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Recent advances in the ability to quantify longitudinal connectivity of riverine systems is enabling a better understanding of how connectivity affects fish assemblages. However, the role of connectivity relative to other factors, such as land use, in structuring biological assemblages is just emerging. We assessed the relevance of a structural connectivity index to stream fish communities at a relatively large scale (across five watersheds of Lake Ontario), while controlling for confounding habitat variables such as land use, elevation and stream topology. The results were assessed to determine whether species’ sensitivities to connectivity are in accordance with expectations of life history. Our results indicated that at large scales, structural connectivity explains significant amounts of variation in community structure (1 to 5.4% as measured by Bray-Curtis similarity), but remains secondary to other habitat components. Connectivity also was significantly related to abundance in 3 of the 7 species assessed. The lower explanatory power of our models compared to studies done at smaller scales suggests that the relevance of connectivity to fish communities is scale dependent and diminishes relative to other environmental factors at larger spatial extents.
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Note: This is a post-print version of a manuscript published in Canadian Journal of
Fisheries and Aquatic Sciences. It is reproduced here under the journal’s Open Access
policy. The published version of the article can be accessed at: Assessing the biological
relevance of aquatic connectivity to stream fish communities (doi: 10.1139/cjfas-2013-
0646)
a Corresponding author
b Current address: P.O. Box 92, Glovertown, NL, A0G 2L0, Canada.
Title: Assessing the Biological Relevance of Aquatic Connectivity to Stream Fish Communities
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Authors:
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Shad Mahluma: Department of Biology, Memorial University, St. John’s, NL A1B 3X9, Canada.
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Email: skm311@mun.ca
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Dan Kehler: Parks Canada, 1869 Upper Water St., Halifax, NS, B3J 1S9, Canada. Email:
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dan.kehler@pc.gc.ca
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David Coteb: Ocean Sciences Centre, Memorial University of Newfoundland, St. John’s NL
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A1C 5S7, Canada. Email: dave.j.cote@gmail.com
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Yolanda F. Wiersma: Department of Biology, Memorial University, St. John’s, NL A1B 3X9,
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Canada. Email: ywiersma@mun.ca
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Les Stanfield: Ontario Ministry of Natural Resources, 41 Hatchery Lane, Picton, ON K0K 2T0,
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Canada. Email: les.stanfield@ontario.ca
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Abstract:
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Recent advances in the ability to quantify longitudinal connectivity of riverine systems is
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enabling a better understanding of how connectivity affect fish assemblages. However, the role
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of connectivity relative to other factors such as land use in structuring biological assemblages is
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just emerging. We assessed the relevance of a structural connectivity index to stream fish
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communities in five watersheds and examined whether species sensitivities to connectivity are
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in accordance with expectations of life history. While controlling for the confounding effect of
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land use, elevation, and stream topology, we demonstrate that structural connectivity explains
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significant amounts of variation in community structure (1 to 5.4% as measured by Bray-Curtis
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similarity) and single species metrics (3 of 7 species abundances). The lower explanatory power
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of our models compared to studies done at smaller scales suggests that the relevance of
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connectivity to fish communities is scale dependent and diminishes relative to other
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environmental factors at larger spatial extents.
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Keywords: Fragmentation, Structural Connectivity, Functional Connectivity
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Introduction:
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The increased awareness of the effects of anthropogenic structures that may act as
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barriers on aquatic ecosystems has prompted new research to understand, quantify, and mitigate
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fragmentation impacts (Fullerton et al. 2010). Previous work has focused on individual barriers
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and how they influence aquatic communities (Coffman 2005, Mahlum et al. 2014, Warren and
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Pardew 1998). However, recent efforts have extended the spatial scope to consider the effects of
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multiple potential barriers (Cote et al. 2009, O’Hanley 2011, Padgham and Webb 2010); which
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theoretically can act in a cumulative fashion at the scales fish operate.
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Terrestrial landscape-scale metrics of connectivity have been well studied over the last 30
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years, with aquatic environments simply being regarded as a habitat feature embedded within the
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terrestrial landscape (Wiens 2002). Increasingly, basic principles from landscape ecology have
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been tailored for river ecosystems (Fausch et al. 2002, Ward 1998, Ward et al. 2002). Following
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this foundational work, several research efforts have developed ways to measure structural
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connectivity that are appropriate for the dendritic nature of aquatic systems. These include score
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and ranking methods (Pess et al. 1998, Poplar-Jeffers et al. 2009, Taylor and Love 2003),
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optimization techniques (Kemp and O'Hanley 2010, O’Hanley 2011), patch-based graphs (Erős
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et al. 2012, Erős et al. 2011, Schick and Lindley 2007), and connectivity indices (Cote et al.
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2009, Padgham and Webb 2010). These methods are particularly accommodating and valuable in
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prioritizing restoration efforts, as reconnecting aquatic habitats can be costly (Bernhardt et al.
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2005, Januchowski-Hartley et al. 2013). However, the use of structural indices are predicated on
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being able to efficiently improve ecological integrity by maximizing assumed biological gains by
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increasing structural connectivity (Cote et al. 2009, O’Hanley 2011, Schick and Lindley 2007),
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from the removal or restoration of particular barriers. Although these indices provide
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conceptually simple methods to systematically improve structural connectivity, it is poorly
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understood whether the recommendations yield biologically meaningful results (see Perkin and
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Gido 2012 for an exception). It is therefore necessary to understand the limitations (both
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statistical and ecological) of structural indices at predicting ecological responses in aquatic
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communities (Kupfer 2012).
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One method to assess the ecological relevance of structural indices is to test for
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relationships between a given structural index and biological community patterns across stream
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systems with variable degrees of fragmentation. For instance, Perkin and Gido (2012) found a
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strong relationship between fish community structure, within second and third order stream units,
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and a structural connectivity index. Understanding the response of structural indices at small
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spatial extents is an important development, yet it remains unknown whether these relationships
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will continue to be present at broader spatial extents where confounding variables may have an
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increased influence on aquatic communities. For example, Branco et al. (2011) found that
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environmental and human pressures, but not the presence of barriers, were the dominant driver of
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the distribution of several potamodromous and resident fish species in a 3600 km2 watershed.
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However, Branco et al. (2011) acknowledged that they used a relatively simple index of
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connectivity and called for a more thorough assessment of connectivity at broader spatial extents.
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We analyzed the relationship between structural connectivity and patterns in the fish
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community using data from five 5th and 6th order watersheds in southern Ontario, Canada,
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(ranging in extent from 98 km2 - 283 km2) which have a high degree of biodiversity (regional
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species richness of 38). The focus of this study was to determine if a relatively simple structural
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index, the Dendritic Connectivity Index (DCI), has biological relevance. Although we expect
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multiple confounding variables (e.g., elevation, watershed land use, stream network topology) to
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contribute to the explanation of patterns in community structure; we expected changes in fish
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community data in response to variation in the DCI. Specifically, once other habitat factors are
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accounted for, elevated connectivity will reflect habitat attributes of increased patch size and
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accessible habitat and should support a broader range of stream biota (Bain and Wine 2009,
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Peterson et al. 2013). Therefore, it is expected that we would see relative increases in species
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richness and fish abundance with increases of the DCI. We also tested the importance of the DCI
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for individual fish species for both presence and abundance data. At an individual species level,
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we expect to see an increase in species presence and abundance as connectivity increases across
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sites. Primarily, it is anticipated that individual species that have life histories that require broad
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scale movements (e.g., salmonids) will be more affected by losses in connectivity than species
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that may not require the same broad scale movements (e.g., cottids).
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Methods:
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Study Area:
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Southern Ontario exhibits a high degree of freshwater fish biodiversity (Chu et al. 2003).
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The diversity is attributed to a combination of postglacial dispersal and the anthropogenic
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introduction of non-native species (Dextrase and Mandrak 2006). The study was conducted in
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the watersheds of Wilmot, Oshawa, Ganaraska, Cobourg, and Duffins in southern Ontario, just
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east of the metropolitan area of Toronto (Figure 1). The five watersheds studied are dominated
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by developed urban areas at their confluence with Lake Ontario, agricultural landscape in the
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mid reaches, and a mixture of forest and low intensity agriculture in the headwaters. They range
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in watershed size of 98 km2 for Wilmot to 283 km2 for Ganaraska.
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Data Layers:
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Fish community data and habitat variables (including the structural index) were
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incorporated into the analysis (Table 1). Fish sampling was conducted from 1997 to 2009 by
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various agencies as part of a collaborative monitoring program (TRCA, 2010) using the Ontario
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Stream Assessment Protocol (Stanfield 2010). Sample site locations are based on random
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stratified designs to characterize conditions within stream segments. A handful of long-term
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monitoring sample sites were initially selected based on their representative conditions which
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were averaged across sampling periods to eliminate pseudo-replication. Sites were a minimum
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length of 40 m and were bounded by “crossovers” (where the thalweg crossed to the opposite
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side of the stream) to ensure adequate sampling of all habitat types (Stanfield 2010).
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Furthermore, sample site lengths reflect from 5 to 10 bankfull widths and have been shown to
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provide reliable measures of fish assemblages across time and space for this study area (Stanfield
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et al., 2012). Single-pass electrofishing was used to capture fish at a targeted effort of 7 to 15
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s/m2. All fish were measured, weighed, and identified to species with the exception of lampreys
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(Petromyzontidae), which were identified to family due to inconsistencies in identification to the
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species level. Finally, we also excluded 16 sites from the analysis that appeared to exhibit
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difficulties with identification of one or more individuals to the species level.
