Sustainability of the Peruvian anchoveta supply chains from sea to shelf: towards a new strategy for optimal use of resources

The research performed a sustainability assessment of supply chains of the anchoveta (Engraulis ringens) in Peru. The corresponding fisheries lands 6.5 million t per year, of which <2% is rendered into products for direct human consumption (DHC) and 98% reduced into feed ingredients (fishmeal and fish oil, FMFO), for export. Several industries compete for the anchoveta resources, generating local and global impacts. The need for understanding these dynamics, towards sustainability-improving management and policy recommendations, determined the development of a sustainability assessment framework: 1) characterisation and modelling of the systems under study (with Life Cycle Assessment and other tools) including local aquaculture, 2) calculation of sustainability indicators (i.e. energy efficiency, nutritional value, socio-economic performances), and 3) sustainability comparison of supply chains; definition and comparison of alternative exploitation scenarios. Future exploitation scenarios were defined by combining an ecosystem and a material flow models: continuation of the status quo (Scenario 1), shift towards increased proportion of DHC production (Scenario 2), and radical reduction of the anchoveta harvest in order for other fish stocks to recover and be exploited for DHC (Scenario 3). Scenario 2 was identified as the most sustainable. Management and policy recommendations include improving of: controls for compliance with management measures, sanitary conditions for DHC, landing infrastructure for small- and medium-scale (SMS) fisheries; the development of a national refrigerated distribution chain; and the assignation of flexible tolerances for discards from different DHC processes.

A behavioral ecology of shermen: hidden stories from trajectory data in the Northern Humboldt Current System

This work proposes an original contribution to the understanding of shermen spatial behavior, based on the behavioral ecology and movement ecology paradigms. Through the analysis of Vessel Monitoring System (VMS) data, we characterized the spatial behavior of Peruvian anchovy shermen at di erent scales: (1) the behavioral modes within shing trips (i.e., searching, shing and cruising); (2) the behavioral patterns among shing trips; (3) the behavioral patterns by shing season conditioned by ecosystem scenarios; and (4) the computation of maps of anchovy presence proxy from the spatial patterns of behavioral mode positions. At the rst scale considered, we compared several Markovian (hidden Markov and semi-Markov models) and discriminative models (random forests, support vector machines and arti cial neural networks) for inferring the behavioral modes associated with VMS tracks. The models were trained under a supervised setting and validated using tracks for which behavioral modes were known (from on-board observers records). Hidden semi-Markov models performed better, and were retained for inferring the behavioral modes on the entire VMS dataset. At the second scale considered, each shing trip was characterized by several features, including the time spent within each behavioral mode. Using a clustering analysis, shing trip patterns were classi ed into groups associated to management zones, eet segments and skippers' personalities. At the third scale considered, we analyzed how ecological conditions shaped shermen behavior. By means of co-inertia analyses, we found signi cant associations between shermen, anchovy and environmental spatial dynamics, and shermen behavioral responses were characterized according to contrasted environmental scenarios. At the fourth scale considered, we investigated whether the spatial behavior of shermen re ected to some extent the spatial distribution of anchovy. Finally, this work provides a wider view of shermen behavior: shermen are not only economic agents, but they are also foragers, constrained by ecosystem variability. To conclude, we discuss how these ndings may be of importance for sheries management, collective behavior analyses and end-to-end models.