GEOG 566

         Advanced spatial statistics and GIScience

June 12, 2017

Final Project: “Spatial Relationships of Vegetation for Restored and Reference Salt Marshes (Salmon River Estuary, Oregon)

Filed under: Final Project @ 2:32 pm

My Spatial Problem Blog Post


  1. A description of the research question that you are exploring.

Salmon River is one of the smallest estuaries on the northern Oregon coast (800 ha), with the largest proportion of tidal marsh (400 ha) for any Oregon estuary. It borders Tillamook and Lincoln counties, and is designated as an Important Bird Area by The National Audubon Society.  Conservation Research at Salmon River Estuary has been a focus of government, non-profit, and educational institutions since the 1970’s due to concern over salmonid habitat and the impacts of sea level rise on the coast. Salmon River consists of public and protected wetlands that have been restored and protected since the U.S. Forest Service removed dikes from three sites in 1978, 1987, and 1996. Tidal flow to the ocean is currently unobstructed on sites that were previously used as pastureland and/or diked. One wetland on the estuary was never diked and is used as a reference marsh for field research, to determine functional equivalency of restored marshes. Salmon River Estuary has been a site for place-based restoration and studies of community recovery over the last 40 years (Flitcroft et al. 2016). Vegetation has been monitored at this site since dike removal, however survey records are still being deciphered from past researchers. If sufficient plot data can be recovered and confirmed, future analyses of vegetation patterns over the last 40 years will be investigated further.

Factors influencing the richness and environmental integrity of Salmon River are associated with the physiognomic and taxonomic features of the plant community. This study focuses on the spatio-temporal distribution patterns of Salmon River vegetation to explore how remnant and restored marshes differ in terms of biodiversity and species composition. I expect that different durations of tidal exclusion through dike establishment will reveal differences between sites in the context of plant species composition and distribution.

  1. A description of the dataset you will be analyzing, including the spatial and temporal resolution and extent.

I have collected species data (ocular estimations of percent coverage) from 1 m2 plots on transects from four tidal marshes: Mitchell Marsh (dike removed 1978), Y Marsh (dike removed in 1987), Salmon Creek Marsh (dike removed 1996), and one remnant marsh adjacent to Y marsh as a control (never diked). I also collected soil samples at each sampled transect plot, and tested them for salinity, conductivity, and bulk density, as well as nitrogen content. Transect plots were square shaped plots, 50 m apart in increasing distance from the tide.  My objective for data analysis was to describe the spatial patterns of the vegetation communities in tidal marshes of Salmon River Estuary after dike removal. I surveyed a total of 74 square meter plots on transects.

Stohlgren plots, also known modified Whittaker plots (MW, 1,000 square meters), were established at each marsh site to collect data on species abundance for comparison with transect data. MW plots were implemented to test for patterns of diversity at multiple scales beyond what transect, square meter plots may capture within the same site. The restored and remnant sites have three MW plots each, for a total of 12 MW plots. The MW, plots were placed at a random distance 50 m along and 20 m offset from the sampled transects at each marsh for a stratified random sample design. Each MW plot is a minimum of 50 meters apart, depending on where they were placed along the transect. At each MW plot, percent cover and presence/absence of species were estimated (with the aid of 1 meter square grids). Samples of all species identified were collected, pressed and are being examined to confirm identification. Elevation data were obtained from LiDAR surveys in 2015, and used to pinpoint elevation for all plots.

The data sets for my project include spreadsheets that describe percent vegetation cover, elevation, and soil characteristics per transect plot. MW plots were not sampled for soil and thus only have percent vegetation cover and elevation. The spatial grain of my study is one square meter, represented by the size of my smallest sampling frame for plots. With the nested sample plots and my stratified random sampling techniques, I have multiple spatial extents for this project. One extent could be considered the length of a transect (which vary by tidal marsh), the area of a MW plot (1,000 m2), or could arguably be extended to the entire Estuary. There are some interesting temporal aspects to my study as well; three of the four tidal marshes have experienced successive dike removal. These marshes have been surveyed for vegetation cover post dike removal and every 5 years subsequently. Incorporating these historical data will add dimension to my spatial and temporal analysis of variation at my study site.

