Showing 16 of total 16 results (show query)
pbs-assess
sdmTMB:Spatial and Spatiotemporal SPDE-Based GLMMs with 'TMB'
Implements spatial and spatiotemporal GLMMs (Generalized Linear Mixed Effect Models) using 'TMB', 'fmesher', and the SPDE (Stochastic Partial Differential Equation) Gaussian Markov random field approximation to Gaussian random fields. One common application is for spatially explicit species distribution models (SDMs). See Anderson et al. (2024) <doi:10.1101/2022.03.24.485545>.
Maintained by Sean C. Anderson. Last updated 18 hours ago.
ecologyglmmspatial-analysisspecies-distribution-modellingtmbcpp
205 stars 11.01 score 848 scripts 1 dependents
rvalavi
blockCV:Spatial and Environmental Blocking for K-Fold and LOO Cross-Validation
Creating spatially or environmentally separated folds for cross-validation to provide a robust error estimation in spatially structured environments; Investigating and visualising the effective range of spatial autocorrelation in continuous raster covariates and point samples to find an initial realistic distance band to separate training and testing datasets spatially described in Valavi, R. et al. (2019) <doi:10.1111/2041-210X.13107>.
Maintained by Roozbeh Valavi. Last updated 5 months ago.
cross-validationspatialspatial-cross-validationspatial-modellingspecies-distribution-modellingcpp
113 stars 10.49 score 302 scripts 3 dependents
evolecolgroup
tidysdm:Species Distribution Models with Tidymodels
Fit species distribution models (SDMs) using the 'tidymodels' framework, which provides a standardised interface to define models and process their outputs. 'tidysdm' expands 'tidymodels' by providing methods for spatial objects, models and metrics specific to SDMs, as well as a number of specialised functions to process occurrences for contemporary and palaeo datasets. The full functionalities of the package are described in Leonardi et al. (2023) <doi:10.1101/2023.07.24.550358>.
Maintained by Andrea Manica. Last updated 26 days ago.
species-distribution-modellingtidymodels
31 stars 8.82 score 51 scriptsevolecolgroup
pastclim:Manipulate Time Series of Climate Reconstructions
Methods to easily extract and manipulate climate reconstructions for ecological and anthropological analyses, as described in Leonardi et al. (2023) <doi:10.1111/ecog.06481>. The package includes datasets of palaeoclimate reconstructions, present observations, and future projections from multiple climate models.
Maintained by Andrea Manica. Last updated 20 days ago.
climate-datapaleoclimatespecies-distribution-modelling
38 stars 8.12 score 49 scripts
theoreticalecology
sjSDM:Scalable Joint Species Distribution Modeling
A scalable and fast method for estimating joint Species Distribution Models (jSDMs) for big community data, including eDNA data. The package estimates a full (i.e. non-latent) jSDM with different response distributions (including the traditional multivariate probit model). The package allows to perform variation partitioning (VP) / ANOVA on the fitted models to separate the contribution of environmental, spatial, and biotic associations. In addition, the total R-squared can be further partitioned per species and site to reveal the internal metacommunity structure, see Leibold et al., <doi:10.1111/oik.08618>. The internal structure can then be regressed against environmental and spatial distinctiveness, richness, and traits to analyze metacommunity assembly processes. The package includes support for accounting for spatial autocorrelation and the option to fit responses using deep neural networks instead of a standard linear predictor. As described in Pichler & Hartig (2021) <doi:10.1111/2041-210X.13687>, scalability is achieved by using a Monte Carlo approximation of the joint likelihood implemented via 'PyTorch' and 'reticulate', which can be run on CPUs or GPUs.
Maintained by Maximilian Pichler. Last updated 1 months ago.
deep-learninggpu-accelerationmachine-learningspecies-distribution-modellingspecies-interactions
69 stars 7.64 score 70 scripts
lifewatch
sdmpredictors:Species Distribution Modelling Predictor Datasets
Terrestrial and marine predictors for species distribution modelling from multiple sources, including WorldClim <https://www.worldclim.org/>,, ENVIREM <https://envirem.github.io/>, Bio-ORACLE <https://bio-oracle.org/> and MARSPEC <http://www.marspec.org/>.
