Occupancy Models Research Articles

Integrating niche and occupancy models to infer the distribution of an endemic fossorial snake (Atractus lasallei).

Understanding species distribution and habitat preferences is crucial for effective conservation strategies. However, the lack of information about population responses to environmental change at different scales hinders effective conservation measures. In this study, we estimate the potential and realized distribution of Atractus lasallei, a semi-fossorial snake endemic to the northwestern region of Colombia. We modelled the potential distribution of A. lasallei based on ecological niche theory (using maxent), and habitat use was characterized while accounting for imperfect detection using a single-season occupancy model. Our results suggest that A. lasallei selects areas characterized by slopes below 10°, with high average annual precipitation (>2500mm/year) and herbaceous and shrubby vegetation. Its potential distribution encompasses the northern Central Cordillera and two smaller centers along the Western Cordillera, but its habitat is heavily fragmented within this potential distribution. When the two models are combined, the species' realized distribution sums up to 935 km2, highlighting its vulnerability. We recommend approaches that focus on variability at different spatio-temporal scales to better comprehend the variables that affect species' ranges and identify threats to vulnerable species. Prompt actions are needed to protect herbaceous and shrub vegetation in this region, highly demanded for agriculture and cattle grazing.

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

Effective control of energy consumption in public buildings holds paramount significance for global sustainable development. However, uncertainty in occupant behavior during operational phase often leads to a substantial discrepancy between the designed and actual energy consumption. While there is existing research on occupant behavior in public buildings utilizing stochastic modeling, statistical modeling, data mining, and agent-based modeling methods, the connotations and formation mechanisms have not been systematically revealed, rendering these developed models weak in applicability and reliability in practical scenarios. Reinforcement learning, which can perceive aspects of external environment and learn from stochastic interactions spontaneously, has been distinguished as a promising method for occupant behavior modeling. This research aims to develop a paradigm for reinforcement learning application in occupant behavior modeling in public buildings. Semi-structured interviews and literature review were combined to collect relevant information, while reasons for using reinforcement learning and applicable algorithms were discerned through qualitative analysis. Finally, detailed frameworks were proposed and validated through focus groups, offering a comprehensive guide that will serve as a reference for future research. This includes steps for collecting occupant-related data, selecting appropriate algorithms, and training and deploying reinforcement learning agents effectively. This research innovatively proposed a “panorama” of reinforcement learning for occupant behavior modeling in public buildings, laying a solid foundation for indoor environment and energy efficiency improvement in public building operations.

Aquatic invasive species exhibit contrasting seasonal detectability patterns based on environmental DNA: Implications for monitoring

Abstract Aquatic invasive species (AIS) are a global threat to freshwater biodiversity and ecosystem services. Documenting AIS prevalence at broad spatial scales is critical to effective management and early detection. However, conventional monitoring for AIS is costly and is rarely applied at the resolution and scale required for effective management. Monitoring of AIS using environmental DNA (eDNA) has the potential to enable broadscale surveillance at a fraction of the cost of conventional methods, but key questions must first be addressed related to how eDNA detection probability varies among environments, seasons, and multiple species with different life histories. To quantify spatiotemporal variation in the detection probability of AIS using eDNA sampling, we surveyed 20 lakes with known populations of four aquatic invasive species: common carp (Cyprinus carpio), rusty crayfish (Faxonius rusticus), spiny waterflea (Bythotrephes longimanus), and zebra mussels (Dreissena polymorpha). We collected water samples at 10 locations per lake, five times throughout the open water season resulting in a total of 1,000 water samples. Quantitative polymerase chain reaction was used with species‐specific assays to determine the presence of each species' eDNA in water samples. With Bayesian occupancy models, we quantified the effects of lake and site characteristics and Julian date on eDNA detection probability. The probability of eDNA detection varied seasonally, and the seasonal variation was species‐specific and related to species life histories. Zebra mussel eDNA was generally the most detectable among the species we targeted, and detection probability peaked in midsummer when only six water samples were required to achieve a 95% probability of detection (80% Bayesian credible interval: 3–12 samples). Spiny waterflea eDNA detections also peaked in mid to late summer, but were overall the most difficult species to detect, requiring 160 samples for a 95% probability of detection (80% Bayesian credible interval: 67–1,616 samples). Common carp eDNA was most detectable in the spring and rusty crayfish eDNA was most detectable in the early autumn, corresponding to key life history events. Sampling for eDNA during the optimal time of the year for each species decreased the number of samples required to reach a 95% probability of detection by an order of magnitude or more. Our results are relevant for decision makers interested in using eDNA as a multi‐species monitoring tool and highlight the importance of life history in the efficacy of eDNA monitoring.

