Abstract. The increasing global concern about wildfires, mostly caused by people, has triggered the development of human-caused fire occurrence models in many countries. The premise is that better knowledge of the underlying factors is critical for many fire management purposes, such as operational decision-making in suppression and strategic prevention planning, or guidance on forest and land-use policies. However, the explanatory and predictive capacity of fire occurrence models is not yet widely applied to the management of forests, fires or emergencies. In this article, we analyse the developments in the field of human-caused fire occurrence modelling with the aim of identifying the most appropriate variables and methods for applications in forest and fire management and civil protection. We stratify our worldwide analysis by temporal dimension (short-term and long-term) and by model output (numeric or binary), and discuss management applications. An attempt to perform a meta-analysis based on published models proved limited because of non-equivalence of the metrics and units of the estimators and outcomes across studies, the diversity of models and the lack of information in published works.

Maize with the insecticidal properties of the entomopathogenic bacterium Bacillus thuringiensis Berliner, known as Bt maize, has been sown in Europe since 1998. For several years, EU and Spanish regulations have required laboratory and field trials to assess risks of genetically modified crops for nontarget organisms prior to their authorization. Thirteen field trials were conducted in Spain to measure the effects of Bt maize on a broad range of arthropod taxa; no effects were found in accordance with most literature records. However, statistical analyses of single trials rarely have the statistical power to detect low effect sizes if they do not have a sufficient sample size. When sample size is low, meta-analysis may improve statistical power by combining several trials and assuming a common measure of effect size. Here we perform a meta-analysis of the results of 13 independent field trials conducted in Spain in which effects of single or stacked Bt traits on several arthropod taxa were measured with no significant results. Since the taxa included in each single trial were not the same for all trials, for the meta-analysis we selected only those taxa recorded in a minimum of six trials, resulting finally in 7, 7, and 12 taxa analyzed in visual counts, pitfall traps and yellow sticky traps, respectively. In comparison with single trial analysis, meta-analysis dramatically increased the detectability of treatment effects for most of the taxa regardless of the sampling technique; of the 26 taxa analyzed, only three showed poorer detectability in the meta-analysis than the best recorded in the 13 single trials. This finding reinforces the conclusion that Bt maize has no effect on the most common herbivore, predatory and parasitoid arthropods found in the maize ecosystems of southern Europe.

Understanding inter-and intra-specific plant interactions and competition over water is challenging because of the lack of effective approaches for accessing and monitoring root distribution and activity. In this context, stable isotopes are excellent ecohydrological tracers that allow characterizing the dynamics of water uptake patterns in trees and shrubs. Here, we studied biotic interactions for water uptake between two typical Mediterranean tree species, Aleppo pine (Pinus halepensis) and holm oak (Quercus ilex), coexisting in a mixed forest. We measured stable isotope composition ( 18 O and  2 H) of xylem water in all trees found in the studied stand during one growing season, covering an exceptionally long summer drought and subsequent recovery. We applied point-process statistics together with stand density information to evaluate tree-to-tree interactions for water use. In pines, we observed a clear uncoupling between soil and xylem water isotope composition after two months of persistent drought. Conversely, the isotope composition of xylem water in oaks tracked observed changes in the soil during the first two months of drought, but began to depart from soil values after three months. These results suggest that during drought the oaks were able to keep active for longer using alternative soil water sources, not available for the pines. Point-process statistics revealed more positive isotope compositions at distances below 4-6 m, but only between con-specific individuals (i.e. pine-pine, oak-oak). These intraspecific responses were first seen in the pines (after two months of drought) and subsequently in oaks (after three months), coinciding with the onset of soil-xylem uncoupling for each species. On the other hand, the isotope composition of individual oaks decreased with increasing neighbor pine density, but increased in response to oak density. Conversely, the pines showed more positive values with increasing oak density. Our results suggest that the use of shallow water in oaks is limited by the presence of

