An integrated sequential deep learning framework with experience replay for large-scale species distribution modeling under data scarcity

Large-scale species distribution models (SDMs) across many taxa are constrained by data scarcity, sampling bias, and environmental heterogeneity. We present an integrated framework combining environmentally informed sampling with sequential deep learning for large-scale multispecies SDMs, demonstrated using 1, 359 food-relevant plant species in South America. A deep feedforward neural network is trained sequentially across species using experience replay to stabilize learning under species imbalance. Sampling integrates historical climate, soil, and land-cover predictors through geographic thinning, ecoregion-constrained augmentation, and background stratification based on multivariate environmental similarity. Performance is evaluated against established SDM algorithms using independent continental and national test datasets, while a future climate projection is used to benchmark model robustness. Results show stable performance across species niche breadths, improved sensitivity for taxa with restricted environmental tolerances, and consistent rankings across scales and climate conditions. However, this stability yields smoother suitability surfaces that may obscure fine-scale niche contrasts. Furthermore, the analyses reveal that algorithmic behavior under environmental novelty is driven primarily by data structure and sampling design rather than the specific type of algorithmic architecture. The framework is suited for large-scale multispecies SDMs under heterogeneous data when predictive stability and cross-scale consistency are prioritized over detailed single-species inference.