Dr. Patrice Descombes

Understanding the ecological rules structuring the organization of species interactions is a prerequisite to predicting how ecosystems respond to environmental changes. While the ecological determinants of single networks have been documented, it remains unclear whether network ecological rules are conserved along spatial and environmental gradients. To address this gap, we reconstructed 48 plant–herbivore interaction networks along six elevation gradients in the Central European Alps in Switzerland, using DNA metabarcoding on orthoptera faeces. We developed hypotheses on the ecological mechanisms expected to structure interaction networks, based on plant phylogeny, plant abundance, leaf toughness, leaf nitrogen content and plant metabolomics. We show that plant phylogenetic relationships and species abundance have the greatest explanatory power regarding the structure of the ecological networks. Moreover, we found that leaf nitrogen content is a key determinant of interactions in warmer environments, while phenolic compounds and tannins are more important in colder environments, suggesting that determinants of species interactions can shift along environmental gradients. With this work, we propose an approach to study the mechanisms that structure the way species interact with each other between bioregions and ecosystems.

The metabolome is the biochemical basis of plant form and function, but we know little about its macroecological variation across the plant kingdom. Here, we used the plant functional trait concept to interpret leaf metabolome variation among 457 tropical and 339 temperate plant species. Distilling metabolite chemistry into five metabolic functional traits reveals that plants vary on two major axes of leaf metabolic specialization-a leaf chemical defense spectrum and an expression of leaf longevity. Axes are similar for tropical and temperate species, with many trait combinations being viable. However, metabolic traits vary orthogonally to life-history strategies described by widely used functional traits. The metabolome thus expands the functional trait concept by providing additional axes of metabolic specialization for examining plant form and function.

Species distribution models (SDMs) are statistical tools that relate species observations to environmental conditions to retrieve ecological niches and predict species' potential geographic distributions. The quality and robustness of SDMs clearly depend on good modelling practices including ascertaining the ecological relevance of predictors for the studied species and choosing an appropriate spatial resolution (or 'grain size'). While past studies showed improved model performance with increasing resolution for sessile organisms, there is still no consensus regarding how inappropriate resolution of predictors can impede understanding and mapping of multiple facets of diversity. Here, we modelled the distribution of 1180 plant species across the European Alps for two sets of predictors (climate and soil) at resolutions ranging from 100-m to 40-km. We assessed predictors' importance for each resolution, calculated taxonomic (TD), relative phylogenetic (rPD) and functional diversity (rFD) accordingly, and compared the resulting diversities across space. In accordance with previous studies, we found the predictive performance to generally decrease with decreasing predictor resolution. Overall, multifaceted diversity was found to be strongly affected by resolution, particularly rPD, as exhibited by weak to average linear relationships between 100-m and 1-km resolutions (0.13 ≤ R² ≤ 0.57). Our results demonstrate the necessity of using highly resolved predictors to explain and predict sessile species distributions, especially in mountain environments. Using coarser resolution predictors might cause multifaceted diversity to be strongly mispredicted, with important consequences for biodiversity management and conservation.

1. Theory predicts that a large fraction of phytochemical diversity - the richness of individual chemical compounds produced by plants - governs the complexity of interactions between plants and their herbivores. While the effect of specific classes of chemical compounds on plant resistance against herbivores has been largely documented, the effect of community-level variation in phytochemical diversity on plant-herbivore interactions has so far received minimal consideration.
2. We hypothesized that plant communities bearing on average higher levels of phytochemical diversity should sustain lower herbivory rates, overall. Yet, the magnitude of this effect could vary across different environmental conditions, potentially because of climate-mediated effects on phytochemical production and changes in herbivore community richness and composition.
3. To address these hypotheses, we used previous knowledge of species-level phytochemical make-up for more than 400 plant species of the Swiss Alps. Using common garden experiments, we estimated season-wide herbivore damage on low (average 3,500 unique molecules) and high (average 4,500 unique molecules) phytochemical diversity plant communities that were planted in the colline, mountain and alpine vegetation sites along two elevation transects in the Alps.
4. We found that high phytochemical diversity plant communities showed reduced levels of herbivore damage in the colline (low elevation) sites, but this pattern reversed in the alpine (high elevation) sites. Our results suggest that the outcome of phytochemical diversity on plant–herbivore interactions depends on the characteristics of the local herbivore communities, together with trade-offs between chemical defences and other plant traits (i.e. physical defences and plant palatability).
5. Synthesis. Phytochemical diversity is a key component of functional diversity, influencing community composition and dynamics. We show that the effect of phytochemical diversity on herbivory is environmental-dependent, generating ecological switches when moving from low to high elevation. Through upward movement of plants under climate change, phytochemical community structure will be likely modified, ultimately disrupting local community assembly processes.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km² resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e., offset) between in-situ soil temperature measurements, based on time series from over 1200 1-km² pixels (summarized from 8500 unique temperature sensors) across all the world’s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in-situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.

