Risk factors for bronchopulmonary dysplasia infants with respiratory score greater than four: a multi-center, prospective, longitudinal cohort study in China.

Bronchopulmonary dysplasia (BPD) is the most common complication of prematurity involving both pre- and post-natal factors. A large, prospective, longitudinal cohort study was conducted to determine whether inflammation-related factors are associated with an increased risk of BPD in preterm infants who were born at a gestational age < 32 weeks, < 72 h after birth and respiratory score > 4. The study included infants from 25 participating hospitals in China between March 1, 2020 and March 31, 2022. The primary outcomes were BPD and severity of BPD at 36 weeks post-menstrual age. A total of 1362 preterm infants were enrolled in the study. After exclusion criteria, the remaining 1088 infants were included in this analysis, of whom, 588 (54.0%) infants were in the BPD group and 500 (46.0%) were in the non-BPD group. In the BPD III model, the following six factors were identified: birth weight (OR 0.175, 95% CI 0.060-0.512; p = 0.001), surfactant treatment (OR 8.052, 95% CI 2.658-24.399; p < 0.001), mean airway pressure (MAP) ≥ 12 cm H2O (OR 3.338, 95% CI 1.656-6.728; p = 0.001), late-onset sepsis (LOS) (OR 2.911, 95% CI 1.514-5.599; p = 0.001), ventilator-associated pneumonia (VAP) (OR 18.236, 95% CI 4.700-70.756; p < 0.001) and necrotizing enterocolitis (NEC) (OR 2.725, 95% CI 1.182-6.281; p = 0.019). Premature infants remained at high risk of BPD and with regional variation. We found that post-natal inflammation-related risk factors were associated with an increased risk of severe BPD, including LOS, VAP, NEC, MAP ≥ 12 cm H2O and use of surfactant.

The relative contributions of soft tissue mass components as risk or protective factors of non-alcoholic fatty liver disease in children.

BACKGROUND/OBJECTIVE: Several body components are known to be associated with non-alcoholic fatty liver disease (NAFLD) in children. However, the relative contributions of soft tissue mass components as risk or protective factors of NAFLD are largely unknown because measurements of these components are often highly correlated. Therefore, we aimed to estimate levels of association between soft tissue mass components and NAFLD. SUBJECTS/METHODS: We collected the medical records of 555 Chinese children (aged 3-18 years). Five mutually exclusive and exhaustive components of soft tissue mass were measured using dual energy X-ray absorptiometry. NAFLD was diagnosed with abdominal B-ultrasound scan. We fit Dirichlet regression and multivariate linear regression models wherein age and NAFLD were used as predictors of the proportional measurements of soft tissue mass components. RESULTS: The proportion of android fat was significantly higher in children with NAFLD than in those without NAFLD (ratio of proportions ranged from 1.18 to 1.30), whereas proportions of trunk lean and limb lean were significantly lower (ratio of proportions ranged from 0.87 to 0.92 for trunk lean and from 0.82 to 0.91 for limb lean). The proportion of gynoid fat was slightly higher in boys with NAFLD than in those without NAFLD (ratio = 1.05), but this proportion was not significantly higher in girls. The association between the proportion of android fat and NAFLD appeared to be somewhat greater than the associations between proportions of trunk lean or limb lean components and NAFLD. CONCLUSION: Our findings suggest that lowering fat mass and increasing lean mass can both be used to combat NAFLD in children and that more studies are needed to determine the association between gynoid fat and NAFLD.

Distribution of multiple chalazia in eyelids of pediatrics requiring surgery in southeast China: a hospital-based cross-sectional study.

