Development Of A Risk Evaluation System For The Establishment Of Gyrodactylus Salaris In Scottish River

Systems

SARF070

David Morris

A REPORT COMMISSIONED BY SARF AND PREPARED BY

Published by the: Scottish Aquaculture Research Forum (SARF) This report is available at: http://www.sarf.org.uk Dissemination Statement This publication may be re-used free of charge in any format or medium. It may only be reused accurately and not in a misleading context. For material must be acknowledged as SARF copyright and use of it must give the title of the source publication. Where third party copyright material has been identified, further use of that material requires permission from the copyright holders concerned. Disclaimer The opinions expressed in this report do not necessarily reflect the views of SARF and SARF is not liable for the accuracy of the information provided or responsible for any use of the content. Suggested Citation Title: Development of a risk evaluation system for the establishment of Gyrodactylus salaris in Scottish river systems ISBN: 978-1-907266-38-6 First published: August 2011 © SARF 2010

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Development of a risk evaluation system for the establishment of Gyrodactylus salaris in Scottish river

systems

Scottish Aquaculture Research Forum final report Project SARF0070 Principal Investigator: David Morris Institution: University of Stirling

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CONTENTS Executive summary 3 Project report Scientific objectives 5 Methods and results Derived river network 5

Landcover 9 Point data 10 Integration of datasets 12 Development of Scottish risk model 13 Risk map of Great Britain 15

Discussion 16 References 18

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EXECUTIVE SUMMARY Gyrodactylus salaris is an ectoparasite of salmon. It is not present in UK rivers, but its introduction into Norwegian river systems has caused substantial economic and environmental damage to salmon fisheries and farming. Once within a river system it can reduce salmon stocks to 1% of their initial levels. Procedures to remove the parasite from infected river systems are expensive, time consuming, can be ecologically damaging and are not always effective. G. salaris is a notifiable pathogen and is regarded as the most important exotic disease threat to wild UK salmon, with the potential to cause major economic losses to fisheries. Current control measures for G. salaris primarily rely on maintaining the parasite free status of the UK. However, if it were introduced then rapid detection would be essential to remove it from an affected river and prevent it spreading within and between neighbouring catchments. However, it is impossible to regularly examine every part of the river network of the UK for parasitic disease, especially when resources are constrained. Therefore techniques are needed that allow for current monitoring regimes to be specifically targeted to those rivers and reaches where both risk of G. salaris introduction and establishment is highest. This would dramatically improve the chances of parasite detection and allow for those areas to be further targeted for monitoring and education regarding the disease. This represents a cost effective strategy that will have a high chance of success. In addition, once identified an area of risk can be included in scenario planning exercises. A recently completed Defra funded project (FC1117) developed a risk map to determine the areas where G. salaris would most likely establish if introduced into English and Welsh river systems. This map was based on environmental factors thought to influence G. salaris establishment with sub basins being rated at low, medium or high risk of establishment. Before this project, English and Welsh monitoring was largely based on geographic coverage and availability of samples rather than being targeted as it had not been possible to identify those areas of potentially high risk. The map was further integrated with information regarding fish farms with the potential to add movement data. The current SARF project was to develop a risk map for G salaris in Scottish rivers, and link this with the map of England and Wales. This would result in an overview of Britain that would highlight the regions most at risk of establishment. It would also inform national policy and procedures regarding G. salaris. To develop the risk map, a similar protocol was used as developed for the Defra project that primarily relied on existing sources of data. The datasets used were obtained through arranged and existing University licences, CEH, and provided by SEPA, Marine Scotland, Fisheries trusts and the SNH, and combined into a Geographic Information System (GIS). Using a digital elevation model a river network was derived for Scotland. This was checked and debugged to form a representative vector line river network for Scotland. Geomorphology and surrounding types of landuse, which may affect water chemistry/quality were then calculated for every stretch of the derived river network. In addition, point source water chemistry data and salmon density data was mapped onto the network, resulting in an overview of local environmental parameters for all Scottish rivers

