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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.

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More than 240 species of snake have been described from Thailand, yet basic natural history and ecology for this group of animals remains scarce in human disturbed environments despite conservation and human medical significance of them in these habitats. We studied snake diversity at Walailak University from March to December 2023, the largest university campus in Thailand (1525 hectares) through standardized walking surveys, opportunistic notifications and observation, road surveys, and traps and evaluated diversity using the Shannon diversity index (H), Pielou's evenness of species (J), detection probabilities (p), and occupancy probabilities (ψ). We observed 195 snakes (21 species, 7 families) at Walailak University and overall snake diversity (H = 2.60) and evenness (J = 0.85) were quite high, although specific site diversity (range H = 0-1.94) and evenness (range J = 0.67-0.91) within the university were variable. The probability of detecting snakes (range p = .10-.40) increased with increasing humidity and decreased with increasing amount of rain, temperature, and wind; site occupancy probability decreased with increased canopy height and increased with increased distance to buildings, increased canopy height loss, increased distance to roads, and increased distance to water. Our findings of relatively high snake diversity, presence of snake species potentially dangerous to humans (six species), and protected snake species (Thailand WARPA and international CITES, five species) suggest significant potential for conservation and further research at Walailak University and other campuses in Thailand.

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Monitoring programs are pivotal to establishing sound management. Due to economic, logistic, and time limitations, monitoring programs often overlook differences among life-history stages. However, species occurrence does not necessarily mean population viability, and it is unclear to what extent monitoring programs that do not consider separately adult presence and reproduction provide effective management indications. Unfortunately, collecting data on certain life stages requires high sampling effort, leading to a trade-off between model reliability and resources needed for monitoring. We collected data on presence and reproduction of amphibians by monitoring 207 waterbodies in Lombardy (northern Italy) in 2017-2022. We then used multistate occupancy models to test whether certain environmental features, namely, pond area, hydroperiod, forest cover, shade, aquatic vegetation, and predators' presence, differentially affected adult occurrence and breeding probabilities of multiple amphibian species. To assess optimal sampling efforts, we modeled the detection probabilities of adults and reproduction across multiple species. Finally, we identified the optimal monitoring strategy under different scenarios of resource availability, comparing adult-only monitoring versus joint assessment of the occurrence of adults and reproduction. In many cases, the main drivers of adult occurrence and reproduction did not coincide because most investigated ecological variables affected one life stage or the other. Forest area, for instance, increased occurrence probabilities of adults of the endemic Rana latastei but showed no effect on their reproduction probabilities. Quantitative estimates of the sampling effort showed that occurrence of adults was easier to spot in 4 out of 7 species. Multicriteria decision analyses showed that when resources were scarce, monitoring adults was the optimal strategy for those 4 species. Conversely, with more resources, monitoring both adults and reproduction emerged as the best strategy for all the considered species. Integrated monitoring of adults and reproduction is essential to comprehensively identify effective conservation measures for amphibians.

