App purpose

This app is designed to look for evidence of statistically significant changes in macrofaunal composition between baseline and monitoring surveys. The app can be used for monitoring of marine licences (e.g. marine aggregate, offshore wind, renewables), MPAs or R&D sites.

Data

The macrofaunal abundance data used in the app come from the OneBenthic database. The app also uses spatial data polygons (see 'Map Layers' tab) to automatically assign samples to treatment groups (e.g. reference, licensed areas and secondary impact zones).

How it works

Select sector, baseline and monitoring surveys for comparison, and whether to use all or paired samples only (i.e. stations with both a baseline and a monitoring sample). Selected samples are shown in the map. For the marine aggregates sector (AGG), users must also select an RSMP survey array. To find surveys for comparison, turn on sample locations (see map 'samples' checkbox) and use the 'Draw a rectangle' tool to highlight an area of interest (N.B. a full list of 'baseline' surveys associated with each of the aggregate industry's RSMP arrays can be seen in 'samples' tab of the OneBenthic Baseline tool). Results will appear in the 'Search' tab. Selected data and assigned treatment groups can be viewed under the 'Data' tab. Data are analysed in various ways, with results available, where relevant, at different spatial scales (see 'Results' tab).

The Boxplot tab provides a graphical representation of differences in mean values of univariate summary metrics (taxon richness and abundance) between baseline and monitoring surveys by treatment category.

The Anova tab provides results of an Analysis of Variance, a statistical test for difference in the mean of groups (e.g. baseline vs monitoring).

The MDS tab shows a series of non-metric multidimensional scaling ordination (nMDS) plots based on Bray Curtis dissimilarity (4th root transformed macrofaunal abundance data). Each dot represents a sample, with positions reflecting similarity/dis-similarity in terms of macrofaunal composition. The stress value provides a measure of how well the 2-d plot represents the multidimensional data (stress values of <0.1 are considered good).

The Anosim test looks for evidence of statistically significant differences between the two groups of samples (i.e. baseline vs monitoring). Test outputs include R and p-values. The R value indicates the size of the difference between the two groups, with 0 indicating no difference and 1 a large difference. A p-value of <0.05 indicates that results are statistically significant. Both R and p-values should be considered when interpreting test results. Note that where there are large numbers of samples it may be possible to find statistically significant, yet very small differences between the groups. It is therefore important to consider the effect size (R value). For this reason we generally consider differences to only be of interest where p<0.05 and R>0.1. Anosim test outputs include a column for 'interpretation', following criteria in Goss-Souza (2015). Press button to see results displayed in the map (only relevant to treatment polygons). ANOSIM tests are performed using the R vegan package. Where ANOSIM finds a meaningful difference (i.e. p<0.05, R>0.1), relevant data are carried forward for a SIMPER test to identify which taxa are responsible for the differences.

The Simper test is carried out using the 'simper' function (vegan) based on Bray-Curtis dissimilarities. Note that analyses only run after clicking on each tab - please be patient. You can change the R value cut-off for SIMPER analysis here:

Contact

This is a beta version of the app. Users should satisfy themselves that comparisons are valid and carefully check the data and results. No liability is accepted by the app developer. For help/advice using the app (or to provide feedback), please get in touch (keith.cooper@cefas.co.uk).