Environ. Res. Lett. 14 (2019) 124073 https://doi.org/10.1088/1748-9326/ab5a94

LETTER Understanding tidal marsh trajectories: evaluation of multiple OPEN ACCESS indicators of marsh persistence

RECEIVED 25 July 2019 Kerstin Wasson1,8 , Neil K Ganju2 , Zafer Defne2 , Charlie Endris1, Tracy Elsey-Quirk3 , REVISED Karen M Thorne4 , Chase M Freeman4 , Glenn Guntenspergen5 , Daniel J Nowacki6 and 31 October 2019 Kenneth B Raposa7 ACCEPTED FOR PUBLICATION 22 November 2019 1 Elkhorn Slough National Estuarine Research Reserve, Royal Oaks, CA 95076, United States of America 2 PUBLISHED Woods Hole Coastal and Marine Science Center, U.S. Geological Survey, United States of America 3 18 December 2019 Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge, LA 70803, United States of America 4 Western Ecological Research Center, U.S. Geological Survey, United States of America 5 Patuxent Wildlife Research Center, U.S. Geological Survey, United States of America Original content from this 6 Pacific Coastal and Marine Science Center, U.S. Geological Survey, United States of America work may be used under 7 the terms of the Creative Narragansett Bay National Estuarine Research Reserve, United States of America Commons Attribution 3.0 8 Author to whom any correspondence should be addressed. licence. E-mail: [email protected], [email protected], [email protected], [email protected], [email protected], kthorne@usgs. Any further distribution of _ this work must maintain gov, [email protected], glenn [email protected], [email protected] and [email protected] attribution to the author(s) and the title of Keywords: resilience, sea-level rise, tidal marsh, wetland, management the work, journal citation Supplementary material for this article is available online and DOI.

Abstract Robust assessments of ecosystem stability are critical for informing conservation and management decisions. Tidal marsh ecosystems provide vital services, yet are globally threatened by anthropogenic alterations to physical and biological processes. A variety of monitoring and modeling approaches have been undertaken to determine which tidal marshes are likely to persist into the future. Here, we conduct the most robust comparison of marsh metrics to date, building on two foundational studies that had previously and independently developed metrics for marsh condition. We characterized pairs of marshes with contrasting trajectories of marsh cover across six regions of the United States, using a combination of remote-sensing and field-based metrics. We also quantified decadal trends in marsh conversion to mudflat/open water at these twelve marshes. Our results suggest that metrics quantifying the distribution of vegetation across an elevational gradient represent the best indicators of marsh trajectories. The unvegetated to vegetated ratio and flood-ebb sediment differential also served as valuable indicators. No single metric universally predicted marsh trajectories, and therefore a more robust approach includes a suite of spatially-integrated, landscape-scale metrics that are mostly obtainable from remote sensing. Data from surface elevation tables and marker horizons revealed that degrading marshes can have higher rates of vertical accretion and elevation gain than more intact counterparts, likely due to longer inundation times potentially combined with internal recycling of material. A high rate of elevation gain relative to local sea-level rise has been considered critical to marsh persistence, but our results suggest that it also may serve as a signature of degradation in marshes that have already begun to deteriorate. This investigation, with rigorous comparison and integration of metrics initially developed independently, tested at a broad geographic scale, provides a model for collaborative science to develop management tools for improving conservation outcomes.

Introduction have been developed, but most investigations only use one or a few, and multiple stability metrics have rarely Globally, scientists conduct assessments of ecosystem been correlated to each other or integrated (Kéfi et al stability to inform policy and management (Sato and 2019). Yet in order to direct monitoring efforts and Lindenmayer 2018). Many different stability metrics provide vital guidance to decision-makers, it is critical

© 2019 The Author(s). Published by IOP Publishing Ltd Environ. Res. Lett. 14 (2019) 124073 to evaluate multiple assessment alternatives, and regional characterizations (e.g. of sea-level trends). potentially integrate them to determine ecosystem The data from these monitoring efforts is used to para- condition (Donohue et al 2016). meterize mechanistic models of marsh dynamics The resilience of tidal marshes in the face of exter- (Craft et al 2008, Swanson et al 2014, Schile et al 2014, nal disturbances such as sea-level rise (SLR) and Byrd et al 2016). Some types of monitoring require coastal development is a concern to coastal managers relatively high investment, such as field measurements throughout the world. Tidal marshes provide a wide involving years of consistent repeat visits to detect range of ecosystem services, including nutrient and trends. Others can be accomplished with less effort, carbon sequestration, habitat provision, and wave such as analysis of aerial photographs. Ideally, coastal ( ) attenuation Mitsch et al 2015, Barbier 2019 . How- managers should invest in the monitoring that has the ever, natural and anthropogenic processes modify the greatest predictive power for the least investment vertical and lateral dynamics of tidal marshes, through necessary. It is still an open question, however, as to both physical and biogeochemical forcings. Physical which metrics will yield the greatest return on that forces, including wind-waves, tidal processes, and investment (Wiberg et al 2019). sediment supply play an important role in regulating Recently, Raposa et al (2016) developed and tested the hydro-geomorphic response of the tidal marsh threemulti-metricindicesbasedonasuiteoftentidal landscape (Fagherazzi et al 2012), while biogeochem- marsh metrics, specifically aimed at evaluating marsh ical processes play a large role in biomass production, soil stability, and vertical accretion (Morris et al 2002, persistence in the face of accelerations in the rate of SLR. Cahoon et al 2019). Animals can also exert strong All metrics were chosen based on previous research sug- influences on marshes, through herbivory and bur- gesting their importance to marsh response to SLR ( ) rowing (Stevenson et al 1985, Holdredge et al 2009). marsh elevation, sediment concentration, etc .This Human activities have altered many of these drivers of assessment approach was applied to 16 NERR marshes marsh integrity and persistence, such as eutrophica- and related resilience scores were used to recommend tion fueling rapid decomposition and resulting in management strategies. Independently, Ganju et al degradation (Deegan et al 2012), decreases in sediment (2013, 2015) linked sediment transport mechanisms supply essential to vertical marsh growth (Weston with marsh trajectory, demonstrating with a pair of sites 2014), and accelerating SLR rates (Kirwan and that intact marshes tend to import sediment, while Megonigal 2013). degrading marshes export sediment. That analysis was Management strategies to enhance tidal marsh per- extended to eight US sites (Ganju et al 2017),andafur- sistence include restoration of riverine sediment supply, ther link was quantifiedbetweenthenetsedimentbud- thin-layer sediment placement, drainage enhancement, get, the unvegetated to vegetated ratio (UVVR), and the shoreline protection, and invasive species management flood-ebb sediment differential. The implication in (Day et al 2007, Blum and Roberts 2009,Wigandet al Ganju et al (2017) was that marshes with high vegetative ) 2017,Thorneet al 2018 . Prioritizing restoration sites coverandlowopen-waterareatendtotrapandretain and identifying the most appropriate techniques sediment, while marshes that are losing plant cover will requires reliable metrics to aid managers in directing further lose sediment and convert to open water. resources to marshes that are vulnerable enough to Here, we jointly compare all metrics developed by require intervention, but not so threatened that invest- Raposa et al (2016) and Ganju et al (2017) to evaluate ment in them will be wasted. Managers also need to which best predict tidal marsh persistence, which we identify marshes that have the greatest chance of persis- define as the maintenance of vegetated marsh cover tence under future SLR conditions, so that they have within the current marsh footprint. Twelve metrics high priority for conservation to protect and sustain (and three multi-metric indices incorporating them) their areal extent and the processes necessary for their were calculated for pairs of geographically similar persistence. marshes with varying trajectories of marsh persistence In the United States, federal organizations includ- ing the National Oceanic and Atmospheric Adminis- in six different US regions. Univariate and multi- tration’s National Estuarine Research Reserve System, variate statistical approaches were employed to deter- the Department of the Interior’s US Geological Sur- mine which metrics, separately or jointly, best vey, US Fish and Wildlife Service and National Park correlated with marsh persistence, and whether this Service, as well as academic researchers have devel- varied across regions. The twelve metrics were also oped and monitored indicators of tidal marsh resi- related to actual decadal change in marsh vegetation at lience and persistence. These range from point each site to determine which best predicted observed measurements (e.g. of sediment accretion or marsh trajectories. This unprecedented analysis allows for elevation change at surface elevation tables) to esti- robust recommendations to coastal decision-makers mates at the landscape scale (e.g. sediment flux or on the highest priority metrics, and provides guidance habitat change assessed from aerial photographs) to for prioritizing future monitoring strategies.