Spatial and temporal ecology of Cerdocyon thous: a mesopredator canid coping with habitat loss, fragmentation, and chronic anthropogenic disturbances

ContextHuman activities are reducing the amount and quality of natural landscapes. Understanding how such changes affect the spatial and temporal ecology of mammal populations will enable us to foresee how communities will be structured in the Anthropocene.ObjectivesHere, we evaluated how the occupancy, intensity of use, and activity patterns of the mesopredator canid Cerdocyon thous are affected by topographic variation, habitat amount, fragmentation and chronic anthropogenic disturbances.MethodsCamera trapping data were obtained between May and September 2014 in 179 sampling points within ten priority areas for conservation in a seasonally dry tropical forest (Caatinga) in Brazil, totaling an effort of 6,701 camera.days. We use occupancy models for analyzed occupancy, generalized linear models (GLM) for the intensity of use and kernel density curve for activity pattern.ResultsCerdoyon thous benefited from human disturbance, showing greater occupancy and intensity of use near anthropogenic habitats, fragmentation, human density and cattle density. Moreover, temporal analyses showed that it modulates its daily activity according to habitat amount, human density, cattle density and altitude. However, C. thous tends to avoid areas with excess of fire, logging and infrastructures.ConclusionsThese results allow us to understand the mechanisms that contribute to the dominance of generalist mesocarnivores in human-dominated environments.

Research on occupant injury severity prediction of autonomous vehicles based on transfer learning

AbstractThe focus of the future of autonomous vehicles has shifted from feasibility to safety and comfort. The seat of an autonomous vehicle may be equipped with a rotational function, and the occupant's sitting position would be diverse. This poses a higher challenge to occupant injury protection during vehicle collisions. The main objective of the current study is to develop occupant injury prediction models for autonomous vehicles that can be used to predict the injury severity of occupants in different seat orientations and sitting positions. The first step is to establish an occupant crash model database with different seat orientations. It is used to simulate the occupant crash injury database of an autonomous vehicle, considering seat rotation and the back inclination angle. The second step is to establish a pre‐training occupant injury prediction model based on the existing database and then train the autonomous vehicle occupant injury prediction model using an in‐house database based on the transfer learning method. Occupant injury prediction models achieve good accuracy (82.8% on the numerical database and 62.9% on the real verification database) and shorter computational time (4.86 ± 0.33 ms) on the prediction tasks. Finally, the influence of the model input variables is analyzed. This study demonstrates the feasibility of using a small‐sample database based on transfer learning for occupant injury prediction in autonomous vehicles.

MULTI-OBJECTIVE OPTIMIZATION STUDY OF RESIDENTIAL ENERGY CONSUMPTION BASED ON EDH AND THE STOCHASTIC OPERATION OF AIR CONDITIONING

ABSTRACT Current requirements for residential energy-efficient design solutions in China are relatively homogeneous, requiring only improvements in overall building energy performance, often ignoring the energy differences between different residential housing units. This paper proposes a multi-objective research framework based on an occupant behavior model of air conditioning and a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to study energy-efficient design solutions for residential envelopes. The optimal energy-saving design scheme has a 22.56% and 35.34% reduction in energy consumption difference between residential units and overall building energy consumption, respectively, both of which have been optimized substantially. Moreover, the overall energy performance of the building can be improved when the “energy performance difference between housing units” (EDH) is reduced, which verifies that EDH is an important criterion for improving the energy performance of residential housing units.