3. Hypotheses: predict the kinds of patterns you expect to see in your data, and the processes that produce or respond to these patterns.

Are tidal marshes that were restored earlier more similar to the Reference Marsh in terms of environmental conditions (soil, elevation), and species composition when compared to those restored more recently?

I predict that restored marshes will be significantly different from the Reference Marsh. I also predict that time since dike removal will not be strong indicator of similarities between restored marshes and the reference marsh. I anticipate that sites which have experience recent dike removal have soils with higher soil salinity and conductivity, compared to remnant marsh plots.

Does species richness captured differ between restored and reference salt marshes?

I predict that Reference Marsh has higher richness compared to restored marshes. I  expect that plots from the reference marsh (Transect C, adjacent to Y Marsh) will be more diverse and heterogeneous than tidal marshes that have been diked. I predict sites that have experienced dike removal more or less recently will both have different species composition and be less diverse compared to reference sites. I hypothesize that the reference marsh will be the most diverse, with the highest richness and spatial heterogeneity of species throughout, compared to the other low marshes that have been diked.

Do Species Area relationships differ between restored and reference salt marshes?

I predict that the Reference Marsh has a greater number of species over area compared to restored marshes. I also predict that there will be spatial correlation of plant species at a larger scale, with fine scale patchiness within my site, suggesting that there may be ‘nesting’ of species or smaller pockets of diversity within the marsh, with similarities in species assemblages occurring at a larger scale.

Do field methods, specifically nested-rectangular (Modified Whittaker) plots and non-nested-square (Transect) plots capture species richness differently?

I predict that Modified Whittaker plots will capture more species than Transect plots, since MW plots will be able to address species richness at greater scales.

4. Approaches: describe the kinds of analyses you completed using your data.

I produced Mantel tests, and ISA (Indicator Species Analysis), as well as species area curves and a map of my site to compare and contrast differences in species assemblages by site. I used PC-Ord, Excel, and Arcmap to complete these analyses.

5. Results: What did you produce/find?

I conducted a Mantel test on all of my data to determine the scale at which my plot data were spatially autocorrelated (self-related). I found that none of my plots were spatially autocorrelated (Mantel’s r statistic Transect: r = 0.037797, p = 0.182182; MW plot: r = 0.027994, p = 0.164164, accept null hypothesis of no relationship). This is a little surprising, but may be indicative of noise within my dataset, and variation of species at this scale. It is possible that autocorrelation is not detectable at this scale, or perhaps I need a larger dataset with less proportional noise to sort out the autocorrelation signal. I was however able to detect spatial autocorrelation at the 1,000 square meter scale for the Modified Whittaker plots (r = 0.638224, p = 0.00010), suggesting that there may be more fine scale patichness, variation, or nestedness among plant species at each of the SRE tidal marshes. Salt may also be a confounding factor that drives spatial diversity of vegetation, in addition to dike removal, as salt is a limiting factor for some salt marsh plants; not all species are equally tolerant of it.

For the ISA (Indicator Species Analysis) test I completed to determine which species were associated with which tidal marsh environments, I found that (1) Triglochin maritima, Sarcocornia perennis, and Distichlis spicata were significant indicators of salty, restored conditions, (2) Dechampsia caespitosa, Juncus arcticus var. littoralis, Potentilla pacifica, Glaux maritima, and Hordeum brachyantherum were significant indicators of upland, high elevation conditions, and (3) Carex lyngbyei was a significant indicator of restored conditions. I broke my dataset up and conducted a mantel test for each of the groups, using only plots that recorded the presence of at least one species in each of the groups. I did not find any significant autocorrelation either with any of the strong indicator species (that were all found in a high proportion of plots surveyed). I am curious if my plots were located closer to each other, and/or I had surveyed more plots over a larger area, spatial autocorrelation patterns would begin to emerge.

an example of PC-Ord output for a Mantel test. The R statistic implies the amount of correlation between species cover and distance, the p value implies the significance of the correlation/similarity in comparison to chance (computed by Monte Carlo randomizations).