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Figure 1. Location of study sites. Paired marshes in six regions were investigated; clockwise from the Northeast these are Narragansett Bay, Rhode Island (Coggeshall and Nag West), Barnegat Bay, New Jersey (Reedy and Dinner Point Creek), Chesapeake Bay, Maryland (Blackwater, Fishing Bay), Gulf coast (Grand Bay, Mississippi and Terrebonne Bay, Louisiana), southern California (Seal Beach, Pt. Mugu Lagoon), and central California (upper and lower Elkhorn Slough).

Methods Flood-ebb sediment differential The difference in suspended-sediment concentration Study sites (SSC) on flood and ebb tides has been shown to To determine which metrics best identify tidal marsh indicate the directionality of tidal marsh channel persistence, we examined a pair of marshes with sediment flux over time scales of months to years varying trajectories: one with a higher and one with a (Ganju et al 2017). It is intuitive that this sediment lower change in unvegetated to vegetated marsh ratio differential, defined as the mean SSC on flood tides (UVVR)(see below) within each region. There were a minus the mean SSC on ebb tides as determined by total of six focal regions (figure 1). Five marshes had velocity direction, is representative of whether a marsh been previously examined by Raposa et al (2016)(both channel imports or exports sediment: one expects that fl Narragansett Bay marshes, both Elkhorn Slough greater ood concentrations would result in sediment marshes; Grand Bay); six other marshes had been import. The sediment differential is preferable to previously examined by Ganju et al (2017)(Reedy mean SSC, a metric sometimes used to characterize Creek, Dinner Point Creek, Blackwater, Fishing Bay, marsh sediment availability, because it can indicate direction with negative values and because its absolute Point Mugu Lagoon, and Seal Beach). A description of value scales with mean SSC, combining flux direction- each site is in the supplemental information (SI) is ality and total sediment availability in a single metric available online at stacks.iop.org/ERL/14/124073/ (Nowacki and Ganju 2019). In this study we represent mmedia. SSC with turbidity as a proxy, given the linearity between the two parameters, and relatively consistent slope in areas dominated by fine sediment (Ganju et al MARS metrics 2007). Details on calculations are in the SI. The ten MARS (MArsh Resilience to SLR) metrics were specifically chosen because they reflect condi- UVVR tions affected by SLR and therefore represent potential Ganju et al (2017) identified the UVVR as an indicator indicators of tidal marsh persistence (Raposa et al of tidal marsh trajectory due to its relationship with 2016). The three vegetation metrics are all derived the marsh sediment budget. A stable tidal marsh, with from recent field assessments in a marsh. The five intact marsh plains and little deterioration tends to a metrics for elevation change, accretion, sediments, UVVR∼0.1. Values greater than this indicate a and tidal range are all from longer-term monitoring trajectory towards marsh plain deterioration, and over the most recent decade or coring, while the two increasing values correspond to increasing runaway SLR metrics are calculated from long-term NOAA tide marsh expansion (e.g. Mariotti 2016). Therefore, fl station data. Each metric can individually re ect SLR despite the UVVR being a snapshot in time, it indicates impacts, but similar metrics are also averaged into one the open-water conversion process; e.g. a high UVVR of five broader categories, which then are further translates to historical deterioration within a pre- integrated into the three multi-metric MARS indices, viously vegetated marsh plain. Conversely, a histori- thus providing opportunities to examine marsh per- cally stable marsh will show a low present-day UVVR. sistence in the face of SLR at three organizational levels For each marsh, UVVR was calculated using a (see tables 1 and S1). reclassification of NAIP imagery into a normalized

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Table 1. Summary of metrics and approaches used for each.

Category Metric Data needs

MARS metrics (Raposa et al 2016)

Marsh elevation Percent of marsh below MHW Frequency distribution of marsh elevations; estimate of mean distributions high water Percent of marsh in lowest third of Frequency distribution of marsh elevations plant distribution Skewness Frequency distribution of marsh elevations − Marsh elevation change Elevation change rate (mm yr 1) Time series data from surface elevations tables (SETs) − Sediment/accretion Short-term accretion rate (mm yr 1) Time-series data from marker horizons − Long-term accretion rate (mm yr 1) Soil cores for radiometric dating Turbidity(NTU) Mean turbidity from water quality sondes Tidal range Tidal range (m) Mean daily tidal range from water quality sondes − Sea-level rise Long-term rate of SLR (mm yr 1) Long-term data from NWLON station Short-term inter-annual variability in Inter-annual variability data from NWLON station water levels (mm)

Ganju et al (2017) metrics

Flood-ebb turbidity differential Mean suspended sediment concentrations on flood and ebb tides UVVR Relative area of vegetated marsh and unvegetated areas from aerial photographs

Observed change in vegetation

Decadal change in UVVR UVVR (see above) assessed at 2+points spanning ∼10 years Percent of marsh plain with vegetation Area of vegetated marsh divided by total marsh landscape area (vegetated+unvegetated) × 100 Decadal change in percent vegetated Change in above, assessed at 2+points spanning ∼10 year

difference vegetation index that allowed us to isolate [((unvegetated area()initial year/ vegetated area () initial year and quantify healthy marsh vegetation from bare mud -())unvegetated area()final year/ vegetated area () final year or water. Tidal marsh boundaries were clipped based /number of years between initial and final] ´ 10 on the wetland classification maps from the US Fish ()1 and Wildlife Service’s National Wetlands Inventory [(((vegetated area/ total area )´ 100 ) (NWI). Specifically, it included the boundaries of ()initial year () initial year -´((vegetated area/ total area ) 100 )) estuarine intertidal subsystem from NWI’s digitization ()final year () final year / ´ of historic maps (as early as 1970s) to recently collected number of years between initial and final] 10 aerial imagery, thus in areas of recent loss, the ()2 boundaries included unvegetated areas that had been formerly vegetated (see SI for details). Analyses To examine differences related to marsh trajectories, Observed vegetation change we calculated the percent difference among marsh For this study, we also evaluated the actual observed pairs within a region for each metric or index. We trends in vegetation occurring at each site, assessing subtracted the value for the site with a trajectory of marsh persistence trajectories by quantifying decadal higher marsh loss from the value for the site with change in two ways. First, we calculated change in higher marsh persistence. This difference was divided UVVR (equation (1)). We calculated this from two or by the largest absolute value for any of the 12 sites, then more data points over an approximate 10 year span. multiplied by 100 to convert this to a percent We found that rates of change were similar when difference. To assess which metrics/indices performed conducted by simply subtracting values approximately well, with a large difference in the expected direction a decade apart versus conducting a linear regression (lower marsh persistence members of the pair scoring with multiple points spanning a decade, though the lower), we averaged the values across all six regions, latter is likely to be more accurate (see SI). and counted the number of regions with negative value Second, we also calculated the change in percent of of <−10. the marsh area that was vegetated (vegetated/(unvege- In order to determine whether the 12 marshes tated+vegetated)) over the same period, using the group by higher versus lower persistence trajectories subtraction method (equation (2)). We present the based on the metrics we assessed, and which metrics raw value for percent vegetated as well as the change best correlate with this grouping, we conducted a value in the results, since the contrasts among sites in suite of related non-metric multi-dimensional scaling percent vegetated are stark. analyses using Primer v. 7.0 (Clarke et al 2014).We

