An inspection-based assessment of obstacles to salmon, trout, eel and lamprey migration and river channel connectivity in Ireland

Journal Pre-proof An inspection-based assessment of obstacles to salmon, trout, eel and lamprey migration and river channel connectivity in Ireland

Siobhán Atkinson, Michael Bruen, John J. O' Sullivan, Jonathan N. Turner, Bernard Ball, Jens Carlsson, Craig Bullock, Colm Casserly, Mary Kelly-Quinn PII:

S0048-9697(20)30725-7

DOI:

https://doi.org/10.1016/j.scitotenv.2020.137215

Reference:

STOTEN 137215

To appear in:

Science of the Total Environment

Received date:

17 June 2019

Revised date:

5 February 2020

Accepted date:

7 February 2020

Please cite this article as: S. Atkinson, M. Bruen, J.J. O' Sullivan, et al., An inspectionbased assessment of obstacles to salmon, trout, eel and lamprey migration and river channel connectivity in Ireland, Science of the Total Environment (2020), https://doi.org/ 10.1016/j.scitotenv.2020.137215

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© 2020 Published by Elsevier.

Journal Pre-proof

An inspection-based assessment of obstacles to salmon, trout, eel and lamprey migration and river channel connectivity in Ireland.

Siobhán Atkinsona,d, Michael Bruenb, John J. O’ Sullivanb, Jonathan N. Turnerc, Bernard Balla,d, Jens Carlssona,d, Craig Bullocke, Colm Casserlyc, Mary Kelly-Quinna. a. School of Biology and Environmental Science and Earth Institute, University College

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Dublin, Dublin 4, Ireland

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University College Dublin, Dublin 4, Ireland

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b. UCD Dooge Centre for Water Resources Research, School of Civil Engineering,

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c. School of Geography and Earth Institute, University College Dublin, Dublin 4, Ireland

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d. Area52 Research Group, School of Biology and Environmental Science and Earth Institute, University College Dublin, Dublin 4, Ireland

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Dublin 4, Ireland

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e. School of Architecture, Planning and Environmental Policy, University College Dublin,

Corresponding author: Siobhán Atkinson, email: [email protected]

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Title: An inspection-based assessment of obstacles to salmon, trout, eel and lamprey migration and river channel connectivity in Ireland. Abstract Knowledge of the location, physical attributes and impacts of obstacles on river connectivity is a requirement for any mitigating action aimed at restoring the connectivity of a river system. Here, we present a study that recorded the numbers and physical diversity of

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obstacles in 10 river catchments in Ireland, together with the impact these structures had

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on overall river connectivity. A total of 372 obstacles were recorded, 3 of these were dams,

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and the remainder were low-head weirs/sluices, obstacles associated with road-river

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crossings and natural structures. The degree of fragmentation was estimated in each

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catchment by calculating obstacle density and the Dendritic Connectivity Index (DCI). DCI scores were calculated for 4 native Irish fish species with different life-histories, namely

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diadromous (Atlantic salmon, sea trout, European eel, sea lamprey) and potamodromous

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(brown trout). Obstacle density ranged between 1.2 and 0.02 obstacles per kilometre of river. Six of the 10 catchments had at least one obstacle located on the mainstem river at least 5 kilometres from its mouth/confluence. These 6 catchments typically had the lowest connectivity scores for diadromous species and ranged between 0.6 and 44.1 (a fully connected river would receive a maximum score of 100). Whilst there was no significant correlation between obstacle density and the DCI score for diadromous fish, a significant negative correlation was detected between obstacle density and the DCI score for potamodromous brown trout. Here, we highlight the merit of these obstacle assessments and associated challenges for decision-making relating to prioritisation of obstacles for removal or modification.

Journal Pre-proof Key words: Barrier, connectivity, Ireland, inventory, fish migration, physical survey,

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potamodromous, diadromous.

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1 Introduction Longitudinal migration either within a river system or between a river and the sea plays an important role in the lifecycle of many fish species. Fish migrate to access a variety of functional habitats, including those that provide suitable feeding, spawning, and refuge conditions (Lucas and Baras 2001; Maitland 2003; Carlsson et al. 2004; Näslund et al. 1998).

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The extent of migration can vary between species (Ovidio and Philippart 2002; Baudoin et

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al. 2015). For example, the long-range migrations of Atlantic salmon (Salmo salar), and the

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European eel (Anguilla anguilla) between salt and freshwater systems are well documented. Most Atlantic salmon growth occurs in the marine environment, and salmon return to

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freshwater habitats only to spawn, typically travelling thousands of kilometres in the

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process (Mills 1991; Baudoin et al. 2015). The European eel, on the other hand, spawns in

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the Sargasso Sea, undertaking a journey of several thousand kilometres through the Atlantic Ocean (Tesch 2003; Aarestrup et al. 2009). European eels migrate into freshwater systems

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where they can live for up to 25 years before making their return journey to the Sargasso Sea (Baudoin et al. 2015). Although the migrations between habitats for potamodromous fish species like resident brown trout (S. trutta) are not as striking as diadromous species, they nevertheless serve an important role in the survival and fitness of individuals (Jonsson 1985; Carlsson et al. 2004; Näslund et al. 1998). A high level of connectivity between habitats in a river system and between a river and the sea is acknowledged as vital for sustaining healthy stream fish populations and assemblages (Perkin and Gido 2012; van Puijenbroek et al. 2019). The connectivity of a river system is largely determined by the presence and impact of obstacles within the river, of both anthropogenic and natural origin. Obstacles include

Journal Pre-proof physical obstructions (Burford et al. 2009; Barry et al. 2018; Silva et al. 2019), low flow or chemical barriers such as those presented by anoxic or highly polluted reaches (Chapman 1996) or sediment barriers (Boubée et al. 1997). Physical obstacles, such as dams, culverts and weirs, fragment riverine habitats (Lucas and Frear 1997; Cote et al. 2009; Lucas et al. 2009; Bourne et al. 2011; Nunn and Cowx 2012; Gauld et al. 2013) and prevent or delay the movement of fish, invertebrates and river material. “Passage efficiency” and “passability”

