Showing papers on "Occupancy published in 2015"

Spatiotemporal hierarchical modelling of species richness and occupancy using camera trap data

Abstract: Summary Over the last two decades, a large number of camera trap surveys have been carried out around the world and camera traps have been proposed as an ideal tool for inventorying and monitoring medium to large-sized terrestrial vertebrates. However, few studies have analysed camera trap data at the community level. We developed a multi-session multi-species occupancy model that allows us to obtain estimates for species richness and occupancy combining data from multiple camera trap surveys (sessions). By estimating species presence at the session-level and modelling detection probability and occupancy for each species and sessions as nested random effects, we could improve parameter estimates for each session, especially for species with sparse data. We developed two variants of our model: one was a binary latent states model while the other used a Royle–Nichols formulation for the relationship between detection probability and abundance. We applied both models to data from eight camera trap surveys from south-eastern Peru including six study sites, 263 camera stations and 17 423 camera days. Sites covered protected areas, a logging concession and Brazil nut concessions. We included habitat (terra firme vs. floodplain) as a covariate for occupancy and trail vs. off-trail as a covariate for detection. Among-camera heterogeneity was a serious problem for our data and the Royle–Nichols variant of our model had a much better fit than the binary-state variant. Both models resulted in similar species richness estimates showing that most of the sites contained intact large mammal communities. Detection probabilities and occupancy values were more variable across species than across sessions within species. Three species showed a habitat preference and four species showed preference or avoidance of trails. Synthesis and applications. Our multi-session multi-species occupancy model provides improved estimates for species richness and occupancy for a large data set. Our model is ideally suited for integrating large numbers of camera trap data sets to investigate regional and/or temporal patterns in the distribution and composition of mammal communities in relation to natural or anthropogenic factors or to monitor mammal communities over time.

Household occupancy monitoring using electricity meters

Abstract: Occupancy monitoring (i.e. sensing whether a building or room is currently occupied) is required by many building automation systems. An automatic heating system may, for example, use occupancy data to regulate the indoor temperature. Occupancy data is often obtained through dedicated hardware such as passive infrared sensors and magnetic reed switches. In this paper, we derive occupancy information from electric load curves measured by off-the-shelf smart electricity meters. Using the publicly available ECO dataset, we show that supervised machine learning algorithms can extract occupancy information with an accuracy between 83% and 94%. To this end we use a comprehensive feature set containing 35 features. Thereby we found that the inclusion of features that capture changes in the activation state of appliances provides the best occupancy detection accuracy.

Using automated recorders and occupancy models to monitor common forest birds across a large geographic region

Abstract: Automated recorders and occupancy models can be used together to monitor population trends of multiple avian species across a large geographic region. Automated recorders are an attractive method for monitoring birds, because they leave a record that can be independently validated and multiple units can be programmed to repeatedly survey different locations at the same daily times. We assessed the use of automated recorders and single-species, single-season occupancy models to monitor common forest birds across a 5.4-million-ha region of northern California. Using a survey protocol of 5-minute recordings at 3 times of the morning repeated over 3 consecutive days at 453 sites, we detected 32 species at >10% of these sites. Five of these species (Steller's jay [Cyanocitta stelleri], mountain chickadee [Poecile gambeli], red-breasted nuthatch [Sitta canadensis], dark-eyed junco [Junco hyemalis], and western tanager [Piranga ludoviciana]) were dominant with occupancies >0.5. We also modeled occupancy associations with elevation and canopy cover for brown creeper (Certhia americana), MacGillivray's warbler (Geothlypis tolmiei), and western tanager and found the environmental conditions at which occupancy was maximized differed by up to 399 m in elevation and 17.9% canopy cover for these species. Given a sampling effort of 100 new sites per year, we demonstrated 80% power (α = 0.1) to detect occupancy declines as small as 2.5% per year over 20 years for the 32 most common species. The effective radius of automated recorder surveys was approximately 50 m. In a field test, surveys conducted concurrently using automated recorders and point counts yielded similar occupancy estimates despite differences in detection probability. Our results suggest that automated recorders, used alone or in conjunction with point counts, can provide a practical means of monitoring common forest birds across a large geographic area. © 2014 The Wildlife Society.