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Data for the prevalence of native salmon Gyrodactylus spp. were obtained from previous monitoring surveys establishing the G. salaris free status of Scotland. The results of these monitoring surveys allowed native Gyrodactylus spp. data to be mapped to the derived river network and allocated to either high or low prevalence dependant upon the level of Gyrodactylus reported. Using logistic regression, with the prevalence as the dependant variable, the environmental factors were examined to determine those factors that could be used to predict Gyrodactylus spp. prevalence. A number of factors were identified, two of which directly coincided with those found in the Defra project and several that were related. The primary identified factors were distance to mouth and water pH and these, together with salmon density were used to develop the Scottish risk map. For the development of the risk map, the derived river network was split into 963 catchments and the characteristics of these used in the regression to obtain predictive scores for catchment risk of Gyrodactylus establishment. The catchment scores were then mapped into the GIS to produce a risk map for Scotland. This map indicated that river systems in that discharge to the east of Scotland were at particular risk of Gyrodactylus establishment compared to those that discharge to the west. Rivers included the Tweed, Tay, Dee and Don. The islands appeared to be of low risk of establishment. It is highlighted that some trout farms and fisheries occur on the high risk systems. The map was successfully linked to the previous one developed for England and Wales, resulting in a risk map of G. salaris establishment that covered Great Britain. Because of their nature, risk maps can only give an indication of risk. They cannot be used to definitively predict when and where a parasite will be introduced or succeed in establishing. However, their strength is that they provide an indication, where none existed before. Such maps become more predictive as more detailed data become available. Future enhancements of the risk map would be the inclusion of more fish density data, hydro-electric schemes, fish farm locations and animal movement data. This could allow further integration with the G. salaris webtool created during the Defra (FC1117) grant for England and Wales that was to aid in developing a rapid, coordinated response if the parasite were introduced. The map produced in this project is the first risk map for G. salaris establishment for Scotland and was generated using parameters that are considered biologically relevant for the parasite. It can therefore be considered to represent a scientifically reasonable basis on which monitoring and awareness campaigns can be targeted in a cost effective way. The maps produced are primarily for government agencies, and river and fisheries trusts, to indicate those areas of Scotland where G. salaris is most likely to establish if introduced. These maps can therefore inform current monitoring strategies, biosecurity measures and aid in contingency planning should an outbreak occur.

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Scientific objectives

1. Build a database characterising rivers across Scotland 2. Integration of environmental database into a GIS together with native

gyrodactylid survey data 3. Develop a Scottish risk model by analysing incidence with environmental

parameters 4. Compare Scottish risk model with that of England and Wales to produce a

mainland Britain risk map for Gyrodactylus salaris. All objectives were met within the project. Methods and results Build and integrate an environmental database into a GIS together with native gyrodactylid survey data. To build the database a variety of datasets were obtained, either in a GIS format or as point data. The datasets in a GIS format included ones for which the existing licences were held by the University of Stirling (e.g. through EDINA-Digimap), or additional licences were obtained during the project. These datasets included the Landcover 2000-25m raster (CEH), River network mainland Scotland (SEPA reduced version-CEH), OS 1:50,000, digital elevation models (90m, 25m), Scotland outline, British Isles outline, rivers detailed and inland water body maps (EDINA-Digimap Sharegeo). Derived river network In developing a GIS database it was necessary to develop a derived river network (DRN), similar to the one used for the related Defra project (FC1117). The purpose of the DRN was to allow geomorphological parameters to be calculated for each section of the river network. It also allows the percentage landcover upstream from each segment of river to be calculated. The existing river network datasets could not easily be translated to calculate catchment properties as this data were essentially hand drawn rather than in a form that could be integrated with other datasets allowing for GIS processing. The DRN used in the Defra project (FC 1117) was developed using a digital elevation model (DEM) of England and Wales and a river centreline map produced by the Environment Agency (Coley 2003). Such a network was unavailable for Scotland and therefore had to be generated using the ARCHydro attachment of ARC GIS together with the DEMs, the inland water body map of Scotland, and the SEPA reduced-river network. An initial attempt at deriving the river network was attempted using the 25m DEM dataset. This however, was not successful due to errors induced when the individual DEM tiles from this dataset were stitched together to form a DEM dataset covering Scotland. Therefore it was decided to use an existing 90m prestitched DEM that covered the whole of Scotland, excluding the Shetland Isles. DEMs use satellite data to measure the height of land, and as such are affected by the curvature of the