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Figure 2. Summary of metrics and indices. MARS metrics, categories, and indices developed by Raposa et al (2016) are shown in the top three sections; two additional metrics developed by Ganju et al (2017) are shown in the fourth section. The assessment of actual marsh persistence trajectories is shown last, with the change in UVVR and change in percent of marsh with vegetation. The percent of marsh with vegetation is also shown to help describe these sites. Green indicates greatest marsh resilience, red lowest marsh resilience. Scoring is summarized in supplemental table 1. included all the metrics in this analysis, but omitted report Pearson’s correlation coefficient for all. These MARS categories and indices, because they are not analyses were conducted in R version 3.5.2 (R Core independent of the metrics. The assessment of Team 2018). observed decadal marsh trajectories was not included in the multivariate analyses, because it was intended to describe, not predict marsh persistence. Data were Results normalized to enable comparison between metrics with different scales. We created a Euclidean similarity Overall comparisons of scores and persistence matrix and visualized differences among higher versus The overall patterns of marsh metric scores were lower marsh persistence sites using a two-dimensional complex, and differences between paired sites classi- fi ordination plot and carried out an analysis of similar- ed as more versus less persistent were not immedi- (fi ) ity (ANOSIM) to test for differences by persistence and ately obvious gure 2 . The simplest expectation by region. We used similarity percentages (SIMPER) would be that more persistent marshes in each of the ( ) to further examine groupings and the metrics that best six pairs have high scores green and less persistent distinguished them. This unconstrained ordination marshes have low scores (red). Instead, differences analysis was followed by a discriminant analysis using between the higher versus lower persistence member canonical analysis of principal coordinates (CAP) of each pair were mixed, and not always occurring in because paired sites were determined within regions the expected direction. a priori (Anderson and Robinson 2003, Anderson and The scoring of the six pairs of sites according to the Willis 2003). Thus, the CAP procedure maximizes various metrics revealed strong contrasts among paired site differences rather than maximizing the regions and metrics (figure 2). Sites did not typically total variation among all sites. CAP was then used to score uniformly (low across all metrics or high across test for significant differences among regions and all metrics), but rather showed a mix of scores. Over- among higher marsh persistence versus lower sites. all, the Rhode Island sites had the greatest number of We also carried out univariate analyses (regres- low scores, though the UVVR scores were relatively sions) to examine the relationship between actual high. An opposite pattern was seen in central observed marsh cover trajectories (decadal change in California, where the lowest UVVR scores were found, UVVR and percent vegetated) and all individual but other scores were moderate. metrics and indices. We further conducted regressions The assessment of change in the UVVR that quan- of these variables to examine the relationship between tified the actual observed change in vegetated marsh vegetation distribution across an elevation gradient loss over the past decade showed high variation among and accretion and elevation change/SLR. We graphi- regions. It is clear that what counts as high marsh per- cally present only the significant relationships and sistence in one region may represent low persistence in

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Figure 3. Differences among more versus less persistent marshes. The difference in value for the metrics and indices shown in figure 2 was calculated for each pair of sites (subtracting the less persistent from the more persistent site). This difference was divided by the largest absolute value for any of the 12 sites, then multiplied by 100 to convert this to a percent difference. The first six data columns show the percent differences for each state. Negative numbers (shown in shades of red for values less than −10) represent cases where the less persistent marsh scored lower on a metric, as would be expected; positive numbers represented the reverse (shown in shades of green for values larger than 10). The next column shows the average difference among pairs. The final column is a count of the number of regions (out of 6 possible) in which the difference was −10 or more negative; this is one way of assessing whether this is a useful indicator of marsh persistence. Counts of 4 are shown in light gray, 5 in dark gray. another; for instance, the low persistence site in New Multivariate analyses Jersey scored higher than many high persistence sites The non-metric multi-dimensional scaling yielded a elsewhere, and the high persistence site in central Cali- robust ordination of sites, with visually apparent fornia scored lower than any low persistence site else- separation among marshes with higher versus lower where (figure 2). persistence, and significant separation in ANOSIM The assessment of percent difference in metric (figure 4). Region was a highly significant factor and scores between higher versus lower marsh persistence CAP ordination illustrated whether regional differ- sites also revealed differences among metrics ences overshadowed paired site differences in persis- (figure 3). Here, we expected the difference to be nega- tence metrics (figure S9). For many sites (e.g. southern tive, indicating that the lower persistence site scored California, Rhode Island, Mississippi/Louisiana) there lower on metrics than the paired site in the same was little separation between higher and lower persis- region, indicated by red shading. This was indeed the tence marshes, owing to regional similarities in metrics case for many metrics. However, for various inter- such as tidal range and SLR rates, and to greater related scores (accretion, marsh elevation change rate, regional differences in the metrics (figure S10). For turbidity), values were higher at the lower marsh per- example, CA-C and CA-S had a higher mean tidal sistence sites, indicating that sediment concentration range (1.6 m and 1.1–1.2 m, respectively) than all but or accretion was greater there. A tally of regions where one (RI-lower) of the other marshes. Relatively high the scores were more than 10% lower at the lower rates of relative SLR and short-term accretion seemed marsh persistence site identified that three MARS to influence the clustering of MS/LA marshes and metrics (all related to vegetation distribution), the higher persistence marshes in NJ and MD. Overall, associated MARS vegetation category average, and the accounting for the regional grouping of paired marshes, flood-ebb sediment differential all functioned as reli- marshes with trajectories of lower persistence were able indicators. significantly different than those with higher persistence

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Figure 4. Non-metric multi-dimensional scaling analyses.

Figure 5. Relationships between decadal change in UVVR and selected metrics. Change in UVVR was positively correlated with (A) Tidal range and (B) UVVR. Higher persistence sites are shown as blue points; lower persistence sites as red points. The Pearson correlation coefficient is shown.

(p=0.033; figure S9).ForsitessuchasMD,NJ,and metric Tidal Range (and related MARS category Tidal CA-C, marshes with lower persistence were clearly Range) and the UVVR metric showed significant different than those with higher persistence. positive relationships (figure 5). Examination of the A SIMPER analysis revealed that average squared color-coding by persistence in this figure makes clear, distance among sites within the higher persistence as noted earlier (figure 2) that scoring of higher versus grouping is much less than within the lower persis- lower persistence was relevant within a region, but tence grouping. The SIMPER analysis identified seven does not hold across regions (otherwise all the red metrics that jointly explained 67% of the difference in points in these figures would have been in the far right higher versus lower persistence, with each contribut- of the graphs). None of the metrics had a significant ing 9%–11% (figure 4). These are very similar to relationship with change in percent of vegetated metrics identified as important by comparing differ- marsh. ences among pairs (figure 3, final columns). We also conducted univariate analyses to more closely examine metrics that had showed unexpected Univariate analyses relationships: accretion, elevation change, and the Examination of the relationship between actual MARS ratio (elevation change/SLR) that were higher observed marsh trajectories (change in UVVR and in lower persistence marshes, on average (figure 3).We percent of vegetated marsh over the past decade) and correlated these metrics to two vegetation metrics, every individual metric, category and index revealed percent of marsh in the lower third of tolerance, and very few significant correlations (table S2). The MARS also the simple percent of the marsh area that is

