Incorporating ecological process and environmental change into spiny lobster population models using a spatially-explicit, individual-based approach

Abstract

Marine fisheries and the ecosystems that sustain them are increasingly beset by environmental deterioration, yet traditional fishery models used for stock prediction typically handle these dynamics poorly if at all. To do so requires the integration of spatio-temporal change in environmental quality and its subsequent effects on habitat suitability and life history dynamics. Spatially-explicit, individual-based simulation models are particularly well suited to this task and, although they are seeing increased use in fisheries ecology and management, this approach has seen limited application in crustacean fisheries. In 1993, we began development of a spatially-explicit individual-based model (IBM) describing the recruitment of Caribbean spiny lobster ( Panulirus argus) in the Florida Keys, Florida (USA) to investigate the impact of regional changes in environmental quality, habitat structure and postlarval supply on lobster recruitment. The shallow coastal waters of the Florida Keys ecosystem have experienced an unprecedented series of environmental perturbations over the past decade. Seagrass die-offs, cyanobacteria blooms, sponge die-offs and dramatic changes in salinity have occurred and these potentially impact the recruitment of spiny lobsters in the region via both direct and indirect means. Here I provide an overview of the unique approach that we have used to examine these dynamics, an approach that links environmental events that occur on large scales (e.g. changes in habitat structure and salinity) with their population-level consequences for lobsters via impacts that operate on the individual-level. Although not applicable in all situations, spatially-explicit IBMs should see wider use in crustacean fishery applications because of both the ecological insight they yield and their ability to integrate data across hierarchical scales of organization.