I also produced a map in Arc, to visualize the patterns of diversity I saw in my data. I coded each of my plot locations (for Transects and Modified Whittaker plots) by the dominant plant strategy type: Carex lyngbyei dominant (A plot that had at least 50% or more cover of Carex lyngbyei ), salt dominant (at least 50% or more cover of one or more salt tolerant species), or mid-high marsh dominant (at least 50%, 0r more cover of a mid-high marsh species that is not Carex lyngbyei). I think the map helps to visualize spatial patterns of diversity well, on a broader scale, by grouping plots into different assemblies or types/guilds based on their content.

Map of my site, indicating species assemblages by plot location; The Reference Marsh is dominated by high elevation species, and the previously diked marshes are all Low Marsh environments, dominated by C. lyngbyei and salt-tolerant species.

I created Species Area curves to examine how species richness increased over an incremental increase in area sampled (from 1 to 10, 100, and 1,000 square meters). A Species-Area curve represents the exponential relationship between species richness and scale of area; as the scale of area sampled increases, you may be more likely to find new species. A steeper slope for a species area curve indicates higher richness (number of species) and a shallow curve indicates lower richness. One of the things that can distinguish a species-area curve from a species accumulation curve, is the relative ‘nestedness’ of the environment being sampled. As I mentioned earlier, ‘nestedness’ is a measure of structure and distribution of species across a location, So an example of nestedness would be a location that may have a few species overall, with subsets of locations with more species, or pockets of diversity and heterogeneity. When nestedness is high, the slope of the species area curve is reduced relative to the species accumulation curve. The opposite occurs when nestedness is low. So in this case, all of the slopes of the species area curves I sampled are lower or less than the slopes of the species accumulation curves, for all sites. This suggests that in terms of the spatial patterns of diversity at Salmon River, there may be nesting or patchiness occurring, within tidal marshes, where there may be fewer species overall, especially at restored sites, with pockets of diversity. And this inference is consistent with what I observed in the field on the ground.

As you can see from this Species-Area graph, the reference marsh has the highest richness of all marshes sampled, though YMarsh is a close second as plot size dimension goes up. Mitchell Marsh and Salmon Creek marsh are both comparably low, suggesting that as scale of area increases, species richness does not increase by much. This is also a slight deviation from the species accumulation curves, which suggested Mitchell Marsh may be more diverse than YMarsh at the 1 meter scale on transect plots. While diversity and richness seem to vary within restored marshes by scale, the Reference Marsh has consistently higher richness, and Salmon Creek has consistently low richness.

This graph, shows the trend for species accumulation average number of species encountered per meter squared plot (for transect) over cumulative plots sampled. These data are the result of a combinatrix from PC-Ord that accounts for every possible combination of x number of plots sampled. You can see from this graph, that the square meter plots on the reference marsh have a significantly higher number of cumulative species, compared to all other reference marshes. Salmon Creek has the lowest, and Mitchell and YMarsh are intermediate.

also compared species accumulation (effort curve) on the square meter plots from the MW plots I sampled and found that species accumulation patterns were overall consistent. The only difference here is that Mitchell Marsh was found to have higher species accumulation over area than YMarsh, which in this case was comparable to Salmon Creek’s low diversity. The difference in YMarsh diversity could be from the placement of MW plots or patch variation, both Salmon Creek and YMarsh have large patches of C. lyngbyei. Ultimately this shows that there is variability in species richness within Restored Marshes, but consistently high richness on the reference marsh.

