A framework for practical and rigorous impact monitoring by field managers of marine protected areas

Monitoring is a crucial component of conservation in marine protected areas (MPAs) as it allows managers to detect changes to biodiversity and to infer cause of change. However, the complexities of sampling designs and associated statistical analyses can impede implementation of monitoring by managers. Two monitoring frameworks commonly used in marine environments are statistical testing and parameter estimation. For many managers these two approaches fail to help them detect change and infer causation for one or more reasons: the complexity of the statistical test, no decision-making structure and a sampling design that is suboptimal. In collaboration with marine park rangers in Egypt, we instigated a monitoring framework to detect impacts by snorkelers in a pragmatic but scientifically rigorous way. First, we used a literature review to define causal criteria to facilitate inference. This was essential because our sampling design was suboptimal due to a lack of baseline data and there was only one impact site. Second, we established a threshold level of coral damage that if exceeded would trigger management to reduce the impact of snorkelers. This provided a clear decision-making structure. Third, we estimated effect sizes with confidence intervals to detect change. For the field managers, this approach to detection was easier to understand than assessing a null hypothesis and provided critical information for decision making. At no stage during the short study period did snorkelers cause damage that exceeded the threshold and thus mitigation was not required. In situations of technical and financial constraints this framework will increase the implementation of effective impact monitoring for many activities in MPAs and enhance management of marine biodiversity.     

Recycling and utilization of metabolic wastes for energy production is an index of biochemical adaptation of fish under environmental pollution stress

The activity levels of lactate dehydrogenase andlactic acid were assessed in various tissues of the fishduring exposure to lethal concentration of group-IIPyrethroids (deltamethrin, cypermethrin, fenvalerate andfluvalinate) for a period of 72 hours. The results showedsteady increased levels in all the tissues (blood,muscle, brain and liver) with response patterncharacteristic of their own. The increased LDH activityand lactic acid levels indicate the shifting of aerobicglycolysis to anaerobiosis and its further utilizationfor energy production during adaptation to toxic stress.     

Using a neural network approach and time series data from an international monitoring station in the Yellow Sea for modeling marine ecosystems

The international marine ecological safety monitoring demonstration station in the Yellow Sea was developed as a collaborative project between China and Russia. It is a nonprofit technical workstation designed as a facility for marine scientific research for public welfare. By undertaking long-term monitoring of the marine environment and automatic data collection, this station will provide valuable information for marine ecological protection and disaster prevention and reduction. The results of some initial research by scientists at the research station into predictive modeling of marine ecological environments and early warning are described in this paper. Marine ecological processes are influenced by many factors including hydrological and meteorological conditions, biological factors, and human activities. Consequently, it is very difficult to incorporate all these influences and their interactions in a deterministic or analysis model. A prediction model integrating a time series prediction approach with neural network nonlinear modeling is proposed for marine ecological parameters. The model explores the natural fluctuations in marine ecological parameters by learning from the latest observed data automatically, and then predicting future values of the parameter. The model is updated in a “rolling” fashion with new observed data from the monitoring station. Prediction experiments results showed that the neural network prediction model based on time series data is effective for marine ecological prediction and can be used for the development of early warning systems.     

Quality control and quality assurance for particle size distribution measurements at an urban monitoring station in Augsburg, Germany

Long-term observations of atmospheric constituents such as aerosol particles are increasingly needed to assess their impact on climate and human health. In contrast to particle mass concentration (MC), there are currently no standardized quality control (QC) and quality assurance (QA) procedures for the measurement of particle size distribution (PSD). This study describes some fundamental QC and QA procedures associated with the collection and evaluation of a 2 year dataset between 2005 and 2006 at an urban background monitoring site in Augsburg, Germany. The considered parametres include ambient PSD between 3 nm and 10 microm (merged from a twin differential mobility and an aerodynamic particle sizer, TDMPS and APS, respectively) as well as total particle number (TNC), length (LC) and MC determined by independent instruments. The hourly 1st and 0th moment of PSD showed good correlations with the independently measured LC (R(2) = 0.86) and TNC (R(2) = 0.79), respectively, the deviation for LC with 4% and for TNC with 22% being rather small. The volume concentration (3rd moment) of hourly measured PSD and the resultant MC (when assuming a realistic apparent density of 1.5 g cm(-3)) correlated well with the independently measured MC of PM(2.5) or PM(10) (R(2) > 0.86) and showed only small deviation from PM(2.5) (1%) or PM(10) (5%), respectively. The study demonstrates that the described QC and QA measures define both a high accuracy of the PSD measurements and their long-term comparability against data obtained in similar measurement programmes.