These are not the trends you are looking for: poorly calibrated single-pass electrofishing data can bias estimates of trends in fish abundance.

There is increasing interest in the potential of single-pass and timed electrofishing to assess status and trends in fish populations. However, where capture probability varies over time, there is a risk that uncalibrated electrofishing data could fail to detect, or provide biased estimates of trends. This study analysed a long-term electrofishing dataset collected over 50 years in an intensively studied catchment where egg deposition and emigrant production declined by c. 82% and 35% over the same time. The electrofishing data were used to illustrate the effects of changing capture probability on estimated trends in juvenile Atlantic salmon Salmo salar abundance. Temporal variability in capture probability was modelled. Trends in abundance were then estimated from uncalibrated single-pass electrofishing count data and compared with estimates from data calibrated for capture probability. The calibrated data revealed significant declines in S. salar fry (age 0) and parr (age ≥ 1) abundance. However, the trend estimates from the uncalibrated data were positively biased and not significant. Exploration of alternative (realistic) scenarios with different trends in true abundance and capture probability suggests that uncalibrated electrofishing data can provide very misleading estimates of trends. The problem is exacerbated in data where capture probability is low. It is recommended that single-pass and timed electrofishing methods should not be used to assess trends in fish populations without regular (annual) calibration.

Drone-based Structure-from-Motion provides accurate forest canopy data to assess shading effects in river temperature models.

Climatic warming will increase river temperature globally, with consequences for cold water-adapted organisms. In regions with low forest cover, elevated river temperature is often associated with a lack of bankside shading. Consequently, river managers have advocated riparian tree planting as a strategy to reduce temperature extremes. However, the effect of riparian shading on river temperature varies substantially between locations. Process-based models can elucidate the relative importance of woodland and other factors driving river temperature and thus improve understanding of spatial variability of the effect of shading, but characterising the spatial distribution and height of riparian tree cover necessary to parameterise these models remains a significant challenge. Here, we document a novel approach that combines Structure-from-Motion (SfM) photogrammetry acquired from a drone to characterise the riparian canopy with a process based temperature model (Heat Source) to simulate the effects of tree shading on river temperature. Our approach was applied in the Girnock Burn, a tributary of the Aberdeenshire Dee, Scotland. Results show that SfM approximates true canopy elevation with a good degree of accuracy (R2 = 0.96) and reveals notable spatial heterogeneity in shading. When these data were incorporated into a process-based temperature model, it was possible to simulate river temperatures with a similarly-high level of accuracy (RMSE <0.7 °C) to a model parameterised using 'conventional' LiDAR tree height data. We subsequently demonstrate the utility of our approach for quantifying the magnitude of shading effects on stream temperature by comparing simulated temperatures against another model from which all riparian woodland has been removed. Our findings highlight drone-based SfM as an effective tool for characterising riparian shading and improving river temperature models. This research provides valuable insights into the effects of riparian woodland on river temperature and the potential of bankside tree planting for climate change adaptation.

Integration of juvenile habitat quality and river connectivity models to understand and prioritise the management of barriers for Atlantic salmon populations across spatial scales.

Diadromous fish populations are strongly affected by in-stream barriers that cause river network fragmentation, constraining productivity or preventing completion of their lifecycle. Removal or reduction of barrier impacts is a restoration measure associated with unambiguous benefits. Management of barriers is therefore often prioritised above other restoration actions. Barrier management is prioritised at local and national scales depending on funding. However, barrier prioritisation is potentially sub-optimal because existing tools do not consider habitat quality. Furthermore, effects of partial barriers (those passable under certain conditions) are uncertain, depending on location and potential cumulative effects. A framework is presented for assessing effects of impassable manmade barriers (IMBs) on longitudinal river network connectivity (percentage of upstream habitat accessible from the river mouth) for Atlantic salmon across spatial scales, using Scotland as an example. The framework integrates juvenile habitat quality and network connectivity models to (1) provide information necessary for local and national prioritisation of barriers, and (2) assess potential effects of passable manmade barriers (PMBs) within a sensitivity framework. If only IMBs are considered, high levels of longitudinal connectivity are observed across most of Scotland's rivers. Barrier prioritisation is sensitive to habitat weighting: not accounting for habitat quality can lead to over- or underestimating the importance of IMBs. Prioritisation is also highly sensitive to the passability of PMBs: if passability drops to <97% (combined up- and downstream passability), the mean effect of PMBs becomes greater than IMBs at the national level. Moreover, impacts on catchment connectivity, and thus production (number of juvenile salmon produced by the river), could be severe, suggesting a better understanding of the passability of PMBs is important for future management of migration barriers. The presented framework can be transferred to other catchments, regions, or countries where necessary data are available, making it a valuable tool to the broader restoration community.

