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species distribution modelling

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  • This package is a versatile tool that aims at (1) defining the minimum background extent necessary to fit Species Distribution Models reliable enough to extract ecologically relevant conclusions from them and (2) optimizing the modelling process in terms of computation demands. See Rotllan-Puig, X. & Traveset, A. (2021) https://www.sciencedirect.com/science/article/pii/S0304380020304191

  • 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.

  • Framework for microclimate and mechanistic niche models. It provides an interface to a suite of biophysical modelling algorithms including the Niche Mapper system.

  • 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.

  • This service aims at running a species distribution modelling (sdm) service based on Maximum Entropy (maxent) algorithm. It is intended to predict the potential geographic distribution of a species based on environmental variables. Maxent algorithm is a probabilistic modelling technique used for making predictions based on incomplete information or limited data. It is based on the principle of maximum entropy, which states that when making predictions, one should choose the probability distribution that is the least informative, or maximally uncertain, while still satisfying a set of known constraints. The application calculates the distribution model of a species by taking as input files a vector of presence and a group of raster files that represent the background or environmental layers and providing five output files: elapid_object.ela, maxent_prediction_model.tif, maxent_prediction_plot.png, dependency_plots.png, and auc_score.txt. The application can take as input also a set of parameters (optional), which have default values in case the user does not want to configure them.

  • 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.

  • A set of functions for error detection and correction in point data quality datasets that are used in species distribution modelling. Includes functions for parsing and converting coordinates into decimal degrees from various formats.