Impacts of coyote colonization on coastal mammalian predators

Extreme ecosystem modification by humans has caused drastic reductions in populations and ranges of top mammalian predators, while simultaneously allowing synanthropic mesopredator species to expand. These conditions often result in inflated local densities of highly adaptable mesopredators that disrupt trophic dynamics and place unsustainable predation pressure on native prey populations. Colonization of a dominant predator may lead to top-down control of mesopredators and restore trophic balance. Coyotes are a novel colonizer of some coastal barrier islands of eastern North America, offering an opportunity to test how the addition of an apex predator impacts an established guild of mesopredators. To assess their trophic impact, we conducted 75,576 camera trapping hours over an 18-month study period, capturing > 1.5 million images across 108 coastal camera sites. Using two-species occupancy and habitat use models, we found sizeable effects of coyote habitat use on that of red foxes and free-ranging domestic cats, suggesting that coyotes function as apex predators in barrier island ecosystems. In fact, the only factor that determined the spatial pattern of highly ubiquitous red foxes was the sympatric habitat use of the largest carnivore in the food web—coyotes. That ‘novel’ apex predators can become established in coastal food webs illustrates the highly dynamic nature of conservation challenges for habitats and species at the edge of the sea.

Western spotted skunk spatial ecology in the temperate rainforests of the Pacific Northwest

AbstractA major threat to small mammalian carnivore populations is human‐induced land use change, but conservation and management are inhibited by limited knowledge about their ecology and natural history. To fill a key knowledge gap of the western spotted skunk (Spilogale gracilis), we investigated their spatial ecology at the landscape and home range scale in the temperate rainforests of the Oregon Cascades during 2017–2019. For the landscape scale analysis, we used detections of western spotted skunks at 112 baited camera traps and fitted a dynamic occupancy model to investigate spatial distribution and drivers of inter‐seasonal and inter‐annual changes in occupancy. Concurrently, we radio‐collared 25 spotted skunks (9 female, 16 male) and collected 1583 relocations. Using continuous‐time movement models, we estimated large home range sizes for both male and female spotted skunks, relative to their body mass, and highly overlapping home ranges that indicated a lack of territoriality. Using these home ranges, we fitted a resource selection function using environmental covariates that we assigned to various hypotheses such as resources, predator avoidance, thermal tolerance, and disturbance. Overall, western spotted skunks were widely distributed across our study area (seasonal occupancy up to 63.7 ± 5.3%) and highly detectable (weekly detection probability = 41.2%). At both spatial scales, spotted skunks selected wetter areas and local valleys, which we attributed to areas with more food resources. At the home range scale, spotted skunks selected locations with lower predation risk and areas surrounded by more previously logged forests. In this montane environment, inter‐seasonal contractions in the spatial distributions of spotted skunks were strongly driven by their response to cold temperature and accumulated snow. This was especially evident when seasonal occupancy declined significantly following a severe heavy snow event in February 2019. Given that there is little information available on the natural history of the western spotted skunk, these results provide essential information about their ecology to focus future monitoring efforts and may help identify potential threats (e.g., forest management, severe snow events, or wildfires) to this species.

Effects of temporal hydrologic shifts on the population biology of an endangered freshwater fish in a dryland river ecosystem

AbstractSpecies occupying dryland river ecosystems often experience “boom‐and‐bust” demographic cycles that coincide with shifts in habitat availability. Knowing whether declines are within natural thresholds versus those caused by acute human disturbance is critical for managing protected species. We investigated temporal shifts in abundance and habitat use of an endangered population of the threespine stickleback Gasterosteus aculeatus in southern California, where a Mediterranean climate leads to ephemeral habitat in one of the regions' least hydrologically modified rivers, the Santa Clara River. We conducted population surveys over a period of below‐average rainfall in the upper watershed in Soledad Canyon, with predefined reaches surveyed multiple times per year to capture different hydrologic conditions. Abundances were stable across years but varied significantly depending on location, with some reaches remaining dry and others drying seasonally to varying degrees. Occupancy models showed that the presence of stable perennial reaches, drying regime, and other site‐specific factors were important predictors of habitat use, and that certain reaches may be key to ensuring source‐sink dynamics as flow dissipates over the dry season. Low occupancy in two sections was driven by different predominant mechanisms, one by diel cycles of evapotranspiration and the other by cattails (Typha spp.), with both having greater effects during the hotter, drier parts of the year. As dryland river ecosystems are vulnerable to the effects of anthropogenic‐induced climate change, this study demonstrates how temporal monitoring can delimit dry‐state benchmarks for improving management interventions (i.e., translocation and habitat restoration) for protected species under conditions that are predicted to worsen in the coming years.

