Site Selection
For this study, we sampled 22 tributaries in the Lower Fraser region, including tributaries that flow directly into the Fraser River as well as those that flow into the Pitt, Coquitlam, and Harrison rivers (rivers that flow into the lower Fraser River). Of these, 18 sites had floodgates of various designs and configurations and four had no floodgates (Fig. 1, Online Resource 1). These non-floodgate sites were chosen to represent fully connected habitats. Candidate sites were selected after reviewing the Lower Fraser Strategic Streams Review (DFO 1999) and Lower Fraser River floodplain maps (BC MFLNRO 2011). Site selection criteria included accessibility for sampling, availability of preexisting data on floodgate opening or a suitable place to secure a time-lapse camera, and a sufficient channel width and length to conduct two seine hauls in the tributary on either side of the floodgates.
Quantifying Floodgate Operations
There is a limited amount of data on floodgate operations in the Lower Fraser, with most published data limited to a few sites and short time frames. Here, we addressed this data gap on floodgate opening and closing by compiling existing data from municipalities and by using time-lapse photography to capture floodgate position at 1-h intervals. Only two sites had available preexisting data—Spencer Creek and Mountain Slough—both of which are controlled manually based on threshold water levels. As such, staff at the District of Kent and the City of Maple Ridge provided opening and closing dates during the study period. At the remaining 16 floodgate sites, we installed Brinno TLC200 time-lapse cameras to photograph the floodgates every daylight hour from July 2014 to January 2015, and then again from April to July 2015. Cameras were removed from January to April 2015 to avoid losing cameras due to vandalism and water damage during particularly high tides or winter storms, when large volumes of water are pumped over the dikes. Once every 4–6 weeks, we visited the sites to check the cameras, change batteries, and download the photos. Cameras were mounted inside of a PVC pipe housing and locked to railings, grates, or fences around the floodgates. Despite attempts to protect cameras within this housing, some data were missing for some sites and time periods due to theft, water damage, and the camera shifting positions.
The collected time-lapse videos were reviewed frame-by-frame to assess gate openness. The gates were described as open or closed based on a minimum threshold of openness set when water was able to visibly flow between the edge of the gate and any adjacent structures such as walls or other gates (typically a ~ 5°–10° opening angle). In Oregon, juvenile coho salmon have been observed passing through a top-hinged floodgate while it was open to angles of 7°–16° (Bass 2010). Although larger fish may be unable to move through floodgates that are only open 5°–10°, the majority of fish captured in this study were under 40 mm fork length, and a wider minimum opening may exclude times when these fish can pass through the floodgates.
Many flood boxes have multiple gates (Online Resource 1), but due to flood box configurations and limited camera mounting positions, we were not able to photograph all gates at all sites. Where possible, we photographed all of the floodgates at a site and classified the flood box as open when at least one floodgate opened. If it was not possible to fit all of the floodgates in the frame, we randomly selected one or one pair of floodgates and mounted the camera to photograph the representative gate or pair of gates (Online Resource 1). High tides or river levels frequently submerged floodgates completely, obscuring them from the view of the cameras. When floodgates were completely under water, we assumed the pressure from the high downstream water level was keeping the gates closed. In order to open, floodgates must have sufficient head differential (i.e., pressure due to differences in water level), with enough water accumulated above the gates to overcome friction in the hinges and the pressure of water downstream of the floodgates holding them closed (Giannico and Souder 2005; Thomson 2005). In the time-lapse footage, floodgates typically closed before the water fully submerged them and were also closed when the tide receded several hours later (personal observation). Furthermore, most floodgates are accompanied by pumping stations that remove excess water from upstream when the downstream water level is high (Thomson 2005), thereby reducing the hydraulic head and the likelihood that floodgates would open when underwater. Accordingly, we are confident that this approach provides reliable information on patterns of floodgate operation.
To quantify floodgate operations, we calculated the proportion of the recording time (i.e., daytime hours) that the floodgates opened for each date and site, and then took the mean value across the entire video recording period (July 2014–July 2015). We calculated the proportion of the day that gates were open instead of counting the number of hours. This was to account for the cameras’ inability to record images at night and the rapidly changing day lengths in the autumn months at this temperate latitude. We also calculated the “mean proportion of the day gates opened” over subset time periods (e.g., data from July and August only) and based on a stricter gate openness threshold (~ 30° opening angle), but found that all openness metrics were highly correlated (r
2 > 0.85), and did not include these other metrics in further analyses.
Fish Sampling
We sampled fish at all sites to understand how floodgate operations influenced fish communities. Each site was sampled once between July 30 and August 29, 2014. Previous studies in the area have identified late summer as a period when the impacts of flood boxes on fish and water quality are most severe (Gordon et al. 2015; Scott et al. 2016).