Integración de datos de presencia adulta y de reproducción para identificar medidas de conservación para los anfibios Resumen Los programas de monitoreo son fundamentales para establecer una gestión adecuada. Debido a limitaciones económicas, logísticas y de tiempo, los programas de seguimiento suelen pasar por alto las diferencias entre las distintas fases del ciclo de vida. Sin embargo, la presencia de especies no significa necesariamente la viabilidad de la población, y no está claro hasta qué punto los programas de seguimiento que no consideran por separado la presencia de adultos y la reproducción proporcionan indicaciones eficaces para la gestión. Desgraciadamente, la recolección de datos sobre etapas determinadas del ciclo de vida requiere un gran esfuerzo de muestreo, lo que lleva a un compromiso entre la fiabilidad del modelo y los recursos necesarios para el seguimiento. Recopilamos datos sobre la presencia y reproducción de anfibios mediante el monitoreo de 207 cuerpos de agua en Lombardía (norte de Italia) entre 2017 y 2022. Después utilizamos modelos de ocupación multiestado para comprobar si determinadas características ambientales (superficie de los estanques, hidroperiodo, cubierta forestal, sombra, vegetación acuática y presencia de depredadores) afectaban de forma diferencial la presencia de adultos y a las probabilidades de reproducción de múltiples especies de anfibios. Para evaluar los esfuerzos de muestreo óptimos, modelamos las probabilidades de detección de adultos y reproducción de múltiples especies. Por último, identificamos la estrategia de seguimiento óptima en diferentes escenarios de disponibilidad de recursos y comparamos el seguimiento exclusivo de adultos frente a la evaluación conjunta de la presencia de adultos y reproducción. En muchos casos, los principales factores en la presencia de adultos y en la reproducción no coincidían, ya que la mayoría de las variables ecológicas investigadas afectaban a una u otra etapa del ciclo. La cubierta forestal, por ejemplo, aumentó la probabilidad de presencia de adultos de la especie endémica Rana latastei pero no mostraron un efecto sobre su probabilidad de reproducción. Las estimaciones cuantitativas del esfuerzo de muestreo mostraron que la presencia de adultos era más fácil de detectar en cuatro de las siete especies. Los análisis de decisión multicriterio mostraron que, cuando los recursos eran escasos, el seguimiento de los adultos era la estrategia óptima para esas cuatro especies. Por el contrario, con más recursos, el seguimiento tanto de los adultos como de la reproducción resultó ser la mejor estrategia para todas las especies consideradas. El seguimiento integrado de los adultos y la reproducción es esencial para identificar de forma exhaustiva medidas de conservación eficaces para los anfibios.

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This commentary critiques the methodology, interpretation of results, and broader implications of a study by Jarcuska et al. (2024). We argue that the study's design and analysis fail to conclusively demonstrate any causal link between solar parks and bird diversity or community composition. Furthermore, focusing solely on species diversity and community composition, the study overlooks the importance of functional diversity and functional structure of communities in assessing the ecological impacts of solar parks on agricultural ecosystems. By exposing these shortcomings and recommending well-established methods for future research, we aim to ensure robust and informative studies that guide balanced decision-making for conservation and all stakeholders.

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Quantifying the cost-effectiveness of alternative sampling methods is crucial for efficient biodiversity monitoring and detection of population trends. In this study, we compared the cost-effectiveness of three novel sampling methods for detecting changes in koala (Phascolarctos cinereus) occupancy: thermal drones, passive acoustic recorders and camera trapping. Specifically, we fitted single-season occupancy-detection models to data recorded from 46 sites in eight bioregions of New South Wales, Australia, between 2018 and 2022. We explored the effect of weather variables on daily detection probability for each method and, using these estimates, calculated the statistical power to detect 30%, 50% and 80% declines in koala occupancy. We calculated power for different combinations of sites (1-200) and repeat surveys (2-40) and developed a cost model that found the cheapest survey design that achieved 80% power to detect change. On average, detectability of koalas was highest with one 24-h period of acoustic surveys (0.32, 95% CI's: 0.26, 0.39) compared to a 25-ha flight of drone surveys (0.28, 95% 0.15, 0.48) or a 24-h period of camera trapping consisting of six cameras (0.019, 95% CI's: 0.014, 0.025). We found a negative quadratic relationship between detection probability and air temperature for all three methods. Our power and cost analysis suggested that 148 sites surveyed with acoustic recorders deployed for 14 days would be the cheapest method to sufficiently detect a 30% decline in occupancy with 80% power. We recommend passive acoustic recorders as the most efficient sampling method for monitoring koala occupancy compared to cameras or drones. Further comparative studies are needed to compare the relative effectiveness of these methods and others when the monitoring objective is to detect change in koala abundance over time.

