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Building temperature setpoints affect both HVAC energy consumption and occupant comfort. To reduce HVAC energy usage, researchers often investigate how system operations can be optimized under weather and occupancy variability subject to a fixed setpoint that minimizes any possible discomfort. While previous research has explored the selection of dynamic setpoints to minimize HVAC energy consumption based on outdoor temperature, they have often neglected the impact of varying occupancy rates on the setpoints. This paper aims to demystify energy savings derived from fixed and dynamic temperature setpoints under weather and occupancy variability and explores the additional energy savings that can be achieved through dynamic temperature setpoints. An exhaustive HVAC zone temperature setpoint optimizer was developed to determine dynamic setpoints with respect to weather and occupancy (i.e., setpoints that minimize HVAC energy consumption at different occupancy rates based on outdoor weather). U.S. DOE reference building energy models for small, medium, and large office buildings were simulated at 17 climate zones, 4 occupancy rates (25%, 50%, 75%, 100%) and 7 setpoints (19.5°C to 25.5°C at 1°C interval). It was found that, both fixed and dynamic setpoints benefit from the energy reduction of approximately 2-4% from the lower heat generated by the occupants at lower occupancy rates. However, at outdoor temperatures between 5°C and 32°C where occupant heat loads can swing the building between heating, free-running, and cooling modes, dynamic setpoints yield additional 2-10% energy savings, compared to fixed setpoints.

Abstract We recently published a study discussing the pitfalls of non‐probability sampling when selecting monitoring sites. We demonstrated that selecting sites based on abundance can often lead to biased inference, and we suggested that researchers use probability sampling. We also called for nuance when interpreting results of monitoring programs that use non‐probability sampling. We suggested that inference from sites of great abundance might still be useful for inference into population dynamics of long‐lived species such as raptors. Perret et al. seem to misinterpret our call for nuance as advocating for non‐probability sampling. They state that we concluded the general recommendation of using probability sampling should be revised. We did not conclude this. In fact, we agree with their recommendation. Perret et al. implemented simulations that are unrealistic within the context of our study. We use empirical data for 12 raptor species to demonstrate that our previous results are valid and that simulations implemented by Perret et al. do not reflect the biology of long‐lived raptors. The time series simulated by Perret et al. fluctuated greatly in abundance with populations often more than doubling within a year. This is extremely unlikely for populations of long‐lived species having high site fidelity. Many historical programs monitor sites of great abundance and thus risk biased results. We demonstrate that this risk is minimal under some important conditions and our results likely apply to other long‐lived species. Acknowledging this nuance could rescue many long‐term monitoring programs and their data thereby preserving efforts of costly conservation programs. Consistent with our original study, these exceptions do not invalidate the general recommendation to avoid non‐probability sampling; however, they do support our call for nuance when interpreting results of studies that monitored animals at sites of great abundance.

Abstract Population monitoring programmes typically rely on sampling because it is impossible to survey all the sites within the study area. In such a situation, the general recommendation to obtain unbiased estimates of population trends is to select monitoring sites using probability sampling. However, site selection not based on probability sampling, such as selecting sites with the largest abundance of individuals at the beginning of the monitoring programme, is common in practice. Nevertheless, these methods carry the risk of obtaining biased trend estimates. Using simulations, McClure & Rolek (2023) investigated whether three non‐probability sampling site selection methods can yield unbiased trend estimates under some specific conditions. For two of these methods, that is selecting high quality sites and selecting sites known to be occupied, the authors conclude that there is a major risk of obtaining biased trend estimates. For the third method, that is selecting sites with the largest initial abundance, they found conditions in which unbiased estimates can be obtained. They conclude that the general recommendation to use probability sampling should be revised. Here, we show that the authors' results, although perfectly correct, do not invalidate this recommendation. First, we point out that the authors made strong assumptions about the populations' functioning in their simulations, especially that inter‐annual variance in abundance is similar for all sites, which is unlikely in most real populations. We show through simple simulations that even slightly relaxing this assumption invalidates the authors' results. We also point out that for most of the hypotheses made by the authors, it is generally not known at the beginning of a study whether they will be respected. Furthermore, the authors did not provide evidence that selecting sites based on high initial abundance leads to more precise trend estimates than probability sampling methods. Therefore, neither the benefits nor the risks of this method are known. We conclude that until evidence is provided that abundance‐based site selection improves estimate precision and the situations in which it provides unbiased estimates are clearly identified, using probability sampling should remain the rule.

Abstract Site occupancy models (SOMs) are a common tool for studying the spatial ecology of wildlife. When observational data are collected using passive monitoring field methods, including camera traps or autonomous recorders, detections of animals may be temporally autocorrelated, leading to biased estimates and incorrectly quantified uncertainty. We presently lack clear guidance for understanding and mitigating the consequences of temporal autocorrelation when estimating occupancy models with camera trap data. We use simulations to explore when and how autocorrelation gives rise to biased or overconfident estimates of occupancy. We explore the impact of sampling design and biological conditions on model performance in the presence of autocorrelation, investigate the usefulness of several techniques for identifying and mitigating bias and compare performance of the SOM to a model that explicitly estimates autocorrelation. We also conduct a case study using detections of 22 North American mammals. We show that a join count goodness‐of‐fit test previously proposed for identifying clustered detections is effective for detecting autocorrelation across a range of conditions. We find that strong bias occurs in the estimated occupancy intercept when survey durations are short and detection rates are low. We provide a reference table for assessing the degree of bias to be expected under all conditions. We further find that discretizing data with larger windows decreases the magnitude of bias introduced by autocorrelation. In our case study, we find that detections of most species are autocorrelated and demonstrate how larger detection windows might mitigate the resulting bias. Our findings suggest that autocorrelation is likely widespread in camera trap data and that many previous studies of occupancy based on camera trap data may have systematically underestimated occupancy probabilities. Moving forward, we recommend that ecologists estimating occupancy from camera trap data use the join count goodness‐of‐fit test to determine whether autocorrelation is present in their data. If it is, SOMs should use large detection windows to mitigate bias and more accurately quantify uncertainty in occupancy model parameters. Ecologists should not use gaps between detection periods, which are ineffective at mitigating temporal structure in data and discard useful data.

Forty years of increasing fire size and severity in California’s Sierra Nevada were embodied by the 2021 Dixie Fire, which burned 389,837 ha between July and October, making it one of the largest, most destructive fires in California’s history. Historical fire regimes burned substantial areas annually, but primarily at low and moderate severities. Studies of the implications of fires like the Dixie Fire, which burn largely at higher severities, have focused on habitat change in the burned area; much less is known about potential edge effects. The hermit warbler (Setophaga occidentalis) and western bluebird (Sialia mexicana) have previously been characterized as species with dramatic, opposing post-wildfire responses: hermit warblers display a strongly negative response to fires while western bluebirds display a strong positive response. We conducted passive acoustic monitoring pre- and post-Dixie Fire inside and outside the burned area and analyzed the audio with the machine learning animal identification tool BirdNET. We then used a multi-season occupancy modeling framework to characterize both species’ site extinction and site colonization responses as a function of fire and habitat variables. We found no influence of edge effects, though this may have been a function of the low density of our recorders relative to the home range of these species. We did, however, observe contrasting relationships between the species. Hermit warbler site extinction was elevated in burned areas, particularly those burned at higher severities, while site colonization in unburned habitat was positively associated with canopy cover. Western bluebirds displayed the exact opposite pattern (elevated colonization in areas of high-severity fire, elevated extinction in unburned areas with high canopy cover). These results suggest contrasting trajectories for fire-influenced bird species under contemporary fire regimes and could guide hypothesis generation for broader studies of biodiversity responses to fire in this ecosystem.

Negative interactions among invasive predators, competitors and native species can often disrupt ecosystem services, particularly when keystone species are affected. The Key Largo woodrat (Neotoma floridana smalli) is an endangered ecosystem engineer, endemic to protected hammocks on the northern third of the island of Key Largo, FL, USA. Invasive predator control efforts have assisted in woodrat recovery, but less is known about how a potential competitor, the black rat (Rattus rattus), interacts with the woodrat. We conducted camera trap surveys at supplemental nest sites throughout the range of the woodrat and used multi-species occupancy models to investigate factors influencing woodrat and black rat co-occurrence. Supplemental nest sites were surveyed each year over a three-year period, during which the population of black rats within the protected hammock declined precipitously. Woodrats and black rats occurred at similar levels in the first survey with occupancy probabilities of 22.8 % and 15.6 %, respectively. Both species occurred at fewer sites in the second survey, but while woodrats later rebounded, black rats were only detected twice in the final survey. There was evidence of species avoidance between the rodents based on a species interaction term, and they exhibited opposing relationships with predictor variables of both detection and occupancy probabilities. As the occupancy of black rats decreased, the relationship between woodrats and distance from developed areas also weakened. Following the black rat decline, woodrats recolonized an area previously dominated by black rats, where they had been presumed extirpated for 2–3 decades. Our results indicate that black rat competition restricted occupancy of Key Largo woodrats, hindering recovery efforts that previously concentrated on invasive predator removal, demonstrating the need to consider influences of both invasive predators and competitors in species recovery programs.