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Earth, datasets that cover mainland Scotland tend to be unreliable for the Shetland Isles and therefore these were excluded from the analysis (Fig 1a). The network was derived following the ARCHydro workflow and the method of Coley (2003). As the DRN is based on elevation data, deriving it can become unreliable in those areas where the ground is relatively flat. Burning in a known river course through these areas improves the reliability of the resulting river network. The first step therefore was to burn in a known river network into the DEM, using the AGREE method of the ArcHydro workflow. The network used was the SEPA reduced river network for Scotland, which contains the larger rivers of Scotland. Following burning in, sinks (holes) within the DEM caused by potential artefact and level areas containing water bodies by overlaying the inland water body map (Fig 1b) were filled to prevent artificial drainage and pooling areas from forming during the derivation of the river network. Fig 1a 90m digital elevation map of Scotland; Fig 1b, DEM of River Tay catchment including locations of inland water bodies. From the filled DEM, a flow direction raster dataset was created. This allocates where water would flow if introduced into each point located on the DEM based on its elevation in relation to 8 neighbouring points (Fig 1c). From this raster grid a flow accumulation raster dataset could be derived. This assigns a value to a point in the raster, dependant on how many points flow into it from the flow accumulation grid (Fig 1d). Fig 1c. Flow direction of the River Tay catchment. Fig 1d Flow accumulation grid of the River Tay catchment derived from flow direction raster.

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From the flow accumulation raster, streams could be defined (Fig 1e). The streams were segmented between confluences and catchments delineated relating to each stream segment. Finally, the segmented streams were converted into vector format resulting in a drainage line (Fig 1f). This drainage line was used as the basis for the derived river network to be used in the project. Fig 1e. Stream definition from flow direction raster of the River Tay catchment. Fig 1f. Drainage line for the River Tay catchment. The drainage line was error checked against Google maps, and the OS 1:50,000 map. Notable errors occurred where the river network map used to burn the drainage line did not always connect with the Scottish coastline. This led the drainage line being forced to misconnect with an adjacent catchment (Fig. 1g). Fig 1g. Map of simplified river network used for burning in a derived river network, together with map of inland water bodies, highlighting Loch Awe. River Awe does not connect with the sea as defined by the Scotland outline map. This erroneously resulting in the River Awe catchment merging into the River Tay catchment. These discrepancies were corrected in the original datasets and the derivation process repeated. Even though steps were taken to avoid artificial braiding of the drainage line, through burning, this artefact still occurred in several locations and had to be manually corrected in line with the OS map (Fig 1h). Finally, other discrepancies

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were noted with derived river network and the OS map. In the majority these were very small, relating to reach length of tributaries, however where they were more substantial (e.g. two tributaries artificially merging) they were manually corrected (Fig 1i). Fig 1h. Formation and removal of braiding artifacts. Fig 1i. Comparison of the drainage line with OS map. An area for manual correction is highlighted. Small deviations between the lengths of the derived tributaries and the map occur, however the accuracy was considered generally acceptable and fit for purpose. The DRN extends through water bodies, ensuring connectivity. From the DRN, it was possible, using Arc Hydro to obtain values for altitude, distance to source, distance to mouth, gradient, and Strahler and Shreve (two methods

