Assessing the functional significance of forest biodiversity using digital imagery

A) Decades of study have attempted to define a generalized environmental heterogeneity - biodiversity relationship, with the traditional MacArthurian niche-based hypothesis remaining as the dominant reference point; i.e., increasing heterogeneity promotes biodiversity by increasing resource opportunities. However, studies have frequently reported negative or non-significant relationships. In a vast majority of them, environmental heterogeneity was defined along a gradient of increasing randomness, towards complete disorder. A new conceptual framework could help to reconcile the array of observed relationships. Using an exhaustive literature review, we test a conceptual framework proposing that the direction of environmental heterogeneity - biodiversity relationships is contingent on the level of human footprint to which an ecosystem is subjected (the anthropocline). The results reveal that highly-modified and semi-natural ecosystems are characterized by a dominance of positive and negative EH-BD relationships, respectively, whereas natural ecosystems show mixed responses. Out of this novel framework arises the revised perspective that natural ecosystems are typified, not by maximal or minimal, but by intermediate levels of environmental heterogeneity.


B) The objective of this study was to use side-view digital photography to describe the structure and dynamics of natural forest stands in the context of the FunDiv Europe exploratory network. From March to December 2012, time-series of digital images were recorded at each forest plot using commercially available digital camera units. Three regions of interest (ROI) representing the over-, mid- and under-storey layers of the forest plots were manually delineated for each image time-series. The image processing workflow produced a list of six variables for each of the three ROIs: 1) Growing season length (days), 2) Growing season onset (day of the year), 3) Growing season offset (day of the year), 4) Mean greenness cover (%), 5) Mean image texture (Mean information gain; MIG), 6) Mean image anisotropy (MIG ratio). Of the phenology variables, the growing season length and the green offset date of the understorey time-series were both positively associated to Shannon tree species richness, which alone explained up to 22% of the variation. Of the structural heterogeneity variables, only the anisotropy of the midstorey ROI was significantly, and positively, correlated to Shannon tree species richness. Further analyses will aim to generalize these results and correlate forest structural heterogeneity and phenology to understorey diversity and productivity variables.

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