation, and reproduction of the species throughout its lifespan. We found that (i) abundant food had little effect on the size at maturation, (ii) maximum fecundity at ultimate age doubled compared to typically lower food availability in the wild, (iii) puberty could not be reached below the food availability corresponding to functional response value of 0.164, and (iv) energy allocated to reproduction was positively correlated with both bivalve size and food availability. Accounting for allometric growth observed in P. nobilis did not affect the findings, prompting us to recommend that isometric growth be assumed when modelling the bivalve using DEB. The model presented here is the first full-life cycle bioenergetic model made for P. nobilis. It can be used standalone for predicting energy budget of individuals at specific environmental conditions, or as a building block for modeling populations and ecosystems under various environmental scenarios. The model can readily incorporate other environmental factors relevant to changes in physiology and energy allocation, such as oxygen and pH.

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