I conducted a multivariate analysis with my field data using the non-metric multidimensional scaling technique which is ideal for ecological data that is non-normally distributed. NMS avoids linear assumptions and uses ranked distances to linearize relationships within the dataset. This allows the user to see a wider variety of structures within the ordination and make a number of insightful observations and conclusions. Now if any one particular graphic could summarize my entire thesis, this would probably be it, and I will do my best to highlight the most salient features here. First I would like to point out that between each of the tidal marshes, which are represented by these amorphous colored convex hulls, there is very little overlap between all sites, and the reference marsh, on the left here in red, is the most divergent from any other marsh.

The reference marsh is also closely associated with a number of high marsh species that are indicative of native, or ‘reference communities.’ The Reference marsh is high up on the elevation axis, also demonstrating that it has high elevation throughout. YMarsh, the blue convex hull at the bottom. Has the longest, widest convex hull, which means that elevation varies throughout the site which is why you see sample units on the lower end of the elevation axis and towards the middle of the elevation axis. The species that are coded here and directly associated with the YMarsh site are halophytic, or salt tolerant, suggesting that these plants are found at low elevations on areas with high soil salinity and conductivity, which describes the conditions of YMarsh. Mitchell Marsh and Salmon Marsh overlap in this case, likely because they both have lower soil salinity and conductivity values from freshwater influence, and mostly mid to low elevation ranges, with the exception of a few high elevation outliers. Both of these sites seem to be associated with introduced species (reed canary grass, PHAR) or pasture grasses like Agrostis stolonifera.

Predominately, there were many instances of homogenous patches of Carex lyngbyei at both of these sites, and at YMarsh as well. In fact the only point at which all three restored sites converge is at the end of the ‘restoration’ axis over Carex lyngbyei. Carex lynbgyei is also divergent from all other species sampled, because it often occupies monotypic swaths of marsh, and is found on the lower end of the restoration axis, based on my coding schematic; areas like the reference marsh were coded with a low number, and restored marshes were given codes with numerical values increasing with the chronological order of dike removals. Carex lynbgyei sits at the end of the axis associated with the highest ‘restoration axis’ values, as it represents a strong pattern within all marshes that have experienced dike removal at any point in time, thus it is indicative of restored ecosystems. Unfortunately, there were no significant differences found between sites and any of the other soil characteristics we sampled for, but this maybe more related to sampling techniques and would be interesting to revisit further. We only collected soil from a surface depth of 10 cm, so perhaps if our samples were collected at a deeper level, we would see stronger patterns related to pH, Bulk Density, and C:N ratios. So in summary, the reference tidal marsh vegetation is richer, more diverse, and complex (heterogenous), in the number and variety of species (high marsh/low marsh) than restored salt marsh vegetation at Salmon River Estuary, across field methods, ~40 years later Carex lyngbyei has persistently dominated restored areas post dike removal which marks a significant departure from patterns of species assemblage on the reference marsh.

NMS ordination for Transect plots, examining species distribution over tidal marshes, elevation and soil salinity.

I also conducted an NMS analysis with my Modified Whittaker plot, and the patterns I observed in species associations with environmental characteristics on transect methods were consistent here as well. Though I we did not collect soil samples . I observed the same things from MW plots as I did from Transect plots. There is one small exception, where the Mitchel Marsh convex hull overlaps with the YMarsh convex hull, and this has more to do with coincidence of similar species found within MW plots on those sites, YMarsh and Mitchell Marsh both had a large presence of C. lyngbyei. In this case, Mitchell Marsh also had instances of salt tolerant species within the MW plots, suggesting that there is variation within Mitchell not only at different scales but at different extents of sampling. This also suggests or reinforces the notion by suggesting that Mitchell Marsh has saltier soils compared to Salmon Creek Marsh, despite both of them having freshwater influences. Also, despite differences in shape, all of the restored marshes’ convex hulls converge over Carex lyngbyei, which is the species mostly strongly correlated with disturbed and restored conditions.

