Showing papers on "Occupancy published in 2019"

Planning for sustainable cities by estimating building occupancy with mobile phones

Abstract: Accurate occupancy is crucial for planning for sustainable buildings. Using massive, passively-collected mobile phone data, we introduce a novel framework to estimate building occupancy at unprecedented scale. We show that, at urban-scale, occupancy differs widely from current estimates based on building types. For commercial buildings, we find typical occupancy rates are 5 times lower than current assumptions imply, while for residential buildings occupancy rates vary widely by neighborhood. Our mobile phone based occupancy estimates are integrated with a state-of-the-art urban building energy model to understand their impact on energy use predictions. Depending on the assumed relationship between occupancy and internal building loads, we find energy consumption which differs by +1% to -15% for residential buildings and by -4% to -21% for commercial buildings, compared to standard methods. This highlights a need for new occupancy-to-load models which can be applied at urban-scale to the diverse set of city building types.

Evidence of region-wide bat population decline from long-term monitoring and Bayesian occupancy models with empirically informed priors.

Abstract: Strategic conservation efforts for cryptic species, especially bats, are hindered by limited understanding of distribution and population trends. Integrating long-term encounter surveys with multi-season occupancy models provides a solution whereby inferences about changing occupancy probabilities and latent changes in abundance can be supported. When harnessed to a Bayesian inferential paradigm, this modeling framework offers flexibility for conservation programs that need to update prior model-based understanding about at-risk species with new data. This scenario is exemplified by a bat monitoring program in the Pacific Northwestern United States in which results from 8 years of surveys from 2003 to 2010 require updating with new data from 2016 to 2018. The new data were collected after the arrival of bat white-nose syndrome and expansion of wind power generation, stressors expected to cause population declines in at least two vulnerable species, little brown bat (Myotis lucifugus) and the hoary bat (Lasiurus cinereus). We used multi-season occupancy models with empirically informed prior distributions drawn from previous occupancy results (2003-2010) to assess evidence of contemporary decline in these two species. Empirically informed priors provided the bridge across the two monitoring periods and increased precision of parameter posterior distributions, but did not alter inferences relative to use of vague priors. We found evidence of region-wide summertime decline for the hoary bat ( λ ^ = 0.86 ± 0.10) since 2010, but no evidence of decline for the little brown bat ( λ ^ = 1.1 ± 0.10). White-nose syndrome was documented in the region in 2016 and may not yet have caused regional impact to the little brown bat. However, our discovery of hoary bat decline is consistent with the hypothesis that the longer duration and greater geographic extent of the wind energy stressor (collision and barotrauma) have impacted the species. These hypotheses can be evaluated and updated over time within our framework of pre-post impact monitoring and modeling. Our approach provides the foundation for a strategic evidence-based conservation system and contributes to a growing preponderance of evidence from multiple lines of inquiry that bat species are declining.

Variable Resolution Occupancy Mapping Using Gaussian Mixture Models

Abstract: Occupancy mapping is fundamental for active perception systems to enable reasoning about known and unknown regions of the environment. The majority of occupancy mapping approaches enforce an a priori discretization on the environment, resulting in a fixed resolution map that limits the expressiveness of the representation. The proposed approach removes this a priori discretization, learns continuous representations for the evidence of occupied and free space to derive the probability of occupancy, and enables occupancy grid maps to be generated at arbitrary resolution. Efficient methods are also presented that accurately evaluate the probability of occupancy in individual cells and enable multi-resolution mapping and local occupancy evaluation. The efficacy of the approach is demonstrated by comparison to state-of-the-art discrete and continuous mapping techniques in both two dimensions and three dimensions. The core contribution of this work is a memory-efficient method for deriving occupancy that is amenable to small or large corrections in pose without the need to regenerate the entire map. The applications under considerations are low-bandwidth scenarios (e.g., multi-robot exploration) and operations in expansive environments, where storing an occupancy grid map of the entire environment would be prohibitive.