Airbnb: A Case Study in Occupancy Regulation and Taxation

Abstract: Six years after its founding, Airbnb stands as one of the most prominent participants in the sharing economy, a group that includes compatriots such as Uber and Lyft. These companies offer a variety of Internet-based platforms and applications that create new ways for people to share goods and services with one another on a previously unimaginable scale.i They also act like virtual matchmakers by lowering transaction costs and facilitating arrangements that might otherwise have been too burdensome. Doing so has vast potential to benefit consumers by encouraging collaborative consumption of assets like cars and homes that are expensive to purchase but often remain idle because their owners cannot make full use of them.2 Although these companies are still in their nascent stages, they are growing rapidly, and their collective revenue was estimated to be in excess of $3.5 billion last year.3 Sharing-economy companies offer a wide variety of services but are united by three common traits. First, they rely on recent technological advances to satisfy age-old consumer demands in ways not previously possible. 4 Second, they exist in and parallel to a wide variety of well-established industries that are fundamentally disrupted by the sharing economy's emergent ability to

Green Certification and Building Performance: Implications for Tangibles and Intangibles

Abstract: Commercial buildings represent a significant share of global energy consumption. In the general absence of regulation, voluntary labeling and green building certification schemes have been introduced to reflect this externality to building owners and tenants. The implications of such schemes have previously been documented to affect building financial performance. However, existing studies have focused mostly on the U.S. market and, more importantly, generally include only a limited set of performance metrics. Using a rich, proprietary dataset from one of the largest building owners/managers in North America, the authors investigate the effects of green building certification on non-financial metrics, such as tenant satisfaction, incentives, and lease renewal. Their empirical results show that buildings certified through voluntary labeling schemes generally have a higher probability of lease renewal, offer lower incentives, and have more satisfied tenants. They then study the effects of green building certification on financial metrics, such as rents and occupancy levels. The findings for the U.S. sample unambiguously confirm previously documented results—LEED and ENERGY STAR certified buildings command a small rent premium and have a lower vacancy risk. For Canada, the effects for LEED certified buildings are consistent with U.S. results. The results show that the national green building scheme is not priced in, but that these buildings still offer greater overall stability versus non-certified buildings through increased releasing rates, lower incentives, and substantially higher tenant satisfaction levels. The findings reported in this article provide an important contribution to the understanding of underlying value drivers in more efficient, sustainable buildings and offer some first evidence for the international validity of otherwise mostly U.S.-based studies on the financial performance of more efficient, “green” commercial buildings.

Evaluating short‐ and long‐term impacts of fuels treatments and simulated wildfire on an old‐forest species

Abstract: Fuels-reduction treatments are commonly implemented in the western U.S. to reduce the risk of high-severity fire, but they may have negative short-term impacts on species associated with older forests. Therefore, we modeled the effects of a completed fuels-reduction project on fire behavior and California Spotted Owl (Strix occidentalis occidentalis) habitat and demography in the Sierra Nevada to assess the potential short- and long-term trade-offs. We combined field-collected vegetation data and LiDAR data to develop detailed maps of forest structure needed to parameterize our fire and forest-growth models. We simulated wildfires under extreme weather conditions (both with and without fuels treatments), then simulated forest growth 30 years into the future under four combinations of treatment and fire: treated with fire, untreated with fire, treated without fire, and untreated without fire. We compared spotted owl habitat and population parameters under the four scenarios using a habitat suitability index developed from canopy cover and large-tree measurements at nest sites and from previously derived statistical relationships between forest structure and fitness (k) and equilibrium occupancy at the territory scale. Treatments had a positive effect on owl nesting habitat and demographic rates up to 30 years after simulated fire, but they had a persistently negative effect throughout the 30-year period in the absence of fire. We conclude that fuels-reduction treatments in the Sierra Nevada may provide long-term benefits to spotted owls if fire occurs under extreme weather conditions, but can have long-term negative effects on owls if fire does not occur. However, we only simulated one fire under the treated and untreated scenarios and therefore had no measures of variation and uncertainty. In addition, the net benefits of fuels treatments on spotted owl habitat and demography depends on the future probability that fire will occur under similar weather and ignition conditions, and such probabilities remain difficult to quantify. Therefore, we recommend a landscape approach that restricts timber harvest within territory core areas of use (;125 ha in size) that contain critical owl nesting and roosting habitat and locates fuels treatments in the surrounding areas to reduce the potential for high-severity fire in territory core areas.

