Showing papers on "Occupancy published in 2018"

Modeling occupancy and behavior for better building design and operation—A critical review

Abstract: People spend more than 90% of their life time in buildings, which makes occupant behavior one of the leading influences of energy consumption in buildings. Occupancy and occupant behavior, which refer to human presence inside buildings and their active interactions with various building system such as lighting, heating, cooling, ventilation, window blinds, and plugs, attract great attention of research with regard to better building design and operation. Due to the stochastic nature of occupant behavior, prior occupancy models vary dramatically in terms of data sampling, spatial and temporal resolution. This paper provides a comprehensive review of the current modeling efforts of occupant behavior, summarizes occupancy models for various applications including building energy performance analysis, building architectural and engineering design, intelligent building operations and building safety design, and presents challenges and areas where future research could be undertaken. In addition, modeling requirement for different applications is analyzed. Furthermore, a few commonly used statistical and data mining models are presented. The purpose of this paper is to provide a modeling reference for future researchers so that a proper method or model can be selected for a specific research purpose.

ednaoccupancy: An r package for multiscale occupancy modelling of environmental DNA data.

Abstract: In this article, we describe ednaoccupancy, an r package for fitting Bayesian, multiscale occupancy models. These models are appropriate for occupancy surveys that include three nested levels of sampling: primary sample units within a study area, secondary sample units collected from each primary unit and replicates of each secondary sample unit. This design is commonly used in occupancy surveys of environmental DNA (eDNA). ednaoccupancy allows users to specify and fit multiscale occupancy models with or without covariates, to estimate posterior summaries of occurrence and detection probabilities, and to compare different models using Bayesian model-selection criteria. We illustrate these features by analysing two published data sets: eDNA surveys of a fungal pathogen of amphibians and eDNA surveys of an endangered fish species.

An agent-based stochastic Occupancy Simulator

Abstract: Occupancy has significant impacts on building performance. However, in current building performance simulation programs, occupancy inputs are static and lack diversity, contributing to discrepancies between the simulated and actual building performance. This paper presents an Occupancy Simulator that simulates the stochastic behavior of occupant presence and movement in buildings, capturing the spatial and temporal occupancy diversity. Each occupant and each space in the building are explicitly simulated as an agent with their profiles of stochastic behaviors. The occupancy behaviors are represented with three types of models: (1) the status transition events (e.g., first arrival in office) simulated with probability distribution model, (2) the random moving events (e.g., from one office to another) simulated with a homogeneous Markov chain model, and (3) the meeting events simulated with a new stochastic model. A hierarchical data model was developed for the Occupancy Simulator, which reduces the amount of data input by using the concepts of occupant types and space types. Finally, a case study of a small office building is presented to demonstrate the use of the Simulator to generate detailed annual sub-hourly occupant schedules for individual spaces and the whole building. The Simulator is a web application freely available to the public and capable of performing a detailed stochastic simulation of occupant presence and movement in buildings. Future work includes enhancements in the meeting event model, consideration of personal absent days, verification and validation of the simulated occupancy results, and expansion for use with residential buildings.

Sampling scales define occupancy and underlying occupancy-abundance relationships in animals

Abstract: Occupancy-abundance (OA) relationships are a foundational ecological phenomenon and field of study, and occupancy models are increasingly used to track population trends and understand ecological interactions. However, these two fields of ecological inquiry remain largely isolated, despite growing appreciation of the importance of integration. For example, using occupancy models to infer trends in abundance is predicated on positive OA relationships. Many occupancy studies collect data that violate geographical closure assumptions due to the choice of sampling scales and application to mobile organisms, which may change how occupancy and abundance are related. Little research, however, has explored how different occupancy sampling designs affect OA relationships. We develop a conceptual framework for understanding how sampling scales affect the definition of occupancy for mobile organisms, which drives OA relationships. We explore how spatial and temporal sampling scales, and the choice of sampling unit (areal vs. point sampling), affect OA relationships. We develop predictions using simulations, and test them using empirical occupancy data from remote cameras on 11 medium-large mammals. Surprisingly, our simulations demonstrate that when using point sampling, OA relationships are unaffected by spatial sampling grain (i.e., cell size). In contrast, when using areal sampling (e.g., species atlas data), OA relationships are affected by spatial grain. Furthermore, OA relationships are also affected by temporal sampling scales, where the curvature of the OA relationship increases with temporal sampling duration. Our empirical results support these predictions, showing that at any given abundance, the spatial grain of point sampling does not affect occupancy estimates, but longer surveys do increase occupancy estimates. For rare species (low occupancy), estimates of occupancy will quickly increase with longer surveys, even while abundance remains constant. Our results also clearly demonstrate that occupancy for mobile species without geographical closure is not true occupancy. The independence of occupancy estimates from spatial sampling grain depends on the sampling unit. Point-sampling surveys can, however, provide unbiased estimates of occupancy for multiple species simultaneously, irrespective of home-range size. The use of occupancy for trend monitoring needs to explicitly articulate how the chosen sampling scales define occupancy and affect the occupancy-abundance relationship.