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The expansion of forest cover and intensification of agriculture represent the main threats to the bush cricket Saga pedo, currently listed as Vulnerable globally by the IUCN and included in Annex IV of the European Union Habitats Directive. Gathering information on its ecology and population size is challenging due to its low abundance and localized distribution. Additionally, the elusive and cryptic behavior of this species reduces the likelihood of its detection, potentially resulting in population underestimations. Thus, in this study, we aimed to (1) estimate S. pedo population size in relation to environmental variables and prey availability and (2) predict abundance of S. pedo in our study area for future monitoring in nearby territories. We found that the population of S. pedo in our study area consists of 197 (±115) individuals with a detection probability of 21.01% (±11.09). Detection probability of S. pedo further decreases on windy days. Moreover, we found that the investigated population of S. pedo occupies suboptimal areas, as highlighted not only by the predicted abundances but also by the association between S. pedo and other subfamilies of orthoptera that are ecologically very distant from our target species and mostly linked to mesophilic biotopes. Most of the individuals we observed are concentrated in small clearings completely within wooded matrices and therefore isolated from each other. Based on our results, it is possible that forest expansion toward open meadows represents the main threat to this population, transforming the clearings and xeric meadows (to which S. pedo is linked) into small and fragmented patches that are suboptimal and insufficient to host viable populations.

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Population density and structure are critical to nature conservation and pest management. Traditional sampling methods such as capture-mark-recapture and catch-effort can't be used in situations where catching, marking, or removing individuals are not feasible. N-mixture models use repeated count data to estimate population abundance based on detection probability. They are widely adopted in wildlife surveys in recent years to account for imperfect detection. However, its application in entomology is relatively new. In this paper, we describe the general procedures of N-mixture models in population studies from data collection to model fitting and evaluation. Using Lycorma delicatula egg mass survey data at 28 plots in seven sites from the field, we found that detection probability (p) was negatively correlated with tree diameter at breast height (DBH), ranged from 0.516 [95 % CI: 0.470-0.561] to 0.614 [95 % CI: 0.566-0.660] between the 1st and the 3rd sample period. Furthermore, egg mass abundance (λ) was positively associated with basal area (BA) for the sample unit (single tree), with more egg masses on tree of heaven (TOH) trees. More egg masses were also expected on trees of other species in TOH plots. Predicted egg mass density (masses/100 m2) ranged from 5.0 (95 % CI: 3.0-16.0) (Gordon) to 276.9 (95 % CI: 255.0-303.0) (Susquehannock) for TOH plots, and 11.0 (95 % CI: 9.00-15.33) (Gordon) to 228.3 (95 % CI: 209.7-248.3) (Burlington) for nonTOH plots. Site-specific abundance estimates from N-mixture models were generally higher compared to observed maximum counts. N-mixture models could have great potential in insect population surveys in agriculture and forestry in the future.

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Ixodid (hard) ticks play important ecosystem roles and have significant impacts on animal and human health via tick-borne diseases and physiological stress from parasitism. Tick occurrence, abundance, activity, and key life-history traits are highly influenced by host availability, weather, microclimate, and landscape features. As such, changes in the environment can have profound impacts on ticks, their hosts, and the spread of diseases. Researchers recognize that spatial and temporal factors influence activity and abundance and attempt to account for both by conducting replicate sampling bouts spread over the tick questing period. However, common field methods notoriously underestimate abundance, and it is unclear how (or if) tick studies model the confounding effects of factors influencing activity and abundance. This step is critical as unaccounted variance in detection can lead to biased estimates of occurrence and abundance. We performed a descriptive review to evaluate the extent to which studies account for the detection process while modeling tick data. We also categorized the types of analyses that are commonly used to model tick data. We used hierarchical models (HMs) that account for imperfect detection to analyze simulated and empirical tick data, demonstrating that inference is muddled when detection probability is not accounted for in the modeling process. Our review indicates that only 5 of 412 (1 %) papers explicitly accounted for imperfect detection while modeling ticks. By comparing HMs with the most common approaches used for modeling tick data (e.g., ANOVA), we show that population estimates are biased low for simulated and empirical data when using non-HMs, and that confounding occurs due to not explicitly modeling factors that influenced both detection and abundance. Our review and analysis of simulated and empirical data shows that it is important to account for our ability to detect ticks using field methods with imperfect detection. Not doing so leads to biased estimates of occurrence and abundance which could complicate our understanding of parasite-host relationships and the spread of tick-borne diseases. We highlight the resources available for learning HM approaches and applying them to analyzing tick data.