Although the importance of edaphic factors and habitat structure for plant growth and survival is known, both are often neglected in favor of climatic drivers when investigating the spatial patterns of plant species and diversity. Yet, especially in mountain ecosystems with complex topography, missing edaphic and habitat components may be detrimental for a sound understanding of biodiversity distribution. Here, we compare the relative importance of climate, soil and land cover variables when predicting the distributions of 2,616 vascular plant species in the European Alps, representing approximately two-thirds of all European flora. Using presence-only data, we built point-process models (PPMs) to relate species observations to different combinations of covariates. We evaluated the PPMs through block cross-validations and assessed the independent contributions of climate, soil, and land cover covariates to predict plant species distributions using an innovative predictive partitioning approach. We found climate to be the most influential driver of spatial patterns in plant species with a relative influence of ~58.5% across all species, with decreasing importance from low to high elevations. Soil (~20.1%) and land cover (~21.4%), overall, were less influential than climate, but increased in importance along the elevation gradient. Furthermore, land cover showed strong local effects in lowlands, while the contribution of soil stabilized at mid-elevations. The decreasing influence of climate with elevation is explained by increasing endemism, and the fact that climate becomes more homogeneous as habitat diversity declines at higher altitudes. In contrast, soil predictors were found to follow the opposite trend. Additionally, at low elevations, human-mediated land cover effects appear to reduce the importance of climate predictors. We conclude that soil and land cover are, like climate, principal drivers of plant species distribution in the European Alps. While disentangling their effects remains a challenge, future studies can benefit markedly by including soil and land cover effects when predicting species distributions.

To cope with environmental challenges, plants produce a wide diversity of phytochemicals, which are also the source of numerous medicines. Despite decades of research in chemical ecology, we still lack an understanding of the organization of plant chemical diversity across species and ecosystems. To address this challenge, we hypothesized that molecular diversity is not only related to species diversity, but also constrained by trophic, climatic, and topographical factors. We screened the metabolome of 416 vascular plant species encompassing the entire alpine elevation range and four alpine bioclimatic regions in order to characterize their phytochemical diversity. We show that by coupling phylogenetic information, topographic, edaphic, and climatic variables, we predict phytochemical diversity, and its inherent composition, of plant communities throughout landscape. Spatial mapping of phytochemical diversity further revealed that plant assemblages found in low to midelevation habitats, with more alkaline soils, possessed greater phytochemical diversity, whereas alpine habitats possessed higher phytochemical endemism. Altogether, we present a general tool that can be used for predicting hotspots of phytochemical diversity in the landscape, independently of plant species taxonomic identity. Such an approach offers promising perspectives in both drug discovery programs and conservation efforts worldwide.

Aim: Ecological gradients are expected to be associated with structural rewiring of species interaction networks. The study of network structures along geographic and ecological gradients, however, remains marginal because documenting species interactions at multiple sites is a methodological challenge. Here, we aimed to study the structural variation in plant–herbivore interaction networks along elevational gradients using molecular metabarcoding.
Location: European Alps.
Taxon: Plant and Orthopteran herbivores.
Methods: We used a standardized DNA metabarcoding method applied to Orthopteran faeces to document the structure of 48 networks of species interactions across six elevational gradients. We examined how structural properties of plant–Orthoptera networks reflecting specialization and robustness vary with elevation. We compared observed variation to null models to account for differences in network size.
Results: We found an increase in the levels of generality and nestedness with decreasing temperature, and the correlation was stronger than in null models. These relationships corresponded to greater robustness and reduced the importance of specific keystone species in alpine habitats compared to lowland grasslands.
Main conclusions: In cold environments, plant–herbivore networks are wired in a way that may reinforce the resilience of the system to species extinction. Documenting ecological networks along ecological gradients allows a better understanding of the influence of climate on the structure of ecological communities.

