M
Manuela D'Amen
Researcher at University of Lausanne
Publications - 50
Citations - 2120
Manuela D'Amen is an academic researcher from University of Lausanne. The author has contributed to research in topics: Climate change & Species richness. The author has an hindex of 17, co-authored 42 publications receiving 1539 citations. Previous affiliations of Manuela D'Amen include National Research Council & Konrad Lorenz Institute for Evolution and Cognition Research.
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ecospat: an R package to support spatial analyses and modeling of species niches and distributions
Valeria Di Cola,Olivier Broennimann,Blaise Petitpierre,Frank T. Breiner,Manuela D'Amen,Christophe F. Randin,Robin Engler,Julien Pottier,Dorothea V. Pio,Anne Dubuis,Loïc Pellissier,Rubén G. Mateo,Wim Hordijk,Nicolas Salamin,Antoine Guisan +14 more
TL;DR: The aim of the ecospat package is to make available novel tools and methods to support spatial analyses and modeling of species niches and distributions in a coherent workflow and stimulate the use of comprehensive approaches in spatial modelling of species and community distributions.
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Within-taxon niche structure: niche conservatism, divergence and predicted effects of climate change
TL;DR: Two divergent datasets are addressed, one on sister species and subspecies from the European herpetofauna, the other on subspecies of breeding birds in North America, on patterns of within-species niche variation.
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Spatial predictions at the community level: from current approaches to future frameworks.
TL;DR: This review focuses on models that were developed for generating spatially explicit predictions of communities, with a particular focus on species richness, composition, relative abundance and related attributes, with special emphasis on conservation needs under climate change.
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Disentangling biotic interactions, environmental filters, and dispersal limitation as drivers of species co-occurrence
TL;DR: In this paper, the authors compare two community modelling frameworks that integrate a combination of environmental and spatial data to identify positive and negative species associations from presence-and absence matrices, and incorporate an additional comparison using joint species distribution models (JSDM).
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Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models
Manuela D'Amen,Anne Dubuis,Rui Fernandes,Julien Pottier,Loïc Pellissier,Loïc Pellissier,Antoine Guisan +6 more
TL;DR: A first test of the SESAM framework by integrating macroecological constraints into S-SDM predictions, and one that is able to improve compositional predictions is reported.