Aitor Gastón

Seasonal and temporal changes in species use of the landscape: how do they impact the inferences from multi-scale habitat modeling?

ContextMulti-scale approaches to habitat modeling have been shown to provide more accurate understanding and predictions of species-habitat associations. It remains however unexplored how spatial and temporal variations in habitat use may affect multi-scale habitat modeling.ObjectivesWe aimed at assessing how seasonal and temporal differences in species habitat use and distribution impact operational scales, variable influence, habitat suitability spatial patterns, and performance of multi-scale models.MethodsWe evaluated the environmental factors driving brown bear habitat relationships in the Cantabrian Range (Spain) based on species presence records (ground observations) for the period 2000–2010, LiDAR data on forest structure, and seasonal estimates of foraging resources. We separately developed multi-scale habitat models for (i) each season (spring, summer, fall and winter) (ii) two sub-periods with different population status: 2000–2004 (with brown bear distribution restricted to the main population nuclei) and 2005–2010 (with expanding bear population and range); and (iii) the entire 2000–2010 period.ResultsScales of effect remained considerably stable across seasonal and temporal variations, but not the influence of certain environmental variables. The predictive ability of multi-scale models was lower in the seasons or periods in which populations used larger areas and a broader variety of environmental conditions. Seasonal estimates of foraging resources, together with LiDAR data, appeared to improve the performance of multi-scale habitat models.ConclusionsWe highlight that the understanding of multi-scale behavioral responses of species to spatial patterns that continually shift over time may be essential to unravel habitat relationships and produce reliable estimates of species distributions.

Species distribution models applied to plant species selection in forest restoration: are model predictions comparable to expert opinion?

An expert on local flora usually is the best option for plant species selection in most ecological restoration projects; although species selection often needs to be dealt with swiftly as well as on a limited budget, and obtaining the opinion of a local expert may not always be an economically viable alternative. In such cases, species distribution models (SDM) may offer a faster and more cost effective alternative. We asked six experts to rank native tree species according to their suitability at 24 forest sites. The predictive performance of the suitability rankings was evaluated by assessing their ability to discriminate present from absent species in the observed tree assemblages at each evaluation site. We used the area under the receiver operating characteristic curve to calculate the probability that the estimated suitability for a species present at a particular evaluation site is greater than the estimate for an absent species (both picked at random). Suitability rankings were also obtained from the predictions of SDM and the same procedure was used to estimate the predictive performance of the set of models at each site. The experts offered concordant suitability rankings at almost every evaluation site. There were no significant differences in the predictive performance of the SDM and four of the experts, although the SDM performed slightly better than the other two experts. Our results point to the suitability of the proposed species distribution modeling approach to obtain fast and cost effective recommendations for species selection in forest restoration projects.