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Figure 6. Relationships between selected metrics. Short-term accretion rate is plotted against percent of vegetation in the lower third of tolerance (A) and percent of marsh plain that is vegetated (B). MARS ratio is plotted against percent of vegetation in the lower third of tolerance (C) and percent of the marsh plain that is vegetated (D). Higher persistence sites are shown as blue points; lower persistence sites as red points. The Pearson correlation coefficient is shown. vegetated. Short-term accretion rate showed a sig- the importance of the elevational distribution of nificant positive relationship with the former vegetation in marsh dynamics (McKee and Patrick (figure 6(A)) but no relationship with the latter 1988, Morris 2006, Schile et al 2011, Kirwan and (figure 6(B)). A similar but slightly weaker pattern Megonigal 2013, Janousek et al 2016, Cole Ekberg et al emerged for the MARS ratio (figures 6(C) versus (D)) 2017). Robust numeric models of marsh response to and elevation change (not shown). SLR depend on identification of critical indicators of marsh persistence (Wiberg et al 2019), thus our results suggest that the elevational distribution of vegetation Discussion across the marsh landscape should be given particular attention in numeric models. Universal indicators of tidal marsh trajectories The sediment differential and UVVR also emerged Are there universal indicators that are closely asso- as relatively strong indicators of marsh trajectories ciated with persistence versus degradation of tidal across regions, with moderately high average differ- marshes across diverse geographies and marsh dyna- ences among marshes with higher versus lower persis- mics?Our results suggest that there are: the distribu- tence (figure 3) and high contributions to dissimilarity tion of vegetation across the elevational landscape of among these groups overall (figure 4). Our results thus the marsh is a reliable indicator of the marsh’s build on the earlier results (Ganju et al 2013, 2015, trajectory. The three metrics related to this (percent of 2017) highlighting the importance of these indicators. marsh below MHW, percent of marsh in lower third The five indicators that were most reliable are all spa- of vegetation tolerance, and skewness of the distribu- tially-integrated metrics that assess marshes at a land- tion of vegetation across elevations) had the highest scape scale, rather than point-measurements. Each of average difference among marshes with higher versus these single metrics outperformed the composite lower persistence across the six regions (figure 3), and multi-metric MARS indices (Raposa et al 2016). were the highest contributors in the expected direction in the multivariate analysis (figure 4). The average of all three of these metrics (the marsh elevation cate- Variability across marshes gory) was the only indicator that scored in the expected While we identified indicators of marsh trajectory that direction across all pairs of marshes (worse in the less responded consistently across regions, we also persistent marshes; figure 3). Thus, the distribution of detected substantial geographic variability. Narragan- vegetation across the elevational landscape appears to sett Bay marshes scored consistently low on MARS be the most robust indicator of marsh persistence. metrics, but relatively high on UVVR, while Elkhorn Marshes with the majority of the vegetation distrib- Slough marshes showed the reverse pattern (figure 2). uted low in the tolerance range degraded, while those For the six regions, five different metrics had the with vegetation high within the marsh landscape were largest relative difference among pairs (skewness in more stable. This supports earlier studies highlighting two regions, tidal range, percent of vegetation in top

8 Environ. Res. Lett. 14 (2019) 124073 third, sediment differential, and UVVR each in one resilience to SLR (Morris et al 2002, Fagherazzi et al region only). The rate of SLR also varied dramatically 2012). across regions, with highest rates experienced by Gulf In our study of paired marshes of higher versus coast and mid-Atlantic marshes (figures 2, S10). This lower persistence trajectories, however, these metrics suggests that evaluation of multiple metrics better related to marsh elevation change and sediment con- characterizes marsh trajectories at a broad, compara- centration showed the opposite relationship with per- tive scale than any single metric, and that assessments sistence as provided in conceptual models of vertical at regional scales may be more effective at identifying stability (e.g. Cahoon et al 2019). In 4 out of 6 of the marsh persistence trajectories than those at larger regions studied, marsh elevation change and indica- ( national or global scales. tors of sediment concentration turbidity and short- ) As such, we found that some metrics are effective term accretion rate were greater in the less persistent as relative, but not absolute indicators of marsh trajec- marsh, and the average value was greater in the lower (fi ) tories. In particular, UVVR was excellent at differ- persistence marsh across all sites gure 3 . The ( entiating the marshes with higher versus lower MARS-ratio ratio of marsh elevation change to local ) persistence within a region, but not across regions— SLR was also higher in low persistence marshes (fi ) Elkhorn Slough’s high persistence site scored worse on gure 3 . Elevation change and accretion rate also emerged as significant in the multivariate analysis—in the UVVR metric than all except one of the low persis- the reverse direction as anticipated. tence sites elsewhere (figure 2). This likely points to A tidal marsh that has already lost much vegetation inherent differences in a baseline, critical UVVR that may have more sediment mobilized through tidal and may be dependent on tide range, vegetation type, and wave processes, and loss of elevation may result in other regionally varying factors. increased accretion due to longer submergence times Ideally, the observed change in vegetation over (Morris et al 2002, Reed 2002, Wiberg et al 2019). the past decade would serve as a method of ground- Thus, for some marshes that have already lost sig- truthing the predictive value of different metrics. nificant areas of vegetation, sediment accretion and However, we found that regional differences such as subsequent elevation gain can be symptoms of those described above made this challenging. For instability (Ganju et al 2015), not drivers of stability. instance, our results suggest that sites with higher tidal This is why we found that many marshes with vegeta- ( ranges degrade faster counter to the general under- tion distributed lower in the tolerance range had ) standing that microtidal systems are at greatest risk , higher accretion and higher ratios of elevation gain to (fi ( )) fl but this result gure 5 A was strongly in uenced by local SLR (figures 6(A), (C)). This is apparently driven Elkhorn Slough marshes, that have high tidal ranges by the low elevation of the remaining marsh, since the and little recent SLR, yet are rapidly degrading. Geo- percent of the area that was vegetated did not show graphical variability thus made this sort of simple these relationships (figures 6(B), (D)). Marshes dis- regression analysis less valuable than our other tributed low in the elevation range are not likely to approaches to assessing marsh trajectories. Future recover despite high accretion rates: once they have work is needed to explore the degree to which tide lost significant elevation, simply tracking SLR is not range, biome, regional sediment supply, and extreme enough to convert a mudflat back into a marsh—the events account for differences between regions. Ulti- elevation gain would have to dramatically exceed SLR. mately, from a land management perspective, com- Greater elevation gain in degrading marshes was not a paring marsh parcels within the same geographic universal pattern however: at Narragansett Bay and region is the most practical approach given the scale Elkhorn Slough, the marshes with lower persistence and jurisdictional aspects of coastal land management had lower elevation gain rates, and lower ratios of ele- (i.e. refuge or state-by-state basis). vation gain to SLR rate than the marshes with higher persistence. We thus conclude that these classic indi- cators of tidal marsh persistence based on networks of Causes versus consequences of marsh dieback point measurements (i.e. SETs) should be recon- For managers, the most useful indicators of marsh sidered, because they do not always perform as con- persistence have predictive power, allowing for timely sistent indicators. For marshes that are still largely intervention to prevent dieback of marshes in their intact, with high elevation and vegetated cover, eleva- current footprint, or consideration of alternative tion gain equal to or surpassing local SLR is likely strategies such as facilitating marsh migration to essential (e.g. Cahoon et al 2019). Conversely, eleva- higher ground. The most robust predictions likely tion loss poses a challenge to tracking SLR, whether come from factors directly related to the drivers of resulting from microbial decomposition fueled by marsh dieback. Some of the metrics included in our nutrient-loading (Deegan et al 2012) or less inorganic analyses have this potential. In particular, the MARS sediment supply and accretion due to changes to river metrics related to marsh elevation change and sedi- morphology (Day et al 2007). However, once elevation ment supply were originally included (Raposa et al and vegetation has been lost, these metrics lose their 2016) because of their role in potentially driving marsh value as predictors of marsh persistence, and instead