Multi‐species occupancy modeling of ground‐dwelling mammals in central Laos: a case study for monitoring in tropical forests

Unsustainable hunting and habitat loss have led to widespread mammal population declines in Southeast Asia, and evidence‐based conservation is needed to prevent further declines. Robust monitoring of population trends is a key component of evidence‐based approaches. We conducted a landscape‐scale systematic camera‐trapping survey in Nakai‐Nam Theun National Park, located within the Annamites region in Laos, with the objective of establishing a population baseline that can be used for long‐term monitoring for ground‐dwelling mammals. We analyzed camera‐trapping data within a multi‐species Royle–Nichols occupancy framework to estimate occupancy and species richness within three management zones. We used anthropogenic and ecological covariates to understand the factors influencing species occurrence in the study site. In total, we detected 40 terrestrial mammals over 20 794 camera‐trap days, including five Annamite endemics and 18 threatened species, of which 26 had sufficient detections to be included in the multi‐species occupancy analysis. We found that overall species richness increased with remoteness and elevation. Mean ± SD predicted species richness in the three management zones was 14.6 ± 1.55 (Zone 1‐Thong Khacheng), 13.18 ± 1.64 (Zone 2‐Thong Kouang), 11.84 ± 1.15 (Zone 3‐Nam Chae), with an average of 13.26 ± 2.03. Our study highlights the importance of Nakai‐Nam Theun National Park as a priority area for biodiversity conservation in the Annamites, and demonstrates the utility of using camera‐trapping and multi‐species occupancy modeling approaches to establish robust baselines for the monitoring of mammal communities in tropical protected areas.

Application of the occupational health risk assessment model in predicting the risk of noise-induced hearing loss in noise-polluted manufacturers in southern China

Objective. This study aimed to evaluate the correlation between occupational noise-induced hearing loss (ONIHL) and noise exposure in different positions within manufacturing facilities that primarily produce electronic devices, wooden furniture and hardware machinery in southern China and to investigate the practical application of the risk assessment model. Methods. Noise exposure data were collected from 115 workers in 10 manufacturing industries in southern China through on-site investigation. Subsequently, sample testing was conducted in accordance with Chinese standards. The 31 job positions investigated were classified into two similar exposure groups (SEGs). The occupational health risk assessment (OHRA) model was used to evaluate the risks of ONIHL in various job positions. Results. More than half of the positions in both groups had noise levels exceeding 85 dB. In both SEGs, male workers exhibited similar risk patterns of acquiring hearing impairment, with a more pronounced and earlier risk of hearing loss compared to female workers. Age was also found to be a risk factor for hearing impairments. Conclusion. The ORHA model demonstrated a positive correlation between noise exposure levels and NIHL. This model can help employers assess job management with noise exposure and take measures to implement an effective risk management system.

An integrated assessment of niche partitioning reveals mechanisms of coexistence between mesocarnivores

Species diversity depends on the capacity of species to coexist, and when these share similar ecological requirements some degree of partitioning of the ecological niche is expected. Within the order Carnivora, direct and indirect interspecific interactions shape community structure and composition. Although strong negative interactions (e.g., interspecific killing) are expected between large carnivores and subordinate species, mesocarnivores that dominate most human disturbed landscapes can still strongly compete for resources given their similar body size and energy requirements. To identify the mechanisms of coexistence between mesocarnivore species in a Mediterranean landscape we assessed the ecological niche overlap between five species (Vulpes vulpes, Meles meles, Martes foina, Herpestes ichneumon, Genetta genetta), by simultaneously measuring the space, time, and trophic resource use. The multi-species occupancy model fit to camera-trapping data revealed spatial co-occurrence between most species’ pairs and an avoidance pattern between only stone marten and Eurasian badger (78 % probability of a negative effect). Using genetically identified scats, we assessed trophic niche breadth and overlap. Niche breadth was narrow for red fox, Eurasian badger, and stone marten, given these species consumed almost exclusively fruits and arthropods, and so trophic niche overlap was high. For the genet and the mongoose, the consumption of different food resources (e.g., mammals, birds, and reptiles) increased niche breadth and decreased the niche overlap with the other sympatric species. Finally, niche segregation was more significant along the temporal axis, since the mongoose has a diurnal activity pattern and the remaining carnivore species, although mostly nocturnal, had small asynchronies in the activity peaks which could decrease the chance of direct encounters. Therefore, our results suggest that similar habitat preferences and ecological requirements rather than interspecific competition shape the mesocarnivores’ resource use in this landscape. Nonetheless, fine-scale temporal segregation seems the main mechanism to facilitate coexistence, together with small differences in trophic resource use, especially for species with high spatial and temporal overlap.

