The effects of livestock and tourism on vegetation include loss of biodiversity and in some cases species extinction. To evaluate these stressor–effect relationships and provide a tool for managing them in Iran's Lar National Park, we developed a multilayer perceptron (MLP) artificial neural network model to predict vegetation diversity related to human activities. Recreation and restricted zones were selected as sampling areas with maximum and minimum human impacts. Vegetation diversity was measured as the number of species in 210 sample plots. Twelve landform and soil variables were also recorded and used in model development. Sensitivity analyses identified human intensity class and soil moisture as the most significant inputs influencing the MLP. The MLP was strong with R² values in training (0.91), validation (0.83), and test data sets (0.88). A graphical user interface was designed to make the MLP model accessible within an environmental decision support system tool for national park managers, thus enabling them to predict effects and develop proactive plans for managing human activities that influence vegetation diversity. Integr Environ Assess Manag 2021;17:42–52. © 2020 SETAC

中文翻译：

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人工神经网络模型预测人类活动对伊朗拉尔国家公园植被多样性的影响。

牲畜和旅游业对植被的影响包括生物多样性的丧失，在某些情况下还包括物种的灭绝。为了评估这些压力效应关系，并提供了在伊朗拉尔国家公园管理它们的工具，我们开发了多层感知器（MLP）人工神经网络模型来预测与人类活动有关的植被多样性。选择休闲区和限制区作为对人类影响最大和最小的采样区域。以210个样地中的物种数量来衡量植被的多样性。还记录了十二个地形和土壤变量，并将其用于模型开发。敏感性分析确定人类强度等​​级和土壤湿度是影响MLP的最重要输入。MLP的R ²实力很强训练（0.91），验证（0.83）和测试数据集（0.88）中的值。设计了图形用户界面，以使国家公园管理员可以在环境决策支持系统工具中访问MLP模型，从而使他们能够预测影响并制定主动计划来管理影响植被多样性的人类活动。整合环境评估管理2021; 17：42–52。©2020 SETAC