Aim To assess the spatial patterns of forest expansion (encroachment and densification) for mountain pine (Pinus uncinata Ram.) during the last 50 years at a whole mountain range scale by the study of different topographic and socioeconomic potential drivers in the current context of global change.Location The study area includes the whole distributional area of mountain pine in the Catalan Pyrenees (north-east Spain). This represents more than 80 municipalities, covering a total area of 6018 km 2 .Methods Forest cover was obtained by image reclassification of more than 200 pairs of aerial photographs taken in 1956 and 2006. Encroachment and densification were determined according to changes in forest cover, and were expressed as binary variables on a 150 ¥ 150 m cell-size grid. We then used logistic regression to analyse the effects of several topographic and socio-economic variables on forest expansion. ResultsIn the period analysed, mountain pine increased its surface coverage by 8898 ha (an increase of more than 16%). Mean canopy cover rose from 31.0% in 1956 to 55.6% in 2006. Most of the expansion was found on north-facing slopes and at low altitudes. Socio-economic factors arose as major factors in mountain pine expansion, as encroachment rates were higher in municipalities with greater population losses or weaker primary sector development. Main conclusionsThe spatial patterns of mountain pine expansion showed a good match with the main patterns of land-use change in the Pyrenees, suggesting that land-use changes have played a more important role than climate in driving forest dynamics at a landscape scale over the period studied. Further studies on forest expansion at a regional scale should incorporate patterns of land-use changes to correctly interpret drivers of forest encroachment and densification.

Aim To assess the effects of climate change, past land uses and physiography on the current position of the tree line in the Catalan Pyrenees and its dynamics between 1956 and 2006.Location More than 1000 linear kilometres of sub-alpine tree line in the Catalan Pyrenees (north-east Spain) Methods Using aerial photographs and supervised classification, we reclassified the images into a binary raster with 'tree' and 'non-tree' values, and determined canopy cover in 1956 and 2006. We then determined the change in position of the tree line between 1956 and 2006 based on changes in forest cover. We used the distance from the position of the tree line in 1956 to the theoretical potential tree line -determined from interpretation of aerial photographs, identifying the highest old remnants of forest for homogeneous areas of the landscape in terms of bioclimatic conditions, bedrock, landform and exposure -as a surrogate of intensity of past land uses. ResultsOur analyses showed that the Pyrenean tree line has moved upwards on average almost 40 m (mean advance ± SE: 35.3 ± 0.5 m, P < 0.001), although in most cases it has remained unchanged (61.8%) or advanced moderately, i.e. between 25 and 100 m (23.7%); only 9.2% of the locations have advanced more than 100 m. Upward shifts of the tree line were significantly larger in locations heavily modified in the past by anthropogenic disturbance (mean advance 50.8 ± 1.1 m) compared with near natural tree line locations (19.7 ± 0.8 m, P < 0.001), where the mean displacement was much lower than expected and was not related to changes in temperature along the study period. Main conclusionsOur results stress the impact of the cessation of human activity in driving forest dynamics at the tree line in the Catalan Pyrenees, and reveal a very low or even negligible signal of climate change in the study area.

Aim Current interest in forecasting changes to species ranges has resulted in a multitude of approaches to species distribution models (SDMs). However, most approaches include only a small subset of the available information, and many ignore smaller-scale processes such as growth, fecundity and dispersal. Furthermore, different approaches often produce divergent predictions with no simple method to reconcile them. Here, we present a flexible framework for integrating models at multiple scales using hierarchical Bayesian methods. Location Eastern North America (as an example).Methods Our framework builds a metamodel that is constrained by the results of multiple sub-models and provides probabilistic estimates of species presence. We applied our approach to a simulated dataset to demonstrate the integration of a correlative SDM with a theoretical model. In a second example, we built an integrated model combining the results of a physiological model with presenceabsence data for sugar maple (Acer saccharum), an abundant tree native to eastern North America. ResultsFor both examples, the integrated models successfully included information from all data sources and substantially improved the characterization of uncertainty. For the second example, the integrated model outperformed the source models with respect to uncertainty when modelling the present range of the species. When projecting into the future, the model provided a consensus view of two models that differed substantially in their predictions. Uncertainty was reduced where the models agreed and was greater where they diverged, providing a more realistic view of the state of knowledge than either source model. Main conclusionsWe conclude by discussing the potential applications of our method and its accessibility to applied ecologists. In ideal cases, our framework can be easily implemented using off-the-shelf software. The framework has wide potential for use in species distribution modelling and can drive better integration of multi-source and multi-scale data into ecological decision-making.

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