ResumenLa Estadística se ha incorporado en la mayoría de las carreras universitarias y entre ellas en el grado de psicología donde la enseñanza de conceptos estadísticos presenta problemas didácticos específicos debido a que los estudiantes que lo cursan tienen una base matemática muy heterogénea. En este trabajo nos centramos en el estudio de sus actitudes, por su influencia en el proceso de aprendizaje, y las analizamos a través de la Escala de Actitudes hacia la Estadística de Estrada EAEE (ESTRADA, 2002). Los resultados indican actitudes en general moderadas o positivas, con una puntuación promedio global ligeramente superior a la posición teórica de indiferencia. El curso y los estudios previos en esta materia inciden en su actitud. Destacamos que la actitud global hacia la estadística empeora con los años de estudio, aunque las puntuaciones totales más bajas se presentan en los estudiantes que nunca estudiaron estadística, resultados que invitan a la reflexión sobre la manera que se enseñan en los diferentes niveles educativos.Palabras clave: Actitudes. Escalas. Estadística. Estudiantes de Psicología. AbstractStatistics has been incorporated in most university courses and among them in the degree of Psychology, where the teaching of statistical concepts presents specific learning problems, because students enrolled have a very heterogeneous mathematical basis. In this work, we focus on the study of those students' attitudes for their influence on the learning process and we analyze it through the Scale of Attitudes toward Statistics of Estrada,

The study of intra-specific variations in growth and plant physiological response to drought is crucial to understand the potential for plant adaptation to global change. Carbon isotope composition (δ(13)C) in plant tissues offers an integrated measure of intrinsic water-use efficiency (WUEi). The intra-specific association between δ(13)C and productivity has been extensively studied in herbaceous crops, but species-specific information on woody plants is still limited and has so far provided contradictory results. In this work we explored the general patterns of the relationship between δ(13)C and growth traits (height, diameter and biomass) using a meta-analysis. We compiled information from 49 articles, including 176 studies performed on 34 species from 16 genera. We found a positive global intra-specific correlation between δ(13)C and growth (Gr=0.28, P<0.0001), stronger for biomass than for height, and non-significant for diameter. The extent of this intra-specific association increased from Mediterranean to subtropical, temperate and boreal biomes, i.e. from water-limited to energy-limited environments. Conifers and shrubs, but not broadleaves, showed consistent positive intra-specific correlations. The meta-analysis also revealed that the relationship between δ(13)C and growth is better characterized at juvenile stages, under near-optimal and controlled conditions, and by analyzing δ(13)C in leaves rather than in wood.

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To assess risks of cultivation of genetically modified crops (GMCs) on non-target arthropods (NTAs), field tests are necessary to verify laboratory results and in situations where exposure pathways are very complex and cannot be reproduced in the laboratory. A central concern in the design of field trials for this purpose is whether the tests are capable of detecting differences in the abundance or activity of NTAs in a treated crop in comparison with a non-treated comparator plot. The detection capacity of a trial depends on the abundance and variability of the taxon, the values assumed for type I (alpha) and II (beta) errors, and the characteristics of the trial and statistical design. To determine the optimal trial layout and statistical analysis, 20 field trials carried out in Spain from 2000 to 2009 to assess risks of GMCs on NTAs were examined with alpha and beta set at 0.05 and 0.20, respectively. In this article we aim to determine the optimal number of sampling dates during a season, or longitudinal samples, in the design of field trials for assessing effects of GM maize on NTAs, and the ones that contribute most to achieving detectable treatment effects (d(c)) less than 50% of the mean of the control. Detection capacities are a function of the number of individual samples taken during the season but a high number of samples is rarely justified because gains of repeated sampling can be relatively low. These gains depend primarily on field tests relative experimental variability in individual samplings (i.e. experimental variability relative to the mean of the control in each sampling date) which in turn depends on the sampling method (visual counts, pitfall traps or yellow sticky traps) and the density (or abundance) of the taxon in question. Taxa showing more density (or abundance) have less relative experimental variability. The smaller the experimental variability, the lower the profit of increasing the number of sampling dates. Sticky traps have a good effect detection capacity and need very few sampling dates, whereas visual counts and pitfall traps have a poorer effect detection capacity and need more individual samples to achieve d(c) values lower than 50%. In maize field trials, it is recommended to concentrate sampling efforts in certain growth stages; the optimal ones for achieving an acceptable detection capacity are variable but, in general, samples in the first half of the season render better detection capacity than samples in the second half.Postprint (published version