Occupancy Models Research Articles

LSTM Gate Disclosure as an Embedded AI Methodology for Wearable Fall-Detection Sensors

In this paper, the concept of symmetry is used to design the efficient inference of a fall-detection algorithm for elderly people on embedded processors—that is, there is a symmetric relation between the model’s structure and the memory footprint on the embedded processor. Artificial intelligence (AI) and, more particularly, Long Short-Term Memory (LSTM) neural networks are commonly used in the detection of falls in the elderly population based on acceleration measures. Nevertheless, embedded systems that may be utilized on wearable or wireless sensor networks have a hurdle due to the customarily massive dimensions of those networks. Because of this, the algorithms’ most popular implementation relies on edge or cloud computing, which raises privacy concerns and presents challenges since a lot of data need to be sent via a communication channel. The current work proposes a memory occupancy model for LSTM-type networks to pave the way to more efficient embedded implementations. Also, it offers a sensitivity analysis of the network hyper-parameters through a grid search procedure to refine the LSTM topology network under scrutiny. Lastly, it proposes a new methodology that acts over the quantization granularity for the embedded AI implementation on wearable devices. The extensive simulation results demonstrate the effectiveness and feasibility of the proposed methodology. For the embedded implementation of the LSTM for the fall-detection problem on a wearable platform, one can see that an STM8L low-power processor could support a 40-hidden-cell LSTM network with an accuracy of 96.52%.

Managing Stress From a Systemic Perspective: Occupational Stress Models

The World Health Organization (WHO) defines stress as a “state of worry or mental tension caused by a difficult situation.” Burnout results from chronic occupational stress that has not been well managed, and is the result of both systemic and personal factors. Systemic structures and planning to optimize work environments are the focus of occupational stress models. To effectively manage stress and avoid the detrimental effects of neglecting both its acute and chronic consequences (such as decreased productivity, low morale, interpersonal conflicts, absenteeism, and other negative outcomes), it is essential to examine various occupational stress models to determine root causes and identify issues at the work unit level. [ Psychiatr Ann . 2024;54(10):e287–e291.]

Development of occupational well-being measurement model in the employee of Abadan University of Medical Sciences

BACKGROUND: Given the importance of healthcare centers and the employees of medical centers and affiliated institutions in providing comprehensive health services to the community, especially patients, as well as training the students in health-related fields, it is crucial to address the occupational well-being of university employees who are prone to physical and mental disorders. Therefore, this study aims to develop a model for the occupational well-being of employees at the Abadan University of Medical Sciences. MATERIALS AND METHODS: Two hundred Abadan University of Medical Sciences employees were selected using stratified sampling. All the selected participants responded to the Demo and Paschoal Well-being at Work Scale (2016). The model parameters were estimated using partial least squares regression based on the collected data. Afterward, the measurement model of occupational well-being was tested using fit indices. Moreover, data analysis was performed using Smart-PLS software. RESULT: The primary results showed that based on the positive correlation indices and t-test results for the factors of occupational well-being, the Demo and Paschoal measurement model had an acceptable fit; moreover, significant internal correlations were observed among the questionnaire items. The composite reliability and Cronbach’s alpha coefficients of the factors indicated a high reliability of the occupational well-being measurement model. CONCLUSION: The measurement model of occupational well-being was examined, and it is recommended that future researchers employ the current questionnaire to assess occupational well-being.

Improving Inference Within Freshwater Community Studies: Accounting for Variable Detection Rates of Amphibians and Fish.

