Geographic variation in Puget Sound tidal channel planform geometry

Geomorphology 230 (2015) 98–108

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Geographic variation in Puget Sound tidal channel planform geometry W. Gregory Hood ⁎ Skagit River System Cooperative, PO Box 368, LaConner, WA 98257, USA

a r t i c l e

i n f o

Article history: Received 10 February 2014 Received in revised form 10 November 2014 Accepted 16 November 2014 Available online 22 November 2014 Keywords: Landform allometry Tidal channel scaling Wave environment Salt marsh restoration

a b s t r a c t Tidal channels are central elements of salt marsh hydrodynamics, sediment dynamics, and habitat. To develop allometric models predicting the number and size of tidal channels that could develop following salt marsh restoration, channels were digitized from aerial photographs of Puget Sound river delta marshes. Salt marsh area was the independent variable for all dependent channel planform metrics. Tidal channel allometry showed similar scaling exponents for channel planform metrics throughout Puget Sound, simplifying comparisons between locations. Y-intercepts of allometric relationships showed geographic variation, which multipleregression indicated was associated with tidal range and storm significant wave height. Channel size and complexity were positively related to tidal range and negatively related to wave height. Four case studies, each with paired regions of similar tidal range and contrasting wave environments, further indicated wave environment affected channel geometry. Wave-mediated sediment delivery may be the mechanism involved, with wave-sheltered areas experiencing relative sediment deficits, such that some salt marshes in Puget Sound are already suffering sea-level rise impacts that are reflected in their channel network geometry. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Tidal channels influence hydrodynamics (Rinaldo et al., 1999), sediment transport (French and Stoddart, 1992), and the distribution and production of flora (Sanderson et al., 2000) and fauna (Simenstad, 1983; Williams and Zedler, 1999). Thus, tidal channels are central to geomorphic and ecological processes in salt marshes. In coastal marshes of the North Pacific, tidal channels provide important rearing habitat for juvenile salmon (Magnusson and Hilborn, 2003; Bottom et al., 2005). However, habitat loss to agricultural and urban development has contributed to the decline of Pacific salmon populations over the last 150 years, so that Chinook (Oncorhynchus tshawytscha Walbaum) and chum (Oncorhynchus keta Walbaum) salmon are threatened species in Puget Sound and Hood Canal (Washington, USA), respectively, as well as elsewhere along the west coast of North America. Consequently, salmon recovery through estuarine habitat restoration is the focus of many federal, state, and local agencies. Similarly, salt marsh losses in Europe have spurred interest in restoration through managed retreat to protect vulnerable shorelines and provide habitat for fish and wildlife (Cooper et al., 2001; Atkinson et al., 2004; Colclough et al., 2005; Wolters et al., 2005). Effective salt marsh restoration requires the ability to predict the outcomes of proposed restoration actions. If dikes are breached or fill removed to restore tidal inundation to historical salt marshes, how

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http://dx.doi.org/10.1016/j.geomorph.2014.11.009 0169-555X/© 2014 Elsevier B.V. All rights reserved.

many tidal channels could develop in the restored area? How much total channel length or surface area could develop, and thus, how many juvenile salmon could rear in the new channels? Tidal channel geometry (number, size, and shape) has been investigated to provide pragmatic tools for design, prediction, and evaluation of habitat restoration (Coats et al., 1995; Williams et al., 2002; Hood, 2007a); to compare presumed anthropogenically degraded sites and natural reference sites (Hood, 2002, 2007a); and to develop metrics and analytic methods that contribute to understanding landform evolution (Rinaldo et al., 1999; Novakowski et al., 2004). However, channel geometry has been characterized for relatively few locations, and geographic variation in channel geometry is not well understood. The goals of this paper were to extend an empirical model for predicting tidal channel planform geometry, previously developed for the Skagit Delta (Hood, 2007a), to other salt marshes throughout Puget Sound and to explore the likely causes of geographical variation in observed tidal channel planform geometry. 2. Setting Puget Sound salt marshes are primarily located in river deltas, where they provide important rearing habitat for juvenile salmon produced upstream. Accretion shoreforms, or barrier beaches, protect much smaller salt marshes in their lee that are sometimes known as pocket estuaries, where creeks or groundwater seeps reduce local salinity (Shipman, 2008). Pocket estuaries provide stepping-stone migratory corridors for juvenile salmon as they transition from natal river deltas through Puget Sound to the ocean. This study examined salt marshes in all of the major river deltas in Puget Sound, except the Duwamish and Puyallup rivers (Fig. 1) which

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3. Methods 3.1. Approach

Fig. 1. Study locations. River delta sites include [1] Lummi, [2] Nooksack, [3] NF Skagit, [4] SF Skagit, [5] Stillaguamish, [6] Snohomish, [7] Nisqually, [8] Union, [9] Tahuya, [10] Skokomish, [11] Dewatto, [12] Hamma-Hamma, [13] Duckabush, [14] Dosewallips, and [15] Quilcene. Salt marshes sheltered by coastal barrier shoreforms include [a] Oak Harbor, [b] Race, [c] North Bluff, [d] Iverson, [e] Point Julia, [f] Thorndyke Bay, and [g] Dabob Bay.

