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Markov Chain Nest Productivity Model (MCnest) predictions compared to endogenous life- cycle model (ELM) predictions for aryl hydrocarbon receptor (AHR) activation leading to embryonic mortality at the median lethal concentration (LC50).
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Recent research has provided valuable momentum for the development and use of population models for ecological risk assessment (ERA). In general, ERA proceeds along a tiered strategy, with conservative assumptions deployed at lower tiers that are relaxed at higher tiers with ever more realistic models. As the tier increases, so do the levels of tim...
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... purpose of that work was to demonstrate the ability of ELMs to integrate toxicological effects to predict fitness effects, taking lifecycle into account. Table 4 reports the magnitude of effects on MCnest predictions versus ELM predictions for embryonic mortality associated with AHR activation at the LC50. For bald eagle, the effects on fitness are much larger than effects on fecundity, whereas, for tree swallow, the effects on fitness are much smaller than effects on fecundity. ...Citations
... At the lower level, ecological risk assessments have focused more on the adverse effects of a single physical or chemical stress on an individual organism or a small group of representative species [4,5]. At higher levels such as populations or communities, more effective assessment of ecological risk in the context of multiple stressors and multiple ecological receptors continues to attract the attention of assessors [6][7][8]. Analyzing ecological risk based on ecosystem services can better describe the way in which external drivers stemming from human well-being can negatively impact internal ecosystem mechanisms [9][10][11]. In rapidly urbanizing areas, the loss of ecological space leads to the loss of ecological services and the degradation of ecological functions [12]. ...
... Regional ecological risk assessment involves evaluating the likelihood of adverse effects and the potential harm when ecological receptors are exposed to natural disasters or man-made disturbances in the context of specific temporal and spatial backgrounds [13][14][15][16]. Given the co-occurrence of multiple risk sources and multiple ecological receptors in the environment on a specific spatial scale, ecological risk analysis on the regional scale faces significant challenges, including complex interactions, variations in the importance and sensitivity of ecological receptors, data acquisition, uncertainty, and the involvement of numerous stakeholders [6,17]. ...
... Following the principles of efficiency of ecological modeling, several methods such as the relative risk model have been developed and used to assess ecological risks in urban areas [18][19][20][21]. High-level ecological models on the regional scale prioritize accuracy [6]. Simulating fine-scale changes in urban land use is an important way to explore the ecological risks manifested by the loss of the ecosystem services provided by an ecological space or by an increased sensitivity of the ecological environment [22,23]. ...
The rapid expansion of different types of urban land continues to erode natural and semi-natural ecological space and causes irreversible ecological damage to rapidly industrialized and urbanized areas. This work considers Quanzhou, a typical industrial and trade city in southeastern China as the research area and uses a Markov chain integrated into the patch-generating land use simulation (PLUS) model to simulate the urban expansion of Quanzhou from 2005 to 2018. The PLUS model uses the random forest algorithm to determine the contribution of driving factors and simulate the organic and spontaneous growth process based on the seed generation mechanism of multi-class random patches. Next, leveraging the importance of ecosystem services and ecological sensitivity as indicators of evaluation endpoints, we explore the temporal and spatial evolution of ecological risks from 2018 to 2031 under the scenarios of business as usual (BAU), industrial priority, and urban transformation scenarios. The evaluation endpoints cover water conservation service, soil conservation service, biodiversity maintenance service, soil erosion sensitivity, riverside sensitivity, and soil fertility. The ecological risk studied in this work involves the way in which different types of construction land expansion can possibly affect the ecosystem. The ecological risk index is divided into five levels. The results show that during the calibration simulation period from 2005 to 2018 the overall accuracy and Kappa coefficient reached 91.77% and 0.878, respectively. When the percent-of-seeds (PoS) parameter of random patch seeds equals 0.0001, the figure of merit of the simulated urban construction land improves by 3.9% compared with the logistic-based cellular automata model (Logistic-CA) considering organic growth. When PoS = 0.02, the figure of merit of the simulated industrial and mining land is 6.5% higher than that of the Logistic-CA model. The spatial reconstruction of multiple types of construction land under different urban development goals shows significant spatial differentiation on the district and county scale. In the industrial-priority scenario, the area of industrial and mining land is increased by 20% compared with the BAU scenario, but the high-level risk area is 42.5% larger than in the BAU scenario. Comparing the spatial distribution of risks under the BAU scenario, the urban transition scenario is mainly manifested as the expansion of medium-level risk areas around Quanzhou Bay and the southern region. In the future, the study area should appropriately reduce the agglomeration scale of urban development and increase the policy efforts to guide the development of industrial land to the southeast.
