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.

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.

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.

Too risky to settle: avian community structure changes in response to perceived predation risk on adults and offspring.

Predation risk is widely hypothesized as an important force structuring communities, but this potential force is rarely tested experimentally, particularly in terrestrial vertebrate communities. How animals respond to predation risk is generally considered predictable from species life-history and natural-history traits, but rigorous tests of these predictions remain scarce. We report on a large-scale playback experiment with a forest bird community that addresses two questions: (i) does perceived predation risk shape the richness and composition of a breeding bird community? And (ii) can species life-history and natural-history traits predict prey community responses to different types of predation risk? On 9 ha plots, we manipulated cues of three avian predators that preferentially prey on either adult birds or offspring, or both, throughout the breeding season. We found that increased perception of predation risk led to generally negative responses in the abundance, occurrence and/or detection probability of most prey species, which in turn reduced the species richness and shifted the composition of the breeding bird community. Species-level responses were largely predicted from the key natural-history trait of body size, but we did not find support for the life-history theory prediction of the relationship between species' slow/fast life-history strategy and their response to predation risk.

Estimating abundance while accounting for rarity, correlated behavior, and other sources of variation in counts.

The class of N-mixture models allows abundance to be estimated from repeated, point count surveys while adjusting for imperfect detection of individuals. We developed an extension of N-mixture models to account for two commonly observed phenomena in point count surveys: rarity and lack of independence induced by unmeasurable sources of variation in the detectability of individuals. Rarity increases the number of locations with zero detections in excess of those expected under simple models of abundance (e.g., Poisson or negative binomial). Correlated behavior of individuals and other phenomena, though difficult to measure, increases the variation in detection probabilities among surveys. Our extension of N-mixture models includes a hurdle model of abundance and a beta-binomial model of detectability that accounts for additional (extra-binomial) sources of variation in detections among surveys. As an illustration, we fit this model to repeated point counts of the West Indian manatee, which was observed in a pilot study using aerial surveys. Our extension of N-mixture models provides increased flexibility. The effects of different sets of covariates may be estimated for the probability of occurrence of a species, for its mean abundance at occupied locations, and for its detectability.

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.

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.

Predicting the geographic distribution of a species from presence-only data subject to detection errors.

Several models have been developed to predict the geographic distribution of a species by combining measurements of covariates of occurrence at locations where the species is known to be present with measurements of the same covariates at other locations where species occurrence status (presence or absence) is unknown. In the absence of species detection errors, spatial point-process models and binary-regression models for case-augmented surveys provide consistent estimators of a species' geographic distribution without prior knowledge of species prevalence. In addition, these regression models can be modified to produce estimators of species abundance that are asymptotically equivalent to those of the spatial point-process models. However, if species presence locations are subject to detection errors, neither class of models provides a consistent estimator of covariate effects unless the covariates of species abundance are distinct and independently distributed from the covariates of species detection probability. These analytical results are illustrated using simulation studies of data sets that contain a wide range of presence-only sample sizes. Analyses of presence-only data of three avian species observed in a survey of landbirds in western Montana and northern Idaho are compared with site-occupancy analyses of detections and nondetections of these species.

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.

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.

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.

Models for inference in dynamic metacommunity systems.

A variety of processes are thought to be involved in the formation and dynamics of species assemblages. For example, various metacommunity theories are based on differences in the relative contributions of dispersal of species among local communities and interactions of species within local communities. Interestingly, metacommunity theories continue to be advanced without much empirical validation. Part of the problem is that statistical models used to analyze typical survey data either fail to specify ecological processes with sufficient, complexity or they fail to account for errors in detection of species during sampling. In this paper, we describe a statistical modeling framework for the analysis of metacommunity dynamics that is based on the idea of adopting a unified approach, multispecies occupancy modeling, for computing inferences about individual species, local communities of species, or the entire metacommunity of species. This approach accounts for errors in detection of species during sampling and also allows different metacommunity paradigms to be specified in terms of species- and location-specific probabilities of occurrence, extinction, and colonization: all of which are estimable. In addition, this approach can be used to address inference problems that arise in conservation ecology, such as predicting temporal and spatial changes in biodiversity for use in making conservation decisions. To illustrate, we estimate changes in species composition associated with the species-specific phenologies of flight patterns of butterflies in Switzerland for the purpose of estimating regional differences in biodiversity.