9 Environ. Res. Lett. 14 (2019) 124073 can signal marsh degradation. The marshes in our snapshot. Given the evidence for ‘runaway’ open- study with trajectories of lower persistence appear water expansion (e.g. Mariotti 2016), as the UVVR likely to disappear in the near future, counter to the increases it is expected that processes leading to expan- common perspective of marshes being able to main- sion begin to dominate and increase in magnitude tain equilibrium through enhanced accretion follow- Point measurements at a network of SETs have ing elevation loss (FitzGerald and Hughes 2019). been promoted as a salt marsh monitoring approach Some of our other metrics, such as the sediment (e.g. Webb et al 2013, Osland et al 2017). For instance, differential and UVVR, may perform well as indicators Lovelock et al (2015) analyzed recent trends in man- due their association with consequences (rather than grove surface elevation changes at 27 sites across the causes) of marsh degradation. The flood-ebb differ- Indo-Pacific region using data from a network of SETs ential implicitly accounts for the source and direction and found that adequate sediment availability can of sediment transport, whereas mean values of SSC do enable mangrove forests to maintain rates of soil-sur- not. Therefore, degrading wetlands will typically have face elevation gain that match or exceed that of SLR. high mean SSC but negative differentials, while an However, obtaining reliable rates of change from SETs expanding wetland will have a positive differential, requires multiple site visits over many years, and, indicating sediment import. We also suggest that given spatial variability, multiple stations per marsh. values of UVVR up to 0.5–1.0 may have predictive The SET methodology can also provide critical infor- value for marsh persistence, but beyond this the marsh mation on mechanisms underlying marsh dynamics, has degraded so far that sensitivity as an indicator has and thus merits continued inclusion in an extensive been lost. Once a system has crossed a UVVR of ∼1.0, portfolio of marsh monitoring. the increasing role of estuarine processes (i.e. wind- ) wave resuspension, larger scale circulation patterns Conclusions may confound application of marsh metrics to pre- dominantly shallow-water systems. Understanding tidal marsh trajectories—of persis- Additional metrics may shed light on causes of tence versus degradation—is critical for coastal man- marsh dieback. For instance, marsh degradation in agement, especially in the face of accelerating SLR. both of the California regions included in this study Marshes likely to persist in place can serve as the appears likely to be driven by deeper subsidence than centerpiece of conservation initiatives that protect what is measured by surface elevation tables, perhaps them from damaging land uses or other stressors; resulting from groundwater overdraft or seismic marshes with moderate persistence should be prior- events. Longer time series of vegetative cover may also itized for restoration action; while those with the have greater predictive power; thus extending the lowest likelihood of persisting may not represent wise UVVR change analysis to begin with the earliest aerials investment opportunities. Our study has identified dating back almost a century might prove to be a pow- that indicators related to the distribution of vegetation erful tool. across the landscape of marsh elevations most strongly predict marsh trajectories, while sediment differential Selecting marsh monitoring approaches and the UVVR are also associated with marsh persis- Our results suggest that geospatially derived metrics tence but to a lesser degree. Our results indicate that can be an effective approach for assessment of marsh overall, the most robust monitoring approach involves stability at the landscape scale. While we calculated the spatially comprehensive characterization of marsh vegetation distributions from field transects of vegeta- ecosystems. tion cover and elevation, this can also be done with We found that persistent marshes across the Uni- aerial image analysis and digital elevation models, ted States resemble each other more than do degrading though some field ground-truthing is essential marshes (figure 4), bringing to mind the opening lines (Buffington et al 2016, Ekberg et al 2017). The UVVR of Tolstoy’s Anna Karenina (‘Happy families are all is also assessed from aerial imagery. Therefore, four of alike; every unhappy family is unhappy in its own the five most effective indicators in this study can way.’) This suggests that marshes must have attributes mostly be obtained from remote sensing. in common to be persistent in the face of sea level rise, Decadal change in UVVR intuitively provides a while marsh degradation can occur through many dif- valuable perspective on vegetation cover trends at the ferent pathways. marshes. Accurate calculation of the rate of change Nonetheless, our investigation also revealed sig- requires consistent analysis of aerial images over time, natures of marsh degradation. While an ample sedi- ideally from 3+years to calculate a robust trend. ment supply and substantial elevation gain can clearly However, we found that for these 12 marshes, current enhance marsh persistence in the long-term, the UVVR was very highly correlated (R2=0.93) with majority of degrading marshes in our study had higher decadal rate of change of UVVR (figure 5). Thus sim- accretion and elevation gain rates on a decadal scale ply assessing the current UVVR is sufficient and pro- than did their paired higher persistence counterparts. vides a suitable metric with a single temporal This finding reinforces the notion that caution must

10 Environ. Res. Lett. 14 (2019) 124073 be applied to the spatial interpretation and extrapola- Zafer Defne https://orcid.org/0000-0003- tion of data from SETs, underscoring the need for a 4544-4310 holistic marsh assessment (Ganju 2019). Marsh accre- Tracy Elsey-Quirk https://orcid.org/0000-0002- tion data must be interpreted in the context of spatial 2068-592X location, elevation and vegetation. For a vegetated Karen M Thorne https://orcid.org/0000-0002- marsh high in the tidal frame, elevation gain will help 1381-0657 with persistence in the face of future SLR. But the find- Chase M Freeman https://orcid.org/0000-0003- ings of our study expand and support earlier work 4211-6709 (Ganju et al 2015) indicating that for a marsh that has Glenn Guntenspergen https://orcid.org/0000- lost significant elevation and vegetation, increased 0002-8593-0244 accretion can signal degradation. Daniel J Nowacki https://orcid.org/0000-0002- This synthesis represented a cross-disciplinary 7015-3710 effort, and involved a critical examination of metrics Kenneth B Raposa https://orcid.org/0000-0002- independently developed by different teams, a highly 9998-3881 unusual endeavor (Kéfi et al 2019). By working across disciplines, we were able to detect weaknesses in earlier References approaches. For instance, we found that the multi- metric MARS indices were less effective at predicting Anderson M J and Robinson J 2003 Generalized discriminant – marsh trajectories than were single metrics. We also analysis based on distances Aust. N. Z. J. Stat. 45 301 18 Anderson M J and Willis T J 2003 Canonical analysis of principal determined that the UVVR metric loses sensitivity as coordinates: a useful method of constrained ordination for an indicator of marsh persistence beyond a certain ecology Ecology 84 511–25 threshold of marsh degradation, and is generally less Barbier E B 2019 The value of coastal wetland ecosystem services ( ) – closely associated with marsh trajectories than is the Coastal Wetlands Amsterdam: Elsevier pp 947 64 Blum M and Roberts H 2009 Drowning of the Mississippi delta due elevational distribution of vegetation. But together we to insufficient sediment supply and global sea-level rise Nat. have also succeeded in identifying an improved Geosci. 2 488–91 approach to monitoring and understanding marsh Buffington K J, Dugger B D, Thorne K M and Takekawa J Y 2016 persistence, which consists of combined application of Statistical correction of lidar-derived digital elevation models with multispectral airborne imagery in tidal marshes Remote a subset of the metrics. Because no single metric was Sens. Environ. 186 616–25 reliable at a broad geographic scale, we recommend Byrd K B, Windham-Myers L, Leeuw T, Downing B, Morris J T and that assessments focus at regional scales and include Ferner M C 2016 Forecasting tidal marsh elevation and vegetation distribution across an elevation gradient, habitat change through fusion of Earth observations and a process model Ecosphere 7 e01582 sediment differential, and UVVR. Our investigation Cahoon D R, Lynch J C, Roman C T, Schmit J P and Skidds D E 2019 identifying the most robust ensemble of metrics to Evaluating the relationship among wetland vertical predict persistence serves as a template for other stu- development, elevation capital, sea-level rise, and tidal marsh – dies developing and testing monitoring approaches sustainability Estuaries Coasts 42 1 15 Clarke K R, Gorley R N, Somerfield P J and Warwick R M 2014 that can inform conservation and management. Change in Marine Communities: An Approach to Statistical Analysis and Interpretation (Plymouth: Primer-E) Cole Ekberg M L, Raposa K B, Ferguson W S, Ruddock K and Acknowledgments Watson E B 2017 Development and application of a method to identify salt marsh vulnerability to sea level rise Estuaries This study was supported by funding from NOAA’s Coasts 40 694–710 Office for Coastal Management and the USGS Coastal Craft C, Clough J, Ehman J, Joye S, Park R, Pennings S, Guo H and / Machmuller M 2008 Forecasting the effects of accelerated and Marine Hazards Resources, Land Change Science sea-level rise on tidal marsh ecosystems services Front. Ecol. and Ecosystems programs. GRG thanks L Schepers for Environ. 7 73–8 the use of his field data at Blackwater and Fishing Bay in Day J W et al 2007 Restoration of the Mississippi Delta: lessons learned – the calculation of certain marsh metrics. Any use of trade, from Hurricanes Katrina and Rita Science 315 1679 84 fi Deegan L A, Johnson D S, Warren R S, Peterson B J, Fleeger J W, product, or rmnamesinthispublicationisfor Fagherazzi S and Wollheim W M 2012 Coastal descriptive purposes only and does not imply endorse- eutrophication as a driver of salt marsh loss Nature 490 388 ment by the US Government. We are grateful to J A Carr Donohue I et al 2016 Navigating the complexity of ecological – and two anonymous reviewers for providing thoughtful stability Ecol. Lett. 19 1172 85 Fagherazzi S et al 2012 Numerical models of salt marsh evolution: suggestions that improved the clarity of the paper. ecological, geomorphic, and climatic factors Rev. Geophys. 50 FitzGerald D M and Hughes Z 2019 Marsh processes and their response to climate change and sea-level rise Annu. Rev. Earth Planet. Sci. 47 481–517 ORCID iDs Ganju N K 2019 Marshes are the new beaches: Integrating sediment transport into restoration planning Estuaries Coasts 42 917–26 ’ Kerstin Wasson https://orcid.org/0000-0002- Ganju N K, Defne Z, Kirwan M L, Fagherazzi S, D Alpaos A and Carniello L 2017 Spatially integrative metrics reveal hidden 1858-4505 vulnerability of microtidal salt marshes Nat. Commun. 8 Neil K Ganju https://orcid.org/0000-0002-1096-0465 14156

