Showing papers on "Occupancy published in 2014"

Advances and applications of occupancy models

Abstract: Summary The past decade has seen an explosion in the development and application of models aimed at estimating species occurrence and occupancy dynamics while accounting for possible non-detection or species misidentification. We discuss some recent occupancy estimation methods and the biological systems that motivated their development. Collectively, these models offer tremendous flexibility, but simultaneously place added demands on the investigator. Unlike many mark–recapture scenarios, investigators utilizing occupancy models have the ability, and responsibility, to define their sample units (i.e. sites), replicate sampling occasions, time period over which species occurrence is assumed to be static and even the criteria that constitute ‘detection’ of a target species. Subsequent biological inference and interpretation of model parameters depend on these definitions and the ability to meet model assumptions. We demonstrate the relevance of these definitions by highlighting applications from a single biological system (an amphibian–pathogen system) and discuss situations where the use of occupancy models has been criticized. Finally, we use these applications to suggest future research and model development.

A systematic approach to occupancy modeling in ambient sensor-rich buildings

Abstract: With ever-rising energy demand and diminishing sources of inexpensive energy resources, energy conservation has become an increasingly important topic. Building heating, ventilation, and air conditioning (HVAC) systems are considered to be a prime target for energy conservation due to their significant contribution to commercial buildings' energy consumption in the US. Knowing a building's occupancy plays a crucial role in implementing demand-response HVAC controls, with a corresponding potential for reduction of HVAC energy consumption, especially in office buildings. This paper evaluates occupancy modeling (both binary detection and multi-class estimation) using twelve ambient sensor variables. Performance of six machine-learning techniques is evaluated in both single-occupancy and multi-occupancy offices. Of the six, the decision-tree technique yielded the best overall accuracy (i.e. 96.0% to 98.2%) and root mean square error (RMSE) (i.e. 0.109 to 0.156). The contribution of each individual ambient sensor variable is evaluated via information gain. It is found that CO2, door status, and light variables have important contributions to the final modeling results. It is observed that the overall accuracy generally increases as the number of sensors increases. This paper also examines the possibility of building a global occupancy model, and explores the reasons for low performance of global occupancy estimation. Lastly, the occupancy model is used to estimate and visualize the accumulative room and thermal zone usage in an office test-bed building for three months. The results reveal that the effective vacancy accounts for a substantial portion of the operational hours, varying from 19.8% to 29.8% with an average of 23.3%, which bears significant potential for energy savings. Furthermore, the authors simulated HVAC energy consumption of the test-bed building for three months in DesignBuilder and EnergyPlus, and compared energy consumption of occupancy-based demand-response HVAC controls using the authors' occupancy-modeling results to the conventional HVAC controls currently implemented in the test-bed building. The results demonstrate that 20% of gas and 18% of electricity could be effectively saved if occupancy-based demand-response HVAC control is implemented.

Occupancy Modeling and Prediction for Building Energy Management

Abstract: Heating, cooling and ventilation accounts for 35p energy usage in the United States. Currently, most modern buildings still condition rooms assuming maximum occupancy rather than actual usage. As a result, rooms are often over-conditioned needlessly. Thus, in order to achieve efficient conditioning, we require knowledge of occupancy. This article shows how real time occupancy data from a wireless sensor network can be used to create occupancy models, which in turn can be integrated into building conditioning system for usage-based demand control conditioning strategies. Using strategies based on sensor network occupancy model predictions, we show that it is possible to achieve 42p annual energy savings while still maintaining American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) comfort standards.

Quantifying restoration effectiveness using multi-scale habitat models: implications for sage-grouse in the Great Basin.