Fast maximum likelihood estimation for general hierarchical models

Hierarchical statistical models are important in applied sciences because they capture complex relationships in data, especially when variables are related by space, time, sampling unit, or other shared features. Existing methods for maximum likelihood estimation that rely on Monte Carlo integration over latent variables, such as Monte Carlo Expectation Maximization (MCEM), suffer from drawbacks in efficiency and/or generality. We harness a connection between sampling-stepping iterations for such methods and stochastic gradient descent methods for non-hierarchical models: many noisier steps can do better than few cleaner steps. We call the resulting methods Hierarchical Model Stochastic Gradient Descent (HMSGD) and show that combining efficient, adaptive step-size algorithms with HMSGD yields efficiency gains. We introduce a one-dimensional sampling-based greedy line search for step-size determination. We implement these methods and conduct numerical experiments for a Gamma-Poisson mixture model, a generalized linear mixed models (GLMMs) with single and crossed random effects, and a multi-species ecological occupancy model with over 3000 latent variables. Our experiments show that the accelerated HMSGD methods provide faster convergence than commonly used methods and are robust to reasonable choices of MCMC sample size.

Evaluation of lap belt-pelvis load transfer in frontal impact simulations using finite element occupant models

Objective The goal of this study was to examine the relationship between lap belt tension and force measured at the iliac wing and the effects of model type and torso posture on this relationship. From this analysis, preliminary transfer functions were developed to predict loads applied to the iliac wing as a function of lap belt tension at magnitudes typically measured in sled and vehicle crash tests. Methods A DOE study was conducted to provide a robust assessment of the lap belt-pelvis load relationship under various conditions. The GHBMC, THUMS, and THOR FE models were positioned in upright and reclined postures with several other intrinsic and extrinsic parameters varied for a total of 360 simulations. For the HBMs, instrumentation was developed to measure ASIS load at each iliac wing. Simulations that resulted in submarining were identified and removed from the subsequent development of lap belt-ASIS force transfer functions. Results The GHBMC exhibited submarining more frequently than the THUMS and THOR models. In addition to submarining, there were several cases in which the lap belt remained below the ASIS instrumentation or roped during the model’s forward excursion. These phenomena, particularly prevalent in the THUMS model, also influenced how the lap belt engaged the ASIS instrumentation and were thus eliminated from the transfer function development. Transfer functions relating peak lap belt tension and corresponding ASIS force magnitudes were developed for the GHBMC and THOR models in upright and reclined postures. In the upright posture, the THOR showed a higher level of ASIS load measured for a given level of lap belt tension than the GHBMC; however, in recline the lap belt-pelvis load relationship was similar between the two models. Conclusions The lap belt-pelvis load relationship was found to be affected by model type, posture, the area in which the ASIS instrumentation was defined, and occupant kinematics. This study showed it was possible to minimize the ASIS force by having the lap belt engage low on the pelvis and upper thighs, though further study is needed to determine if this loading mechanism is truly protective from an injury standpoint or an artifact of bypassing the ASIS instrumentation. The transfer function that showed the highest ASIS force measured for a given level of lap belt tension is recommended for future use.

