Jean-Nicolas Pradervand

Shifts in species richness, herbivore specialization, and plant resistance along elevation gradients

Environmental gradients have been postulated to generate patterns of diversity and diet specialization, in which more stable environments, such as tropical regions, should promote higher diversity and specialization. Using field sampling and phylogenetic analyses of butterfly fauna over an entire alpine region, we show that butterfly specialization (measured as the mean phylogenetic distance between utilized host plants) decreases at higher elevations, alongside a decreasing gradient of plant diversity. Consistent with current hypotheses on the relationship between biodiversity and the strength of species interactions, we experimentally show that a higher level of generalization at high elevations is associated with lower levels of plant resistance: across 16 pairs of plant species, low-elevation plants were more resistant vis-à-vis their congeneric alpine relatives. Thus, the links between diversity, herbivore diet specialization, and plant resistance along an elevation gradient suggest a causal relationship analogous to that hypothesized along latitudinal gradients.

Turnover of plant lineages shapes herbivore phylogenetic beta diversity along ecological gradients

Understanding drivers of biodiversity patterns is of prime importance in this era of severe environmental crisis. More diverse plant communities have been postulated to represent a larger functional trait-space, more likely to sustain a diverse assembly of herbivore species. Here, we expand this hypothesis to integrate environmental, functional and phylogenetic variation of plant communities as factors explaining the diversity of lepidopteran assemblages along elevation gradients in the Swiss Western Alps. According to expectations, we found that the association between butterflies and their host plants is highly phylogenetically structured. Multiple regression analyses showed the combined effect of climate, functional traits and phylogenetic diversity in structuring butterfly communities. Furthermore, we provide the first evidence that plant phylogenetic beta diversity is the major driver explaining butterfly phylogenetic beta diversity. Along ecological gradients, the bottom up control of herbivore diversity is thus driven by phylogenetically structured turnover of plant traits as well as environmental variables.

Very high resolution environmental predictors in species distribution models: Moving beyond topography?

Recent advances in remote sensing technologies have facilitated the generation of very high resolution (VHR) environmental data. Exploratory studies suggested that, if used in species distribution models (SDMs), these data should enable modelling species’ micro-habitats and allow improving predictions for fine-scale biodiversity management. In the present study, we tested the influence, in SDMs, of predictors derived from a VHR digital elevation model (DEM) by comparing the predictive power of models for 239 plant species and their assemblages fitted at six different resolutions in the Swiss Alps. We also tested whether changes of the model quality for a species is related to its functional and ecological characteristics. Refining the resolution only contributed to slight improvement of the models for more than half of the examined species, with the best results obtained at 5 m, but no significant improvement was observed, on average, across all species. Contrary to our expectations, we could not consistently correlate the changes in model performance with species characteristics such as vegetation height. Temperature, the most important variable in the SDMs across the different resolutions, did not contribute any substantial improvement. Our results suggest that improving resolution of topographic data only is not sufficient to improve SDM predictions – and therefore local management – compared to previously used resolutions (here 25 and 100 m). More effort should be dedicated now to conduct finer-scale in-situ environmental measurements (e.g. for temperature, moisture, snow) to obtain improved environmental measurements for fine-scale species mapping and management.

Combining food web and species distribution models for improved community projections.

The ability to model biodiversity patterns is of prime importance in this era of severe environmental crisis. Species assemblage along environmental gradients is subject to the interplay of biotic interactions in complement to abiotic filtering and stochastic forces. Accounting for complex biotic interactions for a wide array of species remains so far challenging. Here, we propose using food web models that can infer the potential interaction links between species as a constraint in species distribution models. Using a plant–herbivore (butterfly) interaction dataset, we demonstrate that this combined approach is able to improve species distribution and community forecasts. The trophic interaction network between butterfly larvae and host plant was phylogenetically structured and driven by host plant nitrogen content allowing forecasting the food web model to unknown interactions links. This combined approach is very useful in rendering models of more generalist species that have multiple potential interaction links, where gap in the literature may occur. Our combined approach points toward a promising direction for modeling the spatial variation in entire species interaction networks.