Habitat loss and fragmentation significantly contribute to the population decline of endangered species and pose conservation challenges. The canopy-living primates are highly susceptible to such habitat loss or fragmentation. Golden langur is one such primate with limited occupancy. In the present study, we aimed to identify factors influencing the occurrence of the endangered golden langur which is confined to a narrow region of north-western Assam, India, and its population characteristics using an occupancy framework. Ten teams of 4–5 people walked 1772.11 km in 1363 grid cells of 50 ha in size. We constructed detection histories of the species for each grid from three replications. We obtained 740 detections of golden langurs and estimated the probability of occupancy to be 0.57 ± 0.04SE in Manas Biosphere Reserve (BR) and 0.69 ± 0.08SE in the southern fragments. The moist deciduous forests positively and rainfall negatively influenced the occupancy probability in Manas BR, while forest cover and canopy height positively influenced the occupancy of golden langurs in the southern fragments. The encounter rate of golden langur in Manas BR was 0.37 groups/km and 0.67 groups/km in the southern fragments. The mean group size of the multi-male multi-female groups was 11± 4 SD (10.71± 4.72 SD in Manas BR and 10.60 ± 3.81 SD in the southern fragments). The mean group size and composition did not vary between the two areas indicating the ability of the species to adapt to changing habitat conditions. Fragment size did not show any relation between the percent grid cells with the langur occurrence, encounter rate, and mean group size. The unique langur group count was multiplied by the mean group size and calculated the minimum population size of golden langurs in India to be 7396 langurs. Since golden langur is highly folivorous, their persistence depends on the availability of suitable habitats, thus, the management of moist deciduous forests and contiguity of the canopy is crucial to ensure their survival.

Predator populations persisting in desert landscapes may be especially vulnerable to habitat fragmentation and changing climates, but many are chronically understudied and at risk of extirpation. The Asiatic subspecies of caracal, Caracal caracal schmitzi, inhabit the mountainous landscapes of the western and southern Arabian Peninsula, but they are thought to be in decline across the region. In Saudi Arabia, a recent extensive study used camera traps and face-to-face questionnaires to survey Arabian leopards and other medium- to large-size mammals, simultaneously generating vast bycatch data on Asiatic caracal presence. We assessed interspecific temporal overlap and identified factors that influence caracal occupancy, and predict their potential distribution across their historical range in Saudi Arabia. From fourteen camera trap surveys, 497 independent captures of caracals were recorded at only the nine south-western sites. Occupancy modelling showed caracals occurred in areas with higher gross primary productivity and elevations, as well as with a higher relative abundance of free-roaming cats and dogs, striped hyaena, and wild prey. Higher abundances of large predators decreased detection of caracals. Caracals displayed a cathemeral activity pattern with peaks of activity around sunrise and sunset, and had a high diel overlap with free-roaming cats and dogs, and wild Arabian wolves. Predictive modelling identified the south western mountains as a stronghold for Asiatic caracals, with low occupancy or recent extirpation in the north and elsewhere, and was highly congruous with predictions from false-positive occupancy modelling from 843 questionnaires. The persistence of caracal populations is likely driven by the increased vegetation and wild prey associated with the southern regions, as well as a more frequent human presence that potentially increases availability of alternative prey, including free-roaming cats. Caracals persisting in dry and desert regions may be particularly vulnerable to climatic changes affecting vegetation and prey abundance, but may have the ability to adapt and benefit from limited human presence if conflict can be avoided.

Abstract For rewilding the depleted crocodylian populations in India, a targeted ‘one-species one area’ based conservation approach was adopted in the early-1970s. Suitable habitats were identified and designated as protected areas, specifically targeted to recover a particular crocodylian species. A ~ 610 km stretch of Chambal River in the Ganga River Basin was declared as National Chambal Sanctuary to restore the ‘Critically Endangered’ gharial (Gavialis gangeticus), where active management of mugger (Crocodylus palustris) was discouraged. In the present study, we examined the population trends, occupancy, and genetic status of mugger by conducting population monitoring and genetic assessment to understand the status of potentially competitive mugger in the Sanctuary. Our finding suggests that the mugger population has notably increased and colonised the Sanctuary. We observed a moderate level of genetic diversity in the mugger, which was relatively higher compared to the gharial in the Sanctuary. The rapid colonization of ecological generalist mugger raises concerns about potential competition with ecological specialist gharial threatening its long-term sustainability. Considering the coexistence dynamics between the species, it is essential to extend adaptive management strategies for mugger to ensure successful recovery of gharial population in the Sanctuary.

Abstract Background Emergency Department (ED) overcrowding is a global concern, with tools like NEDOCS, READI, and Work Score used as predictors. These tools aid healthcare professionals in identifying overcrowding and preventing negative patient outcomes. However, there’s no agreed-upon method to define ED overcrowding. Most studies on this topic are U.S.-based, limiting their applicability in EDs without waiting rooms or ambulance diversion roles. Additionally, the intricate calculations required for these scores, with multiple variables, make them impractical for use in developing nations. Objective This study sought to examine the relationship between prevalent ED overcrowding scores such as EDWIN, occupancy rate, and Work Score, and a modified version of EDWIN newly introduced by the authors, in comparison to the real-time perspectives of emergency physicians. Additionally, the study explored the links between these overcrowding scores and adverse events related to ED code activations as secondary outcomes. Method The method described in the provided text is a correlational study. The study aims to examine the relationship between various Emergency Department (ED) overcrowding scores and the real-time perceptions of emergency physicians in every two-hour period. Additionally, it seeks to explore the associations between these scores and adverse events related to ED code activations. Results The study analyzed 459 periods, with 5.2% having Likert scores of 5–6. EDOR had the highest correlation coefficient (0.69, p

Abstract Time‐to‐detection (TTD) occupancy models are increasingly used to study site occupancy of organisms. Occupancy is a reduced representation of abundance (distinguishing only between 0 and >0 individuals), which is also often a quantity of interest. In this paper, we present a novel framework for TTD occupancy models that address limitations of existing approaches. Our approach incorporates factors that accommodate detection heterogeneity among sites/visits and inter‐visit dependency, allowing for the relaxation of some restrictive assumptions inherent in previous models. As a result, our framework offers a robust and versatile tool for analysing various ecological data sets. We employ a maximum likelihood approach to estimate model parameters and conduct inference for the proposed TTD occupancy models. A key feature of these models is the introduction of a community parameter. This parameter characterizes the similarity of detectabilities, ranging from complete independence to complete identity, across multiple site visits. This framework disentangles the detection rate, abundance and occupancy, similar to the popular N‐mixture model. For situations where abundance estimation is not the primary goal, a family of mixed gamma exponential TTD models is developed, which generally exhibit more stable numerical properties compared to N‐mixture type TTD models. The performance of the proposed models and some reduced models is evaluated through simulation studies. The results indicate that the N‐mixture TTD model tends to considerably overestimate the occupancy probability when the community parameter is less than one, a condition necessary to satisfy the strict closure population assumption. On the other hand, the standard exponential TTD occupancy model underestimates the occupancy probability in the presence of unobserved detection heterogeneity and inter‐visit dependency. An analysis of 63 bird species in the Karoo region of South Africa demonstrates the enhanced flexibility of the proposed TTD occupancy models for data fitting. This paper demonstrates the importance of employing more flexible and general models to accurately capture the complexities of ecological systems or survey data. We provide R‐code to fit all considered models to data. The proposed TTD model framework contributes to enhancing our understanding of species occupancy distributions.

