The Hengduan Mountains (HM) are located on the southeastern edge of the Tibetan Plateau (TP) and feature high mountain ridges (> 6000 m a.s.l.) separated by deep valleys. The HM region also features an exceptionally high biodiversity, believed to have emerged from the topography interacting with the climate. To investigate the role of the HM topography on regional climate, we conduct simulations with the regional climate model COSMO at high horizontal resolutions (at ~12 km and a convection-permitting scale of ~4.4 km) for the present-day climate. We conduct one control simulation with modern topography and two idealised experiments with modified topography, inspired by past geological processes that shaped the mountain range. In the first experiment, we reduce the HM’s elevation by applying a spatially non-uniform scaling to the topography. The results show that, following the uplift of the HM, the local rainy season precipitation increases by ~25%. Precipitation in Indochina and the Bay of Bengal (BoB) also intensifies. Additionally, the cyclonic circulation in the BoB extends eastward, indicating an intensification of the East Asian summer monsoon. In the second experiment, we remove the deep valley by applying an envelope topography to quantify the effects of terrain undulation with high amplitude and frequency on climate. On the western flanks of the HM, precipitation slightly increases, while the remaining fraction of the mountain range experiences ~20% less precipitation. Simulations suggest an overall positive feedback between precipitation, erosion, and valley deepening for this region, which could have influenced the diversification of local organisms.

1. Metabarcoding of environmental DNA (eDNA) has recently improved our understanding of biodiversity patterns in marine and terrestrial ecosystems. However, the complexity of these data prevents current methods to extract and analyze all the relevant ecological information they contain. Therefore, ecological modeling could greatly benefit from new methods providing better dimensionality reduction and clustering. 2. Here we present two new deep learning-based methods that combine different types of neural networks to ordinate eDNA samples and visualize ecosystem properties in a two-dimensional space: the first is based on variational autoencoders (VAEs) and the second on deep metric learning (DML). The strength of our new methods lies in the combination of several inputs: the number of sequences found for each molecular operational taxonomic unit (MOTU), together with the genetic sequence information of each detected MOTU within an eDNA sample. 3. Using three different datasets, we show that our methods represent well three different ecological indicators in a two-dimensional latent space: MOTU richness per sample, sequence α-diversity per sample, and sequence ꞵ-diversity between samples. We show that our nonlinear methods are better at extracting features from eDNA datasets while avoiding the major biases associated with eDNA. Our methods outperform traditional dimension reduction methods such as Principal Component Analysis, t-distributed Stochastic Neighbour Embedding, and Uniform Manifold Approximation and Projection for dimension reduction. 4. Our results suggest that neural networks provide a more efficient way of extracting structure from eDNA metabarcoding data, thereby improving their ecological interpretation and thus biodiversity monitoring.

Human activities can degrade the quality of coral reefs, cause a decline in fish species richness and functional diversity and an erosion of the ecosystem services provided. Environmental DNA metabarcoding (eDNA) has been proposed as an alternative to Underwater Visual Census (UVC) to offer more rapid assessment of marine biodiversity to meet management demands for ecosystem health indices. Taxonomic information derived from sequenced eDNA can be combined with functional traits and phylogenetic positions to generate a variety of ecological indices describing ecosystem functioning. Here, we inventoried reef fish assemblages of two contrasted coastal areas of Curaçao, (i) in close proximity to the island’s capital city and (ii) in a more remote area under more limited anthropogenic pressure. We sampled eDNA by filtering large volumes of sea water (2 x 30L) along 2km boat transects, which we coupled with species ecological properties related to habitat use, trophic level and body size to investigate the difference in fish taxonomic composition, functional and phylogenetic indices recovered from eDNA metabarcoding between these two distinct coastal areas. Despite no marked difference in species richness, we found a higher phylogenetic diversity in proximity to the city, but a higher functional diversity on the more isolated reef. Composition differences between coastal areas were associated with different frequencies of reef fish families. Because of a partial reference database, eDNA only partly matched those detected with UVC, but eDNA surveys nevertheless provided rapid and robust species occupancy responses to contrasted environments. eDNA metabarcoding coupled with functional and phylogenetic diversity assessment can serve the management of coastal habitats under increasing threat from global changes.

Forecasting the trajectories of species assemblages in response to ongoing climate change requires quantifying the time lags in the demographic and ecological processes through which climate impacts species' abundances. Since experimental climate manipulations are typically abrupt, the observed species responses may not match their responses to gradual climate change. We addressed this problem by transplanting alpine grassland turfs to lower elevations, recording species' demographic responses to climate and competition, and using these data to parameterize community dynamics models forced by scenarios of gradual climate change. We found that shifts in community structure following an abrupt climate manipulation were not simply accelerated versions of shifts expected under gradual warming, especially when they missed the rise of species benefiting from moderate warming. Time lags in demography and species interactions controlled the pace and trajectory of changing species' abundances under simulated 21st century climate change, and thereby prevented immediate diversity loss.

Understanding the traits mediating species' responses to climate change is a cornerstone for predicting future community composition and ecosystem function. Although species' eco-physiological properties determine their response to environmental change, most trait-based studies focus on a small subset of easily-measured morphological traits as proxies for physiology. This choice may limit our ability to predict the impacts of climate change on species' demography, and obscure the underlying mechanisms. We conducted a transplantation experiment along a 1000 m Alpine elevation gradient to quantify the degree to which plant demographic responses to climate were predicted by eco-physiological performance versus common morphological traits. Although physiological measurements revealed that warming favored species with a conservative water use strategy, the predictive power of physiological traits did not exceed that of morphological traits. Our results show that while easily-measured morphological traits can successfully predict demographic responses to climate, eco-physiological approaches are needed to understand mechanism.

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. Using a standardized DNA metabarcoding method applied to feces, we examined how structural properties of plant--orthoptera networks reflecting specialization and resilience vary with elevation. We found an increase in 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 importance of keystone species in alpine habitats. 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 ecosystems.