MW NMS that shows consistent species patterns over elevation and tidal marshes. Soil samples were not collected for MW plots.

6. Significance: How is your spatial problem important to science? to resource managers?

Over 40 years later, the tidal marshes of Salmon River Estuary are still very different, and it’s possible they were different to begin with, based on their unique geographies, that influence salt inundation and soil patterns. Salmon River Estuary salt marshes also appear to have responded to and developed from disturbance differently; each is still following a different restoration pathway 40 years later. Soil salinity, elevation, and inundation patterns (channels) vary by geography among these salt marsh sites in the SRE, and have likely played a role in determining species composition by site. Extensive stands dominated by dense cover of C. lyngbyei represent an alternate stable state for vegetation of SRE salt marshes, and would be an important component to understanding novel community functions, as they relate to restoration and future scenarios. Species assemblages vary both by biogeography (soil, elevation, location) and land use history (pasture use, diking, dike removal).

The spatial problem I have chosen to investigate is of importance to scientists, as it provides further insight into how Pacific Northwest Coastal Estuaries recover from land use and disturbance, a phenomenon that has not been thoroughly studied yet. This work is valuable to land managers and conservationists who are tasked with coastal wetland mitigation in the PNW, as this case study severs as one of the few examples of long term estuary esearch on the Oregon coast. Estuaries have historically served as habitat and resources for keystone salmonid fish species, invertebrates, migratory birds, waterfowl, and mammals (such as beavers), particularly in the Pacific Northwest (Klemas 2013). Restoring these habitats is critical for protecting wildlife, managing wetland resources and eco-services, and maintaining our shorelines, especially as we face sea level rise from impending climate change. Threats to environmental stability in the case of wetlands can also harm their cultural value. Wetlands have inherent eco-beauty and are among the many natural systems associated with outdoor recreation. If wetlands are disturbed via pollution, compaction, or compositional change in vegetation, little is understood about the certainty of recovery in the context of reference conditions.

Factors influencing the richness and environmental integrity of estuaries like Salmon River are associated with the physiognomic and taxonomic features of the plant community. This study focuses on the spatio-temporal distribution patterns of Salmon River vegetation to explore how remnant and restored marshes differ in terms of biodiversity and species composition. Salmon River Estuary is especially noteworthy due to its unique management history. Salmon River is exceptional compared to other Oregon coastal wetlands, as it was federally protected before any industries could establish influence. Arguably, Salmon River has avoided most disturbance from development because of its relatively small size; there have been no instances of dredging or jetty construction for the purposes of navigation. Previous use as pastureland with dike establishment in the 1960’s is the dikes established in the 1960s and removed from 1978 onwards encapsulates the majority of known human influence on the marsh. Beginning in 1978, periodic vegetation surveys on site with long term ecological research at Salmon River has created intimate knowledge of the estuary and promoted ecological sustainability.

There has been a dramatic shift with regards to wetland protection and how our government and the public views them. Over the last few decades, policies promoting wetland conversion and development have been exchanged for protection and regulation initiatives. Wetland management goals today are largely focused on restoration to compensate for loss and damage, which has forged new industries tasked with wetland recovery and monitoring. However, some mitigation project datasets and sites are too small to collect useful data or make a meaningful impact on a large environmental scale. It is necessary to amass a variety of high quality data on larger wetland areas over longer periods of time to address how natural recovery processes may be employed for wetland conservation. Salmon River is an excellent long term case study for examining the prospect of rehabilitation for ecosystem functionality and reference conditions (Frenkel and Moran 1991; Frenkel 1995; Flitcroft et al. 2016).