Functional homogenization of bumblebee communities in alpine landscapes under projected climate change

BackgroundBumblebees represent an active pollinator group in mountain regions and assure the pollination of many different plant species from low to high elevations. Plant-pollinator interactions are mediated by functional traits. Shift in bumblebee functional structure under climate change may impact plant-pollinator interactions in mountains. Here, we estimated bumblebee upward shift in elevation, community turnover, and change in functional structure under climate change.MethodWe sampled bumblebee species at 149 sites along the elevation gradient. We used stacked species distribution models (S-SDMs) forecasted under three climate change scenarios (A2, A1B, RCP3PD) to model the potential distribution of the Bombus species. Furthermore, we used species proboscis length measurements to assess the functional change in bumblebee assemblages along the elevation gradient.ResultsWe found species-specific response of bumblebee species to climate change. Species differed in their predicted rate of range contraction and expansion. Losers were mainly species currently restricted to high elevation. Under the most severe climate change scenarios (A2), we found a homogenization of proboscis length structure in bumblebee communities along the elevation gradient through the upward colonization of high elevation by species with longer proboscides.ConclusionsHere, we show that in addition to causing the shift in the distribution of bumblebee species, climate change may impact the functional structure of communities. The colonization of high elevation areas by bumblebee species with long proboscides may modify the structure of plant-pollination interaction networks by increasing the diversity of pollination services at high elevation.

Soil factors improve predictions of plant species distribution in a mountain environment

Explanatory studies suggest that using very high resolution (VHR, 1–5 m resolution) topo-climatic predictors may improve the predictive power of plant species distribution models (SDMs). However, the use of VHR topo-climatic data alone was recently shown not to significantly improve SDM predictions. This suggests that new ecologically-meaningful VHR variables based on more direct field measurements are needed, especially since non topo-climatic variables, such as soil parameters, are important for plants. In this study, we investigated the effects of adding mapped VHR predictors at a 5 m resolution, including field measurements of temperature, carbon isotope composition of soil organic matter (δ13CSOM values) and soil pH, to topo-climatic predictors in SDMs for the Swiss Alps. We used data from field temperature loggers to construct temperature maps, and we modelled the geographic variation in δ13CSOM and soil pH values. We then tested the effect of adding these VHR mapped variables as predictors into 154 plant SDMs and assessed the improvement in spatial predictions across the study area. Our results demonstrate that the use of VHR predictors based on more proximal field measurements, particularly soil parameters, can significantly increase the predictive power of models. Predicted soil pH was the second most important predictor after temperature, and predicted δ13CSOM was fourth. The greatest increase in model performance was for species found at high elevation (i.e. 1500–2000 m a.s.l.). Addition of predicted soil factors thus allowed better capturing of plant requirements in our models, showing that these can explain species distributions in ways complementary to topo-climatic variables. Modelling techniques to generalize edaphic information in space and then predict plant species distributions revealed a great potential in complex landscapes such as the mountain region considered in this study.

The determinants of alpine butterfly richness and composition vary according to the ecological traits of species

Predicting spatial patterns of species diversity and composition using suitable environmental predictors is an essential element in conservation planning. Although species have distinct relationships to environmental conditions, some similarities may exist among species that share functional characteristics or traits. We investigated the relationship between species richness, composition and abiotic and biotic environment in different groups of butterflies that share ecological characteristics. We inventoried butterfly species richness in 192 sites and classified all inventoried species in three traits categories: the caterpillars diet breadth, the habitat requirements and the dispersal ability of the adults. We studied how environment, including influence butterfly species richness and composition within each trait category. Across four modelling approaches, the relative influence of environmental variables on butterfly species richness differed for specialists and generalists. Climatic variables were the main determinants of butterfly species richness and composition for generalists, whereas habitat diversity, and plant richness were also important for specialists. Prediction accuracy was lower for specialists than for generalists. Although climate variables represent the strongest drivers affecting butterfly species richness and composition for generalists, plant richness and habitat diversity are at least as important for specialist butterfly species. As specialist butterflies are among those species particularly threatened by global changes, devising accurate predictors to model specialist species richness is extremely important. However, our results indicate that this task will be challenging because more complex predictors are required.