At each site, we sampled fish communities with four seine hauls using a 15.24 m by 2.44 m net with a 3.175-mm mesh size. At sites with floodgates, we performed two seine hauls on each side of the floodgates (upstream and downstream). To conduct these seine hauls, one crew member held one end of the net on the bank near the water’s edge while another member waded with the other end towards the center of the channel and then back to shore, where crewmembers quickly pulled up the excess net onto the bank and formed a purse to hold the captured fish. The net was fully extended during each set to keep the set area relatively consistent and comparable. Some sites were too deep to safely wade with the seine net. At these locations, we rowed an inflatable raft to pull one edge of the seine net while the other end was held at the waters’ edge. Captured fish were removed from the net, identified to species, measured to fork length, and weighed before being released back to the location of capture. Sampling was approved by the University Animal Care Committee at Simon Fraser University (protocol number 1158B-11) and by scientific collection permits from Fisheries and Oceans Canada and the Ministry of Forests, Lands, and Natural Resource Operations.
Exact locations of seine hauls were chosen based on practical and biological reasons. At the four sites without floodgates, seines were conducted approximately 30–50 m apart and on either side of a place that might have had a floodgate. For example, dikes can often occur under railroads or roads, but at the sites without floodgates, bridges were installed over an interruption in the dike rather than floodgates. Exact seine locations were selected based on the ability to pull the seine net up on the bank (influenced by slope of bank), safe access to the shoreline, and the need to be a safe distance from pump intakes and outfalls. As much as possible, we selected seine locations to represent one or two habitat types and attempted to find similar habitats upstream and downstream where they existed. At some sites, seine locations were limited by short channel length, woody debris snagging the net, and water depth. Furthermore, our sampling may have been affected by differences in capture efficiency during sampling, as well as the volume of water sampled. Although we made efforts to sample a similar-sized area during each set, there were variations in water depths and tides across sites. Due to time restrictions and the high number of sampling sites spread over a large geographic area, we were unable to standardize our sampling to occur at the same tidal stage and depth across all sites.
In addition to fish data, we recorded water quality data, channel width and depth, and weather conditions at each site. Using a YSI device (Model 556 MPS, YSI Incorporated 2009), we measured dissolved oxygen, salinity, conductivity, and temperature at a distance of 15 m from the flood box on its upstream and downstream sides. The YSI probe was placed near the middle of the channel at a depth of approximately 0.5 m. These measurements were collected once at each site during fish community sampling.
Geographic Site Information: Watershed Area, Distance Upriver, and Land Use
This analysis included three geographic variables that may affect fish abundance and diversity: distance up the Fraser River from the ocean to the floodgate, watershed area upstream of the floodgates, and land use within each site’s watershed. Distance upriver was estimated using the Path and Measurement tools within Google Earth to draw and measure a path along the river to the mouth of the river (version 7.1.5.1557, Google Inc 2015). Because the Fraser River splits into north, middle, and south arms in the delta, we took the measurement via the arm that produced the shortest path from the ocean to the floodgates. Watershed areas were estimated in ArcGIS version 10.2 (ESRI 2014) after drawing watershed polygons with the Hydrology tools. In several cases, the watershed’s topography was too flat for the Hydrology tools to correctly draw the watershed boundaries. In these cases, we drew watershed boundaries manually while referencing aerial photos from Google Earth. We summed the land use areas within each watershed into four categories: Agricultural, Urban, Undeveloped, and Other Human Uses (e.g., industrial, transportation, resource extraction, and utilities). The developed percentage of the watershed was obtained by summing the percent areas of all agricultural, urban, and other human land uses (Online Resource 1). Metro Vancouver, the District of Kent, the Fraser Valley Regional District, and the District of Mission provided land use data for their respective jurisdictions.
Statistical Analysis
We conducted two main analyses to (a) examine patterns in gate openings and explore what site characteristics could affect gate openings and (b) to understand how differences in fish communities on either side of the dikes relate to floodgate openness. These analyses also included several site characteristics as variables (Online Resource 1). We also conducted a third analysis to determine whether water quality measurements (e.g., dissolved oxygen concentrations) relate to floodgate operations.
We constructed generalized linear mixed-effects models (GLMMs) to determine whether site characteristics affected the amount of time gates opened. Given that the response data were repeated observations of whether the gates were open or closed, we used the binomial family with a logit-link for this model set. Gate opening data were summarized by date, with the model input formatted as a two-column integer matrix containing the proportions of the day that floodgates were open and closed (Hastie and Pregibon 1992). Initial model comparisons based on Akaike’s information criterion (AIC) indicated strong support for including the daily mean discharge of the Fraser River (Water Survey of Canada station no. 08MH024) as a covariate in all candidate models. Specifically, including daily mean discharge reduced the model’s AIC score by 30.2 ΔAIC units. In addition, all models incorporated a random intercept by site (ΔAIC = 213.7 with a lower AIC score for the model with the random effect) and an AR1 temporal autocorrelation term (ΔAIC = 9626.6 with a lower AIC score for the model with the autocorrelation term) based on results of initial model comparisons between models with and without each of these terms. These three factors were then included in all models in a different set of candidate models, which were compared using AIC model selection to determine which fixed effects were best supported by the data. Candidate models included all subsets of the following fixed effects: distance from the ocean, watershed area, pumps (present/absent), gate type (side-hinged, top-hinged, or manual sliding gate), and the proportion of the watershed with developed land uses. The continuous variables were standardized by their sample standard deviations and centered to aid in model convergence (Schielzeth 2010). The model set also included a “null” model with only the autocorrelation term, daily mean Fraser discharge, and the random effect. No interaction terms were considered due to poor coverage of some variables (e.g., pumps present in larger watersheds but not smaller ones) and failure of models to converge. Models were created using the lme4 package (v. 1.1-9, Bates et al. 2015) in R (v. 3.1.2, R Core Team 2015).