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are the most common terms used to describe the extent to which obstacles or barriers can

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impact fish movement (Kemp and O’Hanley 2010). These can vary depending on the species

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in question (Tudorache et al. 2008), their life stage (Baudoin et al. 2015), flow conditions in the channel (Ovidio et al. 2007; Gauld et al. 2013; Baudoin et al. 2015) and the physical

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nature of the structure (Ovidio and Philippart 2002; Ovidio et al. 2007; Burford et al. 2009;

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Lucas et al. 2009; Tremblay et al. 2016). Delays in migration can have significant impacts on

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the ability of some fish species to access suitable habitats (Lucas et al. 2009; Silva et al. 2019). For example, Silva et al. (2019) showed that the sea lamprey (Petromyzon marinus)

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experienced a mean delay of 6.3 days per river obstacle in the River Ulla (north-west Spain) during its upstream spawning migration. The cumulative effect of this delay was considerable, due to the large number (45 in total) of low-head obstacles within the accessible part of this river (downstream of an impassable dam). From a legislative perspective, the presence of obstacles is important for determining the hydromorphological status of a river in terms of hydrological regime, continuity and morphological condition (EU Water Framework Directive (WFD) 2000/60/EC). Under the WFD, the hydromorphological condition must be considered when assigning “high” status to a river or reducing its status to “good”. In Ireland, there exists a paucity of information on the location of obstacles in Irish river systems and these data limitation continues to present

Journal Pre-proof significant challenges to agencies with responsibility for WFD implementation. To address these data deficits, Inland Fisheries Ireland (IFI) has been tasked with developing a georeferenced database of obstacles in Ireland’s river waterbodies (Department of Housing Planning Community and Local Government, 2018). There has been growing interest in the topic of river fragmentation by physical obstacles in recent years (Garcia de Leániz 2008; Januchowski-Hartley et al. 2013; Kroon and Phillips

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2016; Birnie-Gauvin et al. 2017; Jones et al. 2019). Recent estimates of fragmentation

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suggest that 63% of the worlds rivers which exceed 1,000 km are no longer free-flowing and

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that half of all river reaches globally have diminished connectivity (Grill et al. 2019). It is

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estimated that only 1% of rivers in England, Scotland and Wales are free of artificial obstacles (Jones et al., 2019). Knowledge of the location of obstacles is a necessary

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preliminary requirement for building an initial picture of river fragmentation and

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subsequently, for informing mitigating actions aimed at restoring the connectivity of river systems (Januchowski-Hartley et al. 2013; Kroon and Phillips 2016; Jones et al. 2019).

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Furthermore, details on the physical dimensions of obstacles (Ovidio et al. 2007; Burford et al. 2009) and their associated risk to fish movement can help prioritize specific structures for modification or removal.

The aim of this study was to map the numbers of obstacles and capture their physical features, dimensions and diversity in all rivers in each of 10 catchments in Ireland, and use these data to quantify and compare connectivity estimates relating to four native Irish fish species (salmon, trout, eel and lamprey). Furthermore, this study highlights the value of these exploratory assessments and associated challenges for decision-making relating to prioritisation of obstacles for removal or modification. This study is novel because we

Journal Pre-proof considered all types of obstacles (not just dams like other studies (e.g. van Puijenbroek et al., 2019; Grill et al., 2019)) and also considered multiple species and the passability differences between them, thus highlighting the complexity that must be considered in dealing with river connectivity.

2 Methods

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2.1 Study area

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Ten catchments/ sub-catchments (hereafter “catchments”) were chosen that were

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geographically distributed across Ireland and represented a range of different average

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rainfall conditions and dominant level 1 Corine Land Cover (CLC) classes (2012, version

from 93 km2 and 296 km2.

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18.5.1) (Figure 1). Five catchments were coastal and five were inland and ranged in area

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2.2 Identification of potential river obstacles

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Potential obstacles to fish migration were identified through a desk study using ArcGIS 10.4 software from ESRI following the methodology described in Atkinson et al. (2018a). The methodology involved generating a geo-referenced layer of potential river obstacles based on the intersections between mapped elements of the transport (roads and railways) and river networks, which is useful for identifying culverts and major public bridge aprons, but not weirs or private bridges used for land access. So, in addition, the main stem and tributary networks of the selected river catchments were visually assessed for potential obstacles using 0.5m resolution satellite and aerial imagery (ESRI, 2011-2018), historical 25” (Ordnance Survey Ireland, 1897-1913) and Discovery Series map layers (Ordnance Survey Ireland, 2011-2016). These maps provide detailed information in relation to landscape and

Journal Pre-proof infrastructure features, including rivers, lakes, contours, trails, roads, weirs and waterfalls. Rivers were assessed with the maps and potential obstacles were marked as a point in a new ArcMap shapefile. Furthermore, in places where the resolution of the satellite imagery was poor, the same location was also viewed on Google Maps (https://www.google.ie/maps) and Here Maps (https://maps.here.com/). This additional assessment allowed for the identification of any potential obstacles that were missed with

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the former analysis, such as weirs, waterfalls, or river crossings within farms or forestry.

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Discovery series maps were used preferentially over road-river intersections in river

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catchments where the road network data were sparse. Road and rail network data were acquired from © OpenStreetMap (OSM) (https://www.openstreetmap.org). River network

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data were acquired from the Irish Environmental Protection Agency’s river network GIS

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layer “WFDRiverWaterbodies” (http://gis.epa.ie/). We acknowledge, there is an element of

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subjectivity involved in the manual identification of potential barriers and a possibility that different researchers would identify different numbers of barriers. We are not aware of any

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automated procedure that might reduce this subjectivity but suggest that since the manual methods tends to overestimate the number of actual barriers, this reduces the probability of completely missing an actual obstacle. A spreadsheet outlining the following meta-data for each potential obstacle was generated: identification code, river name, catchment name, obstacle type and its geo-referenced coordinates (latitude and longitude). In addition, maps showing the locations of the potential obstacles along the river network were produced to guide the field survey.

