Guidelines for the conservation and restoration of seagrasses in the United States and adjacent waters

Seagrass ecosystems are protected under the federal "no-net-loss" policy for wetlands and form one of the most productive plant communities on the planet, performing important ecological functions. Seagrass beds have been recognized as a valuable resource critical to the health and function of coastal waters. Greater awareness and public education, however, is essential for conservation of this resource. Tremendous losses of this habitat have occurred as a result of development within the coastal zone. Disturbances usually kill seagrasses rapidly, and recovery is often comparatively slow. Mitigation to compensate for destruction of existing habitat usually follows when the agent of loss and responsible party are known. Compensation assumes that ecosystems can be made to order and, in essence, trades existing functional habitat for the promise of replacement habitat. While ~lant ingse agrass is not technically complex, there is no easy way to meet the goal of maintaining or increasing seagrass acreage. Rather, the entire process of planning, planting and monitoring requires attention to detail and does not lend itself to oversimplification.

A general framework for integrating environmental time series into stock assessment models: model description, simulation testing, and example

We present a method to integrate environmental time series into stock assessment models and to test the significance of correlations between population processes and the environmental time series. Parameters that relate the environmental time series to population processes are included in the stock assessment model, and likelihood ratio tests are used to determine if the parameters improve the fit to the data significantly. Two approaches are considered to integrate the environmental relationship. In the environmental model, the population dynamics process (e.g. recruitment) is proportional to the environmental variable, whereas in the environmental model with process error it is proportional to the environmental variable, but the model allows an additional temporal variation (process error) constrained by a log-normal distribution. The methods are tested by using simulation analysis and compared to the traditional method of correlating model estimates with environmental variables outside the estimation procedure. In the traditional method, the estimates of recruitment were provided by a model that allowed the recruitment only to have a temporal variation constrained by a log-normal distribution. We illustrate the methods by applying them to test the statistical significance of the correlation between sea-surface temperature (SST) and recruitment to the snapper (Pagrus auratus) stock in the Hauraki Gulf–Bay of Plenty, New Zealand. Simulation analyses indicated that the integrated approach with additional process error is superior to the traditional method of correlating model estimates with environmental variables outside the estimation procedure. The results suggest that, for the snapper stock, recruitment is positively correlated with SST at the time of spawning.

Time series analyses of biological and environmental variables for Suisun Bay and the Sacramento and San Joaquin rivers

EXTRACT (SEE PDF FOR FULL ABSTRACT): Arima analysis was used to compute cross-correlations between principal component axes that described environmental variables, chlorophyll concentration and zooplankton density for the Sacramento and San Joaquin rivers and Suisun Bay. ... Cross-correlations among the time series may provide information about links between environmental and biological variables within the estuary and the possible influence of climate.

Sediment flux at selected coastal sites: proposed time series measurment by particle traps

Particle flux in the ocean reflects ongoing biological and geological processes operating under the influence of the local environment. Estimation of this particle flux through sediment trap deployment is constrained by sampler accuracy, particle preservation, and swimmer distortion. Interpretation of specific particle flux is further constrained by indeterminate particle dispersion and the absence of a clear understanding of the sedimentary consequences of ecosystem activity. Nevertheless, the continuous and integrative properties of the particle trap measure, along with the logistic advantage of a long-term moored sampler, provide a set of strategic advantages that appear analogous to those underlying conventional oceanographic survey programs. Emboldened by this perception, several stations along the coast of Southern California and Mexico have been targeted as coastal ocean flux sites (COFS).

Snow accumulation time series and their climatic interpretation

EXTRACT (SEE PDF FOR FULL ABSTRACT): Several snow accumulation time series derived from ice cores and extending over 3 to 5 centuries are examined for spatial and temporal climatic information. ... A significant observation is the widespread depression of net snow accumulation during the latter part of the "Little Ice Age". This initially suggests sea surface temperatures were significantly depressed during the same period. However, prior to this, the available core records indicate generally higher than average precipitation rates. This also implies that influences such as shifted storm tracks or a dustier atmosphere may have been involved. Without additional spatial data coverage, these observations should properly be studied using a coupled (global) ocean/atmosphere GCM.

Effects of observation time on interpretation of climatic time series - a need for consistency

Much of what we know about the climate of the United States is derived from data gathered under the auspices of the cooperative climate network. Particular aspects of the way observations are taken can have significant influences on the values of climate statistics derived from the data. These influences are briefly reviewed. The purpose of this paper is to examine their effects on climatic time series. Two other items discussed are: (1) a comparison of true (24-hour) means with means derived from maximums and minimums only, and (2) preliminary work on the times of day at which maximums and minimums are set.