Suggestions on management measures of pine forest ecosystems invaded by Bursaphelenchus xylophilus

Pine wood nematode （PWN）, Bursaphelenchus xylophilus （Steiner et Buhrer） Nickle is an important invasive alien species in forests of China and has become one of the most destructive forest diseases. In order to improve the resistance and resilience of pine forest ecosystems against B. xylophilus invasion and make the pine forest ecosystem more timely responsive to PWN invasion, we made some recommendations based on five years of intensive observations. We advocate a set of management measures with the theme “Prevention is priority, but integrated with curative techniques and ecological resilience” on the pine forest ecosystem invaded by B. xylophilus; details of accurate measures are proposed. The aim is to discover the underlying problems of present pine forest ecosys-tems and to take, correspondingly, administrative measures and strategies, which will encourage the pine forest ecosystem to develop in a benign way.

Prognosis of forest production using machine learning techniques

Forest production and growth are obtained from statistical models that allow the generation of information at the tree or forest stand level.Although the use of regression models is common in forest measurement,there is a constant search for estimation procedures that provide greater accuracy.Recently,machine learning techniques have been used with satisfactory performance in measuring forests.However,methods such as Adaptive NeuroFuzzy Inference System(ANFIS)and Random Forest are relatively poorly studied for predicting the volume of wood in eucalyptus plantations in Brazil.Therefore,it is essential to check whether these techniques can provide gains in terms of accuracy.Thus,this study aimed to evaluate the use of Random Forest and ANFIS techniques in the prognosis of forest production.The data used come from continuous forest inventories carried out in stands of eucalyptus clones.The data were divided into 70%for training and 30%for validation.The algorithms used to generate rules in ANFIS were Subtractive Clustering and Fuzzy-C-Means.Besides,training was done with the hybrid algorithm(descending gradient and least squares)with the number of seasons ranging from 1 to 20.Several RFs were trained,varying the number of trees from 50 to 850 and the number of observations by five leaves to 35.Artificial neural networks and decision trees were also trained to compare the feasibility of the techniques.The evaluation of the estimates generated by the techniques for training and validation was calculated based on the following statistics:correlation coefficient(r),relative Bias(RB),and the relative root mean square error(RRMSE)in percentage.In general,the techniques studied in this work showed excellent performance for the training and validation data set with RRMSE values<6%,RB<0.5%,and r>0.98.The RF presented inferior statistics about the ANFIS for the prognosis of forest production.The Subtractive Clustering(SC)and Fuzzy-C-Means(FCM)algorithms provide accurate baseline and volume projection estimates;both techniques are good alternatives for selecting variables used in modeling forest production.