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Connectivity index:
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To measure the structural connectivity across the 5 watersheds, we employed the
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Dendritic Connectivity Index (Cote et al. 2009). The DCI is calculated based on the probability
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that an individual can move freely among random points in a dendritic network. This takes into
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consideration the amount of potential habitat between barriers along with a measure of
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passability for each barrier. Furthermore, the DCI is flexible in that it can be modified to address
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the natural connectivity of a stream based on both potamodromous (DCIp) and diadromous
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(DCId) life histories. The DCIp applies to life histories of species that typically live in riverine
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systems and do not require diadromous migration. DCIp is defined as:
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where l is the length of the segment i and j, cij is the connectivity between segments i and j, and L
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is the total stream length of all stream segments. The DCId applies to all life histories that
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migrate between a fixed point (e.g., estuary) and all upstream areas within a riverine system.
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DCId is calculated as:
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where L is the total length of the stream sections, li is the length of section i, cij is the
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connectivity between segments i and j. While the DCIp and DCId measure the overall
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connectedness of a system, it could be beneficial to apply a structural connectivity metric at finer
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spatial scales (e.g., stream reach) to control for local pressures of connectivity on the biotic
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community. As noted in Cote et al. (2009), the DCId can be applied to measure the connectivity
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from any stream segment to the rest of the watershed. We denote this value as DCIs, and used
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this in models for data collected at the scale of the stream segment. We used the Fish Passage
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Extension (FIPEX v2.2.1) for ArcGIS (v9.3.1) using a hydrological stream network provided by
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OMNR to calculate connectivity scores (cij) described above.
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Determining barrier passability:
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Identifying all potential barriers in a system is imperative in order to accurately assess
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connectivity (Cote et al. 2009, Januchowski-Hartley et al. 2013, O’Hanley 2011). A list of
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barrier locations was provided by OMNR which consisted of 298 locations of dams, perched
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culverts, and natural barriers across the 5 watersheds used in this study. We also used the
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
D C I
pc
ij
l
i
L
lj
L
*100
j1
n
i1
n

D C I
dc
ij
l
i
L
*100
i1
n
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National Hydro Network obtained via GeoBase (http://www.geobase.ca/) to identify dams not in
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the OMNR dataset. Furthermore, road culverts are thought to outnumber dams by up to 38 times,
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with as much as 2/3 being designated as complete or partial barriers to fish movement
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(Januchowski-Hartley et al. 2013). Therefore, to help identify potential barriers not in the OMNR
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database, we used ArcGIS and files from GeoBase to identify intersections between streams
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(National Hydro Network) and roads (National Road Network) that would indicate a potential
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barrier and help create an inclusive barrier database to calculate the DCI. All sources of barrier
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locations were cross checked to prevent multiple occurrences of the same barrier in the dataset.
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We calculated and analyzed the DCI with regards to community structure and species richness
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with only known barriers and then again with the inclusion of potential barriers identified
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through GIS (stream/road intersections). The intent of this analysis was to provide insight into
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GIS-derived barrier locations and the potential benefits of modeling all potential barrier
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locations.
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Determining passability values for potential barriers in these watersheds was challenging
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due to their vast number and the limited information available for them. This limitation is not
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unique to this study and underscores some of the common obstacles to riverscape-scale analyses
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in larger watersheds (for an example see Meixler et al. 2009). For the DCI, passabilities are
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defined as a value between 0 (impassable) and 1 (fully passable). Passability scores of zero were
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first assigned to all dams and perched culverts. Culverts were considered perched when the outlet
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bottom elevation was greater than the height of the outlet pool (Stanfield 2010). The remaining
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75% of potential barriers lacked a passability score. Previous studies have found a relationship
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between culvert passabilities and channel slope (McCleary and Hassan 2008, Poplar-Jeffers et al.
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2009), and we followed this approach to infer values for barriers with unknown passability. We
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used an available data set from Terra Nova National Park (TNNP), Newfoundland, Canada that
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contained both passability scores and channel slopes. Passabilities in TNNP were calculated
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using FishXing (Furniss et al. 2006) and were based on the percent of time stream flows were
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within a passable range for brook trout (Salvelinus fontinalis). We calculated channel slope for
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culverts in Newfoundland and Ontario using a 10-m digital elevation model (DEM) by creating a
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100 m diameter buffer around the barrier and taking the difference in elevation between the
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farthest upstream and downstream points and then dividing by the stream length between those
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points. Finally, we used a nonlinear regression model,
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where i = 1 to number of culverts (N), p is passability, x is channel slope, and εi ~ N(0,δ2), to
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estimate the relationship between culvert passability and channel slope in TNNP. This model fits
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a sigmoidal curve with a fixed passability of 1, when channel slope is 0. We then applied that
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relationship to the channel slopes associated with potential barriers in southern Ontario.
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Accounting for confounding variables:
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It is known that stream process and patterns are continually changing along the
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longitudinal gradient of the stream (Vannote et al. 1980) and these changes can significantly
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affect the biotic community (Fausch et al. 2002). Some of these influences can be segregated into
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habitat variables (e.g., elevation and stream width) and landscape use (e.g., urban and farmland).
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Several factors were incorporated into our analysis to control for confounding effects that
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influence community structure (see Table 1). These included elevation (Rahel and Hubert 1991,
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Stanfield and Kilgour 2006), land cover (Allan et al. 1997, Allan 2004, Stanfield and Kilgour
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2006), stream network topology (Betz et al. 2010, Hitt and Angermeier 2008), and stream width
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(Cote 2007). We extracted elevation (ELE) for each sampling site from a 10-m DEM obtained
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
l o g
p
i
1p
i






1
1
x
i
i
10
from OMNR. Land cover metrics that were thought to influence stream biota were quantified
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using the Southern Ontario Land Resource Information System (SOLRIS; Ontario Ministry of
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Natural Resources 2006) by determining the percentage of the watershed in each land cover type
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(Table 1). Using a metric analogous to stream order, we quantified the hydrological locations of
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sampling sites within the dendritic network using the Upstream Cell Count (UCC) which
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consists of the total amount of linear stream habitat above a sampling location (see Betz et al.
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2010 for a detailed description). Lastly, stream width (SW) was measured during biological
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sampling by taking an average of 10 transects measuring SW throughout the sampling site
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(Stanfield 2010).
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To select co-variables (Table 1) for the inclusion in our analysis, we used Akaike’s
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Information Criteria (AIC) to select a candidate model that best explains the data and
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subsequently can be used for the inclusion of confounding variable in the following analysis of
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community structure, species richness, and species abundance (Akaike 1973, Burnham and
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Anderson 2002, Oksanen et al. 2012). Before we identified candidate models, we removed
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collinear variables (Spearman’s rank correlations > 0.7). Next using variables identified in Table
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1, a priori candidate models were created for the distance-based redundancy analysis (db-RDA,
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described below) on community similarities ranging from simple (single variable) to more
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complex (maximum 9 variables in our global model). To assess how well co-variables
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contributed to explaining the community data, we calculated the ∆AIC (difference in AIC values
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from the model with the smallest AIC value) and AIC weights (the amount of support that a
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given model is the best). Only models that were within ∆AIC < 2 of the top model were
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considered for the inclusion in the analysis (Burnham and Anderson 2002). To maintain
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consistency between the analyses of community structure, species richness, and species
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abundances, we incorporated the same variables identified through the model selection procedure
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for all levels of analysis.
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Analysis:
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Is fish community similarity related to the DCI metrics?
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A multivariate db-RDA was used to analyze how connectivity, as measured by the DCIs,
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DCIp, and DCId, affects community structure based on species abundances (Legendre and
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Anderson 1999). Distance based redundancy analysis is a robust analytical method used to assess
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the relationship between meaningful measures of species associations (e.g., Bray-Curtis index)
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and fixed effects within a linear model framework. Furthermore, we chose to use a db-RDA to 1)
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accommodate for non-Euclidean distance measures used in community similarity metrics; 2)
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control for confounding variables; and 3) use nonparametric permutation methods which freed us
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from the assumption of normality (Legendre and Anderson 1999). Prior to the multivariate
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analysis, a fourth root transformation of the abundance data was employed to emphasize
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diversity (Clarke and Warwick 2001). Then, we used the Bray-Curtis Index (Bray and Curtis
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1957) as a measure of the similarity of the fish communities between sites because of its
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robustness and appropriateness for ecological community data (Clarke and Warwick 2001, Faith
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et al. 1987). Finally, a correction factor was not incorporated for the negative-eigenvalues to
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correct for Type 1 errors based on McArdle and Anderson (2001). Significance was determined
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by a pseudo-F statistic at alpha = 0.05.
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Is DCIs related to fish species richness?
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We used a generalized linear mixed model (GLMM) approach to test the effects of
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connectivity as determined by the DCIs on species richness. Treating watershed as a random
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effect allowed us to account for the potential pseudo-replication within watersheds (Bates et al.
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2011). Species richness was quantified by calculating the total number of fish species at each
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site. For sites with repeated sampling, species richness was averaged across sampling periods.
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Our approach to calculate species richness was chosen to provide a more accurate measure of
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this indicator than the single “most recent” observation that was used in the analyses by Stanfield
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and Kilgour (2006). Averaging richness across sampling events captures temporal variability and
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minimizes effects of sampling bias/error, but potentially undervalues diversity where sampling
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effort was lower (Kennard et al. 2006, Stanfield et al. 2013). Finally, using the GLMM, we
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analyzed the relationship between the DCIs and the species richness of a site while controlling
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for confounding variables previously identified. All variables but watershed were treated as fixed
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effects. Significance was determined by the z-statistic at alpha = 0.05.
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Is DCIs related to presence and abundance of individual species?
