Age estimation of black locust (Robinia pseudoacacia) based on morphometric traits

The use of black locust (Robinia pseudoacacia L.) is still controversial, especially in managed forests, due to its invasive nature. has been proven effective reclamation degraded lands when native species are not an alternative the face climate change and desertification. Therefore, importance European ecosystems remains a matter debate. Of course, it adventitious originating from another continent. However, ecological characteristics natural range very useful for ensuring restoration vegetation cover areas that have experienced significant anthropogenic pressure. This large number important features make agent forestry system provide environmental economic benefits. status as species, i.e. one capable uncontrolled spread, speculative. Such conclusion requires various studies, including those identifying exact age plants based on morphometric parameters. In our study, we tested hypothesis plant height trunk diameter will allow accurate assessment age. were measured zone spontaneous self-seeding planted 60-years-old forest. dispersed towards fallow land, formed corner agricultural field. spreading began 15 20 years ago. mainly propagated by seedlings. An expert estimated visually. trees cut obtain cross sections account annual rings. A total 68 examined characters. Black communities during self-dispersal represented individuals different ages. spatial pattern distribution ages can reveal temporal dynamics formation populations locust. For this purpose, critical accurately identify sample. Expert estimation fast fairly accurate, but depends greatly qualifications oftentimes underestimated, early stages population development. Plant also good predictor age, model gives poor predictions older than 10 years. tree ranged 2.3 16.0 meters. at breast (DBH) 1.0 17.5 cm. root collar (DRC) 1.6 21.7 studied trees, determined rings per transect, 6 17 according estimates was 8.2 ± 4.8 statistically significantly different. linearly related size, while showed logarithmic relationship with height. closer height, had slightly lower dependence traits. Regression analysis there linear between traits coefficient determination 0.96, base 0.97. indicates high level multicollinearity these considering them predictors Taking into improved predictive ability trees.