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The world has become urban; cities increasingly shape our worldviews, relation to other species, and the large-scale, long-term decisions we make. Cities are nature, but they need to align better with other ecosystems to avoid accelerating climate change and loss of biodiversity. We need a science to guide urban development across the diverse realities of global cities. This need can be met, in part, by shifts in urban ecology and its linkages to related sciences. This perspective is a "synthesis of syntheses", consolidating ideas from the other articles in the Special Section. It re-examines the role of urban ecology, and explores its integration with other disciplines that study cities. We conclude by summarizing the next steps in the ongoing shift in urban ecology, which is fast becoming an integral part of urban studies. [ABSTRACT FROM AUTHOR]

In this perspective, we present how three initial landmark papers on urban sustainability research contributed to the larger sustainability science scholarship and paved the way for the continued development of urban sustainability research. Based on this, we propose three conceptual innovation pathways to trace the progression of urban sustainability science: First, urban sustainability from a system's perspective, meaning that urban sustainability requires integrative solutions to work in the tripled social-ecological-technological system setting. Second, urban sustainability from a (people and place) relational perspective, meaning urban sustainability is a contested and dynamic social-ecological contract of cities. As a governance mission, urban sustainability requires evidence from research that can inform coordinated action to bridge people, places, meanings, visions and ecosystems. Third, urban sustainability from a transformative science perspective, meaning that for urban sustainability to be achieved and progressed, deep transformations are required in systems, relations, policies and governance approaches. Our proposal for the future of urban sustainability science centres on emphasizing the relevance and policy applicability of systems' thinking, value and place thinking and transitions/transformations thinking as fundamental to how knowledge is co-produced by research science, policy and society and becomes actionable. [ABSTRACT FROM AUTHOR]

We review the current state of knowledge about urban ecosystem services in New York City (NYC) and how these services are regulated, planned for, and managed. Focusing on ecosystem services that have presented challenges in NYC-including stormwater quality enhancement and flood control, drinking water quality, food provisioning and recreation-we find that mismatches between the scale of production and scale of management occur where service provision is insufficient. Adequate production of locally produced services and services which are more accessible when produced locally is challenging in the context of dense urban development that is characteristic of NYC. Management approaches are needed to address scale mismatches in the production and consumption of ecosystem services. By coordinating along multiple scales of management and promoting best management practices, urban leaders have an opportunity to ensure that nature and ecosystem processes are protected in cities to support the delivery of fundamental urban ecosystem services. [ABSTRACT FROM AUTHOR]

Cellular automata (CA) models have increasingly been used to simulate land use/cover changes (LUCC). Metaheuristic optimization algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA) have been recently introduced into CA frameworks to generate more accurate simulations. Although Markov Chain Monte Carlo (MCMC) is simpler than PSO and GA, it is rarely used to calibrate CA models. In this article, we introduce a novel multi-chain multi-objective MCMC (mc-MO-MCMC) CA model to simulate LUCC. Unlike the classical MCMC, the proposed mc-MO-MCMC is a multiple chains method that imports crossover operation from classical evolutionary optimization algorithms. In each new chain, after the initial one, the crossover operator generates the initial solution. The selection of solutions to be crossed over are made according to their fitness score. In this paper, we chose the example of New York City (USA) to apply our model to simulate three conflicting objectives of changes from non-urban to low-, medium- or high-density urban between 2001 and 2016 using USA National Land Cover Database (NLCD). Elevation, slope, Euclidean distance to highways and local roads, population volume and average household income are used as LUCC causative factors. Furthermore, to demonstrate the efficiency of our proposed model, we compare it with the multi-objective genetic algorithm (MO-GA) and standard single-chain multi-objective MCMC (sc-MO-MCMC). Our results demonstrate that mc-MO-MCMC produces accurate simulations of land use dynamics featured by faster convergence to the Pareto frontier comparing to MO-GA and sc-MO-MCMC. The proposed multi-objective cellular automata model should efficiently help to simulate a trade-off among multiple and, possibly, conflicting land use change dynamics at once. • This paper presents a multi-objective CA land use/cover change model. • The model uses a novel multi-objective Markov chain Monte Carlo (MO-MCMC). • We compare MO-MCMC with a multi-objective genetic algorithm (MO-GA). • We apply our model to simulate the expansion of multi-density urban areas in New York. • The results show that the MO-MCMC outperformed MO-GA in terms of accuracy and speed. [ABSTRACT FROM AUTHOR]