From Nonequilibrium to Equilibrium: Insights from a Two-Population Occupation Model

Despite recent progress in the field of urban building energy modelling (UBEM), integration of occupant behaviour models is still an area in need of development. Although abundant for building energy modelling (BEM), stochastic occupancy models tailored for urban-level simulations are lacking, as well as systematic analyses of accuracy versus complexity. Occupancy models aimed for BEM typically introduce a heavy computational load, but a small reduction in accuracy might allow a large increase in speed. This paper proposes methods for scaling up stochastic Markov-chain (MC) occupancy models previously developed for BEM to fit UBEM, and compares them in terms of accuracy and computational efficiency. For the scales normally involved in UBEM (tens to hundreds of occupants per zone and hourly time resolution), a model based on multinomial sampling is proposed that is several hundred times faster than the original MC model and only slightly overestimates the variability in the occupancy patterns.

The number of occupancy and occupant behaviour models developed for building performance simulation (BPS) has steadily increased for the past four decades. However, their use is still limited in practice. This is partly due to the difficulty in understanding their utility and to the challenges related to their implementation into BPS. Both problems can be attributed to the lack of a framework for their description and communication. In this paper, we fill this gap by introducing a framework to document occupant models, that represents the state-of-the-art of available information on the topic. The framework consists of four blocks (description, development, evaluation, and implementation) and can also be regarded as a guideline to help researchers communicate their models transparently. Based on a systematic review, we verify to which degree existing academic papers on occupant models meet the framework, thus providing a self-critical assessment of the state-of-the-art of occupant models’ documentation.

Monitoring studies often use marked animals to estimate population abundance at small spatial scales. However, at smaller scales, occupancy sampling, which uses detection/nondetection data, may be useful where sites are approximately territories, and occupancy dynamics should be strongly correlated with population dynamics. Occupancy monitoring has advantages in that it is less expensive and invasive, and marked animals are not needed. Here, we used empirical data to determine whether and when change in occupancy is a good proxy for population change for a territorial species. As part of this overall goal, we also compared maximum‐likelihood estimates using a model‐averaging approach with a Bayesian MCMC approach. We used field data collected from 1993 to 2013 on three study areas for California spotted owls (Strix occidentalis occidentalis), a territorial species. Although correlations for trajectories of realized population change (Δt) between territory occupancy and Pradel models were moderate to high for Bayesian MCMC‐based estimates and high for model‐averaged estimates, magnitudes of the trajectories were different with the Pradel model reporting greater magnitudes of change. For the two areas showing a decline, Δt for the Pradel model was approximately 20–30% lower than for the occupancy model, and 25% higher in the area showing an increase. These differences can arise because the occupancy model is less sensitive, in that if two owls share a territory, the loss of one may be reflected in survival and, consequently in Δt by the Pradel model, but because the territory remains occupied it is not reflected by the occupancy model. Bayesian MCMC‐based and model‐averaged estimates of Δt were in close agreement in pattern (correlation ≥0.74) and magnitude (relative differences of last Δt were ≤5%) for both occupancy and mark–resight models. Results from the Pradel model may lead to conservation actions necessary to avoid high extinction or extirpation risk for small populations, while results from the territory occupancy model may result in status quo management. We found both Bayesian MCMC‐based and model‐averaged estimates of Δt robust approaches to evaluate population trends. However, we recommend the Bayesian MCMC approach for estimating risk (e.g., probability of declines) for retrospective analyses.

This paper, the last of four, completes the presentation of a model of human occupation. Its purpose is to illustrate how a model of occupation can be applied In clinicai practíce. Three major assumptions concerning occupational therapy that underliethis model are described, the three parts of the model presented earlier are reviewed, and the use of the model is assessmentto generate plans for treatment is discussed. Four case histories are used to demonstrate assessment and intervention.

Recent studies have used occupancy models (OM) and ecological niche models (ENM) to provide a better understanding of species’ distributions at different scales. One of the main ideas underlying the theoretical foundations of both OM and ENM is that they are positively related to abundance: higher occupancy implies higher density and more suitable areas are likely to have more abundant populations. Here, we analyze the relationship between habitat use measured in terms of occupancy probabilities from OM and environmental suitability derived from ENM in three different Neotropical mammal species: Leopardus wiedii, Cuniculus paca, and Dasypus novemcinctus. For ENM, we used climatic and vegetation cover variables and implemented a model calibration and selection protocol to select the most competitive models. For OM, we used a single-species, single-season model with site covariates for camera-trap data from six different sites throughout the Neotropical realm. Covariates included vegetation percentage, normalized difference vegetation index, normalized difference water index, and elevation. For each site, we fit OM using all possible combinations of variables and selected the most competitive (ΔAICc < 2) to build an average OM. We explored relationships between estimated suitability and occupancy values using Spearman correlation analysis. Relationships between ENM and OM tended to be positive for the three Neotropical mammals, but the strength varied among sites, which could be explained by local factors such as site characteristics and conservation status of areas. We conjecture that ENM are suitable to understand spatial patterns at coarser geographic scales because the concept of the niche is about the species as a whole, whereas OM are more relevant to explain the distribution locally, likely reflecting transient dynamics of populations resulting from many local factors such as community composition and biotic processes.

