OAK STANDS AFFECTED BY Polyporaceae AND THEIR VIABILITY IN THE SOUTH-WEST OF THE CENTRAL RUSSIAN UPLAND

Abstract

One of the reasons for the weakened state of English oak Quercus robur L. in coppice oak forests of the southern forest-steppe of European Russia is trunk and butt rots, the causative agents of which are the representatives of pathogenic polypore fungi (PPF). This article is devoted to the study of the state of oak stands due to the prevalence of PPF on the oak. The research was carried out in the south-western part of the Central Russian Upland in the seasons of 2009-2017. The research objects were coppice oak stands in upland and ravine oak forests with English oak Q. robur L. and the PPF species associated with oak. The communities include the following species: Fistulina hepatica, Laetiporus sulphureus, Fomitiporia robusta, Inocutis dryophila, Pseudoinonotus dryadeus, Daedalea quercina, Hapalopilus croceus, Grifola frondosa, Fomes fomentarius, Polyporus squamosus. Field surveys were carried out according to a complex methodology using the methods of traditional phytopathology and modern mycocenology and the adapted methods of mycopathocenological surveys. The methodology of cameral research consisted of general scientific methods of systematization of primary empirical data using the methods of descriptive statistics, correlation and regression analysis and estimation of the difference of sample means. As a result of the studies, a significant positive correlation was revealed between the value of the weighted average category of the state of viability of oak stands (CV1-6) and the value of the total prevalence of species from the PPF communities on the oak (R): r = 0.388 (Student's t-test t(actual) = 2.562; the critical value of Student's t-test t(st) = 2.021; the number of degrees of freedom k = 37; the probability of incorrect estimation P = 0.05). Statistical models reflecting the interdependencies between these values were obtained: CV1-6(R): Y = = 0.0268X + 2.2290; R(CV1-6): Y = 5.6262X - 5.7204. These models allow estimating changes of each of the specified values by changing the other.