Journal Pre-proof 2.3 Ground-truthing obstacles and survey assessment Apart from a small number that were not accessible, each potential obstacle was visited, and its physical details determined. Road crossings that were consistent with natural stream conditions in terms of gradient, width and substrate were not considered obstacles to fish migration and were therefore not recorded as part of the assessment. In addition, backwatered culverts with water depths comparable with the natural stream were not

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recorded as obstacles. Weirs which were breached or washed out (such that their impact on

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fish movement was deemed to be insignificant) were also excluded from the assessment.

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Finally, dry intermittent streams and ditches were omitted from the assessment.

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A Level I IFI (Inland Fisheries Ireland, the state agency responsible for the protection, management and conservation of Ireland's inland fisheries and sea angling resources)

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obstacle assessment (Gargan et al. 2011) was carried out on each structure judged to be an

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obstacle. This is a systematic, standardised, time-efficient assessment which has been developed by IFI. The assessment allows the surveyor to quickly record the locations of

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obstacles within a river catchment, and to collect the basic physical measurements and attributes of each structure. This methodology was developed specifically by IFI as a rapid survey technique and does not account for the time-and flow-related variable nature of passability. Thus, the assessment provided an estimate of the risk of each structure only for the flow conditions on the day of sampling. Surveys were typically conducted in flow conditions that, from direct observation, did not drown out the structure. Discharge data from the nearest relevant flow gauging station were analysed to quantify flow conditions on the date of the survey. First the long-term flow duration curve (FDC) was estimated for the entire record at each gauging station. This characterises the long-term flow regime in the river reach. From this FDC, the percentage exceedance of the actual flow on the days of the

Journal Pre-proof survey was determined and the minimum value for each river is shown in the last column (“Flow index”) of Table 2. Typically, bank full flow conditions, the threshold for flooding, occur in Irish rivers for the 2-year return period flow, i.e. for an exceedance percentages of 0.14% or lower (i.e. such flows are exceeded (0.14% of the time – equivalent to once in 2 years). Table 2 shows that all the surveys were carried out for flows with exceedance percentages of 19% or greater i.e. flows much lower than bankfull (or flood flow) conditions

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and, on most dates, at exceedance percentages very much greater. Surveys were carried out

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by a team of two surveyors between the years 2016 and 2018. Most of the surveys were

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carried out during the summer and autumn months, however, some limited surveys were carried out during winter and spring. Surveys took between 10 and 20 minutes, depending

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on the conditions at the site and the size of the obstacle. Larger structures (e.g. weirs)

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typically took longer than smaller structures (e.g. culverts).

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A subjective assessment of the risk posed by each obstacle to the movement of fish species was undertaken at each obstacle onsite. Risk levels, as per the survey form, ranged from

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impassable, high, moderate, low and no risk, and assessments were conducted for four fish species at each obstacle. The physical attributes of each structure, material composition and flow conditions on the day of survey were used to inform the risk assessment. High or wide structures typically incurred a high or impassable risk category, unless appropriate fish passage facilities were available at these structures, in which case the risk level was reduced (Gargan et al., 2011). The depth of the plunge pool relative to the head height of the drop (if present) was taken into consideration, as was the water level flowing over the structure, as this can present a swim challenge to some species due to laminar flow, shallow water depths, lack of resting places and high velocities (Byrne and Beckett, 2012). Furthermore, the thigmotactic climbing behaviour of eel was taken into consideration. Eel can climb along

Journal Pre-proof the splash zones of waterfalls and weirs or discharge chutes of hydropower facilities, which can affect their ability to surmount an obstacle (Jellyman, 1977; Byrne and Beckett, 2012). However, these ascents are generally not feasible on artificial, dry concrete substrate. In addition, structures with an overhang or perched culverts may be particularly difficult for eel to navigate. Therefore, these structures were typically considered to be high risk or impassable. All data were collected onsite using a digital assessment form.

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2.4 River obstacle classification

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River obstacles were classified based on whether they were road-river crossings, weirs,

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sluices, dams or natural waterfalls. Structures that did not fit into one of these categories

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were listed as “other” and a description of each was provided during the survey. Road-river

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crossings (bridges and culverts) and weirs were further classified based on the presence or absence of common physical attributes relevant for fish passage. Thus, road-river crossings

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were subdivided based on whether they had a downstream apron, a downstream drop, or

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both (Figure 2). Weirs were subdivided based on whether their downstream chutes were sloping, vertical or stepped. Weirs with sloping chutes were further subdivided based on the presence or absence of a downstream apron and/or downstream drop (Figure 2).

2.5 Assessment of fragmentation

The degree of fragmentation in each catchment was calculated using two metrics: river obstacle density and the Dendritic Connectivity Index (DCI) (Cote et al. 2009). River obstacle density in this study was defined as the number of obstacles divided by the total river length in each catchment (Park et al. 2008; Jones et al. 2019). The DCI is a metric for assessing longitudinal connectivity in a river that takes account of the number of obstacles, the passability of each obstacle (this is species dependant), habitat variables between obstacles

Journal Pre-proof in the river channel (e.g. channel reach length or wetted area), whether the obstacle is natural or anthropogenic, the location of obstacles in the channel and the cumulative impact of a number of obstacles in the same channel (Cote et al. 2009). The resulting DCI is a value between 0 and 100, where a fully connected river with no obstacles would receive a value of 100. The DCI is calculated differently depending on the life history of the fish. For instance, DCID (i.e. for diadromous fish) considers that a fish may move in both upstream

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and downstream directions within a river network (e.g. adult sea trout or Atlantic salmon

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migrate from the sea into rivers to spawn, whereas the smolts of these species migrate from