So to recap, there seems to be a false association between restoration and reference conditions, in the case of Salmon River. Though Salmon River Estuary is an example of successful restoration, it does not mirror pre-disturbance ecosystem structure. Thus it seems challenging to manage for pristine environments, since pre-disturbance conditions are often not well known or pristine for that matter, the impacts of disturbance may persist over long periods of time (like C. lyngbyei) and both intact and disturbed wetlands are changing constantly so its impossible to protect them from undue influence. However, we can continue to define and promote functionality in ecosystems as function may change with structure. As a result, from the work I have done, I would recommend adapting our expectations for Salmon River and for the passive, deliberate restoration of estuaries. I would also recommend to continue to restore for function and monitor structural changes so we can understand and infer novel function in ecosystem context (Gray et al 2002)

7. Your learning: what did you learn about software (a) Arc-Info, (b) Modelbuilder and/or GIS programming in Python, (c) R, (d) other?

I have previous experience with Arc that I have developed and expanded upon at Oregon State while pursuing my MS in Geography. I was able to do some work in Arc with mapping my data, but I utilized knowledge that I had acquired previously. Ultimately, I have analyzed most if not all of my data thus far in PC-Ord, under the guidance of Dr. Bruce McCune (the software creator), which enables a variety of ordination and multivariate analysis options. I learned how to conduct Mantel tests, ISA, and other multivariate comparisons within PC-Ord.  I did not learn much additionally about Python or R, except what other students were able to complete from their tutorials.

8.What did you learn about statistics, including (a) hotspot, (b) spatial autocorrelation (including correlogram, wavelet, Fourier transform/spectral analysis), (c) regression (OLS, GWR, regression trees, boosted regression trees), and (d) multivariate methods (e.g., PCA)?

I learned that statistics programs like PC-Ord can be preferable for datasets that have lower or more fine scale spatial resolution; my data were difficult to use in Arc because it’s format was not easily to interpolate. As a result, I learned a lot about Principal Components Analysis techniques to tease out patterns in my data, and look at how species assemblage patterns vary by environmental conditions and site treatments (dike removal). However, I found it helpful to visualize my data in a map, even though I was limited to the point location of my plots, and categorize them based on spatial patterns from my plot data (percent cover of species).  I also learned how to conduct a Mantel test on my data at a variety of spatial scales to look for auto-correlation.

From learning about other student’s tutorials, I learned about geographically weighted regression (GWR), and how one may examine clustering of particular environmental conditions with the GWR tool in Arc. GWR can show that certain environmental characteristics  (like canopy cover, understory vegetation cover, elevation) show positive or negative correlation in different locations. I also learned about hotspot analysis from student tutorials as well and found that it can also be used to infer spatial relationships between environmental variables and location. Hotspot analysis can be useful for looking at density of populations or biodiversity.

9. How did you respond to comments from your peers and mentors/instructors?

I received useful comments from my peers and the instructor (Dr. Julia Jones), about considering how salt inundation on the tidal marshes I study, may have a causal relationship to differences in species assemblages in addition to restoration treatment (diking and dike removal).  It’s important to acknowledge confounding factors within my data, as my study is inductive.Dr. Jones also suggested originally that I investigate spatial autocorrelation with Mantel tests, to see how variable species assemblages are within my data. I was able to incorporate feedback that helped me with the formation of my analysis and interpretation of my results.

Literature Cited

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Flitcroft, RL, Bottom, DL, Haberman, KL, Bierley, KF, Jones, KK, Simenstad, CA, Gray, A,

Ellingson, KS, Baumgartner, E, Cornwell, TJ and Campbell, LA. 2016. Expect the

unexpected: Place-Based Protections can lead to Unforeseen benefits. Aquatic

Conservation: Marine and Freshwater Ecosystems (26): 39-59.

Mather, P.M. 1976. Computational methods of multivariate analysis in physical

geography. J. Wiley & Sons, London. 532 pp.

McCune, B. and J. B. Grace. 2002. Analysis of Ecological Communities. MjM Software,

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National Soil Survey Center, Natural Resources Conservation Service, U.S. Department of

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