Maintained by Salvador Fernandez. Last updated 2 years ago.
bio-oraclelifewatchlifewatchvlizspecies-distribution-modelling
30 stars 7.47 score 218 scripts
consbiol-unibern
SDMtune:Species Distribution Model Selection
User-friendly framework that enables the training and the evaluation of species distribution models (SDMs). The package implements functions for data driven variable selection and model tuning and includes numerous utilities to display the results. All the functions used to select variables or to tune model hyperparameters have an interactive real-time chart displayed in the 'RStudio' viewer pane during their execution.
Maintained by Sergio Vignali. Last updated 4 months ago.
hyperparameter-tuningspecies-distribution-modellingvariable-selectioncpp
25 stars 7.37 score 155 scripts
cmerow
rangeModelMetadata:Provides Templates for Metadata Files Associated with Species Range Models
Range Modeling Metadata Standards (RMMS) address three challenges: they (i) are designed for convenience to encourage use, (ii) accommodate a wide variety of applications, and (iii) are extensible to allow the community of range modelers to steer it as needed. RMMS are based on a data dictionary that specifies a hierarchical structure to catalog different aspects of the range modeling process. The dictionary balances a constrained, minimalist vocabulary to improve standardization with flexibility for users to provide their own values. Merow et al. (2019) <DOI:10.1111/geb.12993> describe the standards in more detail. Note that users who prefer to use the R package 'ecospat' can obtain it from <https://github.com/ecospat/ecospat>.
Maintained by Cory Merow. Last updated 9 months ago.
ecological-metadata-languageecological-modellingecological-modelsecologyspecies-distribution-modellingspecies-distributions
6 stars 6.96 score 16 scripts 3 dependents
hemingnm
SESraster:Raster Randomization for Null Hypothesis Testing
Randomization of presence/absence species distribution raster data with or without including spatial structure for calculating standardized effect sizes and testing null hypothesis. The randomization algorithms are based on classical algorithms for matrices (Gotelli 2000, <doi:10.2307/177478>) implemented for raster data.
Maintained by Neander Marcel Heming. Last updated 5 months ago.
null-modelsrandomizationrasterspatialspatial-analysisspecies-distribution-modelling
7 stars 6.61 score 32 scripts 2 dependents
r-a-dobson
dynamicSDM:Species Distribution and Abundance Modelling at High Spatio-Temporal Resolution
A collection of novel tools for generating species distribution and abundance models (SDM) that are dynamic through both space and time. These highly flexible functions incorporate spatial and temporal aspects across key SDM stages; including when cleaning and filtering species occurrence data, generating pseudo-absence records, assessing and correcting sampling biases and autocorrelation, extracting explanatory variables and projecting distribution patterns. Throughout, functions utilise Google Earth Engine and Google Drive to minimise the computing power and storage demands associated with species distribution modelling at high spatio-temporal resolution.
Maintained by Rachel Dobson. Last updated 1 months ago.
dynamicsdmgoogle-earth-enginegoogledrivesdmspatiotemporalspatiotemporal-data-analysisspatiotemporal-forecastingspecies-distribution-modellingspecies-distributions
6 stars 6.16 score 20 scripts
bmaitner
S4DM:Small Sample Size Species Distribution Modeling
Implements a set of distribution modeling methods that are suited to species with small sample sizes (e.g., poorly sampled species or rare species). While these methods can also be used on well-sampled taxa, they are united by the fact that they can be utilized with relatively few data points. More details on the currently implemented methodologies can be found in Drake and Richards (2018) <doi:10.1002/ecs2.2373>, Drake (2015) <doi:10.1098/rsif.2015.0086>, and Drake (2014) <doi:10.1890/ES13-00202.1>.
Maintained by Brian S. Maitner. Last updated 2 months ago.
