Zooplankton network conditioned by turbidity gradient in small anthropogenic reservoirs

Goździejewska, Anna; Kruk, Marek

doi:10.1038/s41598-022-08045-y

Cited by 17 publications

(32 citation statements)

References 75 publications

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“…but less rapidly cycled in heated reservoirs (in particular in WW), surface water can be colonized by macrophytes that effectively utilize the available nutrients (TN and TOC levels decreased in WM and WW) and compete for food with phytoplankton (Vanderstukken et al, 2011). These energy transformations create trophic conditions which exert the greatest influence on species composition and function, as well as biomass and the type and strength of interspecific interactions in zooplankton communities (Kruk et al, 2021;Goździejewska and Kruk, 2022).…”

Section: Discussionmentioning

confidence: 99%

“…An undirected graph was created to identify all positive and negative interactions between zooplankton species in three thermal classes. Positive interactions denoted co-occurrence patterns or mutualistic relationships between the biomass of zooplankton taxa, whereas negative interactions denoted predatory or competitive relationships Goździejewska and Kruk, 2022). The ranges of correlation coefficient values for the edges were set to ensure that they were significant at P ≤ 0.05 for sample size in each thermal class.…”

Section: Statistical and Network Analysesmentioning

confidence: 99%

“…In weighted graphs, the distance between nodes is defined as the sum of weights (Kamada and Kawai, 1989). The zooplankton network in three thermal classes was compared based on the key network attributes that are applied in ecological studies, including the number of neighbors, closest path, clustering coefficient, network centralization, network density, and network heterogeneity (Pavlopoulos et al, 2011, Kruk andGoździejewska and Kruk, 2022). Four popular node centrality attributes were used to determine the significance of zooplankton taxa in three thermal classes: node degree centrality (NDC) (Pavlopoulos et al, 2011), node closeness centrality (NCC) (Newman, 2005), node betweenness centrality (NBC) (Brandes, 2001), and the clustering coefficient (CCF).…”

Section: Statistical and Network Analysesmentioning

confidence: 99%

“…Planktonic animals are the main link in microbial carbon cycling (Bowszys et al, 2020), and they are sensitive bioindicators of changes in abiotic factors (Goździejewska et al, 2016;Zhou et al, 2018;Wang et al, 2021). Due to their taxonomic and functional diversity, different ecological strategies, phylogenetic distinctness, as well as passive and broad dispersion in the environment, zooplankton are highly useful for the development of ecosystem models/forecasts, including in the context of global warming (Richardson, 2008;Chou, 2012;Arlic, et al 2013;Feitosa et al, 2019;Kruk et al, , 2021Goździejewska and Kruk 2022). Zooplankton respond directly to water temperature at the physiological (by regulating metabolic, growth and development processes) (Gillooly, 2000;Lewandowska et al, 2014 b) and behavioral level (changes in distribution, population size, species composition and phenology) (Ejsmont-Karabin and Wągleńska, 1988;Carter and Schindler, 2012;Ejsmont-Karabin et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Interspecific interactions were examined with the use of the network graph analysis. A network graph model supports the identification and assessment of relationships between species, based on mutualism or neutral coexistence of species in ecological guilds (positive mathematical interactions) or limitation (negative correlations) resulting from predation or competition (Allesina et al, 2005, D'Alelio et al, 2016Goździejewska and Kruk, 2022).…”

Section: Introductionmentioning

confidence: 99%

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The response of zooplankton network indicators to winter geothermal water warming in shallow reservoirs