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used for stream ordering) for each individual segment of the network. These were used in the subsequent analysis. Landcover The landcover 2000 dataset (CEH) maps UK into broad categories and was used to record acid grassland, in shore littoral, littoral, supra littoral, broad leaf woodland, urban/suburban. neutral grassland, improved grassland, calcerous grassland, arable, rock, bog, bracken, coniferous woodland, standing water, heath, montane in a 25m raster format (Fig 2a). Fig 2a. Landcover map of Scotland. 17 different landcover types are colour coded. Each land type was separated from the original mixed raster datset to form a unique raster. The derived river network was then superimposed onto each of these rasters and the percentage land upstream calculated using the flow accumulation tool in Arc GIS toolbox. This generated values for each segment of river for the percentage upstream landuse and followed the method of Coley (2003) (Fig 2b). Fig 2b. Landcover acid grassland raster superimposed with the derived river network to give a value to each stream segment based on percentage landcover above segment. The different values represented in this map by colour variation (green none, yellow low, red high). The catchments associated with each river segment are represented in red.

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Point Data Three types of point data were used in the project; water chemistry, fish densities, gyrodactylid data. Water chemistry: Point data was obtained for water chemistry determinands. This data was obtained from SEPA as part of their ongoing monitoring surveys. The data obtained comprised of 10 years sampling for the following determinands pH, Mg, Zn, Ca, Hardness, NO, Orthophosphate, BOD, O2, Nitrate, Nitrite, Fe, Pb, Alkalinity. Eastings and Northings were included allowing data to be plotted in the GIS. The dataset comprised multiple readings from > 3000 sites across Scotland. This was summarised using Minitab for each determinand resulting in values for trimmed mean (upper and lower 5% values ignored), mean, median, lower quartile, upper quartile and range for each sampling point. This reduced dataset was then examined for non-normally distributed data and potential outliers. Where these were found the original dataset was re-examined in case of error, and adjusted if needed. The dataset was plotted against the DRN using the supplied Eastings and Northings. It was anticipated that the datapoints would not lie exactly on the DRN but would generally be a short distance away. Therefore, to check accuracy a buffer zone was created around the DRN of 100m. If a datapoint was not included within this buffer its location was manually checked for accuracy. The resultant map suggested a reasonable spread of datasites across Scotland, with an expected clustering around the central belt/ Tayside (Fig 3a). Fig 3a. Location of water quality sites throughout Scotland.

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Fish data: Data for salmonid densities was obtained from SEPA and SNH/ Marine Scotland; The SNH/ Marine Scotland data forming part of a report regarding salmonid densities. Both of these datasets provided data from across Scotland and were composed of two different types of electrofishing estimation; timed and depletion studies. Published fish maps of Scotland were examined, to provide further data regarding salmonid distributions. Finally the Rivers and Fisheries Trusts of Scotland (RAFTS) was approached as the contact to obtain the data from individual fisheries trusts and they arranged to obtain formal permissions from the individual trusts. Salmonid density data from a number of trusts was successfully collected and used for the project. The combined datasets produced a map of point data for the sampling sites (Fig 3b). Fig 3b. Salmonid electrofishing survey data. Gyrodactylus data: Data for the distribution of salmonid Gyrodactylus surveys was obtained from Marine Scotland and Stirling University (Dr A. Shinn). The combined dataset resulted in 197 Gyrodactylus surveys. The data was collected as part of ongoing monitoring purposes and PhD, research studies and not explicitly for analysis in this project. Therefore, data cleaning and Gyrodactylus prevalence allocation was conducted for each site. This was preformed in the same manner as the related Defra grant (FC1117). For the Defra study, for purposes of logistic regression analysis the data was split into low prevalence (0) and high prevalence datasets (1). A high prevalence was any site that had more than 1 fish in 16 examined infected. Where a site was identified as low prevalence a minimal sample size of 10 fish was used. This excluded negative sites where the sample size was less than 10. In addition, sites that could not unambiguously be given a grid reference and positive sites where the sample size was not recorded was also not included for analysis. Because gyrodactylids are overdispersed, numbers on individual fish were not taken into account. This reduced the dataset to 77 usable sites for the regression analysis (Fig 3c), with the remaining sites rejected. Visual examination of the location of the Gyrodactylus sites indicated that low prevalence sites occurred more frequently on the islands but no clear pattern was associated with the mainland.

