LiDAR technology has firmly contributed to strengthen the knowledge of habitat structure-wildlife relationships, though there is an evident bias towards flying vertebrates. To bridge this gap, we investigated and compared the performance of LiDAR and field data to model habitat preferences of wood mouse (Apodemus sylvaticus) in a Mediterranean high mountain pine forest (Pinus sylvestris). We recorded nine field and 13 LiDAR variables that were summarized by means of Principal Component Analyses (PCA). We then analyzed wood mouse’s habitat preferences using three different models based on: (i) field PCs predictors, (ii) LiDAR PCs predictors; and (iii) both set of predictors in a combined model, including a variance partitioning analysis. Elevation was also included as a predictor in the three models. Our results indicate that LiDAR derived variables were better predictors than field-based variables. The model combining both data sets slightly improved the predictive power of the model. Field derived variables indicated that wood mouse was positively influenced by the gradient of increasing shrub cover and negatively affected by elevation. Regarding LiDAR data, two LiDAR PCs, i.e. gradients in canopy openness and complexity in forest vertical structure positively influenced wood mouse, although elevation interacted negatively with the complexity in vertical structure, indicating wood mouse’s preferences for plots with lower elevations but with complex forest vertical structure. The combined model was similar to the LiDAR-based model and included the gradient of shrub cover measured in the field. Variance partitioning showed that LiDAR-based variables, together with elevation, were the most important predictors and that part of the variation explained by shrub cover was shared. LiDAR derived variables were good surrogates of environmental characteristics explaining habitat preferences by the wood mouse. Our LiDAR metrics represented structural features of the forest patch, such as the presence and cover of shrubs, as well as other characteristics likely including time since perturbation, food availability and predation risk. Our results suggest that LiDAR is a promising technology for further exploring habitat preferences by small mammal communities.

Seed predation is an antagonistic interaction that negatively affects the performance of individual plants and can limit plant population dynamics. In animal-dispersed plants, crop size is an important determinant of plant reproductive success through its effect on seed dispersers and predators. Seed traits, such as size or chemical composition, can also increase the tolerance to seed predators or reduce their performance. We investigated the interaction between Quercus faginea and two specialized pre-dispersal insect seed predators (weevils and moths) during two years of contrasting crop size to determine the consequences of oak reproductive investment on seed production and insect performance. Crop size was 44% lower and acorns were 32% smaller in the second year, although acorn predation by insects was proportionally similar between both years at the population level. Individual trees producing larger crops showed a lower incidence of insect predators during the year of abundant acorn production, whereas trees producing bigger acorns experienced higher seed predation rates by insects, and acorns held more insect larvae in the low crop year. Competition between insects increased when acorn production was low, and higher tannin content in acorns further constrained the number of weevil larvae developing together in the same acorn. However, the abundance and size of insect larvae produced per tree were similar between the two crop years, and this was due to larvae often depleting acorn reserves when resources were low. Oak reproductive output increased nearly two-fold during the large crop year. Crop size variation, acorn production in a given year and acorn size and chemical composition seem to be important traits for reducing damage by insect predators in Quercus faginea and improve oak reproductive success.

Aim: Understanding how climate affects species distributions remains a major challenge, with the relative importance of direct physiological effects versus biotic interactions still poorly understood. We focus on three species of resource specialists (crossbill Loxia finches) to assess the role of climate in determining the seasonal availability of their food, the importance of climate and the occurrence of their food plants for explaining their current distributions, and to predict changes in their distributions under future climate change scenarios.Location: Europe.Methods: We used datasets on the timing of seed fall in European Scots pine Pinus sylvestris forests (where different crossbill species occur) to estimate seed fall phenology and climate data to determine its influence on spatial and temporal variation in the timing of seed fall to provide a link between climate and seed scarcity for crossbills. We used large‐scale datasets on crossbill distribution, cover of the conifers relied on by the three crossbill species and climate variables associated with timing of seed fall, to assess their relative importance for predicting crossbill distributions. We used species distribution modelling to predict changes in their distributions under climate change projections for 2070.Results: We found that seed fall occurred 1.5–2 months earlier in southern Europe than in Sweden and Scotland and was associated with variation in spring maximum temperatures and precipitation. These climate variables and area covered with conifers relied on by the crossbills explained much of their observed distributions. Projections under global change scenarios revealed reductions in potential crossbill distributions, especially for parrot crossbills.Main conclusions: Ranges of resource specialists are directly influenced by the presence of their food plants, with climate conditions further affecting resource availability and the window of food scarcity indirectly. Future distributions will be determined by tree responses to changing climatic conditions and the impact of climate on seed fall phenology.