Investigating three‐dimensional mesoscale habitat complexity and its ecological implications using low‐cost RGB‐D sensor technology

Abstract

Summary1. Structural complexity, that is, spatial patterns of structural 'units of interest', is one of the most fundamental drivers in habitat use of animals and other functions of habitats. Assessment of habitat structural complexity (e.g. mangrove root complexity) and its ecological implications (e.g. mangroves as nursery sites) has, however, been hampered by the lack of satisfactory methods for realistically measuring and analysing field habitat structure. 2. We demonstrate a simple low-cost approach comprising an RGB_D scanning sensor (Kinect), free and readily accessible software (Meshlab, Matlab (or Octave), Qubicle Constructor and Voxeliser) and fractal dimension to measure mangrove root structure and complexity at spatial scales relevant to juvenile fish use of the habitat. The cube-counting method was used for three-dimensional characterisation of fractal dimensions of space around aerial roots of mangroves from a 'fish point of view'. Space-fish relationship under different ecological situations was assessed by simulating a falling tide and using different rectangular prisms to represent varying fish allometries utilizing the space. 3. Fractal dimension (FD) of space around roots decreased with decreasing height of the 'object' mimicking a falling tide. The same root system may therefore look more complex to aquatic animals as the water level decreases. We also assessed the variation in FD responding to manipulation of the density and arrangement of objects (pneumatophores). Difference in FD was generally small, due attention to replication is necessary for comparisons of habitat structural complexity using this metric. 4. This simple low-cost approach will enable field measurements of habitat structure at spatial scales relevant to its influence on habitat use and animal behaviour, as well as allowing realistic replication of field structure for laboratory-based experiments.

The power of 3D fractal dimensions for comparative shape and structural complexity analyses of irregularly shaped organisms

“…Besides their application at the level of individual organisms, fractal dimension analyses can be applied to the ecosystem scale for studying productivity, biodiversity or resilience (Bradbury & Reichelt, ; Kamal et al., ; Martin‐Garin et al., ; Thistle, Schneider, Gregory, & Wells, ; Young, Dey, Rogers, & Exton, ). Apart from largely descriptive and analytic applications, fractal dimension analyses could also be integrated in modelling approaches, for example to simulate growth processes under different evolutionary scenarios ( sensu Kaandorp, ; Kaandorp & Sloot, ; Merks, Hoekstra, Kaandorp, & Sloot, ; Polly et al., ; Wilke, Pfenninger, & Davis, ).…”

“…Box-counting, Minkowski-Bouligand dimension, or Hausdorff dimension;Schroeder, 1991). Fractal dimensions have also found their way into 3D applications, becoming more and more popular with the increasing availability of 3D documentation methods (Backes, Eler, Minghim, & Bruno, 2010;Kamal, Lee, & Warnken, 2014;Liu, Zhang, & Yue, 2003;Zhang, Liu, Dean, Sahgal, & Yue, 2006).…”

Distribution of fish among Avicennia and Sonneratia microhabitats in a tropical mangrove ecosystem in South China

“…only can't totally represent mangrove structural complexity (Green et al 2012); fractal dimension has limited sensitivity in detecting differences in spatial pattern (Kamal et al 2014). However, the existing methods used to measure the structural complexity of mangrove still have their limitations.…”

Accuracy and Precision of Habitat Structural Complexity Metrics Derived from Underwater Photogrammetry

“…It has also been demonstrated to be superior to this traditional approach when applied to modeled habitat as its not sensitive to the specific placement in 2D space of the measured transect [20]. However, other approaches, such as variance in height [17], slope, or even fractal dimension [16,42,43] may also prove useful as descriptors of structural complexity.…”