Influence of building envelopes, climates, and occupancy patterns on residential HVAC demand

Abstract: Heating, ventilation and air-conditioning (HVAC) demand accounts for 40% of household energy use. While the impacts of local climates, building characteristics, and occupancy patterns on HVAC demand are all well-known, the interplay between these factors has not been systematically studied. This paper provides a quantitative analysis of the complex interplay between these parameters on the annual and peak HVAC demand of residential buildings. Australia is used as a case study because the building construction practices are similar to many other countries, particularly in Europe and the USA, and the Australian climates span the majority of climates found in the world. The wall types considered are cement render, timber clad, brick veneer, reverse brick veneer, and cavity brick. The climates range from tropical in the north of the country, to cold temperate in the south of the country. The occupancy patterns are modeled using six common Occupancy Scenarios. The results show the highest energy reductions are observed in the climates with high diurnal temperature variation, such as Melbourne (51%) and Hobart (54%). The effect of thermal inertia is less in the climates with low diurnal temperature variation such as Darwin (19%). Occupancy scenarios that include unoccupied periods result in lower annual HVAC demand, yet they increase the peak cooling and heating demand. Increasing the indoor thermal mass by using brick veneer internal walls is shown to reduce the demand by approximately 10–15%.

Annual estimates of occupancy for bryophytes, lichens and invertebrates in the UK, 1970–2015

Abstract: Here, we determine annual estimates of occupancy and species trends for 5,293 UK bryophytes, lichens, and invertebrates, providing national scale information on UK biodiversity change for 31 taxonomic groups for the time period 1970 to 2015. The dataset was produced through the application of a Bayesian occupancy modelling framework to species occurrence records supplied by 29 national recording schemes or societies (n = 24,118,549 records). In the UK, annual measures of species status from fine scale data (e.g. 1 × 1 km) had previously been limited to a few taxa for which structured monitoring data are available, mainly birds, butterflies, bats and a subset of moth species. By using an occupancy modelling framework designed for use with relatively low recording intensity data, we have been able to estimate species trends and generate annual estimates of occupancy for taxa where annual trend estimates and status were previously limited or unknown at this scale. These data broaden our knowledge of UK biodiversity and can be used to investigate variation in and drivers of biodiversity change. Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.9977426

Trends and indicators for quantifying moth abundance and occupancy in Scotland

Abstract: Moths form an important part of Scotland’s biodiversity and an up-to-date assessment of their status is needed given their value as a diverse and species-rich taxon, with various ecosystem roles, and the known decline of moths within Britain. We use long-term citizen-science data to produce species-level trends and multi-species indicators for moths in Scotland, to assess population (abundance) and distribution (occupancy) changes. Abundance trends for moths in Scotland are produced using Rothamsted Insect Survey count data, and, for the first time, occupancy models are used to estimate occupancy trends for moths in Scotland, using opportunistic records from the National Moth Recording Scheme. Species-level trends are combined to produce abundance and occupancy indicators. The associated uncertainty is estimated using a parametric bootstrap approach, and comparisons are made with alternative published approaches. Overall moth abundance (based on 176 species) in Scotland decreased by 20% for 1975–2014 and by 46% for 1990–2014. The occupancy indicator (based on 230 species) showed a 16% increase for 1990–2014. Alternative methods produced similar indicators and conclusions, suggesting robustness of the results, although rare species may be under-represented in our analyses. Species abundance and occupancy trends were not clearly correlated; in particular species with negative population trends showed varied occupancy responses. Further research into the drivers of moth population changes is required, but increasing occupancy is likely to be driven by a warming summer climate facilitating range expansion, whereas population declines may be driven by reductions in habitat quality, changes in land management practices and warmer, wetter winters.

The effects of wildfire severity and pyrodiversity on bat occupancy and diversity in fire-suppressed forests

Abstract: Wildfire is an important ecological process that influences species' occurrence and biodiversity generally. Its effect on bats is understudied, creating challenges for habitat management and species conservation as threats to the taxa worsen globally and within fire-prone ecosystems. We conducted acoustic surveys of wildfire areas during 2014-2017 in conifer forests of California's Sierra Nevada Mountains. We tested effects of burn severity and its variation, or pyrodiversity, on occupancy and diversity for the 17-species bat community while accounting for imperfect detection. Occupancy rates increased with severity for at least 6 species and with pyrodiversity for at least 3. Two other species responded negatively to pyrodiversity. Individual species models predicted maximum occupancy rates across burn severity levels but only one species occurred most often in undisturbed areas. Species richness increased from approximately 8 species in unburned forests to 11 in pyrodiverse areas with moderate- to high-severity. Greater accessibility of foraging habitats, as well as increased habitat heterogeneity may explain positive responses to wildfire. Many bat species appear well adapted to wildfire, while a century of fire suppression and forest densification likely reduced habitat quality for the community generally. Relative to other taxa, bats may be somewhat resilient to increases in fire severity and size; trends which are expected to continue with accelerating climate change.