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rivers to the sea), however, this movement begins at the furthest downstream point of the river such as the mouth of the river, a lake, or a confluence. DCIP (i.e. for potamodromous

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fish) is calculated based on the assumption that potamodromous fish are equally likely to

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move in both upstream and downstream directions between two randomly chosen

∑∑

∏

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from Cote et al. (2009):

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segments of the river network (Cote et al. 2009). The DCIP and DCID are calculated as follows

Where, the indices and are used to identify river segments; lj is the length of segement j; ;

is the connectivity between segments I and j (where

downstream passabilities of the

th obstacle (

and

are the up- and

) between segment and

segment j ); M is the total number of obstacles between these segments, and river length.

is the total

Journal Pre-proof Similarly,

∑ (∏

where

is the total river length,

)

is the length of segment , and

and downstream passabilities of the

th obstacle (

and

are the up-

) between the river mouth

(or confluence) and segment respectively. Note that the DCI values for the inland

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catchments in this study (Figure 1) represent the hypothetical connectivity of these

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catchments if they were fully accessible from the sea (i.e. there were no obstacles in the

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river downstream of the catchment).

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To calculate the DCID and DCIP for river catchments, numerical values for the passability of

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individual obstacles are required, which must range between 0 and 1, where 0 represents an impassable structure, and 1 represents a fully passable structure. For the purposes of this

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study, these passability estimates were derived from the subjective risk assessment for

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upstream migration conducted during the field survey. These qualitative risk levels (impassable, high, moderate, low, no risk) were converted into numerical values ranging between 0 and 1. Impassable obstacles were assigned a value of 0, obstacles of high, moderate and low risks were assigned values of 0.25, 0.5 and 0.75, respectively, and obstacles that presented no risk were assigned a value of 1. All obstacles were assumed to be fully passable in the downstream direction. Thus, the DCI calculation was determined by the upstream passability estimates for each obstacle. The DCID and DCIP were calculated for four native Irish fish with different life-histories namely diadromous; Atlantic salmon, sea trout, sea lamprey and the European eel, and potamodromous; brown trout. The DCID and DCIP were calculated for two activities,

Journal Pre-proof spawning and feeding, and included both natural and anthropogenic obstacles. DCIN values, which provided an estimate of the natural connectivity (only natural obstacles, such as waterfalls at bedrock outcrops or boulders, are included in the calculation) were also generated for each species in each catchment. To assess the impact of anthropogenic obstacles only on connectivity, the DCID and DCIP values were scaled relative to the baseline natural connectivity (DCIN) in each catchment, by calculating the DCI as a percentage of DCIN

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(Cote et al., 2009). Wetted area (McGinnity et al. 2003) was chosen as a measure of habitat

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quantity of river segments between or upstream of obstacles, as this was the best proxy for

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spawning/ feeding habitat that could be readily accessed at a large scale. The DCID and DCIP for one or both activities were calculated for each species where relevant (Table 1). DCIs

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were calculated in ArcGIS 10.4 using the Fish Passage Ad-in (FIPEX version 2.2.1) for ArcGIS.

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To provide a more realistic estimate of available habitat quantity, assumptions were made

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relating to habitat use for each species and for each activity, which are summarised in Table 1. In one catchment (the Erriff) a large, impassable natural waterfall (Aasleagh falls) with a

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fish pass was located at the mouth of the river. During the field survey, both structures were assessed independently. However, for the purpose of the DCI calculation, the passability estimate of the fish pass, rather than the waterfall, was used. This structure was recorded as artificial (anthropogenic) for DCI calculations. The relationship between the two metrics of river fragmentation (obstacle density and DCI) was assessed in the R Statistical Environment (R version 3.5.1) using the Pearson correlation co-efficient. It was necessary to log transform the obstacle density estimates and the DCID for sea lamprey in order to meet the assumptions of normality which was determined via Shapiro-Wilk normality tests. The significance level was set at 5%.

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3. Results 3.1 Potential versus actual obstacles A total of 1,589 potential obstacles were identified in the ten study catchments through the desk study (Table 2). Of these, 1,401 structures were visited, and their details recorded. Most of the potential obstacles identified were road-river crossings (83%). A total of 1,160 road-river crossings were visited, and of these, 172, or approximately 1 in 7, were actual

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obstacles.

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An additional 45 obstacles were recorded in 6 of the catchments during field surveys and

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these were included in the assessment. This resulted in a total of 372 actual obstacles

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located during the field survey. There was considerable variability in the numbers of actual

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obstacles between the catchments (mean = 37.2 obstacles, SD = 55.29). Approximately half of all obstacles mapped were in the Dodder, an urban catchment which was heavily

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regulated by weirs in the 18th and 19th centuries by industries which relied on this river as a source of stream power (McEntee and Corcoran, 2016). Obstacles associated with road-river

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crossings were concentrated in the headwaters (1st and 2nd order streams) whereas weirs and waterfalls were more widespread in 1st to 5th order streams (Figure 3).

3.2 River obstacle classification and physical parameters Thirteen river obstacle types were defined in this study (Tables 3 and 4), including four types of “bridge/culvert” and five types of weirs, which captured the physical diversity of the obstacles. Obstacles associated with road-river crossings (bridges, culverts and fords) were most common (n = 172) and more evenly dispersed across the 10 study catchments (Table 2), followed by weirs (n=149). Note however, that approximately 78% of all weirs in this study were found in the Dodder catchment, which is, as mentioned, significantly urbanised.

Journal Pre-proof Of the bridges and culverts, those with a downstream drop but no apron (perched structures) were the most numerous (n = 81) (Figure 2, C.). These typically presented both a jump challenge (mean drop height = 0.32 m, SD = 0.349 m, mean plunge pool depth = 0.297, SD = 0.213), and a depth challenge (mean water depth in the crossing = 0.062 m, SD = 0.085 m) for fish (Table 3). Vertical weirs were the second most common obstacle type (n = 78, mean height = 0.708, SD = 0.599, mean plunge pool depth = 0.325, SD = 0.239) (Table 4).