Research into freshwater communities often aims to link patterns of species distribution in ponds with underlying biotic factors. However, errors with species detection (e.g. false negatives) may underestimate distribution and bias assessments of community structure. Occupancy models that account for imperfect detection offer a solution to this problem. Here, we used three methods (call/visual encounter surveys, dip-netting and newt trapping) to survey amphibians and fish (potential amphibian predators) at 100 ponds in an urbanised landscape in Hungary over one breeding season. We estimated species detection probabilities for amphibians (all life stages combined) and fish using occupancy models to gain insight into amphibian-fish relationships and other survey-specific variables. We detected nine amphibian and 20 fish species. There were relatively low but variable estimated probabilities of detection for amphibians (mean: 0.320, 95% Bayesian credible interval: 0.142-0.598), with three species having detection rates < 0.1. Probabilities of detection peaked in the middle of the breeding season and increased with survey effort. Detection probabilities of five species were negatively associated with the detection of fish at a pond, while there were positive relationships between detection and emergent vegetation cover. We found no substantial differences in detection rates among the three survey methods. The probability of detecting fish was much higher than for amphibians (0.588, 0.503-0.717) but was lower at ponds with high emergent vegetation where amphibian detection was higher. Our results underscore the importance of accounting for the imperfect detection of both response organisms and potentially interacting species in aquatic community studies. We recommend applying multi-species occupancy models to enable inference for both common and rare species at ponds in landscapes subjected to human disturbances.

Occupational Health Nursing models and theories: A critical analysis in the scope of the unitary-transformative perspective.

Occupational Health Nursing (OHN) has followed a complex path to build and strengthen its theoretical basis. Starting with Public Health core principles, theories were shaped by the dualism of person worker and working environment, where sometimes the centre of the thought was given to the latter and other times to the former. The problem was not much on such conflict but on the definition of the correct OHN focus and whether genuine nursing knowledge was being applied. We are worried that other disciplines radically influenced the theoretical path taken by OHN theorists. To approach this problem, a unitary-transformative perspective can help us describe and analyse this phenomenon and engage scholars to reflect upon the accurate epistemological focus of OHN. We aimed to deepen this reflection and uncover a new OHN theoretical focus.

Effects of seatback angle, seat rotation, and impact speed on injury risk of occupants in highly automated vehicles in frontal crashes

Objective The objective of this study is to examine the effects of seatback angle, seat rotation, and impact speed on occupant kinematics and injury risk in highly automated vehicles. Methods The study utilized the Global Human Body Models Consortium midsize male (M50-OS+B) simplified occupant model in a simplified vehicle model (SVM) to simulate frontal crashes. The M50-OS+B model was gravity-settled and belted into the driver and left rear passenger seat. To investigate the effects of seatback angle, seat rotation, and impact speed on occupant kinematics and injury risk in frontal crashes, a design of experiments (DOE) was conducted. The DOE incorporated four seatback angles (13°, 23°, 45°, and 57.5° about vertical), four seat rotation angles (0°, 25°, 45°, and 90°), three impact speeds (25, 35, and 45 kph), and four frontal crash type configurations. All four seatback angles were used with 0° seat rotation, whereas 13° seatback angle was used with the remaining seat rotation configurations because of cabin fit considerations. Injury risks were estimated for the head, neck, shoulder, thorax, pelvis, and lower extremities for both occupants for each simulation (n=588). Results Statistically significant differences between all the groups within each independent variable category were observed based on the analysis of variance. HIC-based head injury risk and chest injury risk decreased and femur force for the driver and tibia force for the passenger increased with an increase in seatback angles. The head injury risk increased with seat rotation. All the injury risks increased with an increase in impact speed. The driver airbag was able to safeguard the driver from head injuries for all seat rotations except at 90° of seat rotation. Conclusion This is the first vehicle modeling study that collectively looked at the effects of seatback angle, seat rotation, and impact speed along with the interaction of occupants on the risk of injury in frontal crashes. The rear passenger experienced higher seatbelt loads than the driver. More reclined seats decreased head and chest injury risk, but increased driver femur injury risk and rear passenger tibia injury risk. Results underscore the necessity for additional anti-submarining mechanisms and driver airbag designs adapted for the anticipated occupant positions.

Human activities reshape the spatial overlap between North Chinese leopard and its wild ungulate prey

