Climate change is progressively increasing severe drought events in the Northern Hemisphere, causing regional tree die-off events and contributing to the global reduction of the carbon sink efficiency of forests. There is a critical lack of integrated community-wide assessments of drought-induced responses in forests at the macroecological scale, including defoliation, mortality, and food web responses. Here we report a generalized increase in crown defoliation in southern European forests occurring during 1987–2007. Forest tree species have consistently and significantly altered their crown leaf structures, with increased percentages of defoliation in the drier parts of their distributions in response to increased water deficit. We assessed the demographic responses of trees associated with increased defoliation in southern European forests, specifically in the Iberian Peninsula region. We found that defoliation trends are paralleled by significant increases in tree mortality rates in drier areas that are related to tree density and temperature effects. Furthermore, we show that severe drought impacts are associated with sudden changes in insect and fungal defoliation dynamics, creating long-term disruptive effects of drought on food webs. Our results reveal a complex geographical mosaic of species-specific responses to climate change–driven drought pressures on the Iberian Peninsula, with an overwhelmingly predominant trend toward increased drought damage.

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Amphibian chytridiomycosis is a disease caused by the fungus Batrachochytrium dendrobatidis (Bd). Whether Bd is a new emerging pathogen (the novel pathogen hypothesis; NPH) or whether environmental changes are exacerbating the host-pathogen dynamic (the endemic pathogen hypothesis; EPH) is debated. To disentangle these hypotheses we map the distribution of Bd and chytridiomycosis across the Iberian Peninsula centred on the first European outbreak site. We find that the infection-free state is the norm across both sample sites and individuals. To analyse this dataset, we use Bayesian zero-inflated binomial models to test whether environmental variables can account for heterogeneity in both the presence and prevalence of Bd, and heterogeneity in the occurrence of the disease, chytridiomycosis. We also search for signatures of Bd-spread within Iberia using genotyping. We show (1) no evidence for any relationship between the presence of Bd and environmental variables, (2) a weak relationship between environmental variables and the conditional prevalence of infection, (3) stage-dependent heterogeneity in the infection risk, (4) a strong association between altitude and chytridiomycosis, (5) multiple Iberian genotypes and (6) recent introduction and spread of a single genotype of Bd in the Pyrenees. We conclude that the NPH is consistent with the emergence of Bd in Iberia. However, epizootic forcing of infection is tied to location and shaped by both biotic and abiotic variables. Therefore, the population-level consequences of disease introduction are explained by EPH-like processes. This study demonstrates the power of combining surveillance and molecular data to ascertain the drivers of new emerging infections diseases.

Assessing the potential future of current forest stands is a key to design conservation strategies and understanding potential future impacts to ecosystem service supplies. This is particularly true in the Mediterranean basin, where important future climatic changes are expected. Here, we assess and compare two commonly used modeling approaches (niche-and process-based models) to project the future of current stands of three forest species with contrasting distributions, using regionalized climate for continental Spain. Results highlight variability in model ability to estimate current distributions, and the inherent large uncertainty involved in making projections into the future. CO 2 fertilization through projected increased atmospheric CO 2 concentrations is shown to increase forest productivity in the mechanistic process-based model (despite increased drought stress) by up to three times that of the non-CO 2 fertilization scenario by the period 2050-2080, which is in stark contrast to projections of reduced habitat suitability from the niche-based models by the same period. This highlights the importance of introducing aspects of plant biogeochemistry into current niche-based models for a realistic projection of future species distributions. We conclude that the future of current Mediterranean forest stands is highly uncertain and suggest that a new synergy between niche-and process-based models is urgently needed in order to improve our predictive ability.

Stem radial growth responds to environmental conditions, and has been widely used as a proxy to study long-term patterns of tree growth and to assess the impact of environmental changes on growth patterns. In this study, we use a tree ring dataset from the Catalan Ecological and Forest Inventory to study the temporal variability of Scots pine (Pinus sylvestris L.) stem growth during the 20th century across a relatively large region (Catalonia, NE Spain) close to the southern limit of the distribution of the species. Basal area increment (BAI) was modelled as a function of tree size and environmental variables by means of mixed effects models. Our results showed an overall increase of 84% in Scots pine BAI during the 20th century, consistent with most previous studies for temperate forests. This trend was associated with increased atmospheric CO 2 concentrations and, possibly, with a general increase in nutrient availability, and we interpreted it as a fertilization effect. Over the same time period, there was also a marked increase in temperature across the study region (0.19 1C per decade on average). This warming had a negative impact on radial growth, particularly at the drier sites, but its magnitude was not enough to counteract the fertilization effect. In fact, the substantial warming observed during the 20th century in the study area did not result in a clear pattern of increased summer drought stress because of the large variability in precipitation, which did not show any clear time trend. But the situation may change in the future if temperatures continue to rise and/or precipitation becomes scarcer. Such a change could potentially reverse the temporal trend in growth, particularly at the driest sites, and is suggested in our data by the relative constancy of radial growth after ca. 1975, coinciding with the warmer period. If this situation is representative of other relatively dry, temperate forests, the implications for the regional carbon balance would be substantial.

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[1] Air temperature is involved in many environmental processes such as actual and potential evapotranspiration, net radiation and species distribution. Ground meteorological stations provide important local data of air temperature, but a continuous surface for large and heterogeneous areas is also needed. In this paper we present a hybrid methodology between Remote Sensing and Geographical Information Systems to retrieve daily instantaneous, mean, maximum and minimum air temperatures (2002)(2003)(2004) as well as monthly and annual mean, maximum and minimum air temperatures (2000-2005) on a regional scale (Catalonia, northeast of the Iberian Peninsula) by means of multiple regression analysis and spatial interpolation techniques. To perform multiple regression analysis we have used geographical and multiresolution remotely sensed variables as predictors. The geographical variables we have included are altitude, latitude, continentality and solar radiation. As remote sensing predictors, we have selected those variables that are most closely related with air temperature such as albedo, land surface temperature (LST) and NDVI obtained from Landsat-5 (TM), Landsat-7 (ETM+), NOAA (AVHRR) and TERRA (MODIS) satellites. The best air temperature models are obtained when remote sensing variables are combined with geographical variables: averaged R 2 = 0.60 and averaged root mean square error (RMSE) = 1.75°C for daily temperatures, and averaged R 2 = 0.86 and averaged RMSE = 1.00°C for monthly and annual temperatures. The results also show that combined models appear in a higher frequency than only geographical or only remote sensing models (87%, 11% and 2% respectively) and that LST and NDVI are the most powerful remote sensing predictors in air temperature modeling.Citation: Cristóbal, J., M. Ninyerola, and X. Pons (2008), Modeling air temperature through a combination of remote sensing and GIS data,