Showing papers on "Occupancy published in 2013"

Occupancy estimation and modeling

Abstract: Occupancy estimation and modeling , Occupancy estimation and modeling , کتابخانه دیجیتال جندی شاپور اهواز

Occupancy pattern detection, estimation and prediction

Abstract: Systems and methods are described for predicting and/or detecting occupancy of an enclosure, such as a dwelling or other building, which can be used for a number of applications. An a priori stochastic model of occupancy patterns based on information of the enclosure and/or the expected occupants of the enclosure is used to pre-seed an occupancy prediction engine. Along with data from an occupancy sensor, the occupancy prediction engine predicts future occupancy of the enclosure. Various systems and methods for detecting occupancy of an enclosure, such as a dwelling, are also described.

Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analysed with occupancy models

Abstract: Many publications documenting large-scale trends in the distribution of species make use of opportunistic citizen data, that is, observations of species collected without standardized field protocol and without explicit sampling design. It is a challenge to achieve reliable estimates of distribution trends from them, because opportunistic citizen science data may suffer from changes in field efforts over time (observation bias), from incomplete and selective recording by observers (reporting bias) and from geographical bias. These, in addition to detection bias, may lead to spurious trends. We investigated whether occupancy models can correct for the observation, reporting and detection biases in opportunistic data. Occupancy models use detection/nondetection data and yield estimates of the percentage of occupied sites (occupancy) per year. These models take the imperfect detection of species into account. By correcting for detection bias, they may simultaneously correct for observation and reporting bias as well. We compared trends in occupancy (or distribution) of butterfly and dragonfly species derived from opportunistic data with those derived from standardized monitoring data. All data came from the same grid squares and years, in order to avoid any geographical bias in this comparison. Distribution trends in opportunistic and monitoring data were well-matched. Strong trends observed in monitoring data were rarely missed in opportunistic data. Synthesis and applications. Opportunistic data can be used for monitoring purposes if occupancy models are used for analysis. Occupancy models are able to control for the common biases encountered with opportunistic data, enabling species trends to be monitored for species groups and regions where it is not feasible to collect standardized data on a large scale. Opportunistic data may thus become an important source of information to track distribution trends in many groups of species.

Occupancy Detection from Electricity Consumption Data

Abstract: Detecting when a household is occupied by its residents is fundamental to enable a number of home automation applications. Current systems for occupancy detection usually require the installation of dedicated sensors, like passive infrared sensors, magnetic reed switches, or cameras. In this paper, we investigate the suitability of digital electricity meters -- which are already available in millions of households worldwide -- to be used as occupancy sensors. To this end, we have collected fine-grained electricity consumption data along with ground-truth occupancy information for 5 households during a period of about 8 months. Our results show that using common classification methods it is possible to achieve occupancy detection accuracies of more than 80%.

Monitoring the Status and Trends of Tropical Forest Terrestrial Vertebrate Communities From Camera Trap Data: A Tool for Conservation

Abstract: Reducing the loss of biodiversity is key to ensure the future well being of the planet. Indicators to measure the state of biodiversity should come from primary data that are collected using consistent field methods across several sites, longitudinal, and derived using sound statistical methods that correct for observation/detection bias. In this paper we analyze camera trap data collected between 2008 and 2012 at a site in Costa Rica (Volcan Barva transect) as part of an ongoing tropical forest global monitoring network (Tropical Ecology Assessment and Monitoring Network). We estimated occupancy dynamics for 13 species of mammals, using a hierarchical modeling approach. We calculated detection-corrected species richness and the Wildlife Picture Index, a promising new indicator derived from camera trap data that measures changes in biodiversity from the occupancy estimates of individual species. Our results show that 3 out of 13 species showed significant declines in occupancy over 5 years (lowland paca, Central American agouti, nine-banded armadillo). We hypothesize that hunting, competition and/or increased predation for paca and agouti might explain these patterns. Species richness and the Wildlife Picture Index are relatively stable at the site, but small herbivores that are hunted showed a decline in diversity of about 25%. We demonstrate the usefulness of longitudinal camera trap deployments coupled with modern statistical methods and advocate for the use of this approach in monitoring and developing global and national indicators for biodiversity change.