Background: Multiple chalazia are common in children, and many are treated by surgery. However, the distribution of different types of multiple chalazia has not been studied. This research aimed to investigate the location and number of multiple chalazia in pediatrics who need surgical treatments. Methods: Patients with multiple chalazia treated by incision and curettage surgery (I&C) in a tertiary children's hospital between June and December 2016 were reviewed. Demographic data, locations, and numbers of chalazia were recorded. Data were analyzed using generalized linear models of the counts and the occurrences of chalazia. Hypotheses were tested using likelihood ratio tests appropriate for each type of data. Results: The study included 128 subjects, most of which were 1-3 years old. The majority of patients had bilateral chalazia (95.3%), and the proportions of patients with internal, external, and marginal chalazion differed dramatically (99.2%, 61.7%, and 2.3%, respectively). The number of internal and external chalazia did not vary significantly with gender, age, or residence of the patients. Internal chalazia were located more frequently in the upper lids (p<0.001). External chalazia showed no preference of localization. The average number of internal chalazia in each eyelid did not relate to the presence of external chalazia. Conclusions: Multiple chalazia are common among younger children in southeast China. The anatomical distribution varies depending on the type of chalazion. Multiple chalazia often occur bilaterally and internally. If doctors are more aware of the anatomical distribution of chalazia, this might result in a higher success rate of I&C.

Risk factors for obesity and overweight in Chinese children: a nationwide survey.

OBJECTIVE: This study aimed to analyze a comprehensive set of potential risk factors for obesity and overweight among Chinese children with a full range of ages and with wide geographical coverage. METHODS: In the Prevalence and Risk Factors for Obesity and Diabetes in Youth (PRODY) study (2017-2019), the authors analyzed 193,997 children aged 3 to 18 years from 11 provinces, autonomous regions, and municipalities that are geographically representative of China. All participants underwent physical examinations, and their caregivers completed questionnaires including dietary, lifestyle, familial, and perinatal information of participants. A multilevel multinomial logistic regression model was used to evaluate the potential risk factors. RESULTS: Among the actionable risk factors that were measured, higher consumption frequencies of animal offal (odds ratios [OR] for an additional time/day = 0.91, 95% CI: 0.88-0.95, same unit for OR below unless specified otherwise), dairy products (0.91, 95% CI: 0.88-0.94), freshwater products (0.94, 95% CI: 0.91-0.96), staple foods (0.94, 95% CI: 0.92-0.96), and coarse food grain (OR for every day vs. rarely = 0.92, 95% CI: 0.86-0.98) were associated with lower relative risk of obesity. However, higher restaurant-eating frequency (OR for >4 times/month vs. rarely = 1.21, 95% CI: 1.15-1.29) and longer screen-viewing duration (OR for >2 hours vs. <30 minutes = 1.16, 95% CI: 1.10-1.22) were associated with higher relative risk of obesity. Increased exercise frequency was associated with the lowest relative risk of obesity (OR for every day vs. rarely = 0.72, 95% CI: 0.68-0.77). CONCLUSIONS: Changes in lifestyle and diet of Chinese children may help relieve their obesity burden.

Risk factors for peripherally inserted central catheterization-associated bloodstream infection in neonates. / ç»å¤–å‘¨é™è„‰ç©¿åˆºä¸­å¿ƒé™è„‰ç½®ç®¡æ‚£å„¿ç›¸å ³æ€§è¡€æºæ„ŸæŸ“çš„å±é™©å› ç´ åˆ†æž.

OBJECTIVES: To study the features of catheter-related bloodstream infection (CRBSI) or central line-associated bloodstream infection (CLABSI) after peripherally inserted central catheterization (PICC) in neonates admitted to the neonatal intensive care unit (NICU) and the risk factors for CRBSI or CLABSI. METHODS: A retrospective analysis was performed on the medical data of the neonates who were treated and required PICC in the NICU of the Children's Hospital, Zhejiang University School of Medicine from June 1, 2018 to May 1, 2020. The catheterization-related data were collected, including placement time, insertion site, removal time, and antimicrobial lock of PICC. The multivariate logistic regression model was used to investigate the risk factors for CRBSI or CLABSI in the neonates. RESULTS: A total of 446 neonates were enrolled, with a mean gestational age of (30.8±4.0) weeks, a mean birth weight of (1 580±810) g, a median age of 9 days, and a median duration of PICC of 18 days. The incidence rates of CLABSI and CRBSI were 5.6 and 1.46 per 1 000 catheter days, respectively. Common pathogens for CLABSI caused by PICC included Staphylococcus epidermidis (n=19) and Klebsiella pneumoniae (n=11), and those for CRBSI caused by PICC included Klebsiella pneumoniae (n=6). The risk of CLABSI caused by PICC increased significantly with prolonged durations of PICC and antibiotic use, and the PICC-related infection probability at head and neck was significantly lower than that in the upper and low limbs (P<0.05), while the above conditions were more obvious in neonates with a birth weight of <1 500 g. The risk of CRBSI caused by PICC decreased with the increase in gestational age (P<0.05). CONCLUSIONS: CRBSI and CLABSI remain serious issues in NICU nosocomial infection. The identification of the risk factors for CRBSI and CLABSI provides a basis for improving the quality of clinical care and management.

