Population Estimates Research Articles

Population size and structure of beisa oryx and gerenuk in Geralle National Park, southeastern Ethiopia.

The study was conducted to determine the population size of endangered Oryx beisa (Rüppell, 1835), and near-threatened Litocranius walleri (Brooke, 1878) of uncertain global population estimates in Geralle National Park, southeastern Ethiopia. Systematic line transects were established with a transect length range of 2.3 to 6.8 km long (a total of 165.4 km long with a sighting distance of 150 m after truncation). The combination of AIC and chi-square p-values was used as model selection criteria for density/population size estimation in distance sampling software. The lowest AIC, ∆AIC (close to zero), and Chi-square tests (p-value > .05) were selected with adequate model fit. The minimum observation was 67 individuals of beisa oryx in the dry season. The maximum observation was 349 individuals of gerenuk during the wet season. The minimum detection probability of oryx was in the wet season (p â = 76 ± 26), and the minimum detection probability of gerenuk was (p â = 75 ± 1) in both seasons. The two-season pooled density analyzed for studied species indicated (0.85 ± 0.34, 1.24 ± 0.47, beisa oryx/km2), and (3.82 ± 0.6, 4.88 ± 0.7 gerenuk/km2) in dry and wet seasons, respectively. It can be concluded from the results of the study that GNP is home to previously undiscovered healthy populations of the endangered beisa oryx and near-threatened gerenuk. So it is recommended to undergo in-depth population studies, including other species available in the national park and their habitat components, so as to design sound, sustainable conservation measures for the wildlife resources in the area.

Adaptive image processing embedding to make the ecological tasks of deep learning more robust on camera traps images

Camera traps serve as a valuable tool for wildlife monitoring, generating a vast collection of images for ecologists to conduct ecological investigations, such as species identification and population estimation. However, the sheer volume of images poses a challenge, and the integration of deep learning into automated ecological investigation tasks remains complex, particularly when dealing with low-quality images in long-term monitoring programs. Existing approaches often struggle to strike a balance between image enhancement and deep learning for ecological tasks, thereby overlooking crucial information contained within low-quality images. This research introduces a pioneering adaptive image processing module (AIP) that seamlessly incorporates image processing into camera trap ecological tasks, elevating the performance of wildlife monitoring activities. Specifically, a differentiable image processing (DIP) module is presented to enhance low-quality images, with its parameters predicted by a Non-local based parameter predictor (NLPP). Additionally, an end-to-end approach based on hybrid data containing both original and synthetic data is proposed, encompassing adaptive image processing methods and downstream tasks for camera traps, adaptable to various scenarios. This approach effectively reduces the manual labor and time required for professional image processing. When applied to real-world camera trap images and synthetic image datasets, our method achieves an accuracy of 92.26% and 86.65% in classifying wildlife, respectively, demonstrating its robustness. By outperforming alternative methods under harsh conditions, the application of the adaptive image processing module instills greater confidence in deep learning applications within complex environments.

Late Holocene vegetation dynamics: degree and regional patterns of the Dark Ages woodland regeneration (ad 300–700) in the Netherlands

AbstractDuring the Dark Ages, which include the Late Roman period (ad 270–450) and the Early Middle Ages (ad 450–1050), large-scale vegetation development in northwest Europe was characterised by widespread regeneration of woodlands. This regeneration phase represents a break from the extensive reduction in woodland from human activities in late Holocene vegetation history. In the Netherlands, possible causes for this reversal can be found in a decreased human impact on the landscape, a colder and possibly wetter climate, and geomorphological changes. This paper presents a synthesis from 38 pollen records from a range of sites across the Netherlands, from which regional differences in the degree of woodland regeneration and vegetation composition before and during the Dark Ages have been identified. Both original data as well as vegetation abundance modelled by REVEALS were used to assess the regrowth of the woodland cover. The observed trends were considered in terms of the landscape setting and population estimates in order to disentangle the relative importance of these forcing factors. The results indicate that landscape and human impact were the most important factors determining the vegetation structure and changes to it. The pollen data show that in the Netherlands, the clearance of woodland in the Roman period followed by its regrowth afterwards were greatest in the river area. This can be linked to a high population density there during the Roman period followed by a strong decrease of population in the Dark Ages and to increased flooding.

