Roads pose an imminent threat to wildlife directly through mortality and changes in individual behavior, and also indirectly through modification of the amount and configuration of wildlife habitat. However, few studies have addressed how these mechanisms interact to determine species response to roads. We used structural equation modeling to assess direct and indirect effects (via landscape modification) of roads on space use by jaguars in Brazil, using radio-tracking data available from the literature. We fit path models that directly link jaguars’ space use to roads and to land cover, and indirectly link jaguars’ space use to roads through the same land cover categories. Our findings show that space use by jaguars was not directly affected by roads, but indirect effects occurred through reductions in natural areas on which jaguars depend, and through urban sprawl. Males´ space use, however, was not negatively influenced by urban areas. Since jaguars seem to ignore roads, mitigation should be directed to road fencing and promoting safe crossings. We argue that planners and managers need to much more seriously take into account the deforestation and the unbridled urban expansion from roads to ensure jaguar conservation in Brazil.

Removal models were proposed over 80 years ago as a tool to estimate unknown population size. More recently, they are used as an effective tool for management actions for the control of non desirable species, or for the evaluation of translocation management actions. Although the models have evolved over time, in essence, the protocol for data collection has remained similar: at each sampling occasion attempts are made to capture and remove individuals from the study area. Within this paper we review the literature of removal modelling and highlight the methodological developments for the analysis of removal data, in order to provide a unified resource for ecologists wishing to implement these approaches. Models for removal data have developed to better accommodate important features of the data and we discuss the shift in the required assumptions for the implementation of the models. The relative simplicity of this type of data and associated models mean that the method remains attractive and we discuss the potential future role of this technique.

The study of the short-term post-burn tree growth in a mixed stand of Pinus nigra and Pinus pinaster and in a pure stand of P. nigra in the Cuenca Mountains (Spain) will enable us to determine the disturbance of prescribed burning conducted in two seasons. Dendrochronological methods and mixed modelling were used to investigate whether tree growth responses are influenced by stand and tree characteristics, fire season and fire severity variables. The findings revealed that prescribed burning scarcely affected tree growth. The type of stand (mixed or pure) was not critical for tree growth. The individual tree characteristics were significant factors in all the scenarios studied. The inclusion of some fire severity variables for the first time in tree growth models showed that the maximum scorch height determined a main part of the variability of tree growth. The time during which the temperature was above 60 °C in the cambium region and temperature was above 300 °C in the bark surface were only significant factors after spring burnings. The litterfall one year after the prescribed burning was not a significant factor in any of the models. Overall, the findings confirm the characteristic resistance of P. nigra to surface fires and favor the potential application of prescribed burning programs for this species in the Mediterranean Basin.

Climatic change is expected to affect forest development in the short term, as well as the spatial distribution of species in the long term. Species distribution models are potentially useful tools for guiding species choices in reforestation and forest management prescriptions to address climate change. The aim of this study is to build spatial and spatio-temporal models to predict the distribution of four different species present in the Spanish Forest Inventory. We have compared the different models and showed how accounting for dependencies in space and time affect the relationship between species and environmental variables.

A comparison of several statistical techniques common in species distribution modeling was developed during this study to evaluate and obtain the statistical model most accurate to predict the distribution of different forest tree species (in our case presence/absence data) according environmental variables. During the process we have developed maximum entropy (MaxEnt), classification and regression trees (CART), multivariate adaptive regression splines (MARS), showing the statistical basis of each model and, at the same time, we have developed a specific additive model to compare and validate their capability. To compare different results, the area under the receiver operating characteristic (ROC) function (AUC) was used. Every AUC value obtained with those models is significant and all of the models could be useful to represent the distribution of each species. Moreover, the additive model with thin plate splines gave the best results. The worst capability was obtained with MARS. This model’s performance was below average for several species. The additive model developed obtained better results because it allowed for changes and calibrations. In this case we were aware of all of the processes that occurred during the modeling. By contrast, models obtained using specific software, in general, perform like “hermetic machines”, because it could sometimes be impossible to understand the stages that led to the final results.