Herbivory and plant defenses exhibit a coupled decline along elevation gradients. However, the current ecological equilibrium could be disrupted under climate change, with a faster upward range shift of animals than plants. Here, we experimentally simulated this upward herbivore range shift by translocating low-elevation herbivore insects to alpine grasslands. We report that the introduction of novel herbivores and increased herbivory disrupted the vertical functional organization of the plant canopy. By feeding preferentially on alpine plants with functional traits matching their low-elevation host plants, herbivores reduced the biomass of dominant alpine plant species and favored encroachment of herbivore-resistant small-stature plant species, inflating species richness. Supplementing a direct effect of temperature, novel biotic interactions represent a neglected but major driver of ecosystem modifications under climate change.

1. Plants protect themselves against herbivore attacks with physical traits and toxic secondary metabolites. Levels of plant defences and herbivore performance might shift under climate warming, particularly in alpine habitats, where herbivore pressure is currently low. Plant responses to warming should be driven by species-specific shifts in physical and chemical defence traits.
2. We investigated the association between plant leaf physical and chemical traits and herbivory under current and warmer climates in three grasslands along a subalpine to alpine gradient. Specifically, we measured the rate of in situ natural herbivory, and performed bioassays to measure overall plant species-level resistance using the extreme generalist non-native caterpillar Spodoptera littoralis. We simulated warmer conditions by using open-top chambers and assessed the effect of warming on leaf physical and chemical traits, and how trait changes affect caterpillar performance.
3. Natural herbivory and caterpillar performance were associated with plant physical traits, including specific leaf area, and with ordination axes representing dimensions of the plant chemical profile. We found that the warming treatment independently decreased the number of distinct chemical compounds per species, and marginally increased specific leaf area. Changes in leaf functional traits were not systematically associated with changes in caterpillar performance.
4. Synthesis. Plant physical traits and chemical profiles are both related to natural herbivory and plant resistance against Spodoptera littoralis. While leaf physical and chemical traits of high elevation plants were modified by the warming treatment, these changes did not result in predictable effects on plant resistance against herbivores.

Ecologically meaningful predictors are often neglected in plant distribution studies, resulting in incomplete niche quantification and low predictive power of species distribution models (SDMs). Because environmental data are rare and expensive to collect, and because their relationship with local climatic and topographic conditions are complex, mapping them over large geographic extents and at high spatial resolution remains a major challenge.
Here, we propose to derive environmental data layers by mapping ecological indicator values in space. We combined ~6 million plant occurrences with expert-based plant ecological indicator values (EIVs) of 3600 species in Switzerland. EIVs representing local soil properties (pH, moisture, moisture variability, aeration, humus and nutrients) and climatic conditions (continentality, light) were modelled at 93 m spatial resolution with the Random Forest algorithm and 16 predictors representing meso-climate, land use, topography and geology. Models were evaluated and predictions of EIVs were compared with soil inventory data. We mapped each EIV separately and evaluated EIV importance in explaining the distribution of 500 plant species using SDMs with a set of 30 environmental predictors. Finally, we tested how they improve an ensemble of SDMs compared to a standard set of predictors for ca 60 plant species.
All EIV models showed excellent performance (|r| > 0.9) and predictions were correlated reasonably (|r| > 0.4) to soil properties measured in the field. Resulting EIV maps were among the most important predictors in SDMs. Also, in ensemble SDMs overall predictive performance increased, mainly through improved model specificity reducing species range overestimation.
Combining large citizen science databases to expert-based EIVs is a powerful and cost–effective approach for generalizing local edaphic and climatic conditions over large areas. Producing ecologically meaningful predictors is a first step for generating better predictions of species distribution which is of main importance for decision makers in conservation and environmental management projects.

Current analyses and predictions of spatially‐explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long‐term average thermal conditions at coarse spatial resolutions only. Hence, many climate‐forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing, or cold‐air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free‐air temperatures, microclimatic ground and near‐surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in.

To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near‐surface temperature data from all over the world. Currently this database contains time series from 7538 temperature sensors from 51 countries across all key biomes. The database will pave the way towards an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes.
 

