Managing undesirable outputs in the Australian construction industry using Data Envelopment Analysis models

Carbon reduction has become one of the most significant challenges for economic growth. This paper presents the preliminary analysis of undesirable output reduction targets and emission schedules in temporal-spatial comparisons based on Data Envelopment Analysis. The reduction targets of undesirable outputs are investigated, including the maximum, input, technical and ideal reduction targets. Four Data Envelopment Analysis models that are based on a sequential benchmark technology and variable returns to scale are introduced to measure these reduction percentages. In order to formulate the optimal emission schedule of undesirable outputs, an optimal model is provided without inflation. Data from the Australian construction industry from 2000 to 2010 are employed to develop the models. The results of the analysis indicate that the Australian Government could achieve its promised carbon reduction targets in the construction industry. Most Australian regions' construction industries possess carbon mitigation potential and some of them could increase their desirable outputs if carbon were decreased. This paper suggests that policymakers can benefit from formulating various suitable undesirable output reduction objectives and schedules through the models developed. The research method can be replicated for other sectors and regions focussing on undesirable output reduction.

Strong influence of local habitat structure on mammals reveals mismatch with edge effects models

Context: What determines mammal occurrence across wildland-urban edges? A better understanding of the variables involved will help update edge effects theory and improve our ability to conserve biota in urbanizing landscapes. Objectives: For the first time, we tested whether the occurrence of mammals across urban-forest edges and forest interiors was best predicted by: (1) edge variables (i.e. edge type and distance to an urban boundary), (2) local habitat structure (e.g. proportion of understory cover), or (3) edge variables after accounting for local habitat structure. Methods: Using 77 camera stations in South-Eastern Australia, we quantified the factors influencing the occurrence of five native mammals (brown antechinus, bush rat, common brushtail possum, black wallaby and long-nosed bandicoot) and three non-native mammals (red fox, cat, and dog). Results: The occurrence of most native and non-native mammals was best predicted by local habitat structure rather than by edge variables. Although edge variables had effects on most species occurrences, local habitat structure outweighed the impacts of edge effects. Conclusions: Our findings are important for management and urban planning as they suggest that local-scale management of habitat and habitat retention at urban edges will mitigate urban impacts on fauna. Our work reveals a critical mismatch in the spatial scale of predictive variables commonly used in edge effects models (edge types and distance to a boundary) compared with the smaller scale of local habitat variables, which underlie most species occurrence. We emphasize the need to consider heterogeneity within patches in predictive frameworks of edge effects.

A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting

Accurate and timely traffic flow prediction is crucial to proactive traffic management and control in data-driven intelligent transportation systems (D2ITS), which has attracted great research interest in the last few years. In this paper, we propose a Spatial-Temporal Weighted K-Nearest Neighbor model, named STW-KNN, in a general MapReduce framework of distributed modeling on a Hadoop platform, to enhance the accuracy and efficiency of short-term traffic flow forecasting. More specifically, STW-KNN considers the spatial-temporal correlation and weight of traffic flow with trend adjustment features, to optimize the search mechanisms containing state vector, proximity measure, prediction function, and K selection. urthermore, STW-KNN is implemented on a widely adopted Hadoop distributed computing platform with the MapReduce parallel processing paradigm, for parallel prediction of traffic flow in real time. inally, with extensive experiments on real-world big taxi trajectory data, STW-KNN is compared with the state-of-the-art prediction models including conventional K-Nearest Neighbor (KNN), Artificial Neural Networks (ANNs), Naïve Bayes (NB), Random orest (R), and C4.. The results demonstrate that the proposed model is superior to existing models on accuracy by decreasing the mean absolute percentage error (MAPE) value more than 11.9% only in time domain and even achieves 89.71% accuracy improvement with the MAPEs of between 4% and 6.% in both space and time domains, and also significantly improves the efficiency and scalability of short-term traffic flow forecasting over existing approaches.