Geographic principles applied to population dynamics: A spatially interpolated integrated population model

Abstract A major impediment to wildlife conservation and management, from a quantitative perspective, is dealing with high degrees of uncertainty associated with population estimates. Integrated population models (IPMs) can help alleviate that challenge, but they are often limited to narrow spatial or temporal windows owing to the financial and logistical burdens of acquiring requisite datasets. To expand the spatiotemporal scope of practical IPM implementation, we developed a novel method that expresses demographic relatedness among sampled and unsampled locations using geographic principles of spatial autocorrelation. We interpolated demographic parameters at unsampled locations using parameter estimates from data‐informed locations. Errors attributable to the interpolative process were corrected using a joint likelihood and locally recorded count data (‘cheaper’ and broadly distributed). We evaluated the spatially interpolated IPM (SIIPM) for precision and accuracy under variable levels of spatial autocorrelation using simulated data and a Leave‐One‐Out Cross‐Validation (LOOCV) technique. Conventional IPMs and state‐space models (SSM) were fit to the same simulated datasets to provide a comparative assessment of the novel method. In a final, empirical demonstration we fit the SIIPM to data collected from Greater Sage‐Grouse (Centrocercus urophasianus; sage‐grouse) populations located in Nevada, U.S.A. during 2013–2021. SIIPMs outperformed conventional IPMs when fit to data possessing moderate‐to‐high levels of spatial autocorrelation. Under moderate levels of autocorrelation, the average improvement in parameter estimation was 13.6% for survival, 65.3% for recruitment and 23.7% for rate of population change (). When spatial autocorrelation was low, the SIIPM still outperformed contemporary approaches in areas that were geographically close (<67 km) to sampling locations. Under low autocorrelation‐near distance scenarios, we observed SIIPM parameters that were 30.8% (recruitment), 32.5% (; IPM comparison) and 54.0% (; SSM comparison) more precise than contemporary models. Spatial autocorrelation is often assumed but rarely tested when comparing population dynamics across regions of large geographic extent. We demonstrated that SIIPMs can improve precision of species' vital rate estimation when extrapolating model inference beyond populations for which long‐term monitoring data exists. Specific to sage‐grouse, these results support previous conclusions of broad‐scale spatial autocorrelation in population dynamics and a reproductive‐survival trade‐off previously documented at smaller scales.

State-level population estimates of sexual minority adolescents in the United States: A predictive modeling study

State-level population estimates of sexual minority adolescents in the United States: A predictive modeling study

Population and spatial dynamics of desert bighorn sheep in Grand Canyon during an outbreak of respiratory pneumonia

IntroductionTerrestrial species in riverine ecosystems face unique constraints leading to diverging patterns of population structure, connectivity, and disease dynamics. Desert bighorn sheep (Ovis canadensis nelsoni) in Grand Canyon National Park, a large native population in the southwestern USA, offer a unique opportunity to evaluate population patterns and processes in a remote riverine system with ongoing anthropogenic impacts. We integrated non-invasive, invasive, and citizen-science methods to address questions on abundance, distribution, disease status, genetic structure, and habitat fragmentation.MethodsWe compiled bighorn sightings collected during river trips by park staff, commercial guides, and private citizens from 2000–2018 and captured bighorn in 2010–2016 to deploy GPS collars and test for disease. From 2011–2015, we non-invasively collected fecal samples and genotyped them at 9–16 microsatellite loci for individual identification and genetic structure. We used assignment tests to evaluate genetic structure and identify subpopulations, then estimated gene flow and recent migration to evaluate fragmentation. We used spatial capture-recapture to estimate annual population size, distribution, and trends after accounting for spatial variation in detection with a resource selection function model.Results and discussionFrom 2010–2018, 3,176 sightings of bighorn were reported, with sightings of 56–145 bighorn annually on formal surveys. From 2012–2016, bighorn exhibiting signs of respiratory disease were observed along the river throughout the park. Of 25 captured individuals, 56% were infected by Mycoplasma ovipneumoniae, a key respiratory pathogen, and 81% were recently exposed. Pellet sampling for population estimation from 2011–2015 yielded 1,250 genotypes and 453 individuals. We detected 6 genetic clusters that exhibited mild to moderate genetic structure (FST 0.022–0.126). The river, distance, and likely topography restricted recent gene flow, but we detected cross-river movements in one section via genetic recaptures, no subpopulation appeared completely isolated, and genetic diversity was among the highest reported. Recolonization of one large stretch of currently empty habitat appears limited by the constrained topology of this system. Annual population estimates ranged 536–552 (95% CrI range 451–647), lamb:ewe ratios varied, and no significant population decline was detected. We provide a multi-method sampling framework useful for sampling other wildlife in remote riverine systems.