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We also tested to see how connectivity, calculated with known barriers and potential
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barriers, affected the presence and abundance of individual species. Seven relatively abundant
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species across three families were selected to represent a wide range of life history characteristics
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(e.g., diadromous) and that were also relatively abundant across sites (Table 2 and 3). We again
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used a GLMM approach, with presence modeled as binomial and abundance as a Poisson
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response variable. Watershed was treated as a random effect to account for potential pseudo-
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replication of observations within watersheds. The same confounding variables identified in the
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model selection procedures described above were also included as fixed effects. Because the
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abundance data exhibited considerable overdispersion, we used a resampling approach (Markov
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Chain Monte Carlo) to assess significance (Hadfield 2010). All statistical analysis was carried
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out with the statistical program R (v. 2.15.2, R Development Core Team 2012).
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Results:
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A total of 273 stream sites were selected across 5 watersheds (range of 27 to 70 sites per
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watershed). We used the selected sites for all levels of analysis within this study. A total of 38
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species were sampled across the study sites with a mean of 25.4 species per watershed (range =
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21 to 28). In addition to the 298 barriers identified by OMNR, we identified an additional 85
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dams and 1,041 potential barriers. The relationship between stream slope and passability
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obtained from barriers in Terra Nova National Park was reasonably strong (r2 = 0.68; Figure 2).
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When applied to potential barriers in southern Ontario, the predicted passabilities of un-surveyed
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barriers ranged from 0.0 to 0.99 with the passabilities strongly skewed towards the right, which
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indicates greater passability (Figure 3). Calculated connectivity scores for our study area
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watersheds in southern Ontario ranged from 0.0 to 41.1 for DCIs at the site scale, 14.9 to 22.6 for
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the DCIp, and 0.3 to 31.2 for the DCId, the latter two versions calculated at the watershed scale
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(Table 4).
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Twenty-two different models were analyzed with AIC scores (Table 5). Results of the
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Spearman’s correlation matrix indicated that SW and UCC were highly correlated (r = 0.8). As a
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result, we did not include SW and UCC in the same model. The top model for the db-RDA of
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community similarity (∆AIC < 2) included ELE, SW, and the land cover metric of built-up area-
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pervious (BUAP), which indicates areas of urban development. All other additional confounding
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variables did not adequately explain community structure given the dataset and were represented
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in models that had ∆AIC > 2. The top model had a weight of evidence of 80 percent in support of
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the top model, and to maintain consistency between the different analyses, we elected to use
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ELE, SW, and BUAP to control for confounding effects in subsequent facets of our analysis.
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Furthermore, while it is likely that we would identify that the selected variables would relate
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differently to each level of analysis (e.g., community structure vs individual species) and within
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different univariate analyses (e.g., individual species), we chose to run a single model selection
294
procedure to simplify the analysis and subsequent interpretation of the results between the
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different levels of the analysis. Moreover, we also found that several variables (e.g., elevation
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and stream width) remained consistent between this study and other studies within the same
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geographic area (see Stanfield et al. 2006), indicating that we would gain relatively little from
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additional model selection procedures.
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We explained 21.1, 21.4, and 24.4 percent of the total variation in species composition
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with the db-RDA models used to analyze the relationships between the DCIs, DCIp, and DCId,
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calculations based on known barriers, and community structure for abundance data. Furthermore,
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we used a type III sum of squares and found all three co-variables significantly related to
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community structure in all three models (Models 1-3; Table 6). The DCIs, DCIp, and DCId was
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significantly related to community structure as well (F = 3.67, df = 1, p < 0.01; F = 4.74, df = 1,
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p < 0.005; F = 15.64, df = 1, p < 0.005 respectively). A positive correlation was also seen for the
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DCIs (r = 0.65) and DCId (r = 0.48) for axis 1 and a negative correlation was seen for the DCIp
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with axis 2 (r = -0.67).
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The db-RDA models used to analyze the relationships between the DCIs, DCIp, and DCId,
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calculated with known barriers and potential barriers, and community structure for abundance
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data, with the co-variables of ELE, SW, and BUAP, explained 21.9, 22.2, and 24.4 percent of the
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total variation in species composition respectively (Models 4-6; Table 6 and Figure 4). Using
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additional barrier information derived from GIS data modestly improved our models and the
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amount of variation explained with our connectivity metric by 1.5, 1.3, and 0.0% respectively.
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Following the trends with the models which used only known barriers (models 1-3), we found
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that all confounding variables for models 4-6 significantly explained community structure (Table
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6).In these models, the DCIs, DCIp, and DCId were also significantly related to community
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structure (F = 6.37, df = 1, p < 0.005; F = 7.64, df = 1, p < 0.005; F = 15.52, df = 1, p < 0.005
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respectively). However, the directions of the relationships were confounded between models for
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elevation, stream width, BUAP and DCIs (Table 6).
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Species richness was not associated with changes in connectivity based on known
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barriers (DCIs; z = 1.26, n = 273, p-value = 0.204; Figure 5a). However, when we included
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potential barriers into the DCI calculation, species richness became weakly correlated with the
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DCIs (z = 1.99, n = 273, p-value = 0.047; Figure 5b) as were ELE and SW (z = -0.003, n = 273,
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p-value < 0.001; z = 0.05, n = 273, p-value < 0.001 respectively). However, the land cover
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variable used (BUAP) did not show a significant relationship with species richness (z = 0.068, n
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= 273, p-value = 0.058).
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The presence of only two species had a positive relationship with the DCIs: rainbow trout
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(Oncorhynchus mykiss) and mottled sculpin (Cottus bairdii; z = 0.07, n = 273, p-value = <0.001
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and z = 0.017, n = 273, p-value = <0.001 respectively; Table 2). Furthermore, abundance
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increased for rainbow trout (mean = 0.07, n = 273, p-value = 0.001), mottled sculpin (mean =
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0.09, n = 273, p-value = 0.001), and longnose dace (mean = 0.05, n = 273, p-value = 0.014;
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Rhinichthys cataractae) with an increase in the DCIs (Table 3; Figure 6). At least one
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confounding variable had a significant relationship in the individual species analysis, where ELE
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was the dominant predictor variable most commonly seen between the species.
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Discussion:
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The use of connectivity indices as a tool to assess the fragmentation of a system and
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assist in prioritizing restoration efforts can be a valuable asset in reconnecting aquatic habitat
339
patches. While minimal, we demonstrated that the DCI has biological relevance with regards to
340
understanding fish communities and individual species distribution and abundance, even in the
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presence of confounding variables such as elevation, stream width, and land cover. Although it is
342
necessary to address alternate pressures simultaneously when improving biological connectivity,
343
selecting barriers to restore based on structural gains in connectivity can contribute to recovery
344
and persistence of the aquatic community.
345
This conclusion is also consistent with findings by Perkin and Gido (2012) who found a
346
significant relationship between the same connectivity index analyzed here and community
347
structure within relatively fine scale study units consisting of second and third order streams.
348
However, the fine spatial extents examined in that study likely minimized confounding variables
349
and showed a much stronger relationship between connectivity and fish communities (r2 = 0.66).
350
Since the importance of environmental factors to stream biota is often scale-dependent (Fausch
351
et al. 2002, Poff 1997, Wiens 2002), it remains unknown whether links between structural
352
connectivity and communities will persist at spatial extents broader than the present study.
353
However, it has been shown that increases in interpatch distance significantly decrease landscape
354
connectivity (Goodwin and Fahrig 2003) and it could be expected that the same trends would
355
persist in aquatic environments. Structural indices have been increasingly used to determine the
356
17
degree of connectivity across watersheds but interpretation of these results are hampered by the
357
lack of demonstrations of biological relevance to aquatic ecosystems (Tischendorf and Fahrig
358
2000). Understanding these relationships is important to provide context into the appropriateness
359
and limitations of simple structural indices, such as the DCI, and their use in aquatic ecosystems.
360
The biology of the species in this study likely impacted the sensitivity of the species to
361
structural connectivity. This study found relationships between the DCIs and the abundance of
362
several species. As expected, we found species that require extensive movements during their
363
life history (e.g., rainbow trout) were significantly influenced by a lack of longitudinal
364
connectivity (DCIs). In contrast, other species (mottled sculpin and longnose dace), less known
365
for extensive migration (Johnston 2003), were also influenced by the presence of anthropogenic
366
barriers. Past studies have found local scale effects of barriers on small stream fishes (Coffman
367
2005, Norman et al. 2009, Warren and Pardew 1998). However, as documented by Meixler et al.
368
(2009), it appears that local scale effects of barriers can translate into population wide impacts on
369
the persistence of at least some small stream fishes. Furthermore, some of our species-specific
370
expectations with regards to connectivity did not bear out. For example, we expected brook trout,
371
a native species to the study area, would be more affected by losses in connectivity than other
372
species because they require a variety of habitats throughout their life cycle, which could result
373
in long migrations (Gowan and Fausch 1996). However, the presence of anthropogenic barriers
374
did not seem to have a significant relationship with brook trout abundance. This may be
375
attributed to low abundance or confounding variables not modeled in this study. For instance,
376
brown trout (Salmo trutta) impact brook trout through competition of important habitat (e.g.,
377
spawning habitat and refugia) and predation (Fausch and White 1981). Similarly, others (e.g.,
378
Stanfield et al. 2006) have found that brook trout distribution and abundance in this area are
379
18
affected by the cumulative effects of competition from multiple salmonids and land use.
380
Supporting Fausch and White (1981) and Stanfield et al (2006), we found a strong elevation
381
influence between these two species implying that brook trout are being pushed into the
382
headwaters where competition is lessened. Although fragmentation may be a factor in the
383
eventual recovery of brook trout and other salmonids, it appears that other confounding variables
384
currently have a greater impact on the persistence of this species. Continuing to improve our
385
understanding of the role of fragmentation in species distributions will assist managers in the
386
recovery of imperiled species and how to mitigate the effects of anthropogenic disturbances.
