Occupancy

About: Occupancy is a research topic. Over the lifetime, 2757 publications have been published within this topic receiving 68288 citations.

Quantitative predictions for patch occupancy of capercaillie in fragmented habitats

Abstract: A major conclusion of studying metapopulation biology is that species conservation should favor regional rather than local population persistence. Regional persistence is tightly linked to size, spatial configuration and quality of habitat patches. Hence it is important for the management of endangered species that priority patches can be identified. We developed a predictive model of patch occupancy by capercaillie, a threatened grouse species, based on a single snapshot of data. We used logistic regression to predict patch occupancy as a function of patch size, isolation, connectivity, relative altitude, and biogeographical area. The probability of a patch being occupied increased with patch size and increasing altitude, and decreased with increasing distance to the next occupied patch. Patch size was the most important predictor although occupied patches varied considerably in size. Our model only uses data on the number, size and spatial configuration of habitat patches. It is a useful tool to designate priority areas for conservation, i.e. large core patches with high resilience in habitat quality, smaller island-patches that still have high probability of being inhabited or becoming recolonised, and patches functioning as ‘‘stepping stones’’. If capercaillie is to be preserved, habitat suitability needs to be maintained in a functional network of patches that account for size and inter-patch distance thresholds as found in this study. We suggest that similar area-isolation relationships are valid for almost any region within the distribution range of capercaillie. The thresholds for occupancy are however likely to depend on characteristics of the respective landscape. The outcome of our study emphasises the need for future investigations that explore the relationship between patch occupancy, matrix quality and its resistance to dispersing individuals.

Occupancy-Based Frequent Pattern Mininga

Abstract: Frequent pattern mining is an important data mining problem with many broad applications. Most studies in this field use support (frequency) to measure the popularity of a pattern, namely the fraction of transactions or sequences that include the pattern in a data set. In this study, we introduce a new interesting measure, namely occupancy, to measure the completeness of a pattern in its supporting transactions or sequences. This is motivated by some real-world pattern recommendation applications in which an interesting pattern should not only be frequent, but also occupies a large portion of its supporting transactions or sequences. With the definition of occupancy we call a pattern dominant if its occupancy value is above a user-specified threshold. Then, our task is to identify the qualified patterns which are both dominant and frequent. Also, we formulate the problem of mining top-k qualified patterns, that is, finding k qualified patterns with maximum values on a user-defined function of support and occupancy, for example, weighted sum of support and occupancy. The challenge to these tasks is that the value of occupancy does not change monotonically when more items are appended to a given pattern. Therefore, we propose a general algorithm called DOFRA (DOminant and FRequent pattern mining Algorithm) for mining these qualified patterns, which explores the upper bound properties on occupancy to drastically reduce the search process. Finally, we show the effectiveness of DOFRA in two real-world applications and also demonstrate the efficiency of DOFRA on several real and large synthetic datasets.

Office building occupancy monitoring through image recognition sensors

Abstract: In the Architecture, Engineering, construction and Operations (AEcO) there is a growing interest in the use of the building Information modelling (bIm). Through integration of information and processes in a digital model, bIm can optimise resources along the lifecycle of a physical asset. Despite the potential savings are much higher in the operational phase, bIm is nowadays mostly used in design and construction stages and there are still many barriers hindering its implementation in Facility management (Fm). Its scarce integration with live data, i.e. data that changes at high frequency, can be considered one of its major limitations in Fm. The aim of this research is to overcome this limit and prove that buildings or infrastructures operations can benefit from a digital model updated with live data. The scope of the research concerns the optimisation of Fm operations. The optimisation of operations can be further enhanced by the use of maintenance smart contracts allowing a better integration between users’ behaviour and maintenance implementation. In this case study research, the Image recognition (Imr), a type of Artificial Intelligence (AI), has been used to detect users’ movements in an office building, providing real time occupancy data. This data has been stored in a bIm model, employed as single reliable source of information for Fm. This integration can enhance maintenance management contracts if the bIm model is coupled with a smart contract. Far from being a comprehensive case study, this research demonstrates how the transition from bIm to the Asset Information model (AIm) and, finally, to the Digital Twin (i.e. a near-real-time digital clone of a physical asset, of its conditions and processes) is desirable because of the outstanding benefits that have already been measured in other industrial sectors by applying the principles of Industry 4.0.

Logging and indigenous hunting impacts on persistence of large Neotropical animals

Abstract: Areas allocated for industrial logging and community-owned forests account for over 50% of all remaining tropical forests. Landscape-scale conservation strategies that include these forests are expected to have substantial benefits for biodiversity, especially for large mammals and birds that require extensive habitat but that are susceptible to extirpation due to synergies between logging and hunting. In addition, their responses to logging alone are poorly understood due to their cryptic behavior and low densities. In this study, we assessed the effects of logging and hunting on detection and occupancy rates of large vertebrates in a multiple-use forest on the Guiana Shield. Our study site was certified as being responsibly managed for timber production and indigenous communities are legally guaranteed use-rights to the forest. We coupled camera-trap data for wildlife detection with a spatially explicit dataset on indigenous hunting. A multi-species occupancy model found a weak positive effect of logging on occupancy and detection rates, while hunting had a weak negative effect. Model predictions of species richness were also higher in logged forest sites compared to unlogged forest sites. Density estimates for jaguars and ocelots in our multiple-use area were similar to estimates reported for fully protected areas. Involvement of local communities in forest management, control of forest access, and nesting production forests in a landscape that includes protected areas seemed important for these positive biodiversity outcomes. The maintenance of vertebrate species bodes well for both biodiversity and the humans that depend on multiple-use forests.