11 Environ. Res. Lett. 14 (2019) 124073

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12 SUPPLEMENTAL INFORMATION Study systems

Narragansett Bay, Rhode Island The two study marshes in Narragansett Bay, Rhode Island are located on Prudence Island within the Narragansett Bay National Estuarine Research Reserve, in the approximate geographic center of the Bay. Both marshes are designated NERR Sentinel Sites and have been the focus of long-term monitoring, which was initiated in 2000. These and other data show that marshes in Rhode Island are in rapid decline, with most evidence pointing to sea-level rise as the primary driver. Impacts include rapid areal loss of marsh (~17% since the 1970s), loss of irregularly-flooded high marsh in favor of flood-tolerant S. alterniflora, interior ponding and lateral creek expansion, vegetation die-off along edges and on the marsh platform, and increases in marsh crab abundance and impacts (Watson et al. 2017, Raposa et al. 2017, Raposa et al. 2018). Changes and impacts of course vary among individual marshes in the state; recent assessments place both Coggeshall (Figure S1.a) and Nag (Figure S1.b) marshes towards the intermediate level of impacts, with Coggeshall exhibiting relatively more degraded conditions than Nag.

Figure S1. Rhode Island study sites. (a) Coggeshall and (b) Nag marshes in Narragansett Bay.

Barnegat Bay, New Jersey The two study sites are located on the mainland shoreline of the Barnegat Bay-Little Egg Harbor estuary (Barnegat Bay for short) in New Jersey (Figure S2). The Bay is approximately ~85 km long, with a ~1- to ~10-km variable width (~ 6km near Dinner Creek and ~2 km near Reedy Creek). It is connected to the ocean through Little Egg Inlet in the south, Barnegat Inlet in

23 the middle and Point Pleasant Canal in the north. Reedy Creek is located about ~7 km to the south of the Canal and Dinner Creek is located about ~18 km to the north of the Little Egg Inlet. The northern part of the bay, where Reedy Creek is located, has lower tidal energy with mean tide range about ~0.1 m than the southern part (mean tide range about ~0.6 m near Dinner Creek). The northern bay also has lower salinity in general, about ~8 to10 g/kg lower in May- October period (Velinsky et al. 2017a), because of larger fresh water input and more restricted connection to the ocean (Defne & Ganju 2015). The northern part of the bay also accommodates more urban developed areas than the south, which seems to be the reason for increased nitrogen loading to the bay even after the point source of nitrogen was redirected offshore (Velinsky et al. 2017b). Marsh accretion rate estimates vary from 1.6 mm y-1 near Reedy Creek to 2.2 mm y-1 near Dinner Creek. Both marsh sites have been subject to intensive mosquito control activities. Both sites had been previously parallel or grid-ditched, and more recently Dinner Creek has been subject to Open Water Marsh Management, where ponds are excavated in the marsh interior and dredge material is sprayed as a thin layer on the marsh platform surrounding the ponds (James- Pirri et al. 2012; Elsey-Quirk and Adamowicz 2016). Previous studies have illustrated that Reedy Creek marsh has relatively low elevation capital and has relatively low accretion rates, while marshes around Dinner Creek (e.g., Channel Creek) have relatively high elevation capital and high rates of accretion (Unger et al. 2016; Boyd et al. 2017).

Figure S2. New Jersey study sites. (a) Dinner and (b) Reedy Creek marshes in Barnegat Bay.

Chesapeake Bay, Maryland The Maryland study sites are located on the Delmarva peninsula, on the east side of Chesapeake Bay. Chesapeake Bay is the largest estuary in the United States, and is bordered by extensive tidal marshes. The two marsh complexes in this study are within the Blackwater National Wildlife Refuge (federal) and the Fishing Bay Wildlife Management Area (state of

24 Maryland). Marshes in Blackwater NWR have a documented history of deterioration (Stevenson et al. 1985), while the adjacent marshes (e.g. along Fishing Bay) have remained intact over the same time period (Ganju et al. 2013). Deterioration at Blackwater NWR was likely caused by a number of factors; Stevenson et al. (1985) indicate causes including invasive rodents, tidal restriction, and sea-level rise/subsidence. Under present conditions, Blackwater NWR continues to lose marsh area and export sediment through edge erosion and open-water conversion, while Fishing Bay marshes are not losing marsh area and are importing sediment (Ganju et al. 2013).

Figure S3. Maryland study sites. (a) Blackwater and (b) Fishing Bay marshes in Chesapeake Bay.

Grand Bay, Mississippi / Terrebonne Bay, Louisiana The Grand Bay site is located in Mississippi within the Grand Bay National Estuarine Research Reserve, in the Gulf of Mexico. Tides are diurnal, with wind-driven events playing a large role in hydrodynamics and sediment transport. Edge erosion is a large driver of marsh loss in this area, due to a large open-water fetch in the seaward region and the loss of seaward Grand Batture barrier islands (Passeri et al. 2015). However, the interior of the marsh is largely intact with little obvious internal deterioration. The Terrebonne Bay site is one of the Louisiana Coastwide Reference Monitoring Stations (CRMS 0310; www.lacoast.gov) located in Terrebonne basin. Terrebonne basin is within the deltaic plain region of southeast Louisiana. Marshes of this area are located in an abandoned delta lobe that was active 3,000 to 4,000 ybp (Coleman 1981). Tidal range is this area averages 30 cm during spring tides and 10 cm during neap tides (in Wang et al. 1993). This area is experiencing rapid wetland loss associated with shifts in the location of delta building, sea- level rise, and hydrologic alterations (Blum and Roberts 2009; Syvitski et al 2009). High rates of relative sea-level rise along the deltaic coast of Louisiana, due to a number of factors, have

25 contributed to high rates of marsh loss. Marsh interiors can be sediment-limited associated with the trapping and deposition of sediments closer to the tidal channel. Productivity of marsh plants is also constrained in the interior marsh because of relatively high water-levels and reduced soil conditions and limited horizontal porewater exchange. Marsh accretion, therefore, is limited by both mineral sediment and organic matter input, and in some places, may not be sufficient to maintain marsh elevation above sea-level. Stages of deterioration in the marsh interior include vegetation death and decomposition, formation of small ponds, and pond expansion (DeLaune et al. 1994; Day et al. 2011). The Terrebonne marsh (CRMS 0310) is representative of marshes within the lower Terrebonne basin and is characterized by low salt marsh vegetation, primarily Spartina alterniflora, and polyhaline conditions (mean salinity: 17.5, 2006-2019).

