A landscape genomics approach to understanding and predicting species distribution

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Related publications (31)

Evaluation of genetic demultiplexing of single-cell sequencing data from model species

Single-cell sequencing (sc-seq) provides a species agnostic tool to study cellular processes. However, these technologies are expensive and require sufficient cell quantities and biological replicates to avoid artifactual results. An option to address thes ...

LIFE SCIENCE ALLIANCE LLC2023

Social media and deep learning reveal specific cultural preferences for biodiversity

Devis Tuia

Social media has created new opportunities to map cultural ecosystem services (CES) related to biodiversity at large scales. However, using these novel data to understand people's preferences in relation to these CES remains a challenge. To address this, w ...

2023

A landmark environmental law looks ahead

Devis Tuia

In late December 1973, the United States enacted what some would come to call “the pitbull of environmental laws.” In the 50 years since, the formidable regulatory teeth of the Endangered Species Act (ESA) have been credited with considerable successes, ob ...

American Association for the Advancement of Science2023

Using continuous electrical conductivity measurements to derive major solute concentrations in karst systems

Paolo Benettin

Hydrochemical data of karst springs provide valuable insights into the internal hydrodynamical functioning of karst systems and support model structure identification. However, the collection of high-frequency time series of major solute species is limited ...

WILEY2023

Chronic warming and dry soils limit carbon uptake and growth despite a longer growing season in beech and oak

Yann Vitasse, Charlotte Grossiord, Margaux Clara Lou Didion-Gency

Progressively warmer and drier climatic conditions impact tree phenology and carbon cycling with large consequences for forest carbon balance. However, it remains unclear how individual impacts of warming and drier soils differ from their combined effects ...

Oxford Univ Press Inc2023

Distributional Regression and Autoregression via Optimal Transport

Laya Ghodrati

We present a framework for performing regression when both covariate and response are probability distributions on a compact and convex subset of

\R^d

. Our regression model is based on the theory of optimal transport and links the conditional Fr'echet m ...

EPFL2023

Interactions between beech and oak seedlings can modify the effects of hotter droughts and the onset of hydraulic failure

Charlotte Grossiord, Eugénie Isabelle Mas, Fernando Valladares, Hervé Cochard, Margaux Clara Lou Didion-Gency, Janisse Deluigi

center dot Mixing species with contrasting resource use strategies could reduce forest vulnerability to extreme events. Yet, how species diversity affects seedling hydraulic responses to heat and drought, including mortality risk, is largely unknown. Using ...

Wiley2023

Frequent asymmetric migrations suppress natural selection in spatially structured populations

Anne-Florence Raphaëlle Bitbol, Alia Abbara

Natural microbial populations often have complex spatial structures. This can impact their evolution, in particular the ability of mutants to take over. While mutant fixation probabilities are known to be unaffected by sufficiently symmetric structures, ev ...

Oxford Univ Press2023

Stay close, but not too close: aerial image analysis reveals patterns of social distancing in seal colonies

Devis Tuia, Benjamin Alexander Kellenberger

Many species aggregate in dense colonies. Species-specific spatial patterns provide clues about how colonies are shaped by various (a)biotic factors, including predation, temperature regulation or disease transmission. Using aerial imagery, we examined the ...

2023

Robust discrete choice models with t-distributed kernel errors

Michel Bierlaire, Prateek Bansal, Thomas Gasos

Outliers in discrete choice response data may result from misclassification and misreporting of the response variable and from choice behaviour that is inconsistent with modelling assumptions (e.g. random utility maximisation). In the presence of outliers, ...

2023

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Hugo Chao Cui, Florent Gérard Krzakala, Lenka Zdeborová

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

2023

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