BackgroundRapidly expanding human activities have profoundly changed the habitat use of both large carnivores and their prey, but whether and how human activities affect the interactions between them has received relatively less attention. In this study, we conducted a systematically designed camera-trapping survey on an endangered large carnivore (North Chinese leopard Panthera pardus japonensis) and its wild ungulate prey (Siberian roe deer Capreolus pygargus and wild boar Sus scrofa) in the Taihang Mountains of central North China. Using conditional two-species occupancy model based on data derived from the extensive sampling effort (15,654 camera-days at 102 camera sites), we examined the relationship of spatial use between leopards and each prey species under the effects of human presence, free-ranging cattle, roads and settlements.ResultsHumans and cattle had contrasting effects on the relationship of spatial use between leopard and roe deer, with higher and lower spatial segregation between them at human and cattle-frequented sites, respectively. Roads might create a shelter for wild boar from leopard predation, with less spatial segregation between them at sites close to the roads.ConclusionsOur findings demonstrate that human activities are reshaping the spatial overlap between large carnivores and their prey, and have non-equivalent effects among different types of human activity. Such effects may further alter the strength of interspecific interactions between predator and prey, with far-reaching influences on the community and ecosystem that require more research.

Keystone species in an urban environment: Do raptors control the Mexican gray squirrel in Mexico City?

Urbanization has forced animals to respond to novel environments. One of the basic questions in urban ecology is if ecological processes that occur in natural conditions also occur in urban environments. In natural ecosystems, key predators can limit prey abundance and have a widespread effect on trophic levels. Here, we examine a predator-prey interaction in Mexico City, an urban ecosystem where prey can be evaluated in the presence or absence of apex predators. Nearly all original ecosystems in Mexico City have been transformed for human purposes. Many of the green spaces in the city contain populations of the Mexican gray squirrel, a species native to this area. These green spaces are also inhabited by different species of raptors, the most frequent being Harris's hawk, Cooper's hawk, and sharp-shinned hawk. Little is known about the interactions between raptors and squirrels, particularly how different environmental factors influence this ecological relationship. We do know, however, that raptors prey on squirrels. We predicted that in parks where raptors were present, there would be fewer squirrels than in parks where there were no raptors. We studied the relationship between raptors and squirrels using occupancy models, which also allowed us to evaluate environmental factors that affect the presence of both squirrels and raptors. We also tested if the presence of raptors influenced the occupancy probability of squirrels, and vice versa. Lastly, we estimated the abundance of squirrels in parks, both where raptors were present and where they were absent. Contrary to our predictions, in our first two sampling periods we found a positive relationship between the presence of raptors and squirrel abundance, but this relationship was absent during our second two survey periods, which was due to a decline in squirrel abundance in parks where raptors were present. These results suggest that predator-prey interactions occur in urban settings much as they do under natural conditions, but that other factors unique to urbanization can also impact the abundance of raptors and squirrels. Our findings also suggest that raptors might be an effective means of controlling squirrel populations under some conditions.

A multiscale analysis of factors influencing Setophaga striata (Blackpoll Warbler) occupancy and abundance during the nonbreeding season in eastern Colombia

Abstract Setophaga striata (Blackpoll Warbler), one of the few boreal-breeding migratory birds that migrates to the Orinoco and Amazon regions, is experiencing steep population declines. However, knowledge of the species’ distribution and habitat use during the nonbreeding season is limited. Here, we explore how stationary nonbreeding season (December to April) occupancy probabilities vary across regional and landscape scales, and with vegetation structure for S. striata in eastern Colombia. By running single-species, single-season hierarchical occupancy models and N-mixture models, we evaluated how regional (annual precipitation, nonbreeding season precipitation, driest month, elevation, and net primary productivity), landscape (habitat type and % forest in the landscape), and vegetation structure variables influenced occupancy by S. striata. In the regional analysis, occupancy was positively influenced by stationary nonbreeding precipitation and net primary productivity, and was predicted to peak at elevations between 500 and 1,000 m. At the landscape scale, occupancy and abundance were higher in agroforestry systems (in order of magnitude: shade-grown cacao, citrus plantations and silvopastures) than in forested habitats. In relation to vegetation structure, occupancy was negatively correlated with a principal component containing shrub density, canopy height, and canopy cover. Within the Orinoco region, a spatial prediction based on our results supported high occupancy rates in moister areas along and close to the Andean foothills and where the Orinoco grasslands transition into Amazonian forests. Our results highlight the importance of landscapes containing agroforestry systems as stationary nonbreeding habitats for S. striata in Colombia’s Orinoco region and the importance of promoting agroforestry systems, like shade-grown cacao, in areas with higher nonbreeding season precipitation and net primary productivity values in order to increase habitat availability for the species.