Figure S4. Louisiana and Mississippi study sites. (a) Terrebonne Bay, Louisiana; (b) Grand Bay, Mississippi.

Elkhorn Slough, central California Elkhorn Slough is a small estuary in the middle of the Monterey Bay, 120 km south of San Francisco. It hosts the most extensive salt marshes in the state south of the Bay area. Substantial marsh loss has occurred due to diking (Van Dyke and Wasson 2005). However, even undiked salt marshes have undergone dieback over the past 50 years, in patterns consistent with excessive inundation. Elkhorn Slough NERR Sentinel Site data from surface elevation tables reveal that marsh elevation gain is less than accretion, indicating shallow subsidence is occurring. Data from repeat surveys of benchmarks suggests deep subsidence is also occurring. Elkhorn Slough lies in a very productive agricultural watershed, and subsidence may be driven by groundwater overdraft and/or eutrophication-fueled decomposition. Elkhorn Slough’s salt marshes that never have been diked were divided into a lower region (Figure S5.a), which has higher persistence, vs. an upper region (Figure S5.b), which has lower persistence, perhaps due to longer residence times enhancing symptoms of eutrophication.

26

Figure S5. Central California study sites. (a) Lower region and (b) upper region of Elkhorn Slough, California.

Point Mugu Lagoon and Seal Beach, southern California The salt marsh of Point Mugu Lagoon is located within the Naval Base Ventura County at the foot of the Santa Monica Mountains in Ventura County, California (Figure S6.a). The central basin of Point Mugu Lagoon is part of an estuary occurring in the flat valley bottom of the Oxnard plain and is directly adjacent to the steep slopes and erodible soils of the Transverse Ranges (Brownlie and Taylor 1981). The combination of steep slopes, relatively few dams in the region (Willis and Griggs 2003), and isolated periods of rainfall result in high suspended sediment concentrations reaching the lagoon and may reflect conditions of a natural southern California watershed during the wet season (Rosencranz et al. 2016). The Seal Beach marsh is part of the US Fish and Wildlife Service’s Seal Beach National Wildlife Refuge and within the Naval Weapons Station Seal Beach (Figure S6.b). Seal Beach is part of the historic Anaheim Bay wetlands network and occurs in a flood plain that is also fed by the Transverse Ranges (Brownlie and Taylor 1981). Historically, the channels of the Santa Ana and San Gabriel Rivers discharged large amounts of sediment into Anaheim Bay near and through Seal Beach salt marsh (Brownlie and Taylor 1981; Stein et al. 2007; Warrick and Farnsworth 2009); however, flood control efforts have changed these rivers so that flows now bypass the Seal Beach salt marshes. In comparison, Point Mugu Lagoon is adjacent to uplands and drains a river system, whereas Seal Beach is located within a highly developed and urbanized region. Surface elevation table data beginning in 2013 at both sites reveal that Point Mugu Lagoon is relatively stable, whereas Seal Beach shows marsh elevation gains are less than accretion, indicating shallow

27 subsidence is occurring, with rates at Seal Beach being four times greater than Point Mugu Lagoon. Rosencranz et al (2016), found that the main difference between the two study sites is that Point Mugu Lagoon has an external watershed sediment source which allows for fluvial sediment mobilization and transport, and therefore it is likely to be more stable in response to SLR. Lastly, Seal Beach is dominated by cordgrass (Spartina foliosa) at its lower elevations and along tidal creeks, whereas Point Mugu Lagoon is characterized by dense shrubby vegetation, including pickleweed (Sarcocornia pacifica) and salt grass (Distichlis spicata), which may be less prone to erosion and increase rates of sediment trapping (Boorman et al. 1998), likely increasing persistence. Both Point Mugu Lagoon and Seal Beach represent modified landscapes that include watershed level modifications and altered sedimentation patterns, making them ideal sites for studying the range of marsh loss and mechanisms representative of southern California salt marshes.

Figure S6. Southern California study sites. (a) Point Mugu Lagoon and (b) Seal Beach marshes in southern California.

MARS metrics For a detailed description of the methods used for collecting data for each of the ten MARS metrics refer to Raposa et al. (2016); a brief overview is provided here. The three vegetation metrics (percent of marsh elevations below mean high water, percent of marsh elevations in the lower third of overall plant distribution, skewness) are calculated from a robust set of recent elevation points collected at intervals across transects that traverse the marsh platform and span the entire elevational range of plants in a marsh; the first metric also requires calculating a tidal datum specific to the marsh. The rate of elevation change metric is calculated using data from surface elevation tables (SETs) in the marsh, preferably multiple SETs monitored for many years. The next three metrics general reflect sediment availability and

28 accumulation. The short-term accretion rate and long-term accretion rate metrics are calculated from marker horizons placed in conjunction with SETs or from dated cores, respectively. The turbidity metric is calculated by taking the mean turbidity value from a local water quality monitoring station in or near each marsh over five recent years; if these data are unavailable, total suspended solids (TSS) data may be used interchangeably. Data from an appropriate local water quality monitoring station in or near each marsh are also used to calculate the tidal range metric, specifically by averaging the mean daily difference in water levels (i.e., highest daily water level minus lowest daily water level) across a recent five-year time period. The last two MARS metrics reflect the degree to which a marsh is exposed to sea-level rise. The long-term rate of SLR and short-term, inter-annual variability in water levels metrics are calculated using data from an appropriate NOAA tide station (tideandcurrents.noaa.gov). The former is the published rate of change in mean sea-level (MSL) from the selected tide station. The latter is the mean monthly water level anomaly over the last 10 years after accounting for seasonal cycles and the long-term trend, also from the same station. The ten metrics are assigned a score ranging from 1-5 according to categorical thresholds (Table S1), with lower scores reflecting low resilience and high scores the opposite. The metrics are then combined into five broader resilience categories by averaging across all metric scores included in each category marsh elevations (three metrics), elevation change (one), sediment/accretion (three), tidal range (one) and sea-level rise (two). The metrics and categories are further aggregated into the three MARS multi-metric resilience indices to potentially provide more comprehensive estimates of marsh resilience than possible with only single metrics. The MARS risk index is calculated by summing the number of categories that scored moderate to high (defined as having a mean category score of ≥3), representing low risk. The MARS average index is calculated by taking the average of the five category scores, and the MARS ratio index by dividing the rate of marsh elevation change by the long-term rate of SLR. A “do-it-yourself” calculation tool is provided so any user can use these methods: https://www.nerra.org/reserves/national-tools/marsh-resilience-assessment/

29 Table S1. Scoring system for all metrics and indices. Scoring for MARS metrics, categories, and indices is identical to Raposa et al. 2016. New scoring thresholds were developed for the other metrics.

