May 2017

Habitat structural complexity is one of the most important factors in structuring biological communities. Recent advances in structure-from-motion and photogrammetry, like the ones used to make the 3D maps featured in this website, allow non-experts to reconstruct 3D digital representations of habitats and organisms from which structural complexity can be measured.

But little attention has been given to quantifying the errors in using these techniques (but see Figueira et al. 2015 in Remote Sensing) and variability in results under different surveying and environmental conditions, errors that are important when we want to examine changes in habitat complexity over space and time.

A super useful paper Characterizing measurement errors of habitat structural complexity from marine ecosystem 3D models just got published by Ecology and Evolution!

 Fig. 4 from Bryson et al. 2017 Imagery mosaics from multi-day surveys at Horseshoe Reef, Lizard Island, Great Barrier Reef, Australia.

In this paper we evaluated the accuracy, precision and bias in measurements of marine habitat structural complexity derived from 3D maps, using repeated surveys of artificial reefs (with known structure) and natural coral reefs. We quantified measurement errors as a function of survey image coverage, actual surface rugosity and the morphological community composition of corals.

We discovered habitat complexity measurements were between 8 -15% lower for in-water reconstructions when compared to the reference reconstruction, but highly repeatable with a standard deviation of 0.065 (4.9% of the true rugosity) for a resolution of 2.5 cm. During our in situ surveys, we discovered measurements could be biased by up to 7.5% because of varying environmental conditions across surveys. The errors were larger (~7.5%) at more complex sites dominated by coarse branching corals, compared to less complex sites dominated by massive or plating corals.

Figure 11 from Bryson et al. 2017 Relationships between 2 × 2m quadrat rugosity errors and quadrat rugosity modeled using OLS
Figure 12 from Bryson et al. 2017 Relationships between 2 × 2m quadrat rugosity errors and quadrat rugosity categorized by dominant coral morphotype coverage class modeled using OLS (adj. R2 = 0.473). Model predicted rugosity error vs. rugosity is shown for each class (solid line) and compared to the model predicted relationship for ‘mixed’ type quadrats (dashed line).

The logistical advantages of these techniques in marine habitats mean they are likely to replace traditional techniques (such as chain-and-tape) in future ecologically-focused research. The quantitative relationships demonstrated in this study have important implications for data collection and the interpretation of measurements when examining changes in habitat complexity using 3D models.



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