Building engineers and managers often treat occupants as passive agents despite their important role in building energy performance. Meanwhile, neglecting occupants' preferences may lead to poor implementations of building controls. The overall goal of this research is to improve buildings' energy performance and occupants' satisfaction by considering occupant-building interactions. This research first assessed the impact of static and dynamic occupant modelling approaches in a simulation-based analysis using the existing occupant models. The results showed the discrepancies between these two approaches in predicting the energy performance. The analysis emphasized the importance of using dynamic modelling approach. Given that the existing occupant models are context-specific, the next steps of this research focused on conducting a monitoring campaign. Therefore, the various monitoring methods exist in the literature and the anecdotal evidence were critically reviewed. Among the methods, environmental conditions are more controllable as well as configuring building designs and control systems are more flexible with laboratories and virtual environments. However, researchers can achieve valuable information on the natural occupants' presence and behaviour by in-situ monitoring on a relatively large sample size at a lower cost in long-term studies. This research adopted the in-situ method in a case study.

Mechanisms driving patterns of occurrence and co‐occurrence among North American freshwater fishes are poorly understood. In particular, the influence of biotic interactions on coexistence among stream reaches and their effects on regional species distribution patterns is not well understood for congeneric headwater fishes.Occupancy models provide a useful framework for examining patterns of co‐occurrence while also accounting for imperfect detection. Occupancy models may be extended to test for evidence that a dominant species influences the occurrence of a subordinate species and thus evaluate support for the hypothesis that species interactions drive patterns of coexistence.We examined patterns of occurrence and co‐occurrence at the stream‐reach scale among three species of darters (Percidae: Etheostomatinae) that occupy headwater streams within a Gulf Coastal Plain drainage in the south‐eastern U.S.A. We assessed species occurrences at 97 sites in first‐ to third‐order streams on one occasion each and used data from four sub‐reaches sampled with equal effort at each site to estimate species‐specific detection probabilities. Following sampling, a suite of habitat variables was collected at three equidistant points along each of the three transects established within a sub‐reach. Coarse (stream‐segment, catchment, network) scale variables were also incorporated using geospatial data. Single‐species and two‐species occupancy models were used to examine patterns of occupancy and coexistence.The occupancy of each species was influenced by distinct habitat variables. Goldstripe darters (Etheostoma parvipinne) were constrained by a stream size gradient, groundwater input appeared to influence the occurrence of Yazoo darters (Etheostoma raneyi), and local habitat heterogeneity (e.g. variation in depth and current velocity) appeared to influence the occupancy of redspot darters (Etheostoma artesiae).We found no evidence that the presence of one species influenced the occurrence of another within a stream‐reach based on two‐species occupancy models. Rather, species co‐occurrences were best explained as independent occurrences within a stream‐reach according to species‐specific habitat associations.Occupancy modelling may provide a suitable framework for evaluating the influence of biotic interactions among congeneric stream fishes along species‐specific habitat gradients at the stream reach scale. Our study offers insight into how habitat variation can influence coexistence of potential competitors across a large river system.

Multi‐species occupancy models for estimating the effects of environmental covariates on species occurrences while accounting for the effects of false‐negative errors in detection were developed more than 20 years ago. Only recently have these models been extended to include correlations in occurrence between species. Tobler et al. (2019) proposed two of these models wherein species occurrences are specified using a probit‐regression model of correlated multivariate binary outcomes. In one model, correlations in occurrence between species are formal parameters. In the other model, a factor‐analytic approximation of these correlations is used. After conducting extensive simulation studies to compare the performance of these models, Tobler et al. (2019) recommended the latter model in favour of the former owing to difficulties in fitting the former to simulated or real data. The same software (JAGS) was used to fit both models. Since the former model specifies the actual process used to generate species occurrences, we hypothesized that shortcomings in the software were responsible for the difficulties reported by Tobler et al. (2019). We therefore devised an efficient Markov chain Monte Carlo algorithm for fitting the data‐generating, multi‐species occupancy model using parameter‐expanded data augmentation. Using this algorithm, we conducted analyses of simulated data sets to re‐evaluate and compare the performance of the two multi‐species occupancy models. We also compared the results of fitting these models to a real dataset collected in camera‐trap surveys of mammalian species on the Tibetan Plateau. Our analyses of simulated data revealed that estimators of species occurrence, correlation and detection parameters had similar performance in terms of average bias, average root‐mean‐squared error and average coverage of 95% credible intervals regardless of whether the data‐generating model or its factor‐analytic approximation was used to analyse the data. However, our analyses also revealed the presence of systematic bias in the correlation estimates of both models, wherein strongly negative correlations were overestimated and strongly positive correlations were underestimated. Systematic bias was not evident in correlations estimated by fitting a multivariate probit regression model to latent species occurrences; therefore, the systematic bias in correlation estimates of the two occupancy models must have been produced by the failure of these models to estimate latent species occurrences accurately.