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Fig 3c. Location of all gyrodactylid presence (red circle) and absence (black circle) sites in Scotland together with landcover map. Fig 3d. Location of high prevalence (red circle) and low prevalence (black circle) sites used in analysis Integration of datasets. For each of the gyrodactylid datapoints the nearest fish density (within 10km) and water chemistry site (within 5km) located on the same river stretch was recorded. Also the percentage upstream landcover composition was recorded together with the Strahler, Shreve, altitude, gradient, distance to source and distance to mouth. From this it became clear that the salmonid density data did not cover a wide enough range to be retained for analysis. However, it was also noted that a large proportion of retained gyrodactylid sites had been sampled on fish farms (58 sites), unlike the situation for the associated Defra project (FC1117) where the gyrodactylid data was largely derived from electrofishing surveys. It was therefore considered that fish density was likely to become a confounding variable during the subsequent analysis. However, this also meant that water chemistry data was available for most of the sites, which could be included in the analysis along with the landcover, and geomorphology data.

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Development of a Scottish risk model Analysis: The integrated dataset was analysed using logistic regression. For the water chemistry data the trimmed mean, median, lower quartile or upper quartile of all variables collected for each site were initially used as independent variables in the analysis. If an upper or lower quartile was found significant, then these were to be replaced with minimum or maximum values and again examined in the analysis. Variables were first plotted against each other to look for evidence of co-linearity. Where this occurred, only one variable was taken forward for the regression analysis. Logistic regression models were built in order to identify independent variables associated with low prevalence/high prevalence of gyrodactylids at a site using SPSS (vers 16). Multivariable models were built using backward and forward stepwise approaches. The initial model included all variables from which the least significant were removed until only significant variables remained. The effect of cases with potentially high levels of leverage on the model was assessed by removing them from the analysis. In addition, Receiver Operating Curve (ROC) analysis was also examined using the prevalence of gyrodactylids as the state variable, and principal component analysis examined to reduce dimension for the logistic regression. From the analysis a number of variables were identified that appeared to be associated with the high/low prevalence of gyrodactylids. However, none of the final models significantly improved upon the null model of the analysis in terms of predictive value. That is, the predictive value of the model was >1.25 times that of the null model. The null model being that all sites had high prevalence. Factors that appeared to have a significant effect in model development included distance to mouth/source, acid grassland, heath, arable, pH (IQR1/ minimum), and standing water. The difficulty associated with model building was possibly reflected in the high numbers of positive sites used to build the models in comparison to the number of negative sites. The model developed during Defra grant (FC1117) identified brown trout density, distance to mouth, and minimum pH as factors, with brown trout density being interpreted as a proxy for salmonid density. When compared to the factors highlighted in the Scottish modelling, pH and distance to mouth are both represented. With regard to the other variables, standing water is likely to be a reflection of the location of fish farms in Scotland, rather than an actual risk factor, while acid grassland, heath and arable could all be associated with pH. Scottish risk map: For the development of a risk map for Scotland, it was decided to defer the model used to the parameters identified in the Defra grant. However, instead of brown trout density, salmon density was used, so the final variables used in the model were, minimum pH, lndistance to mouth, lnsalmon density. The preference to salmon density over brown trout was that G. salaris is known to affect Atlantic salmon, while its affect on brown trout, and its interactions with this species are largely unknown. In addition, the distribution of salmon is similar to brown trout across Scotland, whereas in England and Wales the distribution of these two species is different.

