Regular ArticleAssessment of Spatial and Temporal Variability in Ecosystem Attributes of the St Marks National Wildlife Refuge, Apalachee Bay, Florida
Abstract
Six carbon-flow networks of a seagrass ecosystem were constructed from comprehensive databases collected at three different sites during January and February 1994, respectively. The flow models, which consisted of 51 compartments each, were analysed by means of network analysis and the resultant outputs compared on spatial and temporal scales. Results on the biomass, species diversity, input, rates of exchange and export of material showed distinct differences between the sites in a month, as well as between months at the same site. System-level attributes, such as total system throughput, ascendancy, the structure and magnitude of recycling, flow diversity, etc., derived from network analysis, also showed noticeable differences from site to site and between months. There was an increase in ecosystem activity and in the magnitude of its global properties between months which are ascribed to a rise in temperature between months, increased rates in respiration and growth of resident species populations, as well as to the immigration of large numbers of fish and birds.
References (0)
Cited by (144)
Temporal and spatial differences in nitrogen and phosphorus biogeochemistry and ecosystem functioning of a hypertrophic lagoon (Curonian Lagoon, SE Baltic Sea) revealed via Ecological Network Analysis
2024, Science of the Total EnvironmentIn coastal lagoons, eutrophication and hydrology are interacting factors that produce distortions in biogeochemical nitrogen (N) and phosphorus (P) cycles. Such distortions affect nutrient relative availability and produce cascade consequences on primary producer's community and ecosystem functioning.
In this study, the seasonal functioning of a coastal lagoon was investigated with a multielement approach, via the construction and analysis of network models. Spring and summer networks, both for N and P flows, have been simultaneously compiled for the northern transitional and southern confined area of the hypertrophic Curonian Lagoon (SE Baltic Sea). Ecological Network Analysis was applied to address the combined effect of hydrology and seasonality on biogeochemical processes.
Results suggest that the ecosystem is more active and presents higher N and P fluxes in summer compared to spring, regardless of the area. Furthermore, larger internal recycling characterizes the confined compared to the transitional area, regardless of the season. The two areas differed in the fate of available nutrients. The transitional area received large riverine inputs that were mainly transferred to the sea without the conversion into primary producers' biomass. The confined area had fewer inputs but proportionally larger conversion into phytoplankton biomass. In summer, particularly in the confined area, primary production was inefficiently consumed by herbivores. Most phytoplanktonic N and P, in the confined area more than in the transitional area, were conveyed to the detritus pathway where P, more than N, was recycled, contributing to the unbalance in N:P stoichiometry and favouring N-fixing cyanobacteria over other phytoplankton groups. The findings of this study provide a comprehensive understanding of N and P circulation patterns in lagoon areas characterized by different hydrology. They also support the importance of a stoichiometric approach to trace relative differences in N and P recycling and abundance, that promote blooms, drive algal communities and whole ecosystem functioning.
Structural controllability and observability of complex network with output feedback
2023, Physica A: Statistical Mechanics and its ApplicationsControl theory provides useful tools for steering engineered and natural systems to desired states. Despite recent progress, a framework to control and observe the network with output feedback is still missing. Here we propose some new concepts, such as feedback-stem, feedback-bud and feedback-cactus. From the perspective of feedback-cactus, we propose a graphic necessary and sufficient condition for the structural controllability and observability of the network with output feedback. This condition combined with maximum matching allows us to solve the minimum input and output problem. Applying our framework to real and model networks, we find that some nodes play a dual role, that is, they are both driver nodes and sensor nodes. The proportion of such dual-role nodes is higher in sparse and homogeneous networks and is encoded by the degree distribution. Statistics find that the power-law distribution of the length of the feedback-stem is ubiquitous in real and model networks. Further, we use the proportion of edges in the feedback-cactus to evaluate the edge participation, i.e., the extent to which edges participate in the control and observation of the network with output feedback. Numerical simulations and theoretical analysis show that edge participation is higher in sparse and homogeneous networks.
Mining relationships between performance of link prediction algorithms and network structure
2021, Chaos, Solitons and FractalsThe numerous link prediction algorithms proposed by the network science researchers demonstrate their creativity in this hot topic. However, various algorithms together with the miscellaneous real-world networks put much difficulty on the choice of algorithm when coping with a new network. In this paper, we try to provide some elementary rules through mining the relationships between network structure features and the algorithm mechanisms. We discovered some principles indicating clustering coefficients influences on the prediction accuracy of structure-based algorithms. Besides, our experiment results present some interesting phenomenon neglected previously. The results and discussions may help us understand the link prediction problem better and further.