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The standard deviation of the mean estimates for most of the physical dimensions

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only 7 had formal fish passes installed (Table 4).

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measured for each obstacle type was high (Tables 3 and 4). Of all the obstacles assessed,

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3.3 Indices of river fragmentation

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3.3.1 Obstacle density

Obstacle density varied between catchments. The Dodder catchment had the highest

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obstacle density, with approximately 1.2 obstacles per kilometre of river. Conversely, the

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Kinnegad catchment had the lowest obstacle density, with only 1 obstacle in its entire network of 60 km. Mean obstacle density across all catchments was 0.21 obstacles per kilometre of river (SD = 0.37) (Figures 4 and 5).

3.3.2 Dendritic Connectivity Index – Diadromous life history Similar to obstacle density, the DCID varied between catchments, but also between species (Figure 4). Obstacles on the mainstem of the river were important for determining the DCI values. Out of the 10 catchments surveyed, 6 had at least one obstacle located on the mainstem river at least 5 kilometres from the mouth/confluence. Although the risk these individual obstacles presented to the upstream migration of the different fish species

Journal Pre-proof varied, these 6 catchments typically had the lowest DCID scores for diadromous species, ranging between 0.6 and 44.1. The lowest recorded DCID scores were in the Erriff catchment, where the DCID for sea lamprey and eel were 0.6 and 0.7 respectively due to the potential unsuitability of the fish pass associated with Aasleagh falls for these species. The natural connectivity (DCIN) for sea lamprey and eel in this catchment was also low (DCIN = 1 and 12.3 for sea lamprey and eel

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respectively) due to a series of high risk/impassable waterfalls/chutes immediately

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upstream of Aasleagh Falls. The third lowest DCID recorded was in the Owenboliska

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catchment and was also for sea lamprey (DCID = 2.6). Here again, natural connectivity was

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low for this species due to the natural waterfall located on the mainstem of the river (DCI N =

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10.4).

The catchment with the highest overall connectivity was the Kinnegad, where the DCI D for

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salmon was 100. Only one obstacle was recorded here but was excluded from the DCI

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calculations for Atlantic salmon as it was located on a first order stream (Table 1). The DCID for sea trout, sea lamprey and eel were also high (approximately 99.6) (Figure 4). When scaled relative to the baseline natural connectivity (DCIN) in each catchment, the Nanny and Dodder catchments were identified as being the most impacted by anthropogenic obstacles. Sea lamprey, sea trout, eel and Atlantic salmon scored 5.6%, 5.9%, 8.1% and 8.2% of the natural connectivity in the Nanny, respectively (Figure 4). In the Dodder, eel, sea trout and Atlantic salmon scored 7.1%, 10.8% and 12.2% of the DCIN. Out of all the diadromous species in the Dodder catchment, the DCID (15.2) and the percentage-ofnatural DCID (19.7%) were highest for sea lamprey. This is because only 34% of the river wetted area had suitable gradient (≤1.5%) for sea lamprey spawning. Thus, proportionately,

Journal Pre-proof sea lamprey had less to gain within this catchment in terms of spawning habitat than other species, such as sea trout, which were assumed to use 100% of the wetted area in the catchment for spawning. In addition, scaled relative to the baseline natural connectivity, the Erriff was identified as being considerably impacted by anthropogenic obstacles for eel (percentage-of-natural DCID = 5.9%) and this was a result of anthropogenic modification to one of the chutes upstream of Aasleagh falls.

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3.3.3 Dendritic Connectivity Index - Potamodromous life-history

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The Dodder ranked “worst” in terms of DCIP for brown trout for both spawning (DCIP, spawning

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= 4.1) and feeding activities (DCIP, Feeding = 11.1), followed by the Erriff (DCIP, Feeding= 24.8),

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Glashaboy (DCIP, spawning = 26.7, DCIP, Feeding= 26.7) and Moynalty (DCIP, spawning = 27, DCIP, Feeding=

Feeding =

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27). Connectivity was highest for brown trout in the Kinnegad (DCIP , Spawning = 99.2; DCIP, 99.2), Owenboliska (DCIP, Feeding = 94.9) and Clodiagh catchments (DCIP , Spawning = 76.2;

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DCIP, Feeding = 76.4). The largest differences in DCIP for spawning and feeding activities within

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the same catchment were recorded in the Dromore, Owenboliska and Erriff catchments, and this was a result of the number and size of the lakes within these catchments. Scaling the DCIP values relative to the baseline natural connectivity in each catchment (DCIN), the Dodder remained the most impacted catchment, scoring 5.4% and 13.5% of the natural connectivity (DCIN) for spawning and feeding, respectively. This was followed by the Glashaboy (DCIP, spawning = 26.7%, DCIP, Feeding= 26.7%) and Moynalty (DCIP, spawning = 27%, DCIP, Feeding=

27%).

3.3.4 Relationship between DCI values and obstacle density Although a weak negative relationship was evident, no significant correlations were observed between obstacle density and the DCI for all species with a diadromous life history

Journal Pre-proof (Figure 6). However, a significant negative correlation between the DCIP for brown trout for both feeding and spawning activities and obstacle density (Pearson Correlation = -0.76, p = 0.01, n = 10 (feeding); Pearson Correlation = -0.82, p = 0.003, n = 10 (spawning)) was observed.

4 Discussion

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This study assessed in detail the numbers, types and impact of obstacles on connectivity at

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the catchment level and quantified the degree of fragmentation for four native Irish fish

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species. It highlights the large variation in the physical dimensions of river obstacles, their

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location in river systems, and how the degree of fragmentation varied between catchments

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and between species.

