very high resolution mapping; environmental carrying capacity; virtual simulations; artificial data; biotic interactions; plant and insect communities; global change impact ; integrating scales; Swiss Alps; species distribution models; assemblage modelling
Mateo R. (2017), Biodiversity Models: What If Unsaturation Is the Rule?, in Trends in Ecology & Evolution
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Di Cola V. (2017), ecospat: an R package to support spatial analyses and modeling of species niches and distributions, in Ecography
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D'Amen M. (2017), Improving spatial predictions of taxonomic, functional and phylogenetic diversity., in Journal of Ecology
, 106, 76-86.
Mod H. (2016), What we use is not what we know: environmental predictors in plant distribution models, in Journal of Vegetation Science
, 27, 1308-1322.
D'Amen M. (2015), Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models, in Journal of Biogeography
, 42, 1255-1266.
Guisan A., Climate change impact on mountain biodiversity, in Thomas E. Lovejoy and Lee Hannah (ed.), Yale University Press, New Haven.
D'Amen M., Disentangling biotic interactions, environmental filters, and dispersal limitation as drivers of species co-occurrence., in Ecography
, In press.
Baudraz Maude E.A., Learning from model errors: Can land use, edaphic and very high-resolution topo-climatic factors improve macroecological models of mountain grasslands?, in Journal of Biogeography
, In press.
Background. This project is currently my main group’s project. Through my previous SNF projects, robust distribution data have been collected on plants and insects in an intensively sampled study area of the Swiss Alps. These data were used to develop models for the current and future (under climate change) distribution of plant and insect species, and to attempt predicting communities by stacking individual species’ predictions (S-SDMs), according to community modelling schemes, such as the SESAM framework. Important limits to such species and assemblage modelling were however identified. Specific aims. In this follow-up SESAM’ALP project, I aim at overcoming these limitations by: (i) developing very-high-resolution environmental maps, and accordingly improve associated species distribution predictions, for the study area; (ii) test novel ways to quantify and integrate biotic interactions in S-SDMs and implement the use of macroecological environmental constraints on S-SDMs; (iii) integrate information from larger scales (e.g. invading/colonizing species, uncovered part of the niche) at the regional scale, (iv) test these approaches through novel virtual simulations ; and (v) use these improved models to develop novel regional multi-drivers scenarios of global change impact on plant and insect communities at very high-resolution in the Alps. Methods. Advanced statistical modelling and spatial analyses will be used to improve assemblage and macroecological modelling, and to test and quantify biotic interactions. Dispersal modelling will be used for predicting future distributions of native species, and to model the spread of invasive species. Scripts will be developed for the virtual ecologist approach.Expected value of the proposed project. The knowledge gained at the end of the project, and the new innovative approaches, tools and datasets delivered, should foster important advances in our capacity of modeling and predicting communities across entire landscapes. In particular, it should allow addressing partially the question: will plant and insect communities evolve into novel assemblages under global changes?