Family Planning Practices, Knowledge, Decision Making, and Factors Associated with Unplanned Pregnancies among Women Working in A Selected Garment Industry in Urban Bengaluru, India

Background: According to UN population estimates, India has surpassed China as the world’s most populated nation, marking the biggest upheaval in global demography since records have been kept. Providing universal family planning services is an important strategy to reduce maternal morbidity and to control population growth. Even though the country has multiple programs addressing family planning issues, they are missing out on working women. Thus, this study aims to estimate the prevalence of unmet needs of family planning and determinants for improper access to family planning methods among women working in a selected Garment industry in Urban Bengaluru. Methodology: A community based cross-sectional study was conducted in a selected garment factory for a period of two months. Women workers in Reproductive age group (18-49 years) and who gave voluntary consent to participate in the study were taken up. Total sample of 454 women were included. The data was collected using a semi-structured and validated questionnaire. Collected data was entered in Microsoft Excel and was analysed using SPSS version 16. Results: Among 454 participants, 42.3% (n=192) of them had used contraceptive method. Out of 192 women, most of them (72.9%, n=140) had chosen tubectomy as the contraceptive measure. Among 407 women, 14.3% (n=58), 13.3% (n=42), and 20.9% (n=9) of them had not planned for their 1st pregnancy, 2nd pregnancy, and 3rd pregnancy respectively. Conclusion: Less than half of them had used any form of contraceptive method. Among that half, nearly three-fourths had chosen tubectomy.

Advancing Finite Population Inference: Integrating Sample Surveys and Big Data

"Traditional sample surveys, emphasizing large sample sizes and robust finite population estimates through probability sampling designs, have been a staple in official statistics. However, modernization efforts are underway as statistical agencies explore the integration of big data and web panel information for near real-time estimations. This paper reviews the challenges associated with these endeavours, particularly addressing statistical biases linked to under-coverage in big data and errors in data variables. Kim and Tam (2020) introduced data integration methods, treating big data as an incomplete sampling frame and utilizing calibration weighting. The paper systematically reviews various integration methods, including mass imputation, propensity score, and calibration weighting. Additionally this paper concludes with a simulation study evaluating the performance of Kim and Tam's (2020) proposed estimators, assessing Bias, Standard Error (SE), and Root Mean Square Error (RMSE). The findings contribute to the on-going discourse on modernizing survey methodologies and leveraging diverse data sources for more efficient and timely official statistics."

The cost of providing care by family and friends (informal care) in the last year of life: A population observational study

Introduction: Little is known about replacement costs of care provided by informal carers during the last year of life for people dying of cancer and non-cancer diseases. Aim: To estimate informal caregiving costs and explore the relationship with carer and decedent characteristics. Design: National observational study of bereaved carers. Questions included informal end-of-life caregiving into the 2017 Health Survey for England including estimated recalled frequency, duration and intensity of care provision. We estimated replacement costs for a decedent’s last year of life valuing time at the price of a substitutable activity. Spearman rank correlations and multivariable linear regression were used to explore relationships with last year of life costs. Setting/participants: Adult national survey respondents – England. Results: A total of 7997 adults were interviewed from 5767/9612 (60%) of invited households. Estimated replacement costs of personal care and other help were £27,072 and £13,697 per carer and a national cost of £13.2 billion and £15.5 billion respectively. Longer care duration and intensity, older age, death at home (lived together), non-cancer cause of death and greater deprivation were associated with increased costs. Female sex, and not accessing ‘other care services’ were related to higher costs for other help only. Conclusion: We provide a first adult general population estimate for replacement informal care costs in the last year of life of £41,000 per carer per decedent and highlight characteristics associated with greater costs. This presents a major challenge for future universal care coverage as the pool of people providing informal care diminish with an ageing population.