The physical parameters used to describe the obstacles here (presence or absence of an

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apron, obstacle height, for example) have been highlighted in the literature as being key

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determinants of obstacle passability (Ovidio et al. 2007; Burford et al. 2009; Baudoin et al. 2015). This study further classified obstacle types, and in so doing, highlighted the diversity of obstacle form. The variation in dimensions between structures (particularly height, plunge pool depth and downstream apron length) was generally large and resulted in a wide range of passability values. This highlights the uniqueness of individual obstacles, and while it is useful to classify obstacles for management purposes, it is important to bear in mind that each obstacle is different and can present different challenges for fish species. Previous studies have identified this issue. For example, in a study assessing the passage of westslope cutthroat (Oncorhynchus clarkii lewisi) and brook trout (Salvelinus fontinalis) through culverts, Burford et al. (2009) noted that two large trout succeeded in passing through a

Journal Pre-proof culvert with an outlet drop of 0.61m, but no trout passed through another culvert with an outlet drop of 0.15m. The authors attributed this observation to the fact that the culvert with the larger drop had a small but deep plunge pool, whereas the outlet of the latter culvert fell onto rocks (Burford et al. 2009). The desk study utilised here, described in Atkinson et al. (2018a), overestimated the number of actual river obstacles in each catchment, thus taking a precautionary approach to locating

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river obstacles. However, as noted in Atkinson et al. (2018a), the high number of false

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alarms (crossings that were not obstacles) generated by the desk study is typically coupled

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with a high detection rate. Thus, while ground-truthing of the potential obstacles is

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required, the initial desk study largely eliminates the need to walk entire river catchments to locate obstacles, allowing resources to be directed at more focussed site visits. In addition, a

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large number of the potential obstacles identified were road-river crossings and the

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subsequent ground-truthing associated with these structures is rapid, as, by definition, the site can be readily accessed by road. Indeed, it is possible that some structures were missed

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with the desk study presented here. Yet, 77% of the total river network in Ireland is headwater streams (Strahler 1st and 2nd order), amounting to a total length of 56,743 km (McGarrigle, 2014). These streams are typically in remote locations, overgrown and difficult to access. Walk-over surveys of these streams, while a more robust approach, would be especially time-consuming and difficult. Thus, we feel that the approach taken here is practical and consistent, and therefore valuable for cost-effective, large scale assessments. The DCI provided a more comprehensive metric of river fragmentation than obstacle density, as it incorporated passability estimates for each obstacle, available habitat quantity, cumulative impacts of obstacles and the life-history of each fish species. Obstacle density

Journal Pre-proof was significantly correlated with the DCIP for brown trout; but not for diadromous fish. This correlation was however driven by two extreme cases where obstacle density was either very high (Dodder) or very low (Kinnegad) (Figure 7). The DCID calculation is based on the probability of a fish moving in both upstream and downstream directions between the mouth of the river and another segment of the river network, thus obstacles located at or near the mouth of the river, or along the mainstem, had significant impacts on the DCID in

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this study. This was particularly significant for species with poor swimming/jumping abilities

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(sea lamprey, for example), as seen in the Owenboliska, Nanny, Dodder and Erriff

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catchments. Scaling the DCID values relative to the natural connectivity of each catchment as in Cote et al. (2009) was a useful way to disentangle the effect of anthropogenic obstacles

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from the natural connectivity of the system, and highlighted catchments that were most

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affected by human activity (e.g. the Dodder, Nanny and Glashaboy). Igoe et al. (2004)

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emphasised river obstacles as being “the single biggest factor limiting the distribution of anadromous lamprey” in Ireland. This was reflected in our results, the DCID for sea lamprey

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was typically low across the river catchments, particularly those with obstacles located on the mainstem. Although the passability estimates and habitat quantity variables incorporated into the DCID varied depending on species, the DCID (with the exception of the Erriff, where the DCID was very low for sea lamprey) consistently showed a trend in terms of severity of impact on connectivity. Bourne et al. (2011) noted that catchment scale connectivity estimates were less sensitive to variations in passability estimates for individual obstacles (although some differences were found). These observations highlight the potential value of this index for bringing to the fore those catchments which warrant further assessment. Furthermore, obstacle density could be coupled with the DCI to highlight severely impacted catchments, where removal or remediation of a large number of

Journal Pre-proof obstacles may be required to significantly improve connectivity. For example, both the Dodder and Nanny have poor DCID values for diadromous species. However, the Dodder has 1 obstacle per 0.8 kilometres of river, while the Nanny has 1 obstacle per 19 kilometres. Such metrics can be useful for selecting catchments where removal/remediation action may result in larger benefits, depending on the species in question. For Atlantic salmon, for example, removing the lowermost obstacles in the Nanny would likely be more beneficial

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than removing the lowermost obstacles in the Dodder. Additionally, the DCI can be used as

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a tool to assess the potential effects of obstacle removal or modification on connectivity

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(Cote et al. 2009).

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The DCI has also been used to assess river fragmentation at a European scale for a number of diadromous fish species, by comparing historic and current fish distributions. For

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instance, van Puijenbroek et al. (2019) focused on the impact of the lowermost dams on 33

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large European rivers, and concluded that river fragmentation has species specific consequences, and thus should be assessed on the level of individual species and rivers. Our

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results also highlight the importance of considering multiple species when assessing river fragmentation. Furthermore, the smaller, more numerous low-head structures described here can have impacts on connectivity (Ovidio and Philippart 2002) and should be considered in prioritisation decisions. Low-head weirs, for example, can present as much a challenge for migrating fish as a dam (Garcia de Leániz, 2008). Although in this study the subjective assessment was conducted by the same person, having a subjective element in a field survey done by a team may result in inconsistent assessments of obstacle risk between the different surveyors. However, the inclusion of physical dimensions of the obstacles can help to support the risk assessments in a more objective way. Ideally, obstacle risk or passability can be determined empirically through fine scale resolution methods (Passive

Journal Pre-proof Integrated Transponder (PIT) telemetry, population genetics or human observation/video filming, for example), however implementation of this approach is difficult and costly at a large scale (Kemp and O’Hanley 2010). It is for this reason that theoretical passability estimates which are predominantly objective (such as the UK SNIFFER protocol (SNIFFER 2010)) or entirely objective (such as the French ICE protocol (Baudoin et al. 2015)) and are calculated based on the physical dimensions of obstacles and water flow over the obstacle,

