Critical connectivity thresholds and the role of temperature in parasite metapopulations

Managing the spread of parasite populations is a ubiquitous challenge for managers of natural and farmed systems. A specific current example are “sea lice”, parasitic marine organisms that live on both wild and farmed Salmon sp., posing a persistent challenge to aquaculture operations in high latitude countries. Lice reproduce with pelagic larvae that spread in the water column, meaning that fish farm sites are connected to one another as a result of their relative location and the response of the organism to the environment. Much work has investigated population connectivity for specific systems, using computational models that link the physical and biological processes affecting larval transport. However, although probability and frequency of dispersal events may be calculated, the specific implications for outbreaks on sites is not entirely clear. We therefore used population models to describe the stage-structured dynamics of connected sea lice population networks, with the goal of establishing the critical connection strength (probability of successful dispersal) for occurrence of an outbreak. Our initial investigation deals with the case of “self-infection” of a single isolated population. Vital rates of life-cycle stages respond to temperature, and as a consequence so does critical connectivity. A dispersal success probability of around 0.01 is required for sea lice population development at the optimal temperature, although this is dependent on parameterisation. The analysis is extended to networks of sites interacting via dispersal, and we show how fallow sites can lead to substantial reductions in population growth rates. Our results provide an important, and to date, missing component in assessment of the environmental impact of marine aquaculture sites. They provide a basis upon which site operators and regional planners can appraise specific developments in the context of their surroundings, and may provide a means for future development of adaptive management strategies.