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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This paper presents a revision, an update, and an extension of the generalized single-channel (SC) algorithm developed by Jiménez-Muñoz and Sobrino (2003), which was particularized to the thermal-infrared (TIR) channel (band 6) located in the Landsat-5 Thematic Mapper (TM) sensor. The SC algorithm relies on the concept of atmospheric functions (AFs) which are dependent on atmospheric transmissivity and upwelling and downwelling atmospheric radiances. These AFs are fitted versus the atmospheric water vapor content for operational purposes. In this paper, we present updated fits using MODTRAN 4 radiative transfer code, and we also extend the application of the SC algorithm to the TIR channel of the TM sensor onboard the Landsat-4 platform and the enhanced TM plus sensor onboard the Landsat-7 platform. Five different atmospheric sounding databases have been considered to create simulated data used for retrieving AFs and to test the algorithm. The test from independent simulated data provided root mean square error (rmse) values below 1 K in most cases when atmospheric water vapor content is lower than 2 g • cm −2 . For values higher than 3 g • cm −2 , errors are not acceptable, as what occurs with other SC algorithms. Results were also tested using a land surface temperature map obtained from one Landsat-5 image acquired over an agricultural area using inversion of the radiative transfer equation and the atmospheric profile measured in situ at the sensor overpass time. The comparison with this "ground-truth" map provided an rmse of 1.5 K.

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.