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are used. However, incongruences between passability estimates derived from these

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objective estimates, particularly for structures which may not be complete barriers, have

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been reported (Barry et al. 2018). Furthermore, when compared with empirical evidence, objective passability estimates have also been shown to be misleading. Burford et al. (2009)

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showed that westslope cutthroat and brook trout were indeed capable of upstream

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movement through culverts that were classified as impassable by FishXing (software which

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models fish movement parameters against culvert hydraulics over a range of expected stream discharges (Bourne et al. 2011)) model predictions, and an overall agreement of just

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17% between the model and field data was observed. Due to the ambiguity surrounding estimates of passability (in both upstream and downstream directions) and how available habitat is measured (Kemp and O’Hanley 2010), it is important that the DCI values recorded here are not taken as immutable intrinsic attributes, but only as rather conservative indicators of connectivity. DCI values have been shown to vary depending on methods used to estimate passability (Bourne et al. 2011), and on whether habitat quantity or quality is used in the weighting of river segments upstream and downstream of obstacles (Buddendorf et al. 2017). Bourne et al. (2011) showed that rapid culvert assessments that evaluate passability based on flow conditions on a single day provide different DCI values than those which are generated by passability assessments that

Journal Pre-proof take account of temporal variation, namely FishXing. However, as noted previously, this dynamic model is also not perfect (Burford et al. 2009). It is possible that the available habitat estimated for the fish species in this study is overestimated. However, we feel that taking this precautionary approach is more favourable than potentially underestimating available habitat. In the absence of large-scale data relating to the quality of stream habitat, we feel that wetted area is an acceptable measure of available habitat for spawning and/or

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feeding.

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Regardless of how they are generated, many passability estimates typically capture

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passability at one point in time or during specific flow conditions. This is a key limitation of

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the assessment used in this study and others (for example the UK SNIFFER protocol (SNIFFER 2010) and the French ICE protocol (Baudoin et al. 2015)). In the assessment presented here,

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temporal variability of obstacle dimensions and the risk to passability are not captured.

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While this approach may prove misleading, it is often an inevitable limitation of large-scale projects. Obstacles typically should be accessed during low-medium flow conditions for

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surveying and also for health and safety considerations. In addition, when large numbers of obstacles are recorded, which is often the case, it may not be feasible to return to them a second or third time in periods of varying flow conditions. Furthermore, it is difficult to predict the conditions under which fish will surmount an obstacle (Thorstad et al. 2008), and the timing and consequent water temperature and flow conditions during upstream migration can be different for each species (Lucas and Baras, 2001; Quinn et al. 2006). Nonetheless, where possible or available, empirical data should be used to support passability estimates. A promising new tool that could support these data collection is environmental DNA (eDNA) analysis. Environmental DNA assays for target species and eDNA metabarcoding are both promising new techniques for establishing species presence or

Journal Pre-proof absence and species richness from environmental samples (Deiner et al. 2017). Environmental DNA methods are cheaper to implement at a large scale than traditional methods and can be used to quickly establish the spatial distribution of a target species, and whether obstacles are passable or not (Atkinson et al. 2018b; Bracken et al. 2019). Importantly, eDNA analysis may capture the temporal variability of passability, and answer the question: “Was this obstacle passable at some point in time, by a particular fish

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species?” At least one eDNA assay exists for 4 of the species (brown trout, Atlantic salmon,

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eel and sea lamprey) discussed here (Gustavson et al. 2015; Atkinson et al. 2018b; Knudsen

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et al. 2019). This indirect method of estimating passability may however be limited in situations where impassable barriers are present in the system. As noted by Kemp and

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O’Hanley (2010) if an impassable structure exists downstream of an obstacle of interest, it is

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impossible to assess passability of this obstacle for diadromous fish using direct or indirect

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methods, because the species will simply not be present. In such a case, direct methods (tagging potamodromous fish) or physical survey assessments may be more appropriate.

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The results, methodology and considerations highlighted in this article can provide the basic input information for multicriteria decision support systems to prioritise obstacle removal/modification decisions. Rapid obstacle surveys like the one presented here can provide the necessary data for an automated initial prioritisation/ranking of obstacles for further attention. More detailed, precise and expensive investigations can then be concentrated on the individual obstacles most likely to be modified. This study highlights the variable nature of connectivity for different species, thus emphasising the need for species-specific approaches to addressing the issue of river fragmentation. For example, in order to improve connectivity for diadromous species such as Atlantic salmon or eel, removing the lower most obstacles in the catchment would be the most appropriate

Journal Pre-proof approach. This, however, may not substantially improve connectivity for potamodromous species, like brown trout. Thus, consideration of the conservation and management goals of species within each catchment is important for future decision-making regarding river obstacle removal or modification. However, this study showed that despite differences in the DCI between species, trends in the degree of fragmentation were apparent between catchments, particularly for species with similar life-histories. Thus, the DCI may be a useful

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tool for initially diagnosing general connectivity issues within catchments, which can be

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subsequently considered at a species level. The DCI may also help to identify individual

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obstacles where removal could maximise connectivity for multiple species with the same

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Acknowledgements

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life-history and possibly also for species with differing life-histories.

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This research was funded by the Irish Environmental Protection Agency as part of the Reconnect project (Grant: 2015‐W‐LS‐8). We would like to thank Inland Fisheries Ireland for

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providing the wetted area data for the study catchments. The OSM data are freely available under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF).

