Patterns and processes of recently settled and juvenile American lobster (Homarus americanus) in the lower Bay of Fundy

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University of New Brunswick


In this thesis I quantified, over 4 years, spatial patterns of early life phases of American lobsters (Homarus americanus) at multiple spatial scales in the lower Bay of Fundy, ranging from 0.55 m2 cobble-filled collectors to large regions 127-674 km2 , and I conducted a field experiment and a modeling exercise to investigate which environmental (biological and physical) factors are responsible for the dominant patterns I observed. Benthic recruits and the following two juvenile phases (emergent and vagile lobsters) showed similar spatial patterns at the scales investigated (most patchiness at the “area” scale), but these differed markedly from the pattern (no patchiness at any of the scales investigated) displayed by the fourth life-history phase (adolescents). The number of stage IV larvae (i.e., postlarvae) caught in the plankton by light traps was significantly related to spatial variability in benthic recruitment at the area scale, but the number of stage I larvae was not. The modelling exercise identified four variables that were related to benthic recruitment patterns: North Atlantic Oscillation Index (NAO) over the larval period, fetch of the study area, juvenile abundance, and sea surface temperature over the larval period. Whereas some of these associations appear to be spurious, others suggest, as did the light trap study, that factors affecting postlarval supply are determinants of the spatial patterns observed. Also, the NAO index showed promise in predicting inter-annual variation in benthic recruitment patterns. This work highlights the importance of sparsely distributed nursery grounds to benthic recruitment of this species. As rapid climate change is affecting the distribution of the species and conditions for larval development, continuing monitoring of recruitment is of high and increasing importance. Such monitoring will also allow testing of the predictive ability of the model developed in chapter three, which is an important next step for this work.