These pages explain species distribution modeling (SDM) and how to carry out each step in nimo, from occurrence records to maps of habitat suitability. No programming is needed: every step is done in the nimo interface. Readers who already know SDM can go straight to the page they need. The…
Occurrence records are the foundation of any species distribution model: the model can only be as good as the records it learns from. This page explains where to find records, how to bring them into nimo, and what to check before modeling. Where do occurrence records come from? Common problems…
Pre-modeling prepares everything a model needs: clean records, a calibration area, relevant predictors, training and testing sets, and, for presence-only data, background points or pseudo-absences. Decisions taken at this stage often influence the final maps more than the choice of algorithm. The steps below follow the order of the Pre-Modeling…
With the modeling table ready (see Pre-modeling), models can be fitted, evaluated and combined. This page presents the algorithms available in nimo, their settings, how performance is measured, and how to combine models into an ensemble. Choosing algorithms No algorithm is best in every situation. Comparing several algorithms, and combining…
A fitted model is not the end of the analysis. This page shows how to understand what the models learned, map their predictions, and check where those predictions can be trusted. 1. Which predictors matter? Variable importance Variable importance measures how much a model relies on each predictor. nimo uses…
Science relies on analyses that others can check and repeat. nimo makes this possible without writing code: every step run in the app is recorded, with the exact options chosen, as an R script that reproduces the analysis. Why a script? Following the script in the app Open Script in…
This session goes through a complete analysis with the example data provided with nimo: records of the white-backed vulture (Gyps africanus) in Kruger National Park, South Africa. It takes about 30 minutes and uses the main features presented in the other documentation pages. After each step, a short explanation helps…
The Ensemble of Small Models (ESM) is a method proposed by Lomba et al. 2010 and Breiner et al. 2015 to address the challenges associated with modeling the spatial distributions of rare or poorly known species. This approach is particularly useful when occurrence data for these species are limited, which…