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Journal Pre-proof Table 1. Species assessed for DCI calculations, the relevant activity the DCI was calculated for, and the variables that defined suitable habitat for this activity. Species

Activity

DCI life-history

Variables

Lake Wetted Area

Stream gradient

≥1

All

≥1

All

Excluded

≥2

All

Excluded

≥1

All

Excluded

≥1

≤ 1.5 %

Included

≥1

All

Brown trout

Spawning

DCIP

Excluded

Brown trout

Feeding

DCIP

Included

Atlantic salmon

Spawning

DCID

Sea trout

Spawning

Sea lamprey

Spawning

Eel

Feeding

o J DCID DCID DCID

l a

n r u

f o

Stream order included

r P

e

o r p

Journal Pre-proof Table 2. The total number of potential and actual obstacles in each of the 10 study catchments. In some instances, the number of actual obstacles exceeds the number of potential obstacles because several additional obstacles were recorded during the field survey. Catchment

Potential Obstacles Road-

Weir

Actual Obstacles Dam

Sluice

crossing

Waterf

Other/

all

Unknown

Total

Road-

242

112

2

1

18

17

392

51

Erriff

135

7

-

-

25

10

177

21

Bann

171

11

-

1

-

4

187

36

Moynalty

135

4

-

-

-

1

140

Kinnegad

47

-

-

-

-

47

Nanny

139

8

-

-

-

2

149

e

Dromore

184

3

-

2

3

-

Glashaboy

96

14

-

-

-

Owenboliska

61

13

1

-

3

Clodiagh

108

7

-

-

Total

1318

179

3

4

2

J

51

Dam

f o

crossing

Dodder

Waterfall

Other

Total

Flow Index

6

12

1

189

28%

-

-

24

1

48

26%

-

-

1

-

40

88%

5

-

-

-

-

14

78%

-

-

-

-

-

1

77%

7

7

-

-

-

-

14

97%

192

19

5

-

-

1

-

25

63%

110

18

5

-

-

-

-

23

67%

78

3

1

1

-

1

-

6

48%

-

117

7

4

-

-

1

-

12

19%

34

1589

172

149

3

6

40

2

372

l a

-

r P

9 1

117

Sluice

2

rn

u o

Weir

o r p 2 3

n/a

Flow Index is the lowest percentage of time (over whole flow record) that flows on field survey dates for each river is exceeded. It is derived from long-term flow duration curve for river. Bank full (flood) flows in Ireland typically occur for values < 0.14%

Journal Pre-proof Table 3. The mean (µ) and standard deviation (σ2) of various physical parameters measured for each obstacle type associated with road-river crossings. Here, n = number of obstacles, FP = number of fish passes present, WD = water depth within the structure, H = total height of the downstream drop (for bridges/culverts with an apron and a drop, this is equal to the sum of the apron height and the drop height), PPD = plunge pool depth, DAL = downstream apron length.

µ

f o

Obstacle Type Obstacle Category

Physical Attribute

n

Apron

Drop

Bridge|Culvert

+

+

44

Bridge|Culvert

+

-

Bridge|Culvert

-

Bridge|Culvert

-

Ford

FP

WD (m)

H (m)

µ

σ

0

0.056

11

0

+

81

-

2

PPD (m)

µ

σ

± 0.044

0.449

0.051

± 0.058

1.269

0

0.062

± 0.085

0.32

33

0

0.133

3

0

0.115

o J

NA

± 0.134

0.4

σ

± 0.143

ro

3.465

0.157

± 0.162

17.809

0.297

± 0.213

NA

0.305

± 0.245

NA

0.267

± 0.056

NA

µ

σ

± 0.357

0.273

± 1.07

l a

n r u ± 0.233

2

DAL (m) 2

-p

e r P

± 0.349

± 0.05

2

Slope/Nature of Apron (count) Gentle

Moderate

Steep

± 3.987

38

4

2

± 29.888

3

3

1

Stepped

4

Journal Pre-proof Table 4. The mean (µ) and standard deviation (σ2) of various physical parameters measured for each obstacle type associated with weirs, sluices and natural obstacles (waterfalls). Here, n = number of obstacles, FP = number of fish passes present, H = weir or sluice height, DH = drop height, TH = total height of the structure (H + DH), PPD = plunge pool depth, DAL = downstream apron length, SL = sill length (measured on the horizontal).

f o

Obstacle Type Obstacle Category

Physical Attribute

Apron

Drop

n

FP

H (m)

DH (m)

µ

σ

2

TH (m)

µ

σ

2

µ

PPD (m)

σ

o r p

e

2

r P

µ

DAL (m)

σ

2

SL (m)

2

µ

σ

± 6.339

Weir / Waterfall Slope (count)

2

µ

σ

7.041

± 6.125

Weir

Sloping

+

+

8

2

2.365

± 1.585

0.603

± 0.742

2.967

± 2.115

0.449

± 0.366

5.55

Weir

Sloping

-

+

6

0

1.322

± 1.387

0.268

± 0.12

1.59

± 1.398

0.432

± 0.213

NA

5.487

± 4.04

Weir

Sloping

-

-

26

1

1.111

± 0.787

NA

0.432

± 0.331

NA

4.682

± 5.092

Weir

Vertical

78

1

0.708

± 0.599

NA

0.325

± 0.239

NA

NA

Weir

Stepped

31

1

1.889

± 1.614

NA

NA

0.365

± 0.246

NA

6.303

Sluice

6

0

0.823

NA

NA

0.29

± 0.21

NA

NA

Natural

40

1

2.323

NA

NA

0.509

± 0.392

NA

10.1

Dam

3

1

unknown

o J

n r u

± 0.208 ± 2.132

l a

NA

NA

Gentle

Moderate

Steep

Vertical

8 2

2

2

10

16 78

± 5.781

± 7.718

1

4

25

10

Journal Pre-proof Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Jo ur

na

lP

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of

☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Journal Pre-proof

Highlights 

Obstacle inventories are necessary for making decisions on remediation measures



Ground-truthed assessment of obstacles and connectivity at the river catchment scale Obstacle density ranged between 1.2 and 0.02 obstacles per km of river



88% of obstacles were road-river crossings and low-head weirs



Connectivity estimated at catchment scale shows river and species-specific impacts

Jo ur

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

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7