Revisiting the past to foretell the future: summer temperature and habitat area predict pika extirpations in California

Abstract: Aim The American pika (Ochotona princeps) appears to have experienced climate mediated upslope range contraction in the Great Basin of North America, but this result has not yet been extended to other portions of the pika’s range. Our goals were: first, to determine the environmental parameters that most influence current pika distribution within California; second, to infer whether these constraints explain extirpations that have occurred in California; third, to predict future extirpations; and fourth, to advance methods for assessing the degree to which pikas and other climate-sensitive mammals are threatened by climate change. Location Historical pika record locations in California, USA, spanning four degrees of latitude and longitude, from Mount Shasta to the southern Sierra Nevada. Methods We identified 67 precise historical pika record locations and surveyed them exhaustively, over multiple years, to determine whether pika populations persist at those sites. We used an information theoretic approach and logistic regression to model current pika occupancy as a function of 16 environmental variables, tested our best-performing model as a predictor of historical occupancy, and then used our model to predict future pika occupancy given anticipated climate change. Results Pikas no longer occurred at 10 of 67 (15%) historical sites in California. The best predictors of occupancy were average summer temperature and talus habitat area within a 1-km radius. A logistic model fitted to this relationship correctly predicted current occupancy at 94% of sites and correctly hindcasted past occupancy at 93% of sites, suggesting that the model has strong temporal transferability. Depending on the future climate scenario, our model projects that by 2070 pikas will be extirpated from 39% to 88% of these historical sites in California. Main conclusions Our simple species distribution model for pikas performs remarkably well for both current and historical periods. Pika distribution appears to be governed primarily by behavioural restrictions mediated by summer temperature and by the configuration of talus habitat available to pikas locally. Pikas, and other montane species in the western USA, may be subjected to above-average exposure to climate change because summer temperature is projected to rise more than annual temperature.

The effects of urbanization on population density, occupancy, and detection probability of wild felids

Abstract: Urbanization is a primary driver of landscape conversion, with far-reaching effects on landscape pattern and process, particularly related to the population characteristics of animals. Urbanization can alter animal movement and habitat quality, both of which can influence population abundance and persistence. We evaluated three important population characteristics (population density, site occupancy, and species detection probability) of a medium-sized and a large carnivore across varying levels of urbanization. Specifically, we studied bobcat and puma populations across wildland, exurban development, and wildland-urban interface (WUI) sampling grids to test hypotheses evaluating how urbanization affects wild felid populations and their prey. Exurban development appeared to have a greater impact on felid populations than did habitat adjacent to a major urban area (i.e., WUI); estimates of population density for both bobcats and pumas were lower in areas of exurban development compared to wildland areas, whereas population density was similar between WUI and wildland habitat. Bobcats and pumas were less likely to be detected in habitat as the amount of human disturbance associated with residential development increased at a site, which was potentially related to reduced habitat quality resulting from urbanization. However, occupancy of both felids was similar between grids in both study areas, indicating that this population metric was less sensitive than density. At the scale of the sampling grid, detection probability for bobcats in urbanized habitat was greater than in wildland areas, potentially due to restrictive movement corridors and funneling of animal movements in landscapes influenced by urbanization. Occupancy of important felid prey (cottontail rabbits and mule deer) was similar across levels of urbanization, although elk occupancy was lower in urbanized areas. Our study indicates that the conservation of medium- and large-sized felids associated with urbanization likely will be most successful if large areas of wildland habitat are maintained, even in close proximity to urban areas, and wildland habitat is not converted to low-density residential development.