debouch into the industrial ports of Seattle and Tacoma, respectively, and have lost nearly all of their historical salt marshes. The Skagit Delta has the most widespread extant salt marsh but it has suffered extensive historical habitat loss to agricultural development (Collins et al., 2003). The Nisqually and Skokomish deltas have had extensive but recent restoration, while the Nooksack is an entirely new delta formed over the last century as the result of anthropogenically assisted river avulsion that moved the Nooksack River mouth from Lummi Bay to Bellingham Bay (Bortelson et al., 1980). The same avulsion orphaned the Lummi Delta and allowed it to be more easily diked and converted to farmland, leaving only a fringe of remnant salt marsh. Thus, none of the Puget Sound river deltas are pristine (e.g., Collins et al., 2003), but portions of each delta appear to be in reasonably good condition. Similarly, Puget Sound pocket estuaries have been largely destroyed by agricultural and residential development (Simenstad et al., 2011) but some remnants in good condition were included in this study, four from northern Hood Canal and four from the Whidbey Basin. Salt marsh sites were excluded from analysis when anthropogenic impacts were clearly evident. These included recently restored salt marshes, marshes with large “borrow ditches” from which nearby dikes were constructed, and marshes surrounded by historically abandoned and breached dikes. Some historical legacies of anthropogenic disturbance may not have been recognized, which may account for some of the variance in observed statistical patterns.

The traditional approach to predicting tidal channel geometry relies on application of fluvial hydraulic geometry to tidal systems, where empirically derived power functions describe scaling relationships between tidal prism or its surrogate, tidal channel drainage area, and channel cross-section geometry (Coats et al., 1995; Williams et al., 2002). By extension, planform tidal channel metrics have been shown to also scale with drainage area (e.g., Rinaldo et al., 1999; Marani et al., 2003). However, it is challenging to delineate tidal drainage divides over generally flat salt marshes. One suggested solution is drainage divide identification through numerical modeling of tidal flow. The resulting divides are equidistant from channel axes (Marani et al., 2003). Thus, drainage divides can be estimated easily in a geographic information system (GIS). Nevertheless, simplifying assumptions underlying this approach may be sometimes violated, although it is unclear if these violations are significant. For example, vegetation type may affect tidal flow velocity and direction if vegetation is patchy and if different species have distinct canopy architecture (e.g., Temmerman et al., 2005a), as is the case for many Puget Sound deltas. Flow-significant vegetation patches are not necessarily coincident with theoretical drainage divides. Where salt marsh vegetation senesces during the winter, as is typical in Puget Sound, there are likely seasonal patterns in tidal hydrodynamics. Additionally, where there is significant flooding above the salt marsh surface, also typical in Puget Sound, it is unclear how tidal prism is partitioned between sheet flow and channelized flow, though marsh surface elevation and vegetation are significant influences (Temmerman et al., 2005a,b). Finally, the method assumes very flat topography, but this is clearly not the case for Puget Sound river deltas where relatively high tidal range and high riverine sediment loads contribute to notable elevation gradients and to the presence of natural berms along salt marsh islands and large blind tidal channels. To avoid these potential complications this paper treats salt marsh islands surrounded by river distributaries or the sea as the predictive geomorphic unit rather than the individual tidal channel drainage basin. Thus, the response variables are the planform metrics of the set of tidal channels draining a salt marsh island rather than the geometry of a single channel. This approach is based on the observation that landforms are often self-affine fractals, i.e., form is “stretched” depending on scale (Ouchi and Matsushita, 1992; Rodriguez-Iturbe and Rinaldo, 1997), which is fractal terminology for allometry (Mandelbrot, 1983). Allometry and fractal geometry differ in perspective. Fractal geometry focuses on how measured quantities vary as a power of measurement scale (Milne, 1991), while allometry focuses on proportional relative rates of change between two measured quantities in a system (Church and Mark, 1980; Hood, 2007b). Like hydraulic geometry, fractals and allometric systems are described by power functions. One benefit of the allometric approach is that it is well aligned with the challenges of salt marsh restoration, where the planner, engineer or ecologist is generally confronted with predicting the geomorphic outcome of restoring a given area of salt marsh, rather than the outcome of restoring individual tidal drainage basins. For example, it is usually unclear how many drainage basins and how many tidal channels will result from salt marsh restoration—an allometric analysis can address that basic question and inform engineers how many dike breaches should be made to provide channel outlets. (Dikes are usually breached rather than removed due to cost constraints.) Similarly, biologists need to know how many fish could be produced by a potential restoration site. An allometric analysis allows estimation of the total channel area or length that a site will likely develop, which can then be linked to fish production by applying a typical fish density to the channel estimate. Similarly, engineers want to know how much tidal channel excavation should take place to accelerate channel development and fish occupancy of the restoration site. Allometric scaling of total channel