Bringing circuit theory into spatial occupancy models to assess landscape connectivity

Abstract Occupancy models were originally developed to better understand species distribution while accounting for imperfect detection. Because species distribution is not only shaped by habitat quality but also by the ability of individuals to reach suitable habitats, spatial dynamic occupancy models have been proposed to extend the original framework by defining that site colonisation was a function of the Euclidean distance to occupied sites. However, not all sites in the landscape are equally accessible due to the presence of barriers, that of corridors, etc. To account for connectivity between sites, the Euclidean distance has recently been replaced by a least‐cost path distance, which explicitly accounts for landscape resistance, but assumes that individuals will follow the optimal route. To relax this assumption, we first developed a new spatial occupancy model that incorporates commute‐time distance derived from circuit theory to model accessibility across sites. This distance has the advantage of modelling movement as a random walk and accounting for the fact that colonisation could be achieved from multiple paths. Our approach allows for the explicit estimation of landscape connectivity from detection/non‐detection data and a direct measure of connectivity uncertainty. We implemented the model in the Bayesian framework using the nimble R package, which allows useful R connectivity functions to be called from within the model. Second, we carried out a simulation study to assess the performance of our model by considering four scenarios depicting an increasing level of landscape resistance. Third, to illustrate our new approach, we studied the recolonisation of two carnivores in France. We quantified the degree to which rivers facilitate Eurasian otter (Lutra lutra) colonisation and highways impede Eurasian lynx (Lynx lynx) colonisation. Overall, spatial occupancy models provide a flexible framework to accommodate any distance metric designed to align with species dispersal ecology.

Spatio‐temporal occupancy models with INLA

Abstract Modern methods for quantifying, predicting and mapping species distributions have played a crucial part in biodiversity conservation. Occupancy models have become a popular choice for analysing species occurrence data due to their ability to separate out the observational error induced by imperfect detection of a species, and the sources of bias affecting the occupancy process. However, the spatial and temporal variation in occupancy that is not accounted for by environmental covariates is often ignored or modelled through simple spatial structures as the computational costs of fitting explicit spatio‐temporal models is too high. In this work, we demonstrate how Integrated Nested Laplace Approximation (INLA) may be used to fit complex spatio‐temporal occupancy models and how the R‐INLA package can provide a user‐friendly interface to make such complex models available to users. We show how occupancy models, provided some simplification on the detection process is assumed, can be framed as latent Gaussian models and, as such, benefit from the powerful INLA framework. A large selection of complex modelling features, and random effect models, including spatio‐temporal models, have already been implemented in R‐INLA. These also become available for occupancy models, providing the user with an efficient, reliable and flexible toolbox. We illustrate how INLA provides a flexible and computationally efficient framework for developing and fitting complex occupancy models using two complex case studies. Through these, we show how different spatio‐temporal models that include spatial‐varying trends, smooth terms and spatio‐temporal random effects can be fitted to aggregated detection/non‐detection data. At the cost of limiting the complexity of the detection model structure, INLA can incorporate a range of rather complex structures in the ecological process of interest and hence, extend the functionality of occupancy models. The limitations of occupancy models in terms of scalability for large spatio‐temporal data sets remain a challenge and an active area of research. INLA‐based occupancy models provide an alternative inferential and computational framework to fit complex spatio‐temporal occupancy models. The need for new and more flexible computationally efficient approaches to fit such models makes INLA an attractive option for addressing complex ecological problems, and a promising area of research.

Comparison of Deterministic, Stochastic, and Energy-Data-Driven Occupancy Models for Building Stock Energy Simulation

