Spring salmon on the River South Esk, Scotland: Scottish Marine and Freshwater Science Vol 7 No 10

Report of a three year project carried out by Marine Scotland to investigate the spring component of the River South Esk salmon stock.

Part 3: The status of the juvenile salmon stock

Objective: How does the status of the juvenile salmon stock vary within the River South Esk and how does this relate to spawning regions used by spring fish?

3.1 Introduction

Juvenile assessments are often used to determine the health of fish populations and contribute towards status assessments under the Water Framework Directive ( SNIFFER, 2011 b) and EU Habitats Directive (Godfrey, 2005). Due to density dependent processes, juvenile assessments are potentially less sensitive than other metrics of fish population health, at least at relatively high densities, but can still be a useful indicator where spawner numbers drop below saturation levels or where there are problems with local habitat quality that prevent successful recruitment.

Electrofishing is the most commonly applied sampling approach for juvenile salmonids. Where quantitative multi-pass electrofishing is performed, estimates of capture probability can be obtained. Given estimates of capture probability, it is possible to scale the observed fish numbers, to the total numbers of fish estimated to occur at a site (Millar et al., 2015).

One of the greatest challenges in juvenile salmon assessment relates to the interpretation of data obtained by electrofishing. To determine whether observed salmon numbers are adequate, it is first necessary to determine the expected numbers under natural environmental conditions. Juvenile salmon densities vary in response to a wide range of biotic and abiotic habitat variables including water quality, depth, velocity, substrate, cover and food availability (Armstrong et al., 2003). Although it is not possible to measure all these variables at the large spatial and temporal scales required for salmon assessment, it is possible to model salmon densities in relation to habitat proxies, often obtained from a Geographical Information System ( GIS). Such an approach was recently described by Millar et al. (2015) for Scotland and provides the basis for interpreting electrofishing data in this report. Specifically, observed densities from electrofishing on the River South Esk were compared with the national fish density model described by Millar et al. (2015) to assess whether sites are performing better or worse than expected given the habitat characteristics of the river. Due to the potential for status to change over time, data are presented for each year of the study (2013 and 2014) and compared with historical data (2004, 2005 and 2011), to determine if there is any obvious temporal variation in numbers of salmon fry across sites.

In the context of the current investigation, radio tracking has shown that early running spring salmon spawn predominantly in the upper South Esk (see Section 2). Therefore, it seems reasonable to assume that the status of electrofishing sites in the upper catchment are broadly indicative of the health of spring salmon stocks and it is on this basis that that the electrofishing data are interpreted.

3.2 Methods

Electrofishing data

Only quantitative electrofishing data containing more than two passes was included in this report. Historical electrofishing data were obtained from Scottish Natural Heritage ( SNH) for the years 2004 and 2005 (Godfrey, 2005), and from the Scottish Fisheries Co-ordination Centre ( SFCC) database for 2011. Additionally, Marine Scotland Science undertook programmes of electrofishing in 2013 and 2014 in support of this project. A summary of available data is provided in Table 3.2.1.

Table 3.2.1: Summary of available electrofishing data.

