Occupancy

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

Occupancy dynamics in a tropical bird community: unexpectedly high forest use by birds classified as non‐forest species

Abstract: Summary 1. Worldwide loss of biodiversity necessitates a clear understanding of the factors driving population declines as well as informed predictions about which species and populations are at greatest risk. The biggest threat to the long-term persistence of populations is the reduction and changes in configuration of their natural habitat. 2. Inconsistencies have been noted in the responses of populations to the combined effects of habitat loss and fragmentation. These have been widely attributed to the effects of the matrix habitats in which remnant focal habitats are typically embedded. 3. We quantified the potential effects of the inter-patch matrix by estimating occupancy and colonization of forest and surrounding non-forest matrix (NF). We estimated species-specific parameters using a dynamic, multi-species hierarchical model on a bird community in southwestern Costa Rica. 4. Overall, we found higher probabilities of occupancy and colonization of forest relative to the NF across bird species, including those previously categorized as open habitat generalists not needing forest to persist. Forest dependency was a poor predictor of occupancy dynamics in our study region, largely predicting occupancy and colonization of only non-forest habitats. 5. Our results indicate that the protection of remnant forest habitats is key for the long-term persistence of all members of the bird community in this fragmented landscape, including species typically associated with open, non-forest habitats. 6.Synthesis and applications. We identified 39 bird species of conservation concern defined by having high estimates of forest occupancy, and low estimates of occupancy and colonization of non-forest. These species survive in forest but are unlikely to venture out into open, non-forested habitats, therefore, they are vulnerable to the effects of habitat loss and fragmentation. Our hierarchical community-level model can be used to estimate species-specific occupancy dynamics for focal and inter-patch matrix habitats to identify which species within a community are likely to be impacted most by habitat loss and fragmentation. This model can be applied to other taxa (i.e. amphibians, mammals and insects) to estimate species and community occurrence dynamics in response to current environmental conditions and to make predictions in response to future changes in habitat configurations.

Foraging in a landscape mosaic: selection for energy and minerals in free-ranging cattle.

Abstract: Several studies have indicated the potential importance of nutrients, other than energy, in determining foraging decisions. A model was developed to test this idea, on the assumption of an intake maximization for different nutrients (energy, sodium and phosphorus). The model predictions were tested using field data from cattle grazing in a landscape mosaic of Pleistocene cover-sand and riverine grassland. Observations on foraging behaviour, food intake and diet composition were collected in thirteen 4-day-periods over 2 years. Habitat selection was determined by comparing the proportion of grazing time in different vegetation units with the available area proportion of the units. Two levels of habitat selection were examined: a micro-level (fine-scale, where vegetation units were considered separately) and a macro-level (coarse-scale, where vegetation units were combined to give selection at the landscape level). At the micro-level of habitat selection, no selection was apparent between the vegetation units of the riverine landscape, but the Deschampsia flexuosa unit was significantly selected for in the cover-sand landscape. At the landscape (macro-) level, the animals preferred the riverine landscape. The model revealed poor predictions of habitat occupancy on a micro-level. A much better prediction was obtained when vegetation units were combined at a macro-level. The D. flexuosa unit provided a higher energy intake, whereas the intake of sodium was higher in riverine grassland. Phosphorus proved relatively significant in determining habitat occupancy. Based on energy maximization alone, the model was a very poor predictor of habitat occupancy. It is argued that selection occurred mainly at the macro-level. The incorporation of different nutrient constraints in foraging models can then prove fruitful when seeking explanations of habitat occupancy. At the micro-level, difficulties for the animal in assessing nutrient availability may result in a less selective foraging pattern. However, the costs of increased selectivity may be greater than the benefits.

High occupancy vehicle lanes: Not always more effective than general purpose lanes

Abstract: The success of a high occupancy vehicle lane in motivating people to shift to carpools and buses depends on maintaining a travel time differential between it and the adjacent general purpose lanes. This differential, in turn, depends on the level of continuing delay on the general purpose lanes. Therefore, it is clear that a high occupancy vehicle lane that will motivate people to shift to high occupancy vehicles will not eliminate congestion. Consequently, it is not clear that constructing a high occupancy vehicle lane will necessarily reduce delay more than construction of a general purpose lane. The objective of this research is to determine the circumstances in which this would be the case. The hypothesis is that such circumstances would be quite limited, and this proves to be the case. The intended benefits of high occupancy lanes are defined as reduced person-delay and reduced emissions. A model is developed to calculate these benefits for four alternatives: add a high occupancy vehicle lane, add a general purpose lane, convert an existing lane to a high occupancy vehicle lane, and do nothing. The model takes into account the initial conditions, the dynamic nature of the travel time differential between the high occupancy vehicle lane and other lanes, and the uncertainty regarding the extent to which people will shift modes. It combines queueing theory and mode choice theory and provides a robust method for comparing alternatives using a small amount of easily observed data. Application of the model in typical situations shows that with initial delays on the order of 15 min or more, adding a high occupancy vehicle lane would provide substantial reductions in delay and some reduction in emissions. However, in a wide range of such situations, adding a general purpose lane would be even more effective. Only if the initial delay is long and the initial proportion of high occupancy vehicles falls in a rather narrow range, would an added high occupancy vehicle lane be more effective. The proportion of high occupancy vehicles must be such that it allows good utilization of the high occupancy vehicle lane while maintaining a sufficient travel time differential to motivate a shift to buses or carpools. Adding a high occupancy vehicle lane to a three lane freeway will be more effective than adding a general purpose lane only if the initial maximum delay is on the order of 35 min or more and the proportion of high occupancy vehicles is on the order of 20%. Federal policies encourage construction of high occupancy vehicle lanes and restrict funding for general purpose lanes in areas that have not attained air quality standards. The findings of this research suggest a need to reconsider these policies.

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.