Conditional Monte Carlo Dense Occupancy Tracker

Abstract: Proper modeling of dynamic environments is a core task in the field of intelligent vehicles. The most common approaches involve the modeling of moving objects, through Detection And Tracking of Moving Objects (DATMO) methods. An alternative to a classic object model framework is the occupancy grid filtering domain. Instead of segmenting the scene into objects and track them, the environment is represented as a regular grid of occupancy, in which spatial occupancy is tracked at a sub-object level. In this paper, we present the Conditional Monte Carlo Dense Occupancy Tracker, a generic spatial occupancy tracker, which infers dynamics of the scene through an hybrid representation of the environment, consisting of static occupancy, dynamic occupancy, empty spaces and unknown areas. This differentiation enables the use of state specific models (classic occupancy grid for motion-less components, set of moving particles for dynamic occupancy) as well as proper confidence estimation and management of data-less areas. The approach leads to a compact model that drastically improves the accuracy of the results and the global efficiency in comparison to previous methods.

Hunting, Exotic Carnivores, and Habitat Loss: Anthropogenic Effects on a Native Carnivore Community, Madagascar.

Abstract: The wide-ranging, cumulative, negative effects of anthropogenic disturbance, including habitat degradation, exotic species, and hunting, on native wildlife has been well documented across a range of habitats worldwide with carnivores potentially being the most vulnerable due to their more extinction prone characteristics. Investigating the effects of anthropogenic pressures on sympatric carnivores is needed to improve our ability to develop targeted, effective management plans for carnivore conservation worldwide. Utilizing photographic, line-transect, and habitat sampling, as well as landscape analyses and village-based bushmeat hunting surveys, we provide the first investigation of how multiple forms of habitat degradation (fragmentation, exotic carnivores, human encroachment, and hunting) affect carnivore occupancy across Madagascar's largest protected area: the Masoala-Makira landscape. We found that as degradation increased, native carnivore occupancy and encounter rates decreased while exotic carnivore occupancy and encounter rates increased. Feral cats (Felis species) and domestic dogs (Canis familiaris) had higher occupancy than half of the native carnivore species across Madagascar's largest protected landscape. Bird and small mammal encounter rates were negatively associated with exotic carnivore occupancy, but positively associated with the occupancy of four native carnivore species. Spotted fanaloka (Fossa fossana) occupancy was constrained by the presence of exotic feral cats and exotic small Indian civet (Viverricula indica). Hunting was intense across the four study sites where hunting was studied, with the highest rates for the small Indian civet (mean=90 individuals consumed/year), the ring-tailed vontsira (Galidia elegans) (mean=58 consumed/year), and the fosa (Cryptoprocta ferox) (mean=31 consumed/year). Our modeling results suggest hunters target intact forest where carnivore occupancy, abundance, and species richness, are highest. These various anthropogenic pressures and their effects on carnivore populations, especially increases in exotic carnivores and hunting, have wide-ranging, global implications and demand effective management plans to target the influx of exotic carnivores and unsustainable hunting that is affecting carnivore populations across Madagascar and worldwide.

Contextual occupancy detection for smart office by pattern recognition of electricity consumption data

Abstract: The advent of IoT has resulted in a trend towards more innovative and automated applications. In this regard, occupancy detection plays an important role in many smart building applications such as controlling heating, ventilation and air conditioning (HVAC) systems, monitoring systems and managing lighting systems. Most of the current techniques for detecting occupancy require multiple sensors fusion for attaining acceptable performance. These techniques come with an increased cost and incur extra expenses of installation and maintenance as well. All of these methods are intended to deal with only two states; when a user is present or absent and control the system accordingly. In this paper, we have proposed a non-intrusive approach to detect an occupancy state in a smart office using electricity consumption data and introduced a novel concept of third state as standby for dealing with situations when the user lefts his seat for small breaks. We demonstrated our approach using electricity data collected within our research centre and detected occupancy state with efficiency up to 94%. Furthermore, our solution does not require extra equipment or sensors to deploy for occupancy detection as smart energy meters are already being deployed in most of the smart buildings.

