Abstract
Biocrusts, topsoil communities formed by mosses, lichens, liverworts, algae, and cyanobacteria, are a key biotic component of dryland ecosystems worldwide. Experiments carried out with lichen- and moss-dominated biocrusts indicate that climate change may dramatically reduce their cover and diversity. Therefore, the development of reproducible methods to monitor changes in biocrust diversity and abundance across multiple spatio-temporal scales is key for evaluating how climate change may impact biocrust communities and the myriad of ecosystem functions and services that rely on them. In this study, we collected lichen-dominated biocrust samples from a semi-arid ecosystem in central Spain. Their α-diversity was then evaluated using very high spatial resolution hyperspectral images (pixel size of 0.091 mm) measured in laboratory under controlled conditions. Support vector machines were used to map the biocrust composition. Traditional α-diversity metrics (i.e., species richness, Shannon’s, Simpson’s, and Pielou’s indices) were calculated using lichen fractional cover data derived from their classifications in the hyperspectral imagery. Spectral diversity was calculated at different wavelength ranges as the coefficient of variation of different regions of the reflectance spectra of lichens and as the standard deviation of the continuum removal algorithm (SD_CR). The accuracy of the classifications of the images obtained was close to 100%. The results showed the best coefficient of determination (r2 = 0.47) between SD_CR calculated at 680 nm and the α-diversity calculated as the Simpson’s index, which includes species richness and their evenness. These findings indicate that this spectral diversity index could be used to track spatio-temporal changes in lichen-dominated biocrust communities. Thus, they are the first step to monitor α-diversity of biocrust-forming lichens at the ecosystem and regional levels, a key task for any program aiming to evaluate changes in biodiversity and associated ecosystem services in drylands.
Highlights
Biocrusts are communities formed by photoautotrophic and heterotrophic organisms that live on the soil surface and cover a large part of the non-vegetated surface in drylands worldwide [1]
To evaluate the spectral diversity metrics that better capture the α-diversity of biocrust-forming lichens, we evaluated through linear regression models the relationships between spectral diversity and α-diversity metrics, calculated using the fractional cover of each plot
The high values of the area under the curve (AUC) derived from the receiver operating characteristics (ROC) curves showed that the support vector machine (SVM) used were successfully trained to classify biocrust-forming lichens (Figure 4)
Summary
Biocrusts are communities formed by photoautotrophic (algae, lichens, cyanobacteria, liverworts, and bryophytes) and heterotrophic (bacteria, fungi, protozoa, and nematodes) organisms that live on the soil surface and cover a large part of the non-vegetated surface in drylands worldwide [1]. These communities influence fundamental ecosystem processes in drylands, including—but not limited to—nutrient cycling, soil respiration, and runon-runoff dynamics [2], and are critical for maintaining the multiplicity of ecosystem services they provide [3]. For this reason, finding accurate and operational methods to estimate the cover and diversity of biocrust constituents is a key goal for any ecosystem monitoring program in drylands
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