To examine potential relationships among site-level variables, we conducted a principle component analysis using PAST (v. 2.17, Hammer et al. 2012). These variables included floodgate type, pump presence or absence, watershed area, location on the river, and percentage of the watershed with developed land uses.
We calculated differences between the upstream and downstream fish communities using community dissimilarity metrics and log response ratios. First, we sought to understand how the entire fish communities differed upstream and downstream of floodgates and to investigate how these community differences varied with floodgate operations (i.e., are communities more different where floodgates stay closed?). To do this, we constructed a community dissimilarity matrix using Bray-Curtis differences, taking each upstream/downstream section as a separate site. Given that fish samples were dominated heavily by three-spined stickleback (Gasterosteus aculeatus), we square-root-transformed taxon abundances before calculating Bray-Curtis distances, as this metric can be driven by abundances of a dominant species (Legendre and Legendre 2012). Bray-Curtis distances for the upstream and downstream portions of each site were then extracted from the community dissimilarity matrix for further analysis against floodgate operations. Bray-Curtis dissimilarities were computed using the vegan package in R (v. 2.3-0, Oksanen et al. 2015).
To characterize potential differences between upstream and downstream fish communities, we computed the log response ratios of several metrics based on fish samples. These metrics included the richness, biomass, and number of fish captured upstream and downstream of floodgates. We calculated these metrics for total fish captured and for subgroups of fishes (e.g., native and non-native fishes). The log response ratio (lnRR) is typically used to express the effects of a treatment relative to a control or reference state (Hedges et al. 1999). Here, we treat the downstream fish community as a reference state and the upstream fish community as a treatment, to compute the log response ratio as
$$ \mathrm{lnRR}=\ln \left(1+\frac{\mathrm{Upstream}-\mathrm{downstream}}{\mathrm{Downstream}}\right) $$
To test whether the downstream fish communities would be suitable for use as the “reference state,” we plotted downstream fish captures, biomass, and richness against floodgate openness. We did not find any strong relationships between openness and downstream fish variables, thus we are confident that the log response ratio is an effective metric for this purpose.
After breaking the data out into groups of species (e.g., native or non-native fishes), several sample units had zero values and resulted in undefined or infinite estimates of the log response ratio. These zero values are potentially important features of the data, so we added the minimum non-zero value for that variable to every observation before calculating the log response ratio. This method of adjustment has been used as a conservative estimate of the log response ratio in data where species were not detected in some samples (Viola et al. 2010). We also computed log response ratios for the richness, biomass, and number captured for the four most commonly sampled taxa: three-spined stickleback (Gasterosteus aculeatus), pumpkinseed sunfish (Lepomis gibbosus), prickly sculpin (Cottus asper), and juvenile minnows (Cyprinidae). We captured many unidentified juvenile cyprinids (most of which were under 40-mm fork length), and therefore pooled them with all minnows for calculations of fish taxonomic richness.
The computed Bray-Curtis distances and log response ratios were then used as response variables in a series of linear models to understand relationships between upstream-downstream community differences and floodgate openness. A separate set of candidate models was created for each of the response variables (e.g., species richness, abundance). Each of the candidate models included up to two of the following explanatory variables: mean proportion of the day gates opened, number of floodgates, watershed area, distance upriver, and the percent developed area in the watershed. Top models were selected based on small sample-size corrected AIC (AICc) values, and parameter estimates were obtained by averaging models within 8 ΔAICc units of the top model (Burnham and Anderson 2002). Before model averaging, we checked that the candidate models met the assumptions of linear modeling by examining residuals and normal Q-Q plots.
We also used linear modeling to explore whether floodgate operations were correlated to water quality measurements. We constructed a series of linear models relating dissolved oxygen concentrations to floodgate operations and site characteristics. All models for dissolved oxygen measurements appeared to meet the assumptions of linear modeling, based on residuals, normal Q-Q plots, and Cook’s distances. These models were compared using AICc model comparison and parameter values and weights were estimated using model averaging. We used the direct measurements and modeled upstream and downstream dissolved oxygen separately. For all analyses, model selection and averaging were performed with the AICcmodavg (v. 2.0-3, Mazerolle 2015) and MuMIn (v. 1.15.1, Bartoń 2015) packages implemented R (v. 3.1.2, R Core Team 2015).
Following from these results, we were interested in whether hypoxic conditions above floodgates might affect the differences in observed fish communities at gated sites. In a post-hoc analysis, we examined the fish community data for a relationship with upstream dissolved oxygen concentrations. Neither Bray-Curtis community differences nor the log response ratios for native richness showed strong correlations with upstream dissolved oxygen concentrations (r
2 ~ 0.1, p > 0.05).