Abundance trends for river macroinvertebrates vary across taxa, trophic group and river typology

Powell, Kathryn E; Oliver, Tom H.; Johns, Tim; González‐Suárez, Manuela; England, Judy; Roy, David B.

doi:10.1111/gcb.16549

Cited by 17 publications

(6 citation statements)

References 89 publications

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“…This aspect needs further work, especially where better abundance or biomass data are available (e.g. Powell et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

“…March-May was the most frequently used sample period, so this choice maximised the sample size in space and time, whilst minimising the risk of artefacts caused by variation in sample timing across the study period. A joint quality-assurance scheme for England andWales (1991-2011) indicated that the error rate was nearconstant (Murray-Bligh and Griffiths, 2022), and similar protocols remained in place subsequently for sample processing in the two countries. Sampling sites were included if they: i) had been sampled in at least four years during 1991-2019, including at least one sample in each decade (1991-2000, 2001-2010 and 2011-2019); ii) were not directly downstream from an effluent outfall; iii) included data on altitude, distance from source and channel slope; and iv) could have their catchments delineated from a digital terrain model.…”

Section: Macroinvertebrate Datamentioning

confidence: 99%

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Evidence of biological recovery from gross pollution in English and Welsh rivers over three decades

Pharaoh

Diamond

Ormerod

et al. 2023

Science of The Total Environment

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“…This aspect needs further work, especially where better abundance or biomass data are available (e.g. Powell et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Section: Macroinvertebrate Datamentioning

confidence: 99%

Evidence of biological recovery from gross pollution in English and Welsh rivers over three decades

Pharaoh

Diamond

Ormerod

et al. 2023

Science of The Total Environment

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“…Indeed, Myers & Worm [157] explicitly note that compensatory responses of smaller fish species have been observed even though they were not part of their analysis. Per cent cover (a metric of biomass) of living coral on reefs has also shown large declines globally [71], while diatom abundances show spatially variable trends related to climate, without a strong overall general trend [158,159].…”

Section: (D) Abundance and Biomassmentioning

confidence: 99%

Looking back on biodiversity change: lessons for the road ahead

Dornelas

Chase

Gotelli

et al. 2023

Phil. Trans. R. Soc. B

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Estimating biodiversity change across the planet in the context of widespread human modification is a critical challenge. Here, we review how biodiversity has changed in recent decades across scales and taxonomic groups, focusing on four diversity metrics: species richness, temporal turnover, spatial beta-diversity and abundance. At local scales, change across all metrics includes many examples of both increases and declines and tends to be centred around zero, but with higher prevalence of declining trends in beta-diversity (increasing similarity in composition across space or biotic homogenization) and abundance. The exception to this pattern is temporal turnover, with changes in species composition through time observed in most local assemblages. Less is known about change at regional scales, although several studies suggest that increases in richness are more prevalent than declines. Change at the global scale is the hardest to estimate accurately, but most studies suggest extinction rates are probably outpacing speciation rates, although both are elevated. Recognizing this variability is essential to accurately portray how biodiversity change is unfolding, and highlights how much remains unknown about the magnitude and direction of multiple biodiversity metrics at different scales. Reducing these blind spots is essential to allow appropriate management actions to be deployed. This article is part of the theme issue ‘Detecting and attributing the causes of biodiversity change: needs, gaps and solutions’.

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“…This method could be applied to other chemical species or geographical regions. Future studies could also investigate the method's applicability to river biodiversity indicators such as macroinvertebrate abundance (Powell et al, 2022).…”

Section: Challenges Limitations and Future Workmentioning

confidence: 99%

River reach-level machine learning estimation of nutrient concentrations in Great Britain

Tso,

Magee,

Huxley

et al. 2023

Front. Water

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Nitrogen (N) and phosphorus (P) are essential nutrients necessary for plant growth and support life in aquatic ecosystems. However, excessive N and P can lead to algal blooms that deplete oxygen and lead to fish death and the release of toxins that are harmful to humans. Estimates of N and P levels in rivers are typically calculated at station or grid (>1 km) scale; therefore, it is difficult to visualise the evolution of water quality as water travels downstream. Using a high-resolution reach-scale river network and associating each reach with land cover fractions and catchment descriptors, we trained random forest models on aggregated data (2010–2020) from the Environmental Agency Open Water Quality Data Archive for 2,343 stations to predict long-term nitrate and orthophosphate concentrations at each river reach in Great Britain (GB). We separated the model training and predictions for different seasons to investigate the potential difference in feature importance. Our model predicted concentrations with an average testing coefficient of determination (R2) of 0.71 for nitrate and 0.58 for orthophosphate using 5-fold cross-validation. Our model showed slightly better performance for higher Strahler stream orders, highlighting the challenges of making predictions in small streams. Our results revealed that arable and horticultural land use is the strongest and most reliable predictor for nitrate, while floodplain extents and standard percentage runoff are stronger predictors for orthophosphate. Nationally, higher orthophosphate concentrations were observed in urbanised areas. This study shows how combining a river network model with machine learning can easily provide a river network understanding of the spatial distribution of water quality levels.

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Abundance trends for river macroinvertebrates vary across taxa, trophic group and river typology

Abstract: Biodiversity is rapidly changing across the globe (Díaz et al., 2019).Long-term datasets suggest widespread declines in richness, abundance and biomass of terrestrial insects and other arthropods, including steep declines in biomass of flying insects in areas of

Cited by 17 publications

References 89 publications

Evidence of biological recovery from gross pollution in English and Welsh rivers over three decades

Evidence of biological recovery from gross pollution in English and Welsh rivers over three decades

Looking back on biodiversity change: lessons for the road ahead

River reach-level machine learning estimation of nutrient concentrations in Great Britain

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