WILDPOP: AN INTERACTIVE TOOL FOR ESTIMATING OCCUPANCY AND ABUNDANCE OF WILDLIFE POPULATIONS

Species abundance or population size is an ecological parameter of critical importance for wildlife management and conservation decisions. Widely used data collection methods, such as sign surveys and remote cameras, often count non-identifiable individuals or individuals prone to misidentification. In ecological modeling, these individuals are considered unmarked, and a state-of-the-art modeling approach for such data is occupancy-type modeling for unmarked individuals, which explicitly incorporates imperfect detection. Hierarchical modeling of this kind requires advanced statistical analyses, typically conducted using the R software platform and the “unmarked” package. However, these models can be explored only by researchers with programming skills and a thorough understanding of hierarchical analysis of wildlife population data. To help researchers and practitioners implement these models, we have developed a Shiny web-based interactive tool for wildlife population assessment, which works with data collected by scientists and wildlife managers. This app facilitates the use of occupancy-type modeling for unmarked individuals (single-season single species occupancy and N-mixture models) for non-coder users. The app performs simulations for single-season single species occupancy and N-mixture models with or without covariates, and estimates occupancy and abundance employing users provided data. The results are displayed as text, tables, and graphs, helping users understand hierarchical modeling and answering real-life wildlife management questions.

To stay or not to stay? Temporal shifts in dung beetle occupancy in a cloud forest landscape

Abstract Dynamic occupancy model estimates the probabilities of occupancy, detection, extinction and colonization of individuals throughout seasons and is, thus, a useful tool for identifying whether individuals select a site to improve population phenology and fitness. We used multi‐season dynamics occupancy models to assess how abiotic factors (monthly variation, percentage of forest and edge and temperature) and biotic factors (female abundance) affect the demographic dynamics of coprophagous beetles Deltochilum mexicanum and Dichotomius satanas that differ in their habitat preferences in a human‐modified landscape in a cloud forest in Mexico. For both species, we captured a total of 1069 individuals with a density of 48.2 individuals per trap in forest areas and 14.8 individuals per trap in non‐forest areas. The species differed in their spatiotemporal occupancy dynamics in the landscape. Specifically, we found that monthly variation, percentage of forest cover and percentage of edge affected the models for both species. Each species showed a specific set of explanatory covariates for the model parameters, indicating that phenological and demographic regulatory mechanisms act as drivers of the spatiotemporal occupancy in a year period. We highlight the importance of considering abiotic factors (landscape characteristics) as drivers of occupancy, especially in highly heterogeneous landscapes. Such understanding is relevant to establish the effects of the landscape on the distribution and occurrence of dung beetles by determining how forest and generalist species differ in colonization and extinction patterns with respect to their phenology.

Vegetation canopy height shapes bats’ occupancy: a remote sensing approach

ABSTRACT Anthropogenic activities have significantly altered land cover on a global scale. These changes often have a negative effect on biodiversity limiting the distribution of species. The extent of the effect on species’ distribution depends on the landscape composition and configuration at a local and landscape level. To better understand this effect on a large scale, we evaluated how land cover and vegetation structure shape bat species’ occurrence while considering species’ imperfect detection. We hypothesize that intensification of anthropogenic activities in agriculture, for example, reduces heterogeneity of land cover and vegetation structure, and thereby, limits bat occurrence. To investigate this, we conducted acoustic bat sampling across 59 locations in southern Portugal, each with three spatial replicates. We derived fine-scale vegetation structural metrics by combining spaceborne LiDAR (GEDI) and synthetic aperture radar data (Sentinel-1 and ALOS/PALSAR-2). Additionally, we included land cover metrics and high-resolution climate data from CHELSA. Our findings revealed an important relationship between bat species’ occupancy and vegetation structure, particularly with vegetation canopy height. Moreover, forest and shrubland proportions were the main land cover types influencing bat species responses. All species’ best-ranking occupancy models included at least one climatic variable (temperature, humidity, or potential evapotranspiration), demonstrating the importance of climate when predicting bat distribution. Our acoustic surveys had a species’ detection probability varying from 0.19 to 0.86, and it was influenced by night conditions. These findings underscore the importance of modeling imperfect detection, especially for highly vagile and elusive organisms like bats. Our results demonstrate the effectiveness of using vegetation and landscape metrics derived from high-resolution remote sensing data to model species distribution in the context of biodiversity monitoring and conservation.

Estimating coccidioidomycosis endemicity while accounting for imperfect detection using spatio - temporal occupancy modeling.