Prevalence Estimation of Dementia/Alzheimer’s Disease Using Health and Retirement Study Database in the United States

BackgroundUpdated prevalence estimates along the continuum of Alzheimer’s disease (AD) can foster a more nuanced and effective approach to managing AD within the current healthcare landscape.ObjectivesThis study aims to estimate the prevalence and severity distribution of dementia/AD (including mild, moderate, and severe stages) and all-cause mild cognitive impairment (MCI) in the United States using data from the Health and Retirement Study (HRS).DesignRetrospective study.SettingData from the bi-annual HRS surveys involving in-depth interviews of a representative sample of Americans aged >50 years.ParticipantsDementia/AD and all-cause MCI patients from the 4 most recent HRS surveys (2014, 2016, 2018 and 2020).MeasurementsAD was identified based on diagnosis (self-report). Cognitive performance (modified Telephone Interview of Cognitive Status [TICS-m]) scores in the dementia/AD range were also captured; all-cause MCI was similarly identified using the TICS-m. Dementia/AD and MCI prevalence, as well as the distribution by dementia/AD stage (mild, moderate, or severe), were estimated. Sampling weights developed by HRS were applied to ensure the sample’s representativeness of the target population and unbiased estimates for population parameters.ResultsAcross the four HRS surveys, the total number of HRS respondents ranged from 15,000 to 21,000 (unweighted); 7,000 to 14,000 had TICS-m scores. The estimated prevalence of AD (all severity categories combined) in the 2014, 2016, 2018, and 2020 HRS surveys was 1.2%, 1.2%, 1.3% and 1.0%, respectively using the diagnosis-based approach; using the cognitive performance-based approach, 23–27% patients had scores in the dementia/AD ranges across the 4 surveys. The estimated prevalence of all-cause MCI was consistently 23% in each survey. In the 2020 survey, the distribution of mild, moderate, and severe disease stages was 34%, 45%, and 21%, respectively, in patients self-reporting an AD diagnosis, and 55%, 40%, and 5%, respectively in all patients meeting TICS-m threshold for dementia/AD.ConclusionThe prevalence of AD diagnosis based on self-report was approximately 1% across the 4 most recent HRS surveys and may reflect the proportion of patients who have actively sought healthcare for AD. Among HRS survey respondents with cognitive scores available, over 20% were in the dementia/AD range. The distribution of disease by stage differed for self-reported AD diagnosis vs dementia/AD based on cognitive scores. Discordance in estimates of dementia/AD and stage distributions underscores a need for better understanding of clinical practice patterns in AD diagnosis, use of clinical assessment tools, and severity classification in the United States. Accurate patient identification is needed, especially early in the AD disease continuum, to allow for timely and appropriate initiation of new anti-amyloid treatments.

Economic analysis of foregoing Rh immunoglobulin for bleeding in pregnancy

ObjectivesTo perform cost analyses of foregoing RhD blood type testing and administration of Rh immunoglobulin (RhIg) for bleeding in pregnancy at <12 weeks gestation in the United States. Study DesignWe created a decision-analytic model comparing the current standard treatment pathway for patients who have threatened, spontaneous, or induced abortion in the United States, with a new pathway foregoing RhD testing and administration of RhIg for those who are RhD-negative at <12 weeks gestation, assuming that the risk of sensitization is 0%. We derived population and cost estimates from current literature and calculated the number needed to treat, and number needed to screen, to avoid one case of fatal hemolytic disease of the fetus and newborn. We performed sensitivity analyses assuming Rh-sensitization risks of 1.5% and 3% and varying the subsequent pregnancy rates from 44-100%. ResultsThe annual savings to healthcare payers in the United States of foregoing RhD testing and RhIg administration to RhD-negative patients with bleeding events at <12 weeks is $5.5 million/100,000 total pregnancies, assuming that the risk of sensitization is 0%. In the sensitivity analyses with sensitization risk 1.5% and subsequent pregnancy rate 84.3%, foregoing Rh testing and RhIg administration <12 weeks would save $2.8 million/100,000 pregnancies, with a corresponding number needed to treat of 7,322 and number needed to screen of 48,816. At a 3% sensitization rate, the current standard treatment pathway is the most economical. ConclusionsThere is an opportunity to save the United States healthcare payers as much as $5.5 million/100,000 pregnancies by withholding RhIg in specific situations and conserving it for use later in pregnancy. ImplicationsCost analyses support foregoing RhD blood type screening and RhIg administration at <12 weeks gestation if the sensitization rate is <3%. By de-implementing this low value care, payers in the United States can save as much as $5.5 million/100,000 pregnancies and conserve RhIg for use later in pregnancy.

Multiple Imputation of Race and Hispanic Ethnicity in National Surveillance Data for Chlamydia, Gonorrhea, and Syphilis.