Law enforcement is central to the objectives of managing Protected Areas for the benefit of resident species. The study sought to appraise park patrol performance against variables needed for monitoring of the species, according to the ideologies of occupancy modelling in the Kalakpa Resource Reserve (KRR), by estimating patrol effort and comparing to estimates of survey effort needed for a precise occupancy estimation. Patrol indices were obtained from ranger patrol data and used to estimate patrol effort for rangers in the KRR. The survey effort was estimated using the values provided by the occupancy analysis of key ungulate species in KRR, for all encountered ungulates treated as a single group and four species directly sighted/heard on patrols. Encounter rates were low for ungulate species during the study period. The number of species and illegal activities observed were significantly affected by the number of staff on patrol, duration of patrols, and distance patrolled. The patrolling camp had no significant effect on the observations of species and illegal activities. Patrol effort was 190 survey occasions, with the estimated survey effort ranging from 377 to 1068 survey occasions. The estimated survey effort was significantly higher than the patrol effort and necessitated an increase in the number of patrols per unit time to improve the protection of the species in the KRR.

The German buildings sector is currently facing a double challenge: meeting emission reduction targets and providing affordable housing in rapidly growing cities. Living space per person has a major impact on household energy consumption and it has increased significantly in recent decades. New construction increases the demand for often energy-intensive building materials, and energy demand during the use phase of buildings is positively related to the size of the space. Optimising the allocation of living space therefore offers great potential for addressing both challenges. Sufficiency policies such as flat exchanges, a financial bonus for moving and the provision of moving advice aim to reduce the total floor area in the housing sector by using existing space more efficiently. However, the effectiveness of such measures is not yet fully understood. As the sufficiency approach relies heavily on individual choices, residents’ involvement, perception and acceptance are crucial for the success of such measures. An investigation in selected German housing companies shows that the potential of these measures is not fully exploited under the current political and social framework in Germany. Social acceptance, new norms and business models for landlords are needed for the effective use of these measures. Policy relevance How effective is the implementation of sufficiency measures for space utilisation in the German rental housing sector? The success factors and limiting barriers of the measures along with the acceptability and effectiveness of these measures are investigated by interviewing housing companies (some of them social housing providers) that have the potential to implement such policies in their housing stock. Improvements are needed in the social and technical infrastructure to increase the effectiveness of the measures. Raising awareness, rethinking communications and designing effective financial incentives are recommended to make the measures attractive to the public. Involving all stakeholders, keeping the issue on the political agenda and engaging in dialogue with policymakers are considered effective steps in the process of achieving space sufficiency in the housing sector.

Abstract Remote cameras have become a widespread data‐collection tool for terrestrial mammals, but classifying images can be labor intensive and limit the usefulness of cameras for broad‐scale population monitoring. Machine learning algorithms for automated image classification can expedite data processing, but image misclassifications may influence inferences. Here, we used camera data for three sympatric species with disparate body sizes and life histories – black‐tailed jackrabbits (Lepus californicus), kit foxes (Vulpes macrotis), and pronghorns (Antilocapra americana) – as a model system to evaluate the influence of competing image classification approaches on estimates of occupancy and inferences about space use. We classified images with: (i) single review (manual), (ii) double review (manual by two observers), (iii) an automated‐manual review (machine learning to cull empty images and single review of remaining images), (iv) a pretrained machine‐learning algorithm that classifies images to species (base model), (v) the base model accepting only classifications with ≥95% confidence, (vi) the base model trained with regional images (trained model), and (vii) the trained model accepting only classifications with ≥95% confidence. We compared species‐specific results from alternative approaches to results from double review, which reduces the potential for misclassifications and was assumed to be the best approximation of truth. Despite high classification success, species‐level misclassification rates for the base and trained models were sufficiently high to produce erroneous occupancy estimates and inferences related to space use across species. Increasing the confidence thresholds for image classification to 95% did not consistently improve performance. Classifying images as empty (or not) offered a reasonable approach to reduce effort (by 97.7%) and facilitated a semi‐automated workflow that produced reliable estimates and inferences. Thus, camera‐based monitoring combined with machine learning algorithms for image classification could facilitate monitoring with limited manual image classification.

3D semantic scene completion (SSC) aims to get a dense semantic understanding of an environment in 3D. It requires a geometric and semantic knowledge of the surrounding environment and the filling of void areas. In this paper, we propose an improved algorithm by modifying VoxFormer. VoxFormer consists of two steps for 3D semantic scene completion. First, it predicts the occupancy of an environment. Then, it completes the semantic scene completion through a masked autoencoder. It requires separate training for two stages, which can cause a disconnect of information from input to output. We propose an improved VoxFormer algorithm that makes end-to-end training possible by integrating occupancy prediction and scene completion. We use pseudo-LiDAR computed by depth estimation as input of 3D CNN, which generates queries for cross attention with 2D features. This makes the process end-to-end by connecting occupancy prediction and semantic scene completion. Experimental results using SemanticKITTI show improvement in the proposed algorithm.

Recent research has suggested that category theory can provide useful insights into the field of machine learning (ML). One example is improving the connection between an ML problem and the design of a corresponding ML algorithm. A tool from category theory called a Kan extension is used to derive the design of an unsupervised anomaly detection algorithm for a commonly used benchmark, the Occupancy dataset. Achieving an accuracy of 93.5% and an ROCAUC of 0.98, the performance of this algorithm is compared to state-of-the-art anomaly detection algorithms tested on the Occupancy dataset. These initial results demonstrate that category theory can offer new perspectives with which to attack problems, particularly in making more direct connections between the solutions and the problem’s structure.

Urban-Building Energy Modelling (UBEM) tools play a crucial role in analysing and optimizing energy use within cities. Among the available approaches, the bottom-up physics-based one is the most versatile for urban development and management applications. However, their accuracy is often limited by the inability to capture the dynamic impact of occupants’ presence and actions (i.e., Occupant Behaviour) on building energy use patterns. While recent research has explored advanced Occupant Behaviour (OB) modelling techniques that incorporate stochasticity and contextual influences, current UBEM practices primarily rely on static occupant profiles, due to limitations in the software itself. This paper addresses this topic by conducting a thorough literature review to examine existing OB modelling techniques, data sources, key features and detailed information that could enhance UBEM simulations. Furthermore, the flexibility of available UBEM tools for integrating advanced OB models will be assessed, along with the identification of areas for improvement. The findings of this review are intended to guide researchers and tool developers towards creating more robust and occupant-centric urban energy simulations.

Abstract Population trends are lacking for most threatened species, especially those that are cryptic and difficult to survey. Recent developments in passive acoustics and semi‐automated call recognition provide a cost‐effective option to systematically monitor populations of vocal species. We assessed recent trends for the koala Phascolarctos cinereus, an iconic marsupial, based on 7 years of acoustic monitoring across 224 forested sites. The study period overlapped with a severe drought and extensive megafires in 2019 followed by 2 years of extremely high rainfall. Dynamic occupancy modelling with a range of covariates at multiple landscape scales found that initial occupancy was related to elevation (−ve), NDVI (+ve) and previous selective harvesting (16–30‐year age class; weakly +ve). Extinction probability increased with the extent of high‐severity fire. Colonisation probability was related to a range of factors, with the top model showing a decrease with increasing lagged annual rainfall. However, the null model was also supported, suggesting weak associations for colonisation. Using these relationships, koala occupancy was found to be high and stable over the study period. Fire did not influence regional trends because koalas often persisted with low‐ to moderate‐severity fire and because high‐severity fire was limited to 11% of their habitat. In contrast, bellow rate varied across years, being initially low and declining immediately after the 2019 fires, with the driver of these changes unclear. Neither timber harvesting nor low‐severity fire influenced koala occupancy or bellow rate. Given the extensive area of koala habitat in the region, our results point to the presence of a large population in these public forests, and in recent years, stable occupancy (albeit with site‐scale reductions in density with high‐severity fire). Ongoing monitoring is crucial for tracking future changes, especially with predictions of more frequent, severe forest fires due to climate change.

Determining best methods to detect individuals and monitor populations that balance effort and efficiency can assist conservation and land management. This may be especially true for small, non-charismatic species, such as rodents (Rodentia), which comprise 39% of all mammal species. Given the importance of rodents to ecosystems, and the number of listed species, we tested two commonly used detection and monitoring methods, live traps and camera traps, to determine their efficiency in rodents. An artificial-intelligence machine-learning model was developed to process the camera trap images and identify the species within them which reduced camera trapping effort. We used occupancy models to compare probability of detection and occupancy estimates for six rodent species across the two methods. Camera traps yielded greater detection probability and occupancy estimates for all six species. Live trapping yielded biasedly low estimates of occupancy, required greater effort, and had a lower probability of detection. Camera traps, aimed at the ground to capture the dorsal view of an individual, combined with machine learning provided a practical, noninvasive, and low effort solution to detecting and monitoring rodents. Thus, camera trapping with machine learning is a more sustainable and practical solution for the conservation and land management of rodents.

