Monitoring well-being and changing environmental conditions in coastal communities: development of an assessment method

The intersection of social and environmental forces is complex in coastal communities. The well-being of a coastal community is caught up in the health of its environment, the stability of its economy, the provision of services to its residents, and a multitude of other factors. With this in mind, the project investigators sought to develop an approach that would enable researchers to measure these social and environmental interactions. The concept of well-being proved extremely useful for this purpose. Using the Gulf of Mexico as a regional case study, the research team developed a set of composite indicators to be used for monitoring well-being at the county-level. The indicators selected for the study were: Social Connectedness, Economic Security, Basic Needs, Health, Access to Social Services, Education, Safety, Governance, and Environmental Condition. For each of the 37 sample counties included in the study region, investigators collected and consolidated existing, secondary data representing multiple aspects of objective well-being. To conduct a longitudinal assessment of changing wellbeing and environmental conditions, data were collected for the period of 2000 to 2010. The team focused on the Gulf of Mexico because the development of a baseline of well-being would allow NOAA and other agencies to better understand progress made toward recovery in communities affected by the Deepwater Horizon oil spill. However, the broader purpose of the project was to conceptualize and develop an approach that could be adapted to monitor how coastal communities are doing in relation to a variety of ecosystem disruptions and associated interventions across all coastal regions in the U.S. and its Territories. The method and models developed provide substantial insight into the structure and significance of relationships between community well-being and environmental conditions. Further, this project has laid the groundwork for future investigation, providing a clear path forward for integrated monitoring of our nation’s coasts. The research and monitoring capability described in this document will substantially help counties, local organizations, as well state and federal agencies that are striving to improve all facets of community well-being.

Estimating relative abundance from catch and effort data, using neural networks

We develop and test a method to estimate relative abundance from catch and effort data using neural networks. Most stock assessment models use time series of relative abundance as their major source of information on abundance levels. These time series of relative abundance are frequently derived from catch-per-unit-of-effort (CPUE) data, using general linearized models (GLMs). GLMs are used to attempt to remove variation in CPUE that is not related to the abundance of the population. However, GLMs are restricted in the types of relationships between the CPUE and the explanatory variables. An alternative approach is to use structural models based on scientific understanding to develop complex non-linear relationships between CPUE and the explanatory variables. Unfortunately, the scientific understanding required to develop these models may not be available. In contrast to structural models, neural networks uses the data to estimate the structure of the non-linear relationship between CPUE and the explanatory variables. Therefore neural networks may provide a better alternative when the structure of the relationship is uncertain. We use simulated data based on a habitat based-method to test the neural network approach and to compare it to the GLM approach. Cross validation and simulation tests show that the neural network performed better than nominal effort and the GLM approach. However, the improvement over GLMs is not substantial. We applied the neural network model to CPUE data for bigeye tuna (Thunnus obesus) in the Pacific Ocean.

Premiers résultats sur l'excrétion et la production du zooplancton de la Lagune Ebrié (Côte d'Ivoire)

Biomass and metabolic rates (total nitrogen and phosphorus excretion and respiration) were measured at 4 stations, representative of the lagoon environment, during high-water (Oct-Nov), dry (Dec-Jan) and rainy (July) seasons. In low-salinity waters (4o/oo) Acartia clausi is almost the only species, whereas a marine and diversified fauna is brought in from the ocean during the dry season. O/NT and O/PT atomic ratios between respiration (O) and total nitrogen (NT) and phosphorus (PT) excretions are high (15.1 and 111, respectively) and show a marked hydrocarbon feeding of zooplankton. Production was assessed from excretion via the net growth efficiency coefficient, K2 , calculated from N/P ratios for particles (a1), zooplankton excretion (a2) and constitution (a3). Daily productivity indices (i.e. daily production/biomass ratio) are high and equivalent to 1.2-3.8 day turn-over times. These high values may be ascribed to high temperatures (26.5-30 C) and phytoplankton richness (surface chlorophyll 'a' concentrations are always greater than 4 mg/m-3). Finally, the paper deals with trophic relationships between phyto- and zooplankton (ingestion /primary production ratio and transfer coefficient) and the question of relationships between zooplankton and predators.

Introduction

The 1992 PACLIM meeting featured the Long-Term Ecological Research (LTER) program, sponsored by the National Science Foundation. Ranging from hot to cold and wet to dry climatic regimes, these 18 sites are attempting to understand the web of relationships in different locations as communities evolve over time scales of years to decades to centuries. During this time they are subject to external forcings, including those that vary smoothly and somewhat predictably, like the seasons, upon which are superimposed random "shocks" of various magnitudes.