Accurate modelling of occupancy patterns is critical for reliable estimation of building stock energy demand, which is a key input for the design of district energy systems. Aiming to investigate the suitability of different occupancy-modelling approaches for the design of district energy systems, the present study examines a set of standard-based schedules (from the UK National Calculation Methodology), a widely used stochastic occupancy model, and a novel energy-data-driven occupancy model. To this end, a dynamic energy model of a higher education office building developed within a stock model of London’s Bloomsbury district serves as a testbed to implement the occupancy models, explore their implications for the estimation of annual and peak heating and cooling demand, and extrapolate the findings to the computationally demanding building stock stimulations. Furthermore, the simulations were conducted in two years before and after the COVID-19 pandemic to examine the implications of hybrid working patterns after the pandemic. From the results, the energy-data-driven model demonstrated superior performance in annual heating demand estimations, with errors of ±2.5% compared to 14% and 7% for the standard-based and stochastic models. For peak heating demand, the models performed rather similarly, with the data-driven model showing 28% error compared to 29.5% for both the standard-based and stochastic models in 2019. In cooling demand estimations, the data-driven model yielded noticeably higher annual cooling demand and lower peak cooling demand estimations as compared with the standard-based and stochastic occupancy models. Given the adopted building-modelling approach, these findings can be extended to district-level investigations and inform the decision on the choice of occupancy models for building stock energy simulation.

Anuran occupancy varies with stream characteristics and flow across Arizona wilderness areas

Abstract Riparian ecosystems comprise less than 2% of the landscape in the arid western United States, yet they provide habitat and resources to over half of arid‐land wildlife species, including a broad diversity of anurans (frogs and toads). Despite this, relatively few studies describe riparian herpetofaunal communities in the Southwest, and remote wilderness areas are particularly undersurveyed. We employed passive acoustic monitoring to capture anuran advertisement calls in wilderness area tributaries of the Verde River, Arizona, USA. In the spring and summer of 2021 and 2022, 13–29 autonomous recording units (ARUs) were deployed along perennial, intermittent and ephemeral tributary stream reaches. We characterized sites based on the per cent of pool, riffle, run and side channel habitat within 100 m of each ARU and quantified substrate, wetted channel width and canopy cover. To relate anuran occupancy to environmental and hydrological variables, we evaluated acoustic detection data using single‐species, single‐season occupancy models. Four species were detected in this study: canyon treefrog (Hyla arenicolor), red‐spotted toad (Anaxyrus punctatus), Woodhouse's toad (Anaxyrus woodhousii) and non‐native American bullfrog (Lithobates catesbeianus), with canyon treefrog being the most ubiquitous species observed. Vocal activity of canyon treefrog was higher at perennial and intermittent sites compared to ephemeral sites, and the presence of pools was most strongly associated with canyon treefrog occupancy. Persistent, slow‐moving water best predicted the presence of breeding canyon treefrogs. Notably, this study did not detect several species with historical records in the middle Verde River catchment, including Arizona toad (Anaxyrus microscaphus) and northern leopard frog (Lithobates pipiens). Given climate change‐related flow declines and intensifying demands for water in arid lands globally, maintaining stream flows that provide consistent and suitable hydroregimes for anuran breeding and larval development is of increasing importance. Determining habitat use and flow regimes necessary to support anuran populations can aid in prioritization of conservation actions related to water management and predict how changes in water availability may impact stream‐breeding anurans.

Distribution and habitat use patterns of the endangered Central American clouded oncilla (Leopardus pardinoides oncilla) in Costa Rica.

Montane cloud forests are highly threatened ecosystems that are vulnerable to climate change. These complex habitats harbor many species that suffer the negative consequences of this global phenomenon, such as shifts in their distribution and habitat use. The Central American clouded oncilla (Leopardus pardinoides oncilla) is the smallest and most endangered wild cat in Mesoamerica and is primarily reported in cloud forests throughout its distribution. The species is poorly understood, with no studies conducted in Central America assessing its habitat preferences. To bridge this knowledge gap, we sampled two mountain ranges in Costa Rica with camera traps and conducted an occupancy analysis to understand the anthropogenic and environmental features that influence oncilla habitat use within them. Additionally, we conducted spatial predictions of habitat use across its northern and southern range in Costa Rica to identify priority conservation areas for the species. We found that Central American clouded oncilla habitat use is driven primarily by environmental factors. Our results showed that oncillas select habitats with denser tree cover at high elevations, closer to permanent water sources, which may provide them with high prey density and a favorable habitat structure for their survival. Spatial predictions identified two main regions as conservation priority areas where threat mitigation efforts and monitoring should be implemented: the Caribbean slope of the Talamanca mountains, and the Arenal-Monteverde forest complex. The occupancy modeling approach turned out to be very useful to assess the spatial associations of the species with the environment and mapping the conservation priority areas. Future research and mitigation actions should focus on potential threats that could negatively impact Central American clouded oncilla populations and habitat use, including the role of mesopredators and feral species.