Abstract In the Arctic tundra, predators face recurrent periods of food scarcity and often turn to ungulate carcasses as an alternative food source. As important and localized resource patches, carrion promotes co‐occurrence of different individuals, and its use by predators is likely to be affected by interspecific competition. We studied how interspecific competition and resource availability impact winter use of carrion by Arctic and red foxes in low Arctic Fennoscandia. We predicted that the presence of red foxes limits Arctic foxes' use of carrion, and that competition depends on the availability of other resources. We monitored Arctic and red fox presence at supp lied carrion using camera traps. From 2006 to 2021, between 16 and 20 cameras were active for 2 months in late winter (288 camera‐winters). Using a multi‐species dynamic occupancy model at a week‐to‐week scale, we evaluated the use of carrion by foxes while accounting for the presence of competitors, rodent availability, and supplemental feeding provided to Arctic foxes. Competition affected carrion use by increasing both species' probability to leave occupied carcasses between consecutive weeks. This increase was similar for the two species, suggesting symmetrical avoidance. Increased rodent abundance was associated with a higher probability of colonizing carrion sites for both species. For Arctic foxes, however, this increase was only observed at carcasses unoccupied by red foxes, showing greater avoidance when alternative preys are available. Supplementary feeding increased Arctic foxes' carrion use, regardless of red fox presence. Contrary to expectations, we did not find strong signs of asymmetric competition for carrion in winter, which suggests that interactions for resources at a short time scale are not necessarily aligned with interactions at the scale of the population. In addition, we found that competition for carcasses depends on the availability of other resources, suggesting that interactions between predators depend on the ecological context.

The purple martin (Progne subis) has declined substantially in numbers and geographic range since the 1980s. Surveys and monitoring are needed to better understand the status of the species, in order to set conservation priorities and direct actions. Purple martins in California often breed in habitats that differ from those in other areas of the species’ range. The species’ sparse distribution, colonial nature, and frequent use of inaccessible nests sites require specialized methodologies to determine suitable nesting habitat, occupancy, numbers, and reproductive success. I present a range of methods available to monitor nesting purple martins in the variety of nesting substrates in which they nest in California. These methods should improve efficiency and accuracy of surveys and provide comparable results that can be used to evaluate regional or statewide trends.

Facing the current sustainability challenges requires reduction in building stock energy usage towards achieving the European Green Deal targets. This can be accomplished by adopting techniques such as fault detection and diagnosis and efficiency optimization. Taking an Estonian school as a case study, an occupancy-based algorithm for scheduling ventilation operations in buildings is here developed starting only from energy use data. The aim is optimizing the system’s operation according to occupancy profiles while maintaining a comfortable indoor climate. By relying only on electricity meters without using carbon dioxide or occupancy sensors, we use the historical data of a school to develop a DBSCAN-based clustering algorithm that generates consumption profiles. A novel occupancy estimation algorithm, based on threshold and time-series methods, then creates 12 occupancy schedules that are either based on classical detection with an on-off method or on occupancy estimation for demand-controlled ventilation. We find that the latter replaces the 60% capacity of current on-off schedules by 30% or even 0%, with energy savings ranging from 3.5% to 66.4%. The corresponding costs are reduced from 18.1% up to 62.6%, while still complying with current national regulations for indoor air quality. Remarkably, our method can immediately be extended to other countries, as it relies only on occupancy schedules that ignore weather and other location-specific factors.

Urbanization of natural landscapes and increasing human populations have brought people and our companion animals into closer contact with wildlife, even within protected areas. To provide guidance for human–wildlife coexistence, it is therefore critical to understand the effects of anthropogenic disturbances and how well native wildlife species survive in human-dominated landscapes. We investigated the spatio-temporal responses of 10 vertebrate taxa, with an emphasis on the Endangered Eld's deer Rucervus eldii thamin, to anthropogenic disturbances in Shwesettaw Wildlife Sanctuary, Myanmar. We quantified anthropogenic disturbances as distance from human settlements, distance from a highway, and the presence of people and free-ranging dogs Canis familiaris. Anthropogenic disturbances had stronger negative impacts on the detection of native wildlife species than on occupancy. Eld's deer avoided areas close to human settlements and showed low diel activity overlap with both people and dogs, although we found a positive association with human presence at the camera-trap sites. Five species exhibited lower diel activity overlap with people in the rainy season when human activity is the highest in our study area. All studied wildlife species shifted to nocturnal activity or did not show any clear activity pattern during the cool-dry season when the presence of dogs increased. The ecological and conservation impacts of dogs are underestimated in South-east Asia, particularly in Myanmar, and this case study highlights the impacts of dogs on the temporal use of habitat by wildlife and the need for better practices in the management of dogs within protected areas.

Abstract Species distribution models (SDMs) are widely applied to understand the processes governing spatial and temporal variation in species abundance and distribution but often do not account for measurement errors such as false negatives and false positives. We describe unmarked, a package for the freely available and open‐source R software that provides a complete workflow for modelling species distribution and abundance while explicitly accounting for measurement errors. Here we focus on recent advances in unmarked functionality to support multi‐species, multi‐state, and multi‐season data, as well as support for fitting models with random effects. For illustration, we present an analysis of Acadian Flycatcher Empidonax virescens abundance on Roanoke River National Wildlife Refuge, North Carolina, USA, over 18 years. We found that Acadian Flycatcher abundance was initially greater in hardwood plantation habitat relative to bottomland hardwood forest along river levees but that abundance declined over time in both habitats. We plan for unmarked development to keep pace with advances in hierarchical modelling in ecology, including better handling of continuous‐time data from camera trap and automated recording units and integrated models for multiple data streams.

Abstract Site selection bias can occur when researchers monitor animals at sites of great abundance, occupancy or quality. The prevailing heuristic is therefore that ecologists should never select sites using these criteria. There is thus concern that common monitoring schemes including surveys at colonies, migratory bottlenecks and artificial breeding sites intentionally monitor sites of great abundance. Whether such routine monitoring schemes likely succumb to site selection bias is unexamined. We simulate the dynamics of long‐lived vertebrate populations to test three potential pitfalls hypothesized to occur when sites are selected for monitoring based on abundance, occupancy or quality. (1) For regression to the mean, population levels might appear to decline at monitored sites because they were chosen during a peak of abundance and random fluctuations cause populations to decrease from that point. (2) Preferential sampling occurs when some sites are preferred by the focal organism and have an above‐average probability of being selected for monitoring. (3) The missing zero effect occurs when occupied sites are chosen more often for monitoring because of initial or historical occupancy. Our simulations demonstrate that regression to the mean should only occur when inter‐annual fluctuations in abundance are relatively large compared with the average difference between sites. If researchers monitor sites that are truly of great average abundance, regression to the mean is avoided. Preferential sampling can cause a delay in the detection of a decline because monitored sites are preferred by the focal species and thus should be the first to be occupied and the last to be abandoned. Finally, the missing zero effect can cause a perceived decline in a stable population because sites were chosen when they were initially occupied and thus can only be abandoned, whereas potential colonizations at initially unoccupied sites cannot be observed. Regression to the mean occurred during specific circumstances that do not seem biologically plausible for some study systems (e.g. colony surveys). It is therefore impractical to apply a single simple heuristic such as ‘never monitor animals at sites of great abundance’ across organisms of varying life histories.

Abstract The relatively recent rediscovery of an American marten (Martes americana) population that was reintroduced over 30 years ago in southern Vermont provides an opportunity to investigate the relative importance of other mesocarnivores, and forest stand (e.g., DBH, downed logs, vertical structure) and habitat variables to their presence on the Green Mountain National Forest. Marten are state‐listed as an endangered species in Vermont and occur there at the southern extent of their range in the eastern United States. We collected detection data from camera surveys in 5 km2 units between 2019 and 2021 (December–April; n = 40 units, 238 cameras). We examined activity patterns and applied an occupancy modeling framework to the detection data to assess the relative importance of covariates at unit and camera levels and assess interactions of marten with other mesocarnivores. We did not find any unit‐level occupancy models with significant covariates that were better supported than the base model in the single‐season unit‐level analysis. Distance to the nearest release site was the covariate most supported for detectability at both spatial scales, and marten occupancy at the camera level was positively influenced by the amount of canopy cover. Two species interaction models did not indicate any positive or negative association beyond random with other mesocarnivores and activity patterns among mesocarnivores had substantial overlap. Marten recovery since the time of the reintroduction appears slow, and even 30 years later, the marten distribution is limited and suggests that dispersal is restricted at some level. We recommend a further investigation of the possible impact of other mesocarnivores to juvenile survival or other vital demographic rate (e.g., recruitment) in marten that were not explicitly measured in this study.

