Damaris Zurell (@damariszurell)
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  • Global Rank 421,862 (Top 15 %)
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  • Registered about 8 years ago
  • Most used languages
    R
    57.1 %
    HTML
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  • Location ๐Ÿ‡ฉ๐Ÿ‡ช Germany
  • Country Total Rank 18,348
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Top repositories

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SSDM-JSDM

Codes for Zurell et al. (2020) Testing species assemblage predictions from stacked and joint species distribution models. Journal of Biogeography 47: 101-113. DOI: 10.1111/jbi.13608.
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EFForTS-workshop-2023

Upscaling workshop CRC-990 EFForTS Gรถttingen 14-Feb-2023
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4D-niche-overlap

Codes for: Zurell D, Gallien L, Graham CH, Zimmermann NE (2018) Do long-distance migratory birds track their niche through seasons? Journal of Biogeography 45: 1459-1468.doi: 10.1111/jbi.13351
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damariszurell.github.io

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EEC-SDM

The course **Introduction to species distribution modelling*** is part of the Master module "Macroecology and global change" in the Master programme "Ecology, Evolution and Conservation (EEC)" at the University of Potsdam.
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Rcodes_MapNovelEnvironments_SDMs

Inflated response curves and environmental overlap masks for species distribution models SDMs (R codes). Here you can download the R codes for inflated SDM response curves and environmental overlap masks introduced in Zurell et al. (2012) DDI. These simple functions facilitate visualisation of multi-dimensional SDM response and of model extrapolations to novel (=unsampled) environments. Inflated response curves are an extension of conventional partial dependence plots that show the effects of one variable on the response while accounting not only for the average effects of all other variables but also for minimum and maximum (and median and quartile) values. Thus, they are basically an abstracted 2D version of the multidimensional response surfaces. Their advantages are that (1) they are explicit about the shape of the response at different values of all other variables, and (2) make the responses clear if interactions are (implicitly or explicitly) included in SDMs. Environmental overlap masks compare two datasets (sampled data and prediction data, e.g. climate change scenario or different geographical area) and identify novel environments, meaning both environmental conditions beyond the sampled ranges of the single variables and novel combinations of environmental variables.
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EEC-Macro

R practicals for the course "Macroecological analyses" at Univ. Potsdam
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SDM-Intro

Introduction to species distribution modelling
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RangeshiftR-tutorial

This tutorial will introduce the main features of the new R package RangeShiftR. Examples follow those provided in the original RangeShifter publication (Bocedi et al. 2014): https://doi.org/10.1111/2041-210X.12162.
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EEC-MGC

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