Emergy-based indicators for evaluating ecosystem health: A case study of three benthic ecosystem networks influenced by coastal upwelling in northern Chile (SE Pacific coast)

Abstract

It has been hypothesized that ecosystem health describes the state in which all processes operating within an ecosystem are functioning at a level of optimum efficiency to maximize system empower. In this study, systems analysis of networks and information flows is used within this definition of ecosystem health to assess the condition of the benthic ecosystems in three coastal bays in northern Chile. These highly productive ecosystems are characterized by the inflow of cold, nutrient-rich waters of low oxygen concentration derived from coastal upwelling of deep waters and the interruption of upwelling flow during El Niño events when warmer waters with higher oxygen and lower nutrient concentrations enter these coastal systems. Also, these ecosystems support important artisanal benthic fisheries and are affected by industrial activities in the coastal zone. Energy Systems Theory (EST) and Emergy Analysis (EA) (Emergy is defined as the sum of the available energy (i.e., energy with the potential to do work) of one kind, e.g., solar joules, which is used-up, directly and indirectly, within an ecosystem for the production of goods and services were applied to quantify the health of these benthic ecosystems and evaluate differences in their structure, organization and functional capacities, which are related to their emergy signatures. The marked dominance of these benthic ecosystems by nitrate from upwelling resulted in unbalanced emergy signatures, suggesting less development and system diversity compared to other coastal ecosystems with more balanced emergy signatures. Macro-descriptors and network properties, such as emergy-based ascendency and the quality-adjusted Shannon diversity index (the proportion of total system emergy flow allocated to each component) were highest for Mejillones Bay, followed by Antofagasta and then Tongoy Bay. The Average Mutual Information (AMI) index adjusted for energy quality (the number and diversity of emergy-weighted interactions among the system components) and the emergy-based A/C (ascendency to capacity) ratio (this index has been suggested as a maturity index), were higher for Tongoy Bay, suggesting functional differences in health among the three ecosystems. Thus, the emergy-based macro descriptors and other indicators used in our analysis indicate that the benthic networks examined have different structural and functional characteristics that lead to different characterizations of their states of health. As a result of this complexity, management policies should be implemented within a systemic context for analysis that considers all the factors determining the relative health of each ecosystem.