index.qmd

---
title: A Julia toolkit for species distribution data
authors:
  - name: Timothée Poisot
    affiliation:
        - ref: udem
    roles: writing
    email: timothee.poisot@umontreal.ca
    corresponding: true
affiliations:
    - id: udem
      name: Département de Sciences Biologiques, Université de Montréal, Montréal, Canada
bibliography: references.bib
engine: julia
abstract: LATER
---

## Introduction

ref to Kaas 2024 on integration - interoperability is done through interfaces, show some examples

## Application description

### Component packages

### Software information

### Integration with other packages

The **SDT** package benefits from close integration with other packages in the Julia universe. Notably, this includes **Makie** (and all related backends) for plotting and data visualisation, where usual plot types are overloaded for layer and occurrence data. Most data can be exported using the **Tables** interface, which allows data to be consumed by other packages like **DataFrames** and **MLJ**. Interfaces internal to Julia are also implemented whenever they make sense. Layers behave like arrays, are iterable, and broadcastable; occurrences collections are arrays and iterables. Beyong supporting external interfaces, **SDT** defines its own internally. Access to raster data is supported by a trait-based interface for **SimpleSDMDatasets**, and one of the component packages (**OccurrencesInterface** implements a minimalist interface to facilite the consumption of occurrence data.

Internal use of other interfaces like StatsAPI

## Worked out example

In this section, we provide a worked out example of (i) collecting data on historical and future climate, (ii) projecting these data into a new CRS, (iii) collecting occurrence data within a country boundaries, (iv) training a random-forest classifier for the species, and (v) transfering this model to future climate data. This provides an illutration of most of the components of the package.

### Collecting climate data

```{julia}
provider = RasterData{CHELSA2, BioClim}
```

### CS 2

something with clustering?

### CS 3

## References