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.

Sampling-Based Approaches to Estimating Two-Dimensional Large Wood Area from UAS Imagery

Large in-stream wood (LW) is a critical component of riparian systems that increases heterogeneity of flow regimes and provides high quality habitat for salmonids and other fishes. We present four sampling-based methods to estimate two-dimensional LW for a 61-hectare river restoration project on the South Fork McKenzie River near Rainbow, OR (USA). We manually delineated LW area, from unoccupied aircraft systems (UAS) multispectral imagery for 40 randomly selected 51.46 m2 hexagonal plots. Seven auxiliary variables were extracted from the imagery and imagery derivatives to be incorporated in four estimators by summarizing spectral statistics for each plot including Random forest (RF) classification of segmented imagery (Cohen’s kappa = 0.75, balanced accuracy = 0.86). The four estimators were: difference estimator, simple linear regression estimator with one auxiliary variable, general regression estimator with seven auxiliary variables, and simple random sample without replacement. We assessed variance of the estimators and found that the simple random sample without replacement produced the largest estimate for LW area and widest confidence interval (17,283 m2, 95% CI 10,613 - 23,952 m2) while the generalized regression approach resulted in the smallest estimate and narrowest confidence interval (16,593 m2, 95% CI 13,054 - 20,133 m2). These methods facilitate efficient estimates of critical habitat components, that are especially suited to efforts that seek to quantify large amounts of these components through time. When combined with traditional sampling methods, classified imagery acquired via UAS promises to enhance the temporal resolution of the data products associated with restoration efforts while minimizing the necessity for potentially hazardous field work.