Landscape-level patterns in photosynthetic marine fouling biofilm compositional heterogeneity as revealed by hyperspectral classification
Authors: Longyear Jennifer , Stoodley Paul 
Published: July 2020

&lt;p&gt;&lt;span&gt;Marine fouling biofilms typically have diverse community assemblages in which microalgae are strongly represented.&amp;#160; The visible light absorption properties of microalgal photosynthetic pigments typically drive the overall visible light reflectance spectra of these biofilms.&amp;#160; In some cases diagnostic spectral features can be used to infer algal taxonomy, while in mixed communities the overlapping pigment signatures of the constituent species often blur together.&amp;#160; In this study, we apply methods common in remote sensing approaches to spectral data to extract information from subtle variations in the reflectance spectra of mixed composition marine biofilms.&amp;#160; We demonstrate that marine biofilm community composition, as evidenced by their reflectance spectra, is both spatially heterogenous and spatially structured. &lt;/span&gt;&lt;/p&gt;
&lt;p&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
&lt;p&gt;&lt;span&gt;Visible-NIR hyperspectral images (3.3nm x 200 bands) of biofilms grown on 7.5cm x 7.5cm panels (n=9), immersed in a coastal marina at ~1m depth for 13 months, were captured with a benchtop line-scan imager.&amp;#160;&amp;#160; The hyperspectral data were smoothed and transformed to consolidate the major aspects of spectral variability.&amp;#160; A novel active learning spectral classification method incorporating iterative spectral library building by k-means clustering and spectral angle mapping, followed by hierarchical clustering by spectral similarity, discovered more than 70 distinct spectral classes present in the biofilms.&amp;#160; Accordingly, the hyperspectral images of the fouling biofilms were converted to spatially explicit spectral class maps, where each class was assumed representative of a distinct community compositional mix.&amp;#160; Hyperspectral indexing calibrated to chl &lt;em&gt;a&lt;/em&gt; surface area density was used to map biomass for the same images.&amp;#160; &lt;/span&gt;&lt;/p&gt;
&lt;p&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
&lt;p&gt;&lt;span&gt;Cross-tabulating the spectral class and biomass data, it was apparent that for these biofilms, different biomass density levels were consistently associated with specific community compositions (spectral classes.)&amp;#160; Only a small number of the possible classes were represented in the densest areas of biofilm, suggesting that these species composition mixes have a competitive advantage.&amp;#160; In contrast, the full diversity of class types was present in the low biomass areas.&amp;#160; &lt;/span&gt;&lt;/p&gt;
&lt;p&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
&lt;p&gt;&lt;span&gt;Our hyperspectral approach does not convey exact species composition, as would pooled metagenomic sampling or in-depth microscopy.&amp;#160; However it does allow for the examination of spatially explicit changes in biofilm composition at relatively large scales (the landscape), and so may be a useful tool in hypothesis generation, long term monitoring, and other environmental biofilm applications. &lt;/span&gt;&lt;/p&gt;