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length and area have also revealed non-linear cumulative effects of salt marsh area; total channel area and length increase disproportionately with salt marsh area (allometric exponents N1), which indicates to planners that it is better to restore large intact salt marsh areas rather than splitting restoration effort into many separate, smaller parcels, e.g., one 100-ha salt marsh parcel supports much greater total channel area or length than ten 10-ha parcels, and thus much greater fish production (Hood, 2007a). The most significant caution regarding use of allometric analysis for restoration is that the confidence limits of the prediction are large, so that the prediction estimates provide guidance rather than narrow prescription. Further investigation into the causes of variance in allometric analysis (such as the current study) should narrow the prediction limits. Recently, a new metric, overmarsh flow length, has been developed that is more sensitive to variation in channel geometry than are traditional Hortonian metrics (Marani et al., 2003). When applied to individual tidal channel drainage basins, this metric responds to variation in basin shape, channel branching pattern (frequency and orientation), and channel meandering. While this metric has promise as a diagnostic tool, its great sensitivity might be problematic when applied in a prescriptive fashion. Given the apparent high degree of spatial variation in this metric (Marani et al., 2003), which value of this metric is an appropriate target for an engineer planning restoration? In addition, given a target value, how should an engineer design excavated channels? A 1:1 mapping of basin shape, channel branching pattern, or channel meandering with this metric seems unlikely since these aspects of channel geometry interact to affect the metric. Exploration of these issues is left for another study. 3.2. GIS analysis Tidal channel margins and other shorelines were manually digitized from true color orthophotos in a GIS to create polygon shape files from which basic metrics, such as salt marsh island area, channel surface area, and channel perimeter could be easily measured by GIS routines (Fig. 2). Analysis was limited to salt marshes characterized by herbaceous and shrub vegetation. Unvegetated tidal flats and densely forested floodplains were not examined. Channel margins and shorelines were defined by the abrupt transition from vegetated to unvegetated intertidal areas. Unvegetated tidal flats, vegetated salt marshes, and water had characteristic photo-signatures that were generally easily distinguished. Nevertheless, multiple sets of orthophotos were consulted for each study site, because different sets sometimes differed in interpretability due to differences in shadows from nearby forests, sun angle, vegetation growth, tidal stage, and image color balance or resolution. For each site a single photo set providing optimal interpretability was chosen as the baseline for channel delineation (Table 1), but reference to secondary photo sets (e.g., Google Earth) sometimes improved channel detection.

Table 1 Principal aerial photos used for GIS delineation of tidal channels and salt marsh. Site

Data (aerial photo) source

Year

Resolution

Dabob Bay Dewatto Bay Dosewallips Delta Duckabush Delta Hamma-Hamma Delta Lummi Delta Nisqually Delta Nooksack Delta Pt. Julia Lagoon Quilcene Delta Seabeck Lagoon Skagit Delta Skokomish Delta Snohomish Delta Stillaguamish Delta Tahuya Delta Thorndyke Bay Union River Delta

Bing (via ESRI ArcGIS) Bing (via ESRI ArcGIS) Port Gamble S'Klallam Tribe Bing (via ESRI ArcGIS) Washington Dept. of Transportation Lummi Nation Nisqually Tribe Lummi Nation Kitsap County Bing (via ESRI ArcGIS) Bing (via ESRI ArcGIS) Skagit River System Cooperative Bing (via ESRI ArcGIS) Snohomish County Snohomish County Bing (via ESRI ArcGIS) Bing (via ESRI ArcGIS) Mason County

2011 2011 2002 2011 2009 2010 2012 2010 2007 2011 2011 2004 2011 2011 2009 2011 2011 2006

22.5 22.5 cm 22.5 cm cm cm cm cm 22.5 22.5 cm 22.5 22.5 22.5 22.5 22.5 cm

20 30 20 25 20 15

15

15

cm cm cm

cm cm cm cm cm cm cm

Area and perimeter of salt marsh islands and blind tidal channels (channels with a downstream but no upstream connection to another water body) were calculated with a GIS. Island area did not include the area of the tidal channels draining the islands to avoid spurious correlations between island area and channel area. Channel length was calculated as half of the channel perimeter and included the channel main stem and all tributaries. Channel count was the number of channel outlets on an island's perimeter and was counted manually. Magnitude was the count of first-order channels (Strahler, 1957) in a channel network, and provided an indication of network complexity; this was also counted manually, with repeated counts for very large networks to minimize error. Channel surface area, length, and magnitude were summed over all channels draining an island to provide a collective total for each island. They were also calculated and analyzed for the largest channels draining each island. The number of tributary channels to the main stem of each island's largest channel was another indication of network complexity and was also counted manually. The channel metrics used in this paper were chosen because of their utility for engineers, planners, and biologists involved in salt marsh channel habitat restoration. For example, tidal channel surface area and length can be used by biologists to estimate potential fish abundance in the restored habitat by combining these metrics with fish density values from reference salt marsh channels standardized by channel length or surface area. Channel count is a metric that is necessary for engineers to determine how many dike breaches should be made to provide channel outlets (if the dikes are not removed entirely). Too few breaches likely limit fish accessibility to the restoration site. The surface area and length of the largest channel draining a salt marsh island could be useful to biologists to determine whether large

Fig. 2. Detail of the South Fork Skagit Delta, showing photo interpretation. Two salt marsh islands are shown in their entirety (A and B), bounded by river distributaries (dark in photo, white in GIS map; arrows show river flow direction). Dendritic tidal channels draining the salt marsh islands (black in the map) show more detail in the map than visible in the photo because they were digitized at higher resolution than the image shown. Island A has 19 dendritic channel outlets; Island B has 2. Various shades of gray in the photo correspond to different dominant plant species on the salt marsh islands; darkest gray = cattail (Typha angustifolia), white and light gray = predominance of sedge (Carex lyngbyei); other species are also present but are more difficult to distinguish in this gray-scale depiction of the true color photos.

