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38 lines
1.5 KiB
TeX
38 lines
1.5 KiB
TeX
\section{Supplementary: Feature Importance Analysis}
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\label{app:features}
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% ══════════════════════════════════════════════════════════════════════════════
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We analyze feature importance using integrated gradients
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\citep{chen2020deep} applied to the feature encoder. Across all 847
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species, the most influential variables are:
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\begin{table}[H]
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\centering
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\caption{Top 10 features by mean integrated gradient magnitude across all
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species.}
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\label{tab:features}
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\begin{tabular}{clc}
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\toprule
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\textbf{Rank} & \textbf{Feature} & \textbf{Importance} \\
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\midrule
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1 & Annual mean temperature (BIO1) & 0.187 \\
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2 & Annual precipitation (BIO12) & 0.156 \\
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3 & Elevation & 0.134 \\
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4 & Tree cover fraction & 0.121 \\
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5 & Temperature seasonality (BIO4) & 0.098 \\
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6 & NDVI mean & 0.089 \\
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7 & Precipitation seasonality (BIO15) & 0.076 \\
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8 & Distance to water & 0.062 \\
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9 & Slope & 0.048 \\
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10 & Temperature annual range (BIO7) & 0.041 \\
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\bottomrule
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\end{tabular}
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\end{table}
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Feature importance varies substantially across taxa: for amphibians,
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precipitation variables dominate; for birds, temperature and NDVI are
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most influential; for mammals, tree cover and elevation are primary
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drivers.
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