387
In the absence of anthropogenic barriers, alternate pressures can influence ecological
388
processes and patterns (Fagan 2002, Hargis et al. 1999). In addition to the modest effects of the
389
DCI, elevation, stream width, and land cover had a strong relationship with community structure
390
as well as with individual species (as observed by Stanfield et al. 2006). This supports previous
391
connectivity studies that found environmental factors affected metapopulations (e.g., land cover
392
and water quality; Branco et al. 2011, Meixler et al. 2009). Confounding variables such as the
393
ones modeled here are an important aspect associated with stream communities and controlling
394
for these environmental variables will help assist in determining how structural indices influence
395
stream biota.
396
Presenting connectivity at watershed scales is useful to estimate watershed health or to
397
prioritize restoration actions, but can be limiting for analyses aimed at local scales (e.g., studies
398
targeting site-specific relationships between fish communities and habitat variables; Cote 2007).
399
To address the need for locally-focused studies, we modified this watershed scale index into a
400
local habitat variable (DCIs) and matched it to corresponding biotic information. We consider
401
this a useful addition to typical quantification methods of connectivity that either focused
402
19
primarily on barrier prioritization (Kemp and O'Hanley 2010, O’Hanley 2011, Poplar-Jeffers et
403
al. 2009) or are overly simplistic (e.g., count of the number of barriers; Branco et al. 2011), and
404
therefore miss important aspects of fragmentation (for a review see Kindlmann and Burel 2008,
405
Padgham and Webb 2010). Measuring connectivity at a scale coincident with other aquatic
406
community variables will expand the understanding of how connectivity processes relate to biota
407
and will be useful in theoretical and management applications.
408
Identifying barrier locations is an important aspect in the management of aquatic systems.
409
The failure to account for all barriers may result in costly management actions that produce
410
negligible ecological benefits if the analysis fails to identify limiting factors (Bernhardt et al.
411
2005, Januchowski-Hartley et al. 2013). Although minimal barrier information (known barriers)
412
significantly explained community structure, we saw an improvement with the inclusion of
413
potential barriers (stream/road intersections) both in explaining community structure and species
414
richness. This conclusion lends support to Januchowski-Hartley et al. (2013) who advocate for
415
the incorporation of all potential barriers into current barrier databases.
416
We had relatively low explanatory power to explain community structure and species
417
richness and we were unable to predict abundance of several species (4 of 7 species) with aquatic
418
connectivity. One explanation could be in our methodology for calculating passability.
419
Identifying the passability of barriers was the largest obstacle in assessing connectivity over the
420
relatively large study area. While direct site evaluations of all known and potential barriers in a
421
system is recommended and could potentially improve our predictive power, the large number of
422
barriers within this study required us to identify an alternate method to assess passability. A
423
priority for future work in these watersheds should be a more comprehensive inventory of dams
424
on private lands (e.g., ponds). The use of GIS allowed us to identify potential barriers based on
425
20
locations where streams and roadways intersected. However, assigning passability values
426
required estimates based on known relationships with channel slope in another well studied area.
427
Furthermore, our passabilities were based on brook trout movements. This is not appropriate for
428
all species and likely overestimates passage for many species (e.g., Cyprinidae; Coffman 2005,
429
McLaughlin et al. 2006). Thus functional connectivity for these species may actually be lower in
430
these five watersheds than predicted by our model. Similarly, for species (e.g., Salmo salar)
431
thought to have higher swimming/jumping ability than brook trout, these watersheds may
432
actually have higher functional connectivity than predicted here. While the relationship between
433
channel slope and passability allowed us to identify potential barrier passabilities, it is
434
recommended that managers accurately inventory and assess the passability of all barriers across
435
study areas to allow them to maximize habitat gains with current connectivity models.
436
Based on organisms’ response to fragmentation in terrestrial systems, it is reasonable to
437
expect that thresholds of aquatic connectivity also exist and are associated with the biology of
438
the focal organism or community. Within our five watersheds, only the lower end of the
439
connectivity spectrum were captured and thus critical thresholds may exist outside the range
440
studied here. Capturing the full spectrum of possible connectivity scores at watershed scales may
441
be difficult as pristine and highly fragmented stream systems will likely differ from one another
442
in many other ways. However, identifying ecological thresholds for connectivity will assist with
443
setting management goals for protection and recovery of focal species..
444
As in terrestrial landscape ecology, where work has been done to link structural
445
connectivity metrics with ecological response (i.e., functional connectivity, Kindlmann and Burel
446
2008, Tischendorf and Fahrig 2000), we have shown that aquatic structural connectivity indices
447
can do the same. The structural indices, derived from relatively straightforward physical
448
21
parameters (e.g., stream length, barrier properties), help to explain biologically relevant
449
phenomena such as habitat quality and observed fish movement across barriers. It remains
450
necessary to further incorporate the organisms’ perceptions of its landscape into structural
451
indices to achieve meaningful measures of connectivity (Kindlmann and Burel 2008), but doing
452
so comes with tradeoffs such as increased data requirements, computational complexity, and
453
decreased ease of interpretation (Kupfer 2012). Moreover, incorporating more functional metrics
454
without understanding their limitations may not necessarily increase their validity (Kupfer 2012).
455
Recent work by Bourne (2013) found that incorporating a more functional habitat variable into
456
structural indices influenced the magnitude of fragmentation of a system but not necessarily the
457
qualitative conclusions (i.e., prioritization of the restoration action) when compared to physical
458
properties of habitat. This indicates that, at least in some cases, simple physical measurements
459
may be appropriate, and can save considerable time and resources.
460
Considerable work remains to understand how processes associated with aquatic
461
connectivity relates to faunal communities. The availability of structural connectivity metrics and
462
indices that have been evaluated for their ecological relevance and an understanding of their
463
limitations will prove useful in future research and management efforts in this field.
464
Acknowledgments:
465
The authors are grateful to C. Tu and J. Moryk from the Toronto and Regional
466
Conservation Authority for assistance in local knowledge in the study area. We also thank M.
467
Langdon, T. Mulrooney, R. Collier (PC), and C. Bourne for work done in Terra Nova National
468
Park. Furthermore the authors would like to acknowledge the input and guidance from R.
469
Randall, and M. Underwood, and the Landscape Ecology & Spatial Analysis Lab group at
470
Memorial University for valuable feedback throughout the study. Support for this research was
471
22
provided by Parks Canada Action on the Ground Funding, a Canadian Foundation for Innovation
472
and NSERC Discovery grants to YFW and by AMEC Environment and Infrastructure (DC).
473
Members of the Southern Ontario Stream Monitoring and Research Team provided the field data
474
and made data available for this study. Finally, thanks to D. Mercer of Memorial University’s
475
Queen Elizabeth II map library for assistance in obtaining GIS layers from The Ontario Ministry
476
of Natural Resources database used throughout this study.
477
478
23
Literature Cited:
479
480
Akaike, H. 1973. Information theory and an extension of the maximum likelihood principle,
481
Second International Symposium on Information Theory, pp. 267-281.
482
Allan, D., Erickson, D., and Fay, J. 1997. The influence of catchment land use on stream
483
integrity across multiple spatial scales. Freshwater Biology 37(1): 149-161.
484
Allan, J.D. 2004. Landscapes and riverscapes: the influence of land use on stream ecosystems.
485
Annual Review of Ecology, Evolution, and Systematics: 257-284.
486
Bain, M.B., and Wine, M.L. 2009. Testing predictions of stream landscape theory for fish
487
assemblages in highly fragmented watersheds. Folia Zoologica 59(3): 231-239.
488
Bates, D., Maechler, M., and Bolker, B. 2011. lme4: Linear mixed-effects models using S4
489
classes. In R Package verstion 0.999375-42. http://CRAN.R-project.org/package=lme4.
490
Bernhardt, E.S., Palmer, M.A., Allan, J.D., Alexander, G., Barnas, K., Brooks, S., Carr, J.,
491
Clayton, S., Dahm, C., and Follstad-Shah, J. 2005. Synthesizing US river restoration efforts.
492
Science 308: 636-637.
493
Betz, R., Hitt, N., Dymond, R.L., and Heatwole, C.W. 2010. A method for quantifying stream
494
network topology over large geographic extents. Journal of Spatial Hydrology 10: 16-29.
495
Bourne, C. 2013. How to quantify aquatic connectivity? Verifying the effectivness of the
496
dendritic connectivity indes as a tool for assessing stream fragmentation. M. Sc. thesis,
497
Department fo Biology, Memorial University of Newfoundland, St. John's, NL.
498
Branco, P., Segurado, P., Santos, J.M., Pinheiro, P., and Ferreira, M.T. 2011. Does longitudinal
499
connectivity loss affect the distribution of freshwater fish? Ecological Engineering 48: 70-78.
500
Bray, R.J., and Curtis, J.T. 1957. An ordination of the upland forest communities of southern
501
wisconsin. Ecological Monographs 27(4): 326-349.
502
Burnham, K.P., and Anderson, D.R. 2002. Model selection and multi-model inference: a
503
practical information-theoretic approach. Springer.
504
Chu, C., Minns, C.K., and Mandrak, N.E. 2003. Comparative regional assessment of factors
505
impacting freshwater fish biodiversity in Canada. Canadian Journal of Fisheries and Aquatic
506
Sciences 60(5): 624-634.
507
Clarke, K.R., and Warwick, R.M. 2001. Change in marine communities: an approach to
508
statistical analysis and interpretation. PRIMER-E, Plymouth.
509
Coffman, J.S. 2005. Evaluation of a predictive model for upstream fish passage through culverts.
510
M. Sc. thesis, Department of Biology, James Madison Universtiy, Harrisonburg, VA.
511
24
Cote, D. 2007. Measurements of salmonid population performance in relation to habitat in
512
eastern Newfoundland streams. Journal of Fish Biology 70(4): 1134-1147.