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predatory fish might be present in the restored channel network (fish size is correlated with channel size, especially depth) or to provide guidance to engineers in excavating at least the largest channel draining a site. Likewise, if engineers chose to excavate tidal channels, they often want to know how many tributaries the mainstem of their excavated channel should have. Engineers rarely, if ever, excavate channel networks in their entirety, but they often excavate “starter” channels to accelerate channel development and speed the occupation of a site by fish and other aquatic organisms. Channel magnitude is probably the least necessary metric for engineers, planners, or biologists, but it is easy to calculate and was provided as a simple indication of network complexity. Among the aerial photos, pixel size ranged from 15 to 30 cm. To address the possibility that photo resolution could affect channel scaling patterns, two sensitivity experiments were performed. First, channel scaling relationships derived from 90-cm pixel historical (1937, 1956, 1991) black and white aerial photos for the South Fork (SF) Skagit Delta salt marshes were compared to those derived from 15-cm pixel 2004 false-color infra-red aerial photos. Second, channel scaling for a random selection of SF Skagit Delta salt marsh islands observed in the 2004 photo was compared against the same set of salt marsh islands with all 1st-order channels removed. This was intended to mimic the case where photo resolution differences primarily affect detection of 1st-order channels. 3.3. Statistical analysis Island area and dependent variables describing channel geometry were log-transformed to equalize variance in the residuals and fit power functions in a linear regression. Slopes of the fitted logtransformed regression lines are equal to the exponents of the power functions, i.e., the scaling exponents. Regression lines were compared among study site locations by analysis of covariance (ANCOVA) using model I regression (Zar, 1984). Model I regression was appropriate firstly because measurement error for island area was low compared to dependent channel metrics, i.e., island boundaries were easy to distinguish while small channels were comparatively more difficult to distinguish depending on vegetation cover and photo quality; and secondly because there was a theoretical basis for a causal link between independent and dependent variables, i.e., salt marsh area affects the amount of tidal prism available to maintain channel form (Sokal and Rohlf, 1995). ANCOVA tested first for homogeneity of regression slopes (i.e., the null hypothesis that all regressions had the same slope). When regression slopes were not significantly different amongst study sites, a common regression slope was calculated from the ANCOVA (Zar, 1984). Because an ANCOVA was performed for each of nine planform channel metrics, a Bonferroni approximation was used to avoid inflated risk of Type I inference error, resulting in a critical alpha of 0.005 for the ANCOVAs (0.01 for the four resolution sensitivity tests). Given homogeneous regression slopes, ANCOVA next tested for homogeneity of regression intercepts. Following this second test, stepwise multiple regression was used to explore potential causes of observed differences amongst study sites in the y-intercepts of parallel regression lines. Tidal range, storm significant wave height, and river drainage basin area were the candidate explanatory independent variables. Tidal range was chosen because it could affect tidal prism and therefore channel geometry; storm significant wave height was chosen because a previous study comparing the North Fork (NF) and SF Skagit deltas suggested this could be a factor influencing channel geometry (Hood, 2007a); river drainage basin area was chosen because it is correlated with river discharge, sediment discharge, and sediment grain size (Orton and Reading, 1993) which could all potentially affect tidal channel geometry. Tidal range data were obtained from NOAA tidal stations nearest each study site (http://tidesandcurrents.noaa.gov/tides06/tab2wc1b. html). Almost all tidal stations were within 7 km of the study sites, with many within 3 km; one exception was the SF Skagit Delta which

101

was 17 km from the nearest NOAA station. Storm significant wave height data were from Finlayson (2006). The Nooksack and Lummi deltas were not included in Finlayson (2006), so wave heights for these sites were estimated by regression of Finlayson's wave heights and measured southerly fetch (significant winter storms in Puget Sound are predominantly southerly). For all statistical tests other than the ANCOVAs, the criterion for statistical significance was p b 0.05. Residual plots were routinely examined for all regression analyses to assure residuals were random. 4. Results 4.1. Sensitivity to photo resolution Testing the sensitivity of channel metrics to photo resolution (see Section 3.2) produced similar results for both experiments (Fig. 3). The 1937, 1956, and 1991 channel networks exhibited consistently similar scaling relationships even though significant salt marsh progradation and new channel development occurred during this time (e.g., Hood, 2006). ANCOVA indicated scaling exponents for channel count, total channel length, total channel surface area, and total channel magnitude were unaffected by photo resolution (F3,113 ranging from 0.200 to 0.983 for the four tests comparing the 90-cm pixel historical photos to the 15-cm pixel 2004 photo), but regression intercepts were generally lower for the lower resolution photos (p b 0.01 for total length, p b 0.001 for channel count, p bb 0.001 for total magnitude) and for channel networks where 1storder channels were culled. The exception was scaling of channel surface area, which was insensitive to photo resolution (F 3, 116 = 0.890), because 1st-order channels contribute appreciably to channel network length but have minimal surface area. In the results presented below, the geographical variation in scaling patterns related to channel surface area (insensitive to photo resolution) is similar to that for other planform channel network parameters (sensitive to significant differences in photo resolution), indicating that modest variations in aerial photo scale (15 cm to 30 cm) between different study sites were not significant. 4.2. Puget Sound scaling patterns The greatest number of reference-quality salt marsh islands were found in the largest deltas, the SF Skagit delta (n = 48) and the NF Skagit delta (n = 27). Other deltas had ten or fewer such salt marsh islands. Deltas with fewer than 4 salt marsh islands (Dewatto and Tahuya) were not included in statistical analysis, but they were included in graphical displays of results to ascertain their consistency with other sites. Pocket estuaries typically had three or fewer salt marsh islands behind a coastal barrier shoreform, so they were lumped together and treated as a distinct class containing 17 salt marsh islands. This resulted in 14 study sites being included in statistical analysis. Linear regression of log-transformed data explained considerable data variation for each study site when planform channel metrics were regressed on salt marsh island area. R2 values typically (83% of the regressions) ranged from 0.60 to 0.99, and often (45% of regressions) from 0.80 to 0.99. Regressions with R2 b 0.60 were most common for channel count (6 of 14 sites) and outlet width (5 of 14 sites), and for the pocket estuary group (5 of 9 planform metrics). Separate ANCOVAs for each planform metric indicated the 14 study sites had statistically indistinguishable regression slopes for all planform metrics except outlet width of the largest channel draining a salt marsh island (Table 2). However, regression elevations were significantly different for at least some sites (Fig. 4). ANCOVA-derived regression intercepts were regressed against the possible explanatory variables of tidal range, storm-significant wave height, and river drainage basin area (pocket estuaries were omitted from this analysis). However, correlation (R) between tidal range and