Evolutionary Deep Learning for Car Park Occupancy Prediction in Smart Cities

Abstract: This study presents a new technique based on Deep Learning with Recurrent Neural Networks to address the prediction of car park occupancy rate. This is an interesting problem in smart mobility and we here approach it in an innovative way, consisting in automatically design a deep network that encapsulates the behavior of the car occupancy and then is able to make an informed guess on the number of free parking spaces near to the medium time horizon. We analyze a real world case study consisting of the occupancy values of 29 car parks in Birmingham, UK, during eleven weeks and compare our results to other predictors in the state-of-the-art. The results show that our approach is accurate to the point of being useful for being used by citizens in their daily lives, as well as it outperforms the existing competitors.

Effects of human impacts on habitat use, activity patterns and ecological relationships among medium and small felids of the Atlantic Forest

Abstract: Competition theory and niche theory suggest that two morphologically similar species may coexist by reducing the overlap of at least one dimension of their ecological niche. The medium and small Neotropical felids are an interesting group of carnivore species for studying intraguild competition. Due to differences in size it is expected that the larger ocelot exert strong interference competition on the smaller felids (southern tiger cat, margay and jaguarundi); which, in turn, may exert exploitative competition among themselves. Moreover, landscape changes due to human activities may alter these interspecific interactions. We studied the habitat use and the spatial and temporal interspecific relations of the medium and small Atlantic Forest felids, in a landscape with different levels of anthropogenic impact. We estimated the detection probability, and occupancy probability of these cats and whether these parameters are affected by environmental and anthropogenic variables or by the estimated occupancy and detection probability of the ocelot. We estimated the overlap in daily activity patterns between pairs of the four species and changes in their activity in response to anthropogenic impact. We also studied the potential changes that may have occurred in the daily activity of the small felids in relation to ocelot's occupancy probability. The probability of habitat use of the small- and medium-size felids was negatively associated to the intensity of landscape use by humans. Co-occurrence models indicated that the probability of habitat use by southern tiger cats decreased with ocelot occupancy probability. This effect was higher as human disturbance increased. Moreover, the ocelot and the southern tiger cat became more nocturnal in sites with higher human access, suggesting that they may be temporally avoiding encounters with humans or dogs. Conservation of medium and small felids in the Atlantic Forest depends not only on the establishment and implementations of protected areas but also on the management of human's land uses.

Modeling Avian Full Annual Cycle Distribution and Population Trends with Citizen Science Data

Abstract: Information on species9 distributions and abundances, environmental associations, and how these change over time are central to the study and conservation of wildlife populations. This information is challenging to obtain at relevant scales across range-wide extents for two main reasons. First, local and regional processes that affect populations vary throughout the year and across species9 ranges, requiring fine-scale, year-round information across broad - sometimes hemispheric - spatial extents. Second, while citizen science projects can collect data at these scales, using these data requires additional steps to address known sources of bias. Here we present an analytical framework to address these challenges and generate year-round, range-wide distributional information using citizen science data. To illustrate this approach, we apply the framework to Wood Thrush (Hylocichla mustelina), a long distance Neotropical migrant and species of conservation concern, using data from the citizen science project eBird. We estimate relative occupancy and abundance with enough spatiotemporal resolution to support inference across a range of spatial scales throughout the annual cycle. This includes intra-annual estimates of the range (quantified as the area of occupancy), intra-annual estimates of the associations between species and features of their local environment, and inter-annual season-specific trends in relative abundance. This is the first example of an analysis to capture intra- and inter-annual distributional dynamics across the entire range of a broadly distributed, highly mobile species.