Abstract: A recurrent challenge in the conservation of wide-ranging, imperiled species is understanding which habitats to protect and whether we are capable of restoring degraded landscapes. For Greater Sage-grouse (Centrocercus urophasianus), a species of conservation concern in the western United States, we approached this problem by developing multi-scale empirical models of occupancy in 211 randomly located plots within a 40 million ha portion of the species' range. We then used these models to predict sage-grouse habitat quality at 826 plots associated with 101 post-wildfire seeding projects implemented from 1990 to 2003. We also compared conditions at restoration sites to published habitat guidelines. Sage-grouse occupancy was positively related to plot- and landscape-level dwarf sagebrush (Artemisia arbuscula, A. nova, A. tripartita) and big sagebrush steppe prevalence, and negatively associated with non-native plants and human development. The predicted probability of sage-grouse occupancy at treated plots was low on average (0.09) and not substantially different from burned areas that had not been treated. Restoration sites with quality habitat tended to occur at higher elevation locations with low annual temperatures, high spring precipitation, and high plant diversity. Of 313 plots seeded after fire, none met all sagebrush guidelines for breeding habitats, but approximately 50% met understory guidelines, particularly for perennial grasses. This pattern was similar for summer habitat. Less than 2% of treated plots met winter habitat guidelines. Restoration actions did not increase the probability of burned areas meeting most guideline criteria. The probability of meeting guidelines was influenced by a latitudinal gradient, climate, and topography. Our results suggest that sage-grouse are relatively unlikely to use many burned areas within 20 years of fire, regardless of treatment. Understory habitat conditions are more likely to be adequate than overstory conditions, but in most climates, establishing forbs and reducing cheatgrass dominance is unlikely. Reestablishing sagebrush cover will require more than 20 years using past restoration methods. Given current fire frequencies and restoration capabilities, protection of landscapes containing a mix of dwarf sagebrush and big sagebrush steppe, minimal human development, and low non-native plant cover may provide the best opportunity for conservation of sage-grouse habitats.

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.

BOES: Building Occupancy Estimation System using sparse ambient vibration monitoring

Abstract: In this paper, we present a room-level building occupancy estimation system (BOES) utilizing low-resolution vibration sensors that are sparsely distributed. Many ubiquitous computing and building maintenance systems require fine-grained occupancy knowledge to enable occupant centric services and optimize space and energy utilization. The sensing infrastructure support for current occupancy estimation systems often requires multiple intrusive sensors per room, resulting in systems that are both costly to deploy and difficult to maintain. To address these shortcomings, we developed BOES. BOES utilizes sparse vibration sensors to track occupancy levels and activities. Our system has three major components. 1) It extracts features that distinguish occupant activities from noise prone ambient vibrations and detects human footsteps. 2) Using a sequence of footsteps, the system localizes and tracks individuals by observing changes in the sequences. It uses this tracking information to identify when an occupant leaves or enters a room. 3) The entering and leaving room information are combined with detected individual location information to update the room-level occupancy state of the building. Through validation experiments in two different buildings, our system was able to achieve 99.55% accuracy for event detection, less than three feet average error for localization, and 85% accuracy in occupancy counting.

Prioritizing tiger conservation through landscape genetics and habitat linkages.

Abstract: Even with global support for tiger (Panthera tigris) conservation their survival is threatened by poaching, habitat loss and isolation. Currently about 3,000 wild tigers persist in small fragmented populations within seven percent of their historic range. Identifying and securing habitat linkages that connect source populations for maintaining landscape-level gene flow is an important long-term conservation strategy for endangered carnivores. However, habitat corridors that link regional tiger populations are often lost to development projects due to lack of objective evidence on their importance. Here, we use individual based genetic analysis in combination with landscape permeability models to identify and prioritize movement corridors across seven tiger populations within the Central Indian Landscape. By using a panel of 11 microsatellites we identified 169 individual tigers from 587 scat and 17 tissue samples. We detected four genetic clusters within Central India with limited gene flow among three of them. Bayesian and likelihood analyses identified 17 tigers as having recent immigrant ancestry. Spatially explicit tiger occupancy obtained from extensive landscape-scale surveys across 76,913 km2 of forest habitat was found to be only 21,290 km2. After accounting for detection bias, the covariates that best explained tiger occupancy were large, remote, dense forest patches; large ungulate abundance, and low human footprint. We used tiger occupancy probability to parameterize habitat permeability for modeling habitat linkages using least-cost and circuit theory pathway analyses. Pairwise genetic differences (FST) between populations were better explained by modeled linkage costs (r>0.5, p<0.05) compared to Euclidean distances, which was in consonance with observed habitat fragmentation. The results of our study highlight that many corridors may still be functional as there is evidence of contemporary migration. Conservation efforts should provide legal status to corridors, use smart green infrastructure to mitigate development impacts, and restore habitats where connectivity has been lost.

