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Developing a sound urban green infrastructure system is critical for addressing climate change. Green infrastructure (GI) also plays an essential role in the urban system by providing ecosystem services for urban residents. Although some research on GI has been published in Taiwan, there is a lack of understanding of how changes in land use and GI affect the landscape patterns in urban fringe areas. This study explores the impact of changes in GI on the landscape pattern of the urban fringe and urban core areas in the Taipei metropolitan area (TMA). We used intensity analysis to investigate changes in land area and land use intensity between 1981 and 2015 at three levels: interval, category, and transition. Landscape metrics were employed to analyze changes in GI patterns. We found, firstly, that although the rate of change in the urban core area of the TMA was faster than the rate of change in the urban fringe area during 1981-1995 and 1995-2006, changes in the urban fringe area nevertheless maintained a state of rapid change in 1995-2006 and 2006-2015. Secondly, forest and agricultural lands in urban fringe areas were the GI categories with the greatest changes in area from 1981 to 2015. Thirdly, transition areas among forest, agricultural, and built-up areas in urban fringe areas were larger during 1995-2015 than 1981-1995. Finally, the results of landscape pattern analysis indicate that the urban fringe area of the TMA is experiencing landscape fragmentation. Although forest land remained the main category in the urban fringe from 1981 to 2015, the integrity of its patch area decreased over time, and the prevalence of small and complex patches of built-up and agricultural land increased. Spatial planning should therefore consider how to build a GI system to promote ecosystem services in urban fringe areas so as to enhance the ability to respond to climate change., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Accurate information provided by reliable models is essential for identifying hotspots and mitigating roadkill. However, existing methods, such as kernel density estimation (KDE) and maximum entropy modeling (ME) may individually identify only a subset of the suitable locations for mitigation, because KDE cannot detect hotspots once local abundances are depressed, and ME may only partially identify current hotspots due to imperfect discrimination skill. Here, we propose a hybrid consensus modeling (HCM) approach that leverages the strengths of both KDE and ME by using their consensus to identify the core subset of hotspots. We collected herpetofauna (amphibians and reptiles) roadkill data (N = 839) along four roads in Taiwan (R.O.C.) to evaluate the statistical performance and theoretical mitigation efficiency of HCM, KDE and ME, and to compare the allocation among roads, spatial clustering, and environmental conditions in the identified hotspots. HCM was applied on the herpetofauna dataset as well as separately on amphibians and reptiles. Although the discrimination skill of KDE and ME models for both target clades together was good to excellent (AUC KDE  = 0.944, AUC ME  = 0.822), the highest theoretical mitigation efficiency, was displayed by HCM Consensus (2.89), followed by KDE (2.58), and ME (1.91). Furthermore, we show that theoretical mitigation efficiency increases with decreasing spatial clustering (Moran's I). Given pervasive budget constraints, we recommend to limit permanent mitigation measures such as fenced culverts to HCM Consensus hotspots, temporary measures to KDE hotspots, and to target additional monitoring at ME hotspots., (Copyright © 2021 Elsevier Ltd. All rights reserved.)