Habitat filtering determines the functional niche occupancy of plant communities worldwide

Abstract: How the patterns of niche occupancy vary from species-poor to species-rich communities is a fundamental question in ecology that has a central bearing on the processes that drive patterns of biodiversity. As species richness increases, habitat filtering should constrain the expansion of total niche volume, while limiting similarity should restrict the degree of niche overlap between species. Here, by explicitly incorporating intraspecific trait variability, we investigate the relationship between functional niche occupancy and species richness at the global scale. 2.We assembled 21 datasets worldwide, spanning tropical to temperate biomes and consisting of 313 plant communities representing different growth forms. We quantified three key niche occupancy components (the total functional volume, the functional overlap between species and the average functional volume per species) for each community, related each component to species richness, and compared each component to the null expectations. 3.As species richness increased, communities were more functionally diverse (an increase in total functional volume), and species overlapped more within the community (an increase in functional overlap) but did not more finely divide the functional space (no decline in average functional volume). Null model analyses provided evidence for habitat filtering (smaller total functional volume than expectation), but not for limiting similarity (larger functional overlap and larger average functional volume than expectation) as a process driving the pattern of functional niche occupancy. 4.Synthesis. Habitat filtering is a widespread process driving the pattern of functional niche occupancy across plant communities and coexisting species tend to be more functionally similar rather than more functionally specialized. Our results indicate that including intraspecific trait variability will contribute to a better understanding of the processes driving patterns of functional niche occupancy

Occupancy Counting With Burst and Intermittent Signals in Smart Buildings

Abstract: Zone-level occupancy counting is a critical technology for smart buildings and can be used for applications, such as building energy management, surveillance, and public safety. Existing occupancy counting techniques typically require installation of large number of occupancy monitoring sensors inside a building and an established wireless network. In this paper, in order to achieve occupancy counting, we consider the use of Wi-Fi probe requests that are continuously transmitted from Wi-Fi enabled smart devices for discovering nearby access points. To this end, Wi-Fi Pineapple equipment are used for passively capturing ambient probe requests from Wi-Fi devices, such as smart phones and tablets, where no connectivity to a Wi-Fi network is required. This information is then used to localize users within coarsely defined occupancy zones, and subsequently to obtain occupancy count within each zone at different time scales. An interacting multimodel (IMM) Kalman filter technique is developed to improve occupancy counting accuracy. Our numerical results using Wi-Fi data collected at a university building show that the use of Wi-Fi probe requests in conjunction with IMM-based Kalman filters can be a viable solution for zone-level occupancy monitoring in smart buildings.

Occupancy-Based HVAC Control with Short-Term Occupancy Prediction Algorithms for Energy-Efficient Buildings

Abstract: This study aims to develop a concrete occupancy prediction as well as an optimal occupancy-based control solution for improving the efficiency of Heating, Ventilation, and Air-Conditioning (HVAC) systems. Accurate occupancy prediction is a key enabler for demand-based HVAC control so as to ensure HVAC is not run needlessly when when a room/zone is unoccupied. In this paper, we propose simple yet effective algorithms to predict occupancy alongside an algorithm for automatically assigning temperature set-points. Utilizing past occupancy observations, we introduce three different techniques for occupancy prediction. Firstly, we propose an identification-based approach, which identifies the model via Expectation Maximization (EM) algorithm. Secondly, we study a novel finite state automata (FSA) which can be reconstructed by a general systems problem solver (GSPS). Thirdly, we introduce an alternative stochastic model based on uncertain basis functions. The results show that all the proposed occupancy prediction techniques could achieve around 70% accuracy. Then, we have proposed a scheme to adaptively adjust the temperature set-points according to a novel temperature set algorithm with customers’ different discomfort tolerance indexes. By cooperating with the temperature set algorithm, our occupancy-based HVAC control shows 20% energy saving while still maintaining building comfort requirements.