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To develop a usable risk map, distance to mouth, and both the salmon density and minimum pH point data were interpolated by ordinary Kriging using ARC Geostatistical Analyst. This produced continuous surface maps from the point data that could be used for subsequent analysis (Fig 4). Fig 4. Interpolated map of Scotland for minimum pH. To produce an effective mapping tool, the maps had to be zoned into areas relevant to the river network. This involved developing a catchment layer, which contained enough catchments to represent the river network of Scotland at a fine enough level to be meaningful in analysis and the development of the mapping tool. To develop this, an applet (written by Dr Darren Green; University of Stirling) was used that analysed the connections in the river network layer and ascribed river segments to a catchment based on adjustable parameters. This applet was previously developed during the Defra grant (FC1117) and was adjusted to accommodate the Scottish data, resulting in the DRN being split into 963 segments, each of which was associated with a unique catchment area and river basin Fig 5. Fig 5. Catchment map of Scotland used in risk mapping (left). Derived river basin map (right)

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Average values for each of the catchments was then obtained from the continuous surfaces of the pH, fish density and distance to mouth maps using the zonal statistics tool of ARC. These values were then introduced into the SPSS statistics package, and prediction values calculated using the English and Welsh gryodactylid data in the logistic regression. This data was used as it contained fish densities data used in the model. This generated prediction values for each of the catchments that could then be mapped, resulting in a risk map of Scotland that indicated areas of high, medium and low risk (Fig 6). Fig 6. Risk map of Scotland showing prediction values (left). Risk map of Scotland based on high, medium and low risk values in respect to river basins (right). Risk map of Great Britain: A key requirement of the project was to develop a risk map that covered Great Britain. Extending the use of the English and Welsh risk mapping model to Scotland allowed for relatively straight forward integration of the two risk maps based on Atlantic salmon density, distance to mouth and pH (Fig 7). The catchment map of Scotland was necessarily made at a lower resolution than the English/ Welsh map as there were fewer fish sites and therefore the map of Scotland appears more homogenous. Despite this the combined maps provide an overview of the main locations where G. salaris could colonise.

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Fig. 7. Risk map for the establishment of Gyrodactylus salaris in Great Britain based on environmental parameters. It was noted from the GB risk map that some sites in England are rated medium when arguably there could be considered to be no salmon present. This potential artifact was due to the use of interpolation when developing the maps. These methods can assign values to areas where data are absent. To reduce the impact of this, maps were developed that automatically assigned low risk to an area in England and Wales if no salmon had been recorded in the fish surveys. From Fig 7 it is clear that outside Scotland, the North of England and Wales are particular areas where the model based on salmon indicates high levels of risk for G. salaris establishment. Cornwall, that represents another area with a high number of salmon rivers, appears to be at lower risk of establishment. Discussion The aim of the project was to develop a risk map for Scotland to determine where the parasite Gyrodactylus salaris could establish if introduced. The project was successful in achieving this. The risk map and the relevant associated datasets are to be made available to relevant the stakeholder(s).