Abstract Competition drives community composition and structure in many ecosystems. Spatial and temporal niche partitioning, in which competing species divide the environment in space or time, are mechanisms that may allow for coexistence among ecologically similar species. Such division of resources may be especially important for carnivores in African savannas, which support diverse carnivore assemblages. We used camera traps to explore patterns of spatial and temporal niche partitioning among four mesocarnivore species in Mozambique's Gorongosa National Park: large‐spotted genet (Genetta maculata), African civet (Civettictis civetta), honey badger (Mellivora capensis) and marsh mongoose (Atilax paludinosus). We applied a multispecies occupancy model to evaluate spatial partitioning among mesocarnivores and to quantify the environmental factors that affect species‐specific habitat use, including relative lion (Panthera leo) activity. We also analyzed the temporal activity overlap of the four focal species. We identified species‐specific habitat covariates that influenced detection probabilities but found no evidence of spatial or temporal partitioning among mesocarnivores in the study system. Indeed, we found some evidence for spatial co‐occurrence between two of our focal species: African civet and marsh mongoose. There may be limited competition among mesocarnivores in this system, perhaps due to niche and diet differentiation among these species and an abundance of resources. While we found limited evidence that lion activity impacts mesocarnivores, ongoing monitoring of intraguild interactions is vital as apex predator populations recover in the system. This study adds to a growing understanding of African mesocarnivore ecology and highlights the importance of understanding these dynamics for effective multispecies conservation and restoration.

With the frequent global breakouts of infectious diseases such as Covid-19 and the likes, passengers feel unsafe traveling in crowded trains. The reluctance to share public transport with others due to the risk of disease transmission may lower the ridership as well as decrease the comfort level of passengers. Providing them with future crowdedness levels may allow them to plan accordingly, hence regaining the lost confidence and improving their patronage. This study explores the less frequently investigated relationship among occupancy levels at a particular station over several train runs, to predict the future occupancy level with a delay of one run (day). Tackling the issue as a classification problem rather than a regression problem, train occupancy data, station data, and weather data are merged to develop the final dataset. Training data is stepwise increased from 1 month to 3 months. Similarly, 1–5 days of known occupancy levels are added to each data instance. Among the three classifiers used, XGBoost provides the best results. Some practical challenges to occupancy level prediction are also discussed at the end.

Abstract Snakes are difficult to study due to their cryptic coloration, minimal movements, and use of inaccessible habitats. Although well‐timed surveys during a species' active season can result in higher detection rates and conserve survey resources (i.e., time and money), survey effort may not ensure the detection of rare and cryptic species. Thus, in such instances, a strategic species‐specific sampling design is needed. The Kirtland's snake (Clonophis kirtlandii) is a rare, cryptic species assumed to be experiencing range‐wide declines. Naturalists have noted the disappearance of Kirtland's snakes from various habitats since the early 1970s. The primary objective of our study was to determine detection of Kirtland's snakes and the environmental and temporal factors influencing detection. We calculated the effort needed to detect individuals at sites by estimating detection probabilities of 3 known Kirtland's snake populations in Illinois from 2019 to 2021. Based on 77 Kirtland's snake detections over 226 site visits (34.1%) across 3 study sites, we found that high cloud cover, moderate air temperature, and low relative humidity enhanced the detection probability of this species. The middle of May to the beginning of July was the best time to conduct surveys when detection rates were highest. As our results suggested, it is imperative to establish strategic monitoring programs maximizing conservation resources to document populations for conservation action and range shifts for species of conservation concern, such as Kirtland's snakes.

Abstract Camera traps are increasingly used in wildlife monitoring and citizen science to address an array of ecological questions on a wide variety of species. However, despite the ability of modern camera traps to capture high‐quality video, the majority of studies collect still images, in part because of concerns with video performance. We conducted a camera trap survey of a forested landscape in the UK, using a grid of paired camera traps, to quantify the impact of using video compared to photos on the outcomes of ecological research and for participation and engagement of citizen scientists. Ecological outputs showed no difference between photo and video datasets, but comparison between expert and citizen science classifications showed citizen scientists were able to classify videos more accurately (average accuracy of 95% for video, 86% for photo). Furthermore, citizen scientists were more likely to volunteer additional information on age (provided for 61% videos and 30% photos) and sex (provided for 63% videos and 45% photos) of animals in video footage. Concerns over slow trigger speeds for videos did not appear to affect our datasets or the inferences gained. When combined with citizen science, video datasets are likely to be of higher quality due to increased classification accuracy. Consequently, we encourage researchers to consider the use of video for future camera‐trapping projects.

The blue-billed curassow Crax alberti is an endemic Colombian species categorized as Critically Endangered on the IUCN Red List because of the effects of hunting and habitat loss. Conservation and management actions are required to ensure its persistence in the forest remnants across its range. We conducted a population viability analysis for a population in the municipality of Yondó, Antioquia, based on data collected in the field and available information on the reproductive ecology of the species. We evaluate seven realistic conservation scenarios by comparing the effects that changes in mortality from hunting, carrying capacity and initial population size have on the survival probability of the population. Our results indicate that: (1) the studied population is not viable over a 100-year period under current conditions; (2) mortality as a result of hunting and the size of the initial population have the greatest impacts on the mean time to extinction; (3) a strategy based on eliminating hunting in the two sites with the largest forest remnants in the landscape could ensure the viability of the population over a 100-year period; and (4) other strategies (i.e. population supplementation with captive-bred individuals, reduction of deforestation in the landscape) do not guarantee the viability of the population if mortality from hunting remains constant, even at low levels. These results confirm the susceptibility of the blue-billed curassow to the threats it faces in this landscape, particularly hunting, and provide information on the conservation actions that could allow this remaining population to prevail in the long term.

Autonomous recording units (ARUs) emerged as a novel technology for avian acoustic monitoring in the 2000s. They have since been primarily used as a substitute for human observers during the breeding season. Autonomous recording units have potential uses in springtime soundscapes, such as to study spring departure or arrival of migratory birds and territory establishment of resident breeding birds. We described springtime bird communities of agricultural landscapes based on data collected between 1 April and 15 May 2015–2019, from ARUs deployed at 32 locations across 13 counties in Iowa, USA. We compared bird communities across site types, analyzed trends in migrant arrival dates and detection rates, and examined springtime occupancy of five species that commonly use grassland cover: Common Yellowthroat ( Geothlypis trichas ), Field Sparrow ( Spizella pusilla ), Grasshopper Sparrow ( Ammodramus savannarum ), Savannah Sparrow ( Passerculus sandwichensis ), and Vesper Sparrow ( Pooecetes gramineus ). We made 4029 detections of 86 bird species. The most frequent detections were of common farmland species, including Red-winged Blackbird ( Agelaius phoeniceus ), American Robin ( Turdus migratorius ), Brown-headed Cowbird ( Molothrus ater ), Ring-necked Pheasant ( Phasianus colchicus ), and Eastern Meadowlark ( Sturnella magna ); however, detections also included 18 species of greatest conservation need. Sites with reconstructed prairie vegetation, conventionally grown corn ( Zea mays ) and soybean crops ( Glycine max ), crops with prairie strips, and crops with terraces had similar species richness. We found that, in addition to documenting species richness and departure/arrival times of migratory species, ARUs generated species-level detection probabilities similar to or higher than existing studies on breeding season occupancy. Detection probabilities of five focal species ranged from 0.36–0.89. Occupancy models revealed significant springtime land cover associations for Common Yellowthroats, Field Sparrows, Savannah Sparrows, and Vesper Sparrows. We conclude that springtime deployment of ARUs can provide valuable information on phenological patterns and springtime habitat use of birds.