Chapter 14 – Species Co-Occurrence

Abstract: So far in this book, we have focused on scenarios where the occupancy status of only a single species is of interest. As outlined in Chapter 2 , there are a number of ecological applications where inference is desired about the presence or absence of multiple species across a set of sampling units. In this chapter we turn our attention to investigations of co-occurrence for two or more species, from detection/nondetection data. Specifically, we demonstrate how imperfect detection can lead to misleading conclusions about the nature of any co-occurrence interaction between two species, and describe modeling approaches that have been developed to examine patterns, and the underlying dynamic processes, of species co-occurrence. These modeling approaches enable detection probabilities to be incorporated directly into our inferential framework, and are very flexible: allowing detection probabilities to depend on the number of species present at a unit, and for a potential lack of independence of species detections in the same survey occasions. Our presentation of the modeling approaches draws heavily from the methods described in previous chapters. Examples are provided of their application, investigating co-occurrence patterns for two salamander species in Great Smoky Mountains National Park, USA, and dynamics of northern spotted owl and barred owl interactions in western Oregon, USA.

Lions and leopards coexist without spatial, temporal or demographic effects of interspecific competition

Abstract: Although interspecific competition plays a principal role in shaping species behaviour and demography, little is known about the population-level outcomes of competition between large carnivores, and the mechanisms that facilitate coexistence. We conducted a multilandscape analysis of two widely distributed, threatened large carnivore competitors to offer insight into coexistence strategies and assist with species-level conservation. We evaluated how interference competition affects occupancy, temporal activity and population density of a dominant competitor, the lion (Panthera leo), and its subordinate competitor, the leopard (Panthera pardus). We collected camera-trap data over 3 years in 10 study sites covering 5,070 km2 . We used multispecies occupancy modelling to assess spatial responses in varying environmental and prey conditions and competitor presence, and examined temporal overlap and the relationship between lion and leopard densities across sites and years. Results showed that both lion and leopard occupancy was independent of-rather than conditional on-their competitor's presence across all environmental covariates. Marginal occupancy probability for leopard was higher in areas with more bushy, "hideable" habitat, human (tourist) activity and topographic ruggedness, whereas lion occupancy decreased with increasing hideable habitat and increased with higher abundance of very large prey. Temporal overlap was high between carnivores, and there was no detectable relationship between species densities. Lions pose a threat to the survival of individual leopards, but they exerted no tractable influence on leopard spatial or temporal dynamics. Furthermore, lions did not appear to suppress leopard populations, suggesting that intraguild competitors can coexist in the same areas without population decline. Aligned conservation strategies that promote functioning ecosystems, rather than target individual species, are therefore advised to achieve cost- and space-effective conservation.

Reduced occupancy of hedgehogs (Erinaceus europaeus) in rural England and Wales: The influence of habitat and an asymmetric intra-guild predator

Abstract: Agricultural landscapes have become increasingly intensively managed resulting in population declines across a broad range of taxa, including insectivores such as the hedgehog (Erinaceus europaeus). Hedgehog declines have also been attributed to an increase in the abundance of badgers (Meles meles), an intra-guild predator. The status of hedgehogs across the rural landscape at large spatial scales is, however, unknown. In this study, we used footprint tracking tunnels to conduct the first national survey of rural hedgehog populations in England and Wales. Single and two-species occupancy modelling was used to quantify hedgehog occupancy in relation to habitat and predator covariates. Hedgehog occupancy was low (22% nationally), and significantly negatively related to badger sett density and positively related to the built environment. Hedgehogs were also absent from 71% of sites that had no badger setts, indicating that large areas of the rural landscape are not occupied by hedgehogs. Our results provide the first field based national survey of hedgehogs, providing a robust baseline for future monitoring. Furthermore, the combined effects of increasing badger abundance and intensive agriculture may have provided a perfect storm for hedgehogs in rural Britain, leading to worryingly low levels of occupancy over large spatial scales.

