Habitat Complexity Affects the Structure but Not the Diversity of Sessile Communities on Tropical Coastal Infrastructure

Increasing human population, urbanisation, and climate change have resulted in the proliferation of hard coastal infrastructure such as seawalls and breakwaters. There is increasing impetus to create multifunctional coastal defence structures with the primary function of protecting people and property in addition to providing habitat for marine organisms through eco-engineering - a nature-based solutions approach. In this study, the independent and synergistic effects of physical complexity and seeding with native oysters in promoting diversity and abundances of sessile organisms were assessed at two locations on Penang Island, Malaysia. Concrete tiles with varying physical and biological complexity (flat, 2.5 cm ridges and crevices, and 5 cm ridges and crevices that were seeded or unseeded with oysters) were deployed and monitored over 12 months. The survival of the seeded oysters was not correlated with physical complexity. The addition of physical and biological complexity interacted to promote distinct community assemblages, but did not consistently increase the richness, diversity, or abundances of sessile organisms through time. These results indicate that complexity, whether physical or biological, is only one of many influences on biodiversity on coastal infrastructure. Eco-engineering interventions that have been reported to be effective in other regions may not work as effectively in others due to the highly dynamic conditions in coastal environment. Thus, it is important that other factors such as the local species pools, environmental setting (e.g., wave action), biological factors (e.g., predators), and anthropogenic stressors (e.g., pollution) should also be considered when designing habitat enhancements. Such factors acting individually or synergistically could potentially affect the outcomes of any planned eco-engineering interventions.

Spatially Variable Effects of Artificially-Created Physical Complexity on Subtidal Benthos

In response to the environmental damage caused by urbanization, Nature-based Solutions (NbS) are being implemented to enhance biodiversity and ecosystem processes with mutual benefits for society and nature. Although the field of NbS is flourishing, experiments in different geographic locations and environmental contexts have produced variable results, with knowledge particularly lacking for the subtidal zone. This study tested the effects of physical complexity on colonizing communities in subtidal habitats in two urban locations: (1) Plymouth, United Kingdom (northeast Atlantic) and (2) Tel Aviv, Israel (eastern Mediterranean) for 15- and 12-months, respectively. At each location, physical complexity was manipulated using experimental tiles that were either flat or had 2.5 or 5.0 cm ridges. In Plymouth, biological complexity was also manipulated through seeding tiles with habitat-forming mussels. The effects of the manipulations on taxon and functional richness, and community composition were assessed at both locations, and in Plymouth the survival and size of seeded mussels and abundance and size of recruited mussels were also assessed. Effects of physical complexity differed between locations. Physical complexity did not influence richness or community composition in Plymouth, while in Tel Aviv, there were effects of complexity on community composition. In Plymouth, effects of biological complexity were found with mussel seeding reducing taxon richness, supporting larger recruited mussels, and influencing community composition. Our results suggest that outcomes of NbS experiments are context-dependent and highlight the risk of extrapolating the findings outside of the context in which they were tested.

SVD Entropy Reveals the High Complexity of Ecological Networks

Quantifying the complexity of ecological networks has remained elusive. Primarily, complexity has been defined on the basis of the structural (or behavioural) complexity of the system. These definitions ignore the notion of “physical complexity,” which can measure the amount of information contained in an ecological network, and how difficult it would be to compress. We present relative rank deficiency and SVD entropy as measures of “external” and “internal” complexity, respectively. Using bipartite ecological networks, we find that they all show a very high, almost maximal, physical complexity. Pollination networks, in particular, are more complex when compared to other types of interactions. In addition, we find that SVD entropy relates to other structural measures of complexity (nestedness, connectance, and spectral radius), but does not inform about the resilience of a network when using simulated extinction cascades, which has previously been reported for structural measures of complexity. We argue that SVD entropy provides a fundamentally more “correct” measure of network complexity and should be added to the toolkit of descriptors of ecological networks moving forward.

Hotspot generation for unique identification with nanomaterials

Nanoscale variations in the structure and composition of an object are an enticing basis for verifying its identity, due to the physical complexity of attempting to reproduce such a system. The biggest practical challenge for nanoscale authentication lies in producing a system that can be assessed with a facile measurement. Here, a system is presented in which InP/ZnS quantum dots (QDs) are randomly distributed on a surface of an aluminium-coated substrate with gold nanoparticles (Au NPs). Variations in the local arrangement of the QDs and NPs is shown to lead to interactions between them, which can suppress or enhance fluorescence from the QDs. This position-dependent interaction can be mapped, allowing intensity, emission dynamics, and/or wavelength variations to be used to uniquely identify a specific sample at the nanoscale with a far-field optical measurement. This demonstration could pave the way to producing robust anti-counterfeiting devices.

SVD entropy reveals the high complexity of ecological networks

Quantifying the complexity of ecological networks has remained elusive. Primarily, complexity has been defined on the basis of the structural (or behavioural) complexity of the system. These definitions ignore the notion of 'physical complexity', which can measure the amount of information contained in an ecological network, and how difficult it would be to compress. We present relative rank deficiency and SVD entropy as measures of 'external' and 'internal' complexity respectively. Using bipartite ecological networks, we find that they all show a very high, almost maximal, physical complexity. Pollination networks, in particular, are more complex when compared to other types of interactions. In addition, we find that SVD entropy relates to other structural measures of complexity (nestedness, connectance, and spectral radius), but does not inform about the resilience of a network when using simulated extinction cascades, which has previously been reported for structural measures of complexity. We argue that SVD entropy provides a fundamentally more 'correct' measure of network complexity and should be added to the toolkit of descriptors of ecological networks moving forward.

Weight Encode Reconstruction Network for Computed Tomography in a Semi-Case-Wise and Learning-Based Way

Classic algebraic reconstruction technology (ART) for computed tomography requires pre-determined weights of the voxels for the projected pixel values to build the equations. However, such weights cannot be accurately obtained due to the high physical complexity and computation resources required. In this study, we propose a semi-case-wise learning-based method named Weight Encode Reconstruction Network (WERNet) to co-learn the target voxel values and intrinsic physics of the case in a self-supervised manner without labeling the target voxel set. With the help of gradient normalization, the WERNet reconstructed voxel set with a high accuracy and showed a higher capability of denoising compared to the classic ART methods. Moreover, the encoder of the network is transferable from a voxel set with complex structures to unseen cases without the deduction of the accuracy. Our method can be applied in tomography-related applications and similar inversion problems even with unclear intrinsic physics.