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Fig. 3. Sensitivity tests of aerial photo resolution effects on planform channel geometry. Left-side frames compare 15-cm pixel resolution 2004 infra-red photos (+, solid line) of the SF Skagit Delta salt marshes with 90-cm pixel resolution black and white photos from 1937 (white diamonds, dashed line), 1956 (white circles, dashed line), and 1991 (gray squares, solid line). Right-side frames compare a random selection of SF Skagit Delta salt marsh islands from 2004 where the 1st-order channels have been either culled (black squares) or not (white circles). Note differences in y-axis scale between the right- and left-side frames.

storm significant wave height was 0.80 (p b 0.05) without the Lummi Delta and 0.44 (not statistically significant) with (Fig. 5). Thus, stepwise regression was done with and without the Lummi Delta to better interpret the results. Without the Lummi Delta (i.e., with high autocorrelation between tidal range and wave height) tidal range was the only significant predictor of ANCOVA-derived regression elevations for six of eight planform channel metrics (Table 3). For channel count and total channel length tidal range was the principal predictor with

significant additional predictive contribution from wave height. When the Lummi Delta was included in the analysis wave height became the principal predictor for almost all channel metrics, with tidal range providing a secondary contribution. Channel count, length, surface area, and magnitude were positively correlated with tidal range and negatively correlated with wave height. Several sites with contrasting wave exposure but similar tidal range were examined more closely to further explore the effect of wave

Table 2 ANCOVA summary. Bonferroni compensation for multiple ANCOVAs resulted in critical p = 0.005. Except for Total Area, tests for homogeneity of regression slopes did not exceed even the standard threshold of 0.05. When slopes did not differ significantly, a common slope was calculated from the ANCOVA with lower and upper 95% confidence limits (LCL and UCL). Finally, regression intercepts were tested for homogeneity (last two columns). Planform metric

F13,141 (slopes)

p

Common slope

LCL

UCL

F13,154 (intercepts)

p

Channel count Total length Total area Total magnitude Largest length Largest area Largest magnitude Tributaries to largest channel Largest outlet width

0.913 1.079 2.264 0.736 0.849 2.000 1.173 0.978 3.174

NS NS NS NS NS NS NS NS b0.001

0.61 1.24 1.52 1.00 1.07 1.40 0.85 0.65 –

0.48 1.10 1.25 0.81 0.89 1.03 0.64 0.53 –

0.73 1.38 1.78 1.20 1.24 1.76 1.05 0.78 –

6.304 13.813 11.618 10.663 7.104 6.697 6.672 7.007 –

bb0.001 bb0.001 bb0.001 bb0.001 bb0.001 bb0.001 bb0.001 bb0.001 –

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Fig. 4. Scaling of planform channel geometry with salt marsh island area for Puget Sound river deltas and pocket estuaries. For graphical clarity, regression lines are shown only for representative systems (Nisqually Delta with the largest tidal range, gray dashed line; Skagit SF Delta with the largest sample size, gray solid line; NF Skagit with the second-largest sample size, black solid line; Nooksack Delta with the smallest tidal range, black dashed line). ANCOVA found no differences in regression slopes, but y-intercepts differed significantly.

Fig. 5. Correlation of tidal range and storm significant wave height for Puget Sound river deltas (gray circles, solid regression line, R = 0.80, p b 0.05), contrasted with inclusion of the Lummi Delta (white circle, dashed regression line, R = 0.44, NS).

exposure on tidal channel planform geometry. The Nooksack Delta has a 22-km southerly fetch, while the adjacent Lummi Delta has a 5.5-km fetch. Remnant salt marshes fringe the dikes protecting the agriculturally reclaimed Lummi Delta. Two salt marsh islands are located within a seaward ring dike that protects an aquaculture facility, while the other salt marsh islands are either within the lee of the aquaculture facility or are shielded from waves by a palisade, an historical means of protecting the agricultural dikes from wave attack. Thus, Lummi Delta salt marsh islands are exposed to a short fetch and further protected from wind by the sheltering influence of the aquaculture facility and an historical palisade. Regression analysis indicates the waveprotected Lummi Delta has 10-fold greater total channel surface area, total channel length, and total channel magnitude than the waveexposed Nooksack Delta (Fig. 6; p bb 0.001 for all). There was no difference in channel count. In the Hamma-Hamma Delta (Fig. 7), salt marshes are formed primarily by deltaic sedimentation, but the southern margin of the delta is a barrier beach spit formed by south-to-north longshore sediment transport, with salt marsh sheltered leeward. In the 1950s the

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Table 3 Stepwise multiple regression of ANCOVA elevations (y-intercepts) on wave height (Finlayson, 2006), drainage basin area (Czuba et al., 2011), and tidal range (NOAA). Dependent parameters are in the first column; significant predictors are in the second and fifth columns. Analyses were done with and without the Lummi Delta to highlight the effect of storm significant wave height when inclusion of the Lummi Delta reduced autocorrelation between wave height and tidal range. Without Lummi Delta