Developing English domestic occupancy profiles

Abstract: Occupancy patterns are necessary to estimate energy demand and evaluate thermal comfort in households. Because of this, many European countries are developing representative domestic schedules to replace outdated criteria. This paper evaluates the state of knowledge of UK domestic occupancy patterns and develops new domestic occupancy profiles for England. The presented research (1) characterizes methods for collecting occupancy data and inferring patterns; (2) identifies and assesses the quality of categories of occupancy patterns used in building simulation; and (3) develops updated occupancy profiles. A systematic scoping review identified social and monitoring surveys as the most deployed data-collection methods. A systematic literature review also established that the occupancy categories most frequently used in UK building simulation are (a) a family with dependent children where the parents work full time; and (b) a retired elderly couple who spend most of their time indoors. The interview sample from the English Housing Survey 2014–15 was used to map household typologies. Results show that categories (a) and (b) combined amount to only 19% of England’s households, which suggest models are over-reliant on these groups. Considering this result, the paper develops occupancy patterns for England derived from 2015 UK Time Use Survey diaries for each household typology previously identified.

Human activities influence the occupancy probability of mammalian carnivores in the Brazilian Caatinga

Abstract: The Caatinga is a semi‐arid domain, characterized by reduced humidity and high rates of anthropogenic impact. In addition to the low availability of water, carnivorous mammals are still exposed to a number of threats related to landscape modifications. We used data from camera traps and occupancy models to investigate the habitat use by carnivores in an area of Caatinga in northeastern Brazil. We found a negative correlation between the distance from wind farms and the occupancy probability of the jaguar, and a positive correlation with the occupancy probability of the jaguarundi. Puma and jaguarundi occupied primarily sites near watercourses, whereas the occupancy of the crab‐eating fox was correlated positively with the presence of poachers. The ocelot was detected more frequently at sites distant from human settlements, whereas the jaguar was detected more often in areas far from wind farms. We found a negative correlation between the distance of water and the detection of the ocelot. The detection of the crab‐eating fox was influenced positively by the detection of cattle. In addition to the negative influence of some anthropic activities, our results indicate that water is a very important resource for species, and the few permanent sources of this resource available in the area must be preserved. The replication of our research in other systems, worldwide, that are experiencing similar pressures, should permit a systematic evaluation of the management and conservation strategies needed to rebuild or maintain populations, restore ecosystems, and support conservation policies in human‐altered landscapes. Abstract in Portuguese is available with online material.

Fire Structural Response of the Plasco Building: A Preliminary Investigation Report

Abstract: The fire-induced collapse of the 16-story Plasco building in Tehran city in January 2017 caused many deaths and serious injuries. This paper investigates the structural response of the building during that fire to understand how the building became unstable. As documented, the fire initiated on the tenth floor, and then involved stories 11–14 through a vertically traveling fire. As the occupancy type of the building had changed over time, the analysis was performed based on an estimated fire load density of 1900 MJ/m2 on the day of the catastrophe. The results showed that the structure was able to resist the applied loads when the fire remained within stories 10 and 11. When the fire involved stories 10–14, however, the structure began to fail, starting with the failure of the ceiling trusses. The results also indicated that as the columns were protected by a layer of masonry, they were able to tolerate their applied loads. The analysis was also re-performed based on a fire load density of 511 MJ/m2, which is the fire load recommended by most fire codes for a similar occupancy type. The results showed that no structural failure would have occurred under that fire load. This study indicates that changing the occupancy type of a building without adopting proper mitigation strategies can be catastrophic.