513
Cote, D., Kehler, D.G., Bourne, C., and Wiersma, Y.F. 2009. A new measure of longitudinal
514
connectivity for stream networks. Landscape Ecology 24(1): 101-113.
515
Dextrase, A.J., and Mandrak, N.E. 2006. Impacts of alien invasive species on freshwater fauna at
516
risk in Canada. Biological Invasions 8(1): 13-24.
517
Erős, T., Olden, J.D., Schick, R.S., Schmera, D., and Fortin, M.-J. 2012. Characterizing
518
connectivity relationships in freshwaters using patch-based graphs. Landscape Ecology 27(2):
519
303-317.
520
Erős, T., Schmera, D., and Schick, R.S. 2011. Network thinking in riverscape conservation A
521
graph-based approach. Biological Conservation 144(1): 184-192.
522
Fagan, W.F. 2002. Connectivity, fragmentation, and extinction risk in dendritic metapopulations.
523
Ecology 83(12): 3243-3249.
524
Faith, D.P., Minchin, P.R., and Belbin, L. 1987. Compositional dissimilarity as a robust measure
525
of ecological distance. Plant Ecology 69(1): 57-68.
526
Fausch, K.D., Torgersen, C.E., Baxter, C.V., and Hiram, W.L. 2002. Landscapes to riverscapes:
527
bridging the gap between research and conservation of stream fishes. BioScience 52(6): 483-498.
528
Fausch, K.D., and White, R.J. 1981. Competition between brook trout (Salvelinus fontinalis) and
529
brown trout (Salmo trutta) for positions in a Michigan stream. Canadian Journal of Fisheries and
530
Aquatic Sciences 38(10): 1220-1227.
531
Fullerton, A.H., Burnett, K.M., Steel, E.A., Flitcroft, R.L., Pess, G.R., Feist, B.E., Torgersen,
532
C.E., Miller, D.J., and Sanderson, B.L. 2010. Hydrological connectivity for riverine fish:
533
measurement challenges and research opportunities. Freshwater Biology 55(11): 2215-2237.
534
Furniss, M., Love, M., Firor, S., Moynan, K., Llanos, A., Guntle, J., and Gubernick, R. 2006.
535
FishXing Version 3.0. US Forest Sevice, San Dimas Technology and Development Center, San
536
Dimas, California.
537
Goodwin, B.J., and Fahrig, L. 2003. How does landscape structure influence landscape
538
connectivity? Oikos 99(3): 552-570.
539
Gowan, C., and Fausch, K.D. 1996. Mobile brook trout in two high-elevation Colorado streams:
540
reevaluating the concept of restricted movement. Canadian Journal of Fisheries and Aquatic
541
Sciences 53(6): 1370-1381.
542
Hadfield, J.D. 2010. MCMC methods for multi-response generalized linear mixed models: the
543
MCMCglmm R package. Journal of Statistical Software 33(2): 1-22.
544
25
Hargis, C.D., Bissonette, J., and Turner, D.L. 1999. The influence of forest fragmentation and
545
landscape pattern on American martens. Journal of Applied Ecology 36(1): 157-172.
546
Hitt, N.P., and Angermeier, P.L. 2008. Evidence for fish dispersal from spatial analysis of stream
547
network topology. Journal of the North American Benthological Society 27(2): 304-320.
548
Januchowski-Hartley, S.R., McIntyre, P.B., Diebel, M., Doran, P.J., Infante, D.M., Joseph, C.,
549
and Allan, J.D. 2013. Restoring aquatic ecosystem connectivity requires expanding inventories
550
of both dams and road crossings. Frontiers in Ecology and the Environment 11(4): 211-217.
551
Johnston, C.E. 2003. Movement patterns of imperiled blue shiners (Pisces: Cyprinidae) among
552
habitat patches. Ecology of Freshwater Fish 9(3): 170-176.
553
Kemp, P.S., and O'Hanley, J.R. 2010. Procedures for evaluating and prioritising the removal of
554
fish passage barriers: a synthesis. Fisheries Management and Ecology 17: 297-322.
555
Kennard, M.J., Pusey, B.J., Harch, B.D., Dore, E., and Arthington, A.H. 2006. Estimating local
556
stream fish assemblage attributes: sampling effort and efficiency at two spatial scales. Marine
557
and Freshwater Research 57(6): 635-653.
558
Kindlmann, P., and Burel, F. 2008. Connectivity measures: a review. Landscape Ecology 23(8):
559
879-890.
560
Kupfer, J.A. 2012. Landscape ecology and biogeography rethinking landscape metrics in a post-
561
FRAGSTATS landscape. Progress in Physical Geography 36(3): 400-420.
562
Legendre, P., and Anderson, M.J. 1999. Distance-based redundancy analysis: testing
563
multispecies responses in multifactorial ecological experiments. Ecological Monographs 69(1):
564
1-24.
565
Mahlum, S.K., Cote, D., Kehler, D.G., Wiersma, Y.F., and Clarke, K.R. 2014. Evaluating the
566
barrier assessment technique FishXing and the upstream movement of fish through road culverts.
567
Transactions of the American Fisheries Society 143: 39 - 48.
568
McArdle, B.H., and Anderson, M.J. 2001. Fitting multivariate models to community data: a
569
comment on distance-based redundancy analysis. Ecology 82(1): 290-297.
570
McCleary, R.J., and Hassan, M.A. 2008. Predictive modeling and spatial mapping of fish
571
distributions in small streams of the Canadian Rocky Mountain foothills. Canadian Journal of
572
Fisheries and Aquatic Sciences 65(2): 319-333.
573
McLaughlin, R.L., Porto, L., Noakes, D.L.G., Baylis, J.R., Carl, L.M., Dodd, H.R., Goldstein,
574
J.D., Hayes, D.B., and Randall, R.G. 2006. Effects of low-head barriers on stream fishes:
575
taxonomic affiliations and morphological correlates of sensitive species. Canadian Journal of
576
Fisheries and Aquatic Sciences 63(4): 766-779.
577
Meixler, M.S., Bain, M.B., and Todd Walter, M. 2009. Predicting barrier passage and habitat
578
suitability for migratory fish species. Ecological Modelling 220(20): 2782-2791.
579
26
Monkkonen, M., and Reunanen, P. 1999. On critical thresholds in landscape connectivity: a
580
management perspective. Oikos 84(2): 302-305.
581
Norman, J.R., Hagler, M.M., Freeman, M.C., and Freeman, B.J. 2009. Application of a
582
multistate model to estimate culvert effects on movement of small fishes. Transactions of the
583
American Fisheries Society 138(4): 826-838.
584
O’Hanley, J.R. 2011. Open rivers: barrier removal planning and the restoration of free-flowing
585
rivers. Journal of Environmental Management 92(12): 3112-3120.
586
Oksanen, J., Blanchet, F.G., Kindt, R., Legendre, P., Minchin, P.R., O'Hara, R.B., Simpson,
587
G.L., Henry, H., Wagner, S., and Wagner, H. 2012. Vegan: community ecology package. R
588
package version 2.0-3.
589
Ontario Ministry of Natural Resources. 2006. Southern Ontario Land Resource Information
590
System (SOLRIS). Science & Information Branch.
591
Padgham, M., and Webb, J.A. 2010. Multiple structural modifications to dendritic ecological
592
networks produce simple responses. Ecological Modelling 221(21): 2537-2545.
593
Perkin, J.S., and Gido, K.B. 2012. Fragmentation alters stream fish community structure in
594
dendritic ecological networks. Ecological Applications 22(8): 2176-2187.
595
Pess, G.R., McHugh, M.E., Fagen, D., Stevenson, P., and Drotts, J. 1998. Stillaguamish
596
salmonid barrier evaluation and elimination project‚ Phase III. Final report to the Tulalip Tribes,
597
Marysville, Washington.
598
Peterson, D.P., Rieman, B.E., Horan, D.L., and Young, M.K. 2013. Patch size but not short
599
term isolation influences occurrence of westslope cutthroat trout above humanmade barriers.
600
Ecology of Freshwater Fish.
601
Poff, N.L. 1997. Landscape filters and species traits: towards mechanistic understanding and
602
prediction in stream ecology. Journal of the North American Benthological Society 16(2): 391-
603
409.
604
Poplar-Jeffers, I.O., Petty, J.T., Anderson, J.T., Kite, S.J., Strager, M.P., and Fortney, R.H. 2009.
605
Culvert replacement and stream habitat restoration: implications from brook trout management
606
in an appalachian watershed, U.S.A. Restoration Ecology 17(3): 404-413.
607
R Development Core Team. 2012. R: A Language and Environment for Statistical Computing. R
608
Foundation for Statistical Computing, Vienna, Austria. Retrieved from http://www.R-
609
project.org/.
610
Rahel, F.J., and Hubert, W.A. 1991. Fish assemblages and habitat gradients in a Rocky
611
Mountain-Great Plains stream: biotic zonation and additive patterns of community change.
612
Transactions of the American Fisheries Society 120(3): 319-332.
613
27
Schick, R.S., and Lindley, S.T. 2007. Directed connectivity among fish populations in a riverine
614
network. Journal of Applied Ecology 44(6): 1116-1126.
615
Stanfield, L. 2010. Ontario stream assessment protocol. Version 8.0. Fisheries Policy Section.
616
Ontario Ministry of Natural Resources, Peterborough, Ontario.
617
Stanfield, L.W., Gibson, S.F., and Borwick, J.A. 2006. Using a landscape approach to identify
618
the distribution and density patterns of salmonids in Lake Ontario tributaries, 2006, American
619
Fisheries Society, p. 601.
620
Stanfield, L.W., and Kilgour, B.W. 2006. Effects of percent impervious cover on fish and
621
benthos assemblages and instream habitats in Lake Ontario tributaries, 2006, pp. 577-599.
