Ecosystem health monitoring in the Shanghai-Hangzhou Bay Metropolitan Area: A hidden Markov modeling approach

Impact of Urbanization on Ecosystem Health: A Case Study in Zhuhai, China

“…The expanded constructed area and rapidly increasing population have caused an obvious decline in ESH [53]. These results are similar to studies in other rapidly developing cities in China such as Shanghai [46], Shenzhen [25] and Beijing [54]. From the temporal perspective, the results showed a clear declining trend in ecosystem conditions from 1999 to 2013, as the areas with weak health level increased by nearly six times during this period.…”

“…This implies that Zhuhai paid more attention to economic development and urban expansion during the study period without considering ESH protection. In areas with less human activity, such as mountains and nature reserves, existing studies have shown that ESH always remained at the relatively high level [46,55]. In Zhuhai City, there are many mountains and much forest land distributed in areas such as College Town and DouMen Town with low POPD, which also plays a certain role in ESH protection, indicating that the Zhuhai government carried out related management of the ecosystem of these regions [32].…”

“…A possible reason for this may be that urban areas continue to exchange energy and materials with neighboring areas. Changes of environmental elements (e.g., precipitation, temperature and carbon dioxide) in a given location are likely to propagate to surrounding areas through natural processes such as atmospheric movement and animal migration, of the ability of the surrounding areas to provide ESs [46,56]. This helps us to understand why not only urban centers, but also suburban areas, were observed to have large areas of low ESH and high urbanization.…”

“…Urbanization levels can be investigated from several respects: (1) population growth, which is the main feature of the development of a modern city; (2) economic development, which is the main means of urban development; (3) the expansion of constructed land, which links directly to population growth and economic development and (4) living standards improvement, which is the result of urbanization [45]. As it is difficult to obtain accurate socioeconomic data that measures the improvement of living standards, let alone assesses their spatial heterogeneity, this study used only the first three kinds of data to represent urbanization levels: the population growth by population density (POPD; person km −2 ), economic development by density of gross domestic product (GDPD; 10 4 yuan km −2 ) and expansion of constructed land by constructed area proportion (CAP) [46,47,48].…”

Uncovering ecological state dynamics with hidden Markov models

“…This is likely attributable to many factors, including the difficulty of obtaining and integrating observational data at the large spatio‐temporal scales required (Jones et al ., 2006; Bohmann et al ., 2014; Dietze et al ., 2018; Estes et al ., 2018; Compagnoni et al ., 2019). Although there are fewer examples in the literature, HMMs have been used to make ecosystem‐level inferences about stability and regime shifts (Gal and Anderson, 2010; Gennaretti et al ., 2014; Economou and Menary, 2019), climate‐driven community and disease dynamics (Moritz et al ., 2008; Martinez et al ., 2016; Miller et al ., 2018a), the effects of management action on habitat dynamics (Breininger et al ., 2010), climatic niches (Tingley et al ., 2009) and ecosystem health (Xiao et al ., 2019). HMMs are also frequently used by atmospheric scientists, hydrologists and landscape ecologists to describe regional‐ to global‐scale ecosystem processes such as precipitation (Zucchini and Guttorp, 1991; Srikanthan and McMahon, 2001), streamflow (Jackson, 1975; Bracken et al ., 2014), wetland dynamics (Siachalou et al ., 2014) and land cover dynamics (Aurdal et al ., 2005; Lazrak et al ., 2010; Trier and Salberg, 2011; Abercrombie and Friedl, 2015; Siachalou et al ., 2015).…”