Non-Intrusive Occupancy Monitoring using Smart Meters

Abstract: Detailed information about a home's occupancy is necessary to implement many advanced energy-efficiency optimizations. However, monitoring occupancy directly is intrusive, typically requiring the deployment of multiple environmental sensors, e.g., motion, acoustic, CO2, etc. In this paper, we explore the potential for Non-Intrusive Occupancy Monitoring (NIOM) by using electricity data from smart meters to infer occupancy. We first observe that a home's pattern of electricity usage generally changes when occupants are present due to their interact with electrical loads. We empirically evaluate these interactions by monitoring ground truth occupancy in two homes, then correlating it with changes in statistical metrics of smart meter data, such as power's mean and variance, over short intervals. In particular, we use each metric's maximum value at night as a proxy for its maximum value in an unoccupied home, and then signal occupancy whenever the daytime value exceeds it. Our results highlight NIOM's potential and its challenges.

POEM: power-efficient occupancy-based energy management system

Abstract: Buildings account for 40% of US primary energy consumption and 72% of electricity. Of this total, 50% of the energy consumed in buildings is used for Heating Ventilation and Air-Conditioning (HVAC) systems. Current HVAC systems only condition based on static schedules; rooms are conditioned regardless of occupancy. By conditioning rooms only when necessary, greater efficiency can be achieved. This paper describes POEM, a complete closed-loop system for optimally controlling HVAC systems in buildings based on actual occupancy levels. POEM is comprised of multiple parts. A wireless network of cameras called OPTNet is developed that functions as an optical turnstile to measure area/zone occupancies. Another wireless sensor network of passive infrared (PIR) sensors called BONet functions alongside OPTNet. This sensed occupancy data from both systems are then fused with an occupancy prediction model using a particle filter in order to determine the most accurate current occupancy in each zone in the building. Finally, the information from occupancy prediction models and current occupancy is combined in order to find the optimal conditioning strategy required to reach target temperatures and minimize ventilation requirements. Based on live tests of the system, we estimate ~30.0% energy saving can be achieved while still maintaining thermal comfort.

Spatial occupancy models for large data sets

Abstract: Since its development, occupancy modeling has become a popular and useful tool for ecologists wishing to learn about the dynamics of species occurrence over time and space. Such models require presence–absence data to be collected at spatially indexed survey units. However, only recently have researchers recognized the need to correct for spatially induced overdisperison by explicitly accounting for spatial autocorrelation in occupancy probability. Previous efforts to incorporate such autocorrelation have largely focused on logit-normal formulations for occupancy, with spatial autocorrelation induced by a random effect within a hierarchical modeling framework. Although useful, computational time generally limits such an approach to relatively small data sets, and there are often problems with algorithm instability, yielding unsatisfactory results. Further, recent research has revealed a hidden form of multicollinearity in such applications, which may lead to parameter bias if not explicitly addressed. Com...

Towards good practice guidance in using camera‐traps in ecology: influence of sampling design on validity of ecological inferences

Abstract: Summary The development of camera-traps has provided an opportunity to study ecological relationships and population dynamics of species that are rare, difficult to observe or capture. Their use has seen a major increase recently, particularly with the recent progress in methods adapted to species for which individuals cannot be identified. We took advantage of extensive camera-trap data sets from large spatiotemporal-scale studies of a diverse assemblage of avian and mammalian scavengers in subarctic/arctic tundra to determine sampling designs that minimize detection errors (false-negative) and to evaluate the influence of sampling design on estimation of site occupancy. Results showed that raw error rates in daily presence varied between 5 and 30% among species when using time-triggered cameras with a 5-min interval. Using movement-triggered cameras resulted in larger raw error rates, between 30 and 70%, as well as a lower number of daily presences detected. Increasing the time interval from 5 to 20 min greatly increased the raw error rate in daily presence, but it had negligible impacts on estimates and precision of occupancy and detection probability. Occupancy estimates were mostly influenced by variation in the number of days included during the sampling period. For most species, a threshold of between 20 and 30 problem-free days (i.e. without camera-related technical problems) was required to stabilize occupancy and detection probability, as well as to maximize their precision. Based on the results, we discuss guidelines for establishing sampling designs according to the different ecological questions researchers might want to answer. To our knowledge, our study is the first to directly test the influence of sampling design in camera-trap studies, providing guidelines that are likely to be directly applicable to a large range of species and ecosystems.

