Molecular Proxies for Climate Maladaptation in a Long-Lived Tree (Pinus pinaster Aiton, Pinaceae).

Understanding adaptive genetic responses to climate change is a main challenge for preserving biological diversity. Successful predictive models for climate-driven range shifts of species depend on the integration of information on adaptation, including that derived from genomic studies. Long-lived forest trees can experience substantial environmental change across generations, which results in a much more prominent adaptation lag than in annual species. Here, we show that candidate-gene SNPs (single nucleotide polymorphisms) can be used as predictors of maladaptation to climate in maritime pine (Pinus pinaster Aiton), an outcrossing long-lived keystone tree. A set of 18 SNPs potentially associated with climate, 5 of them involving amino acid-changing variants, were retained after performing logistic regression, latent factor mixed models, and Bayesian analyses of SNP-climate correlations. These relationships identified temperature as an important adaptive driver in maritime pine and highlighted that selective forces are operating differentially in geographically discrete gene pools. The frequency of the locally advantageous alleles at these selected loci was strongly correlated with survival in a common garden under extreme (hot and dry) climate conditions, which suggests that candidate-gene SNPs can be used to forecast the likely destiny of natural forest ecosystems under climate change scenarios. Differential levels of forest decline are anticipated for distinct maritime pine gene pools. Geographically defined molecular proxies for climate adaptation will thus critically enhance the predictive power of range-shift models and help establish mitigation measures for long-lived keystone forest trees in the face of impending climate change.

What is the influence of vaccination's routes on the regression of tumors located at mucosal sites?

Tumor-regressions following tumor-associated-antigen vaccination in animal models contrast with the limited clinical outcomes in cancer patients. Most animal studies however used subcutaneous-tumor-models and questions arise as whether these are relevant for tumors growing in mucosae; whether specific mucosal-homing instructions are required; and how this may be influenced by the tumor.

Bacteria diversity and microbial biomass in forest, pasture and fallow soils in the southwestern Amazon basin

It is well-known that Amazon tropical forest soils contain high microbial biodiversity. However, anthropogenic actions of slash and burn, mainly for pasture establishment, induce profound changes in the well-balanced biogeochemical cycles. After a few years the grass yield usually declines, the pasture is abandoned and is transformed into a secondary vegetation called "capoeira" or fallow. The aim of this study was to examine how the clearing of Amazon rainforest for pasture affects: (1) the diversity of the Bacteria domain evaluated by Polymerase Chain Reaction and Denaturing Gradient Gel Electrophoresis (PCR-DGGE), (2) microbial biomass and some soil chemical properties (pH, moisture, P, K, Ca, Mg, Al, H + Al, and BS), and (3) the influence of environmental variables on the genetic structure of bacterial community. In the pasture soil, total carbon (C) was between 30 to 42 % higher than in the fallow, and almost 47 % higher than in the forest soil over a year. The same pattern was observed for N. Microbial biomass in the pasture was about 38 and 26 % higher than at fallow and forest sites, respectively, in the rainy season. DGGE profiling revealed a lower number of bands per area in the dry season, but differences in the structure of bacterial communities among sites were better defined than in the wet season. The bacterial DNA fingerprints in the forest were stronger related to Al content and the Cmic:Ctot and Nmic:Ntot ratios. For pasture and fallow sites, the structure of the Bacteria domain was more associated with pH, sum of bases, moisture, total C and N and the microbial biomass. In general microbial biomass in the soils was influenced by total C and N, which were associated with the Bacteria domain, since the bacterial community is a component and active fraction of the microbial biomass. Results show that the genetic composition of bacterial communities in Amazonian soils changed along the sequence forest-pasture-fallow.

Do PES Improve the Governance of Forest Restoration?

Payments for Environmental Services (PES) are praised as innovative policy instruments and they influence the governance of forest restoration efforts in two major ways. The first is the establishment of multi-stakeholder agencies as intermediary bodies between funders and planters to manage the funds and to distribute incentives to planters. The second implication is that specific contracts assign objectives to land users in the form of conditions for payments that are believed to increase the chances for sustained impacts on the ground. These implications are important in the assessment of the potential of PES to operate as new and effective funding schemes for forest restoration. They are analyzed by looking at two prominent payments for watershed service programs in Indonesia-Cidanau (Banten province in Java) and West Lombok (Eastern Indonesia)-with combined economic and political science approaches. We derive lessons for the governance of funding efforts (e.g., multi-stakeholder agencies are not a guarantee of success; mixed results are obtained from a reliance on mandatory funding with ad hoc regulations, as opposed to voluntary contributions by the service beneficiary) and for the governance of financial expenditure (e.g., absolute need for evaluation procedures for the internal governance of farmer groups). Furthermore, we observe that these governance features provide no guarantee that restoration plots with the highest relevance for ecosystem services are targeted by the PES

Bootstrapping pairs in Distance-Based Regression

La regressió basada en distàncies és un mètode de predicció que consisteix en dos passos: a partir de les distàncies entre observacions obtenim les variables latents, les quals passen a ser els regressors en un model lineal de mínims quadrats ordinaris. Les distàncies les calculem a partir dels predictors originals fent us d'una funció de dissimilaritats adequada. Donat que, en general, els regressors estan relacionats de manera no lineal amb la resposta, la seva selecció amb el test F usual no és possible. En aquest treball proposem una solució a aquest problema de selecció de predictors definint tests estadístics generalitzats i adaptant un mètode de bootstrap no paramètric per a l'estimació dels p-valors. Incluim un exemple numèric amb dades de l'assegurança d'automòbils.