open-sciencerange-modellingrare-speciesspecies-distribution-modelingspecies-distribution-modelling
4 stars 5.97 score 33 scripts
lleisong
itsdm:Isolation Forest-Based Presence-Only Species Distribution Modeling
Collection of R functions to do purely presence-only species distribution modeling with isolation forest (iForest) and its variations such as Extended isolation forest and SCiForest. See the details of these methods in references: Liu, F.T., Ting, K.M. and Zhou, Z.H. (2008) <doi:10.1109/ICDM.2008.17>, Hariri, S., Kind, M.C. and Brunner, R.J. (2019) <doi:10.1109/TKDE.2019.2947676>, Liu, F.T., Ting, K.M. and Zhou, Z.H. (2010) <doi:10.1007/978-3-642-15883-4_18>, Guha, S., Mishra, N., Roy, G. and Schrijvers, O. (2016) <https://proceedings.mlr.press/v48/guha16.html>, Cortes, D. (2021) <arXiv:2110.13402>. Additionally, Shapley values are used to explain model inputs and outputs. See details in references: Shapley, L.S. (1953) <doi:10.1515/9781400881970-018>, Lundberg, S.M. and Lee, S.I. (2017) <https://dl.acm.org/doi/abs/10.5555/3295222.3295230>, Molnar, C. (2020) <ISBN:978-0-244-76852-2>, Štrumbelj, E. and Kononenko, I. (2014) <doi:10.1007/s10115-013-0679-x>. itsdm also provides functions to diagnose variable response, analyze variable importance, draw spatial dependence of variables and examine variable contribution. As utilities, the package includes a few functions to download bioclimatic variables including 'WorldClim' version 2.0 (see Fick, S.E. and Hijmans, R.J. (2017) <doi:10.1002/joc.5086>) and 'CMCC-BioClimInd' (see Noce, S., Caporaso, L. and Santini, M. (2020) <doi:10.1038/s41597-020-00726-5>.
Maintained by Lei Song. Last updated 2 years ago.
isolation-forestoutlier-detectionpresence-onlymodelshapley-valuespecies-distribution-modelling
4 stars 5.59 score 65 scripts
miguel-porto
eicm:Explicit Interaction Community Models
Model fitting and species biotic interaction network topology selection for explicit interaction community models. Explicit interaction community models are an extension of binomial linear models for joint modelling of species communities, that incorporate both the effects of species biotic interactions and the effects of missing covariates. Species interactions are modelled as direct effects of each species on each of the others, and are estimated alongside the effects of missing covariates, modelled as latent factors. The package includes a penalized maximum likelihood fitting function, and a genetic algorithm for selecting the most parsimonious species interaction network topology.
Maintained by Miguel Porto. Last updated 2 years ago.
community-modelingecological-modellinginteraction-networkjoint-modelsjsdmmissing-covariatesspecies-distribution-modellingspecies-interactions
6 stars 4.48 score 2 scripts
iiasa
ibis.iSDM:Modelling framework for integrated biodiversity distribution scenarios
Integrated framework of modelling the distribution of species and ecosystems in a suitability framing. This package allows the estimation of integrated species distribution models (iSDM) based on several sources of evidence and provided presence-only and presence-absence datasets. It makes heavy use of point-process models for estimating habitat suitability and allows to include spatial latent effects and priors in the estimation. To do so 'ibis.iSDM' supports a number of engines for Bayesian and more non-parametric machine learning estimation. Further, the 'ibis.iSDM' is specifically customized to support spatial-temporal projections of habitat suitability into the future.
Maintained by Martin Jung. Last updated 5 months ago.
bayesianbiodiversityintegrated-frameworkpoisson-processscenariossdmspatial-grainspatial-predictionsspecies-distribution-modelling
21 stars 4.36 score 12 scripts 1 dependents
lance-waller-lab
envi:Environmental Interpolation using Spatial Kernel Density Estimation
Estimates an ecological niche using occurrence data, covariates, and kernel density-based estimation methods. For a single species with presence and absence data, the 'envi' package uses the spatial relative risk function that is estimated using the 'sparr' package. Details about the 'sparr' package methods can be found in the tutorial: Davies et al. (2018) <doi:10.1002/sim.7577>. Details about kernel density estimation can be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>. More information about relative risk functions using kernel density estimation can be found in J. F. Bithell (1991) <doi:10.1002/sim.4780101112>.
Maintained by Ian D. Buller. Last updated 5 months ago.
ecological-nicheecological-niche-modellinggeospatialgeospatial-analysiskernel-density-estimationniche-modelingniche-modellingnon-euclidean-spacespoint-patternpoint-pattern-analysisprincipal-component-analysisspatial-analysisspecies-distribution-modelingspecies-distribution-modelling
1 stars 4.22 score 33 scripts
howl-anderson
sdmvspecies:Create Virtual Species for Species Distribution Modelling
A software package help user to create virtual species for species distribution modelling. It includes several methods to help user to create virtual species distribution map. Those maps can be used for Species Distribution Modelling (SDM) study. SDM use environmental data for sites of occurrence of a species to predict all the sites where the environmental conditions are suitable for the species to persist, and may be expected to occur.
Maintained by Xiaoquan Kong. Last updated 9 years ago.
r-languagespecies-distribution-modellingvirtual-species
1 stars 3.70 score 8 scripts