Occupancy models estimate distributions of imperfectly detected species, but violations of the closure assumption can bias results. However, researchers working with mobile animals may find it impossible to eliminate such violations. Here, we tested the hypothesis that occupancy models fit to realistic sampling data can generate unbiased occupancy estimates for an itinerant Wood Thrush (Hylocichla mustelina) population. In 2013 and 2014, we tracked movements of 41 breeding Wood Thrush males. We modelled territory shift probabilities using logistic exposure models and within‐territory movements using continuous‐time stochastic process models. We then constructed an individual‐based model, simulated (1000 iterations) spatiotemporal locations for individuals and simulated sampling these populations using 162 different point count protocols with variable spatial (sampling radius and point placement method), and temporal (survey length, between‐survey intervals and number of surveys) characteristics. We compared occupancy estimates with true values of instantaneous, daily and seasonal occupancy from the simulations. We parameterized continuous time stochastic process models based on movements within 34 unique territories and estimated a daily territory shift probability of 0.0099 (95% CI: 0.0060, 0.0152). Simulated data indicated that estimates of occupancy ranged from 0.18 (0.06, 1.00) to 0.80 (0.71, 0.89) depending on protocol characteristics. Occupancy estimates increased with increasing survey radius, survey length and between‐survey interval. Protocols using shorter surveys and between‐survey intervals were good estimators for instantaneous occupancy (low bias and mean‐squared error) but poor estimators for daily and seasonal occupancy; longer surveys and intervals generated unbiased estimators of daily occupancy but underestimated seasonal occupancy. Logistic regression models that ignored imperfect detection outperformed occupancy models for estimating instantaneous occupancy but not daily or seasonal occupancy. For mobile animals, occupancy of sampling sites changes in space and time. Consequently, the spatial and temporal aspects of a sampling protocol have strong, but predictable, effects on occupancy model parameter estimates. Our results demonstrate that how these factors interact is critical for designing surveys that produce occupancy estimates representative of the biological process of interest to a researcher.

Occupant model environments (MADYMO) are developed for four surrogate vehicles for injury risk computation in frontal crash fleet simulations: a small passenger car, a midsize passenger car, a midsize sport utility vehicle and a full size pickup truck. This research supports the initial implementation of a novel methodology for Evaluating Fleet, i.e., self and partner, Protection (EFP) of new vehicle designs through a systems modeling approach driven by structural and occupant modeling and real world crash and full scale test data. A two part general framework for development of occupant models for fleet vehicles is established: model development (Part I) and model verification and robustness evaluation (Part II). In Part I, current generic occupant models with seat belt and airbag restraints are obtained from restraint manufacturers and then modified to reflect the interior geometry and clearances of the desired vehicle. Data from finite element structural simulations, including the occupant compartment geometry, crash pulse, and toe pan intrusions, are utilized to drive the MADYMO models. Restraint system and dummy seating changes are incorporated to achieve a realistic match for both midsize male and small female driver dummy responses from available frontal crash tests. Part II involves comparing occupant responses from simulation and test data, and assessing trends in occupant responses in selected crash configurations for application in frontal crash fleet simulations.

Freshwater shrimps are an important biotic component of tropical ecosystems. However, they can have a low probability of detection when abundances are low. We sampled 3 of the most common freshwater shrimp species, Macrobrachium olfersii, Macrobrachium carcinus, and Macrobrachium heterochirus, and used occupancy modeling and logistic regression models to improve our limited knowledge of distribution of these cryptic species by investigating both local- and landscape-scale effects at La Selva Biological Station in Costa Rica. Local-scale factors included substrate type and stream size, and landscape-scale factors included presence or absence of regional groundwater inputs. Capture rates for 2 of the sampled species (M. olfersii and M. carcinus) were sufficient to compare the fit of occupancy models. Occupancy models did not converge for M. heterochirus, but M. heterochirus had high enough occupancy rates that logistic regression could be used to model the relationship between occupancy rates and predictors. The best-supported models for M. olfersii and M. carcinus included conductivity, discharge, and substrate parameters. Stream size was positively correlated with occupancy rates of all 3 species. High stream conductivity, which reflects the quantity of regional groundwater input into the stream, was positively correlated with M. olfersii occupancy rates. Boulder substrates increased occupancy rate of M. carcinus and decreased the detection probability of M. olfersii. Our models suggest that shrimp distribution is driven by factors that function at local (substrate and discharge) and landscape (conductivity) scales.