Predicting bobcat abundance at a landscape scale and evaluating occupancy as a density index in central Wisconsin

Abstract: The abundance of low-density species like carnivores is logistically difficult to directly estimate at a meaningful scale. Predictive distribution models are often used as a surrogate for density estimation. But because density can continue to increase as occupancy asymptotes at 1, occupancy may have little value as an index, and home range expansion in marginal habitat may further confound the association. We sought to estimate bobcat population size at a landscape scale (14,286 km2) in central Wisconsin, which provided an opportunity to relate predicted occurrence to individual space use and population density. We sampled bobcats using motion-sensitive trail cameras at 9 arrays across central Wisconsin. We estimated bobcat site-specific occupancy, and regressed these estimates as linear or asymptotic functions of site-specific density to determine the strength and shape of their association. We subsequently modeled both parameters relative to habitat covariates and repeated the regression process. A linear functional relationship between density and occupancy was most supported when detection parameters were held constant (wi= 0.97, R2 = 0.72) and when detection, occurrence, and density were modeled as a function of habitat covariates (wi= 0.99, R2 = 0.95). This suggests that repeated presence-absence data alone may be an efficient and reliable method for inferring spatial patterns in bobcat density or identifying habitat types with greater density potential in the northern parts of its range. Bobcat occupancy and density were both positively associated with surrounding woody cover and wetland edge density. Our most supported spatially explicit capture-recapture model estimated bobcat abundance as 362 adult individuals (95% CI 272–490) across the study area. © 2015 The Wildlife Society.

Synergistic effects of fire and elephants on arboreal animals in an African savanna

Abstract: Disturbance is a crucial determinant of animal abundance, distribution and community structure in many ecosystems, but the ways in which multiple disturbance types interact remain poorly understood. The effects of multiple-disturbance interactions can be additive, subadditive or super-additive (synergistic). Synergistic effects in particular can accelerate ecological change; thus, characterizing such synergies, the conditions under which they arise, and how long they persist has been identified as a major goal of ecology. We factorially manipulated two principal sources of disturbance in African savannas, fire and elephants, and measured their independent and interactive effects on the numerically dominant vertebrate (the arboreal gekkonid lizard Lygodactylus keniensis) and invertebrate (a guild of symbiotic Acacia ants) animal species in a semi-arid Kenyan savanna. Elephant exclusion alone (minus fire) had negligible effects on gecko density. Fire alone (minus elephants) had negligible effects on gecko density after 4 months, but increased gecko density twofold after 16 months, likely because the decay of fire-damaged woody biomass created refuges and nest sites for geckos. In the presence of elephants, fire increased gecko density nearly threefold within 4 months of the experimental burn; this occurred because fire increased the incidence of elephant damage to trees, which in turn improved microhabitat quality for geckos. However, this synergistic positive effect of fire and elephants attenuated over the ensuing year, such that only the main effect of fire was evident after 16 months. Fire also altered the structure of symbiotic plant-ant assemblages occupying the dominant tree species (Acacia drepanolobium); this influenced gecko habitat selection but did not explain the synergistic effect of fire and elephants. However, fire-driven shifts in plant-ant occupancy may have indirectly mediated this effect by increasing trees' susceptibility to elephant damage. Our findings confirm the importance of fire × elephant interactions in structuring arboreal wildlife populations. Where habitat modification by megaherbivores facilitates co-occurring species, fire may amplify these effects in the short term by increasing the frequency or intensity of herbivory, leading to synergy. In the longer term, tree mortality due to both top kill by fire and toppling by large herbivores may reduce overall microhabitat availability, eliminating the synergy.

A Market-Specific Methodology for a Commercial Building Energy Performance Index

Abstract: The scaling of energy efficiency initiatives in the commercial building sector has been hampered by data limitations, information asymmetries, and benchmarking methodologies that do not adequately model patterns of energy consumption, nor provide accurate measures of relative energy performance. The reliance on simple metrics, such as Energy Use Intensity (EUI), fails to account for significant variation across occupancy, construction characteristics and other elements of a building – both its design and its users – that influence building energy consumption. Using a unique dataset of actual building energy use, physical, spatial, and occupancy characteristics – collected from New York City’s Local Law 84 energy disclosure database, the Primary Land Use Tax Lot Output (PLUTO) database, and the CoStar Group – this paper analyzes energy consumption across commercial office buildings and presents a new methodology for a market-specific benchmarking model to measure relative energy performance across peer buildings. A robust predictive model is developed to normalize across multiple building characteristics and to provide the basis for a multivariate energy performance index. The paper concludes with recommendations for data collection standards, computational approaches for building energy disclosure data, and targeted policies using k-means clustering and market segmentation.