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Predicting the spatial occurrence of wildlife is a major challenge for ecology and management. In Latin America, limited knowledge of the number and locations of vampire bat roosts precludes informed allocation of measures intended to prevent rabies spillover to humans and livestock. We inferred the spatial distribution of vampire bat roosts while accounting for observation effort and environmental effects by fitting a log Gaussian Cox process model to the locations of 563 roosts in three regions of Peru. Our model explained 45% of the variance in the observed roost distribution and identified environmental drivers of roost establishment. When correcting for uneven observation effort, our model estimated a total of 2340 roosts, indicating that undetected roosts (76%) exceed known roosts (24%) by threefold. Predicted hotspots of undetected roosts in rabies-free areas revealed high-risk areas for future viral incursions. Using the predicted roost distribution to inform a spatial model of rabies spillover to livestock identified areas with disproportionate underreporting and indicated a higher rabies burden than previously recognized. We provide a transferrable approach to infer the distribution of a mostly unobserved bat reservoir that can inform strategies to prevent the re-emergence of an important zoonosis.

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There are many advantages to transitioning from conducting marine wildlife surveys via human observers onboard light-aircraft, to capturing aerial imagery using drones. However, it is important to maintain the validity of long-term data series whilst transitioning from observer to imagery surveys. We need to understand how the detection rates of target species in images compare to those collected from observers in piloted aircraft, and the factors influencing detection rates from each platform. We conducted trial ScanEagle drone surveys of dugongs in Shark Bay, Western Australia, covering the full extent of the drone's range (∼100 km), concurrently with observer surveys, with the drone flying above or just behind the piloted aircraft. We aimed to test the assumption that drone imagery could provide comparable detection rates of dugongs to human observers when influenced by same environmental conditions. Overall, the dugong sighting rate (i.e., count of individual dugongs) was 1.3 (95% CI [0.98-1.84]) times higher from the drone images than from the observers. The group sighting rate was similar for the two platforms, however the group sizes detected within the drone images were significantly larger than those recorded by the observers, which explained the overall difference in sighting rates. Cloud cover appeared to be the only covariate affecting the two platforms differently; the incidence of cloud cover resulted in smaller group sizes being detected by both platforms, but the observer group sizes dropped much more dramatically (by 71% (95% CI [31-88]) compared to no cloud) than the group sizes detected in the drone images (14% (95% CI [-28-57])). Water visibility and the Beaufort sea state also affected dugong counts and group sizes, but in the same way for both platforms. This is the first direct simultaneous comparison between sightings from observers in piloted aircraft and a drone and demonstrates the potential for drone surveys over a large spatial-scale.

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The Pollard-Yates transect is a widely used method for sampling butterflies. Data from these traditional transects are analyzed to produce density estimates, which are then used to make inferences about population status or trends. A key assumption of the Pollard-Yates transect is that detection probability is 1.0, or constant but unknown, out to a fixed distance (generally 2.5 m on either side of a transect line). However, species-specific estimates of detection probability would allow for sampling at farther distances, resulting in more detections of individuals. Our objectives were to (1) evaluate butterfly density estimates derived from Pollard-Yates line transects and distance sampling, (2) estimate how detection probabilities for butterflies vary across sampling distances and butterfly wing lengths, and (3) offer advice on future butterfly sampling techniques to estimate population density. We conducted Pollard-Yates transects and distance-sampling transects in central Iowa in 2014. For comparison to densities derived from Pollard-Yates transects, we used Program DISTANCE to model detection probability (p) and estimate density (D) for eight butterfly species representing a range of morphological characteristics. We found that detection probability among species varied beyond 2.5 m, with variation apparent even within 5 m of the line. Such variation correlated with wing size, where species with larger wing size generally had higher detection probabilities. Distance sampling estimated higher densities at the 5-m truncation for five of the eight species tested. At this truncation, detection probability was <0.8 for all species, and ranged from 0.53 to 0.79. With the exception of the little yellow (Pyrisitia lisa), species with median wing length <5.0 mm had the lowest detection probabilities. We recommend that researchers integrate distance sampling into butterfly sampling and monitoring, particularly for studies utilizing survey transects >5 m wide and when smaller species are targeted.