Towards link inference attack against network structure perturbation
2021, Knowledge-Based SystemsThe increasing popularity and diversity of social media sites have resulted in an emergent number of available social networks. These social networks are now the source of information for third-party consumers, such as researchers and advertisers, to understand user social activities. In a privacy-preserving viewpoint, a full assessment of social relationships between individuals may violate privacy. Different network structure perturbation methods have been proposed to limit the disclosure of sensitive user data. However, despite the proliferation of these methods, currently, there are no robustness studies on the methods for link prediction-based hidden inference structure. In this study, we survey the state-of-the-art network structure perturbation methods for privacy-preservation and the classic link prediction methods for structure inference. To restore the perturbed network structure effectively, we propose a novel Multi-Layer Linear Coding-based link prediction method (MLLC) with a closed-form solution. Furthermore, we provide vulnerability analysis on network structure perturbation methods in the context of link prediction-based structure inference. We also compare the methods on the preservation of utility metrics for social network analysis, where a structure perturbation method is preferred if the metrics of the perturbed network are similar to those of the original network. Our experimental study indicates that the MLLC algorithm outperforms conventional methods for hidden structure inference, and that it is important to provide robustness to network structure perturbation methods against these attacks.
Link Prediction through Deep Generative Model
2020, iScienceInferring missing links based on the currently observed network is known as link prediction, which has tremendous real-world applications in biomedicine, e-commerce, social media, and criminal intelligence. Numerous methods have been proposed to solve the link prediction problem. Yet, many of these methods are designed for undirected networks only and based on domain-specific heuristics. Here we developed a new link prediction method based on deep generative models, which does not rely on any domain-specific heuristic and works for general undirected or directed complex networks. Our key idea is to represent the adjacency matrix of a network as an image and then learn hierarchical feature representations of the image by training a deep generative model. Those features correspond to structural patterns in the network at different scales, from small subgraphs to mesoscopic communities. When applied to various real-world networks from different domains, our method shows overall superior performance against existing methods.
Risk assessment simulation for shelf ecosystems based on the ecoscreening and dynamic methods synthesis
2020, Estuarine, Coastal and Shelf ScienceThe paper simulates ecological risk assessment for shelf ecosystems based on the ecoscreening and dynamic methods synthesis. Both approaches and environmental risk model are described, which results make it possible to estimate probability of acceptable impact on the shelf ecosystem. In contrast to existing approaches related to ranking and compiling risk matrices, the proposed method based on probabilistic ecoscreening risk assessment takes into account not only the altering conditions of external influence on the shelf ecosystem, but also the intra-annual dynamics of its main components. This makes it possible to calculate the ecological risk not as a fixed value for the ecosystem under study, but as its component changing over the course of a year along with the intra-annual functioning dynamics. The latter is especially important for the coastal basins, where processes of various genesis are concentrated, high gradients of biomass values and concentrations of components containing it are presented. To simulate various scenarios of combining the effect of external stressors with the intra-annual dynamics of the ecosystem functioning, the Northern Caspian shelf basin was selected, which is subject to intense river flow and to exposure to mineral resources development technologies, including oil and gas. Ecological risk values were calculated taking into account combination of adverse natural factors and implementation of emergency and regular anthropogenic impacts at different periods of the shelf ecosystem components natural dynamics. Results made it possible to assess probabilities of the allowable exposure of shelf ecosystem, to identify areas with the 100% probability of allowable impact and especially critical areas with less than 5% of the impact probability. Calculations show not only efficiency of the proposed approach, but also open possibility of harmonizing ecological and economic requirements in the development of shelf resources. For areas with high allowable impact probability, it is possible to reduce the economic costs of ensuring ecological safety. Ecological risk areas, which are characterized by low estimate of allowable impact probability require an increase in the safety economic expenses. The proposed approach to estimating the intra-annual risk dynamics is of interdisciplinary nature and could be useful both to specialists in the ecology area, in the related fields, and to management personnel in making decisions on redistributing economic expenses and minimizing them while maintaining the priority of environmental safety.
- f1
Corresponding author.