Markov models and network analysis reveal sex-specific differences in the space-use of a coastal apex predator

Understanding the links between external variables such as habitat and interactions with conspecifics and animal space-use is fundamental to developing effective management measures. In the marine realm, automated acoustic tracking has become a widely used method for monitoring the movement of free-ranging animals, yet researchers generally lack robust methods for analysing the resulting spatial-usage data. In this study, acoustic tracking data from male and female broadnose sevengill sharks Notorynchus cepedianus, collected in a system of coastal embayments in southeast Tasmania were analyzed to examine sex-specific differences in the sharks' coastal space-use and test novel methods for the analysis of acoustic telemetry data. Sex-specific space-use of the broadnose sevengill shark from acoustic telemetry data was analysed in two ways: The recently proposed spatial network analysis of between-receiver movements was employed to identify sex-specific space-use patterns. To include the full breadth of temporal information held in the data, movements between receivers were furthermore considered as transitions between states of a Markov chain, with the resulting transition probability matrix allowing the ranking of the relative importance of different parts of the study area. Both spatial network and Markov chain analysis revealed sex-specific preferences of different sites within the study area. The identification of priority areas differed for the methods, due to the fact that in contrast to network analysis, our Markov chain approach preserves the chronological sequence of detections and accounts for both residency periods and movements. In addition to adding to our knowledge of the ecology of a globally distributed apex predator, this study presents a promising new step towards condensing the vast amounts of information collected with acoustic tracking technology into straightforward results which are directly applicable to the management and conservation of any species that meet the assumptions of our model.

Selecting umbrella species for conservation: a test of habitat models and niche overlap for beach-nesting birds

Umbrella species are rarely selected systematically from a range of candidate species. On sandy beaches, birds that nest on the upper beach or in dunes are threatened globally and hence are prime candidates for conservation intervention and putative umbrella species status. Here we use a maximum-likelihood, multi-species distribution modeling approach to select an appropriate conservation umbrella from a group of candidate species occupying similar habitats. We identify overlap in spatial extent and niche characteristics among four beach-nesting bird species of conservation concern, American oystercatchers (Haematopus palliatus), black skimmers (Rynchops niger), least terns (Sterna antillarum) and piping plovers (Charadrius melodus), across their entire breeding range in New Jersey, USA. We quantify the benefit and efficiency of using each species as a candidate umbrella on the remaining group. Piping plover nesting habitat encompassed 86% of the least tern habitat but only 15% and 13% of the black skimmer and American oystercatcher habitat, respectively. However, plovers co-occur with all three species across 66% of their total nesting habitat extent (~ 649 ha), suggesting their value as an umbrella at the local scale. American oystercatcher nesting habitat covers 100%, 99% and 47% of piping plover, least tern and black skimmer habitat, making this species more appropriate conservation umbrellas at a regional scale. Our results demonstrate that the choice of umbrella species requires explicit consideration of spatial scale and an understanding of the habitat attributes that an umbrella species represents and to which extent it encompasses other species of conservation interest. Notwithstanding the attractiveness of the umbrella species concept, local conservation interventions especially for breeding individuals in small populations may still be needed.

Environmental controls on spatial patterns in the long-term persistence of giant kelp in central California