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Camera trap data are biased when an animal passes through a camera's field of view but is not recorded. Cameras that operate using passive infrared sensors rely on their ability to detect thermal energy from the surface of an object. Optimal camera deployment consequently depends on the relationship between a sensor array and an animal. Here, we describe a general, experimental approach to evaluate detection errors that arise from the interaction between cameras and animals. We adapted distance sampling models and estimated the combined effects of distance, camera model, lens height, and vertical angle on the probability of detecting three different body sizes representing mammals that inhabit temperate, boreal, and arctic ecosystems. Detection probabilities were best explained by a half-normal-logistic mixture and were influenced by all experimental covariates. Detection monotonically declined when proxies were ≥6 m from the camera; however, models show that body size and camera model mediated the effect of distance on detection. Although not a focus of our study, we found that unmodeled heterogeneity arising from solar position has the potential to bias inferences where animal movements vary over time. Understanding heterogeneous detection probabilities is valuable when designing and analyzing camera trap studies. We provide a general experimental and analytical framework that ecologists, citizen scientists, and others can use and adapt to optimize camera protocols for various wildlife species and communities. Applying our framework can help ecologists assess trade-offs that arise from interactions among distance, cameras, and body sizes before committing resources to field data collection.

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Mounting evidence of bumble bee declines and the listing of the rusty patched bumble bee (Bombus affinis Cresson) as federally endangered in the United States in 2017 and Canada in 2012 has stimulated an interest in monitoring and conservation. Understanding the influence of land use on occupancy patterns of imperiled species is crucial to successful recovery planning. Using detection data from community surveys, we assessed land use associations for 7 bumble bee species in Minnesota, USA, including B. affinis. We used multispecies occupancy models to assess the effect of 3 major land use types (developed, agricultural, and natural) within 0.5 and 1.5 km on occupancy of 7 Bombus (Hymenoptera: Apidae) species, while accounting for detection uncertainty. We found that B. affinis occupancy and detection were highest in developed landscapes and lowest in agricultural landscapes, representing an inverse relationship with the relative landcover ratios of these landscapes in Minnesota. Occupancy of 2 bumble bee species had strong positive associations with natural landscapes within 1.5 km and 2 species had strong negative associations with agricultural landscapes within 1.5 km. Our results suggest that best practices for imperiled Bombus monitoring and recovery planning depends upon the surrounding major land use patterns. We provide recommendations for urban planning to support B. affinis based on conservation success in the metropolitan areas of Minneapolis-St. Paul. We also encourage substantial survey effort be employed in agricultural and natural regions of the state historically occupied by B. affinis to determine the current occupancy state.

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Aerial LiDAR measurements at 7474 oil and gas production facilities in the Permian Basin yield a measured methane emission rate distribution extending to the detection sensitivity of the method, 2 kg/h at 90% probability of detection (POD). Emissions are found at 38.3% of facilities scanned, a significantly higher proportion than reported in lower-sensitivity campaigns. LiDAR measurements are analyzed in combination with measurements of the heavy tail portion of the distribution (>600 kg/h) obtained from an airborne solar infrared imaging spectrometry campaign by Carbon Mapper (CM). A joint distribution is found by fitting the aligned LiDAR and CM data. By comparing the aerial samples to the joint distribution, the practical detection sensitivity of the CM 2019 campaign is found to be 280 kg/h [256, 309] (95% confidence) at 50% POD for facility-sized emission sources. With respect to the joint model distribution and its confidence interval, the LiDAR campaign is found to have measured 103.6% [93.5, 114.2%] of the total emission rate predicted by the model for equipment-sized emission sources (∼2 m diameter) with emission rates above 3 kg/h, whereas the CM 2019 campaign is found to have measured 39.7% [34.6, 45.1%] of the same quantity for facility-sized sources (150 m diameter) above 10 kg/h. The analysis is repeated with data from CM 2020-21 campaigns with similar results. The combined distributions represent a more comprehensive view of the emission rate distribution in the survey area, revealing the significance of previously underreported emission sources at rates below the detection sensitivity of some emissions monitoring campaigns.