Year Date Easting Northing Number of passes Data Source Area fished (m 2)
2004 01/09/2004 330489 774254 3 SNH 372
2004 01/09/2004 336311 769818 3 SNH 136
2004 01/09/2004 336353 768610 3 SNH 355
2004 02/09/2004 340149 759445 3 SNH 155
2004 02/09/2004 349945 756805 3 SNH 152
2004 02/09/2004 325779 769888 3 SNH 228
2004 05/08/2004 364147 758240 3 SNH 592
2004 31/08/2004 328095 778108 3 SNH 337
2004 31/08/2004 328043 776048 3 SNH 220
2005 08/07/2005 325779 769888 3 SNH 136
2005 08/08/2005 328095 778108 3 SNH 241
2005 22/09/2005 334178 762769 3 SNH 46
2005 22/09/2005 329253 767866 3 SNH 77
2005 27/09/2005 352457 758502 3 SNH 53
2005 27/09/2005 338751 762338 3 SNH 48
2011 01/08/2011 328095 778108 3 SFCC 96
2011 06/10/2011 325779 769888 3 SFCC 125
2011 23/08/2011 334173 762925 3 SFCC 37
2011 23/08/2011 329253 767866 3 SFCC 77
2011 29/07/2011 352457 758502 3 SFCC 98
2011 29/09/2011 338751 762338 3 SFCC 73
2011 31/07/2011 340090 758522 3 SFCC 80
2013 11/09/2013 332177 773323 3 MSS 56
2013 11/09/2013 333988 762936 3 MSS 83
2013 18/09/2013 335398 771511 3 MSS 41
2013 18/09/2013 337218 769781 4 MSS 85
2013 24/09/2013 364178 756374 3 MSS 47
2013 25/09/2013 354159 758510 3 MSS 31
2013 25/09/2013 352456 758508 3 MSS 50
2013 27/09/2013 328813 768113 3 MSS 71
2014 02/09/2014 335343 758440 3 MSS 86
2014 02/09/2014 338566 758327 3 MSS 115
2014 03/09/2014 348402 760161 3 MSS 90
2014 04/09/2014 329693 768000 3 MSS 77
2014 04/09/2014 331455 766776 3 MSS 144
2014 05/09/2014 332767 765773 3 MSS 65
2014 05/09/2014 332618 762968 3 MSS 76
2014 08/09/2014 363075 755529 3 MSS 65
2014 08/09/2014 363400 755634 3 MSS 81
2014 09/09/2014 335418 771535 3 MSS 66
2014 09/09/2014 337293 769889 3 MSS 78
2014 10/09/2014 328167 777375 3 MSS 110
2014 10/09/2014 326674 775757 3 MSS 105
2014 11/09/2014 352457 758502 3 MSS 58
2014 11/09/2014 353096 758438 3 MSS 51
2014 12/09/2014 345010 754416 3 MSS 67
2014 12/09/2014 345952 754667 3 MSS 49
2014 15/09/2014 364170 756362 3 MSS 57
2014 15/09/2014 364882 756968 3 MSS 74
2014 15/09/2014 365071 757618 3 MSS 79
2014 16/09/2014 332697 773054 3 MSS 55
2014 16/09/2014 334178 762769 3 MSS 50
2014 17/09/2014 335291 763673 3 MSS 45
2014 17/09/2014 335305 763600 3 MSS 115
2014 18/09/2014 347362 755222 3 MSS 65
2014 24/09/2014 349187 756818 3 MSS 74

During 2004 and 2005, 15 electrofishing sites were fished. Sites were chosen to maximise the value of historical data consistent with a reasonable geographical spread of sites throughout the SAC (Godfrey, 2005). During 2013, eight sites were selected to provide data on salmon abundance in the upper and lower South Esk catchment. The sites were subsequently micro-sited to include only areas where local physical habitat appeared suitable for salmon. This is likely to be broadly consistent with historical site selection approaches. During 2014, 26 sites were regularly distributed across the catchment, within the geographical range occupied by Atlantic salmon, subject to a number of logistical constraints. These constraints were that the site had to be accessible with respect to access permission or stalking activity, and that the site had to be of fishable dimensions in terms of wet width (generally < 8 m) and water depth (suitable for safe and comfortable wading). This approach removed surveyor bias towards "good" salmon fry and parr habitat, increased the range of habitat types surveyed, and provided good overall spatial coverage.

Estimating salmon densities

Salmon were separated into fry (age 0+) and parr (age 1+ and older) using site specific length breakpoints. Densities were subsequently estimated for salmon fry. Full details of the methods used to estimate salmon fry densities are described by Millar et al. (2015). However, in brief, capture probabilities were modelled from multiple-pass (depletion) electrofishing data as a function of the organisation carrying out the electrofishing, the year, day of the year, geographical location, distance to sea, channel width, channel gradient, catchment area and altitude. The modelled capture probabilities were then used to raise observed fry counts to provide an estimate of the total number of fish occurring at a site.

Assessing the performance of electrofishing sites

The salmon fry densities that would be expected for each electrofishing location under natural environmental conditions were modelled in relation to local habitat characteristics using a national fry density model. For full details of the model see Millar et al. (2015). The resulting predictions from the model were specified such that they represent an average national expectation for salmon fry in a good year, given the local habitat characteristics of the site but excluding the effect of the negative anthropogenic impacts including "urban area" and "conifer forest".

Expected (modelled) salmon densities were compared with observed densities to determine the relative fry numbers at each electrofishing site, and the resulting differences in densities (model residuals, observed - predicted values) were plotted using a simple colour coded system where sites close to average national expectation were coded green, those exceeding expectation were coloured shades of blue and those below expectation were coded shades of red.