Ecological dynamics and coexistence patterns of wild and domestic mammals in an abandoned landscape

The issue of agricultural land abandonment in southern Europe has raised concerns about its impact on biodiversity. While abandoned areas can lead to positive developments like creating new habitats and restoring native vegetation, they can also result in human–wildlife conflicts, particularly in areas with extensive farming and free‐ranging livestock. To understand habitat selection and use of livestock and wild ungulates, it is essential to study their spatial and temporal distribution patterns. In this context, we conducted a long‐term large mammal monitoring project using camera traps in the Peneda‐Gerês National Park in northern Portugal. Our primary focus was on exploring habitat preferences, occupancy dynamics, and potential spatial use correlations between domestic and wild species, utilizing dynamic occupancy models. Most wild species exhibited stable area use patterns, while domestic species experienced marginal declines, and the Iberian ibex displayed signs of repopulation. We observed distinct effects of habitat variables on occupancy, colonization, and extinction, revealing species‐specific patterns of habitat utilization. Human disturbance had a notable impact on domestic species but did not affect wild ones. Camera sensitivity emerged as a critical factor, enhancing detection probability for all species. Additionally, habitat and weather variables exerted varying effects on detection probabilities, underscoring the necessity of accounting for these factors in modeling the detection process. We found shared habitat preferences between cattle and horses, both positively correlated with wolves, suggesting potential human–wildlife conflicts. Despite extensive spatial overlap, domestic and wild species seem to exhibit ecological independence possibly due to distinct strategies and low predation pressure. Overall, the study emphasizes the multifaceted factors influencing habitat use. The observed species associations contribute to understanding ecological relationships and potential resource competition, emphasizing the importance of considering environmental variables for effective wildlife conservation and management.

Evaluating effects of natural and anthropogenic factors on American black bear occupancy in northern Georgia, USA

AbstractAlthough previously close to extirpation in the southeastern United States, American black bear (Ursus americanus) abundance and distribution have increased with habitat management and harvest regulation. The north Georgia black bear population, the most abundant and widely distributed in the state, is currently being exposed to pressures from increasing anthropogenic disturbances, such as recreation and vehicle traffic. We evaluated the effects of environmental factors and anthropogenic activity on habitat use of black bears within a wildlife management area open to public recreation. We used detection/non‐detection data from camera surveys conducted at 448 locations from April to December 2023 to fit hierarchical single‐species occupancy models. Black bear detection was best explained by Julian date, its quadratic effect, and weekly human activity index. Detection increased through the spring, peaked in June, and declined throughout late summer and fall. In contrast to detection, occupancy was not affected by seasonal differences in black bear activity. Occupancy increased with increasing elevation, distance to water, and distance to high‐use recreation areas. The spatial distribution of food resources likely contributed to the observed black bear occupancy pattern related to elevation and distance to water, while human disturbance in the study area also influenced black bear occupancy. Thus, our results suggest that in a landscape open to public recreation, black bear occupancy was driven by a combination of environmental and anthropogenic factors. Identifying patterns of occupancy at a scale typical of management provides important information for managing black bear populations throughout the large, connected network of national forests in the Appalachian region.

A post‐processing framework for assessing BirdNET identification accuracy and community composition

Passively collected acoustic data have become increasingly common in wildlife research and have prompted the development of machine‐learning approaches to extract and classify large sets of audio files. BirdNET is an open‐source automatic prediction model that is popular because of its lack of training requirements for end users. Several studies have sought to test the accuracy of BirdNET and illustrate its potential in occupancy modelling of single or multiple species. However, these techniques either require extensive statistical knowledge or computational power to be applied to large datasets. In addition, there is a lack of comparisons of occupancy and community composition calculated using BirdNET and typical field methods. Here we develop a framework for assessing the accuracy of BirdNET using generalized linear mixed models to determine species‐specific confidence score thresholds. We then compare community composition under our model and another post‐processing approach to field data collected from co‐located point count surveys in northeastern Vermont. Our framework outperformed the other post‐processing method and resulted in species composition similar to that of point count surveys. Our work highlights the potential mismatch between accuracy and confidence score and the importance of developing species‐specific thresholds. The framework can facilitate research on large acoustic datasets and can be applied to output from BirdNET or other automatic prediction models.

