Modeling Life History and Population Dynamics of American Lobster and Atlantic Sea Scallops in a Warming Gulf of Maine

Climate change is impacting many marine species distributions, life histories, and behaviors, as well as their associated fisheries and overall production. This is perhaps especially true for the Gulf of Maine (GOM). Here, warming rates are exceeding a vast majority of the world’s oceans. This highly dynamic system supports myriad species, but is both economically recognized and culturally known for its Atlantic sea scallop (Placopecten magellanicus) and American lobster (Homarus americanus) fisheries. This dissertation examines the influence of regional climate change on these species in an effort to predict how these stocks and their fisheries may change in the future. For scallops, this was accomplished by examining and aging shells collected throughout the GOM to determine if spatial and temporal differences in growth patterns could be explained by regional thermal habitats and salinities. For lobster, a five-step process was developed. Firstly, I conducted a simulation study to evaluate the stock assessment model performance under possible changes in lobster molting probability, lobster molt increment size, and size-at-maturity as a result of changes in thermal habitat. Secondly, using two temperature covariates important for early survival and development, a stock-wide, thermally-explicit Beverton-Holt stock-recruit relationship was estimated for the GOM. This relationship served as the basis of a framework to be used by management to test what levels of spawning biomass are necessary in the current year to achieve the desired levels of recruitment in the near future. Thirdly, a delta-generalized linear mixed model was used to predict lobster spatial density throughout the GOM. This spatial density informed a stock-wide abundance index which was used to replace the traditionally used design-based indices in the stock assessment model. Fourthly, a stock forecasting model was developed that could utilize the aforementioned stock-recruit relationship and consequences of ignoring this thermal influence on recruitment estimations were explored. Lastly, a bioclimate envelope model was used to determine relationships of multiple habitat covariates to lobster abundance from trawl survey data before using these relationships to map and forecast lobster habitat in the GOM

Spatial dynamics of Maine lobster landings in a changing coastal system

Continued warming of oceans has caused global shifts in marine species distributions. This can result in changes in the spatial distribution of landings and have distributional impacts on marine resource-dependent communities. We evaluated the spatial dynamics of American lobster (Homarus americanus) landings in coastal Maine, which supports one of the most valuable U.S. fisheries. We coupled a bioclimate envelope model and a generalized additive model to project spatial dynamics of lobster landings under possible climate scenarios. This coupled model was then used to forecast future lobster habitat suitability based on IPCC RCP climate scenarios and predict distributions of fishery landings from this projected lobster habitat suitability. The historical spatial distribution of fishery landings shows the highest proportional landings in Maine’s Southern (southwest) regions. The current distribution of landings shows higher proportional landings in Downeast (northeast) regions with the highest proportional landings in Midcoast (middle) regions. Our results suggest that while the proportion of landings in each zone will remain stable, changes in habitat suitability in the spring and fall will reduce total landings. Future habitat suitability is projected to decrease in spring but increase in fall in Downeast areas. Downeast landings are projected to decrease in the next 30 years, then increase over the subsequent 80 years, depending on RCP scenarios and abundance regimes. Midcoast landings are projected to decrease while Southcoast landings are expected to stay constant. This study develops an approach to link climate change effects to fishery landings. These findings have long-term implications for sustainable, localized management of the Maine lobster fishery in a changing climate

A framework to incorporate environmental effects into stock assessments informed by fishery-independent surveys: a case study with American lobster (Homarus americanus)

Stock assessments for a majority of the world’s fisheries often do not explicitly consider the effects of environmental conditions on target species, which can raise model uncertainty and potentially reduce forecasting quality. Model-based abundance indices were developed using a delta generalized linear mixed model that incorporates environmental variability for use in stock assessment to understand how the incorporation of environmental variability impacts our understanding of population dynamics. For this study, multiple model-based abundance indices were developed to test the incorporation of environmental covariates in a length-structured assessment of the American lobster (Homarus americanus) stock in the Gulf of Maine – Georges Bank on the possible improvement of stock assessment quality. Comparisons reveal that modelled indices with environmental covariates appear to be more precise than traditional indices, but model performance metrics and hindcasted fishery statuses revealed that these improvements to indices may not necessarily mean an improved assessment. Model-based abundance indices are not intrinsically better than design-based indices and should be tested for each species individually.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Comparing a suite of surplus-production-based stock status identification approaches and management procedures

Different approaches have been used to identify fishery stock status when only biomass and catch data are available. However, the performance of the approaches may be affected by the uncertainties derived from different sources (e.g., model misspecification, stock productivity changing, observation error). Here, we propose that the observed biomass associated with the highest calculated surplus production can be used as an indicator (Bhighest_S) to identify stock status. We develop a management procedure (MP) atop a widely used method (i.e., Gcontrol) by incorporating Bhighest_S in the harvest control rule. Two simulations are conducted to compare the stock status identification approaches and corresponding MPs. Using Bhighest_S to identify stock status performs better than surplus production modeling approaches in simulated regime shift scenarios. Compared with the old version of Gcontrol, incorporating Bhighest_S or estimated BMSY in the harvest control rule provides more stable and higher yields. This study contributes to the development and evaluation of indicator-based stock status identification approaches and MPs that only require biomass and catch data.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author