Goździejewska

Kruk²

2022

Preprint

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Abstract. The increase in the temperature of surface waters has been studied for many decades, and various methods have been used to predict the most probable scenarios. The present study was undertaken to test the following research hypothesis: the warming of surface waters in winter (caused by the inflow of geothermal water) significantly modifies the dynamics, significance and type of relationships in zooplankton communities colonizing mine pit reservoirs. These relationships were examined with the use of network graph analysis for three thermal variants: warm winters (WW), moderate winters (MW), and cold winters (CW). The CW network was most cohesive, and it was controlled by eutrophic Rotifera (Trichocerca pusilla, Pompholyx sulcata, Keratella tecta) and Copepoda, with an equivalent number of positive and negative interspecific relationships. An increase in water temperature in winter led to a decrease in primary production, a decrease in the values of centrality attributes in MW and WW networks, and an increase in the significance of species that communicated with the highest number of species across sub-networks. Moderate winters increased the role of ecologically and functionally diverse species, which contributed to the heterogeneity of the MW network. The WW network was least cohesive, and it was controlled by small-sized psammophilous and phytophilous rotifers (Monommata maculata, Cephalodella spp.) and littoral cladocerans Alona spp. Adults Copepoda were not identified in the network, and the significance of antagonistic relationships decreased, which indicates that the WW network structure was weak and unstable. The results of the impact of warm winters and the flattening of the annual water temperature amplitude on the zooplankton network may be a projection of the expected global changes. These effects are particularly important in water reservoirs exposed to anthropogenic pressure and where changes in the thermal regime can influence future ecosystem services.

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Section: Discussionmentioning

confidence: 99%

Section: Statistical and Network Analysesmentioning

confidence: 99%

Section: Statistical and Network Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The response of zooplankton network indicators to winter geothermal water warming in shallow reservoirs

Goździejewska

Kruk²

2022

Preprint

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Clarifying water clarity: A call to use metrics best suited to corresponding research and management goals in aquatic ecosystems

Turner

Fall

Friedrichs

2022

Limnol Oceanogr Letters

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Water clarity is a subjective term and can be measured multiple ways. Different metrics such as light attenuation and Secchi depth vary in effectiveness depending on the research or management application. In this essay, we argue that different questions merit different water clarity metrics. In coastal and inland waters, empirical relationships to estimate light attenuation can yield clarity estimates that either under-or overestimate the underwater light climate for restoration goals, such as potential habitat available for submerged aquatic vegetation. Best practices in reporting water clarity measurements include regionally specific, temporally representative calibrations and communicating the metric that was actually measured. An intentional choice of the water clarity metric best suited to the research or management question yields the most useful results.The term "water clarity" is inherently ambiguous. Water clarity generally refers to the distance that light penetrates through water, as well as the visibility of objects through water. In lakes, rivers, estuaries, coastal zones, and the open ocean, water clarity is an essential measurement for monitoring programs and a wide variety of research applications. For example, water clarity is used to assess habitat quality for submerged aquatic vegetation (SAV), to study visual predation, and to model primary production. Water clarity is measured using multiple methods, some focused on the depth of light penetration, some based on the depth of object visibility, and others based on the amounts of components present (Fig. 1).General metrics presented here include: Secchi disk depth (Z SD ) (Secchi and Cialdi 1866; Tyler 1968; Preisendorfer 1986), the downwelling and scalar light attenuation coefficients of photosynthetically active radiation (K d (PAR) and K o (PAR)) (Kirk 1994), turbidity (Zaneveld et al. 1980Davies-Colley and Smith 2001;Sampedro and Salgueiro 2015;Eidam et al. 2022), and beam attenuation (Bishop 1999). In this paper, the terms K d and K o will refer to K d (PAR) and K o (PAR). Z SD , K o , and K d are apparent optical properties, descriptors of water bodies that depend on both the substances present and the light field (Mobley 2022). Component-based metrics include colored dissolved organic matter (CDOM) commonly measured by its light absorption (aCDOM; m À1 ) (Green and Blough 1994) or

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The undiscovered country: ten grand challenges in rotifer biology

2023

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Zooplankton network conditioned by turbidity gradient in small anthropogenic reservoirs

Cited by 17 publications

References 75 publications

The response of zooplankton network indicators to winter geothermal water warming in shallow reservoirs

The response of zooplankton network indicators to winter geothermal water warming in shallow reservoirs

Clarifying water clarity: A call to use metrics best suited to corresponding research and management goals in aquatic ecosystems

The undiscovered country: ten grand challenges in rotifer biology

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