Recommended survey designs for occupancy modelling using motion-activated cameras: insights from empirical wildlife data

Abstract: Motion-activated cameras are a versatile tool that wildlife biologists can use for sampling wild animal populations to estimate species occurrence. Occupancy modelling provides a flexible framework for the analysis of these data; explicitly recognizing that given a species occupies an area the probability of detecting it is often less than one. Despite the number of studies using camera data in an occupancy framework, there is only limited guidance from the scientific literature about survey design trade-offs when using motion-activated cameras. A fuller understanding of these trade-offs will allow researchers to maximise available resources and determine whether the objectives of a monitoring program or research study are achievable. We use an empirical dataset collected from 40 cameras deployed across 160 km(2) of the Western Slope of Colorado, USA to explore how survey effort (number of cameras deployed and the length of sampling period) affects the accuracy and precision (i.e., error) of the occupancy estimate for ten mammal and three virtual species. We do this using a simulation approach where species occupancy and detection parameters were informed by empirical data from motion-activated cameras. A total of 54 survey designs were considered by varying combinations of sites (10-120 cameras) and occasions (20-120 survey days). Our findings demonstrate that increasing total sampling effort generally decreases error associated with the occupancy estimate, but changing the number of sites or sampling duration can have very different results, depending on whether a species is spatially common or rare (occupancy = ψ) and easy or hard to detect when available (detection probability = p). For rare species with a low probability of detection (i.e., raccoon and spotted skunk) the required survey effort includes maximizing the number of sites and the number of survey days, often to a level that may be logistically unrealistic for many studies. For common species with low detection (i.e., bobcat and coyote) the most efficient sampling approach was to increase the number of occasions (survey days). However, for common species that are moderately detectable (i.e., cottontail rabbit and mule deer), occupancy could reliably be estimated with comparatively low numbers of cameras over a short sampling period. We provide general guidelines for reliably estimating occupancy across a range of terrestrial species (rare to common: ψ = 0.175-0.970, and low to moderate detectability: p = 0.003-0.200) using motion-activated cameras. Wildlife researchers/managers with limited knowledge of the relative abundance and likelihood of detection of a particular species can apply these guidelines regardless of location. We emphasize the importance of prior biological knowledge, defined objectives and detailed planning (e.g., simulating different study-design scenarios) for designing effective monitoring programs and research studies.

Using existing network infrastructure to estimate building occupancy and control plugged-in devices in user workspaces

Abstract: Buildings are a major consumer of energy. We believe that energy can be saved with the notion of implicit occupancy sensing where existing IT infrastructure can be used to replace and/or supplement explicit dedicated sensors to determine building occupancy and drive building operation. Implicit sensing has the promise to be both lower in cost than explicit sensing based on PIR and ultrasound sensors and to offer additional useful data about the occupants of a building. Our implicit sensing methods are largely based on monitoring IP and MAC addresses in Wi-Fi access points and in routers, and then correlating these addresses to the occupancy of a floor, area, or room of a building. We experimentally evaluate the feasibility of this dual-use of IT infrastructure. We demonstrate an application of implicit sensing to sense the pending occupancy of a user workspace and automatically control the plugged-in devices in the workspace.