622
Stanfield, L.W., Lester, N.P., and Petreman, I.C. 2013. Optimal Effort Intensity in Backpack
623
Electrofishing Surveys. North American Journal of Fisheries Management 33(2): 277-286.
624
Taylor, R.N., and Love, M. 2003. California salmonid stream habitat restoration manual - Part
625
IX fish passage evaluation at stream crossings. CA: California Department of Fish and Game.
626
Tischendorf, L., and Fahrig, L. 2000. On the usage and measurement of landscape connectivity.
627
Oikos 90(1): 7-19.
628
Vannote, R.L., Minshall, G.W., Cummins, K.W., Sedell, J.R., and Cushing, C.E. 1980. The river
629
continuum concept. Canadian Journal of Fisheries and Aquatic Sciences 37(1): 130-137.
630
Ward, J.V. 1998. Riverine landscapes: biodiversity patterns, disturbance regimes, and aquatic
631
conservation. Biological Conservation 83(3): 269-278.
632
Ward, J.V., Malard, F., and Tockner, K. 2002. Landscape ecology: a framework for integrating
633
pattern and process in river corridors. Landscape Ecology 17: 35-45.
634
Warren, J.M.L., and Pardew, M.G. 1998. Road crossings as barriers to small-stream fish
635
movement. Transactions of the American Fisheries Society 127(4): 637-644.
636
Wiens, J.A. 2002. Riverine landscapes: taking landscape ecology into the water. Freshwater
637
Biology 47(4): 501-515.
638
639
640
28
Tables:
641
Table 1: Categories of variables used in the analysis with the associated symbol used within the
642
text. Predictions of the db-RDA for abundance is included in the table with (+) indicating a
643
predicted change in community structure and (-) indicating no predicted change in community
644
structure.
645
646
Category
Variable
Symbol
Units
Fish Community
Abundance
A
Count
Structural Index
DCId
DCId
Percentage of natural
connectivity
DCIp
DCIp
Percentage of natural
connectivity
DCIs
DCIs
Percentage of natural
connectivity
Stream Position
Up-Stream
Cell Count
UCC
Count
Elevation
ELE
Meters
Stream Width
SW
Meters
Land Cover
Build-up area
Pervious
BUAP
Proportion of
watershed
Build-up area
Impervious
BUAI
Proportion of
watershed
Cropland
CR
Proportion of
watershed
Pasture and
Abandoned
Fields
PAF
Proportion of
watershed
Mixed forest
MF
Proportion of
watershed
Deciduous
forest
DF
Proportion of
watershed
29
Table 2. The results of the single species presence analysis. Predictions represent the expected
647
relationship between the species and variable. Positive values indicate that species presence is
648
predicted to increase with increases in the corresponding variable while negative values indicate
649
that species presence is predicted to decrease with increases in the corresponding variable.
650
Species
Prediction
Variable
n
Estimate
SE
z-value
p-value
Oncorhynchus mykiss
+
ELE
273
-0.018
0.004
-4.417
< 0.001*
-
BUAP
-0.518
0.467
-1.111
0.267
+
SW
0.111
0.057
1.936
0.053
+
DCIs
0.058
0.016
3.757
< 0.001*
Salmo trutta
+
ELE
273
0.003
0.004
0.718
0.473
-
BUAP
-0.837
0.217
-3.854
< 0.001*
+
SW
0.349
0.062
5.641
< 0.001*
+
DCIs
0.020
0.016
1.255
0.209
Salvelinus fontinalis
+
ELE
273
0.030
0.005
6.471
< 0.001*
-
BUAP
-0.674
0.308
-2.191
0.028*
+
SW
0.109
0.063
1.723
0.085
+
DCIs
0.002
0.016
0.121
0.903
Rhinichthys obtusus
-
ELE
273
-0.018
0.004
-4.485
< 0.001*
-
BUAP
0.557
0.282
1.977
0.048*
-
SW
0.014
0.063
0.214
0.830
+
DCIs
-0.019
0.015
-1.253
0.210
Rhinichthys cataractae
-
ELE
273
-0.018
0.005
-3.721
< 0.001*
-
BUAP
0.375
0.538
0.696
0.486
-
SW
0.760
0.112
6.758
< 0.001*
+
DCIs
0.019
0.021
0.883
0.377
Semotilus atromaculatus
-
ELE
273
-0.013
0.003
-4.070
< 0.001*
-
BUAP
0.531
0.210
2.531
0.011*
-
SW
-0.051
0.051
-0.990
0.322
+
DCIs
0.008
0.013
0.639
0.523
Cottus bairdii
-
ELE
273
-0.005
0.004
-1.310
0.190
-
BUAP
0.021
0.560
0.037
0.971
+
SW
0.196
0.059
3.289
0.001*
+
DCIs
0.081
0.017
4.917
< 0.001*
* indicates significance at = 0.05
651
652
653
30
Table 3. The results of the single species abundance analysis. Predictions represent the expected
654
relationship between the species and variable. Positive values indicate that species abundance is
655
predicted to increase with increases in the corresponding variable while negative values indicate
656
that species abundance is predicted to decrease with increases in the corresponding variable.
657
Species
Prediction
Variable
n
Estimate
SE
p-value
Oncorhynchus mykiss
+
ELE
273
-0.02
0.00
0.001*
-
BUAP
-0.50
0.02
0.310
+
SW
0.16
0.00
0.001*
+
DCIs
0.07
0.00
0.001*
Salmo trutta
+
ELE
273
0.02
0.00
0.001*
-
BUAP
-0.96
0.02
0.082
+
SW
0.42
0.00
0.001*
+
DCIs
0.02
0.00
0.126
Salvelinus fontinalis
+
ELE
273
0.03
0.00
0.001*
-
BUAP
-0.62
0.02
0.084
+
SW
0.02
0.00
0.792
+
DCIs
-0.01
0.00
0.722
Rhinichthys obtusus
-
ELE
273
-0.02
0.00
0.001*
-
BUAP
0.58
0.02
0.154
-
SW
-0.07
0.00
0.212
+
DCIs
0.00
0.00
0.756
Rhinichthys cataractae
-
ELE
273
-0.02
0.00
0.001*
-
BUAP
0.06
0.04
0.920
-
SW
0.65
0.00
0.001*
+
DCIs
0.05
0.00
0.014*
Semotilus atromaculatus
-
ELE
273
-0.02
0.00
0.001*
-
BUAP
0.74
0.03
0.262
-
SW
-0.18
0.00
0.004*
+
DCIs
0.00
0.00
0.898
Cottus bairdii
-
ELE
273
-0.01
0.00
0.060
-
BUAP
0.08
0.06
0.978
+
SW
0.18
0.00
0.001*
+
DCIs
0.09
0.00
0.001*
* indicates significance at = 0.05
658
659
660
661
31
Table 4. Dendritic Connectivity Index scores for each watershed.
662
Known Barriers
Known Barriers with Stream/River
Intersects
Watershed
DCIp
DCId
DCIs Range
DCIp
DCId
DCIs Range
Duffins
35.4
2.3
0.0 - 58.52
16.1
1.7
0.0 - 35.0
Oshawa
24.2
42.0
0.0 - 46.63
16.8
24.8
0.4 - 33.7
Cobourg
20.4
32.4
0.0 - 32.35
14.9
22.1
0.0 - 26.2
Ganaraska
24.4
0.4
0.0 - 46.63
18.4
0.3
0.5 - 39.1
Wilmot
51.3
67.0
0.0 - 67.02
22.6
31.2
14.9 - 41.1
663
664
32
Table 5. The results of co-variable selection based on the Akiake’s Information Criterion for 22
665
combinations of predictor variables against the db-RDA of community similarity using
666
abundance data (CS).
667
Model
K
AIC
AIC
Exp
Weight
CS ~ ELE + SW + BUAP
4
1181.69
0.00
1.000
0.805
CS ~ ELE + UCC + BUAP
4
1184.79
3.09
0.213
0.171
CS ~ ELE + SW + BUAI
4
1189.69
8.00
0.018
0.015
CS ~ ELE + UCC + BUAI
4
1192.71
11.01
0.004
0.003
CS ~ ELE + SW + PAF
4
1194.27
12.58
0.002
0.001
CS ~ ELE + SW + FAP
4
1194.27
12.58
0.002
0.001
CS ~ ELE + SW + MF
4
1194.54
12.85
0.002
0.001
CS ~ ELE + SW + DF
4
1197.75
16.05
0.000
0.000
CS ~ ELE + UCC + PAF
4
1197.76
16.06
0.000
0.000
CS ~ ELE + UCC + FAP
4
1197.76
16.06
0.000
0.000
CS ~ ELE + SW + CR
4
1197.90
16.21
0.000
0.000
CS ~ ELE + SW + CR
4
1197.90
16.21
0.000
0.000
CS ~ ELE + UCC + MF
4
1198.08
16.39
0.000
0.000
CS ~ ELE + UCC + MF
4
1198.08
16.39
0.000
0.000
CS ~ ELE + SW
3
1201.23
19.54
0.000
0.000
CS ~ ELE + UCC + DF
4
1201.56
19.87
0.000
0.000
CS ~ ELE + UCC + CR
4
1202.14
20.44
0.000
0.000
CS ~ ELE + UCC
3
1205.64
23.95
0.000
0.000
CS ~ ELE
2
1211.63
29.93
0.000
0.000
CS ~ SW
2
1216.68
34.98
0.000
0.000
CS ~ UCC
2
1219.02
37.32
0.000
0.000
aCS ~ ELE + UCC + SW + BUAP + BUAI + CR + PAF + MF + DF
8
1226.36
44.66
0.000
0.000
a Represents the global model (model that includes all variables) used in the model selection.