A landscape-scale assessment of tropical mammals reveals the effects of habitat and anthropogenic disturbance on community occupancy

Abstract: With biodiversity facing unparalleled threats from anthropogenic disturbance, knowledge on the occurrences of species and communities provides for an effective and fast approach to assess their status and vulnerability. Disturbance is most prominent at the landscape-level, for example through habitat loss from large-scale resource extraction or agriculture. However, addressing species responses to habitat changes at the landscape-scale can be difficult and cost-ineffective, hence studies are mostly conducted at single areas or habitat patches. Moreover, there is a relative lack of studies on communities, as opposed to focal species, despite the former may carry more comprehensive information. Here, we used a multi-region, multi-species hierarchical occupancy model to study a meta-community of mammals detected by camera traps across five distinct areas within a heterogeneous landscape in Tanzania, and aimed to assess responses to human disturbance and environmental variables. Estimated species richness did not vary significantly across different areas, even though these held broadly different habitats. Moreover, we found remarkable consistency in the positive effect of distance to human settlements, a proxy for anthropogenic disturbance, on community occupancy. The positive effect of body size and the positive effect of proximity to rivers on community occupancy were also shared by communities. Results yield conservation relevance because: (1) the among-communities consistency in responses to anthropogenic disturbance, despite the heterogeneity in sampled habitats, indicates that conservation plans designed at the landscape-scale may represent a comprehensive and cost-efficient approach; (2) the consistency in responses to environmental factors suggests that multi-species models are a powerful method to study ecological patterns at the landscape-level.

Development of a Consecutive Occupancy Estimation Framework for Improving the Energy Demand Prediction Performance of Building Energy Modeling Tools

Abstract: To improve the energy prediction performance of a building energy model, the occupancy status information is very important. This is more important in real buildings, rather than under construction buildings, because actual building occupancy can significantly influence its energy consumption. In this study, a machine learning based framework for a consecutive occupancy estimation is proposed by utilizing internet of things data, such as indoor temperature and luminance, CO2 density, electricity consumption of lighting, HVAC (heating, ventilation, and air conditioning), electric appliances, etc. Three machine learning based occupancy estimation algorithms (decision tree, support vector machine, artificial neural networks) are selected and evaluated in terms of the performance of estimating the occupancy status for each season. The selection process of the input variables that have crucial impact on the algorithms’ performance are described in detail. Finally, an occupancy estimation framework that can repeat model training and estimation consecutively in a situation when time-series data are continuously provided over the entire measurement period is suggested. In addition, the performance of the framework is evaluated to identify how it improves the energy prediction performance of the building energy model compared to conventional energy modeling practices. The suggested framework is distinguished from similar previous studies in two ways: (1) The proposed framework reveals that input variables for the occupancy estimation model can be occasionally changed by an occupant response to certain times and seasons, and (2) the framework incorporates time-series indirect occupancy sensing data and classification algorithms to consecutively provide occupancy information for the energy modeling effort.

Bats in a changing landscape: Linking occupancy and traits of a diverse montane bat community to fire regime.

Abstract: Wildfires are increasing in incidence and severity across coniferous forests of the western United States, leading to changes in forest structure and wildlife habitats. Knowledge of how species respond to fire-driven habitat changes in these landscapes is limited and generally disconnected from our understanding of adaptations that underpin responses to fire.We aimed to investigate drivers of occupancy of a diverse bat community in a fire-altered landscape, while identifying functional traits that underpinned these relationships.We recorded bats acoustically at 83 sites (n = 249 recording nights) across the Plumas National Forest in the northern Sierra Nevada over 3 summers (2015-2017). We investigated relationships between fire regime, physiographic variables, forest structure and probability of bat occupancy for nine frequently detected species. We used fourth-corner regression and RLQ analysis to identify ecomorphological traits driving species-environment relationships across 17 bat species. Traits included body mass; call frequency, bandwidth, and duration; and foraging strategy based on vegetation structure (open, edge, or clutter).Relationships between bat traits and fire regime were underpinned by adaptations to diverse forest structure. Bats with traits adapting them to foraging in open habitats, including emitting longer duration and narrow bandwidth calls, were associated with higher severity and more frequent fires, whereas bats with traits consistent with clutter tolerance were negatively associated with fire frequency and burn severity. Relationships between edge-adapted bat species and fire were variable and may be influenced by prey preference or habitat configuration at a landscape scale.Predicted increases in fire frequency and severity in western US coniferous forests are likely to shift dominance in the bat community to open-adapted species and those able to exploit postfire resource pulses (aquatic insects, beetles, and snags). Managing for pyrodiversity within the western United States is likely important for maintaining bat community diversity, as well as diversity of other biotic communities.