Occupancy estimation for smart buildings by an auto-regressive hidden Markov model

Abstract: One of the primary energy consumers in buildings are the Heating, Ventilation, and Air-Conditioning (HVAC) systems, which usually operate on a fixed schedule, i.e., running from early morning until late evening during the weekdays. This fixed operation schedule does not take the dynamics of occupancy level in the building into consideration, therefore may lead to waste of energy. An estimate of the number of occupants in the building with time can contribute to improving the control policy of the building's HVAC system by reducing energy consumption. In this paper, the auto-regressive hidden Markov model (ARHMM), is investigated to estimate the number of occupants in a research laboratory in a building using a wireless sensor network deployed. The network is composed of stand-alone sensing nodes with wireless data transmission capability, a base station that collects data from the sensing nodes, and a server to analyze the data from the base station. Experimental results and numerical simulation demonstrate that the ARHMM is more effective in estimating the number of occupants in the laboratory than the HMM algorithm, especially when the occupancy level fluctuates frequently.

Hybrid sampling Bayesian Occupancy Filter

Abstract: Modeling and monitoring dynamic environments is a complex task but is crucial in the field of intelligent vehicle. A traditional way of addressing these issues is 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 each cell is tracked at a sub-object level. The Bayesian Occupancy Filter is a generic occupancy grid framework which predicts the spread of spatial occupancy by estimating cell velocity distributions. However its velocity model, corresponding to a transition histogram per cell, leads to huge data management which in practice makes it hardly compatible to severe computational and hardware constraints, like in many embedded systems. In this paper, we present a new representation for the BOF, describing the environment through a mix of static and dynamic occupancy. This differentiation enables the use of a model adapted to the considered nature: static occupancy is described in a classic occupancy grid, while dynamic occupancy is modeled by a set of moving particles. Both static and dynamic parts are jointly generated and evaluated, their distribution over the cells being adjusted. This approach leads to a more compact model and to drastically improve the accuracy of the results, in particular in term of velocities. Experimental results show that the number of values required to model the velocities have been reduced from a typical 900 per cell (for a 30x30 neighborhood) to less than 2 per cell in average. The massive data compression allows to plan dedicated embedded devices.

On a Dhole Trail: Examining Ecological and Anthropogenic Correlates of Dhole Habitat Occupancy in the Western Ghats of India

Abstract: Although they play a critical role in shaping ecological communities, many threatened predator species are data-deficient. The Dhole Cuon alpinus is one such rare canid with a global population thought to be <2500 wild individuals. We assessed habitat occupancy patterns of dholes in the Western Ghats of Karnataka, India, to understand ecological and anthropogenic determinants of their distribution and habitat-use. We conducted spatially replicated detection/non-detection surveys of dhole signs along forest trails at two appropriate scales: the entire landscape and a single wildlife reserve. Landscape-scale habitat occupancy was assessed across 38,728 km2 surveying 206 grid cells of 188-km2 each. Finer scale habitat-use within 935 km2 Bandipur Reserve was studied surveying 92 grid cells of 13-km2 km each. We analyzed the resulting data of dhole signs using likelihood-based habitat occupancy models. The models explicitly addressed the problematic issue of imperfect detection of dhole signs during field surveys as well as potential spatial auto-correlation between sign detections made on adjacent trail segments. We show that traditional ‘presence versus absence’ analyses underestimated dhole habitat occupancy by 60% or 8682 km2 [naive = 0.27; (SE) = 0.68 (0.08)] in the landscape. Addressing imperfect sign detections by estimating detection probabilities [(L) (SE) = 0.12 (0.11)] was critical for reliable estimation. Similar underestimation occurred while estimating habitat-use probability at reserve-scale [naive = 0.39; (SE) = 0.71 (0.06)]. At landscape scale, relative abundance of principal ungulate prey primarily influenced dhole habitat occupancy. Habitat-use within a reserve, however, was predominantly and negatively influenced by anthropogenic disturbance. Our results are the first rigorous assessment of dhole occupancy at multiple spatial scales with potential conservation value. The approach used in this study has potential utility for cost-effectively assessing spatial distribution and habitat-use in other species, landscapes and reserves.