Regional Disparities in Obesity Among a Heterogeneous Population of Chinese Children and Adolescents.

Importance: Obesity is a public health challenge in China, but the geographical profiles of overweight and obesity among Chinese children are limited. Objective: To examine regional disparities in the prevalence of obesity among the heterogeneous population of Chinese children and adolescents to provide a more accurate profile of obesity among children in China. Design, Setting, and Participants: The Prevalence and Risk Factors for Obesity and Diabetes in Youth (PRODY) study was a cross-sectional survey study conducted from January 1, 2017, to December 31, 2019, among 201 098 children aged 3 to 18 years from 11 provinces, autonomous regions, and municipalities that produced a sample of Chinese children with a full range of ages and wide geographical coverage using a multistage, stratified, cluster-sampling design. Exposures: Five regions geographically representative of China (northern, eastern, southern, western, and central). Main Outcomes and Measures: The body weights and heights of all participants were measured. Multilevel, multinomial logistic regression models were used to estimate the prevalence of overweight and obesity. Results: Among 201 098 healthy children (105 875 boys [52.6%]; mean [SD] age, 9.8 [3.8] years) from eastern, southern, northern, central, and western China, the highest obesity prevalence was estimated for children aged 8 to 13 years in northern China (from 18.8% [95% CI, 16.2%-21.7%] to 23.6% [95% CI, 20.5%-26.9%]) and for boys aged 3 to 6 years in western China (from 18.1% [95% CI, 10.4%-29.4%] to 28.6% [95% CI, 14.3%-49.0%]). Boys had a higher prevalence than girls of obesity only in eastern and northern China, with a mean difference in prevalence of 4.6% (95% CI, 3.8%-5.4%) and 7.6% (95% CI, 6.5%-8.6%), respectively. Conclusions and Relevance: In this survey study, substantial geographic disparities in the prevalence of obesity and overweight were found among the heterogeneous population of Chinese children. The results suggest that special attention should be paid to vulnerable children and that regionally adapted interventions are needed to efficiently mitigate obesity in children.

The Use of Morning Urinary Gonadotropins and Sex Hormones in the Management of Early Puberty in Chinese Girls.

CONTEXT: Although gonadotropin-releasing hormone stimulation test (GnRHST) is the gold standard in diagnosing central precocious puberty (CPP), it is invasive, expensive, and time-consuming, requiring multiple blood samples to measure gonadotropin levels. OBJECTIVE: We evaluated whether urinary hormones could be potential biomarkers for prepuberty or postpuberty, aiming to simplify the current diagnosis and prognosis procedure. METHODS: We performed a cross-sectional study of a total of 355 girls with CPP in National Clinical Research Center for Child Health in China, including 258 girls with positive and 97 girls with negative results from GnRHST. Twenty patients received GnRH analogue (GnRHa) treatment and completed a 6-month follow up. We measured luteinizing hormone (LH), follicle-stimulating hormone (FSH), estradiol, prolactin, progesterone, testosterone, and human chorionic gonadotropin in the first morning voided urine samples. RESULTS: Their urinary LH levels and the ratios of LH to FSH increased significantly with the advancement in Tanner stages. uLH levels were positively associated with basal and peak LH levels in the serum after GnRH stimulation. A cutoff value of 1.74 IU/L for uLH reached a sensitivity of 69.4% and a specificity of 75.3% in predicting a positive GnRHST result. For the combined threshold (uLH ≥ 1.74 + uLH-to-uFSH ratio > 0.4), the specificity reached 86.6%. After 3 months of GnRHa therapy, the uLH and uFSH levels decreased accordingly. CONCLUSION: uLH could be a reliable biomarker for initial CPP diagnosis and screening; uLH could also be an effective marker for evaluating the efficacy of clinical treatment.

Using environmental DNA and occupancy modelling to estimate rangewide metapopulation dynamics.

