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SDM

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  • The package allows users to create and evaluate ensembles of species distribution model (SDM) predictions. Functionality is offered through R functions or a GUI (R Shiny app). This tool can assist users in identifying spatial uncertainties and making informed conservation and management decisions.

  • A set of functions for reproducible and shareable analysis of models from an online repository, comparing and diagnostic models. The package has been developed specifically to improve reproducibility and comparability of SDMs in R by allowing users to encode entire SDM analyses as repeatable and extensible workflows consisting of independently executable, community-contributed modules. The module-workflow structure enables scientists to more easily create and share components of their analysis, and then, access, modify, reuse and combine the components of others.

  • The package provides a framework for generating virtual species distributions, a procedure increasingly used in ecology to improve species distribution models. It integrates the existing methodological approaches with the objective of generating virtual species distributions with increased ecological realism.

  • The package provides tools for designing comprehensive multi-factor SDM ensemble experiments, combining multiple sources of uncertainty (e.g. baseline climate, pseudo-absence realizations, SDM techniques, future projections) and allowing to assess their contribution to the overall spread of the ensemble projection. In addition, mopa is seamlessly integrated with the climate4R bundle and allows straightforward retrieval and post-processing of state-of-the-art climate datasets (including observations and climate change projections), thus facilitating the proper analysis of key uncertainty factors related to climate data.

  • A 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.

  • The packages provides a set of tools for post processing the outcomes of species distribution modeling exercises. It includes novel methods for comparing models and tracking changes in distributions through time. It further includes methods for visualizing outcomes, selecting thresholds, calculating measures of accuracy and landscape fragmentation statistics, etc. This package was made possible in part by financial support from the Australian Research Council & ARC Research Network for Earth System Science.

  • The package analyses species distribution models and evaluates their performance. It includes functions for variation partitioning, extracting variable importance, computing several metrics of model discrimination and calibration performance, optimizing prediction thresholds based on a number of criteria, performing multivariate environmental similarity surface (MESS) analysis, and displaying various analytical plots.

  • The package provides a likelihood-based approach to modeling species distributions using presence-only data. In contrast to the popular software program Maxent, this approach yields estimates of the probability of occurrence, which is a natural descriptor of a species' distribution.

  • Algorithms for presence-only (Bioclim, Domain, Mahalanobis distance), presence-background (Maxent), and presence-absence (BRT). The package includes functions for comparing models.

  • A set of functions for preparing data, training and evaluating dismo models, and comparing ecological niches. It complements to dismo. The package includes functions for implementing species distribution models (SDMs) and ecological niche models (ENMs). The heart of the package is a set of "training" functions which automatically tune and parameterize a model based on several popular algorithms (e.g., MaxEnt, GLMs, BRTs, GAMs, etc.). Ancillary tools include estimation of spatial sampling bias, model evaluation, and calculation of biotic velocity (speed and direction at which a species' range moves through time).