A new parameterization for estimating co-occurrence of interacting species.

Models currently used to estimate patterns of species co-occurrence while accounting for errors in detection of species can be difficult to fit when the effects of covariates on species occurrence probabilities are included. The source of the estimation problems is the particular parameterization used to specify species co-occurrence probability. We develop a new parameterization for estimating patterns of co-occurrence of interacting species that allows the effects of covariates to be specified quite naturally without estimation problems. In our model, the occurrence of one species is assumed to depend on the occurrence of another, but the occurrence of the second species is not assumed to depend on the presence of the first species. This pattern of co-occurrence, wherein one species is dominant and the other is subordinate, can be produced by several types of ecological interactions (predator-prey, parasitism, and so on). A simulation study demonstrated that estimates of species occurrence probabilities were unbiased in samples of 50-100 locations and three surveys per location, provided species are easily detected (probability of detection > or = 0.5). Higher sample sizes (>200 locations) are needed to achieve unbiasedness when species are more difficult to detect. An analysis of data from treefrog surveys in southern Florida indicated that the occurrence of Cuban treefrogs, an invasive predator species, was highest near the point of its introduction and declined with distance from that location. Sites occupied by Cuban treefrogs were 9.0 times less likely to contain green treefrogs and 15.7 times less likely to contain squirrel treefrogs compared to sites without Cuban treefrogs. The detection probabilities of native treefrog species did not depend on the presence of Cuban treefrogs, suggesting that the native treefrog species are naive to the introduced species.

Species richness and occupancy estimation in communities subject to temporary emigration.

Species richness is the most common biodiversity metric, although typically some species remain unobserved. Therefore, estimates of species richness and related quantities should account for imperfect detectability. Community dynamics can often be represented as superposition of species-specific phenologies (e.g., in taxa with well-defined flight [insects], activity [rodents], or vegetation periods [plants]). We develop a model for such predictably open communities wherein species richness is expressed as the sum over observed and unobserved species of estimated species-specific and site-specific occurrence indicators and where seasonal occurrence is modeled as a species-specific function of time. Our model is a multispecies extension of a multistate model with one unobservable state and represents a parsimonious way of dealing with a widespread form of "temporary emigration." For illustration we use Swiss butterfly monitoring data collected under a robust design (RD); species were recorded on 13 transects during two secondary periods within < or = 7 primary sampling periods. We compare estimates with those under a variation of the model applied to standard data, where secondary samples are pooled. The latter model yielded unrealistically high estimates of total community size of 274 species. In contrast, estimates were similar under models applied to RD data with constant (122) or seasonally varying (126) detectability for each species, but the former was more parsimonious and therefore used for inference. Per transect, 6-44 (mean 21.1) species were detected. Species richness estimates averaged 29.3; therefore only 71% (range 32-92%) of all species present were ever detected. In any primary period, 0.4-5.6 species present were overlooked. Detectability varied by species and averaged 0.88 per primary sampling period. Our modeling framework is extremely flexible; extensions such as covariates for the occurrence or detectability of individual species are easy. It should be useful for communities with a predictable form of temporary emigration where rigorous estimation of community metrics has proved challenging so far.

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.

Modeling unobserved sources of heterogeneity in animal abundance using a Dirichlet process prior.

In surveys of natural populations of animals, a sampling protocol is often spatially replicated to collect a representative sample of the population. In these surveys, differences in abundance of animals among sample locations may induce spatial heterogeneity in the counts associated with a particular sampling protocol. For some species, the sources of heterogeneity in abundance may be unknown or unmeasurable, leading one to specify the variation in abundance among sample locations stochastically. However, choosing a parametric model for the distribution of unmeasured heterogeneity is potentially subject to error and can have profound effects on predictions of abundance at unsampled locations. In this article, we develop an alternative approach wherein a Dirichlet process prior is assumed for the distribution of latent abundances. This approach allows for uncertainty in model specification and for natural clustering in the distribution of abundances in a data-adaptive way. We apply this approach in an analysis of counts based on removal samples of an endangered fish species, the Okaloosa darter. Results of our data analysis and simulation studies suggest that our implementation of the Dirichlet process prior has several attractive features not shared by conventional, fully parametric alternatives.

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.