Towards the automation of Species Distribution Modelling: The AutoMaxEnt routine
Albaladejo Robles, G.; Abbate, J. L.; Redding, D. W.
Show abstract
Species distribution models are widely used for the evaluation and analysis of species spatial distributions and responses to environmental changes. Due to this, there's a wide diversity of tools and algorithms to perform these types of analyses. However, despite being a mature field of study, SDM still requires a large amount of research intervention and decision-making to produce reliable models. This is a natural consequence of each species unique response to environmental factors, but in practice it hinders the potential of SDMs to be used effectively and with confidence across large sets of species. Here we present the AutoMaxEnt repository, a collection of functions and tools for the automation of maximum entropy species distribution models. AutoMaxEnt offers a flexible environment to model species distribution models, automating most of the data pre-processing and preparation and giving the user the possibility of exploring multiple model scenarios automatically or with control over how those scenarios are generated. AutoMaxent offers model fit control, multiple background points generation algorithms, automatic model and variable selection, and study area configuration, among other parameters. AutoMaxEnt allows for the automation of model fitting, evaluation and model selection across different species, study areas and time periods.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- PointedSDMs -- an R package to help facilitate the construction of integrated species distribution models 95%
- embarcadero: Species distribution modelling with Bayesian additive regression trees in R 94%
- Thinking like a naturalist: enhancing computer vision of citizen science images by harnessing contextual data 93%
Similar papers in this journal
Similar papers in this journal
- Integrating biodiversity assessments into local conservation planning: the importance of assessing suitable data sources 94%
- A pipeline for assessing the quality of iNaturalist data and images and the importance of metadata and image quality control when using crowd-sourced databases. 93%
- Data stochasticity and model parametrisation impact the performance of species distribution models: insights from a simulation study 93%
Similar papers in this journal
- ALGR: A multi-purpose agricultural landscape generator in R 93%
- Automatic variable selection in ecological niche modeling: A case study using Cassins Sparrow (Peucaea cassinii) 92%
- SecSel, a new software tool for conservation prioritization that is applicable to ordinal-scale data for multiple biodiversity features 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.