668
669
33
Table 6. The output of 6 different models for abundance to determine the relationship between
670
longitudinal connectivity as measured by the Dendritic Connectivity Index (Cote et al. 2009) and
671
community structure as measured by the Bray-Curtis similarity. Abundance 1 models used DCI
672
values calculated with only known barriers whereas Abundance 2 models used DCI values
673
calculated with known barriers and potential barriers.
674
Model
df
% Variation
Explained
Pseudo-F
p-value
Axis 1
Axis 2
Abundance 1:
Full Model 1
4
21.1
17.93
0.005
ELE
1
8
17.83
0.005
0.91
-0.16
BUIP
1
8.7
21.79
0.005
0.11
0.88
SW
1
3.8
12.6
0.005
-0.77
-0.28
DCIs
1
1.2
3.76
0.01
-0.49
-0.14
Residuals
268
78.3
Full Model 2
4
21.4
18.23
0.005
ELE
1
9.5
20.06
0.005
-0.92
0.17
BUIP
1
6.5
17.11
0.005
-0.13
-0.87
SW
1
3.9
12.82
0.005
0.79
0.25
DCIp
1
1
4.74
0.005
0.31
0.46
Residuals
268
79
Full Model 3
4
24.4
21.64
0.005
ELE
1
9.4
20.6
0.005
-0.77
0.54
BUIP
1
6.5
18.01
0.005
-0.43
-0.68
SW
1
4.3
13.65
0.005
0.74
-0.19
DCId
1
5.4
15.64
0.005
0.54
0.41
Residuals
268
74.4
Abundance 2:
Full Model 4
4
21.9
18.74
0.005
ELE
1
7.1
16.63
0.005
-0.88
-0.28
BUIP
1
8.4
21.33
0.005
-0.2
0.85
SW
1
3.6
12.39
0.005
0.77
-0.16
DCIs
1
2.7
6.37
0.005
0.65
-0.16
Residuals
268
78.1
Full Model 5
4
22.2
19.14
0.005
ELE
1
9.8
20.69
0.005
0.93
-0.16
BUIP
1
4.4
12.35
0.005
0.13
0.86
SW
1
4
13.01
0.005
-0.79
-0.23
DCIp
1
2.3
7.64
0.005
-0.22
-0.67
Residuals
268
79.6
34
Full Model 6
4
24.4
21.6
0.005
ELE
1
9.3
20.64
0.005
-0.78
0.52
BUIP
1
7.5
20.24
0.005
-0.41
-0.69
SW
1
4.4
13.66
0.005
0.75
-0.17
DCId
1
5.4
15.52
0.005
0.48
0.37
Residuals
268
73.4
675
676
677
35
Figures
678
Figure 1. The study area in southern Ontario with the barrier locations. The insert illustrates an
679
example area of the Duffins.
680
681
Figure 2. Relationship between channel slope and passability in Terra Nova National Park,
682
Newfoundland and Labrador, Canada. We applied this relationship to barriers in Southern
683
Ontario to determine the passability of unidentified barriers.
684
685
Figure 3. Histogram of barrier passabilities in the study watersheds based on the relationship
686
between channel slope and culvert passability in Terra Nova National Park, Newfoundland and
687
Labrador, Canada.
688
689
Figure 4. The distance based redundancy analysis comparing the DCIs, DCIp and DCId (panels A,
690
B, and C respectively) calculated with known barriers and potential barriers; and associated co-
691
variables (ELE = Elevation, SW = Stream Width, and BUAP = Built-up area-pervious) for
692
abundance data in southern Ontario.
693
694
Figure 5. Relationship between species richness and the DCIs in 5 southern Ontario streams
695
while controlling for elevation, stream width, and built-up area-pervious. The DCIs in panel A is
696
calculated using only known barriers and the DCIs in panel B is calculated using known barriers
697
and potential barriers.
698
699
36
Figure 6. Relationship of the DCI and species abundances (solid line) and 95% confidence
700
intervals (dashed line) for rainbow trout, longnose dace, and mottled sculpin.
701
... Landscape metrics, such as patch connectedness and fragment size, need not be exclusively terrestrial; in fact, aquatic connectivity is increasingly recognized as an important factor shaping lotic fish communities (Ward et al. 2002, Mahlum et al. 2014). It has long been accepted that dams have negative consequences for anadromous fish, such as Chinook Salmon (Oncorhynchus tshawytscha; Raymond 1979), but the extent of the impact of impassable culverts and other features that create impasses for fish communities as a whole is a relatively new area of study (Benton et al. 2008, Alexandre and Almeida 2010, Perkin and Gido 2012, Mahlum et al. 2014, Jackson 2016. ...
... Landscape metrics, such as patch connectedness and fragment size, need not be exclusively terrestrial; in fact, aquatic connectivity is increasingly recognized as an important factor shaping lotic fish communities (Ward et al. 2002, Mahlum et al. 2014). It has long been accepted that dams have negative consequences for anadromous fish, such as Chinook Salmon (Oncorhynchus tshawytscha; Raymond 1979), but the extent of the impact of impassable culverts and other features that create impasses for fish communities as a whole is a relatively new area of study (Benton et al. 2008, Alexandre and Almeida 2010, Perkin and Gido 2012, Mahlum et al. 2014, Jackson 2016. For example, Perkin and Gido (2012), in their study of 12 stream networks in the Great Plains, found that road crossings were inversely correlated with fish species richness and high levels of homogeneity between fish communities at different sites. ...
... However, dams and culverts do not exist in isolation from other environmental factors. Mahlum et al. (2014) addressed this issue in their study of stream connectivity at five watersheds in southern Ontario by accounting for such confounding variables as elevation and land cover. ...
Thesis
Full-text available
Lewis Creek has the highest known fish diversity of any watershed in Vermont. In 2016, I used a backpack electrofisher to sample the fish community at 28 sites throughout the watershed to determine the distribution of individual species and to understand some of the relationships between the terrestrial environment and the fish communities. I collected 30 samples, with an average effort of 1188 seconds and captured a total of 3707 individual fish, representing 28 species, 2 of which were new for the watershed. The site-specific species richness was best explained by elevation (r2 = 0.5), flow accumulation (r2 = 0.4), and stream power (r2 = 0.35), all of which can be considered proxies of the River Continuum Concept. Sites where the richness deviated the most from the value predicted by elevation alone can be explained in part by additional environmental information – stream gradient, dendritic connectivity, and proximity to source populations – or by sampling inconsistencies. In addition, efforts by the Vermont Fish and Wildlife Service to improve angling in the Champlain Valley are likely responsible for the decline and/or distributional changes of several native species, although these projects have been successful in accomplishing their stated goals, at least within Lewis Creek. Also several stream reaches can be identified where anthropogenic modifications to the terrestrial environment likely play an outsized role in shaping the fish community. A random forest regression model offered few novel insights into the patterns of fish assemblages in Vermont—perhaps due to the relatively small sample size, strong environmental correlations, and lack of replicate watersheds.
... In contrast, structural connectivity indices are not data-intensive and can be calculated with relative ease across broader spatial scales. However, they provide only a crude estimate of connectivity, which may or may not reflect actual conditions at the scale of their application (Mahlum et al 2014). Given these drawbacks, potential connectivity metrics present a more suitable choice in the absence of empirical data. ...
... Since different species perceive habitats at different spatial scales across their life-history stages, their response to fragmentation and flow alteration will likely be scale-dependent, and also influenced by their habitat and resource requirements (Rossi andvan Halder 2010, Llausàs andJoan 2012). Generally, as spatial scales of analysis increases, other confounding landscape-level variables (such as elevation, land use, discharge) begin to influence response communities (Mahlum et al 2014). The application of spatial graph and network models across hierarchical river networks presents an opportunity to better understand factors influencing ecological connectivity across spatial scales (Erős and Lowe 2019). ...
Article
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Background. Rivers continue to be harnessed to meet humanity's growing demands for electricity, water, and flood control. While the socioecological impacts of river infrastructure projects (RIPs) have been well-documented, methodological approaches to quantify river fragmentation and flow alteration vary widely in spatiotemporal scope, required data, and interpretation. Aim. In this review, we first present a framework to visualise the effects of different kinds of RIPs on river fragmentation and flow alteration. We then review available methods to quantify connectivity and flow alteration, along with their data requirements, scale of application, advantages, and disadvantages. Finally, we present decision-making trees to help stakeholders select among these methods based on their objectives, resource availability, and the characteristics of the project(s) being evaluated. Methods. Thematic searches of peer-reviewed literature using topic-relevant keywords were conducted on Google Scholar. The bibliography of selected papers was also reviewed, resulting in the selection of 79 publications. Papers that did not define or apply a specific metric were excluded. With respect to fragmentation, we selected papers focused on instream connectivity and excluded those dealing with overland hydrologic connections. For flow alteration, we selected papers that quantified the extent of alteration and excluded those aimed at prescribing environmental flows. Results. The expected hydrological consequences of various RIP types were 'mapped' on a conceptual fragmentation-flow alteration plot. We compiled 29 metrics of river fragmentation and 13 metrics to flow alteration, and used these to develop decision-making trees to facilitate method selection. Discussion. Despite recent advances in metric development, further work is needed to better understand the relationships between and among metrics, assess their ecological significance and spatiotemporal scale of application, and develop more informative methods that can be effectively applied in data-scarce regions. These objectives are especially critical given the growing use of such metrics in basin-wide conservation and development planning.
... Dams, reservoirs and diversions act as barriers causing river networks to fragment longitudinally and laterally [19,33,35], resulting in the loss of connectivity between different segments of a river and between the river and the sea (Figure 1). Fragmentation degrades the health of river ecosystems, often leading to the decline in migratory fish species [12,35,40,41]. The DCI has two subindices, measuring the degree of connectivity between different segments of a river (internal connectivity) and that measures the degree of connectivity between the river and the sea (external connectivity). ...
