[general]
name=QMaxent
qgisMinimumVersion=3.44
qgisMaximumVersion=3.99
description=Maxent species distribution modeling (SDM) in QGIS via the elapid Python library — full workflow from data preparation through post-prediction survey planning.
version=0.1.7
author=Byeong-Hyeok Yu
email=bhyu@knps.or.kr
about=QMaxent runs the full Maxent SDM workflow inside QGIS, integrating
    the elapid Python library so the user never has to leave the GIS.
    The Main Analysis Dock guides the user through five sequential tabs:

    ① Data:
    - Presence layer + environmental raster selection
    - Check Raster Consistency + Harmonize to Folder workflow
      (CRS, extent, resolution alignment)
    - One-click example datasets (Bradypus, Ariolimax) with
      Pre-harmonized and Mismatch demo variants
    - Export for external Maxent: Samples + Raster (samples CSV
      + .asc layers, single-command maxent.jar fit with projection
      raster) OR SWD (CSV pair, extracted values), each with a
      generic maxent.jar command-line README

    ② Parameters:
    - Feature types (LQPHT) — Auto (Phillips et al. 2017 sample-size
      rule) or Manual
    - Beta multiplier
    - Categorical variable support via one-hot encoding
    - Optional distance-weight bias correction (Phillips 2009)
    - Cross-validation: Geographic K-Fold (default), Random K-Fold,
      Checkerboard, Buffered LOO, or None (training AUC only)
    - Jackknife variable importance
    - Permutation importance (sklearn permutation_importance,
      configurable repeats; normalized as %% of total to match
      maxent.jar's convention)
    - Fixed random seed (default 42) for reproducibility

    ③ Training:
    - Live training log with deterministic progress bar
    - Clear log / Copy log / Save log as… buttons for manual export
    - training_log.txt auto-saved next to model.pkl at the end of
      Run Maxent

    ④ Results — four sub-tabs:
    - Response Curves: per-variable marginal-effect plots
    - Jackknife Importance: per-variable train + held-out test AUC
      bars
    - Permutation Importance: AUC-drop bars (%% of total), with
      Save PNG / Save CSV buttons for individual export
    - Spatial Projection: cloglog / logistic / raw output transforms;
      auto-styled habitat-suitability layer; save / load (.pkl) with
      guided variable-mapping dialog; optional auto-save of all four
      analysis charts (response curves, ROC, jackknife, permutation)
      as PNG alongside the prediction raster

    ⑤ Priority Sites for Survey:
    - Discovery mode: random or top-N sampling within a high-
      suitability band (auto-set to raster max × 0.9), with spacing
      constraints relative to existing presences and between
      candidate sites
    - Validation mode: stratified sampling across four suitability
      quartiles (Rhoden et al. 2017), with MTP / T10 / MaxSSS /
      Custom threshold methods defining the lower bound
    - OpenStreetMap Nominatim reverse geocoding (no API key)

    Auxiliary dialogs (outside the main flow):
    - Dependency Installer — first-run isolated-venv setup
    - Example Dataset Downloader — one-click example datasets

    Results export: multi-sheet styled XLSX (Times New Roman,
    academic-paper Supplementary Table convention) covering
    experimental setup, variable inventory, cross-validation,
    jackknife importance, permutation importance, and Priority Sites
    thresholds — ready to paste into a manuscript supplement.

    Bilingual UI (English / Korean). Dependencies install into a
    plugin-private virtual environment that does not affect QGIS
    (~590 MB on disk).
tracker=https://github.com/osgeokr/qmaxent/issues
repository=https://github.com/osgeokr/qmaxent
tags=species distribution model,maxent,sdm,ecology,biogeography,habitat suitability,spatial cross-validation,jackknife,priority sites,conservation,raster,vector,korean
homepage=https://osgeokr.github.io/qmaxent/
category=Analysis
icon=icons/icon.png
experimental=False
deprecated=False
hasProcessingProvider=False
