Occupancy

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

Tourism and climate conditions in San Juan, Puerto Rico, 2000-2010

Abstract: The general behavior of the tourism sector in Puerto Rico, with its marked seasonality, hints at a close relationship between tourism activities and climate conditions. Even if weather condition is only one of many variables considered by travelling tourists, climate conditions weigh heavily in the majority of the decisions. The effect of climate variability on the environment could be manifested in warmer temperature, heat waves, and changes in the frequency of extreme weather events, such as severe storms and hurricanes, floods, and sea level rise. These conditions affect different sectors of society, among them public health and the economy. Therefore, our research has two main objectives: to establish a tourism climate index (TCI) for Puerto Rico and to analyze if occupancy rates in hotels correspond to local weather conditions. Even though there are many other variables that could have positive or negative effects on tourism activities, results showed a significant association between occupancy rate in Puerto Rico and climate indexes. According to both TCI and the mean historical climate for tourism indexes, the most favorable months for tourism in Puerto Rico were February and March (winter), whereas the worst season was the end of August and the beginning of September (summer-fall). Although winter represents dry conditions and lower temperatures in San Juan, it also represents the highest occupancy rate during the years examined. In summer and fall, data showed high occupancy rates, yet climate conditions were not suitable; these months also correspond to the hurricane season. During this season, high relative occupancy rates responded to internal and local tourism patterns. It can therefore be assumed that until the climate-tourism relationship is well characterized, there is little hope of fully understanding the potential economic effects, detrimental or beneficial, of global climate change, not only on tourism in Puerto Rico, but on other economic sectors as well.

Occupancy and detectability modelling of vertebrates in northern Australia using multiple sampling methods

Abstract: Understanding where species occur and how difficult they are to detect during surveys is crucial for designing and evaluating monitoring programs, and has broader applications for conservation planning and management. In this study, we modelled occupancy and the effectiveness of six sampling methods at detecting vertebrates across the Top End of northern Australia. We fitted occupancy-detection models to 136 species (83 birds, 33 reptiles, 20 mammals) of 242 recorded during surveys of 333 sites in eight conservation reserves between 2011 and 2016. For modelled species, mean occupancy was highly variable: birds and reptiles ranged from 0.01-0.81 and 0.01-0.49, respectively, whereas mammal occupancy was lower, ranging from 0.02-0.30. Of the 11 environmental covariates considered as potential predictors of occupancy, topographic ruggedness, elevation, maximum temperature, and fire frequency were retained more readily in the top models. Using these models, we predicted species occupancy across the Top End of northern Australia (293,017 km2) and generated species richness maps for each species group. For mammals and reptiles, high richness was associated with rugged terrain, while bird richness was highest in coastal lowland woodlands. On average, detectability of diurnal birds was higher per day of surveys (0.33 ± 0.09) compared with nocturnal birds per night of spotlighting (0.13 ± 0.06). Detectability of reptiles was similar per day/night of pit trapping (0.30 ± 0.09) as per night of spotlighting (0.29 ± 0.11). On average, mammals were highly detectable using motion-sensor cameras for a week (0.36 ± 0.06), with exception of smaller-bodied species. One night of Elliott trapping (0.20 ± 0.06) and spotlighting (0.19 ± 0.06) was more effective at detecting mammals than cage (0.08 ± 0.03) and pit trapping (0.05 ± 0.04). Our estimates of species occupancy and detectability will help inform decisions about how best to redesign a long-running vertebrate monitoring program in the Top End of northern Australia.

Passive acoustics and sound recognition provide new insights on status and resilience of an iconic endangered marsupial (koala Phascolarctos cinereus) to timber harvesting.

Abstract: Retention forestry aims to mitigate impacts of native forestry on biodiversity, but data are limited on its effectiveness for threatened species. We used acoustics to investigate the resilience of a folivorous marsupial, the koala Phascolarctos cinereus, to timber harvesting where a key mitigation practice is landscape exclusion of harvesting. We deployed acoustic recorders at 171 sites to record male bellows (~14,640 hours) for use in occupancy modelling and for comparisons of bellow rate (bellows night-1). Surveys targeted modelled medium-high quality habitat, with sites stratified by time since logging and logging intensity, including old growth as a reference. After scanning recordings with software to identify koala bellows, we found a high probability of detection (~0.45 per night), but this varied with minimum temperature and recorder type. Naive occupancy was ~ 64% across a broad range of forests, which was at least five times more than expected based on previous surveys using alternative methods. After accounting for imperfect detection, probability of occupancy was influenced by elevation (-ve), cover of important browse trees (+ve), landscape NDVI (+ve) and extent of recent wildfire (-ve, but minor effect). Elevation was the most influential variable, though the relationship was non-linear and low occupancy was most common at tableland elevations (> 1000 m). Neither occupancy nor bellow rate were influenced by timber harvesting intensity, time since harvesting or local landscape extent of harvesting or old growth. Extrapolation of occupancy across modelled habitat indicates that the hinterland forests of north-east NSW support a widespread, though likely low density koala population that is considerably larger than previously estimated. Retention forestry has a significant role to play in mitigating harvesting impacts on biodiversity, including for forest specialists, but localised studies are needed to optimise prescriptions for koalas.

The pervasive influence of sampling and methodological artefacts on a macroecological pattern: the abundance–occupancy relationship

Abstract: Aim To investigate the influence of sampling and methodological artefacts on the correlation between abundance and occupancy. Location Global scope. Methods A fixed effects weighted regression model was fitted to standardized effect size for 175 examples of correlations between abundance and occupancy. A regression tree model with standard effect size as the dependent variable was also fitted to the data. Results Standard effect size, and therefore the correlation between abundance and occupancy, was found to be strongly influenced by the type of abundance measure used to characterize the abundance–occupancy relationship. Local mean abundance (also referred to as ecological mean abundance) was primarily responsible for negative correlations. Negative correlations also resulted from a mismatch in the sampling extents of abundance and occupancy measures. Main conclusions The combination of abundance and occupancy measures selected to characterize the abundance–occupancy relationship for a given set of data has a profound impact on the sign of the correlation between the selected measures. Previous attempts to understand the processes giving rise to the pattern represented by the abundance–occupancy relationship have confounded sampling artefacts (e.g. spatial extent of abundance and occupancy information) and methodological artefacts (e.g. combining a truncated abundance measure such as local mean abundance with an untruncated occupancy measure such as proportion of occupied samples). Thus, a revision of the approach currently used to define and evaluate competing explanatory models of the abundance–occupancy relationship appears to be necessary.

WalkSense: Classifying Home Occupancy States Using Walkway Sensing

Abstract: Home automation systems can save a huge amount of energy by detecting home occupancy and sleep patterns to automatically control lights, HVAC, and water heating. However, the ability to achieve these benefits is limited by a lack of sensing technology that can reliably detect zone occupancy states. We present a new concept called Walkway Sensing based on the premise that motion sensors are more reliable in walkways than occupancy zones, such as hallways, foyers, and doorways, because people are always moving and always visible in walkways. We present a methodology for deploying motion sensors and a completely automated algorithm called WalkSense to infer zone occupancy states. WalkSense can operate in both offline (batch) and online (real-time) mode. We evaluate our system on 350 days worth of data from 6 houses. Results indicate that WalkSense achieves 96% and 95% average accuracies in offline and online modes, respectively, which translates to over 47% and 30% of reduced energy wastage, and 71% and 30% of reduced comfort issues per day, in comparison to the conventional offline and online approaches.