A spatio-temporal statistical model of maximum daily river temperatures to inform the management of Scotland's Atlantic salmon rivers under climate change.

The thermal suitability of riverine habitats for cold water adapted species may be reduced under climate change. Riparian tree planting is a practical climate change mitigation measure, but it is often unclear where to focus effort for maximum benefit. Recent developments in data collection, monitoring and statistical methods have facilitated the development of increasingly sophisticated river temperature models capable of predicting spatial variability at large scales appropriate to management. In parallel, improvements in temporal river temperature models have increased the accuracy of temperature predictions at individual sites. This study developed a novel large scale spatio-temporal model of maximum daily river temperature (Twmax) for Scotland that predicts variability in both river temperature and climate sensitivity. Twmax was modelled as a linear function of maximum daily air temperature (Tamax), with the slope and intercept allowed to vary as a smooth function of day of the year (DoY) and further modified by landscape covariates including elevation, channel orientation and riparian woodland. Spatial correlation in Twmax was modelled at two scales; (1) river network (2) regional. Temporal correlation was addressed through an autoregressive (AR1) error structure for observations within sites. Additional site level variability was modelled with random effects. The resulting model was used to map (1) spatial variability in predicted Twmax under current (but extreme) climate conditions (2) the sensitivity of rivers to climate variability and (3) the effects of riparian tree planting. These visualisations provide innovative tools for informing fisheries and land-use management under current and future climate.

Stream temperature under contrasting riparian forest cover: Understanding thermal dynamics and heat exchange processes.

Climate change is likely to increase summer temperatures in many river environments, raising concerns that this will reduce their thermal suitability for a range of freshwater fish species. As a result, river managers have pursued riparian tree planting due to its ability to moderate stream temperatures by providing shading. However, little is known about the relative ability of different riparian forest types to moderate stream temperatures. Further research is therefore necessary to inform best-practise riparian tree planting strategies. This article contrasts stream temperature and energy fluxes under three riparian vegetation types common to Europe: open grassland terrain (OS), semi-natural deciduous woodland (SNS), and commercial conifer plantation (CS). Data was recorded over the course of a year by weather stations installed in each of the vegetation types. Mean daily stream temperature was generally warmest at OS and coolest at CS. Energy gains at all sites were dominated by shortwave radiation, whereas losses where principally due to longwave and latent heat flux. The magnitude of shortwave radiation received at the water surface was strongly dependent upon vegetation type, with OS and SNS woodland sites receiving approximately 6× and 4× (respectively) the incoming solar radiation of CS. Although CS lost less energy through longwave or latent fluxes than the other sites, net surface heat flux was ordered OS>SNS>CS, mirroring the stream temperature results. These findings demonstrate that energy fluxes at the air-water interface vary substantially between different riparian forest types and that stream temperature response to bankside vegetation depends upon the type of vegetation present. These results present new insights into the conditions under which riparian vegetation shading is optimal for the reduction of surface heat fluxes and have important implications for the development of 'best-practice' tree planting strategies to moderate summer temperature extremes in rivers.

Is the composition of dissolved organic carbon changing in upland acidic streams?

The quantity and composition of dissolved organic carbon (DOC) exported from upland soils to surface waters is a key link in the global carbon cycle and economically important for treating potable waters. The relationship between ultraviolet (UV) absorbance and DOC concentrations can be used to infer changes in the proportion of hydrophobic (aromatic, recalcitrant) carbon and hence biodegradability of DOC. This study describes a significant change in the relationship between UV absorbance and DOC over 22 years at two upland moorland catchments in Scotland, UK. Despite increases in long-term DOC concentrations, analysis suggests that the proportion of hydrophobic material has declined. A statistical mixed-effect modeling approach was used to examine the likely mechanisms that could explain these observations. Annual nonmarine sulfate load was the only significant forcing factor that could explain the observed long-term trend in the UV absorbance-DOC relationship at both sites. It is hypothesized that enhanced heterotrophic decomposition of organic matter and increased solubility of carbon compounds in soils where sulfate driven acidification is being reversed are the dominant mechanisms behind this change in DOC composition. These trends will impact on carbon substrate dynamics by potentially increasing biodegradability of exported organic matter, influencing carbon cycling in terrestrial and aquatic ecosystems.