Occupancy monitoring using environmental & context sensors and a hierarchical analysis framework

Abstract: Saving energy in residential and commercial buildings is of great interest due to diminishing resources Heating ventilation and air conditioning systems, and electric lighting are responsible for a significant share of energy usage, which makes it desirable to optimise their operations while maintaining user comfort Such optimisation requires accurate occupancy estimations In contrast to current, often invasive or unreliable methods we present an approach for accurate occupancy estimation using a wireless sensor network (WSN) that only collects non-sensitive data and a novel, hierarchical analysis method We integrate potentially uncertain contextual information to produce occupancy estimates at different levels of granularity and provide confidence measures for effective building management We evaluate our framework in real-world deployments and demonstrate its effectiveness and accuracy for occupancy monitoring in both low- and high-traffic area scenarios Furthermore, we show how the system is used for analysing historical data and identify effective room misuse and thus a potential for energy saving

Effects of forest management on California Spotted Owls: implications for reducing wildfire risk in fire-prone forests

Abstract: Management of many North American forests is challenged by the need to balance the potentially competing objectives of reducing risks posed by high-severity wildfires and protecting threatened species. In the Sierra Nevada, California, concern about high-severity fires has increased in recent decades but uncertainty exists over the effects of fuel-reduction treatments on species associated with older forests, such as the California Spotted Owl (Strix occidentalis occidentalis). Here, we assessed the effects of forest conditions, fuel reductions, and wildfire on a declining population of Spotted Owls in the central Sierra Nevada using 20 years of demographic data collected at 74 Spotted Owl territories. Adult survival and territory colonization probabilities were relatively high, while territory extinction probability was relatively low, especially in territories that had relatively large amounts of high canopy cover (≥70%) forest. Reproduction was negatively associated with the area of medium-intensity timber harvests characteristic of proposed fuel treatments. Our results also suggested that the amount of edge between older forests and shrub/sapling vegetation and increased habitat heterogeneity may positively influence demographic rates of Spotted Owls. Finally, high-severity fire negatively influenced the probability of territory colonization. Despite correlations between owl demographic rates and several habitat variables, life stage simulation (sensitivity) analyses indicated that the amount of forest with high canopy cover was the primary driver of population growth and equilibrium occupancy at the scale of individual territories. Greater than 90% of medium-intensity harvests converted high-canopy-cover forests into lower-canopy-cover vegetation classes, suggesting that landscape-scale fuel treatments in such stands could have short-term negative impacts on populations of California Spotted Owls. Moreover, high-canopy-cover forests declined by an average of 7.4% across territories during our study, suggesting that habitat loss could have contributed to declines in abundance and territory occupancy. We recommend that managers consider the existing amount and spatial distribution of high-canopy forest before implementing fuel treatments within an owl territory, and that treatments be accompanied by a rigorous monitoring program.

Optimal HVAC building control with occupancy prediction

Abstract: Buildings account for about 41% of primary energy consumption and 75% of the electricity. Space heating, space cooling, and ventilation are the dominant end uses, accounting for 41% of all energy consumed in the buildings sector. Growing interest in sustainability has resulted in research efforts to reduce energy consumption while providing adequate comfort to users. In this work, we present a Model Predictive Control (MPC) framework for optimal HVAC control that minimizes energy consumption while staying within the comfort bounds of the occupants. The novelty of our approach lies in the use of prediction occupancy models derived from data traces and incorporating those models within the MPC framework. We use a Blended Markov Chain (BMC) occupancy prediction model in order to predict thermal load and occupancy of each zone in the building. We test our approach in simulation and compare it with occupancy schedules and control rules currently use in our university buildings. Our preliminary results show that 15.5% savings in cooling in the summer, and 9.4% savings in heating in the winter are achievable when conditioning the building using our MPC/BMC control framework.