We demonstrate the power of combining two emergent tools for resolving rangewide metapopulation dynamics. First, we employed environmental DNA (eDNA) surveys to efficiently generate multiseason rangewide site occupancy histories. Second, we developed a novel dynamic, spatial multiscale occupancy model to estimate metapopulation dynamics. The model incorporates spatial relationships, explicitly accounts for non-detection bias and allows direct evaluation of the drivers of extinction and colonization. We applied these tools to examine metapopulation dynamics of endangered tidewater goby, a species endemic to California estuarine habitats. We analysed rangewide eDNA data from 190 geographically isolated sites (813 total water samples) surveyed from 2 years (2016 and 2017). Rangewide estimates of the proportion of sites that were occupied varied little between 2016 (0.52) and 2017 (0.51). However, there was evidence of extinction and colonization dynamics. The probability of extinction of an occupied site (0.106) and probability of colonization of an unoccupied site (0.085) were nearly equal. Stability in site occupancy proportions combined with nearly equal rates of extinction and colonization suggests a dynamic equilibrium between the 2 years surveyed. Assessment of covariate effects revealed that colonization probability increased as the number of occupied neighbouring sites increased and as distance between occupied sites decreased. We show that eDNA surveys can rapidly provide a snapshot of a species distribution over a broad geographic range and, when these surveys are paired with occupancy modelling, can uncover metapopulation dynamics and their drivers.

Objective prior distributions for Jolly-Seber models of zero-augmented data.

Statistical models of capture-recapture data that are used to estimate the dynamics of a population are known collectively as Jolly-Seber (JS) models. State-space versions of these models have been developed for the analysis of zero-augmented data that include the capture histories of the observed individuals and an arbitrarily large number of all-zero capture histories. The number of all-zero capture histories must be sufficiently large to include the unknown number N of individuals in the population that were ever alive during all sampling periods. This definition of N is equivalent to the "superpopulation" of individuals described in several JS models. To fit JS models of zero-augmented data, practitioners often assume a set of independent, uniform prior distributions for the recruitment parameters. However, if the number of capture histories is small compared to N, these uniform priors can exert considerable influence on the posterior distributions of N and other parameters because the uniform priors induce a highly skewed prior on N. In this article, I derive a class of prior distributions for the recruitment parameters of the JS model that can be used to specify objective prior distributions for N, including the discrete-uniform and the improper scale priors as special cases. This class of priors also may be used to specify prior knowledge about recruitment while still preserving the conditions needed to induce an objective prior on N. I use analyses of simulated and real data to illustrate the inferential benefits of this class of prior distributions and to identify circumstances where these benefits are most likely to be realized.

Environmental DNA sampling reveals high occupancy rates of invasive Burmese pythons at wading bird breeding aggregations in the central Everglades.

The Burmese python (Python bivittatus) is now established as a breeding population throughout south Florida, USA. However, the extent of the invasion, and the ecological impacts of this novel apex predator on animal communities are incompletely known, in large part because Burmese pythons (hereafter "pythons") are extremely cryptic and there has been no efficient way to detect them. Pythons are recently confirmed nest predators of long-legged wading bird breeding colonies (orders Ciconiiformes and Pelecaniformes). Pythons can consume large quantities of prey and may not be recognized as predators by wading birds, therefore they could be a particular threat to colonies. To quantify python occupancy rates at tree islands where wading birds breed, we utilized environmental DNA (eDNA) analysis-a genetic tool which detects shed DNA in water samples and provides high detection probabilities. We fitted multi-scale Bayesian occupancy models to test the prediction that pythons occupy islands with wading bird colonies at higher rates compared to representative control islands containing no breeding birds. Our results suggest that pythons are widely distributed across the central Everglades in proximity to active wading bird colonies. In support of our prediction that pythons are attracted to colonies, site-level python eDNA occupancy rates were higher at wading bird colonies (ψ = 0.88, 95% credible interval [0.59-1.00]) than at the control islands (ψ = 0.42 [0.16-0.80]) in April through June (n = 15 colony-control pairs). We found our water temperature proxy (time of day) to be informative of detection probability, in accordance with other studies demonstrating an effect of temperature on eDNA degradation in occupied samples. Individual sample concentrations ranged from 0.26 to 38.29 copies/µL and we generally detected higher concentrations of python eDNA in colony sites. Continued monitoring of wading bird colonies is warranted to determine the effect pythons are having on populations and investigate putative management activities.