With Lummi Delta

Parameter

Predictors

p-value

R

Predictors

p-value

R2

Channel count

Tidal range, wave height Tidal range

b0.002

0.77

b0.0007

0.77

b0.0005

0.72

b0.002

0.72

b0.0007

0.81

b0.0005

0.79

Total magnitude

Tidal range, wave height Tidal range

b0.0004

0.74

b0.002

0.84

Area of largest channel Length of largest channel

Tidal range Tidal range

b0.002 b0.001

0.65 0.68

b0.004 b0.002

0.56 0.72

Magnitude of largest channel

Tidal range

b0.0005

0.73

b0.004

0.82

Tributary count of largest channel

Tidal range

b0.0006

0.71

Tidal range, wave height Wave height, tidal range Wave height, tidal range Wave height, tidal range Wave height Wave height, tidal range Wave height, tidal range Wave height, tidal range

b0.003

0.71

Total channel area Total channel length

principal Hamma-Hamma River distributary was anthropogenically diverted from its course north of the spit and straightened to bisect the spit and its leeward salt marsh. Training levees were built along the rerouted river to maintain its course on the southern margin of the delta and direct riverine sediments away from tidal flats used to culture oysters. Thus, the amputated tip of the bisected salt marsh is

2

sheltered from waves by the natural sandy barrier and from the river by a high training levee, forming a protective barrier enclosing two salt marsh islands. The channel geometry of the smaller barrier salt marsh island could not be statistically compared to that of the deltaic salt marsh islands because it was outside the size range of the deltaic islands, but consistent with the Nooksack-Lummi comparison, the

Fig. 6. Top Frame: Lummi and Nooksack delta salt marsh islands (open white polygons). Sandy tidal flats are equally extensive in both deltas; photos comprising the photo mosaic were taken at different tidal stages. A dike encloses an aquaculture facility in the Lummi Bay tidal flats. Bottom frame: Contrasting channel geometry between the Lummi (two-toned diamonds) and Nooksack (gray squares) deltas.

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Fig. 7. Top Frame: Location of seven salt marsh islands in the Hamma-Hamma Delta (1–7) and two barrier salt marsh islands (a, b). The barrier salt marshes are bounded by a seaward sandy spit and a river levee. Bottom frame: Contrasting channel geometry between Hamma-Hamma delta salt marshes (#) versus Hamma-Hamma barrier salt marshes (dotted circles). Statistical testing of differences was applied only to the larger barrier salt marsh island because the smaller was outside the size range of the delta salt marshes.

larger barrier salt marsh island had tidal channels with 3- to 4-fold greater total channel length, total channel area, and total channel magnitude than those of the deltaic salt marsh islands (p b 0.05 in all cases). There was no difference in channel count. Finally, the NF and SF Skagit deltas share similar tidal ranges, but the NF delta at the northern edge of Skagit Bay has an 11-km southerly fetch subject to strong winter storms. The SF delta at the southeastern edge of the bay has a southwesterly fetch of 3–6 km subject to mostly mild summer winds (Raubenheimer et al., 2013). The comparatively sheltered SF delta has generally twice the channel count, total channel area, and total channel magnitude as the wave-exposed NF delta, but both have similar total channel length (Fig. 4; Hood, 2007a). Further decomposition of the NF salt marsh islands into a windward group directly adjacent to Skagit Bay, a leeward group sheltered from the bay by intervening salt marsh, and an intermediate group shows that even at this scale the leeward, most sheltered salt marsh islands have tidal channels with 2- to 4-fold greater channel surface area, length, and magnitude than the windward, most wave-exposed salt marsh islands (Fig. 8; p bb 0.001 for all). There was no difference between groups in channel count or the outlet width of the largest channel draining a salt marsh island. 5. Discussion Uniform allometric scaling exponents among different river deltas reflect similar salt marsh- and channel-forming processes throughout the region. The blind tidal channels that drain salt marsh islands in

these river deltas have been shown to be the remnants of historical delta distributaries (Hood, 2006). A distributary divides two salt marsh islands, but after distributary senescence, through shoaling and closure at one end, the formerly separate islands are joined and become one larger island drained by this new blind channel. This process can happen repeatedly so that a salt marsh island and its blind tidal channel network grow recursively. A simple simulation of this recursive process has reproduced the empirically observed scaling exponents (Hood, 2007a). Nevertheless, significant variation in allometric intercepts reflects spatial heterogeneity in tidal channel geometry. Tidal range was positively correlated with tidal channel size and complexity while storm-significant wave height was negatively correlated, but wave height was generally the stronger predictor of the two. Positive correlation of tidal channel count and total length or total area with tidal range is consistent with numerical modeling experiments in idealized systems (Jiménez et al., 2014a,b). However, the influence of waves on tidal channel geometry has been rarely explored. Typically, modeling efforts have shown that the wave environment controls the transition between two distinct stable states—that of a low-elevation, unvegetated tidal flat characterized by high wave energy or that of a high-elevation salt marsh characterized by low wave energy (Fagherazzi et al., 2006; Mariotti and Fagherazzi, 2013a; Hunt et al., 2015). However, the empirical results presented here suggest more subtle wave influences on tidal channel geometry are also possible. Other potential influences on channel geometry that were not examined in this empirical study include salt marsh elevation, sediment grain size, and vegetation density,

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Fig. 8. Channel geometry for leeward, windward, and intermediate salt marsh islands of the NF Skagit Delta.