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A derived river network was created for Scotland, onto which several pertinent geomorphological features were mapped and allowed the landuse data to be incorporated into the network. This methodology replicated the one used to generate the derived river network of England and Wales by Coley (2003) and allowed the parameters collected to be directly comparable between the Scottish study and the Defra FC1117 study. The spread of water chemistry points was distributed across the country at a relatively high density allowing interpolation techniques to be employed. Again this followed roughly the trend for spread of Environment Agencies water quality datapoints utilised in the Defra FC1117 study. The gyrodactylid data and the fish data however, appeared different between the two studies. The gyrodactylid data for Scotland used in the modelling was collected predominantly from fish farm sites, which was the counter to the situation for the Defra project where sampling of wild sites was predominant. This effectively meant that, in Scotland, fish density data in neighbouring rivers did not conceivably contribute to the farm site gyrodactlid status as the farms represented artificially high fish densities. This effect was notable during the modelling process where standing water was considered as a risk factor in Scotland, a parameter that probably reflects the use of standing waters for fish farming practices. If G. salaris were to be introduced its impact on a farm would likely to be limited as on farm treatments could be employed. However, these treatments would not extend to the river system upon which the impact would be severe if the parasite became established. Therefore, modelling using the gyrodactylid data but excluding fish density data was conducted to try and obtain environmental factors pertinent to establishment. This process did identify several factors that could affect gyrodactylid distribution but did not generate models that were statistically, significantly better at predicting gyrodactylid establishment over the null model. Again, this may have in part been a reflection of the available Scottish gyrodactylid data that had many more high prevalence sites than low prevalence sites. However, it was notable that many of the parameters identified during the modelling process correlated to those environmental factors highlighted in the Defra study, notably pH and distance to mouth. It is well established that pH affects Gyrodactylus species and therefore this parameter was included in the development of the risk map. In the Defra study, distance to mouth was considered a proxy measurement related to the nature of the river habitat and therefore it was also retained in the development of the risk map. Standing water, that appeared as a risk factor in the Scottish modelling was not included, due to the concerns regarding fish farm sites outlined above. To match the English/ Welsh risk mapping, wild salmon densities were included. However it is to be noted that the dataset used was not as representative as that used for England/ Wales and therefore a lower resolution was used for the risk map. Higher resolutions of this map may be obtainable if more fish data were to become available. The risk map generated, successfully divided Scotland into areas of high, medium and low risk. These categories are for relative risk, and do not imply that G. salaris establishment is impossible in areas of low risk. The map however, clearly indicated that the short river reaches of the west of Scotland and the Western Isles were of low risk of infection, as opposed to the catchments draining to the east of Scotland. The borders and north of England represented a roughly continuous area that would be at risk of G. salaris establishment

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Improvements on the Scottish risk map would be the incorporation of fish density data. This may allow further modelling and provide a higher resolution of catchment risk. It would also be desirable to incorporate the effect of hydro-electric schemes and artificial water diversions between catchments to provide additional information regarding catchment delineation and the ability of G. salaris to be transported and spread among catchments. Joining of the risk map to the English/ Welsh map highlights the desirability of integrating possible movements of fish between the river catchments of Great Britain through aquaculture activities. This would allow for modelling potential spread of the infection across GB and very rapid contact tracing if the parasite were introduced. The inclusion of the Scottish data into the webtool, developed during the Defra project, for English and Welsh fish movements and intra river farm connectivity would facilitate these aims and allow government agencies to rapidly identify which catchments were at risk if the parasite were introduced. Currently, fish movements between freshwater sites are recognised as the most likely way that G. salaris would spread between and within catchments during the initial phases of establishment. It is to be noted that the majority of salmon farms occupy rivers draining to the west coast, however there are trout farms and fisheries located in other areas, some of which were highlighted as high risk. It is therefore desirable that existing monitoring regimes take these into consideration. In conclusion, risk maps were generated to highlight those river catchments that may be at higher risk of G. salaris establishment. These risk maps can be used by government agencies to inform sampling strategies for monitoring purposes. They could also be included in contingency planning by the agencies and rivers and fisheries trusts regarding parasite introduction. Summary of findings

• The first risk map for G. salaris establishment was made for Scotland. • In general, rivers discharging to the east were considered more at risk than

those flowing to the west of Scotland and the islands. • Trout farms and fisheries occur on some of these high risk river systems.

Ackowledgements: The researchers would like to thank the Scottish Environment Protection Agency, Marine Scotland, Scottish Natural Heritage and the Rivers and Fisheries Trusts of Scotland for kindly contributing data for this study. We would also like to thank SARF for funding this work. References: Coley A. (2003) Relationship between juvenile salmonid populations and catchment features. R&D technical report W2-065/TR. Environment Agency.