A practical guide for combining data to model species distributions.

Understanding and accurately modeling species distributions lies at the heart of many problems in ecology, evolution, and conservation. Multiple sources of data are increasingly available for modeling species distributions, such as data from citizen science programs, atlases, museums, and planned surveys. Yet reliably combining data sources can be challenging because data sources can vary considerably in their design, gradients covered, and potential sampling biases. We review, synthesize, and illustrate recent developments in combining multiple sources of data for species distribution modeling. We identify five ways in which multiple sources of data are typically combined for modeling species distributions. These approaches vary in their ability to accommodate sampling design, bias, and uncertainty when quantifying environmental relationships in species distribution models. Many of the challenges for combining data are solved through the prudent use of integrated species distribution models: models that simultaneously combine different data sources on species locations to quantify environmental relationships for explaining species distribution. We illustrate these approaches using planned survey data on 24 species of birds coupled with opportunistically collected eBird data in the southeastern United States. This example illustrates some of the benefits of data integration, such as increased precision in environmental relationships, greater predictive accuracy, and accounting for sample bias. Yet it also illustrates challenges of combining data sources with vastly different sampling methodologies and amounts of data. We provide one solution to this challenge through the use of weighted joint likelihoods. Weighted joint likelihoods provide a means to emphasize data sources based on different criteria (e.g., sample size), and we find that weighting improves predictions for all species considered. We conclude by providing practical guidance on combining multiple sources of data for modeling species distributions.

Time series sightability modeling of animal populations.

Logistic regression models-or "sightability models"-fit to detection/non-detection data from marked individuals are often used to adjust for visibility bias in later detection-only surveys, with population abundance estimated using a modified Horvitz-Thompson (mHT) estimator. More recently, a model-based alternative for analyzing combined detection/non-detection and detection-only data was developed. This approach seemed promising, since it resulted in similar estimates as the mHT when applied to data from moose (Alces alces) surveys in Minnesota. More importantly, it provided a framework for developing flexible models for analyzing multiyear detection-only survey data in combination with detection/non-detection data. During initial attempts to extend the model-based approach to multiple years of detection-only data, we found that estimates of detection probabilities and population abundance were sensitive to the amount of detection-only data included in the combined (detection/non-detection and detection-only) analysis. Subsequently, we developed a robust hierarchical modeling approach where sightability model parameters are informed only by the detection/non-detection data, and we used this approach to fit a fixed-effects model (FE model) with year-specific parameters and a temporally-smoothed model (TS model) that shares information across years via random effects and a temporal spline. The abundance estimates from the TS model were more precise, with decreased interannual variability relative to the FE model and mHT abundance estimates, illustrating the potential benefits from model-based approaches that allow information to be shared across years.

ednaoccupancy: An r package for multiscale occupancy modelling of environmental DNA data.

In this article, we describe ednaoccupancy, an r package for fitting Bayesian, multiscale occupancy models. These models are appropriate for occupancy surveys that include three nested levels of sampling: primary sample units within a study area, secondary sample units collected from each primary unit and replicates of each secondary sample unit. This design is commonly used in occupancy surveys of environmental DNA (eDNA). ednaoccupancy allows users to specify and fit multiscale occupancy models with or without covariates, to estimate posterior summaries of occurrence and detection probabilities, and to compare different models using Bayesian model-selection criteria. We illustrate these features by analysing two published data sets: eDNA surveys of a fungal pathogen of amphibians and eDNA surveys of an endangered fish species.

A hierarchical model for estimating the spatial distribution and abundance of animals detected by continuous-time recorders.