Untangling the relationships among regional occupancy, species traits, and niche characteristics in stream invertebrates

Abstract: The regional occupancy and local abundance of species are affected by various species traits, but their relative effects are poorly understood. We studied the relationships between species traits and occupancy (i.e., proportion of sites occupied) or abundance (i.e., mean local abundance at occupied sites) of stream invertebrates using small-grained data (i.e., local stream sites) across a large spatial extent (i.e., three drainage basins). We found a significant, yet rather weak, linear relationship between occupancy and abundance. However, occupancy was strongly related to niche position (NP), but it showed a weaker relationship with niche breadth (NB). Abundance was at best weakly related to these explanatory niche-based variables. Biological traits, including feeding modes, habit traits, dispersal modes and body size classes, were generally less important in accounting for variation in occupancy and abundance. Our findings showed that the regional occupancy of stream invertebrate species is mostly related to niche characteristics, in particular, NP. However, the effects of NB on occupancy were affected by the measure itself. We conclude that niche characteristics determine the regional occupancy of species at relatively large spatial extents, suggesting that species distributions are determined by environmental variation among sites.

Predator–Primate Distribution, Activity, and Co-occurrence in Relation to Habitat and Human Activity Across Fragmented and Contiguous Forests in Northeastern Madagascar

Abstract: Predator–primate interactions are understudied, yet predators have been shown to influence primate behavior, population dynamics, and spatial distribution. An understanding of these interactions is important for the successful management and conservation of these species. Novel approaches are needed to understand better the spatial relationships between predators and primates across changing landscapes. We combined photographic surveys of predators and humans with line-transect sampling of lemurs across contiguous and fragmented forests in Madagascar to 1) compare relative activity; 2) estimate probability of occupancy and detection; 3) estimate predator–primate and local people–primate co-occurrence; and 4) assess variables influencing these parameters across contiguous and fragmented forests. In fragmented (compared to contiguous) forest sites endemic predator and lemur activity were lower whereas introduced predator and local people activity were higher. Our two-species interaction occupancy models revealed a higher number of interactions among species across contiguous forest where predator and lemur occupancy were highest. Mouse lemurs show evidence of “avoidance” (SIF 1.0) with feral cats and local people in contiguous forest. Feral cats demonstrated the highest number of interactions with lemurs, despite their distribution being limited to only contiguous forest. Distance to forest edge and distance to nearby villages were important in predicting predator occupancy and detection. These results highlight the growing threat to endemic predators and lemurs as habitat loss and fragmentation increase throughout Madagascar. We demonstrate the effectiveness of a novel combination of techniques to investigate how predator species impact primate species across a gradient of forest fragmentation.

Accounting for false-positive acoustic detections of bats using occupancy models

Abstract: Summary 1. Acoustic surveys have become a common survey method for bats and other vocal taxa. Previous work shows that bat echolocation may be misidentified, but common analytic methods, such as occupancy models, assume that misidentifications do not occur. Unless rare, such misidentifications could lead to incorrect inferences with significant management implications. 2. We fit a false-positive occupancy model to data from paired bat detector and mist-net surveys to estimate probability of presence when survey data may include false positives. We compared estimated occupancy and detection rates to those obtained from a standard occupancy model. We also derived a formula to estimate the probability that bats were present at a site given its detection history. As an example, we analysed survey data for little brown bats Myotis lucifugus from 135 sites in Washington and Oregon, USA. 3. We estimated that at an unoccupied site, acoustic surveys had a 14% chance per night of producing spurious M. lucifugus detections. Estimated detection rates were higher and occupancy rates were lower under the false-positive model, relative to a standard occupancy model. Un-modelled false positives also affected inferences about occupancy at individual sites. For example, probability of occupancy at individual sites with acoustic detections but no captures ranged from 2% to 100% under the false-positive occupancy model, but was always 100% under a standard occupancy model. 4. Synthesis and applications. Our results suggest that false positives sufficient to affect inferences may be common in acoustic surveys for bats. We demonstrate an approach that can estimate occupancy, regardless of the false-positive rate, when acoustic surveys are paired with capture surveys. Applications of this approach include monitoring the spread of WhiteNose Syndrome, estimating the impact of climate change and informing conservation listing decisions. We calculate a site-specific probability of occupancy, conditional on survey results, which could inform local permitting decisions, such as for wind energy projects. More generally, the magnitude of false positives suggests that false-positive occupancy models can improve accuracy in research and monitoring of bats and provide wildlife managers with more reliable information.

