Mangroves are species of halophytic intertidal trees and shrubs derived from tropical genera and are likely delimited in latitudinal range by varying sensitivity to cold. There is now sufficient evidence that mangrove species have proliferated at or near their poleward limits on at least five continents over the past half century, at the expense of salt marsh. Avicennia is the most cold-tolerant genus worldwide, and is the subject of most of the observed changes. Avicennia germinans has extended in range along the USA Atlantic coast and expanded into salt marsh as a consequence of lower frost frequency and intensity in the southern USA. The genus has also expanded into salt marsh at its southern limit in Peru, and on the Pacific coast of Mexico. Mangroves of several species have expanded in extent and replaced salt marsh where protected within mangrove reserves in Guangdong Province, China. In south-eastern Australia, the expansion of Avicennia marina into salt marshes is now well documented, and Rhizophora stylosa has extended its range southward, while showing strong population growth within estuaries along its southern limits in northern New South Wales. Avicennia marina has extended its range southwards in South Africa. The changes are consistent with the poleward extension of temperature thresholds coincident with sea-level rise, although the specific mechanism of range extension might be complicated by limitations on dispersal or other factors. The shift from salt marsh to mangrove dominance on subtropical and temperate shorelines has important implications for ecological structure, function, and global change adaptation.

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H olding is one of the most commonly used real-time control strategies in transit operations. Given a transit network and its operations plan, the holding problem is to decide at a given time at a control station, which vehicle is to be held and for how long, such that the total passenger cost along the route is minimized over a time period. Previous research on the holding problem has always assumed no real-time information available. Such an assumption not only poses great difficulties in solving the problem, but also limits practical applications in a real-time, dynamic operations environment. In this paper we formulate the holding problem as a deterministic quadratic program in a rolling horizon scheme, and develop an efficient solution algorithm to solve it. Using headway data collected by an automated system, we tested the algorithm and evaluated the impact of the resulting holding policies. Important and interesting properties of the holding solution, obtained from both theoretical and computational analyses, are presented.Regardless of how well a transit schedule is designed, problems are likely to arise as the day progresses. The purpose of real-time operations control is to remedy these problems. In general, real-time control strategies can be divided into two main categories: station control and interstation control, though other strategies do exist. Station control strategies (see, for example, Eberlein 1995 andEberlein et al. 1998) are by far the most common, and we consider one such strategy here, namely holding.Put simply, holding is the process of intentionally delaying a vehicle at a station after passengers have alighted and boarded. Holding seems to be a common real-time control strategy because it is easier to implement and frustrates passengers less than station skipping strategies (e.g., deadheading, expressing and short-turning). The holding problem is to determine which vehicles should be held, when they should be held (i.e., at which station), and for how long they should be held. The objective of the holding problem is usually to minimize some aggregate measure of passenger cost.The holding problem has attracted considerable attention in the literature (see, for example, Osuna and Newell 1972, Barnett 1974, Newell 1974, Koffman et al. 1978, Turnquist and Blume 1980, Engelstein 1983, Powell 1985, and Abkowitz et al. 1984, 1986. Most of this research does not consider real-time information, ignores dwell time effects on headway variation, and ignores constraints on departure and layover times. Due to the complexity of the holding problem, only extremely simple analytical models have been developed. Even for the simplest form of holding strategy and idealized transit systems, researchers have shown the holding problem was difficult to analyze, and suggested that analytic models may not be very helpful. Hence, the results in these papers cannot be directly applied to the control of modern transit systems that collect real-time information (e.g., using automatic vehicle location (AVL), autom...

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