Statistical Analysis and Modeling of Occupancy Patterns in Open-Plan Offices using Measured Lighting-Switch Data

Abstract: Occupancy profile is one of the driving factors behind discrepancies between the measured and simulated energy consumption of buildings. The frequencies of occupants leaving their offices and the corresponding durations of absences have significant impact on energy use and the operational controls of buildings. This study used statistical methods to analyze the occupancy status, based on measured lighting-switch data in five-minute intervals, for a total of 200 open-plan (cubicle) offices. Five typical occupancy patterns were identified based on the average daily 24-hour profiles of the presence of occupants in their cubicles. These statistical patterns were represented by a one-square curve, a one-valley curve, a two-valley curve, a variable curve, and a flat curve. The key parameters that define the occupancy model are the average occupancy profile together with probability distributions of absence duration, and the number of times an occupant is absent from the cubicle. The statistical results also reveal that the number of absence occurrences decreases as total daily presence hours decrease, and the duration of absence from the cubicle decreases as the frequency of absence increases. The developed occupancy model captures the stochastic nature of occupants moving in and out of cubicles, and can be used to generate a more realistic occupancy schedule. This is crucial for improving the evaluation of the energy saving potential of occupancy based technologies and controls using building simulations. Finally, to demonstrate the use of the occupancy model, weekday occupant schedules were generated and discussed.

Real-time building occupancy sensing using neural-network based sensor network

Abstract: Current occupancy sensing technologies may limit the effectiveness of buildings controls, due to a number of issues ranging from unreliable data, sensor drift, privacy concerns, and insufficient commissioning. More effective control of Heating, Ventilation and Air-conditioning (HVAC) systems may be possible using a smart and adaptive sensing network for occupancy detection, capable of turning off services out of hours, and not over-ventilating, thus enabling energy savings, and not under-ventilating during occupied periods, giving comfort and health benefits. A low-cost and non-intrusive sensor network was deployed in an open-plan office, combining information such as sound level, case temperature, carbon-dioxide (Co2) and motion, to estimate occupancy numbers, while an infrared camera was implemented to establish ground truth occupancy levels. Symmetrical uncertainty analysis was used for feature selection, and a genetic based search to evaluate an optimal sensor combination. Selected multi-sensory features were fused using a neural network. From initial results, estimation accuracy reaching up to 75% for occupied periods was achieved. The proposed system offers promising opportunities for improved comfort control and energy efficiency in buildings.

Estimation of building occupancy levels through environmental signals deconvolution

Abstract: We address the problem of estimating the occupancy levels in rooms using the information available in standard HVAC systems. Instead of employing dedicated devices, we exploit the significant statistical correlations between the occupancy levels and the CO2 concentration, room temperature, and ventilation actuation signals in order to identify a dynamic model. The building occupancy estimation problem is formulated as a regularized deconvolution problem, where the estimated occupancy is the input that, when injected into the identified model, best explains the currently measured CO2 levels. Since occupancy levels are piecewise constant, the zero norm of occupancy is plugged into the cost function to penalize non-piecewise constant inputs. The problem then is seen as a particular case of fused-lasso estimator by relaxing the zero norm into the e1 norm. We propose both online and offline estimators; the latter is shown to perform favorably compared to other data-based building occupancy estimators. Results on a real testbed show that the MSE of the proposed scheme, trained on a one-week-long dataset, is half the MSE of equivalent Neural Network (NN) or Support Vector Machine (SVM) estimation strategies.

Species distribution modelling and imperfect detection: comparing occupancy versus consensus methods

Abstract: Aim We assessed the influence of species non-detection in modelling species distributions with an ensemble consensus approach that did not account for imperfect detection, compared with an occupancy model that did. Location The hydrographic network of France. Methods We compared range maps of 35 stream fish species with differing degrees of detectability predicted using a consensus approach combining eight species distribution models (SDMs) to maps produced using an occupancy model. Using a spatially and temporally extensive monitoring database of fish populations (France), we modelled the occurrence of species as a function of several climatic and habitat variables and projected species distributions across the whole of the French hydrographic network. The benefits of occupancy models were then assessed from the differences in both predictive performance and species distribution. Results We found that although the occupancy models enhanced the performance for difficult to detect species, consensus models outperformed occupancy models for highly detectable species. In contrast to the minor differences observed in performance measures, estimates of species distributions were severely affected by whether or not imperfect detection was accounted for and varied linearly according to species detectability. Main conclusions This study demonstrated that false absences could have major consequences in estimating species distribution ranges. However, accounting for imperfect detection may not be enough to improve conventional SDMs. These findings could have important implications for conservation, notably in developing large-scale distribution models and documenting species range shifts in the context of recent climate change.