Abstract Anthropogenic developments alter the environment and resources available to wildlife communities. In response to these real or perceived threats from this development, species may adjust their spatial occurrence. Additionally, wildlife species may adjust when in diel time (24‐h light–dark cycle) they occupy sites on the landscape to adapt to changing conditions. However, many wildlife studies only focus on where a species does and does not occur, ignoring how species may shift their diel activity at sites to mitigate threats. We used a multi‐state diel occupancy modeling framework to investigate how a community of mammals (mesocarnivores, urban‐adapted omnivores, and herbivore/small mammals) respond to differing levels of anthropogenic development and forest cover across two climatic seasons. We collected camera trap data at 240 survey locations across the summer and winter of 2021–2022. We modeled multi‐state diel occupancy for 14 mammal species with extent of development/forest and season hypothesized to influence diel occupancy and season hypothesized to influence the probability of detection. We found that all species displayed heterogeneity in both diel occupancy and detection either by extent of development/forest and or season. Within the mesocarnivore species group, coyote and red fox were less sensitive to development and had higher occupancy probability at these sites in general but used them more during the night, while more sensitive mesocarnivores including fisher and bobcat occupied the day state only when there was increasing forest cover. Our results highlight the importance of incorporating diel activity in habitat modeling to better understand the relationship between a species and its landscape, particularly in a region that is vulnerable to increased anthropogenic pressure.

Abstract Occupancy modeling is an essential tool for understanding species‐habitat associations, thereby helping to plan the conservation of rare and threatened wildlife species. The conservation status and ecology of several avian species, particularly ground‐dwelling birds, are poorly known in Ethiopia. We used camera trap‐based occupancy modeling to investigate habitat covariate influence on occupancy (Ψ) and detection probability (ρ) estimates of Moorland Francolins Scleroptila psilolaema from spatially replicated surveys across both relatively pristine and disturbed landscapes in the Afroalpine biome of Ethiopia. Model‐averaged estimate of ψ̂ across all sites was 0.76 (SD = 0.28) and ρ̂ was 0.77 (SD = 0.13) in the pristine landscape. The ψ̂ of the species in the disturbed landscape was 0.56 (SD = 0.19) and ρ̂ was 0.48 (SD = 0.06). As hypothesized, based on our model‐averaged beta coefficient estimates (βmean ± SE), predators significantly negatively influenced the occupancy of Moorland Francolins in pristine habitat. We also found a significant positive association of occupancy with herb species richness. Contrary to our prediction, distance to road significantly negatively influence the occupancy of the species, suggesting that occupancy probability was highest in proximity to roadsides and trails in the pristine habitat. There was no significant influence of habitat covariates on the occupancy of the species in the disturbed habitat. The most important covariates that significantly influence the detectability of the species in pristine habitat included sampling occasion and precipitation. The greater occupancy and detectability of this endemic species in the pristine habitat could be linked with the particular conservation status and management of this biodiversity hotspot in the central highlands of Ethiopia. Our results suggest that strict legal enforcement is required to sustainably preserve Moorland Francolins and the ecological integrity of the entire Afroalpine biome. We recommend using camera traps in order to develop realistic and effective conservation and management strategies for rare, sensitive, cryptic, and ground‐dwelling animals in the region.

Abstract Nepal initiated numerous hydropower and irrigation‐related infrastructure projects to enhance and promote green energy, water security, and agricultural productivity. However, these projects may pose risks to natural habitats and the well‐being of aquatic fauna, leading to significant effects on delicate ecosystems. To understand these potential impacts, it is crucial to gather reliable baseline data on the population status and habitat characteristics of species. This study specifically focuses on Gharials (Gavialis gangeticus), a critically endangered species. We recorded data on pre‐determined habitat variables at stations spaced 500 m apart along the two major river streams of Bardia National Park, as well as at locations where Gharials were sighted between February and March 2023. We used binary logistic regression with a logit link function to investigate the habitat characteristics related to the occurrence of Gharials. The presence/absence of Gharials at sampling points served as the dependent variable, while 10 other predetermined variables (ecological variables and disturbance variables) served as independent variables. Our study recorded 23 Gharials, comprising 14 adults, six sub‐adults, and three juveniles, with a sex ratio of 55.56 males per 100 females. Most individuals (83%) were found basking. Among the 10 habitat predictors, three variables (mid‐river depth, river width, and water temperature) were significantly correlated (p

It is important to understand species-habitat relationships to implement effective adaptive management for rare species. However, it can be challenging to assess habitat associations and their relationships to abiotic stressors in dynamic habitats without the insights that can be gained from long-term monitoring. We report results from the first six years of extensive track tube monitoring of the largest two of three remaining extant populations of federally endangered Pacific pocket mouse (Perognathus longimembris pacificus) in a coastal Mediterranean-type ecosystem on Marine Corps Base, Camp Pendleton in southern California, USA. We used dynamic occupancy and structural equation modeling to assess potential drivers of population trends that included habitat, fire history, rainfall, disturbance, and the presence of other small mammals. We found that the variables that best predicted mouse occupancy were moderate to high forb and perennial herb cover (40–80%), and moderate to high open ground (20–70%) and low non-native grass cover (20%) was also a strong predictor of lower PPM colonization and increased extinction probabilities, with the extent of non-native grass cover being strongly influenced by annual rainfall and recency of fire. Our study adds to the growing literature on effects of invasive annual grasses on native species in Mediterranean-type ecosystems. We suggest that habitat management could be based upon promotion of open forb and perennial herb dominated habitats with reduction of non-native grasses by prescribed fire and other methods. These types of spatial and temporal monitoring programs can support land managers by creating a monitoring and management feedback loop. They can reveal landscape and environmental variables associated with species persistence, inform habitat management goals, and help managers to assess the success of management actions on populations of conservation concern.

Recurring congestion on highways is the primary cause of traffic congestion in the urban traffic system while jams on merging sections are the worst section along the expressway. Due to the presence of heavy congestion in the site of University of Technology, this study aims to discover the traffic characteristics of this site to determine the appropriate solutions. Therefore, this study selects a merging section on Mohammed Al-Qassim expressway to investigate some characteristics of such a section in Iraq. Field data have been collected from this site for 4 h. The data were collected from video cameras installed along the expressway in Baghdad city which is called Mohammed Al-Qassim Expressway. These data present flow of upstream and ramp. The Advanced Interactive Microscopic Simulator for Urban and Non-Urban Networks (AIMSUN) program was also adopted to investigate other characteristics which are difficult to get such as speed and occupancy for the current case. The results showed that the simulated and real data significantly corresponded. In this study, the AIMSUN program received recognition by utilizing Geoffrey E. Havers a method for comparing two sets of traffic volumes that are used in traffic engineering, traffic forecasting, and traffic modeling as it was discovered to be comparable to reality, and the road management was then enhanced by the addition of ramp metering and cycle (green) time control.

Abstract Understanding factors associated with coexistence of human and wildlife in human‐dominated landscapes is crucial for effective species conservation. Among the wildlife species, the sloth bears Melursus ursinus are found both inside and outside the protected areas of Nepal, and with increasing cases of human and bear conflicts in both areas. This highlights the necessity for a comprehensive understanding of anthropogenic and ecological factors that affect the occurrence of sloth bear. The understanding of these factors is important for its coexistence and conservation in human‐dominated areas through establishing management and conservation action plan. We studied the sloth bear's occupancy and their coexistence in human‐dominated environments with other large predators in the Parsa–Koshi Complex of Nepal using camera traps from December 2022 to March 2023. We identified the occupancy and detection probability of the sloth bear as 0.12 and 0.31, respectively. Our analysis reveals a positive relationship between sloth bear occurrence and the presence of large predators (βpredators = 3.104 ± 0.968), such as tigers (Panthera tigris) and leopards (Panthera pardus), as well as the number of humans detected (βhuman = 1.428 ± 1.216) and canopy cover percentage (βcc = 1.002 ± 0.737). However, the number of livestock detected shows a negative interaction with the occurrence of sloth bears (βlivestock = −2.240 ± 1.467). There was insignificant interaction between sloth bear occupancy and distance to human settlements, roads, and water bodies. These findings underscore the complex dynamics between sloth bears, humans, large predators, and livestock in human‐dominated landscapes. To ensure the long‐term survival of sloth bear populations and promote species conservation, comprehensive conservation strategies that account for both ecological and socio‐economic factors are essential.