Substituting space for time: Empirical evaluation of spatial replication as a surrogate for temporal replication in occupancy modelling

Abstract: 1. Occupancy models that account for detection probability are important analytical tools in conservation monitoring. Traditionally, occupancy models relied on detection/non-detection data generated from temporal replicates for estimating detectability. Due to logistical challenges and financial costs involved, many large-scale field studies instead use spatial replication as a surrogate. The efficacy of the two approaches and their statistical validity has generally sought support from simulation-based inferences rather than empirical data. 2. Using the sloth bear Melursus ursinus as an example, we compared estimates of occupancy and detection probabilities obtained from temporal and spatial sampling designs. We carried out temporally replicated camera trap surveys and spatially replicated sign surveys across a 754-km2 area around Bhadra Tiger Reserve in the Western Ghats of India. 3. We sampled along forest/coffee plantation roads in 58 grid cells of 13-km2 each, treating these cells as independent sites. We used the standard single-season model for the camera trap survey data, and the single-season correlated-detections model (with Markovian dependence) for the sign survey data, and incorporated ecological covariates that likely influenced occupancy and detection probabilities. 4. Occupancy estimates from the two surveys and corresponding modelling approaches were similar [ψ^c (SE)^ = 0.58 (0.03) for camera trap surveys; ψ^s (SE)^ = 0.56 (0.03) for sign surveys]. In both cases, the influence of covariates corroborated our a priori predictions. Site-level estimates of occupancy from the two methods were highly correlated (r = 0.78). We generated a combined estimate of sloth bear occupancy in the region as an inverse-variance weighted average of the two estimates [ψ^(SE)^=0.57(0.02)]. 5. Synthesis and applications. Studies that aim to evaluate occupancy models should account for spatial variation in occupancy/detection probabilities, particularly when making inferences on species-habitat relationships. We show that spatial replication can serve as a good surrogate for temporal replication in occupancy studies, which may be useful for distribution assessments of species when field resources are limited or logistical challenges preclude traditional survey approaches that yield temporally replicated data. Our results therefore provide basis for efficient targeting of funds and field resources, particularly for practitioners involved in monitoring species at large landscape scales. This article is protected by copyright. All rights reserved.

Factors influencing ocelot occupancy in Brazilian Atlantic Forest reserves

Abstract: Over 80% of Atlantic Forest remnants are <50 ha and protected areas are embedded in a matrix dominated by human activities, undermining the long-term persistence of carnivores. The ocelot (Leopardus pardalis) is an opportunistic species, but little is known about its tolerance to habitat alterations and the influence of other species on its occupancy in Atlantic Forest remnants. We used camera traps to assess ocelot occupancy in protected areas of Atlantic Forest in southeastern Brazil. We found a positive correlation between the occupancy of ocelots and top predators (jaguars, Panthera onca, and pumas, Puma concolor), and a weaker negative effect between the number of domestic dogs (Canis familiaris) detected and ocelot occupancy. Ocelot detection was higher at sites with more eucalyptus, suggesting that ocelots frequently use these areas. Better-protected areas surrounded by permeable matrices may be critical to the persistence of ocelots in the fragmented Atlantic Forest.

Thermal-Sensor-Based Occupancy Detection for Smart Buildings Using Machine-Learning Methods

Abstract: In this article, we propose a novel approach to detect the occupancy behavior of a building through the temperature and/or possible heat source information. The new method can be used for energy reduction and security monitoring for emerging smart buildings. Our work is based on a building simulation program, EnergyPlus, from the Department of Energy. EnergyPlus can model various time-series inputs to a building such as ambient temperature; heating, ventilation, and air-conditioning (HVAC) inputs; power consumption of electronic equipment; lighting; and number of occupants in a room, sampled each hour, and produce resulting temperature traces of zones (rooms). Two machine-learning-based approaches for detecting human occupancy of a smart building are applied herein, namely support vector regression (SVR) and recurrent neural network (RNN). Experimental results with SVR show that the four-feature model provides accurate detection rates, giving a 0.638 average error and 5.32% error rate, and the five-feature model delivers a 0.317 average error and 2.64% error rate. This indicates that SVR is a viable option for occupancy detection. In the RNN method, Elman’s RNN can estimate occupancy information of each room of a building with high accuracy. It has local feedback in each layer and, for a five-zone building, it is very accurate for occupancy behavior estimation. The error level, in terms of number of people, can be as low as 0.0056 on average and 0.288 at maximum, considering ambient, room temperatures, and HVAC powers as detectable information. Without knowing HVAC powers, the estimation error can still be 0.044 on average, and only 0.71% estimated points have errors greater than 0.5. Our article further shows that both methods deliver similar accuracy in the occupancy detection. But the SVR model is more stable for adding or removing features of the system, while the RNN method can deliver more accuracy when the features used in the model do not change a lot.