Above- and below-ground herbivory are key ecosystem processes that can be substantially altered by environmental changes. However, direct comparisons of the coupled variations of above- and below-ground herbivore communities along elevation gradients remain sparse. Here, we studied the variation in assemblages of two dominant groups of herbivores, namely, aboveground orthoptera and belowground nematodes, in grasslands along six elevation gradients in the Swiss Alps. By examining variations of community properties of herbivores and their food plants along montane clines, we sought to determine whether the structure and functional properties of these taxonomic groups change with elevation. We found that orthoptera decreased in both species richness and abundance with elevation. In contrast with aboveground herbivores, the taxonomic richness and the total abundance of nematode did not covary with elevation. We further found a stronger shift in above- than below-ground functional properties along elevation, where the mandibular strength of orthoptera matched a shift in leaf toughness. Nematodes showed a weaker pattern of declined sedentary behavior and increased mobility with elevation. In contrast to the direct exposal of aboveground organisms to the surface climate, conditions may be buffered belowground, which together with the influence of edaphic factors on the biodiversity of soil biota, may explain the differences between elevational patterns of above- and below-ground communities. Our study emphasizes the necessity to consider both the above- and below-ground compartments to understand the impact of current and future climatic variation on ecosystems, from a functional perspective of species interactions.

Livestock farmers rely on a high and stable grassland productivity for fodder production to sustain their livelihoods. Future drought events related to climate change, however, threaten grassland functionality in many regions across the globe. The introduction of sustainable grassland management could buffer these negative effects. According to the biodiversity-productivity hypothesis, productivity positively associates with local biodiversity. The biodiversity-insurance hypothesis states that higher biodiversity enhances the temporal stability of productivity. To date, these hypotheses have mostly been tested through experimental studies under restricted environmental conditions, hereby neglecting climatic variations at a landscape-scale. Here, we provide a landscape-scale assessment of the contribution of species richness, functional composition, temperature, and precipitation on grassland productivity. We found that the variation in grassland productivity during the growing season was best explained by functional trait composition. The community mean of plant preference for nutrients explained 24.8% of the variation in productivity and the community mean of specific leaf area explained 18.6%, while species richness explained only 2.4%. Temperature and precipitation explained an additional 22.1% of the variation in productivity. Our results indicate that functional trait composition is an important predictor of landscape-scale grassland productivity.

We develop a spatially explicit model of diversification based on palaeohabitat to explore the predictions of four major hypotheses potentially explaining the latitudinal diversity gradient (LDG), namely, the 'time-area', 'tropical niche conservatism', 'ecological limits' and 'evolutionary speed' hypotheses. We compare simulation outputs to observed diversity gradients in the global reef fish fauna. Our simulations show that these hypotheses are non-mutually exclusive and that their relative influence depends on the time scale considered. Simulations suggest that reef habitat dynamics produced the LDG during deep geological time, while ecological constraints shaped the modern LDG, with a strong influence of the reduction in the latitudinal extent of tropical reefs during the Neogene. Overall, this study illustrates how mechanistic models in ecology and evolution can provide a temporal and spatial understanding of the role of speciation, extinction and dispersal in generating biodiversity patterns.

Reconstructing the processes that have shaped the emergence of biodiversity gradients is critical to understand the dynamics of diversification of life on Earth. Islands have traditionally been used as model systems to unravel the processes shaping biological diversity. MacArthur and Wilson's island biogeographicmodel predicts diversity to be based on dynamic interactions between colonization and extinction rates, while treating islands themselves as geologically static entities. The current spatial configuration of islands should influence meta-population dynamics, but long-term geological changes within archipelagos are also expected to have shaped island biodiversity, in part by driving diversification. Here, we compare two mechanistic models providing inferences on species richness at a biogeographic scale: a mechanistic spatial-temporal model of species diversification and a spatial meta-population model. While the meta-population model operates over a static landscape, the diversification model is driven by changes in the size and spatial configuration of islands through time.We compare the inferences of both models to floristic diversity patterns among land patches of the Indo-Australian Archipelago. Simulation results from the diversification model better matched observed diversity than a meta-population model constrained only by the contemporary landscape. The diversification model suggests that the dynamic repositioning of islands promoting land disconnection and reconnection induced an accumulation of particularly high species diversity on Borneo, which is central within the island network. By contrast, the meta-population model predicts a higher diversity on the mainlands, which is less compatible with empirical data. Our analyses highlight that, by comparing models with contrasting assumptions, we can pinpoint the processes that are most compatible with extant biodiversity patterns.