which modeling studies indicate can influence channel network geometry (cf., Mariotti and Fagherazzi, 2013b; Jiménez et al., 2014a). Nevertheless, salt marsh area, tidal range, and wave environment explained a large proportion of the variation in tidal channel geometry seen in Puget Sound. Evidence for the importance of wave environment to tidal channel geometry extends beyond the multiple regression results. Four case studies (Nooksack vs. Lummi salt marshes, Hamma-Hamma deltaic vs. barrier salt marshes, NF vs. SF Skagit salt marshes, and windward vs. leeward NF Skagit salt marshes) provide consistent results, indicating wave-sheltered locations have larger tidal channels than do waveexposed locations. The influence of wave energy on channel geometry is likely mediated through sediment supply. Wave-sheltered areas receive less wave-borne sediment so salt marsh accretion is less (Lagomasino et al., 2013). Slow marsh accretion rates coupled with sea-level rise can lead to channel widening and lengthening through erosion (D'Alpaos et al., 2007; Kirwan et al., 2008). The wavesheltered Lummi salt marshes, abandoned by the Nooksack River through an historical avulsion, are isolated from riverine and waveborne sediment supply and show a 10-fold greater channel size relative

to their wave-exposed and river-supplied sister, the Nooksack Delta. In the Hamma-Hamma delta the principal distributary has been rerouted away from the delta, but a secondary distributary, 1/3 the width of the primary distributary, continues to run through the northern half of the delta. Thus, the Hamma-Hamma Delta salt marshes have experienced significantly reduced riverine sediment supply and consequently have large tidal channels compared to other Puget Sound systems. The Hamma-Hamma Delta consistently ranked second in channel size behind the Nisqually Delta, which at 4.4 m has the largest tidal range of all the Puget Sound deltas, while the Hamma-Hamma has an unremarkable tidal range of 3.5 m. Consequently, with diminished but not completely eliminated riverine sediment supply, there is only a 3- to 4-fold difference in tidal channel size between the Hamma-Hamma barrier salt marshes and delta salt marshes. In contrast, the North and SF Skagit deltas both experience high riverine sediment supply, so the principal contrast is in wave environment and the differences in tidal channel size are only 2-fold for some channel metrics and nonexistent for others (see also Hood, 2007a). Finally, the leeward salt marshes of the NF Skagit Delta are located near the margins of the mainstem NF distributary. The windward salt marshes are supplied by

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secondary NF distributaries less than 1/4 the width of the NF mainstem. Thus, it is likely the leeward salt marshes receive greater riverine sediment supply than do the more distal windward salt marshes. The net differences in wave- and river-borne sediments leads to a 2- to 4-fold difference in channel size and complexity between the windward and leeward salt marshes. Thus, there appears to be a consistent relationship between the relative amounts of wave- and river-borne sediments in these four case studies and the relative differences in tidal channel size between wave-exposed and wave-sheltered salt marshes. Confirmation of this hypothesis will require measurement of suspended sediment concentrations and salt marsh accretion rates in these locations. Nevertheless, the apparent accretion deficit suggested by comparatively larger tidal channels in the SF Skagit delta compared to the NF delta is consistent with greater declines in historical salt marsh progradation rates in the SF compared to NF delta, with recent decades showing net salt marsh erosion in the SF delta (Hood et al., in press). Thus, the results presented here suggest that sediment-challenged salt marshes in Puget Sound are already showing impacts from 20th Century sea-level rise. These results also suggest observed allometric relationships reflect not only natural controls on tidal channel form but also anthropogenic constraints on system controls, such as sediment supply. In addition to the Lummi and Hamma-Hamma, other deltas with known anthropogenic constraints on river flow or sediment supply include the Nisqually (an upstream dam traps sediments), the Skokomish (40% flow diversion to hydropower projects; Jay and Simenstad, 1996), and the Quilcene (33% flow diversion and dam). Perhaps not coincidentally, the Nisqually, Hamma-Hamma, Quilcene, and Skokomish were ranked 1st through 4th in channel size (as indexed by the mean rank of their allometric intercepts), respectively, of the 13 river delta systems examined. The Lummi Delta does not appear among the top four in this list, in spite of sediment starvation through river abandonment, probably because it experiences the lowest tidal range in Puget Sound, a meter less than most other sites. However, the channel geometry of coastal barrier salt marshes, presumably similarly sediment deprived relative to fully functional river systems, is comparable to that of deltas experiencing the greatest amount of anthropogenic sediment diversion. Restoration of system-scale natural processes through relaxation of anthropogenic constraints on sediment supply could likely change allometric patterns within the affected deltas and make their salt marshes more resilient to sea-level rise. 6. Conclusions (i) For almost every planform channel metric examined, allometric scaling exponents were uniform among all Puget Sound salt marsh systems. The one exception was outlet width of the largest channel draining a salt marsh island. However, the y-intercepts of the scaling relationships were geographically variable and correlated with tidal range (positively) and wave energy (negatively). (ii) Closer examination of several paired sites with contrasting wave exposure but similar tidal range confirmed that tidal channel networks were generally proportionally larger in wave-sheltered salt marshes. Furthermore, the degree to which paired sites contrasted in connectivity to sediment sources (relative isolation from rivers and protection from waves) was qualitatively correlated with the degree to which their channel networks differed in size. Additionally, river systems with known anthropogenic reductions in river sediment delivery, through dam construction and flow diversion, also tended to have larger channel networks. (iii) A likely explanation for the observed relationship between tidal channel network size and wave energy (and degree of river connectivity) is that low sediment supply leads to slow salt marsh accretion relative to sea-level rise, which in turn leads to erosion and expansion of tidal channel networks. However, this