Disease burden of sexually transmitted infections such as chlamydia, gonorrhea, and syphilis is often compared across age categories, sex categories, and race and ethnicity categories. Missing data may prevent researchers from accurately characterizing health disparities between populations. This article describes the methods used to impute race and Hispanic ethnicity in a large national surveillance data set. All US cases of chlamydia, gonorrhea, and syphilis (excluding congenital syphilis) reported through the National Notifiable Diseases Surveillance System from the year 2019 were included in the analyses. We used fully conditional specification to impute missing race and Hispanic ethnicity data. After imputation, reported case rates were calculated, by disease, for each race and Hispanic ethnicity category using Vintage 2019 Population and Housing Unit Estimates from the US Census. We then used case counts from subsets that contained only complete race and Hispanic ethnicity information to investigate if the confidence intervals from the multiply imputed data included the observed number of cases in each race and Hispanic ethnicity category. Among the 2,553,038 cases reported in 2019, race and Hispanic ethnicity were multiply imputed for 9% of syphilis cases, 22% of gonorrhea cases, and 33% of chlamydia cases. In the subset analyses, every nonzero rate of reported cases was contained within the confidence intervals that were calculated from multiply imputed data. Confidence intervals that account for the uncertainty of the predictions are an advantage of multiple imputation over complete-case analysis because a realistic variance estimate allows for valid hypothesis testing results.

Sustainable Food Supply: The Interplay between Population Growth and Land Productivity Changes as a Pathway to 2030 and Beyond for Nigeria

Context and Background: Sustainable food supply is a critical global challenge, particularly in developing countries like Nigeria, where rapid population growth and limited land resources create a complex interplay with far-reaching implications. Thus, there will be a need to increase food production to keep pace with the population increase. Goal and Objectives: This study seeks to analyse the interplay between population growth and land productivity changes in Nigeria as a pathway towards achieving sustainable food supply by 2030 and beyond, in line with Goal 2 of the 2030 Sustainable Development Goals (SDGs) which is aimed at “ending hunger, achieving food security, improving nutrition, and promoting sustainable agriculture. Methodology: Data on land productivity and Nigeria's population and Population growth for 1961-2022 was sourced from the Economic Research Service of the United States Department of Agriculture (ERS-USDA, 2023). The data was analysed using descriptive statistics such as mean; standard errors and graphs; the Autoregressive Integrated Moving Average (ARIMA) was used to explain the interplay between population growth and land productivity changes in Nigeria. Results: The results of the population estimates revealed that the model successfully converged and significantly (p&lt;0) explained 98.3% of the variance of the endogenous variable that is, the population at an intercept of -2500.7. Of the hypothesized variables, year (1.212), cropland (9.14E-4), irrigated (0.283), and pasture (0.003). These findings imply that the future population would increase in each period until the series becomes explosive because, the coefficient of the lagged variable of population is positive and greater than 1, which is in contrast with the assumptions of the ARIMA model which should be less than 1. Thus, the study recommends adopting precision agriculture techniques such as GPS mapping, soil analysis, crop health monitoring, and automated irrigation to maximize crop yields, reduce costs, and transform farming practices in Nigeria.

Formation of left-handed helices by C2'-fluorinated nucleic acids under physiological salt conditions.

Recent findings in cell biology have rekindled interest in Z-DNA, the left-handed helical form of DNA. We report here that two minimally modified nucleosides, 2'F-araC and 2'F-riboG, induce the formation of the Z-form under low ionic strength. We show that oligomers entirely made of these two nucleosides exclusively produce left-handed duplexes that bind to the Zα domain of ADAR1. The effect of the two nucleotides is so dramatic that Z-form duplexes are the only species observed in 10 mM sodium phosphate buffer and neutral pH, and no B-form is observed at any temperature. Hence, in contrast to other studies reporting formation of Z/B-form equilibria by a preference for purine glycosidic angles in syn, our NMR and computational work revealed that sequential 2'F…H2N and intramolecular 3'H…N3' interactions stabilize the left-handed helix. The equilibriumbetween B- and Z- forms is slow in the 19F NMR time scale (≥ms), and each conformation exhibited unprecedented chemical shift differences in the 19F signals. This observation led to a reliable estimation of the relative population of B and Z species and enabled us to monitor B-Z transitions under different conditions. The unique features of 2'F-modified DNA should thus be a valuable addition to existing techniques for specific detection of new Z-binding proteins and ligands.

