Information on available software

Selected Models

EFForTS-ABM

Within the subproject B10 of the ongoing collaborative research center EFForTS (Collaborative Research Centre 990: Ecological and Socioeconomic Functions of Tropical Lowland Rainforest Transformation Systems (Sumatra, Indonesia)) we developed the integrated ecological-economic land-use change model EFForTS-ABM. The land-use change model in its current form, consists of a socio-economic and an ecological submodel. Within the socio-economic submodel, households make land use decisions by evaluating alternative management options. These decisions indirectly affect the ecological functions, which are part of the ecological submodel. The model rules and parameterization are mainly based on the results from other sub-projects within the CRC 990.

Find more information and download the model on:
EFForTS-ABM PlosOne publication

Corresponding publication:
Dislich C, Hettig E, Salecker J, Heinonen J, Lay J, et al. (2018) Land-use change in oil palm dominated tropical landscapes—An agent-based model to explore ecological and socio-economic trade-offs. PLOS ONE 13(1): e0190506. https://doi.org/10.1371/journal.pone.0190506

As part of the Night of Science 2019, we have released a simplified version of EFForTS-ABM, the EFForTS-ABM education version. This version is available in German and English language and allows to explore the model dynamics with a simplified interface and game-like challenges. The model files and corresponding instructions are available on our github repository.

Contact:
Jan Salecker

EFForTS-LGraf

Selected R-Packages

NLMR

NLMR is an R package for simulating neutral landscape models (NLM). Designed to be a generic framework like NLMpy, it leverages the ability to simulate the most common NLM that are described in the ecological literature. NLMR builds on the advantages of the raster package and returns all simulation as RasterLayer objects, thus ensuring a direct compability to common GIS tasks and a flexible and simple usage. Furthermore, it simulates NLMs within a self-contained, reproducible framework.

Find more information on:
NLMR@github

Corresponding publication:
Sciaini M, Fritsch M, Scherer C, Simpkins CE. NLMR and landscapetools: An integrated environment for simulating and modifying neutral landscape models in R. Methods Ecol Evol. 2018;00:1–9. https://doi.org/10.1111/2041-210X.13076

Contact:
Marco Sciaini

landscapetools

Landscapetools provides utility functions to complete tasks involved in most landscape analysis. It includes functions to coerce raster data to the common tibble format and vice versa, it helps with flexible reclassification tasks of raster data and it provides a function to merge multiple raster. Furthermore, 'landscapetools' helps landscape scientists to visualize their data by providing optional themes and utility functions to plot single landscapes, rasterstacks, -bricks and lists of raster.

Find more information on:
landscapetools@github

Corresponding publication:
Sciaini M, Fritsch M, Scherer C, Simpkins CE. NLMR and landscapetools: An integrated environment for simulating and modifying neutral landscape models in R. Methods Ecol Evol. 2018;00:1–9. https://doi.org/10.1111/2041-210X.13076

Contact:
Matthias Fritsch

nlrx

The nlrx package provides tools to setup, run and analyze NetLogo model simulations in R. nlrx experiments use a similar structure as NetLogos Behavior Space experiments. However, nlrx offers more flexibility and additional tools for running and anlyzing complex simulation designs and sensitivity analyses. The user defines all information that is needed in an intuitive framework, using S4 class objects. Experiments are submitted from R to NetLogo via XML files that are dynamically written, based on specifications defined by the user. By nesting model calls in future environments, large simulation design with many runs can be executed in parallel. This also enables simulating NetLogo experiments on remote HPC machines.

Find more information on:
nlrx@github

Corresponding publication:
Salecker J, Sciaini M, Meyer KM, Wiegand K. The nlrx r package: A next‐generation framework for reproducible NetLogo model analyses. Methods Ecol Evol. 2019; 10:1854-1863. https://doi.org/10.1111/2041-210X.13286

Contact:
Jan Salecker

landscapemetrics

landscapemetrics is an R package for calculating landscape metrics for categorical landscape patterns in a tidy workflow. The package can be used as a drop-in replacement for FRAGSTATS (McGarigal et al. 2012), as it offers a reproducible workflow for landscape analysis in a single environment. Besides the calculation of widely used landscape metrics, the package also includes several utility functions such as connected components labeling, correlation between metrics or a sample function for metrics around spatial sample points.

Find more information on:
landscapemetrics@github

Citation:
Maximillian H.K. Hesselbarth, Marco Sciaini and Jakub Nowosad (2018). landscapemetrics: Landscape Metrics for Categorical Map Patterns. R package version 0.1.1. https://r-spatialecology.github.io/landscapemetrics

Contact:
Maximilian Hesselbarth

spatialDemography

The responses of species and populations to changes in the environment (e.g. changes in climate and land use) are often complex and difficult to predict. We have created the SpatialDemography model (R package: spatialdemography). The model is a spatially explicit, stage-structured, matrix-based metacommunity model, with the potential for modeling species’ and populations’ potential responses to environmental heterogeneity and change. The SpatialDemography model assumes a cellular landscape populated by organisms with four life stages: a mobile dispersing stage, two sessile non-reproductive stages, and a reproductive adult stage. Individuals are assumed to originate at the center of a given cell and disperse according to a specified dispersal kernel (e.g. log-normal). All adult individuals are capable of producing offspring. The model approach and framework are described in the context of a hypothetical example with multiple competing species in a four cell landscape. In this example simulation, both spatial location and species interactions were important for understanding population dynamics. SpatialDemography can be applied to questions where an understanding of transient and long-term demographic responses to spatiotemporal changes is desired. It is primarily applicable to metapopulations and metacommunities of organisms with early dispersal and sessile adults (i.e. modular organisms such as plants and some marine organisms). SpatialDemography differs from other population models in that it is spatially explicit, can incorporate biotic interactions, and is implemented in R.

Find more information on:
spatialDemography@github

Citation:
Keyel, A. C., Gerstenlauer, J. L. and Wiegand, K. (2016), SpatialDemography: a spatially explicit, stage‐structured, metacommunity model. Ecography, 39: 1129-1137. doi:10.1111/ecog.02295

multirich

The R package multirich provides functions to calculate Unique Trait Combinations (UTC) and scaled Unique Trait Combinations (sUTC) as measures of multivariate richness. The package can also calculate beta-diversity for trait richness and can partition this into nestedness-related and turnover components. The code will also calculate several measures of overlap.

Find more information on:
multirich@github

Citation:
Keyel, A. C. and Wiegand, K. (2016), Validating the use of unique trait combinations for measuring multivariate functional richness. Methods Ecol Evol, 7: 929-936. doi:10.1111/2041-210X.12558