As marine management measures increasingly protect static areas of the oceans, it is important to make sure protected areas capture and protect persistent populations. Rocky reefs in many temperate areas worldwide serve as habitat for canopy-forming macroalgae and these structure-forming species of kelps (order Laminariales) often serve as important habitat for a great diversity of species. Macrocystis pyrifera is the most common canopy-forming kelp species found along the coast of California, but the distribution and abundance of M. pyrifera varies in space and time. The purpose of this study is to determine what environmental parameters are correlated with and their relative contribution to the spatial and temporal persistence of M. pyrifera along the central coast of California and how well those environmental parameters can be used to predict areas where this species is more likely to persist. Nine environmental variables considered in this study included depth of the seafloor, structure of the rocky reef, proportion of rocky reef, size of kelp patch, biomass of kelp within a patch, distance from the edge of a kelp patch, sea surface temperature, wave orbital velocities, and population connectivity of individual kelp patches. Using a generalized linear mixed effects model (GLMM), the persistence of M. pyrifera was significantly associated with seven of the nine variables considered: depth, complexity of the rocky reef, proportion of rock, patch biomass, distance from the edge of a patch, population connectivity, and wave orbital velocities. These seven environmental variables were then used to predict the persistence of kelp across the central coast, and these predictions were compared to a reserved dataset of M. pyrifera persistence, which was not used in the creation of the GLMM. The environmental variables were shown to accurately predict the persistence of M. pyrifera within the central coast of California (r = 0.71, P < 0.001). Because persistence of giant kelp is important to the community structure of kelp forests, understanding those factors that support persistent populations of M. pyrifera will enable more effective management of these ecosystems.

Sensitivity of fine-scale species distribution models to locational uncertainty in occurrence data across multiple sample sizes

To generate realistic predictions, species distribution models require the accurate coregistration of occurrence data with environmental variables. There is a common assumption that species occurrence data are accurately georeferenced; however, this is often not the case. This study investigates whether locational uncertainty and sample size affect the performance and interpretation of fine-scale species distribution models. This study evaluated the effects of locational uncertainty across multiple sample sizes by subsampling and spatially degrading occurrence data. Distribution models were constructed for kelp (Ecklonia radiata), across a large study site (680 km2) off the coast of southeastern Australia. Generalized additive models were used to predict distributions based on fine-resolution (2·5 m cell size) seafloor variables, generated from multibeam echosounder data sets, and occurrence data from underwater towed video. The effects of different levels of locational uncertainty in combination with sample size were evaluated by comparing model performance and predicted distributions. While locational uncertainty was observed to influence some measures of model performance, in general this was small and varied based on the accuracy metric used. However, simulated locational uncertainty caused changes in variable importance and predicted distributions at fine scales, potentially influencing model interpretation. This was most evident with small sample sizes. Results suggested that seemingly high-performing, fine-scale models can be generated from data containing locational uncertainty, although interpreting their predictions can be misleading if the predictions are interpreted at scales similar to the spatial errors. This study demonstrated the need to consider predictions across geographic space rather than performance alone. The findings are important for conservation managers as they highlight the inherent variation in predictions between equally performing distribution models, and the subsequent restrictions on ecological interpretations.

Spatial dynamics of the knob-tailed gecko Nephrurus stellatus in a fragmented agricultural landscape

In fragmented landscapes, a species' dispersal ability and response to habitat condition are key determinants of persistence. To understand the relative importance of dispersal and condition for survival of Nephrurus stellatus (Gekkonidae) in southern Australia, we surveyed 92 woodland remnants three times. This gecko favours early post-fire succession conditions so may be at risk of extinction in the long-unburnt agricultural landscape. Using N-mixture models, we compared the influence of four measures of isolation, patch area and two habitat variables on the abundance and occurrence of N. stellatus, while taking into account detection probability. Patch occupancy was high, despite the long-term absence of fire from most remnants. Distance to the nearest occupied site was the most informative measure of patch isolation, exhibiting a negative relationship with occupancy. Distance to a nearby conservation park had little influence, suggesting that mainland-island metapopulation dynamics are not important. Abundance and occurrence were positively related to %-cover of spinifex (Triodia), indicating that niche-related factors may also contribute to spatial dynamics. Patterns of patch occupancy imply that N. stellatus has a sequence of spatial dynamics across an isolation gradient, with patchy populations and source-sink dynamics when patches are within 300 m, metapopulations at intermediate isolation, and declining populations when patches are separated by >1-2 km. Considering the conservation needs of the community, habitat condition and connectivity may need to be improved before fire can be reintroduced to the landscape. We speculate that fire may interact with habitat degradation and isolation, increasing the risk of local extinctions.