Research on the coexistence of congeneric species is essential for understanding community assemblages. Smaller competitors are expected to avoid larger ones, either spatially or temporally, to reduce interspecific competition. According to the spatial scaling law, the greater the difference in body size, the weaker the competitive interactions of the competitors. However, this is not confirmed in the guild of tropical forest ungulates. In this study, we assessed the competitive interactions of Williamson’s mouse deer (Tragulus williamsoni), an endangered species and one of the smallest ungulates in the world, with sympatric larger ungulates. We hypothesized that: 1) because of its extremely small body size, competition with the larger ungulates would be relatively weak, allowing spatial co-existence but still requiring temporal avoidance, and 2) the strength of avoidance would increases with decreasing differences in body size. We set up 238 camera traps from January 2017 to January 2021 to survey Williamson’s mouse deer and the sympatric larger ungulate species, that is, northern red muntjac (Muntiacus vaginalis), wild boar (Sus scrofa), Chinese serow (Capricornis milneedwardsii), and sambar (Rusa unicolor), in the protected areas of Mengla County, southwestern China. We then performed spatio-temporal analyses, including occupancy models, daily activity patterns, and a time interval analysis. Spatially, there was no significant avoidance. Temporally, Williamson’s mouse deer had different daily activity patterns and direct temporal avoidance of all larger ungulate species. The lack of spatial avoidance and strong temporal avoidance supported our first hypothesis, but the stronger avoidance of much larger species ran counter to our second hypothesis. Our results revealed the coexistence mechanism between Williamson’s mouse deer and sympatric larger ungulates and suggested that the difference in body size is limited in explaining the competitive interactions of tropical forest ungulates due to the effects of multiple ecological processes. This deepens our understanding of the relationship between species trait differences and community assembly in tropical forest ecosystems.

The Himalayan goral (Naemorhedus goral) is facing threats, such as habitat degradation and human disturbances, which are increasing. To aid their conservation, it is crucial to understand the anthropogenic and ecological factors that influence Himalayan goral occurrence. We conducted a study in Dhorpatan Hunting Reserve, Nepal using remote cameras to detect Himalayan gorals during late winter (March 2022) and spring (May 2022). Himalayan gorals were more likely to be detected in late winter (p = 0.253 ± 0.064) compared to spring (p = 0.187 ± 0.056) and the effects of anthropogenic and ecological factors on Himalayan gorals varied between these periods. During late winter, the distance to the nearest human settlement (βsettlement = 1.971 ± 1.147) and the number of other wild ungulates detected (βungulates = 2.954 ± 1.193) had a positive effect on Himalayan goral occurrence. In contrast, only the number of wild ungulate detections (βungulates = 3.262 ± 1.292) had a positive impact on Himalayan goral occupancy during spring. The number of livestock and carnivores detected during each season did not influence Himalayan goral occupancy. Our results highlight the importance of considering ecological and anthropogenic variables in understanding occupancy and distribution of Himalayan gorals to inform species conservation strategies.

Human-induced forest loss has had devastating impacts on biodiversity. Mammal populations in the tropics have been hit particularly hard by the resulting habitat loss, fragmentation and degradation, as well as by overhunting which often goes hand-in-hand. While declines in these populations are generally well documented, few studies offer a pathway for their recovery. Here, we test the association between changes in forest habitat and occupancy trends of Sumatran megafauna (elephant and tiger) and key tiger prey species (wild boar and sambar) in the Leuser Ecosystem: a large forest landscape on the Indonesian island of Sumatra. For elephant and tiger, we develop additional occupancy models to predict their respective spatial distribution under different scenarios of forest loss and gain (through restoration and increased connectivity) to provide a blueprint for avoiding future species loss and assisting with their population recovery. From 2000 to 2019, 254,722 ha (6.7%) of natural forest was converted, primarily to plantations and shrubs. The species-specific responses over the study period revealed that the occurrence of elephant declined along the west, with a range shift to the northeast of Leuser, whereas wild boar underwent a dramatic widespread decline and although sambar experienced losses around the forest edge, it remained widespread in the interior forest, while tiger occupancy remained stable. Modelling habitat loss and fragmentation led to an unsurprising demise of Sumatran megafauna, whereas strategic investments that reconnected several forest patches provided disproportionately large benefits for their recovery through the recolonization of former parts of their range. Indonesia has achieved six consecutive years of declining forest loss rates, and our study’s findings can build off this conservation success by supporting improved provincial spatial planning and field-based restoration efforts that avoid declines of threatened megafauna species and act as a catalyst for rewilding a landscape of global importance.

Anthropogenic influences on a global scale have caused negative impacts on the environment, among the most prominent being the increase in the concentration of carbon dioxide (CO2). In this study, the objective was then to estimate the potential of carbon flux (CO2 flux) in the riparian vegetation of the Jacareí–Jaguari reservoir, using the digital processing of orbital images of the CBERS 4A system. To determine the CO2 flux, vegetative indices were used: Normalized Difference Vegetation Index (NDVI); Photochemical Reflectance Index (PRI) and the scaled Photochemical Reflectance Index (sPRI), associating them with land use and occupation classifications from the MapBiomas collection, determining the histogram of each class for CO2 flux analysis, revealing CO2 flux between −0.136 and 0.4049. The lower values of CO2 flux in the reservoir are due to the decrease in vegetative classes, indicating the need for (re)planting and plant conservation, confirming the importance of areas with an ecosystem function, of carbon sink. HIGHLIGHTS The vegetation indices show the need for plantation.; The indices were established to have no relationship with aquatic vegetation.; The sPRI presents good correlations with the efficiency of solar energy for photosynthesis.; The NDVI can be used to detect seasonal and temporal variations along the various vegetative stages.; Many factors, diurnal and seasonal, can affect PRI patterns at foliar, canopy and ecosystem levels.;

The article explores issues related to the reconstruction of buildings of the old housing stock. For the first time, an analysis of the physical state of operated buildings of mass series built in the 1970s and 1980s is given, on the basis of which a conclusion was made about the possibility of mass modernization with the addition of additional floors. The article substantiates the possibility of performing work on the compaction of the existing building with the addition of additional floors in 5-storey buildings without increasing the load on the existing engineering networks. The article shows that such an approach to the transformation of the urban environment fully satisfies the principles of sustainable development of the city.

Background: Detailed data on intensive care unit (ICU) occupancy in Japan are lacking. Using a nationwide inpatient database in Japan, we aimed to assess ICU bed occupancy to guide critical care utilization planning. Methods: We identified all ICU patients admitted from January 1, 2015 to December 31, 2018 to ICU-equipped hospitals participating in the Japanese Diagnosis Procedure Combination inpatient database. We assessed the trends in daily occupancy by counting the total number of occupied ICU beds on a given day divided by the total number of licensed ICU beds in the participating hospitals. We also assessed ICU occupancy for patients with mechanical ventilation, patients with extracorporeal membrane oxygenation, and patients without life-supportive therapies. Results: Over the 4 study years, 1,379,618 ICU patients were admitted to 495 hospitals equipped with 5,341 ICU beds, accounting for 75% of all ICU beds in Japan. Mean ICU occupancy on any given day was 60%, with a range of 45.0% to 72.5%. Mean ICU occupancy did not change over the 4 years. Mean ICU occupancy was about 9% higher on weekdays than on weekends and about 5% higher in the coldest season than in the warmest season. For patients with mechanical ventilation, patients with extracorporeal membrane oxygenation, and patients without life-supportive therapies, mean ICU occupancy was 24%, 0.5%, and 30%, respectively. Conclusion: Only one-fourth of ICU beds were occupied by mechanically ventilated patients, suggesting that the critical care system in Japan has substantial surge capacity under normal temporal variation to care for critically ill patients.

Abstract Public interest in nature‐based recreation is growing, including visitation to protected areas. However, the level of recreation in these areas that causes detectable changes in wildlife behaviour remains unknown, and many studies that investigate wildlife responses to humans do so in high‐visitation areas. We used camera traps to investigate the spatial and temporal responses of brown bears (Ursus arctos), black bears (Ursus americanus), moose (Alces alces) and wolves (Canis lupis) to experimentally manipulated levels of human activity in Glacier Bay National Park, Alaska during summers 2017 and 2018. Human activity was restricted at some sites and concentrated at others, and these human impact treatments were swapped mid‐season. The park has very low on‐land visitation (~40,000 on‐land tourists per year), making it a unique study system to investigate wildlife responses to low levels of human activity. Detections did not exceed five per week for any species unless human activity was absent (zero photos of humans were taken). However, spatial and temporal patterns of wildlife activity in relation to human activity were nuanced and species specific. Moose shifted their activity patterns to better align with when people were most active. Black bears were more likely to be detected in areas of high human activity but used high‐use areas less intensely than low‐use areas. Wolves used areas of high human impact more intensely, but shifted their activity to be more strongly nocturnal. Our results highlight the importance of considering both spatial and temporal responses of wildlife to human activity. Additionally, and arguably most importantly, we detected changes in wildlife behaviour in response to humans in a national park with relatively low tourism. Although natural processes may dominate in protected areas, our results indicate that even low levels of human activity can alter wildlife behaviour. Synthesis and applications. We demonstrated that nearly any level of human activity in a protected area may alter wildlife behaviour. However, it is unreasonable to expect protected areas to be completely devoid of human activity. Thus, management of these areas will need to balance the desires of humans to view wildlife with the likely impacts. Read the free Plain Language Summary for this article on the Journal blog.

