Spatial and evolutionary predictability of phytochemical diversity.

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.

DNA-based networks reveal the ecological determinants of plant-herbivore interactions along environmental gradients.

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.

ForestClim-Bioclimatic variables for microclimate temperatures of European forests.

Microclimate research gained renewed interest over the last decade and its importance for many ecological processes is increasingly being recognized. Consequently, the call for high-resolution microclimatic temperature grids across broad spatial extents is becoming more pressing to improve ecological models. Here, we provide a new set of open-access bioclimatic variables for microclimate temperatures of European forests at 25 × 25 m2 resolution.

ForestTemp - Sub-canopy microclimate temperatures of European forests.

Ecological research heavily relies on coarse-gridded climate data based on standardized temperature measurements recorded at 2 m height in open landscapes. However, many organisms experience environmental conditions that differ substantially from those captured by these macroclimatic (i.e. free air) temperature grids. In forests, the tree canopy functions as a thermal insulator and buffers sub-canopy microclimatic conditions, thereby affecting biological and ecological processes. To improve the assessment of climatic conditions and climate-change-related impacts on forest-floor biodiversity and functioning, high-resolution temperature grids reflecting forest microclimates are thus urgently needed. Combining more than 1200 time series of in situ near-surface forest temperature with topographical, biological and macroclimatic variables in a machine learning model, we predicted the mean monthly offset between sub-canopy temperature at 15 cm above the surface and free-air temperature over the period 2000-2020 at a spatial resolution of 25 m across Europe. This offset was used to evaluate the difference between microclimate and macroclimate across space and seasons and finally enabled us to calculate mean annual and monthly temperatures for European forest understories. We found that sub-canopy air temperatures differ substantially from free-air temperatures, being on average 2.1°C (standard deviation ± 1.6°C) lower in summer and 2.0°C higher (±0.7°C) in winter across Europe. Additionally, our high-resolution maps expose considerable microclimatic variation within landscapes, not captured by the gridded macroclimatic products. The provided forest sub-canopy temperature maps will enable future research to model below-canopy biological processes and patterns, as well as species distributions more accurately.

Contrasting responses of above- and below-ground herbivore communities along elevation.

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.

Ecological constraints coupled with deep-time habitat dynamics predict the latitudinal diversity gradient in reef fishes.

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.

Plant physical and chemical defence variation along elevation gradients: a functional trait-based approach.

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.

Forecasted coral reef decline in marine biodiversity hotspots under climate change.

Coral bleaching events threaten coral reef habitats globally and cause severe declines of local biodiversity and productivity. Related to high sea surface temperatures (SST), bleaching events are expected to increase as a consequence of future global warming. However, response to climate change is still uncertain as future low-latitude climatic conditions have no present-day analogue. Sea surface temperatures during the Eocene epoch were warmer than forecasted changes for the coming century, and distributions of corals during the Eocene may help to inform models forecasting the future of coral reefs. We coupled contemporary and Eocene coral occurrences with information on their respective climatic conditions to model the thermal niche of coral reefs and its potential response to projected climate change. We found that under the RCP8.5 climate change scenario, the global suitability for coral reefs may increase up to 16% by 2100, mostly due to improved suitability of higher latitudes. In contrast, in its current range, coral reef suitability may decrease up to 46% by 2100. Reduction in thermal suitability will be most severe in biodiversity hotspots, especially in the Indo-Australian Archipelago. Our results suggest that many contemporary hotspots for coral reefs, including those that have been refugia in the past, spatially mismatch with future suitable areas for coral reefs posing challenges to conservation actions under climate change.

Adapting to change: Exploring the consequences of climate-induced host plant shifts in two specialist Lepidoptera species.

Asynchronous migration of insect herbivores and their host plants towards higher elevations following climate warming is expected to generate novel plant-insect interactions. While the disassociation of specialised interactions can challenge species' persistence, consequences for specialised low-elevation insect herbivores encountering novel high-elevation plants under climate change remain largely unknown. To explore the ability of two low-elevation Lepidoptera species, Melitaea celadussa and Zygaena filipendulae, to undergo shifts from low- to high-elevation host plants, we combined a translocation experiment performed at two elevations in the Swiss Alps with experiments conducted under controlled conditions. Specifically, we exposed M. celadussa and Z. filipendulae to current low- and congeneric high-elevation host plants, to test how shifts in host plant use impact oviposition probability, number of eggs clutches laid, caterpillar feeding preference and growth, pupation rate and wing size. While our study shows that both M. celadussa and Z. filipendulae can oviposit and feed on novel high-elevation host plants, we reveal strong preferences towards ovipositing and feeding on current low-elevation host plants. In addition, shifts from current low- to novel high-elevation host plants reduced pupation rates as well as wing size for M. celadussa, while caterpillar growth was unaffected by host plant identity for both species. Our study suggests that populations of M. celadussa and Z. filipendulae have the ability to undergo host plant shifts under climate change. However, these shifts may impact the ability of populations to respond to rapid climate change by altering developmental processes and morphology. Our study highlights the importance of considering altered biotic interactions when predicting consequences for natural populations facing novel abiotic and biotic environments.

Multispecies deep learning using citizen science data produces more informative plant community models.

In the age of big data, scientific progress is fundamentally limited by our capacity to extract critical information. Here, we map fine-grained spatiotemporal distributions for thousands of species, using deep neural networks (DNNs) and ubiquitous citizen science data. Based on 6.7 M observations, we jointly model the distributions of 2477 plant species and species aggregates across Switzerland with an ensemble of DNNs built with different cost functions. We find that, compared to commonly-used approaches, multispecies DNNs predict species distributions and especially community composition more accurately. Moreover, their design allows investigation of understudied aspects of ecology. Including seasonal variations of observation probability explicitly allows approximating flowering phenology; reweighting predictions to mirror cover-abundance allows mapping potentially canopy-dominant tree species nationwide; and projecting DNNs into the future allows assessing how distributions, phenology, and dominance may change. Given their skill and their versatility, multispecies DNNs can refine our understanding of the distribution of plants and well-sampled taxa in general.