A dataset of human capital-weighted population estimates for 185 countries from 1970 to 2100

We provide a novel dataset of human capital-weighted population size (HCWP) for 185 countries from 1970 to 2100. HCWP summarizes a population’s productive capacity and human capital heterogeneity in a single metric, enabling comparisons across countries and over time. The weights are derived from Mincerian earnings functions applied to multi-country census data on educational attainment. The model used to compute the returns to schooling accounts for the diminishing positive relative relationship between education and wages as the overall education of populations rises. The population weights are adjusted by a skills assessment factor representing differences in education quality across countries and years. HCWP is calculated by applying these adjusted human capital weights to population estimates and projections disaggregated by age, sex and education, spanning the period 1970–2020 and 2020–2100 for five Shared Socioeconomic Pathway scenarios. Validation analyses demonstrate the utility of the new HCWP data in explaining national income trends. As a more comprehensive population measure than basic size and age-sex indicators, HCWP enhances the power of statistical models aimed at the assessment of socioeconomic change impacts and forecasting.

The dance of neurons: Exploring nonlinear dynamics in brain networks

The brain is a complex, nonlinear system, exhibiting ever-evolving patterns of activities, whether in the presence or absence of external stimuli or task demands. Nonlinearity can notably obscure the link between structural constraints enforced on the interaction and its dynamical consequences. Suitable nonlinear dynamical models and their analysis serve as essential tools not only for bridging structural and functional understanding of the brain but also for predictably altering the complex dynamical organization of the brain. Here, starting from a large-scale network of threshold Hodgkin–Huxley style neurons, we formulate the average nonlinear dynamics implicitly following from the Wilson–Cowan assumptions. We investigate the influence of biophysical and structural properties on the complexity of neural dynamics at the microscale level and its relationship with the macroscopic Wilson–Cowan model. Incorporating the elements in the model can help identify more realistic regimes of activity and connect the mathematical prediction of increasing nonlinearity to physical manipulations. Our simulations of the temporal profiles reveal dependency on the binary state of interacting subpopulations and the random property of structural network at the transition points, when different synaptic weights are considered. For substantial configurations of stimulus intensity, our model provides further estimates of the neural population’s dynamics, ranging from simple-periodic to aperiodic patterns and phase transition regimes. This reflects the potential contribution of the microscopic nonlinear scheme to the mean-field approximation in studying the collective behaviour of individual neurons with particularly concentrating on the occurrence of critical phenomena. We show that finite-size effects kick the system in a state of irregular modes to evolve differently from predictions of the original Wilson–Cowan reference. Additionally, we report that the complexity and temporal diversity of neural dynamics, especially in terms of limit cycle trajectory, and synchronization can be induced by either small heterogeneity in the degree of various types of local excitatory connectivity or considerable diversity in the external drive to the excitatory pool.

Disturbance of hibernating bats due to researchers entering caves to conduct hibernacula surveys

Estimating population changes of bats is important for their conservation. Population estimates of hibernating bats are often calculated by researchers entering hibernacula to count bats; however, the disturbance caused by these surveys can cause bats to arouse unnaturally, fly, and lose body mass. We conducted 17 hibernacula surveys in 9 caves from 2013 to 2018 and used acoustic detectors to document cave-exiting bats the night following our surveys. We predicted that cave-exiting flights (i.e., bats flying out and then back into caves) of Townsend’s big-eared bats (Corynorhinus townsendii) and western small-footed myotis (Myotis ciliolabrum) would be higher the night following hibernacula surveys than on nights following no surveys. Those two species, however, did not fly out of caves more than predicted the night following 82% of surveys. Nonetheless, the activity of bats flying out of caves following surveys was related to a disturbance factor (i.e., number of researchers × total time in a cave). We produced a parsimonious model for predicting the probability of Townsend’s big-eared bats flying out of caves as a function of disturbance factor and ambient temperature. That model can be used to help biologists plan for the number of researchers, and the length of time those individuals are in a cave to minimize disturbing bats.

Uncertainty and precaution in hunting wolves twice in a year: Reanalysis of Treves and Louchouarn.

