Urban forest patches are a type of urban green infrastructure that can contribute to ecological functioning and sustainability in cities and to residents’ well-being. At the same time, urban forest patches are subject to a variety of pressures, including increasing housing demand; deposition of atmospheric pollutants; spread of invasive species, pests, and pathogens; urban heat island effects; and isolation effects on recruitment and dispersal. Many urban restoration and afforestation projects are being implemented as a result of growing awareness about the benefits of urban forest patches, and the potential of urban land to conserve local biodiversity and provide habitat for rare plants is also increasingly clear. Conceptualizing these forests as a social-ecological system and integrating systems thinking into land management decision-making are key for long-term efforts to promote health, sustainability, and conservation of these important habitats. We developed a conceptual model through an interdisciplinary process engaging social and ecological scientists and urban land management decision makers, with a focus on temperate forest social-ecological systems.
Results/Conclusions
We introduce a conceptual model of the urban forest patch as a complex social-ecological system, incorporating cross-scale interactions. This conceptual model identifies how spatial and temporal social-ecological drivers interact with patch-level conditions at multiple scales. In this model, we place the production and management of urban forest patches in historical perspective and within a broader regional context. We have identified a series of research questions to highlight future directions for research on urban forest patches and developed both qualitative and quantitative inquiries based on the model. Our integrative approach can provide insights into the role of social-ecological drivers in shaping forest health, biodiversity, and benefits forest patches provide to people in urban and urbanizing regions, with direct implications for decision-making to improve management outcomes.