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explanation needs to be confirmed by field measurement of suspended sediments and accretion rates, especially during storms. Acknowledgments Funding was provided by the Washington Department of Fish and Wildlife, Estuary and Salmon Restoration Program (contract # 121872). Aerial photographs were provided by Gerry Gabrisch (Lummi Nation), Jennifer Cutler (Nisqually Tribe), Todd Zackey (Tulalip Tribe), Ron Barnes (Skokomish Tribe), Bruce Jones (Northwest Indian Fisheries Commission), Luke Cherney (Hood Canal Coordinating Council), Jeremy Lucas (Hood Canal Salmon Enhancement Group), Diane Mark (Kitsap County), and Matt Stull (Mason County). Permission to access restricted property for ground-truthing was provided by the Lummi Nation, the Nisqually National Wildlife Refuge, and private property owners in the Hamma-Hamma Delta. Karen Wolf and Kate Ramsden (SRSC) assisted in GIS channel digitization that was later edited by the author. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.geomorph.2014.11.009. References Atkinson, P.W., Crooks, S., Drewitt, A., Grant, A., Rehfisch, M.M., Sharpe, J., Tyas, C.J., 2004. Managed realignment in the UK—the first 5 years of colonization by birds. Ibis 146, 101–110. Bortelson, G.C., Chrzastowski, M.J., Helgerson, A.K., 1980. Historical Changes of Shoreline and Wetland at Eleven Major Deltas in the Puget Sound Region, Washington. U.S. Geological Survey, Atlas HA-617. Bottom, D.L., Simenstad, C.A., Burke, J., Baptista, A.M., Jay, D.A., Jones, K.K., Casillas, E., Schiewe, M.H., 2005. Salmon at River's End: The Role of the Estuary in the Decline and Recovery of Columbia River Salmon. US Department of Commerce (NOAA Tech Memo, NMFS-NWFSC-68). Church, M., Mark, D.M., 1980. On size and scale in geomorphology. Prog. Phys. Geogr. 4, 342–390. Coats, R.N., Williams, P.B., Cuffe, C.K., Zedler, J.B., Reed, D., Waltry, S.M., Noller, J.S., 1995. Design Guidelines for Tidal Channels in Coastal Wetlands. Waterways Experiment Station, Report 934. U.S. Army Corps of Engineers, Vicksburg, Mississippi. Colclough, S., Fonseca, L., Astley, T., Thomas, K., Watts, W., 2005. Fish utilisation of managed realignments. Fish. Manag. Ecol. 12, 351–360. Collins, B.D., Montgomery, D.R., Sheikh, A.J., 2003. Reconstructing the Historical Riverine Landscape of the Puget Lowland. In: Montgomery, D.R., Bolton, S.M., Booth, D.B., Wall, L. (Eds.), Restoration of Puget Sound Rivers. University of Washington Press, Seattle, pp. 79–128. Cooper, N.J., Cooper, T., Burd, F., 2001. 25 years of salt marsh erosion in Essex: implications for coastal defence and nature conservation. J. Coast. Conserv. 7, 31–40. Czuba, J.A., Magirl, C.S., Czuba, C.R., Grossman, E.E., Curran, C.A., Gendaszek, A.S., Dinicola, R.S., 2011. Sediment Load from Major Rivers into Puget Sound and its Adjacent Waters. U.S. Geological Survey Fact Sheet 2011–3083. D'Alpaos, A., Lanzoni, S., Marani, M., Rinaldo, A., 2007. Landscape evolution in tidal embayments: modeling the interplay of erosion, sedimentation, and vegetation dynamics. J. Geophys. Res. 112, F01008. http://dx.doi.org/10.1029/2006JF000537. Fagherazzi, S., Carniello, L., D'Alpaos, L., Defina, A., 2006. Critical bifurcation of shallow microtidal landforms in tidal flats and salt marshes. Proc. Natl. Acad. Sci. 103, 8337–8341. Finlayson, D., 2006. The geomorphology of Puget Sound beaches. Seattle: Washington Sea Grant Program, Puget Sound Nearshore Partnership Report No. 2006–02. French, J.R., Stoddart, D.R., 1992. Hydrodynamics of salt marsh creek systems: implications for marsh morphological development and material exchange. Earth Surf. Process. Landf. 17, 235–252. Hood, W.G., 2002. Application of landscape allometry to restoration ecology. Restor. Ecol. 10, 213–222. Hood, W.G., 2006. A conceptual model of depositional, rather than erosional, tidal channel development in the rapidly prograding Skagit River Delta (Washington, USA). Earth Surf. Process. Landf. 31, 1824–1838. Hood, W.G., 2007a. Scaling tidal channel geometry with marsh island area: a tool for habitat restoration, linked to channel formation process. Water Resour. Res. 43, W03409. http://dx.doi.org/10.1029/2006WR005083. Hood, W.G., 2007b. Landscape allometry and prediction in estuarine ecology: linking landform scaling to ecological patterns and processes. Estuar. Coasts 30, 895–900. Hood, W.G., Veldhuisen, C., Grossman, E.E., 2015. Assessing tidal marsh vulnerability to sea-level rise in the Skagit River Delta. Northwest Science (in press). Hunt, S., Bryan, K.R., Mullarney, J.C., 2015. The influence of wind and waves on the existence of stable intertidal morphology in meso-tidal estuaries. Geomorphology 228, 158–174.

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