Image background assessment as a novel technique for insect microhabitat identification

The effects of climate change, urbanisation and agriculture are changing the way insects occupy habitats. Some species may utilise anthropogenic microhabitat features for their existence, either because they prefer them to natural features, or because of no choice. Other species are dependent on natural microhabitats. Identifying and analysing these insects' use of natural and anthropogenic microhabitats is important to assess their responses to a changing environment, for improving pollination and managing invasive pests. Traditional studies of insect microhabitat use can now be supplemented by machine learning-based insect image analysis. Typically, research has focused on automatic insect classification, but valuable data in image backgrounds has been ignored. In this research, we analysed the image backgrounds available on the ALA database to determine their microhabitats. We analysed the microhabitats of three insect species common across Australia: Drone flies, European honeybees and European wasps. Image backgrounds were classified as natural or anthropogenic microhabitats using computer vision and machine learning tools benchmarked against a manual classification algorithm. We found flies and honeybees in natural microhabitats, confirming their need for natural havens within cities. Wasps were commonly seen in anthropogenic microhabitats. Results show these insects are well adapted to survive in cities. Management of this invasive pest requires a thoughtful reduction of their access to human-provided resources. The assessment of insect image backgrounds is instructive to document the use of microhabitats by insects. The method offers insight that is increasingly vital for biodiversity management as urbanisation continues to encroach on natural ecosystems and we must consciously provide resources within built environments to maintain insect biodiversity and manage invasive pests.Comment: Submitted in Ecological Informatics journal, first review completed, 19 pages, 10 figure

Development of an interface using penalisation method for improving computational performance of bushfire simulation tools

Wind is the dominant environmental factor affecting wildland fire intensity and spread. Previously, fire analysts and managers have relied on local measurements and site-specific forecasts to determine how winds influence fire. The advancements in computer hardware, increased availability of electronic topographical and experimental data, and advances in numerical methods for computing winds, have led to the development of new tools capable of simulating wind flow. Several numerical models have been developed for fire prediction and forecasting. Modelling wind in physics-based models like Fire Dynamics Simulator (FDS) has been shown to produce promising results, but at an inordinate cost. Because of the high computational expense, physics-based models are not suitable for operational use. Little research has been conducted to improve the computational speed of these models. The current study intends to decrease the computational cost of physics-based fire simulations and improve physics-based models by including more complicated driving winds. Physics-based wildfire simulations are driven by inlet boundary conditions which model the atmospheric boundary layer. Various inlet conditions, such as the 1/7-powerlaw or the log law models with artificial turbulence (e.g. the synthetic eddy method, [SEM]) can be used as an inlet to generate a statistically steady wind field for a fire simulation. The power-law inlet is the default inlet condition used in FDS where the wind develops turbulence as it sweeps through the domain, and is often used with wall-of-wind type methods. The log-law inlet generates a log wind profile similar to Atmospheric Boundary Layer (ABL). Development of techniques for imposing inlet conditions and initial conditions for flow simulations have been topics of interest for the past few decades. Current inlet and initial conditions requires time in a scale order of 100s of CPU hours, for generating an appropriate condition to start a fire simulation, hence resulting in increased computational expense. A novel nesting method has been implemented, which involves two regions : penalisation and blending, named as the PenaBlending method. The initial conditions of the fire simulations in FDS are set to the initial condition prescribed by an external model or simulation. This is achieved by a one-way coupling method. External wind data, for which u,v,w can vary in space and time, can be obtained. The precursor data can be generated either from any reduced wind model such as Windninja, which gives terrain modified wind data, or by using analytical methods such as generating logarithmic windfield using Matlab. These external data can be introduced into the FDS domain through a penalization region at the inlet/outlet. A blending region has also been implemented near the specified inlet/outlet which allows a smooth mixing of a precursor wind field to that in the simulation domain. This new inlet condition allows complicated terrain modified temporally and spatially varying wind fields, obtained from precursor simulations or any other models, to be implemented relatively easily in the FDS domain. To test the implementation of this method, a flat terrain is considered in the current study. However, this method could also be used for complicated terrain structures, as a part of future studies. The PenaBlending method provides appropriate flow conditions with reduced computational effort (up to ~ 80%), to start a fire simulation, and, hence, reduces the computational expense of physics-based models. The results obtained using the PenaBlending method have been compared with that obtained using the existing inlet conditions of FDS, like the SEM method, wall-of-wind method and mean-forcing methods, using the 1/7 power-law or log-law inlets. To test these three methods, a set of fire simulations have been conducted and tested against the PenaBlending method. It was found that the results of the PenaBlending methods agree well with that of existing methods, with small variations for both the wind and fire cases. FDS 6.6.0 (the version used in this study) requires a very fine grid to obtain grid convergence. This is not feasible in the case of a large-scale simulation because of very high computation cost. FDS 6.2.0, with a reaction-rate-limiter combustion model, needs less fine grids to obtain grid convergence. Therefore, this combustion model is re-introduced into FDS 6.6.0, providing an option of choosing between two different combustion models, as a part of this study. For all the simulations, the reaction-rate-limiter combustion model has been used. The simulations are carried out in a neutral-atmospheric stability condition. However, the PenaBlending method can apply any general driving wind, and the effect of atmospheric stability, could be included, as part of future studies. The PenaBlending method could be extended in conjunction with Monin-Obukhov Similarity Theory (introduced in FDS 6.6.0) to model fire in various atmospheric stability conditions

Image background assessment as a novel technique for insect microhabitat identification

Habitat fragmentation under increased urbanisation, industrial agriculture and land clearing, are changing the way insects occupy habitat. Some species are highly adaptable and may occupy urbanised areas, utilising anthropogenic microhabitat-scale features. Other species are dependent on natural elements of their habitats, having to locate small regions of natural microhabitat within increasingly hostile landscapes. Consequently, humans are encountering insects in new settings. Identifying and analysing insects’ use of natural and anthropogenic microhabitats is therefore important to assess their responses to a changing environment, for instance to improve pollination or manage invasive pests. But such studies are labour-intensive. Traditional studies of insect microhabitat use can now be supplemented by machine learning-based insect image analysis. Typically, research has focused on automatic insect classification, but valuable data appearing in image backgrounds has been ignored. In this research, we analysed the backgrounds of insect images available in the Atlas of Living Australia database to determine the microhabitats in which they were commonly photographed. We analysed the image backgrounds of three globally distributed insect species that are common across Australia: Drone flies (Eristalis tenax), European honey bees (Apis mellifera), and European wasps (Vespula germanica). Image backgrounds were classified broadly as either natural or anthropogenic using computer vision and machine learning tools benchmarked against a manual classification algorithm. Our automated image background classification achieved 97.4% accuracy when compared against manual classification. Mis-classifications were scarce, usually less than 1%, and primarily for backgrounds of wood and soil or bare ground. Our results indicate that drone flies and European honey bees were predominantly photographed against natural backgrounds (flies manual classifier 95±3%, automated classifier 94%, bees 89±2%,87%), implying frequent observations by humans in natural microhabitat. European wasps were less frequently photographed against natural backgrounds (70±6%,63%). Within this data set, observations of the wasps in anthropogenic microhabitats were more common than for flies and bees. Our results are aligned with the expectation that the wasps are relatively well-suited to urban environments, and that European honey bees and drone flies utilise natural features of their environment. In general, although biases in data collected without formal protocols limits their application, our new automated approach for image background analysis can provide valuable data about insects’ interactions with humans, our artefacts, and natural features of their environments.</p