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River invertebrate communities across Europe have been changing over recent decades in response to variations in water quality, but the underlying drivers of change are difficult to identify because of the complex stressors and environmental heterogeneity involved. Across England and Wales, inter-annual and longer-term changes have been linked to variations in water chemistry, discharge and land cover. Obtaining a better understanding of spatio-temporal changes, their underlying drivers and how these vary across the country, would provide both greater insight into the current state and inform potential management interventions, from local to national scales.
This study uses almost 30 years of data collected across England and Wales to: i) map changes in macroinvertebrate communities; ii) examine how water chemistry, catchment land cover and climate might account for changing invertebrate richness and community composition; and iii) investigate how apparent relationships between water quality, land cover, climate and invertebrate communities vary geographically and temporally.
Methodology
The study used macroinvertebrate and environmental data from around 4000 locations across England and Wales collected between 1991 and 2019. Data were filtered so that only years which contained macroinvertebrate, water chemistry and discharge data were retained, resulting in a final sample size of 672 locations. The catchment for each location was derived from a digital elevation model using ArcHydro tools and the percentages of urban land cover, improved grassland and arable agriculture within each catchment was calculated. Three time periods (1991–1993, 2004–2006, 2017–2019) were mapped. The same approach was used to generate water chemistry maps to provide context for biological changes.
Structural equation modelling (SEM) was applied to i) distinguish land cover, water quality and climatic influences and ii) assess the relative importance of two pathways through which catchment land cover can affect macroinvertebrate communities: via its effect upon water chemistry or through pathways not mediated by water chemistry. SEM models were fitted for richness and community composition and included nine environmental variables. SEMs were constructed using the piecewiseSEM package with land cover, pH and discharge treated as exogeneous variables and the remaining variables modelled using linear mixed-effects models (Table 1).
Table 1. Response and explanatory variables included in the five mixed-effect models that formed the Structural Equation Model. LC = catchment percentage land cover.
| Model | Response | Explanatory variables |
|---|---|---|
| 1 | Nitrate | Urban LC, improved grassland LC, arable LC, discharge |
| 2 | Phosphate | Urban LC, improved grassland LC, arable LC, discharge |
| 3 | BOD | Phosphate, urban LC, improved grassland LC, arable LC, discharge |
| 4 | Water temperature | Urban LC, improved grassland LC, arable LC, discharge |
| 5 | Richness or CA1 | Nitrate, phosphate, pH, temperature, BOD, discharge, urban LC, improved grassland LC, arable LC |
The following conceptual model was constructed to distinguish direct and indirect links between variables (Figure 1).
Figure 1. Conceptual model for the effects of catchment land cover, climate and water chemistry on invertebrate richness and community composition (CA1). Arrows represent hypothesised relationships between the abiotic variables and the invertebrate community.
Geographically weighted regression (GWR) was applied to investigate how invertebrate communities were related to water quality, land cover and climate across time and space.
Outcomes
Mapping confirmed widespread increases in richness and the proportion of pollution-sensitive taxa across much of England and Wales. Communities in the low nutrient, cleaner, cooler waters of the upland north and west were characterised by pollution-sensitive taxa which shifted to more pollution-tolerant taxa in the warmer, nutrient rich waters with higher biochemical oxygen demand in the south and east. It also revealed regions where pollution-sensitive taxa or overall richness declined, the former primarily in the uplands (Figure 2).
Figure 2. Maps of macroinvertebrate richness (top row) and community composition (CA1 score; bottom row) for 1991–93, 2004–06 and 2017–19 and change (Δ) from the first to last time period.
SEMs confirmed strong increases in average biochemical oxygen demand and nutrient concentrations related to urban and agricultural land cover, but only a minority of land cover's effect upon invertebrate communities was explained by average water chemistry, highlighting potential factors such as episodic extremes or emerging contaminants (Figure 3).
Figure 3. Structural equation models of water quality, climate and land use effects on macroinvertebrate richness (left) and composition (right). Arrows represent the direction of effects. Path widths are proportional to the standardised path coefficients, which are also printed in the path labels: green paths and labels = negative effects; brown = positive.
GWR identified strong geographical variation in estimated relationships between macroinvertebrate communities and environmental variables, with evidence that the estimated negative impacts of nutrients and water temperature were increasing through time (Figure 4).
Figure 4. GWR coefficients (slope coefficients) from richness (two left hand columns) and community composition (CA1) score (two right hand columns) models for the two time periods, representing the direction (positive or negative) and strength of the estimated relationships between abiotic variables and biological responses.
The combination of SEM and GWR expanded understanding of spatial patterns beyond time series-based analyses and provided new insights into the possible drivers of macroinvertebrate changes. SEM took a step towards resolving the roles of land cover, water quality and climate by separating out the direct and indirect effects upon stream ecosystems, and better characterizing the relationships among variables. GWR emphasized the complexity of the apparent relationships between communities and the abiotic environment, and how this has evolved in space and time.
Photos: (L) River Lambourn, (R) River Thames by Helen Jarvie
Conclusions
The study confirmed some aspects of our understanding about English and Welsh rivers, for example the widespread improvement in biological quality post-1990 and the roles of water quality, climate and land cover in that improvement. The study also provided new insights, for example by identifying regions of decline, possible increasing climate sensitivity, a modest role for average water chemistry relative to land cover and variations in apparent influences on invertebrates in time and space. These results highlight the importance of considering geographically disaggregated changes alongside average (inter-)national trends to gain a better understanding of how rivers are changing, as average trends can disguise regions of decline.
Whilst the condition of rivers draining urban areas and large parts of lowland England and Wales have improved over the last 30 years, overall diversity and sensitive taxa have declined in areas such as southwest Britain and the uplands. These areas now represent research and management priorities. Moreover, the SEM and GWR results emphasize the complexity and variability in the apparent drivers of changing biological quality. The changing mix of multiple, interacting stressors in river environments – not all of which were included within this study – has the potential to change the apparent relationships between focal variables and invertebrates. Better characterization of the changing mix of potential stressors through space and time, and the ways in which they interact, should help to unpack these complex relationships.
Read more in Science of The Total Environment
Pharaoh, E., Diamond, M., Jarvie, H. P., Ormerod, S. J., Rutt, G., Vaughan, I. P. Potential drivers of changing ecological conditions in English and Welsh rivers since 1990. Science of The Total Environment, Volume 946, 2024. https://doi.org/10.1016/j.scitotenv.2024.174369.
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