Long-Term Monitoring of Fecal Steroid Hormones in Female Snow Leopards (Panthera uncia) during Pregnancy or Pseudopregnancy

Knowledge of the basic reproductive physiology of snow leopards is required urgently in order to develop a suitable management conditions under captivity. In this study, the long-term monitoring of concentrations of three steroid hormones in fecal matter of three female snow leopards was performed using enzyme immunoassays: (1) estradiol-17β, (2) progesterone and (3) cortisol metabolite. Two of the female animals were housed with a male during the winter breeding season, and copulated around the day the estradiol-17β metabolite peaked subsequently becoming pregnant. The other female was treated in two different ways: (1) first housed with a male in all year round and then (2) in the winter season only. She did not mate with him on the first occasion, but did so latter around when estradiol-17β metabolite peaked, and became pseudopregnant. During pregnancy, progesterone metabolite concentrations increased for 92 or 94 days, with this period being approximately twice as long as in the pseudopregnant case (31, 42, 49 and 53 days). The levels of cortisol metabolite in the pseudopregnant female (1.35 µg/g) were significantly higher than in the pregnant females (0.33 and 0.24 µg/g) (P<0.05). Similarly, during the breeding season, the levels of estradiol-17β metabolite in the pseudopregnant female (2.18 µg/g) were significantly higher than those in the pregnant females (0.81 and 0.85 µg/g) (P<0.05). Unlike cortisol the average levels of estradiol-17β during the breeding season were independent of reproductive success

Analysis of the Socioeconomic Situation in the Regions of Russia

The rapid socioeconomic changes that are taking place in the regions of Russia have necessitated the development of special computer technology for their monitoring. This technology is based upon the creation and monthly update of a data base, which contains a small number of key socioeconomic indicators, a special program based upon cluster analysis, and a special program for the automatic classification of regions that displays the results on a screen or a laser color printer in the form of a geographical map of Russia, color-coded for each class. We chose a somewhat unusual scale of classes to display the results and deliberately rejected the traditional scale of ranks that can be used to determine each region's rating. The scale of classes does not indicate which region is "better" or "worse." Each simply has its own complex of socioeconomic characteristics, the meaningful interpretation of which can range from the evaluation of the degree of investment risk to the creation of prerequisites for analyzing the possible results of future choices.