Occupancy Rights and the Wrong of Removal

Abstract: In 1864, the United States Army removed ten thousand Navajo Indians from their homeland in Arizona to a reservation in New Mexico. Two hundred people died on the trek, which the Navajos still refer to as their “Long Walk.” Prior to the Long Walk, the Navajos had been a raiding society, stealing livestock from white settlements. To stop the raiding, the United States moved them to an isolated location. But the removal was not a success: the Navajos were unused to farming and their crops failed; they were required to live in adobe villages rather than their traditional hogans; and they had to share their reservation with the Apaches, tribal enemies. During their time at the reservation, the Navajos became dispirited and suffered from hunger, drought, and disease. Many tribe members died, and more ran away. Finally, in 1868, the United States allowed the survivors to return to their lands in Arizona, unlike most other tribes it relocated during the War for the West. The moral issues raised by the Navajo case are typical of territorial removals, including the expulsions of Germans and Poles following

Influence of prey depletion and human disturbance on tiger occupancy in Nepal

Abstract: Tigers are globally endangered and continue to decline due to poaching, prey depletion and habitat loss. In Nepal, tiger populations are fragmented and found mainly in four protected areas (PAs). To establish the use of standard methods, to assess the importance of prey availability and human disturbance on tiger presence and to assess tiger occupancy both inside and outside PAs, we conducted a tiger occupancy survey throughout the Terai Arc Landscape of Nepal. Our model-average estimate of the probability of tiger site occupancy was 0.366 [standard error (se) = 0.02, a 7% increase from the naive estimate] and the probability of detection estimate was 0.65 (se = 0.08) per 1 km searched. Modeled tiger site occupancy ranged from 0.04 (se = 0.05) in areas with a relatively lower prey base and higher human disturbance to 1 (se = 0 and 0.14) in areas with a higher prey base and lower human disturbance. We estimated tigers occupied just 5049 (se = 3) km2 (36%) of 13 915 km2 potential tiger habitat (forests and grasslands), and we detected sign in four of five key corridors linking PAs across Nepal and India, respectively indicating significant unoccupied areas likely suitable for tigers and substantial potential for tiger dispersal. To increase tiger populations and to promote long-term persistence in Nepal, otherwise suitable areas should be managed to increase prey and minimize human disturbance especially in critical corridors linking core tiger populations.

Dynamic occupancy models for analyzing species' range dynamics across large geographic scales.

Abstract: Large-scale biodiversity data are needed to predict species' responses to global change and to address basic questions in macroecology While such data are increasingly becoming available, their analysis is challenging because of the typically large heterogeneity in spatial sampling intensity and the need to account for observation processes Two further challenges are accounting for spatial effects that are not explained by covariates, and drawing inference on dynamics at these large spatial scales We developed dynamic occupancy models to analyze large-scale atlas data In addition to occupancy, these models estimate local colonization and persistence probabilities We accounted for spatial autocorrelation using conditional autoregressive models and autologistic models We fitted the models to detection/nondetection data collected on a quarter-degree grid across southern Africa during two atlas projects, using the hadeda ibis (Bostrychia hagedash) as an example The model accurately reproduced the range expansion between the first (SABAP1: 1987–1992) and second (SABAP2: 2007–2012) Southern African Bird Atlas Project into the drier parts of interior South Africa Grid cells occupied during SABAP1 generally remained occupied, but colonization of unoccupied grid cells was strongly dependent on the number of occupied grid cells in the neighborhood The detection probability strongly varied across space due to variation in effort, observer identity, seasonality, and unexplained spatial effects We present a flexible hierarchical approach for analyzing grid-based atlas data using dynamical occupancy models Our model is similar to a species' distribution model obtained using generalized additive models but has a number of advantages Our model accounts for the heterogeneous sampling process, spatial correlation, and perhaps most importantly, allows us to examine dynamic aspects of species ranges

Influence of Drought on Salamander Occupancy of Isolated Wetlands on the Southeastern Coastal Plain of the United States

Abstract: In the southeastern U.S., changes in temperature and precipitation over the last three decades have been the most dramatic in winter and spring seasons. Continuation of these trends could negatively impact pond-breeding amphibians, especially those that rely on winter and spring rains to fill seasonal wetlands, trigger breeding, and ensure reproductive success. From 2009 to 2012, we monitored Spring and Fall presence of aquatic stages (larval and paedomorphic, gilled adult) of a winter-breeding amphibian (the mole salamander, Ambystoma talpoideum) and used multi-season models to estimate occupancy, local colonization and extinction. Seasonal estimates of occupancy, corrected for imperfect detection, declined from 22.3 % of ponds in Spring 2009 to 9.9 % in Fall 2012. Our best supported model suggested that changes in occupancy were driven by increased rates of extinction that corresponded with drought-related drying of ponds. Based on uncertainty in climate change projections for the Southeast, we present a conceptual model of predicted changes in wetland hydroperiods across a landscape with projected decreases and increases in future precipitation. Such precipitation changes could alter wetland hydroperiods, facilitate extinctions of species adapted to short, intermediate or long hydroperiod environments and, ultimately, modify the composition of amphibian communities within freshwater wetland ecosystems.