3.3 Results

Expected salmon fry densities

Figure 3.3.1 shows the expected densities of salmon fry for monitoring locations in the River South Esk. Spatial differences in expected densities reflect the spatial variability of habitat variables. The greatest spatial variability was driven by altitude, with lower densities expected at higher altitudes. The next greatest effects were associated with distance to sea (densities increase with increasing distance) and upstream catchment area (densities increase with increasing catchment area up to ca. 50 km 2). Expectations are also reduced at a local level by the presence of mixed forestry. In general, higher densities are expected at the bottom of larger tributaries in the lower catchment which are associated with low altitudes, moderate upstream catchment areas and a moderate distance to sea. Very low density expectations are predicted for higher altitude sites in the upper catchment that also have relatively small upstream catchment areas.


Figure 3.3.1: Predicted (expected) densities of salmon fry for monitoring locations on the River South Esk. The expectations are derived from a national salmon fry abundance model described by Millar et al. (2015). The predictions were made for a good year (2003) and time of year (day of the year 150) when the highest densities are expected. The effect of negative anthropogenic impacts "urban area" and "conifer forest" have been excluded from predictions. The predictions can be considered a mean national expectation under good conditions and in the absence of anthropogenic pressures. Scale represents number of salmon fry expected m -2. Solid purple bar indicates an approximate delineation between the upper catchment where spring fish predominate and the lower catchment.

Observed salmon fry densities

Across all years, fry densities ranged from 0 - 3.9 fish m -2 ( Figure 3.3.2). Observed fry densities were more variable during later years (2011 to 2014) than earlier years (2004 and 2005), although the numbers and distribution of sites and sampling strategies also varied between years. Fry were found at all sites in 2004, 2005 and 2011, although these surveys contained few sites in the lower catchment. Fry were absent from 38 % and 46 % of sites in 2013 and 2014 respectively. Given the lack of a consistent sampling methodology, sample locations and sample numbers across years it was not possible to identify any clear temporal trends in fry densities. However, a qualitative assessment of the data suggests that densities may have been lower in the upper catchment in recent survey years (2013 and 2014). Unfortunately there were very few sampling locations in the lower catchment prior to 2013.


Figure 3.3.2: Observed densities of salmon fry (fish m -2) calculated by raising fry counts observed during electrofishing by an estimate of capture probability derived from a national fry capture probability model (Millar et al., 2015). Electrofishing data are presented for each year where > 2 electrofishing sites were available.

Performance of electrofishing sites relative to average national expectation

The percentage of sites meeting or exceeding the national expectation for fry densities was 100 %, 100 %, 83 %, 80 % and 69 % in 2004, 2005, 2011, 2013 and 2014 respectively ( Figure 3.3.3). There were few electrofishing sites in the lower catchment prior to 2013 and substantial numbers of sites were only fished in 2014. Of the sites fished in the lower catchment in 2014, only 17 % exceeded the average national expectation for fry numbers, although these sites greatly exceeded expectation.


Figure 3.3.3: Differences (model residuals) between expected fry densities obtained from a national fry density model (Millar et al., 2015) and observed densities estimated from electrofishing data. Positive residuals (blue) indicate that sites have done better than expected based on a national expectation; negative residuals (red) indicate that sites have done worse than expected. All values are fish m -2.

3.4 Discussion

Given the lack of consistent experimental design, spatial coverage and site selection across years and the fact that density expectations vary by location, it was not possible to provide a quantitative assessment of any changes in fry density over time. However, it was possible to assess the performance of individual electrofishing sites relative to a recently published national expectation model (Millar et al., 2015). Based on the available radio tracking data it was also possible to broadly delineate the upper South Esk, which is considered to reflect the status of spring fish populations.

For the upper South Esk, a qualitative consideration of the data suggests an increase in the number of sites failing to meet the national expectation in 2014, relative to previous years. However, some care is required in the interpretation of these data. In contrast to previous years, there was no attempt to electrofish sites that were assessed as suitable for salmon during 2014. This would have the benefit of providing an unbiased assessment of fry densities across the upper catchment, but would inevitably increase the chances of fishing sites where salmon fry would be scarce due to local habitat characteristics. Setting aside the issue of survey design, nine out of the thirteen sites surveyed in the upper South Esk during 2014 remained above expectation, providing reasonable evidence that the upper catchment as a whole continues to perform adequately relative to the average national expectation.

There were relatively few data available for the lower South Esk. However, for the sites that were sampled, it is evident that there were substantial deviations from expected densities. Sites on the Noran Water, a northern tributary of the lower South Esk, performed considerably above expectation, whereas sites on two southern tributaries, the Pow Burn and Lemno Burn, performed particularly badly suggesting local anthropogenic impacts that prevent expected levels of salmon production across substantial areas of the lower catchment or differentially poorer adult returns to these areas.


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