Land-use change and windstorms legacies drove the recolonization dynamics of laurel forests in Tenerife, Canary islands

Laurel forests are quite relevant for biodiversity conservation and are among the island ecosystems most severely damaged by human activities.In the past,Canary laurel forests have been greatly altered by logging,livestock and agriculture.The remains of laurel forests are currently protected in the Canary Islands(Spain).However,we miss basic information needed for their restoration and adaptive management,such as tree longevity,growth potential and responsiveness to natural and anthropogenic disturbances.Using dendrochronological methods,we studied how forest dynamic is related to land-use change and windstorms in two well-preserved laurel forests on Tenerife Island.Wood cores were collected from over 80 trees per stand at three stands per forest.We used ring-width series to estimate tree ages and calculate annual basal area increments(BAI),cumulative diameter increases,and changes indicative of released and suppressed growth.Twelve tree species were found in all stands,with Laurus novocanariensis,Ilex canariensis and Morella faya being the most common species.Although some individuals were over 100 years old,61.8%-88.9% of the trees per stand established between 1940 and 1970,coinciding with a post-war period of land abandonment,rural exodus and the onset of a tourism economy.Some trees have shown growth rates larger than 1 cm diameter per year and most species have had increasing BAI trends over the past decades.Strong growth releases occurred after windstorms at both sites,but the effects of windstorms were site-dependent,with the 1958 storm affecting mainly the eastern tip of the island(Anaga massif)and the 1991 storm the western tip(Teno massif).Given the great ability of laurel forest trees to establish after land use cessation and to increase growth after local disturbances such as windstorms,passive restoration may be sufficient to regenerate this habitat in currently degraded areas.

Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests

Background:Black alder(Alnus glutinosa)forests are in severe decline across their area of distribution due to a disease caused by the soil-borne pathogenic Phytophthora alni species complex(class Oomycetes),“alder Phytopththora”.Mapping of the different types of damages caused by the disease is challenging in high density ecosystems in which spectral variability is high due to canopy heterogeneity.Data obtained by unmanned aerial vehicles(UAVs)may be particularly useful for such tasks due to the high resolution,flexibility of acquisition and cost efficiency of this type of data.In this study,A.glutinosa decline was assessed by considering four categories of tree health status in the field:asymptomatic,dead and defoliation above and below a 50% threshold.A combination of multispectral Parrot Sequoia and UAV unmanned aerial vehicles-red green blue(RGB)data were analysed using classical random forest(RF)and a simple and robust three-step logistic modelling approaches to identify the most important forest health indicators while adhering to the principle of parsimony.A total of 34 remote sensing variables were considered,including a set of vegetation indices,texture features from the normalized difference vegetation index(NDVI)and a digital surface model(DSM),topographic and digital aerial photogrammetry-derived structural data from the DSM at crown level.Results:The four categories identified by the RF yielded an overall accuracy of 67%,while aggregation of the legend to three classes(asymptomatic,defoliated,dead)and to two classes(alive,dead)improved the overall accuracy to 72% and 91% respectively.On the other hand,the confusion matrix,computed from the three logistic models by using the leave-out cross-validation method yielded overall accuracies of 75%,80% and 94% for four-,three-and two-level classifications,respectively.Discussion:The study findings provide forest managers with an alternative robust classification method for the rapid,effective assessment of areas affected and non-affected by the disease,thus enabling them to identify hotspots for conservation and plan control and restoration measures aimed at preserving black alder forests.