GEOG 566






         Advanced spatial statistics and GIScience

April 28, 2017

Tutorial 1: Species Area Curves with Vegetation Community Data

Filed under: 2017,Tutorial 1 2017 @ 1:58 pm

Salmon River Estuary: Map of study site and plot locations

Species Area curves represent the relationship between a specified area of habitat and the number of species present there. Species-area curves are useful for comparing species richness data from sample units that differ in area, especially nested sampling plots. Empirically, the larger the area, the larger the number of species you are likely to capture; a principle that has held mathematically true in ecology. Species-area curves can address the adequacy of sample size and estimate the overall diversity and spatial heterogeneity within a community dataset. Species-area curves help to conceptualize the relationship between species richness and spatial scale in a sampled environment. Typically species-area curves take only species presence, into account and demonstrates average number of species per plot sampled. Species-area curves can also help researchers determine the appropriate spatial grain for a study area, as the spatial grain (the size/dimensions of the smallest sample unit) has a strong impact on the shape of a species area curve. Species area curves are a basic diversity measure that are helpful for describing the heterogeneity of a community sample. Species accumulation curves, are derived by a log transformation of the species area curve, and describe a linear trend of how species richness increases over area sampled. The species accumulation curve is also called the ‘effort’ or ‘discovery’ curve, as the species richness measured is often related to the amount of time and space sampled by a researcher. The goal for my tutorial is to demonstrate how I have created species-area and species accumulation curves to investigate spatial pattern of vegetative richness within and between tidal marshes at my study site.

Equation for Species Area Relationship: S = cAz  >>>>> log(S) = c + zlog(A)

Where S = Species Richness, A = area, and c and z are empirically determined constants from plotted data (Rosenzweig 1995).

The research question I have relevant to this exercise/tutorial is: Do species area relationships differ between restored and remnant tidal marshes at Salmon River Estuary? How do field methods, specifically, nested, rectangular (Modified Whittaker) plots and square, non-nested (Transect) plots capture these species-area relationships differently?

Between the two types of sampling techniques I applied, I would ultimately expect the Modified Whittaker plots, which cover more area, to capture the same number if not more salt marsh species compared to transect plots. I hypothesize that the reference marsh would have a greater number of species, and a steeper slope for the species-area curve compared to restored marshes. I would also expect more recently restored marshes to have less ‘steep curves’ than marshes that were restored less frequently.

Name of the tool or approach that you used:

PC-ORD is statistical software counterpart to R that that performs ordinations and multivariate analyses on community datasets. Microsoft Excel was also used for graphics. Initially I used PC-ORD to generate the average number of species per plot over cumulative area sampled, for each plot size in each tidal marsh. I then exported the data to excel and produced graphs of cumulative species over log10 area and figured out the slope of each line. I kept it simple and used software I am familiar with, to expedite the process of producing useful results I can interpret and be confident in.

Brief description of steps you followed to complete the analysis:

Step 1: Enter data into an excel spreadsheet and format for PC-ORD

Open PC-ORD. Start a new project (File > new project). Name the project appropriately and import the excel sheet into the ‘main matrix’ input. Save the project. Use the summary command to produce a species-area curve output. The output will include average number of species per plot over sampled distance. Repeat this for every plot size sampled ((x2)1 m2, 10 m2, 100 m2, 1,000 m2).

Save PC-ORD output and import back into Excel. Graph average species richness against area sampled for species area curves. Do this for transect 1 m2 plots and MW 1 m2 plots, for each tidal marsh, using Excel commands (highlight data > insert > scatter plot graph). Calculate the log10 of the area sampled and log10 of species richness, for all nested MW plots (1 m2, 10 m2, 100 m2, 1,000 m2). The trendline is a fitted linear trend that describes log species richness vs. log species area.

Brief description of results:

The species curve for transect data appears to rise and plateau quickly, whereas the MW species curve rises more steadily and plateaus later (Figure 1 a-d, Figure 2 a-d). MW plots had higher overall species richness (21) and lower species turnover (1.7) compared to transect data species richness (17) and species turnover (4.5). This is an expected result, as there were fewer larger MW plots that covered more area compared to transect plots. Furthermore, the transect plots exist on environmental gradients related to salinity and elevation, suggesting greater species turnover within sample units, compared to MW plots that are not aligned with gradients (McCune and Grace 2002; Rosenzweig 1995). Average species accumulation for transects is similar (16.26) to average species accumulation for MW plots (17.93), suggesting that both represent adequate sample size and appropriate spatial grain for this study site (Figure 2 a, b, c, d, Table 2). ANOVA and post hoc t tests indicated that all tidal marshes have significantly different species accumulation rates.

Critique of the method – what was useful, what was not?

I would describe this method as useful, fast and simple. It was reasonably accessible and easy to do, without requiring extensive knowledge of statistical programs or software packages. However, I would not have found the method as easy if I had not just completed a course at OSU that instructed me on how to use PC-ORD (taught by Dr. Bruce McCune, who created the software). Needing to use two programs to get my results may be a bit cumbersome, however with an initial quick google search I was not able to find any helpful code on how to produce semilog species area graphs and species area curves with R. It seems like pursuing R would have been more frustrating and the results would not have been as clear to me. I am satisfied with my approach and pleased with my outcome, as it is simple and the results make sense to me.

Example of Results:

 

 

 

 

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