Article
Full-text available
This paper illustrates an approach to measuring economic benefits and ecological and social impacts of various configurations of reservoir systems for basin-wide planning. It suggests indicators and examines their behavior under several reservoir arrangement scenarios using two river basins in Sri Lanka as examples. A river regulation index is modified to take into account the volume of flow captured by reservoirs and their placement and type. Indices of connectivity illustrate that the lowest river connectivity in a basin results from a single new reservoir placed on the main stem of a previously unregulated river between the two locations that command 50% and 75% of the basin area. The ratio of the total affected population to the total number of beneficiaries is shown to increase as the cumulative reservoir capacity in a river basin increases. An integrated index comparing the performance of different reservoir system configurations shows that while results differ from basin to basin, the cumulative effects of a large number of small reservoirs may be comparable to those with a few large reservoirs, especially at higher storage capacities.
... Because all the dams in the Yangtze River Basin were built on the tributaries before 1980, the impact on connectivity was small, and the DCI was higher than 80, which represents an almost natural state. (Katopodis, 2005;Mahlum, Kehler, Cote, Wiersma, & Stanfield, 2014), as well as the local ecology and economy. In addition, fish migration and spawning can also be realized by means of joint reservoir operation scheduling for cascade hydropower stations to improve the connectivity, but this method can only be used for short periods of time and it is not a long-term solution (Cai et al., 2020). ...
Article
Dams are built on rivers for power generation, flood prevention and control, and water resources utilization. However, dams also reduce the connectivity of rivers, which hinders the exchange of material and organisms within rivers. The Yangtze River Basin and the Yellow River Basin are the two largest river basins in China. In this study, the connectivity of these two huge and highly impacted systems was investigated. The Dendritic Connectivity Index (DCI) was applied to evaluate the impact on river connectivity of dams with a reservoir capacity of larger than 0.1 km³. The results show that river connectivity decreased following an increase in dam construction. The connectivity of the Yangtze River Basin was close to 80 (0 is completely disconnected and 100 is connectivity under natural condition) in the 1980s, but declined significantly after the Gezhouba Dam was constructed on the mainstream of the Yangtze River. From 1980 to 2010, the connectivity of the Yellow River Basin was always lower than that of the Yangtze River Basin. The changes in the connectivity indices of potamodromous fish (DCIp) and diadromous fish (DCId) were determined for the period of 1980–2010. In the Yangtze River Basin, the DCIp decreased by 67.28% and the DCId decreased by 65.72%. In the Yellow River Basin, the DCIp decreased by 43.8% and the DCId decreased by 100%. In conclusion, the construction of dams, especially those on the main stream, has reduced the connectivity of the basin. The connectivity of the Yangtze River Basin and the Yellow River Basin has been severely affected.
... Tout d'abord, les effets qu'on attribue à ces milieux ne sont pas pleinement démontrés, au contraire même, plusieurs études semblent en désamorcer les impacts (e.g. Touchart, 1999 ;Mahlum et al., 2014 ;Van Looy et al., 2014 ;Villeneuve et al., 2015 ;Bravard, 2018). Ensuite, les travaux de restauration écologique comportent un certain niveau d'incertitudes, en raison de la jeunesse de ces pratiques et du grand nombre de variables auxquelles elles sont confrontées (Graf, 2008). ...
Article
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Au fil du temps, quasiment tous les réseaux hydrographiques de la planète ont été lourdement aménagés avec des ouvrages hydrauliques transversaux à l’écoulement des cours d’eau, qui ont enrichi le panorama des milieux aquatiques continentaux créant des retenues de seuil en rivière, des étangs et des lacs de barrage. Toutefois, aujourd’hui ceux-ci sont au centre de préoccupations de politiques environnementales qui ont comme objectif leur effacement, afin de rétablir les conditions supposées antérieures à leur création. Plusieurs membres de la communauté scientifique se montrent prudents face aux résultats que ces opérations de restauration écologique devraient atteindre et ils proposent d’intégrer ces milieux aquatiques artificiels au sein des politiques environnementales, pour protéger leurs particularités et les bénéfices qu’ils apportent aux sociétés. Toutefois, pour faire cela il est nécessaire de comprendre leur fonctionnement et de développer une vision globale qui les assimile aux milieux aquatiques considérés comme naturels. Ainsi l’objectif de cet article est de passer en revue la littérature scientifique à propos de certaines caractéristiques du biotope des retenues de seuils en rivière, des étangs et des lacs de barrages et de les comparer avec celles des cours d’eau et des lacs naturels, pour voir de combien le fonctionnement des milieux aquatiques artificiels et naturels s’écarte. Les résultats de cette synthèse ont mis en avant le caractère hybride des milieux aquatiques artificiels examinés, qui croisent en même temps des traits lentiques et des traits lotiques. Cela a permis le développement d’une vision globale qui intègre les milieux aquatiques artificiels et naturels, selon un gradient de conditions abiotiques.
... Based on the perceptions of the local fishers, the production of 15 ethnospecies declined following the impoundment (Table 1). Many studies have shown that impoundments for hydroelectric projects may provoke a reduction in fish populations due to a number of different types of impact, such as the blocking of migration routes (Perkin and Gido 2012;Mahlum et al. 2014), injuries to the fish in the turbines (McKinstry et al. 2007), shifts in the flood pulse (Santos, et al. 2018), changes in the physicalchemical properties of the water (Preece and Jones 2002;Olden and Naiman 2010), and alterations in the trophic structure of the impounded ecosystem (Agostinho et al. 2016;Oliveira et al. 2018). All these factors may have contributed to the decline in fishery production in the Madeira Basin. ...
Article
Full-text available
This study aimed to investigate the environmental impacts generated by the hydroelectric complex in the Madeira River, Brazilian Amazon, based on the perceptions of local fishers and fishery database, it focus attention on three main impacts: (i) on local fishery stocks; (ii) in fish fauna and (iii) on the aquatic ecosystems. The local fishers were selected through the ‘‘snowball’’ approach for the application of semi-structured interviews. All the local fishers confirmed having perceived a decline in fishery productivity following the impounding of the Madeira River. Changes in the condition of the fish were also perceived by the local fishers, including exophthalmia (82%), a reduction in the weight or length of the fish (25%), and irregular breeding patterns (14%). In the case of impacts on the river, changes in the hydrological cycle were the process remembered most frequently (75%). The results elucidated a range of environmental impacts caused by the hydroelectric dams of the Madeira River.
... Thus, for resident or potamodromous fish, connectivity is expected to depend more on the "largest fragment", whereas for diadromous fish it depends on the position of the barrier in relation to the river mouth [15]. This index has been successfully used to assess effects of fragmentation on diversity, abundance and distribution patterns of riverine fish in some river systems [21][22][23]. Some limitations of DCI have also been recognised, most importantly the consideration of the barrier placement only as a theoretical approximation expressed as the distance to the lowest point of the network [17] included an additional metric for placement of barriers within the river network, namely the river volume related to discharge and channel dimensions. ...
Article
Full-text available
Background: Fragmentation (establishment of barriers e.g., hydropower dams, reservoirs for irrigation) is considered one of the greatest threats to conservation of river systems worldwide. In this paper we determine the fragmentation status of central Chilean river networks using two indices, namely Fragmentation Index (FI) and Longest Fragment (LF). These are based on the number of barriers and their placement as well as river length available for fish movement. FI and LF were applied to eight Andean river basins of central Chile in order to assess their natural, current (2018) and future (2050) fragmentation at the doorstep of a hydropower boom. Subsequently, we exemplify the use of these indices to evaluate different placement scenarios of new hydropower dams in order to maximize hydropower use and at the same time minimize impact on fish communities. Results: In the natural scenario 4 barriers (waterfalls) were present. To these 4 barriers, 80 new ones of anthropogenic origin were added in the current (2018) scenario, whereas 377 new barriers are expected in near future (2050). Therefore, compared to the ‘natural’ scenario, in 2050 we expect 115-fold increase in fragmentation in analysed river systems, which is clearly reflected by the increase of the FI values in time. At the same time, the LF diminished by 12% on average in the future scenario. The fastest increase of fragmentation will occur in small and medium rivers that correspond to 1st, 2nd and 3rd Strahler orders. Finally, case study on configuration of potential hydropower plants in the Biobio basin showed that hydropower output would be maximized and negative effects on fish communities minimised if new hydropower plants would be located in tributaries of the upper basin. Conclusions: Fragmentation of Chilean Andean river systems is expected to severely increase in near future, affecting their connectivity and ecological function as well as resilience to other anthropogenic stressors. Indices proposed here allowed quantification of this fragmentation and evaluation of different planning scenarios. Our results suggest that in order to minimise their environmental impact, new barriers should be placed in tributaries in the upper basin and river reaches above existing barriers.
Article
Full-text available
Infrastructure-induced fragmentation of riverine ecosystems has prompted the need for more effective aquatic restoration efforts globally. Fragmentation assessments have been extensively undertaken to inform connectivity restoration efforts for fish and other aquatic biota, but they have potentially underestimated the extent of fragmentation by fixating on large dams and overlooking the contribution of other barriers like road crossings and small irrigation structures. The current study addresses this limitation in Mekong region countries (MReCs) of Southeast Asia, by assessing the fragmentation impacts of road crossings and small irrigation structures together with large dams. Our analysis indicates that the basin-scale fragmentation impact of road crossings is similar to that of large dams in MReCs, while small irrigation structures have a far greater impact. These findings raise concerns about the real global extent of aquatic fragmentation, and highlight the need for decision-makers to think beyond dams when attempting to restore connectivity for aquatic biota.
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