This paper addresses the determination of the realized thermal niche and the effects of climate change on the range distribution of two brown trout populations inhabiting two streams in the Duero River basin (Iberian Peninsula) at the edge of the natural distribution area of this species. For reaching these goals, new methodological developments were applied to improve reliability of forecasts. Water temperature data were collected using 11 thermographs located along the altitudinal gradient, and they were used to model the relationship between stream temperature and air temperature along the river continuum. Trout abundance was studied using electrofishing at 37 sites to determine the current distribution. The Representative Concentration Pathways RCP4·5 and RCP8·5 change scenarios adopted by the International Panel of Climate Change for its Fifth Assessment Report were used for simulations and local downscaling in this study. We found more reliable results using the daily mean stream temperature than maximum daily temperature and their respective 7 days moving average to determine the distribution thresholds. Thereby, the observed limits of the summer distribution of brown trout were linked to thresholds between 18·1 and 18·7°C. These temperatures characterize a realized thermal niche narrower than the physiological thermal range. In the most unfavourable climate change scenario, the thermal habitat loss of brown trout increased to 38% (Cega stream) and 11% (Pirón stream) in the upstream direction at the end of the century; however, at the Cega stream, the range reduction could reach 56% due to the effect of a 'warm-window' opening in the piedmont reach.

Climate change affects aquatic ecosystems altering temperature and precipitation patterns, and the rear edge of the distribution of cold-water species is especially sensitive to them. The main goal was to predict in detail how change in air temperature and precipitation will affect streamflow, the thermal habitat of a cold-water fish (brown trout, Salmo trutta Linnaeus 1758), and their synergistic relationships at the rear edge of its natural distribution. 31 sites in 14 mountain rivers 15 and streams were studied in Central Spain. Models at several sites were built using regression trees for streamflow, and a non-linear regression method for stream temperature. Nine global climate models simulations for the RCP4.5 and RCP8.5 (Representative Concentration Pathways) scenarios were downscaled to a local level. Significant streamflow reductions were predicted in all basins (max. -49 %) by the year 2099, showing seasonal differences between them. The stream temperature models showed relationships between models parameters, geology and hydrologic responses. Temperature was sensitive to 20 the streamflow in one set of streams, and summer reductions contributed to additional stream temperature increases (max.3.6ºC), although the most deep-aquifer dependent sites better resisted warming. The predicted increase in water temperature reached up to 4.0ºC. Temperature and streamflow changes will cause a shift of the rear edge of the species distribution.However, geology conditioned the extent of this shift. Approaches like these should be useful in planning the prevention and mitigation of negative effects of climate change by differentiating areas based on the risk level and viability of fish 25 populations.

An integral understanding of forest biodiversity requires the exploration of the many aspects it comprises and of the numerous potential determinants of their distribution. The landscape ecological approach provides a necessary complement to conventional local studies that focus on individual plots or forest ownerships. However, most previous landscape studies used equally-sized cells as units of analysis to identify the factors affecting forest biodiversity distribution. Stratification of the analysis by habitats with a relatively homogeneous forest composition might be more adequate to capture the underlying patterns associated to the formation and development of a particular ensemble of interacting forest species. Here we used a landscape perspective in order to improve our understanding on the influence of large-scale explanatory factors on forest biodiversity indicators in Spanish habitats, covering a wide latitudinal and altitudinal range. We considered six forest biodiversity indicators estimated from more than 30,000 field plots in the Spanish national forest inventory, distributed in 213 forest habitats over 16 Spanish provinces. We explored biodiversity response to various environmental (climate and topography) and landscape configuration (fragmentation and shape complexity) variables through multiple linear regression models (built and assessed through the Akaike Information Criterion). In particular, we took into account the inherent model uncertainty when dealing with a complex and large set of variables, and considered different plausible models and their probability of being the best candidate for the observed data. Our results showed that compositional indicators (species richness and diversity) were mostly explained by environmental factors. Models for structural indicators (standing deadwood and stand complexity) had the worst fits and selection uncertainties, but did show significant associations with some configuration metrics. In general, biodiversity increased in habitats covering wider topographic ranges and comprising forest patches with more complex shapes. Patterns in other relationships varied between indicators (e.g. species richness vs. diversity), or even were opposed (trees vs. shrubs). Our study (1) allowed deepening the understanding of biodiversity patterns in a large set of Spanish forest habitats and (2) highlighted the increasing complexity of identifying common landscape conditions favouring forest biodiversity as the range of analysed biodiversity aspects is widened beyond the more commonly assessed species richness indicators.

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