damariszurell
I am a researcher working on topics related to macroecology and biodiversity modelling.
Universität PotsdamPotsdam, Germany
Pinned Repositories
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
damariszurell.github.io
EEC-Macro
R practicals for the course "Macroecological analyses" at Univ. Potsdam
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.
EFForTS-workshop-2023
Upscaling workshop CRC-990 EFForTS Göttingen 14-Feb-2023
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.
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.
RangeShifter-software-and-documentation
Executables, manuals and tutorial data for RangeShifter v2 GUI and batch mode application
RangeShiftR-package
⚠️ Archive ⚠️ of the old structure of the R package as interface to the RangeShifter simulation platform. Please use the new structure: https://github.com/RangeShifter/RangeShiftR-pkg
ODMAP
damariszurell's Repositories
damariszurell/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.
damariszurell/EFForTS-workshop-2023
Upscaling workshop CRC-990 EFForTS Göttingen 14-Feb-2023
damariszurell/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
damariszurell/damariszurell.github.io
damariszurell/EEC-Macro
R practicals for the course "Macroecological analyses" at Univ. Potsdam
damariszurell/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.
damariszurell/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.
damariszurell/EEC-MGC
damariszurell/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.
damariszurell/SDM-Intro
Introduction to species distribution modelling
damariszurell/CLEWS-EDB
Practicals for the CLEWS-Master module Ecosystem Dynamics and Biodiversity
damariszurell/EEC-QCB
damariszurell/EEC-R-prep
damariszurell/IBM_OptimalForaging
C++ source code for the individual-based model of optimal foraging in white storks introduced in Zurell et al. (2015) Oikos. The model predicts the spatial structure and breeding success of white stork populations in heterogeneous landscapes by explicitly simulating foraging behaviour and home range formation of individuals competing for resources. Because resource depletion is modelled explicitly, the model can predict the maximum carrying capacity of white stork breeding populations in different landscapes, and density-dependent breeding success by inducing fixed stork density levels below carrying capacity. Different behavioural options for the optimal foraging routine and the home range optimisation can be made, and the model also allows tracking individual movement paths. Please note that the code is not optimised for custom use. For any questions, please feel free to contact me via email.
damariszurell/sdmverse
sdmverse is a collaborative metapackage for listing all the metadata of the Species Distribution Modelling (SDM) packages. sdmverse includes a graphical interface available locally or online (see below). sdmverse integrates metadata from SDM packages and checks their validity against the CRAN metadata for perenity.