Macroinvertebrate community traits and nitrate removal in stream sediments

Abstract

correlated with nitrate removal were silt and mud with microphytes (as substrate 57 preference), and with fine sediment with microorganisms, and dead animals (as food 58 sources). These results agreed with the hypothesis of top-down control and enhanced 59 understanding of the influence of hydromorphological factors on nitrate removal. 60 5. This study highlights the involvement of the macroinvertebrate community in 61 in-stream nitrate processing, and demonstrates the usefulness of applying a functional 62 approach to explaining relationships between biodiversity and ecosystem function. 63 64

Human activities' fingerprint on multitrophic biodiversity and ecosystem functions across a major river catchment in China

“…At each site, the taxonomic (species richness [Chao1 richness], Shannon's diversity index [ H′ ], Pielou's evenness index [ J′ ]), and phylogenetic (Faith's phylogenetic diversity index [ PD ]) diversity index of five trophic groups were calculated using alpha_diversity.py script in QIIME toolkit. Species traits of invertebrates, protozoa, and algae were summarized using different functional categories including body size, reproduction, respiration, food preference, and feeding habits (Abonyi et al., 2018; Bruno et al., 2019; Usseglio‐Polatera et al., 2000; Yao et al., 2017). Three functional diversity indices were calculated to characterize all communities above (Bruno et al., 2019): functional richness ( FRic ), Rao's quadratic entropy ( FD ), and functional redundancy ( FRed ).…”

Denitrification in wetlands: A review towards a quantification at global scale

“…Models at watershed scale that integrate wetlands have shown that the performance improves, if the role of wetlands is included, the nutrients outputs are more accurate to what is reported in situ as emissions (vertical fluxes) and exports to the oceans (horizontal fluxes). Different watershed compartments and their spatial distribution of nutrient transformation are more detailed (Bernard-Jannin et al, 2017;Czuba et al, 2018;Hansen et al, 2018;Yao et al, 2017). Kirschke et al (2013) calculated decadal budgets for methane sources and sinks between 1980 and 2010, using a combination of atmospheric chemical transport models, ecosystem models, climate chemistry models and inventories of anthropogenic emissions.…”

Extreme drought pushes stream invertebrate communities over functional thresholds

“…This is difficult or impossible to validate as a causal driver-response relationship in correlational studies, which are often beset by confounding influences beyond the stressor of interest, and instead favours detection in an experimental setting (Kayler et al, 2015;Kreyling et al, 2014). Mesocosms are thus suitable as they can isolate trait responses to stream drought from possible confounding factors (Woodward et al, 2016), such as changing pollutant levels, underlying climatic and hydrological regimes and other site-specific contingencies, including surrounding land use (Ding et al, 2017;Durance & Ormerod, 2009;Floury et al, 2017;Thomson et al, 2012;Yao et al, 2017). Crucially, of all experimental approaches, mesocosms also allow for the greatest compromise between realism and replicability (Stewart et al, 2013).…”