The survival of an increasing number of species is threatened by climate change: 20%–30% of plants and animals seem to be at risk of range shift or extinction if global warming reaches levels projected to occur by the end of this century. Plant range shifts may determine whether animal species that rely on plant availability for food and shelter will be affected by new patterns of plant occupancy and availability. Brown bears in temperate forested habitats mostly forage on plants and it may be expected that climate change will affect the viability of the endangered populations of southern Europe. Here, we assess the potential impact of climate change on seven plants that represent the main food resources and shelter for the endangered population of brown bears in the Cantabrian Mountains (Spain). Our simulations suggest that the geographic range of these plants might be altered under future climate warming, with most bear resources reducing their range. As a consequence, this brown bear population is expected to decline drastically in the next 50 years. Range shifts of brown bear are also expected to displace individuals from mountainous areas towards more humanized ones, where we can expect an increase in conflicts and bear mortality rates. Additional negative effects might include: (a) a tendency to a more carnivorous diet, which would increase conflicts with cattle farmers; (b) limited fat storage before hibernation due to the reduction of oak forests; (c) increased intraspecific competition with other acorn consumers, that is, wild ungulates and free‐ranging livestock; and (d) larger displacements between seasons to find main trophic resources. The magnitude of the changes projected by our models emphasizes that conservation practices focused only on bears may not be appropriate and thus we need more dynamic conservation planning aimed at reducing the impact of climate change in forested landscapes.

The prediction of growing stock volume is one of the commonest applications of remote sensing to support the sustainable management of forest ecosystems. In this study, we used data from the 4th Spanish National Forest Inventory (SNFI-4) and from the 1st nationwide Airborne Laser Scanning (ALS) survey to develop predictive yield models for the three major commercial tree forest species (Eucalyptus globulus, Pinus pinaster and Pinus radiata) grown in north-western Spain. Integration of both types of data required prior harmonization because of differences in timing of data acquisition and difficulties in accurately geolocating the SNFI plots. The harmonised data from 477 E. globulus, 760 P. pinaster and 191 P. radiata plots were used to develop predictive models for total over bark volume, mean volume increment and total aboveground biomass by relating SNFI stand variables to metrics derived from the ALS data. The multiple linear regression methods and several machine learning techniques (k-nearest neighbour, random trees, random forest and the ensemble method) were compared. The study findings confirmed that multiple linear regression is outperformed by machine learning techniques. More specifically, the findings suggest that the random forest and the ensemble method slightly outperform the other techniques. The resulting stand level relative RMSEs for predicting total over bark volume, annual increase in total volume and total aboveground biomass ranged from 30.8-38.3%, 34.2-41.9% and 31.7-38.3% respectively. Although the predictions can be considered accurate, more precise geolocation of the SNFI plots and coincide temporarily with the ALS data would have enabled use of a much larger and robust field database to improve the overall accuracy of estimation.

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