Sediment differential The sediment differential can be computed for any time period greater than a single tidal cycle, and we used the longest possible for a given data set. Floods and ebbs are defined by velocity direction and not the rate of water-level change, although for standing tidal waves these two approaches may yield similar results. The mean SSC for all ebb periods is subtracted from the mean SSC for all flood periods to produce the differentials for each site. Due to variability in the quality of SSC-to-turbidity calibrations across sites, we use turbidity as a proxy for SSC when computing the flood-ebb differential. For New Jersey, Maryland, southern California, and Grand Bay sites, we used the turbidity data collected by the U.S. Geological Survey, and served at the U.S. Geological Survey’s Oceanographic Time-Series Database (stellwagen.er.usgs.gov). Details on instrument location, sampling frequency, and quality assurance are provided for each site. In all cases,

30 turbidity data were separated by flood and ebb using co-located velocity data, and averaged over the entire deployment. The difference between flood and ebb is reported. For Terrebonne, the only available SSC data that were collected with similar methods to the other sites spanned 24 hours, so the value should be interpreted with caution. SSC calibration slopes in fine-sediment dominated areas typically range between 1.25 - 2 (Ganju et al., 2013; Suttles et al., 2017), therefore we used a nominal slope of 1.75 (+/- 0.25), to convert the 10.4 mg/L to 6 NTU (+/- 1). For Elkhorn Slough, the sediment differential was estimated using three YSI sondes collecting turbidity and depth measurements every 15 minutes. These sondes were not ideally located according to the Ganju et al. method, by which they should be in the channel draining the focal marsh. One sonde (Vierra Marsh) was located near the channel draining the entire small estuary that encompasses the two marsh areas in this study. One sonde (Hester Marsh) was located in the channel draining an area where marsh restoration was due to occur (which had not been used for the other marsh parameters due to imminent changes); this is in a similar part of the estuary as the Lower Slough focal marsh area. Another sonde (South Marsh) was located in an area that has very degraded salt marshes due to former diking; this also was not a focal area for the other measurements, but may be representative of the Upper Slough focal area, which has highly degraded marshes. We therefore averaged the differential in flooding/ebbing NTU for Vierra Marsh (-1.28) and Hester Marsh (+0.85) from 2014-2016 to obtain an estimate for the Lower Slough focal marsh area (-0.22), and Vierra Marsh (-1.28) and South Marsh (+0.51) to obtain an estimate for the Upper Slough focal marsh area (-0.39). Despite the uncertainties, it seemed that including an estimate would be more accurate in representing conditions (which appear to be near zero for the sediment differential) for the analyses than missing/blank values.

UVVR Tidal marsh boundaries were clipped based on the wetland classification maps from the U.S. Fish and Wildlife Service’s National Wetlands Inventory (NWI). Unvegetated (e.g. ponds, pannes, channels, flats) and vegetated areas within each marsh complex boundary were determined using 4-band (red, green, blue, near-IR) 1-m resolution imagery from the National Agriculture Imagery Program (NAIP). For each time period we used the Image Analysis toolset in ArcGIS Desktop to reclassify the NAIP imagery into a normalized difference vegetation index (NDVI) with values that ranged from -1 to 1. The goal was to identify an NDVI threshold value that adequately segregated the unvegetated areas of the marsh from the vegetated areas. If necessary, a marsh would be divided into separate zones that required different NDVI thresholds in order to more accurately classify unvegetated vs. vegetated areas. Areas of open water and large channels that did not exceed 100 m in width were excluded from the NDVI classification to avoid the potential of classifying submerged aquatic vegetation, but were included in the final unvegetated area calculations. Total unvegetated and vegetated areas were then calculated and reported as a ratio.

31 The marsh boundaries for New Jersey sites were determined by the watershed analysis approach in Ganju et al. (2017). The boundaries of Mississippi and Louisiana sites were determined with the same approach, but without assigning a specific pour point, which results in a number individual of marsh units within a domain. For pairwise comparisons of these sites, marsh boundaries determined with this approach were expanded such that they surpass the size of the marsh unit containing the field measurement. The boundaries were extended up to natural delineation determined by creeks and open water. UVVR for Mississippi and Louisiana sites was calculated similar to Ganju et al. (2017), using 4-band (blue, green, red and near infrared), 1-meter resolution NAIP, but with the addition of elevation as the fifth band. An unsupervised classification tool that combines isodata clustering and maximum likelihood classification methods was used to classify image pixels into 32 different classes, which were then manually assigned to a vegetated or unvegetated class by comparing to the original image.

Observed change in vegetation Change in UVVR and change in percent of marsh plain that was vegetated was calculated as a simple subtraction between the two time periods for all regions (Fig. S7). For three regions, change in UVVR was compared to a linear regression approach with multiple time points; for these three regions, the value obtained from the regression was used in all the analyses.

Figure S7. Change in UVVR example from Dinner Creek showing (a) the marsh delineation, (b) zoom into the white box in (a) with NAIP 2010 imagery, (c) with NAIP 2017 imagery, and (d) the change in vegetated cover.

Variability in the linear trend of change was apparent when UVVR was calculated using more than two snapshots in time (Figure S8). Factors contributing to variation include seasonal changes in vegetation, episodic events that may have occurred between the times of image acquisition, image quality, variability in classification algorithms and the variability introduced by the operator in cases where interpretation is required to supplement the machine-performed classification. For this reason, it is likely that a larger uncertainty is involved when the rate of change calculated based on only two points (start and endpoints). Using values from two consecutive years had the tendency to produce the most variability (e.g. for Reedy Creek, UVVR2010-2013 = 0.0236, UVVR2013-2015 = -0.0171, UVVRregress = 0.0040). However, using the

32 end members from the largest time span for each dataset yielded similar result with linear regression rates (Reedy Creek: UVVR2010-2017 = 0.0040, UVVRregress = 0.0031; Dinner Creek: UVVR2010-2017 = 0.0015, UVVRregress = 0.0028; Grand Bay: UVVR2010-2017 = -0.0003, UVVRregress = -0.0006; Terrebonne: UVVR2010-2017 = 0.0015, UVVRregress = 0.0023; Elkhorn Slough Upper: UVVR2005-2016 = 0.0647, UVVRregress = 0.0642; Elkhorn Slough Lower: UVVR2005-2016 = 0.0376, UVVRregress = 0.0364). The rate of change was calculated with linear regression for the three regions where more than two imagery datasets were processed; for the other two regions, the imagery data with the largest time span was processed to be used with two-point calculation.

(a)

(b) Figure S8. (a) UVVR values from multiple years for (a) Dinner and Reedy Creeks in New Jersey; Grand Bay in Alabama, Terrebonne in Mississippi, and (b) Elkhorn Slough in California. .

33 Additional analyses The results from all regressions between decadal change in UVVR and the other metrics and indices are shown in Supplemental Table 2.

Table S2. Results of linear regression between each metric, category or index and decadal change in UVVR. The color coded bars illustrate the Pearson correlation coefficient values (red indicates that an increase in the metric correlates with marsh loss (increased UVVR or decreased percent vegetated; green indicates the reverse). Only three relationships were siginficant: * indicates P value < 0.05, *** indicates P value < 0.001.

Relationship with change in UVVR Relationship with change in percent vegetated MARS metrics (Raposa et al. 2016)

Percent of marsh below MHW

Percent of marsh in lowest third

Skewness

Elevation change (mm yr‐1)

Short‐term accretion (mm yr‐1)

Long‐term accretion (mm yr‐1)

Turbidity (NTU)

Tidal range (m) * Long‐term SLR rate (mm yr‐1)

Short‐term SLR variability (mm)

MARS categories

Marsh elevations

Elevation change

Sediment/accretion

Tidal range * Sea‐level rise

MARS indices

MARS‐risk

MARS‐average

MARS‐ratio

Ganju et al. 2017 metrics

Flood‐ebb turbidity differential (NTU)

UVVR ***

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Figure S9. Constrained ordination and CAP output for marsh metrics with a priori group factors by region.

Figure S10. Correlations of metrics with the two CAP axes illustrating the metrics that are most related to site separation.

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