Guide to best practices for ocean CO2 measurements

CHAP 1 - Introduction to the Guide CHAP 2 - Solution chemistry of carbon dioxide in sea water CHAP 3 - Quality assurance CHAP 4 - Recommended standard operating procedures (SOPs) SOP 1 - Water sampling for the parameters of the oceanic carbon dioxide system SOP 2 - Determination of total dissolved inorganic carbon in sea water SOP 3a - Determination of total alkalinity in sea water using a closed-cell titration SOP 3b - Determination of total alkalinity in sea water using an open-cell titration SOP 4 - Determination of p(CO2) in air that is in equilibrium with a discrete sample of sea water SOP 5 - Determination of p(CO2) in air that is in equilibrium with a continuous stream of sea water SOP 6a - Determination of the pH of sea water using a glass/reference electrode cell SOP 6b - Determination of the pH of sea water using the indicator dye m-cresol purple SOP 7 - Determination of dissolved organic carbon and total dissolved nitrogen in sea water SOP 7 en Español - Determinacion de carbono organico disuelto y nitrogeno total disuelto en agua de mar SOP 11 - Gravimetric calibration of the volume of a gas loop using water SOP 12 - Gravimetric calibration of volume delivered using water SOP 13 - Gravimetric calibration of volume contained using water SOP 14 - Procedure for preparing sodium carbonate solutions for the calibration of coulometric CT measurements SOP 21 - Applying air buoyancy corrections SOP 22 - Preparation of control charts SOP 23 - Statistical techniques used in quality assessment SOP 24 - Calculation of the fugacity of carbon dioxide in the pure gas or in air CHAP 5 - Physical and thermodynamic data Errata - to the hard copy of the Guide to best practices for ocean CO2 measurements

Catches per unit of effort of bigeye tuna: a new analysis with regression trees and simulated annealing

ENGLISH: We analyzed catches per unit of effort (CPUE) from the Japanese longline fishery for bigeye tuna (Thunnus obesus) in the central and eastern Pacific Ocean (EPO) with regression tree methods. Regression trees have not previously been used to estimate time series of abundance indices fronl CPUE data. The "optimally sized" tree had 139 parameters; year, month, latitude, and longitude interacted to affect bigeye CPUE. The trend in tree-based abundance indices for the EPO was similar to trends estimated from a generalized linear model and fronl an empirical model that combines oceanographic data with information on the distribution of fish relative to environmental conditions. The regression tree was more parsimonious and would be easier to implement than the other two nl0dels, but the tree provided no information about the nlechanisms that caused bigeye CPUEs to vary in time and space. Bigeye CPUEs increased sharply during the mid-1980's and were more variable at the northern and southern edges of the fishing grounds. Both of these results can be explained by changes in actual abundance and changes in catchability. Results from a regression tree that was fitted to a subset of the data indicated that, in the EPO, bigeye are about equally catchable with regular and deep longlines. This is not consistent with observations that bigeye are more abundant at depth and indicates that classification by gear type (regular or deep longline) may not provide a good measure of capture depth. Asimulated annealing algorithm was used to summarize the tree-based results by partitioning the fishing grounds into regions where trends in bigeye CPUE were similar. Simulated annealing can be useful for designing spatial strata in future sampling programs. SPANISH: Analizamos la captura por unidad de esfuerzo (CPUE) de la pesquería palangrera japonesa de atún patudo (Thunnus obesus) en el Océano Pacifico oriental (OPO) y central con métodos de árbol de regresión. Hasta ahora no se han usado árboles de regresión para estimar series de tiempo de índices de abundancia a partir de datos de CPUE. EI árbol de "tamaño optimo" tuvo 139 parámetros; ano, mes, latitud, y longitud interactuaron para afectar la CPUE de patudo. La tendencia en los índices de abundancia basados en árboles para el OPO fue similar a las tendencias estimadas con un modelo lineal generalizado y con un modelo empírico que combina datos oceanográficos con información sobre la distribución de los peces en relación con las condiciones ambientales. EI árbol de regresión fue mas parsimonioso y seria mas fácil de utilizar que los dos otros modelos, pero no proporciono información sobre los mecanismos que causaron que las CPUE de patudo valiaran en el tiempo y en el espacio. Las CPUE de patudo aumentaron notablemente a mediados de los anos 80 y fueron mas variables en los extremos norte y sur de la zona de pesca. Estos dos resultados pueden ser explicados por cambios en la abundancia real y cambios en la capturabilidad. Los resultados de un arbal de regresión ajustado a un subconjunto de los datos indican que, en el OPO, el patudo es igualmente capturable con palangres regulares y profundos. Esto no es consistente con observaciones de que el patudo abunda mas a profundidad e indica que clasificación por tipo de arte (palangre regular 0 profundo) podría no ser una buena medida de la profundidad de captura. Se uso un algoritmo de templado simulado para resumir los resultados basados en el árbol clasificando las zonas de pesca en zonas con tendencias similares en la CPUE de patudo. El templado simulado podría ser útil para diseñar estratos espaciales en programas futuros de muestreo. (PDF contains 45 pages.)

Coastal plant growth and CO2 enrichment: Can the productivity of black needle rush keep pace with sea level rise?

The rate of sea level change has varied considerably over geological time, with rapid increases (0.25 cm yr-1) at the end of the last ice age to more modest increases over the last 4,000 years (0.04 cm yr-1; Hendry 1993). Due to anthropogenic contributions to climate change, however, the rate of sea level rise is expected to increase between 0.10 and 0.25 cm year-1 for many coastal areas (Warrick et al. 1996). Notwithstanding, it has been predicted that over the next 100 years, sea levels along the northeastern coast of North Carolina may increase by an astonishing 0.8 m (0.8 cm yr-1); through a combination of sea-level rise and coastal subsidence (Titus and Richman 2001; Parham et al. 2006). As North Carolina ranks third in the United States with land at or just above sea level, any additional sea rise may promote further deterioration of vital coastal wetland systems. (PDF contains 4 pages)