Management of wolves is controversial in many jurisdictions where wolves live, which underscores the importance of rigor, transparency, and reproducibility when evaluating outcomes of management actions. Treves and Louchouarn 2022 (hereafter TL) predicted outcomes for various fall 2021 hunting scenarios following Wisconsin's judicially mandated hunting and trapping season in spring 2021, and concluded that even a zero harvest scenario could result in the wolf population declining below the population goal of 350 wolves specified in the 1999 Wisconsin wolf management plan. TL further concluded that with a fall harvest of > 16 wolves there was a "better than average possibility" that the wolf population size would decline below that 350-wolf threshold. We show that these conclusions are incorrect and that they resulted from mathematical errors and selected parameterizations that were consistently biased in the direction that maximized mortality and minimized reproduction (i.e., positively biased adult mortality, negatively biased pup survival, further halving pup survival to November, negatively biased number of breeding packs, and counting harvested wolves twice among the dead). These errors systematically exaggerated declines in predicted population size and resulted in erroneous conclusions that were not based on the best available or unbiased science. Corrected mathematical calculations and more rigorous parameterization resulted in predicted outcomes for the zero harvest scenario that more closely coincided with the empirical population estimates in 2022 following a judicially prevented fall hunt in 2021. Only in scenarios with simulated harvest of 300 or more wolves did probability of crossing the 350-wolf population threshold exceed zero. TL suggested that proponents of some policy positions bear a greater burden of proof than proponents of other positions to show that "their estimates are accurate, precise, and reproducible". In their analysis, TL failed to meet this standard that they demanded of others.

Coverage of Indoor Smoking and Vaping Restrictions in the U.S., 1990–2021

Secondhand smoke exposure increases the risk of premature death and disease in children and non-smoking adults. As a result, many US states and local jurisdictions have enacted comprehensive indoor smoking restrictions (ISR). Indoor vaping restrictions (IVR) have also been adopted to protect against exposure to secondhand e-cigarette aerosol. This study aimed to quantify state and national US coverage of policies restricting indoor cigarette and e-cigarette use over time. Data from the American Nonsmokers Rights' Foundation on US ISR from 1990 to 2021 and IVR from 2006 to 2021 were analyzed. Combining these data with 2015 US Census population estimates, the percentage of state and national residents covered by partial and comprehensive restrictions in bars, restaurants, and workplaces, were calculated (analysis in 2023-2024) over time. Between 1990-2021, national coverage of comprehensive ISR increased for bars (0% to 67.3%), restaurants (0% to 78.2%), and workplaces (0% to 77.5%). Partial ISR coverage decreased for bars (14.8% to 13.9%), restaurants (40.2% to 15.4%) and workplaces (40.2% to 22.5%). From 2006 to 2021, comprehensive IVR coverage increased for bars (0% to 43.5%), restaurants (0% to 51.5%), and workplaces (0% to 53.2%). Despite these increases in coverage, by the end of 2021, <50% of the population was protected by comprehensive ISR for bars, restaurants, and workplaces in 19, 12, and 14 states, respectively. The percentage of the US population protected by ISR and IVR has increased over time. However, gaps in coverage remain, which may contribute to disparities in tobacco-related disease and death.

Acoustic estimation of the manatee population and classification of call categories using artificial intelligence

The population sizes of manatees in many regions remain largely unknown, primarily due to the challenging nature of conducting visual counts in turbid and inaccessible aquatic environments. Passive acoustic monitoring has shown promise for monitoring manatees in the wild. In this study, we present an innovative approach that leverages a convolutional neural network (CNN) for the detection, isolation and classification of manatee vocalizations from long-term audio recordings. To improve the effectiveness of manatee call detection and classification, the CNN works in two phases. First, a long-term audio recording is divided into smaller windows of 0.5 seconds and a binary decision is made as to whether or not it contains a manatee call. Subsequently, these vocalizations are classified into distinct vocal classes (4 categories), allowing for the separation and analysis of signature calls (squeaks). Signature calls are further subjected to clustering techniques to distinguish the recorded individuals and estimate the population size. The CNN was trained and validated using audio recordings from three different zoological facilities with varying numbers of manatees. Three different clustering methods (community detection with two different classifiers and HDBSCAN) were tested for their suitability. The results demonstrate the ability of the CNN to accurately detect manatee vocalizations and effectively classify the different call categories. In addition, our study demonstrates the feasibility of reliable population size estimation using HDBSCAN as clustering method. The integration of CNN and clustering methods offers a promising way to assess manatee populations in visually challenging and inaccessible regions using autonomous acoustic recording devices. In addition, the ability to differentiate between call categories will allow for ongoing monitoring of important information such as stress, arousal, and calf presence, which will aid in the conservation and management of manatees in critical habitats.