MOTIVATION: Several spatial capture-recapture (SCR) models have been developed to estimate animal abundance by analyzing the detections of individuals in a spatial array of traps. Most of these models do not use the actual dates and times of detection, even though this information is readily available when using continuous-time recorders, such as microphones or motion-activated cameras. Instead most SCR models either partition the period of trap operation into a set of subjectively chosen discrete intervals and ignore multiple detections of the same individual within each interval, or they simply use the frequency of detections during the period of trap operation and ignore the observed times of detection. Both practices make inefficient use of potentially important information in the data. MODEL AND DATA ANALYSIS: We developed a hierarchical SCR model to estimate the spatial distribution and abundance of animals detected with continuous-time recorders. Our model includes two kinds of point processes: a spatial process to specify the distribution of latent activity centers of individuals within the region of sampling and a temporal process to specify temporal patterns in the detections of individuals. We illustrated this SCR model by analyzing spatial and temporal patterns evident in the camera-trap detections of tigers living in and around the Nagarahole Tiger Reserve in India. We also conducted a simulation study to examine the performance of our model when analyzing data sets of greater complexity than the tiger data. BENEFITS: Our approach provides three important benefits: First, it exploits all of the information in SCR data obtained using continuous-time recorders. Second, it is sufficiently versatile to allow the effects of both space use and behavior of animals to be specified as functions of covariates that vary over space and time. Third, it allows both the spatial distribution and abundance of individuals to be estimated, effectively providing a species distribution model, even in cases where spatial covariates of abundance are unknown or unavailable. We illustrated these benefits in the analysis of our data, which allowed us to quantify differences between nocturnal and diurnal activities of tigers and to estimate their spatial distribution and abundance across the study area. Our continuous-time SCR model allows an analyst to specify many of the ecological processes thought to be involved in the distribution, movement, and behavior of animals detected in a spatial trapping array of continuous-time recorders. We plan to extend this model to estimate the population dynamics of animals detected during multiple years of SCR surveys.

Detection limits of quantitative and digital PCR assays and their influence in presence-absence surveys of environmental DNA.

A set of universal guidelines is needed to determine the limit of detection (LOD) in PCR-based analyses of low-concentration DNA. In particular, environmental DNA (eDNA) studies require sensitive and reliable methods to detect rare and cryptic species through shed genetic material in environmental samples. Current strategies for assessing detection limits of eDNA are either too stringent or subjective, possibly resulting in biased estimates of species' presence. Here, a conservative LOD analysis grounded in analytical chemistry is proposed to correct for overestimated DNA concentrations predominantly caused by the concentration plateau, a nonlinear relationship between expected and measured DNA concentrations. We have used statistical criteria to establish formal mathematical models for both quantitative and droplet digital PCR. To assess the method, a new Grass Carp (Ctenopharyngodon idella) TaqMan assay was developed and tested on both PCR platforms using eDNA in water samples. The LOD adjustment reduced Grass Carp occupancy and detection estimates while increasing uncertainty-indicating that caution needs to be applied to eDNA data without LOD correction. Compared to quantitative PCR, digital PCR had higher occurrence estimates due to increased sensitivity and dilution of inhibitors at low concentrations. Without accurate LOD correction, species occurrence and detection probabilities based on eDNA estimates are prone to a source of bias that cannot be reduced by an increase in sample size or PCR replicates. Other applications also could benefit from a standardized LOD such as GMO food analysis and forensic and clinical diagnostics.

State-Dependent Resource Harvesting with Lagged Information about System States.

Markov decision processes (MDPs), which involve a temporal sequence of actions conditioned on the state of the managed system, are increasingly being applied in natural resource management. This study focuses on the modification of a traditional MDP to account for those cases in which an action must be chosen after a significant time lag in observing system state, but just prior to a new observation. In order to calculate an optimal decision policy under these conditions, possible actions must be conditioned on the previous observed system state and action taken. We show how to solve these problems when the state transition structure is known and when it is uncertain. Our focus is on the latter case, and we show how actions must be conditioned not only on the previous system state and action, but on the probabilities associated with alternative models of system dynamics. To demonstrate this framework, we calculated and simulated optimal, adaptive policies for MDPs with lagged states for the problem of deciding annual harvest regulations for mallards (Anas platyrhynchos) in the United States. In this particular example, changes in harvest policy induced by the use of lagged information about system state were sufficient to maintain expected management performance (e.g. population size, harvest) even in the face of an uncertain system state at the time of a decision.

Statistical Models for the Analysis and Design of Digital Polymerase Chain Reaction (dPCR) Experiments.