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Abundance surveys are commonly used to estimate plant or animal densities and frequently require estimating detection probabilities to account for imperfect detection. The estimation of detection probabilities requires additional measurements that take time, potentially reducing the efficiency of the survey when applied to high-density populations. We conducted quadrat, removal, and distance surveys of zebra mussels (Dreissena polymorpha) in three central Minnesota lakes and determined how much survey effort would be required to achieve a pre-specified level of precision for each abundance estimator, allowing us to directly compare survey design efficiencies across a range of conditions. We found that the required sampling effort needed to achieve our precision goal depended on both the survey design and population density. At low densities, survey designs that could cover large areas but with lower detection probabilities, such as distance surveys, were more efficient (i.e., required less sampling effort to achieve the same level of precision). However, at high densities, quadrat surveys, which tend to cover less area but with high detection rates, were more efficient. These results demonstrate that the best survey design is likely to be context-specific, requiring some prior knowledge of the underlying population density and the cost/time needed to collect additional information for estimating detection probabilities.

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Monitoring on the basis of sound recordings, or passive acoustic monitoring, can complement or serve as an alternative to real-time visual or aural monitoring of marine mammals and other animals by human observers. Passive acoustic data can support the estimation of common, individual-level ecological metrics, such as presence, detection-weighted occupancy, abundance and density, population viability and structure, and behaviour. Passive acoustic data also can support estimation of some community-level metrics, such as species richness and composition. The feasibility of estimation and certainty of estimates is highly context dependent, and understanding the factors that affect the reliability of measurements is useful for those considering whether to use passive acoustic data. Here, we review basic concepts and methods of passive acoustic sampling in marine systems that often are applicable to marine mammal research and conservation. Our ultimate aim is to facilitate collaboration among ecologists, bioacousticians, and data analysts. Ecological applications of passive acoustics require one to make decisions about sampling design, which in turn requires consideration of sound propagation, sampling of signals, and data storage. One also must make decisions about signal detection and classification and evaluation of the performance of algorithms for these tasks. Investment in the research and development of systems that automate detection and classification, including machine learning, are increasing. Passive acoustic monitoring is more reliable for detection of species presence than for estimation of other species-level metrics. Use of passive acoustic monitoring to distinguish among individual animals remains difficult. However, information about detection probability, vocalisation or cue rate, and relations between vocalisations and the number and behaviour of animals increases the feasibility of estimating abundance or density. Most sensor deployments are fixed in space or are sporadic, making temporal turnover in species composition more tractable to estimate than spatial turnover. Collaborations between acousticians and ecologists are most likely to be successful and rewarding when all partners critically examine and share a fundamental understanding of the target variables, sampling process, and analytical methods.

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Standard occupancy models enable unbiased estimation of occupancy by accounting for observation errors such as missed detections (false negatives) and, less commonly, incorrect detections (false positives). Occupancy models are fitted to data from repeated site visits in which surveyors record evidence of species presence. Use of indirect sign (e.g., scat, tracks) as evidence of presence can vastly improve survey efficiency for inconspicuous species but can also introduce additional sources of error. We developed a "multi-sign" occupancy approach to model the detection process separately for unique sign types and used this method to improve estimates of occupancy dynamics for an inconspicuous species, the American pika (Ochotona princeps). We investigated how estimates of pika occupancy and environmental drivers differed under four increasingly realistic representations of the observation process: (1) perfect detection (commonly assumed for modeling pika occupancy), (2) standard occupancy model (single observation process without possibility of false detection), (3) multi-sign with no false detections (non-false positive model), and (4) multi-sign with false detections (full model). For the multi-sign occupancy models, we modeled the detection of each sign type (fresh scat, fresh haypiles, pika calls, and pika sightings) separately as a function of climatic and environmental covariates. Estimates of occupancy processes and inferences about environmental drivers were sensitive to different detection models. Simplified representations of the detection processes generally resulted in higher occupancy estimates and higher turnover rates than the full multi-sign model. Environmental drivers also varied in their influence on occupancy models, where (e.g.) forb cover was estimated to more strongly influence occupancy in the full multi-sign model than the simpler models. As has been reported previously in other contexts, unmodeled heterogeneity in the observation process can lead to biases in occupancy processes and uncertainty in the relationships between occupancy and environmental covariates. Overall, our multi-sign approach to dynamic occupancy modeling, which accounts for spatio-temporal variation in reliability among sign types, has strong potential to generate more realistic estimates of occupancy dynamics for inconspicuous species.

