No abstract

The populations of brook lamprey Lampetra planeri of Portuguese Rivers were analysed phylogeographically using a fragment of 644 bp of the mitochondrial control region of 158 individuals from six populations. Samples representing L. planeri and migratory lampreys Lampetra fluviatilis of rivers draining to the North Sea and the Baltic Sea were also included to assess the relationships of Portuguese samples. The data support a clear differentiation of all the populations studied. Several populations, which are isolated among themselves and also from the migratory lampreys, proved to be entirely composed of private haplotypes, a finding that supports some time of independent evolutionary history for these populations. This, combined with the geographic confinement to small water bodies, justifies the recognition of at least four conservation units in the Portuguese rivers Sado, São Pedro, Nabão and Inha.

Eucalyptus Longhorned Borers (ELB) are some of the most destructive pests in regions with Mediterranean climate. Low rainfall and extended dry summers cause stress in eucalyptus trees and facilitate ELB infestation. Due to the difficulty of monitoring the stands by traditional methods, remote sensing arises as an invaluable tool. The main goal of this study was to demonstrate the accuracy of unmanned aerial vehicle (UAV) multispectral imagery for detection and quantification of ELB damages in eucalyptus stands. To detect spatial damage, Otsu thresholding analysis was conducted with five imagery-derived vegetation indices (VIs) and classification accuracy was assessed. Treetops were calculated using the local maxima filter of a sliding window algorithm. Subsequently, large-scale mean-shift segmentation was performed to extract the crowns, and these were classified with random forest (RF). Forest density maps were produced with data obtained from RF classification. The normalized difference vegetation index (NDVI) presented the highest overall accuracy at 98.2% and 0.96 Kappa value. Random forest classification resulted in 98.5% accuracy and 0.94 Kappa value. The Otsu thresholding and random forest classification can be used by forest managers to assess the infestation. The aggregation of data offered by forest density maps can be a simple tool for supporting pest management.