Statistical methods for the analysis and design of experiments using digital PCR (dPCR) have received only limited attention and have been misused in many instances. To address this issue and to provide a more general approach to the analysis of dPCR data, we describe a class of statistical models for the analysis and design of experiments that require quantification of nucleic acids. These models are mathematically equivalent to generalized linear models of binomial responses that include a complementary, log-log link function and an offset that is dependent on the dPCR partition volume. These models are both versatile and easy to fit using conventional statistical software. Covariates can be used to specify different sources of variation in nucleic acid concentration, and a model's parameters can be used to quantify the effects of these covariates. For purposes of illustration, we analyzed dPCR data from different types of experiments, including serial dilution, evaluation of copy number variation, and quantification of gene expression. We also showed how these models can be used to help design dPCR experiments, as in selection of sample sizes needed to achieve desired levels of precision in estimates of nucleic acid concentration or to detect differences in concentration among treatments with prescribed levels of statistical power.

Estimating the Effects of Habitat and Biological Interactions in an Avian Community.

We used repeated sightings of individual birds encountered in community-level surveys to investigate the relative roles of habitat and biological interactions in determining the distribution and abundance of each species. To analyze these data, we developed a multispecies N-mixture model that allowed estimation of both positive and negative correlations between abundances of different species while also estimating the effects of habitat and the effects of errors in detection of each species. Using a combination of single- and multispecies N-mixture modeling, we examined for each species whether our measures of habitat were sufficient to account for the variation in encounter histories of individual birds or whether other habitat variables or interactions with other species needed to be considered. In the community that we studied, habitat appeared to be more influential than biological interactions in determining the distribution and abundance of most avian species. Our results lend support to the hypothesis that abundances of forest specialists are negatively affected by forest fragmentation. Our results also suggest that many species were associated with particular types of vegetation as measured by structural attributes of the forests. The abundances of 6 of the 73 species observed in our study were strongly correlated. These species included large birds (American Crow (Corvus brachyrhynchos) and Red-winged Blackbird (Agelaius phoeniceus)) that forage on the ground in open habitats and small birds (Red-eyed Vireo (Vireo olivaceus), House Wren (Troglodytes aedon), Hooded Warbler (Setophaga citrina), and Prairie Warbler (Setophaga discolor)) that are associated with dense shrub cover. Species abundances were positively correlated within each size group and negatively correlated between groups. Except for the American Crow, which preys on eggs and nestlings of small song birds, none of the other 5 species is known to display direct interactions, so we suspect that the correlations may have been associated with species-specific responses to habitat components not adequately measured by our covariates.

Environmental DNA (eDNA) sampling improves occurrence and detection estimates of invasive burmese pythons.

Environmental DNA (eDNA) methods are used to detect DNA that is shed into the aquatic environment by cryptic or low density species. Applied in eDNA studies, occupancy models can be used to estimate occurrence and detection probabilities and thereby account for imperfect detection. However, occupancy terminology has been applied inconsistently in eDNA studies, and many have calculated occurrence probabilities while not considering the effects of imperfect detection. Low detection of invasive giant constrictors using visual surveys and traps has hampered the estimation of occupancy and detection estimates needed for population management in southern Florida, USA. Giant constrictor snakes pose a threat to native species and the ecological restoration of the Florida Everglades. To assist with detection, we developed species-specific eDNA assays using quantitative PCR (qPCR) for the Burmese python (Python molurus bivittatus), Northern African python (P. sebae), boa constrictor (Boa constrictor), and the green (Eunectes murinus) and yellow anaconda (E. notaeus). Burmese pythons, Northern African pythons, and boa constrictors are established and reproducing, while the green and yellow anaconda have the potential to become established. We validated the python and boa constrictor assays using laboratory trials and tested all species in 21 field locations distributed in eight southern Florida regions. Burmese python eDNA was detected in 37 of 63 field sampling events; however, the other species were not detected. Although eDNA was heterogeneously distributed in the environment, occupancy models were able to provide the first estimates of detection probabilities, which were greater than 91%. Burmese python eDNA was detected along the leading northern edge of the known population boundary. The development of informative detection tools and eDNA occupancy models can improve conservation efforts in southern Florida and support more extensive studies of invasive constrictors. Generic sampling design and terminology are proposed to standardize and clarify interpretations of eDNA-based occupancy models.