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With the growing importance of single-photon-counting (SPC) techniques, researchers are now designing high-performance systems based on single-photon avalanche diodes (SPADs). SPADs with high performances and low cost allow the popularity of SPC-based systems for medical and industrial applications. However, few efforts were put into the design optimization of SPADs due to limited calibrated models of the SPAD itself and its related circuits. This paper provides a perspective on improving SPAD-based system design by reviewing the development of SPAD models. First, important SPAD principles such as photon detection probability (PDP), dark count rate (DCR), afterpulsing probability (AP), and timing jitter (TJ) are discussed. Then a comprehensive discussion of various SPAD models focusing on each of the parameters is provided. Finally, important research challenges regarding the development of more advanced SPAD models are summarized, followed by the outlook for the future development of SPAD models and emerging SPAD modeling methods.

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Predator-prey dynamics are a fundamental part of ecology, but directly studying interactions has proven difficult. The proliferation of camera trapping has enabled the collection of large datasets on wildlife, but researchers face hurdles inferring interactions from observational data. Recent advances in hierarchical co-abundance models infer species interactions while accounting for two species' detection probabilities, shared responses to environmental covariates, and propagate uncertainty throughout the entire modeling process. However, current approaches remain unsuitable for interacting species whose natural densities differ by an order of magnitude and have contrasting detection probabilities, such as predator-prey interactions, which introduce zero inflation and overdispersion in count histories. Here, we developed a Bayesian hierarchical N-mixture co-abundance model that is suitable for inferring predator-prey interactions. We accounted for excessive zeros in count histories using an informed zero-inflated Poisson distribution in the abundance formula and accounted for overdispersion in count histories by including a random effect per sampling unit and sampling occasion in the detection probability formula. We demonstrate that models with these modifications outperform alternative approaches, improve model goodness-of-fit, and overcome parameter convergence failures. We highlight its utility using 20 camera trapping datasets from 10 tropical forest landscapes in Southeast Asia and estimate four predator-prey relationships between tigers, clouded leopards, and muntjac and sambar deer. Tigers had a negative effect on muntjac abundance, providing support for top-down regulation, while clouded leopards had a positive effect on muntjac and sambar deer, likely driven by shared responses to unmodelled covariates like hunting. This Bayesian co-abundance modeling approach to quantify predator-prey relationships is widely applicable across species, ecosystems, and sampling approaches and may be useful in forecasting cascading impacts following widespread predator declines. Taken together, this approach facilitates a nuanced and mechanistic understanding of food-web ecology.

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Having an estimate of the number of under-reported cases is crucial in determining the true burden of a disease. In the COVID-19 pandemic, there is a great need to quantify the true disease burden by capturing the true incidence rate to establish appropriate measures and strategies to combat the disease. This study investigates the under-reporting of COVID-19 cases in Victoria, Australia, during the third wave of the pandemic as a result of variation in geographic area and time. It is aimed to determine potential under-reported areas and generate the true picture of the disease in terms of the number of cases. A two-tiered Bayesian hierarchical model approach is employed to estimate the true incidence and detection rates through Bayesian model averaging. The proposed model goes beyond testing inequality across areas by looking into other covariates such as weather, vaccination rates, and access to vaccination and testing centres, including interactions and variations between space and time. This model aims for parsimony yet allows a broader range of scope to capture the underlying dynamic of the reported COVID-19 cases. Moreover, it is a data-driven, flexible, and generalisable model to a global context such as cross-country estimation and across time points under strict pandemic conditions.

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