神经网络最大熵模型：带学习非线性的通用扩展，应用于沙漠蝗分布建模的时间序列
Neural-Network Maxent: a general extension with learned nonlinearity, applied to time-series for Desert Locust distribution modelling
- SISTEMA GmbH（SISTEMA有限公司）
- Beyond EO
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出RNN Maxent，将Maxent与GRU结合以学习非线性，用于沙漠蝗分布建模，在ROC AUC和F1指标上均优于标准Maxent。
AI中文摘要：
物种分布建模（Species Distribution Modelling, SDM）对于理解环境条件如何塑造生物多样性至关重要，尤其针对沙漠蝗（Schistocerca gregaria）这类破坏性害虫，其繁殖动态与快速变化的环境条件紧密相关。最大熵模型（Maxent）已成为仅利用存在数据的主流方法，但它依赖人工选择的特征变换的线性组合，限制了其捕捉生态监测中常见的非线性、时间关系的能力，这类监测中降水、土壤湿度和植被指数等协变量会随时间发生有意义的演变。标准实现将时间序列协变量展平为独立特征，丢失了携带关键信号的序列结构。我们提出RNN Maxent，这是Maxent框架的扩展，用神经网络（具体为门控循环单元（Gated Recurrent Unit, GRU））替代固定特征字典，通过反向传播端到端训练。该方法保留了Maxent仅利用存在数据的统计基础、背景归一化和概率校准，仅有的差异在于非线性是从数据中学习而非预先固定。我们利用RNN Maxent，基于ERA5 Land、MODIS和Sentinel 3的50天环境时间序列绘制沙漠蝗适宜栖息地图，在协变量与存在记录间保持7天间隔以生成预测行为。与标准Maxent相比，RNN Maxent在各指标上均提升了性能：ROC AUC为0.862（标准差0.036），而标准Maxent为0.792；F1值为0.671（标准差0.056），而标准Maxent为0.590。
英文摘要：
Species Distribution Modelling (SDM) is essential for understanding how environmental conditions shape biodiversity, particularly for destructive pests such as the Desert Locust (Schistocerca gregaria), whose breeding dynamics are tightly coupled to rapidly evolving environmental conditions. Maxent has become the dominant method for presence-only data, but its reliance on a linear combination of hand chosen feature transforms limits its ability to capture the nonlinear, temporal relationships common in ecological monitoring, where covariates such as precipitation, soil moisture, and vegetation indices evolve meaningfully over time. Standard implementations flatten time-series covariates into independent features, discarding sequential structure that carries critical signal. We introduce RNN Maxent, an extension of the Maxent framework that replaces the fixed feature dictionary with a neural network, specifically a Gated Recurrent Unit (GRU), trained end to end via backpropagation. The approach preserves Maxent's presence only statistical foundations, background normalization, and probability calibration, differing only in that the nonlinearity is learned from data rather than fixed in advance. We apply RNN Maxent to map suitable habitat for the Desert Locust using 50 day environmental time series derived from ERA5 Land, MODIS, and Sentinel 3, maintaining a 7 day gap between covariates and presence records to yield forecasting behavior. Compared against standard Maxent, RNN Maxent improves performance across metrics (ROC AUC 0.862 std 0.036 vs. 0.792; F1 0.671 std 0.056 vs. 0.590).
