Effect of deuterium-depleted water on reproduction of rainbow trout

During the artificial reproduction of salmonides, the fecundity can be increased either by improving the viability of spermatozoa, or by extending the time period during which a roe micropile remains open, thus allowing its fecundation. Practically, this can be achieved by the use of some fertilising techniques suitable for fish species. Here, we show that the reproduction of rainbow trout in a 1:1 solution of deuterium-depleted water and distilled water led to a significant increase in survival of roes during their embryonic development. Moreover, the addition of glucose and fructose into the deuterium-depleted fertilising solutions led to a further increase in roe survival during embryonic development. The increase in survival is mainly explained by an increase in the motility of spermatozoa.     

SELECTING TREND TESTS FOR WATER QUALITY SERIES WITH SERIAL CORRELATION AND MISSING VALUES1

ABSTRACT: The use of nonparametric tests for monotonic trend has flourished in recent years to support routine water quality data analyses. The validity of an assumption of independent, identically distributed error terms is an important concern in selecting the appropriate nonparametric test, as is the presence of missing values. Decision rules are needed for choosing between alternative tests and for deciding whether and how to pre-process data before trend testing. Several data pre-processing procedures in conjunction with the Mann-Kendall tau and the Seasonal Kendall test (with and without serial correlation correction) are evaluated using synthetic time series with generated serial correlation and missing data. A composite test (pre-testing for serial correlation followed by one of two trend tests) is evaluated and was found to perform satisfactorily.     

The Spatiotemporal Pattern of Fires in Northern Taiga Larch Forests of Central Siberia

The periodicity of fires in larch forests of Evenkia and their relationship with landscape elements have been studied. Cross-sections with “burns” in them caused by past fires have been analyzed in 72 test plots; the fire chronology encompassed the period from the 15th to the 20th century. The between-fire intervals (BFIs) have been calculated by two methods: (I) on the basis of burns alone and (II) on the basis of burns and the start of growth of the new generation of larch after the earliest fire. The BFI depends on local orographic features; it is 86 ± 11 (105 ± 12), 61 ± 8 (73 ± 8), 139 ± 17 (138 ± 18), and 68 ± 14 (70 ± 13) years for northeastern slopes, southwestern slopes, bogs, and flatlands, respectively. The mean BFIs calculated by methods I and II are 82 ± 7 and 95 ± 7 years, respectively. The permafrost horizon rises at a mean rate of 0.3 cm per year after a forest fire. It has been shown that the number of fires regularly peaks at periods of 36 and 82 years. There is also a temporal trend in fire frequency: the mean BFI was approximately 100 years in the 19th century and 65 years in the 20th century.     

Using fuzzy set theory to address the uncertainty of susceptibility to drought

This paper presents the technical aspects of a new methodology for assessing the susceptibility of society to drought. The methodology consists of a combination of inference modelling and fuzzy logic applications. Four steps are followed: (1) model input variables are selected—these variables reflect the main factors influencing susceptibility in a social group, population or region, (2) fuzzification—the uncertainties of the input variables are made explicit by representing them as ‘fuzzy membership functions’, (3) inference modelling—the input variables are used to construct a model made up of linguistic rules, and (4) defuzzification—results from the model in linguistic form are translated into numerical form, also through the use of fuzzy membership functions. The disadvantages and advantages of this methodology became apparent when it was applied to the assessment of susceptibility from three disciplinary perspectives: Disadvantages include the difficulty in validating results and the subjectivity involved with specifying fuzzy membership functions and the rules of the inference model. Advantages of the methodology are its transparency, because all model assumptions have to be made explicit in the form of inference rules; its flexibility, in that informal and expert knowledge can be incorporated through ‘fuzzy membership functions’ and through the rules in the inference model; and its versatility, since numerical data can be converted to linguistic statements and vice versa through the procedures of ‘fuzzification’ and ‘defuzzification’.