Food Quality Affects Secondary Consumers Even at Low Quantities: An Experimental Test with Larval European Lobster

The issues of food quality and food quantity are crucial for trophic interactions. Although most research has focussed on the primary producer – herbivore link, recent studies have shown that quality effects at the bottom of the food web propagate to higher trophic levels. Negative effects of poor food quality have almost exclusively been demonstrated at higher food quantities. Whether these negative effects have the same impact at low food availability in situations where the majority if not all of the resources are channelled into routine metabolism, is under debate. In this study a tri-trophic food chain was designed, consisting of the algae Rhodomonas salina, the copepod Acartia tonsa and freshly hatched larvae of the European lobster Homarus gammarus. The lobster larvae were presented with food of two different qualities (C∶P ratios) and four different quantities to investigate the combined effects of food quality and quantity. Our results show that the quality of food has an impact on the condition of lobster larvae even at very low food quantities. Food with a lower C∶P content resulted in higher condition of the lobster larvae regardless of the quantity of food. These interacting effects of food quality and food quantity can have far reaching consequences for ecosystem productivity

FRDC Project 1998/302 – Rock Lobster Enhancement and Aquaculture Subprogram:

Towards establishing techniques for large scale harvesting of pueruli and obtaining a better understanding of mortality rates B.F. Phillips, R. Melville-Smith, M. Rossbach, Y.W. Cheng

Towards establishing techniques for large scale harvesting of pueruli and obtaining a better understanding of mortality rates

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Resource Occurrence and Productivity in Existing and Proposed Wind Energy Lease Areas on the Northeast US Shelf

States in the Northeast United States have the ambitious goal of producing more than 22 GW of offshore wind energy in the coming decades. The infrastructure associated with offshore wind energy development is expected to modify marine habitats and potentially alter the ecosystem services. Species distribution models were constructed for a group of fish and macroinvertebrate taxa resident in the Northeast US Continental Shelf marine ecosystem. These models were analyzed to provide baseline context for impact assessment of lease areas in the Middle Atlantic Bight designated for renewable wind energy installations. Using random forest machine learning, models based on occurrence and biomass were constructed for 93 species providing seasonal depictions of their habitat distributions. We developed a scoring index to characterize lease area habitat use for each species. Subsequently, groups of species were identified that reflect varying levels of lease area habitat use ranging across high, moderate, low, and no reliance on the lease area habitats. Among the species with high to moderate reliance were black sea bass (Centropristis striata), summer flounder (Paralichthys dentatus), and Atlantic menhaden (Brevoortia tyrannus), which are important fisheries species in the region. Potential for impact was characterized by the number of species with habitat dependencies associated with lease areas and these varied with a number of continuous gradients. Habitats that support high biomass were distributed more to the northeast, while high occupancy habitats appeared to be further from the coast. There was no obvious effect of the size of the lease area on the importance of associated habitats. Model results indicated that physical drivers and lower trophic level indicators might strongly control the habitat distribution of ecologically and commercially important species in the wind lease areas. Therefore, physical and biological oceanography on the continental shelf proximate to wind energy infrastructure development should be monitored for changes in water column structure and the productivity of phytoplankton and zooplankton and the effects of these changes on the trophic system.</jats:p

965. Partnering with State Health Departments: A Road Map for Collaboration Using Public Health Enhanced HIV/AIDS Reporting System (eHARS)

Abstract Background Academic and public health partnerships are a critical component of the Ending the HIV Epidemic: A Plan for America (EHE). The Enhanced HIV/AIDS Reporting System (eHARS) is a standardized document-based surveillance database used by state health departments to collect and manage case reports, lab reports, and other documentation on persons living with HIV. Innovative analysis of this data can inform targeted, evidence-based interventions to achieve EHE objectives. We describe the development of a distributed data network strategy at an academic institution in partnership with public health departments to identify geographic differences in time to HIV viral suppression after HIV diagnosis using eHARS data. Figure 1. Distributed Data Network Methods This project was an outgrowth of work developed at the University of Alabama at Birmingham Center for AIDS Research (UAB CFAR) and existing relationships with the state health departments of Alabama, Louisiana, and Mississippi. At a project start-up meeting which included study investigators and state epidemiologists, core objectives and outcome measures were established, key eHARS variables were identified, and regulatory and confidentiality procedures were examined. The study methods were approved by the UAB Institutional Review Board (IRB) and all three state health department IRBs. Results A common data structure and data dictionary across the three states were developed. Detailed analysis protocols and statistical code were developed by investigators in collaboration with state health departments. Over the course of multiple in-person and virtual meetings, the program code was successfully piloted with one state health department. This generated initial summary statistics, including measures of central tendency, dispersion, and preliminary survival analysis. Conclusion We developed a successful academic and public health partnership creating a distributed data network that allows for innovative research using eHARS surveillance data while protecting sensitive health information. Next, state health departments will transmit summary statistics to UAB for combination using meta-analytic techniques. This approach can be adapted to inform delivery of targeted interventions at a regional and national level. Disclosures All Authors: No reported disclosures </jats:sec

Building use‐inspired species distribution models: Using multiple data types to examine and improve model performance

Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery dependent (conventional mark-recapture tags, fisheries observer records) and two fishery independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage the strengths of individual data types while statistically accounting for limitations, such as sampling biases

Building use‐inspired species distribution models: using multiple data types to examine and improve model performance

Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery dependent (conventional mark-recapture tags, fisheries observer records) and two fishery independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage the strengths of individual data types while statistically accounting for limitations, such as sampling biases.</p