Relaxing the closure assumption in occupancy models: staggered arrival and departure times

Abstract: Occupancy statistical models that account for imperfect detection have proved very useful in several areas of ecology, including species distribution and spatial dynamics, disease ecology, and ecological responses to climate change. These models are based on the collection of multiple samples at each of a number of sites within a given season, during which it is assumed the species is either absent or present and available for detection while each sample is taken. However, for some species, individuals are only present or available for detection seasonally. We present a statistical model that relaxes the closure assumption within a season by permitting staggered entry and exit times for the species of interest at each site. Based on simulation, our open model eliminates bias in occupancy estimators and in some cases increases precision. The power to detect the violation of closure is high if detection probability is reasonably high. In addition to providing more robust estimation of occupancy, this model permits comparison of phenology across sites, species, or years, by modeling variation in arrival or departure probabilities. In a comparison of four species of amphibians in Maryland we found that two toad species arrived at breeding sites later in the season than a salamander and frog species, and departed from sites earlier.

Conservation Assessments of Arboreal Mammals in Difficult Terrain: Occupancy Modeling of Pileated Gibbons (Hylobates pileatus)

Abstract: Long-term monitoring programs, wildlife surveys, and other research involving species population assessment require reliable data on population status. Given the logistically challenging nature of some species' habitats and cryptic behaviors, collecting these data can prove to be a considerable barrier. We used detection/nondetection data from pileated gibbons (Hylobates pileatus) in the Carda- mom Mountains of southwest Cambodia to estimate their population occupancy and detectability. We modeled occupancy using elevation, tree height, tree density, tree diversity, and disturbance covariates. Modeling demonstrated that 83% of the sites are occupied by Hylobates pileatus and that the detectability of the species varies positively with elevation. No clear relationship between habitat quality covariates and occupancy of Hylobates pileatus emerged. Effort analysis based on model estimates demonstrated that at high elevations, less than half the number of site visits is needed to attain the same detectability estimate precision as across all elevations. We suggest that human activities at low elevations, which affect forest composition, are the central factors impacting the detectability and occupancy of Hylobates pileatus. Longer sampling durations and/or a higher number of site visits, especially at lower elevations, increase precision of the occupancy estimator for the least effort. For effective future monitoring and research for this and similar species, using this relatively simple method, applied with repeat site visits, would allow a longitudinal comparison of detection at sites in difficult terrain.

Building analysis systems and methods

Abstract: Certain embodiments relate to systems and methods for monitoring and improving the efficiency by which systems maintain building environments A building management system may analyze values observed by sensors and other devices and determine whether the observed values fall within a range If the observed values do not fall within the range and the building's control system has not taken appropriate action to ensure that the observed values do fall within the range, the building may generate a floor plan specifying where the failure occurred A building ranking system may rank a plurality of buildings based upon their relative efficiency The building ranking system may determine each building's efficiency based on a set of parameters related to the building's energy usage that are normalized for weather, occupancy, and other external parameters

Can camera traps monitor Komodo dragons a large ectothermic predator

Abstract: Camera trapping has greatly enhanced population monitoring of often cryptic and low abundance apex carnivores. Effectiveness of passive infrared camera trapping, and ultimately population monitoring, relies on temperature mediated differences between the animal and its ambient environment to ensure good camera detection. In ectothermic predators such as large varanid lizards, this criterion is presumed less certain. Here we evaluated the effectiveness of camera trapping to potentially monitor the population status of the Komodo dragon (Varanus komodoensis), an apex predator, using site occupancy approaches. We compared site-specific estimates of site occupancy and detection derived using camera traps and cage traps at 181 trapping locations established across six sites on four islands within Komodo National Park, Eastern Indonesia. Detection and site occupancy at each site were estimated using eight competing models that considered site-specific variation in occupancy (ψ)and varied detection probabilities (p) according to detection method, site and survey number using a single season site occupancy modelling approach. The most parsimonious model [ψ (site), p (site survey); ω = 0.74] suggested that site occupancy estimates differed among sites. Detection probability varied as an interaction between site and survey number. Our results indicate that overall camera traps produced similar estimates of detection and site occupancy to cage traps, irrespective of being paired, or unpaired, with cage traps. Whilst one site showed some evidence detection was affected by trapping method detection was too low to produce an accurate occupancy estimate. Overall, as camera trapping is logistically more feasible it may provide, with further validation, an alternative method for evaluating long-term site occupancy patterns in Komodo dragons, and potentially other large reptiles, aiding conservation of this species.