Abstract Background Antiepileptic drugs, levetiracetam (LEV) and brivaracetam (BRV), bind to synaptic vesicle glycoprotein 2A (SV2A). In their anti-seizure activity, speed of brain entry may be an important factor. BRV showed faster entry into the human and non-human primate brain, based on more rapid displacement of SV2A tracer 11C-UCB-J. To extract additional information from previous human studies, we developed a nonlinear model that accounted for drug entry into the brain and binding to SV2A using brain 11C-UCB-J positron emission tomography (PET) data and the time-varying plasma drug concentration, to assess the kinetic parameter K 1 (brain entry rate) of the drugs. Method Displacement (LEV or BRV p.i. 60 min post-tracer injection) and post-dose scans were conducted in five healthy subjects. Blood samples were collected for measurement of drug concentration and the tracer arterial input function. Fitting of nonlinear differential equations was applied simultaneously to time-activity curves (TACs) from displacement and post-dose scans to estimate 5 parameters: K 1 (drug), K 1(11C-UCB-J, displacement), K 1(11C-UCB-J, post-dose), free fraction of 11C-UCB-J in brain (f ND(11C-UCB-J)), and distribution volume of 11C-UCB-J (V T(UCB-J)). Other parameters (K D(drug), K D(11C-UCB-J), f P(drug), f P(11C-UCB-J, displacement), f P(11C-UCB-J, post-dose), f ND(drug), k off(drug), k off(11C-UCB-J)) were fixed to literature or measured values. Results The proposed model described well the TACs in all subjects; however, estimates of drug K 1 were unstable in comparison with 11C-UCB-J K 1 estimation. To provide a conservative estimate of the relative speed of brain entry for BRV vs. LEV, we determined a lower bound on the ratio BRV K 1/LEV K 1, by finding the lowest BRV K 1 or highest LEV K 1 that were statistically consistent with the data. Specifically, we used the F test to compare the residual sum of squares with fixed BRV K 1 to that with floating BRV K 1 to obtain the lowest possible BRV K 1; the same analysis was performed to find the highest LEV K 1. The lower bound of the ratio BRV K 1/LEV K 1 was ~ 7. Conclusions Under appropriate conditions, this advanced nonlinear model can directly estimate entry rates of drugs into tissue by analysis of PET TACs. Using a conservative statistical cutoff, BRV enters the brain at least sevenfold faster than LEV.

Tigers (Panthera tigris) have disappeared from over 90% of their historical range, and extant populations face habitat loss, direct poaching, and prey depletion in otherwise suitable habitats. In Thailand, tiger numbers continue to decline due to prey depletion, yet a few strongholds remain. Recently, tigers have been detected in the Southern Western Forest Complex (sWEFCOM), following intensification of conservation efforts. However, there is still a lack of primary data on the status of tigers and their prey in the sWEFCOM. To fill this knowledge gap, we conducted camera trapping surveys between 2019 and 2020 in Khuean Srinagarindra National Park (KSR) and Salakphra Wildlife Sanctuary (SLP). Located near a tiger source population in Thungyai Naresuan and Huai Kha Khaeng, these areas are potential areas for tiger recovery. In particular, our study assessed the status of prey, a prerequisite to the persistence and recovery of tigers. Based on relative abundance indices, time overlap and occupancy models, we analysed the effect of anthropogenic and ecological factors on the spatial and temporal habitat use of the main prey species. We highlight that anthropogenic factor impacted species-specific habitat relationships. Mainly, shifts in ungulate temporal and spatial habitat use was linked to human activities. These relationships, however, differed between the two protected areas. As tiger recovery depends on prey recovery, we suggest that increased conservation law enforcement and greater engagement with villages within and adjacent to protected areas are essential to minimising unsustainable resource use practices that currently affect prey.

Abstract Habitat loss and fragmentation are the leading causes of species range contraction and extirpation, worldwide. Factors that predict sensitivity to fragmentation include high trophic level, large body size, and extensive spatial requirements. Pumas (Puma concolor) exemplify these qualities, making them particularly susceptible to fragmentation and subsequent reductions in demographic connectivity. The chaparral‐dominated ecosystems surrounding the greater San Francisco Bay Area encompass over 10,000 km2 of suitable puma habitat, but inland waterways, croplands, urban land uses, and extensive transportation infrastructure have resulted in widespread habitat fragmentation. Pumas in this region now exist as a metapopulation marked by loss of genetic diversity, collisions with vehicles, and extensive human–puma conflict. Given these trends, we conducted a photo survey from 2017 to 2021 across 19 patches of predicted habitat and compiled a dataset of >6584 puma images. We used a logistic regression analytical framework to evaluate the hypothesis that puma patch occupancy would exhibit a threshold response explained by patch size, isolation, and habitat quality. Contrary to predictions, only variables related to patch size demonstrated any power to explain occupancy. On average, occupied patches were 18× larger than those where they were not detected (825 ± 1238 vs. 46 ± 101 km2). Although we observed pumas in patches as small as 1 km2, logistic regression models indicated a threshold occupancy probability between 300 and 400 km2, which is remarkably close to the mean male puma home range size in coastal California (~381 km2). Puma populations dependent on habitats below this value may be susceptible to inbreeding depression and human–wildlife conflict, and therefore vulnerable to extirpation. For species conservation, we suggest conflicts might be ameliorated by identifying the largest, isolated patches for public education campaigns with respect to management of domestic animals, and remaining connective parcels be identified, mapped, and prioritized for targeted mitigation.

The surface of a golf ball is provided with innumerable dents called dimples to improve the flight distance. In this study, we manufactured 15 model balls in which the dimple occupancy and the dimple volume ratio was changed by the dimple diameter and the number of dimples, and the dimple shapes. Using these balls, we carried out lift and drag measurements of the rotating balls in wind tunnel experiments. Then, a flight trajectory simulation was performed based on the obtained experimental results. As a result, the influence of the base shape showed different results for both drag and lift coefficients for each occupancy. However, the drag coefficient decreased and the lift coefficient increased as the dimple volume ratio increased regardless of the dimple shape. In addition, the relationship between the lift-drag ratio and the dimple volume ratio showed that the lift-drag ratio decreased as the dimple volume ratio increased. The flight trajectory simulation results showed that the flying distance was greater for O=52.6% and O=63.1% with a conical base when the dimple volume ratio was small and the dimple depth is DD/d=4.55×10-3. Two spheres with a spherical base and a conical base at O=83.1%, also exhibited similar flying distances. However, the flight distance tends to increase as the dimple volume ratio decreases. The ball's trajectory was lower when the volume ratio was small, and higher trajectory was obtained when the occupancy ratio was high.

Abstract Increasingly frequent megafires are dramatically altering landscapes and critical habitats around the world. Across the western United States, megafires have become an almost annual occurrence, but the implication of these fires for the conservation of native wildlife remains relatively unknown. Woodland savannas are among the world's most biodiverse ecosystems and provide important food and structural resources to a variety of wildlife, but they are threatened by megafires. Despite this, the great majority of fire impact studies have only been conducted in coniferous forests. Understanding the resistance and resilience of wildlife assemblages following these extreme perturbations can help inform future management interventions that limit biodiversity loss due to megafire. We assessed the resistance of a woodland savanna mammal community to the short‐term impacts of megafire using camera trap data collected before, during, and after the fire. Specifically, we utilized a 5‐year camera trap data set (2016–2020) from the Hopland Research and Extension Center to examine the impacts of the 2018 Mendocino Complex Fire, California's largest recorded wildfire at the time, on the distributions of eight observed mammal species. We used a multispecies occupancy model to quantify the effects of megafire on species' space use, to assess the impact on species size and diet groups, and to create robust estimates of fire's impacts on species diversity across space and time. Megafire had a negative effect on the detection of certain mammal species, but overall, most species showed high resistance to the disturbance and returned to detection and site use levels comparable to unburned sites by the end of the study period. Following megafire, species richness was higher in burned areas that retained higher canopy cover relative to unburned and burned sites with low canopy cover. Fire management that prevents large‐scale canopy loss is critical to providing refugia for vulnerable species immediately following fire in oak woodlands, and likely other mixed‐forest landscapes.