Ariolimax¶

The Pacific banana slug Ariolimax columbianus is the second worked example. Its purpose is different from Bradypus's feature tour — this dataset is deliberately messy: the supplied environmental rasters do not share a common CRS, extent, or resolution. The example walks through QMaxent's Check Raster Consistency preflight and Harmonize to Folder… workflow, showing the failure mode you would otherwise hit silently and the one-click fix that makes the data Maxent-ready.

Dataset¶

The Ariolimax dataset is the default that ships with elapid. After running Plugins → QMaxent → Download Example Dataset → Ariolimax, the layers appear on the QGIS canvas spanning California's coastal range. Presence points are coloured blue:

Ariolimax presence points overlaid on environmental rasters across coastal California

Visually the data already looks unified. The raster tiles, however, were authored by different remote-sensing pipelines and inherit different projections and resolutions — exactly the situation that breaks Maxent silently.

The mismatch problem¶

Open Plugins → QMaxent → QMaxent Analysis. On ① Data, choose the ariolimax-ca presence layer (3,732 points), then Add from project to register every loaded raster. Click Check Raster Consistency:

Data tab with Ariolimax loaded and Check Raster Consistency reporting Grid mismatch — CRS, extent, resolution differ across rasters

The status line turns amber and reports:

⚠ Grid mismatch — CRS, extent, resolution differ across rasters. Click "Harmonize to Folder…" to align.

Crucially, the Run Maxent button is not blocked — Maxent itself would still produce numbers. Those numbers, however, would be silently wrong: covariates would be sampled from the cells nominally underneath each presence point but actually belonging to misaligned rasters. This is the single most common silent-failure mode in operational SDM and the entire reason this preflight exists.

Running Harmonize to Folder…¶

A new button appears next to Check Raster Consistency as soon as a mismatch is detected: Harmonize to Folder…. Click it and choose an output directory. QMaxent picks the highest-resolution raster as the reference grid, reprojects every other raster to that grid using gdalwarp under the hood (nearest-neighbour for categoricals, bilinear for continuous), and writes new GeoTIFFs into the chosen folder. The new files are auto-loaded into the project and the old ones are removed from the QMaxent raster list.

The Data tab refreshes to show the harmonized stack:

Data tab with the harmonized stack — files prefixed 00_, 01_, … and Check Raster Consistency reporting All 6 rasters share grid (CRS:32610, 1000×1000)

The status line is now green:

✓ All 6 rasters share grid (CRS: EPSG:32610, resolution: 1000 × 1000).

Note the file-name convention: harmonized rasters get a 00_, 01_, … prefix that locks their order. This survives a .qgz save+reload cycle — the model variable order is part of the model's identity, and the prefix makes that order visible at file-system level too.

Running the model¶

With the stack harmonized, the rest of the workflow is identical to Bradypus. Accept the defaults on ② Parameters, click ▶ Run Maxent, and let the training complete. The 5-fold ROC curve shows healthy separation between training and CV performance:

ROC curve for Ariolimax — Training AUC 0.922, mean CV AUC 0.798

The jackknife panel confirms which environmental variables carry the species' signal:

Jackknife variable importance for Ariolimax — surface temperature stdv and leaf-area index mean dominate; cloud cover variables are weakest

05_ca-surfacetemp-stdv (variability of land-surface temperature) carries the strongest univariate signal — biologically sensible for an organism whose activity windows depend on cool, moist microclimates. The mean-temperature variable has lower stand-alone power but matters when removed (the without bar drops), which is the signature of a variable that contributes information not redundant with the others.

The full set of marginal response curves shows the partial dependence of predicted suitability on each variable across its training range:

Marginal response curves for the six Ariolimax variables

Comparing models with and without harmonization¶

We strongly recommend running this once on the unharmonized stack as a teaching exercise. With Maxent's permissive raster handling you will get a finished model and a finished AUC, but the AUC will typically be 0.05–0.10 higher than the harmonized run — not because the model is better, but because covariate misalignment introduces spurious patterns that the model fits to. The cross-validation gap (training vs. CV AUC) widens correspondingly. Always run Check Raster Consistency before drawing conclusions.

Priority sites for survey¶

After projection, switch to ⑤ Priority Sites for Survey, choose Discovery mode, and extract candidates. The sites land on the same coastal mountain ranges that the suitability map highlighted, and survey teams can take the resulting GeoPackage straight to the field:

Priority sites for Ariolimax overlaid on the suitability map

What this example demonstrates¶

The silent-failure mode of Maxent when rasters disagree, and
QMaxent's preflight + harmonize tooling that turns a project-killing mistake into a one-click fix.

Carry the same habit into your own work: every time you assemble a new raster stack, run Check Raster Consistency before training. If it fails, harmonize first, train second.