The role of squid for food web structure and community level metabolism. Denéchère et al., 2024, Ecological modelling

Code and Data linked to the publication.

General information:

• Designed using Matlab R2021b
• FEISTY/ Contains the source code of the FEISTY-squid version developped. see also [1].
• Data/ Contains all the data used in the publication: All the data are in .mat format and .csv
  • Data from Denéchère et al., 2022 are only available in .mat. Data in .csv are available from the publication [2]
• Baserun_Publication.m Contains the code for the core of the publication
• Baserun_Supplementary.m Contains the code for the supplementary materials
• Additionnal code/ Contains additional code for producing the figures

Abstract

Squid differ from fish by their high growth rate, short life span, and feeding behavior. Their fast life strategy is thought to impose a high predation pressure on zooplankton, fish, and other squid preys, and a rapid transfer of energy to upper trophic levels of marine food webs. However, there is a lack of understanding of howsquid’s fast life cycle affects the food-web structure, which is needed to project squid biomass across marine regions under shifting climatic conditions. Here, we examine the role of squid on community metabolism and biomass by collecting data on squid somatic growth and incorporating squid in a size- and trait-based fish community model. We show that squid have a 5 times higher average somatic growth rate than fish. Due to their high food demands, squid are constrained to regions of high pelagic secondary production. The presence of squid in these systems is associated with a reduction in total consumer biomass. This decline is caused by an increase in community-level respiration losses associated with squid. Our results indicate that squid might have a large impact on ecosystem structure even at relatively low standing stock biomass. Consequently, the recent proliferation of squid in ecosystems around the world is likely to have significant ecological and socio-economicimpacts.

Reference

[1] van Denderen, P.D., Petrik, C.M., Stock, C.A., Andersen, K.H., 2021. Emergent globalbiogeography of marine fish food webs. Global Ecol. Biogeogr. 30 (9), 1822–1834

[2] Denéchère, R., van Denderen, P.D., Andersen, K.H., 2022. Deriving population scalingrules from individual-level metabolism and life history traits. Amer. Nat. 199 (4),564–575.