Incorporating Network Connectivity into Stream Classification Frameworks

Denison, Colby D.; Scott, Mark C.; Kubach, Kevin M.; Peoples, Brandon K.

doi:10.1007/s00267-020-01413-2

Cited by 8 publications

(3 citation statements)

References 59 publications

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“…Lowland streams are dominated by harsh environmental conditions, frequent exposure to extreme weather, and poorly defined stream channels. These streams are also connected more closely to large mainstem rivers and reservoirs and share more species among watersheds due to historical sea level change (Marion et al, 2015;Miller et al, 2020;Denison et al, 2021b). Moreover, the species inhabiting the lowland streams tend to have more dispersal-associated traits and have populations that respond more to connectivity than local-scale abiotic factors (Midway and Peoples, 2019).…”

Section: Discussionmentioning

confidence: 99%

Effects of environment and metacommunity delineation on multiple dimensions of stream fish beta diversity

et al. 2023

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IntroductionBeta diversity represents changes in community composition among locations across a landscape. While the effects of human activities on beta diversity are becoming clearer, few studies have considered human effects on the three dimensions of beta diversity: taxonomic, functional, and phylogenetic. Including anthropogenic factors and multiple dimensions of biodiversity may explain additional variation in stream fish beta diversity, providing new insight into how metacommunities are structured within different spatial delineations.MethodsIn this study, we used a 350 site stream fish abundance dataset from South Carolina, United States to quantify beta diversity explainable by spatial, natural environmental, and anthropogenic variables. We investigated three spatial delineations: (1) a single whole-state metacommunity delineated by political boundaries, (2) two metacommunities delineated by a natural geomorphic break separating uplands from lowlands, and (3) four metacommunities delineated by natural watershed boundaries. Within each metacommunity we calculated taxonomic, functional, and phylogenetic beta diversity and used variation partitioning to quantify spatial, natural environmental, and anthropogenic contributions to variations in beta diversity.ResultsWe explained 25–81% of the variation in stream fish beta diversity. The importance of these three factors in structuring metacommunities differed among the diversity dimensions, providing complementary perspectives on the processes shaping beta diversity in fish communities. The effect of spatial, natural environmental, and anthropogenic factors varied among the spatial delineations, which indicate conclusions drawn from variation partitioning may depend on the spatial delineation chosen by researchers.DiscussionOur study highlights the importance of considering human effects on metacommunity structure, quantifying multiple dimensions of beta diversity, and careful consideration of user-defined metacommunity boundaries in beta diversity analyses.

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Section: Discussionmentioning

confidence: 99%

Effects of environment and metacommunity delineation on multiple dimensions of stream fish beta diversity

et al. 2023

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“…Similarly, we observed a change in model accuracy for HM simulation from low to high gradient streams. At the statewide scale, stream gradient and natural land cover are highly correlated (Denison et al, 2021a; Judson et al, 2020), and this pattern may also reflect the role of human disturbance (Denison et al, 2021b). Accounting for differences in land cover across the landscape is important for modelling instream flows, and the differences in model performance in this study have important implications for conducting proceeding flow–ecology analyses using simulated HMs.…”

Section: Discussionmentioning

confidence: 99%

Predictability of flow metrics calculated using a distributed hydrologic model across ecoregions and stream classes: Implications for developing flow–ecology relationships

et al. 2022

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Developing environmental flow standards requires an empirical understanding of the relationship between species' ecology and instream flow. However, when congruent biological and hydrologic data are lacking, the accurate simulation of hydrologic metrics (HMs) corresponding to the locations of biological data is needed. Methods to predict HMs vary in formulation (i.e., statistical vs. process‐based hydrologic models), ability to simulate HMs across the full range of the hydrologic regime (i.e., magnitude, duration, frequency, rate of change and timing) and ability to transfer HMs from gaged to ungaged locations. Yet, despite the breadth of modelling approaches, less attention has been paid to the variability in HMs associated with each approach. In this study, we apply a distributed hydrologic model to the diverse watersheds of South Carolina to examine the predictability of HMs from simulated daily time series of streamflow across ecoregions, stream classifications and level of human alteration. In doing so, we contextualize the predictability of HMs, giving managers and researchers in South Carolina the flexibility of choosing HMs that are best suited for quantifying flow–ecology relationships based on the location, flow regime components of interest and uncertainty of HMs. We found that at least one HM within each of the five flow regime components (out of a selected subset of 41 non‐redundant HMs) was consistently and accurately predicted across the diverse streams of the study area. We discuss the patterns of predictability related to site characterizations and individual HMs and their implications for developing environmental flow standards.

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“…In a certain period of time, devices tend to be in a disconnected state, which leads to the need to select a collection of devices that are in working state during each iteration of federated learning. For various reasons of actual needs, network connectivity has become a hot topic in the field of information technology (see Cheung and Bell [1], Yemini et al [2], Burla et al [3], Denison et al [4], Hummer et al [5], Ma et al [6], Gong et al [7], Gao et al [8,9], and Kumar et al [10]).…”

Section: Introductionmentioning

confidence: 99%

Connectivity Analysis of Bipolar Fuzzy Networks

Gong

Hua

Gao

2022

Mathematical Problems in Engineering

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Network connectivity is an important factor in data transmission, information sharing, and network defense, and it is a crucial indicator which determines the performance of a network. When a network node or channel contains uncertainties in both positive and negative aspects, the entire network can be modeled with bipolar fuzzy graphs. This paper analyzes the influence of each vertex on the connectivity of the entire network through the definition of the connected state on the bipolar fuzzy graph. The solid results are given, and the new concepts are applied in campus network connectivity analysis. This approach helps to analyze the hidden dangers of the bipolar network, find the weaknesses in the connection, and prevent network attacks in advance.

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Incorporating Network Connectivity into Stream Classification Frameworks

Cited by 8 publications

References 59 publications

Effects of environment and metacommunity delineation on multiple dimensions of stream fish beta diversity

Effects of environment and metacommunity delineation on multiple dimensions of stream fish beta diversity

Predictability of flow metrics calculated using a distributed hydrologic model across ecoregions and stream classes: Implications for developing flow–ecology relationships

Connectivity Analysis of Bipolar Fuzzy Networks

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