Development of a rapid and reliable surveillance method for Ornithodoros turicata americanus in gopher tortoise (Gopherus polyphemus) burrows in the southeastern United States.

The soft tick Ornithodoros turicata Duges (Acari: Argasidae) is a potential vector of African swine fever virus (ASFV). We evaluated the efficacy of two methods to collect soft ticks rapidly and efficiently from gopher tortoise (Gopherus polyphemus) burrows, which are ubiquitous throughout large regions of the southeastern United States and their burrows are a known microhabitat of O. turicata. Burrow vacuuming was an effective and efficient tick collection method; no tick was captured employing CO2 trapping. Using an occupancy modelling framework, we estimated that the probability of detecting ticks from an infested burrow each time a sample was taken with this method was 58% and increased with the average relative humidity. With the occupancy model, we estimated that 70% of the burrows in the study area were infested with O. turicata. Manual sifting of the burrow material yielded more ticks (6.6 individuals/sample) than using a set of three sieves (2.9 individuals/sample), yet the probability of detecting the species was not different between the two methods (Pval = 0.7). These methods can inform the development of ASF vector surveillance and outbreak response plans in areas of high risk for ASFV introduction in the region.

Global population size and conservation priority areas for the endangered Titicaca grebe

AbstractThe Titicaca grebe (Rollandia microptera) is a poorly studied endangered species, only found in the Lake Titicaca watershed of Peru and Bolivia. Population surveys undertaken in the early twenty‐first century suggested that the species had declined in number by &gt;70%. We conducted a population survey of the grebe between March and August 2022 in Peru and Bolivia, using both maximum entropy and Bayesian occupancy models to estimate factors affecting habitat suitability for the grebe. We conducted surveys between March and August 2022 in Peru and Bolivia. Contrary to the International Union for Conservation of Nature (IUCN) population estimates of &lt;1,000 individuals, there are likely tens of thousands of individual grebes. The population estimation was sensitive to the type of model used. Distance from shore and fishing intensity were the most significant factors influencing grebe populations. The Reserva Nacional del Titicaca, the only protected area in the lake, covers some of the most suitable Titicaca grebe habitat across its entire range. The population of the Titicaca grebe is significantly higher than previous estimates, but we do not recommend a change to its conservation status because of the lack of conservation efforts for this species, and the long‐term risks associated with global warming and fisheries bycatch.

Wildlife resilience in an urban landscape: understanding land-use impacts in Cape Town

AbstractUrbanisation is rapidly transforming and fragmenting natural habitats, disrupting ecosystems and negatively impacting biodiversity. The City of Cape Town (CoCT) is situated in a global biodiversity hotspot, but sustained anthropogenic activities have resulted in the local extirpation of most medium and large mammals. A recent survey of mammals within urban protected areas of CoCT revealed that a few, mostly medium-sized generalist species, persist. It is uncertain which native mammal species, if any, inhabit the unprotected green belts and parks in suburban and urban areas of the city. A total of 37 camera trap sites were established along four transects for a period of four months between 31 January and 31 May 2022. A total of 12 terrestrial mammal species were detected, nine of which were wild native mammals and three domestic species. Most detections were in natural habitat followed by suburban, with urban areas having the lowest detection rate of wildlife. Single season hierarchical multi-species occupancy models revealed that tree cover had a significant positive effect on both community and individual species occupancy. Contrary to our predictions, neither human population density nor the extent of the impervious surface at sites significantly affected occupancy. Cape grysbok (Raphicerus melanotis) were significantly more likely to occur at sites with a higher proportion of impervious surfaces supporting other recent research, which showed this species together with water mongoose (Atilax paludinosus) and Cape porcupine (Hystrix africaeaustralis) are one of only a few native mammals that appear to persist and may even thrive in human-modified landscapes. Our findings underscore the complexity of urban biodiversity conservation and the species-specific responses to environmental factors, emphasising the importance of tree cover in urban wildlife management.