Coccidioidomycosis, or Valley fever, is an infectious disease caused by inhaling Coccidioides fungal spores. Incidence has risen in recent years, and it is believed the endemic region for Coccidioides is expanding in response to climate change. While Valley fever case data can help us understand trends in disease risk, using case data as a proxy for Coccidioides endemicity is not ideal because case data suffers from imperfect detection, including false positives (e.g., travel-related cases reported outside of endemic area) and false negatives (e.g., misdiagnosis or underreporting). We proposed a Bayesian, spatio-temporal occupancy model to relate monthly, county-level presence/absence data on Valley fever cases to latent endemicity of Coccidioides, accounting for imperfect detection. We used our model to estimate endemicity in the western United States. We estimated high probability of endemicity in southern California, Arizona, and New Mexico, but also in regions without mandated reporting, including western Texas, eastern Colorado, and southeastern Washington. We also quantified spatio-temporal variability in detectability of Valley fever, given an area is endemic to Coccidioides. We estimated an inverse relationship between lagged 3- and 9-month precipitation and case detection, and a positive association with agriculture. This work can help inform public health surveillance needs and identify areas that would benefit from mandatory case reporting.

Risk assessment models for development of carpal tunnel syndrome: clinical, anthropometric, and neuromuscular ultrasound predictors

BackgroundCarpal tunnel syndrome (CTS) is the most common entrapment neuropathy with multifactorial etiologies. We intended in this case–control study to identify, out of a comprehensive set of risk factors, the relatively important ones and develop a quantitative risk assessment model for this disorder. It was also legitimate to define the hazard of those factors in a dose-related manner and the acceptable safe limit especially for work-related stresses.ResultsAge and female predominance were comparable between the 60 patients (89 hands with electrophysiologically confirmed CTS) and 50 controls (100 hands). Occupation of the studied sample varied between housewives only, employed housewives, and manual workers with a distribution that differed significantly between patients and controls. Significantly higher body mass index (BMI) and mean wrist depth were found in patients than controls. Wrist ratio (clinically or sonographically) was significantly squarer in patients than controls. Overall workload and number of hours spent daily performing work with repeated hand movements or awkward hand position were significantly higher among patients than controls.ROC curves were constructed for wrist measurements and occupational stresses. Cut-off points of wrist ratio and internal carpal tunnel ratio (by ultrasound) to discriminate subjects with CTS were > 0.68 and ≤ 1.854, respectively. The best cut-off value for number of hours spent daily performing work with repeated hand movements was 3 h/ day. As for working with awkward hand position or cold exposure, cut-off value was 0.6 h/day for both.Two logistic regression models were conducted to investigate nonoccupational and occupational predictors of CTS. The independent predictors concluded from the first model were BMI, positive family history of CTS, wrist ratio, and decrease grip strength. As for the occupational model, predictors were tasks requiring awkward hand position and cold exposure.ConclusionOccupational risk assessment by clinical, anthropometric, and ultrasonographic measurement should be used in professions requiring repetitive or awkward hand movements, so that in overweight persons with square wrists appropriate workplace setup measures or assistive technology at work or home could be taken to prevent or decrease the impact of work hazards or help choose individuals with low risk for appropriate jobs.

Shifts in elevational distributions of montane birds in an arid ecosystem

Montane species are generally predicted to respond to climate change via upslope movement. Elevational range shifts of birds rarely have been examined in arid regions. Here, we examine shifts in the elevational distributions of breeding birds from two regions of the Great Basin, a desert in the western USA, over 10 to 20 years. We collected data annually from 2001 to 2020, a relatively long and consistent time series that is uncommon in research on distributional shifts. We used single‐species occupancy models of 32 bird species to examine shifts along the full elevational gradient (1650–3200 m a.s.l.) and within the lowest and highest edges (25%) of the gradient. We then conducted simulations to test whether population stochasticity could confound inferences about shifts. We examined whether temperature, precipitation, and primary productivity (normalized difference vegetation index) were associated with occupancy and shifts. The elevational distributions of 23 species shifted, and simulations indicated that shifts in the distributions of 18 species were unlikely to be stochastic. The majority of shifts in the western Great Basin were downslope, whereas those in the central Great Basin were upslope. More shifts occurred at the edges of the elevational gradient than along the full gradient. Elevational shifts lacked a consistent climate‐response signal, but those of some species appeared to follow changes in primary productivity. We found regional differences in elevational shifts and climate associations, and our work suggests that these desert bird populations may be relatively resilient to climate change.