Occupancy‐based monitoring has become an important tool in wildlife conservation and management. Nonetheless, meeting occupancy modeling assumptions and providing biologically accurate information are difficult tasks over long time periods, large areas, or when monitoring multiple species. In occupancy modeling frameworks, derived grids are commonly used to divide landscapes into discrete units. Grid sizes that match the home range size of the species of interest are considered optimal, but this practice is complicated as home range size may vary by sex, habitat quality, or among species. Additionally, studies often assume their survey methods sample an entire grid cell when the actual effective sampling area may be much smaller. The effect of reduced effective sampling area on occupancy estimation has received little attention to date, despite being flagged as a critical issue. In this study, we assessed (1) how the relationship between effective area, home range size, and grid size affects power to detect trends in occupancy; (2) how varying the sampling design factors of effective area, duration, detection probability, and resurvey interval influence monitoring efficiency; and (3) determine whether a single sampling design can simultaneously detect declines in two species with different home range sizes. We used a spatially explicit simulation framework to create biologically realistic declining populations over 10 yr and assessed statistical power to detect known declines using occupancy modeling. We found that effective area and detection probability had the greatest influence on statistical power. We could not reliably detect declines when detection probability was low or when effective sampling area was <1/4 cell. We conclude that failing to account for effective area less than the cell size will result in overestimation of statistical power. Our simulations suggest occupancy models can detect declines for two species with different home range sizes using the same grid cell size under certain conditions, for instance, surveying >25% of the landscape, ≥25% effective area, and fixed sampling locations. Further, increasing resampling interval greatly increased monitoring efficiency. Our results show monitoring planning requires explicit consideration of effective sampling area and methods with sufficient detectability to detect population declines.

Private lands provide key habitat for imperiled species and are core components of function protectected area networks; yet, their incorporation into national and regional conservation planning has been challenging. Identifying locations where private landowners are likely to participate in conservation initiatives can help avoid conflict and clarify trade-offs between ecological benefits and sociopolitical costs. Empirical, spatially explicit assessment of the factors associated with conservation on private land is an emerging tool for identifying future conservation opportunities. However, most data on private land conservation are voluntarily reported and incomplete, which complicates these assessments. We used a novel application of occupancy models to analyze the occurrence of conservation easements on private land. We compared multiple formulations of occupancy models with a logistic regression model to predict the locations of conservation easements based on a spatially explicit social-ecological systems framework. We combined a simulation experiment with a case study of easement data in Idaho and Montana (United States) to illustrate the utility of the occupancy framework for modeling conservation on private land. Occupancy models that explicitly accounted for variation in reporting produced estimates of predictors that were substantially less biased than estimates produced by logistic regression under all simulated conditions. Occupancy models produced estimates for the 6 predictors we evaluated in our case study that were larger in magnitude, but less certain than those produced by logistic regression. These results suggest that occupancy models result in qualitatively different inferences regarding the effects of predictors on conservation easement occurrence than logistic regression and highlight the importance of integrating variable and incomplete reporting of participation in empirical analysis of conservation initiatives. Failure to do so can lead to emphasizing the wrong social, institutional, and environmental factors that enable conservation and underestimating conservation opportunities in landscapes where social norms or institutional constraints inhibit reporting.

BackgroundWe evaluated whether occupancy modeling, an approach developed for detecting rare wildlife species, could overcome inherent accuracy limitations associated with rapid disease tests to generate fast, accurate, and affordable SARS-CoV-2 prevalence estimates. Occupancy modeling uses repeated sampling to estimate probability of false negative results, like those linked to rapid tests, for generating unbiased prevalence estimates.MethodsWe developed a simulation study to estimate SARS-CoV-2 prevalence using rapid, low-sensitivity, low-cost tests and slower, high-sensitivity, higher cost tests across a range of disease prevalence and sampling strategies.ResultsOccupancy modeling overcame the low sensitivity of rapid tests to generate prevalence estimates comparable to more accurate, slower tests. Moreover, minimal repeated sampling was required to offset low test sensitivity at low disease prevalence (0.1%), when rapid testing is most critical for informing disease management.ConclusionsOccupancy modeling enables the use of rapid tests to provide accurate, affordable, real-time estimates of the prevalence of emerging infectious diseases like SARS-CoV-2.

Reinforcement learning for occupant behavior modeling in public buildings: Why, what and how?