Rakholia Shrey, Yosef Reuven, Yadav Neelesh, Karimloo Laura, Pleitner Michaela, Kothari Ritvik
Bioinformatics Center, Forest Research Institute, Dehradun 248006, Uttarakhand, India.
Eilat Campus, Ben Gurion University of the Negev, P.O. Box 272, Eilat 881020, Israel.
Animals (Basel). 2025 Jun 22;15(13):1844. doi: 10.3390/ani15131844.
Urbanization, habitat fragmentation, and intensifying urban heat island (UHI) effects accelerate biodiversity loss and diminish ecological resilience in cities, particularly in climate-vulnerable regions. To address these challenges, we developed TreeGrid, a functionality-based spatial tree planning tool designed specifically for urban settings in the Northern Plains of India. The tool integrates species trait datasets, ecological scoring metrics, and spatial simulations to optimize tree placement for enhanced ecosystem service delivery, biodiversity support, and urban cooling. Developed within an R Shiny framework, TreeGrid dynamically computes biodiversity indices, faunal diversity potential, canopy shading, carbon sequestration, and habitat connectivity while simulating localized reductions in land surface temperature (LST). Additionally, we trained a deep neural network (DNN) model using tool-generated data to predict bird habitat suitability across diverse urban contexts. The tool's spatial optimization capabilities are also applicable to post-fire restoration planning in wildland-urban interfaces by guiding the selection of appropriate endemic species for revegetation. This integrated framework supports the development of scalable applications in other climate-impacted regions, highlighting the utility of participatory planning, predictive modeling, and ecosystem service assessments in designing biodiversity-inclusive and thermally resilient urban landscapes.
城市化、栖息地破碎化以及城市热岛（UHI）效应加剧，加速了生物多样性丧失，削弱了城市尤其是气候脆弱地区的生态恢复力。为应对这些挑战，我们开发了TreeGrid，这是一种基于功能的空间树木规划工具，专为印度北部平原的城市环境设计。该工具整合了物种特征数据集、生态评分指标和空间模拟，以优化树木布局，增强生态系统服务、支持生物多样性并实现城市降温。TreeGrid在R Shiny框架内开发，在模拟局部地表温度（LST）降低的同时，动态计算生物多样性指数、动物多样性潜力、树冠遮荫、碳固存和栖息地连通性。此外，我们使用该工具生成的数据训练了一个深度神经网络（DNN）模型，以预测不同城市环境下鸟类栖息地的适宜性。该工具的空间优化能力还适用于城市与荒野交界地区的火灾后恢复规划，通过指导选择合适的本地物种进行植被恢复。这种综合框架支持在其他受气候影响地区开发可扩展应用程序，突出了参与式规划、预测建模和生态系统服务评估在设计具有生物多样性包容性和热适应性的城市景观中的作用。