Spatially Explicit Power Analyses for Occupancy-Based Monitoring of Wolverine in the U.S. Rocky Mountains

Abstract: Conservation scientists and resource managers often have to design monitoring programs for species that are rare or patchily distributed across large landscapes. Such programs are frequently expensive and seldom can be conducted by one entity. It is essential that a prospective power analysis be undertaken to ensure stated monitoring goals are feasible. We developed a spatially based simulation program that accounts for natural history, habitat use, and sampling scheme to investigate the power of monitoring protocols to detect trends in population abundance over time with occupancy-based methods. We analyzed monitoring schemes with different sampling efforts for wolverine (Gulo gulo) populations in 2 areas of the U.S. Rocky Mountains. The relation between occupancy and abundance was nonlinear and depended on landscape, population size, and movement parameters. With current estimates for population size and detection probability in the northern U.S. Rockies, most sampling schemes were only able to detect large declines in abundance in the simulations (i.e., 50% decline over 10 years). For small populations reestablishing in the Southern Rockies, occupancy-based methods had enough power to detect population trends only when populations were increasing dramatically (e.g., doubling or tripling in 10 years), regardless of sampling effort. In general, increasing the number of cells sampled or the per-visit detection probability had a much greater effect on power than the number of visits conducted during a survey. Although our results are specific to wolverines, this approach could easily be adapted to other territorial species.

Urban predator–prey association: coyote and deer distributions in the Chicago metropolitan area

Abstract: We examined how predator or prey presence, as well as local and landscape factors, influence the distribution of coyotes (Canis latrans) and white-tailed deer (Odocoileus virginianus) in the Chicago metropolitan area. We collected data for 2 years at 93 study sites along 3 transects of urbanization using motion-triggered cameras. Our primary objective was to determine the relationship among coyote and deer spatial and temporal distribution, habitat characteristics, and human activity using multi-season patch occupancy models. Coyote occupancy was most strongly linked to rates of site visitation by humans and dogs, and was more likely farther from the urban center, with coyote colonization of sites inversely related to road density, housing density, and human and dog site visitation. Deer more frequently occupied sites with high canopy cover near water sources and colonized smaller sites with reduced housing density and human and dog presence. Expected predator–prey dynamics were altered in this highly urban system. Though we predicted deer would avoid coyotes on the landscape based on an “ecology of fear” framework, deer and coyote occupancy showed a strong positive association. We suggest that a scarcity of quality habitat in urban areas may cause the species to co-occupy habitat despite potential fawn predation. Modifying human foot traffic in green spaces may represent a useful tool for management and conservation of large urban mammals.

Spatial occupancy models applied to atlas data show Southern Ground Hornbills strongly depend on protected areas

Abstract: Determining the range of a species and exploring species–habitat associations are central questions in ecology and can be answered by analyzing presence–absence data. Often, both the sampling of sites and the desired area of inference involve neighboring sites; thus, positive spatial autocorrelation between these sites is expected. Using survey data for the Southern Ground Hornbill (Bucorvus leadbeateri) from the Southern African Bird Atlas Project, we compared advantages and disadvantages of three increasingly complex models for species occupancy: an occupancy model that accounted for nondetection but assumed all sites were independent, and two spatial occupancy models that accounted for both nondetection and spatial autocorrelation. We modeled the spatial autocorrelation with an intrinsic conditional autoregressive (ICAR) model and with a restricted spatial regression (RSR) model. Both spatial models can readily be applied to any other gridded, presence–absence data set using a newly introduced R packag...