Aim: The importance of quantifying the contribution of historical processes in shaping current biodiversity patterns is now recognized, but quantitative approaches that explicitly link speciation, extinction and dispersal processes to palaeo-environmental changes are currently lacking. Here, we propose a spatial diversification model of lineages through time (SPLIT) based on the reconstruction of palaeo-environments. We illustrate our approach using mangroves as a case study and evaluate whether habitat changes caused by plate tectonics explain the current biodiversity patterns of this group.
Innovations: The SPLIT model allows one to simulate the evolutionary dynamics of species ranges by spatially linking speciation, extinction and dispersal processes to habitat changes over geological time periods. The SPLIT model provides a mechanistic expectation of speciation and extinction assuming that species are ecologically identical and not interacting. The likelihood of speciation and extinction is equivalent across species and depends on two dispersal parameters interacting with habitat dynamics (d a maximum dispersal distance and ds a distance threshold beyond which gene flow is absent). Beyond classical correlative approaches, this model tracks biodiversity dynamics under palaeo-environmental changes and provides multiple expectations (i.e., α-, β-diversity, phylogenies) that can be compared to empirical patterns.
Main conclusions: The SPLIT model allows a better understanding of the origin of biodiversity by explicitly accounting for habitat changes over geological times. The simulations applied to the mangrove case study reproduced the observed longitudinal gradient in species richness, the empirical pattern of β-diversity and also provided inference on diversification rates. Future developments may include niche evolution and species interactions to evaluate the importance of non-neutral mechanisms. The method is fully implemented in the InsideDNA platform for bioinformatics analyses, and all modelling results can be accessed via interactive web links.

Predicting variation in plant functional traits related to anti-herbivore defences remains a major challenge in ecological research, considering that multiple traits have evolved in response to both abiotic and biotic conditions. Therefore, understanding variation in plant anti-herbivore defence traits requires studying their expression along steep environmental gradients, such as along elevation, where multiple biotic and abiotic factors co-vary. We expand on plant defence theory and propose a novel conceptual framework to address the sources of variations of plant resistance traits at the community level. We analysed elevation patterns of within-community trait dissimilarity using the RaoQ index, and the community-weighted-mean (CWM) index, on several plant functional traits: plant height, specific leaf area (SLA), leaf-dry-matter-content (LDMC), silicium content, presence of trichomes, carbon-to-nitrogen ratio (CN) and total secondary metabolite richness. We found that at high elevation, where harsh environmental conditions persist, community functional convergence is dictated by traits relating to plant growth (plant height and SLA), while divergence arises for traits relating resource-use (LDMC). At low elevation, where greater biotic pressure occurs, we found a combination of random (plant height), convergence (metabolite richness) and divergence patterns (silicium content). This framework thus combines community assembly rules of ecological filtering and niche partition with plant defence hypotheses to unravel the relationship between environmental variations, biotic pressure and the average phenotype of plants within a community.

1. Climate change is expected to modify current ecological conditions sustaining the coexistence of species within cold‐adapted plant communities. It will primarily act upon the existing structure of communities, whose response should depend on the functional differences governing coexistence among alpine species. We postulated that a possible trade‐off between (1) plant growth in response to temperature, (2) plant competition and (3) resistance to herbivory, modulates the rate of plant community turnover under climate change.
2. We reviewed the literature investigating functional traits variability within communities along elevation gradients. Despite environmental filtering, our review indicates that interspecific plant functional variation within communities does not necessarily decrease with elevation. While the diversity of traits related to abiotic affinities or competition are well documented, the variability in species resistance to herbivory within communities is poorly known.
3. Using a Lotka–Volterra‐based model, we simulated the impact of climate change on plant communities through (1) a direct effect on plant growth and competition and (2) an indirect effect via an increase in herbivore pressure. We showed that different shapes of trade‐offs between temperature‐related growth, competition and resistance to herbivory modulate community turnover under climate change.
4. We documented the existence of two independent trade‐offs axes using a detailed dataset of functional traits within two alpine grasslands in the European Alps. Plant competitive traits (i.e. leaf area and height) where negatively associated to cold tolerance, whereas traits of the leaf economic spectrum were associated to traits related to the physical resistance to herbivory.
5. We propose that the immediate effect of climate change on alpine plant communities will depend on existing functional variability and how functional axes trade‐off with each other. Documenting ecological constraints between plant functional axes should provide indications to anticipate winners and losers in alpine plant communities.