The global AI Age Detector Software market is experiencing robust growth, projected to reach $202 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 6.3% from 2025 to 2033. This expansion is driven by increasing demand for accurate age verification across diverse sectors. Businesses leverage this technology for age-restricted content access, preventing underage gambling and alcohol sales, while household applications focus on parental control and enhanced security features. The market's segmentation reveals a strong preference for cloud-based solutions due to their scalability and cost-effectiveness, compared to on-premises deployments. Leading players like FaceFirst, NEC Corporation, and Microsoft are actively shaping the market landscape through continuous innovation and strategic partnerships. The market is geographically diverse, with North America holding a significant market share due to early adoption and advanced technological infrastructure. However, rapid growth is anticipated in Asia Pacific regions like China and India fueled by increasing smartphone penetration and a growing awareness of AI-driven solutions. Challenges remain in areas such as data privacy concerns, the need for robust regulatory frameworks, and ensuring the accuracy and fairness of age detection algorithms across diverse demographics. The future trajectory of the AI Age Detector Software market hinges on several key factors. Advancements in deep learning and computer vision algorithms will further enhance accuracy and efficiency. Increased integration with other security systems and platforms will broaden application possibilities. The growing focus on ethical considerations and responsible AI development will drive the creation of more transparent and unbiased age detection technologies. Addressing concerns surrounding data privacy and security will be crucial for market expansion and consumer trust. Competition among existing players and the emergence of new entrants will continue to shape the market dynamics, leading to price optimization and technological advancements. The continuous evolution of regulatory landscapes will also play a significant role in shaping market access and growth opportunities.

Population Density

## Overview

 Population Density is a dataset for object detection tasks - it contains Crowd People annotations for 2,581 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

We developed a model for analyzing multi-year demographic data for long-lived animals and used data from a population of Agassiz’s desert tortoise (Gopherus agassizii) at the Desert Tortoise Research Natural Area in the western Mojave Desert of California, USA, as a case study. The study area was 7.77 square kilometers and included two locations: inside and outside the fenced boundary. The wildlife-permeable, protective fence was designed to prevent entry from vehicle users and sheep grazing. We collected mark-recapture data from 1,123 tortoises during 7 annual surveys consisting of two censuses each over a 34-year period. We used a Bayesian modeling framework to develop a multistate Jolly-Seber model because of its ability to handle unobserved (latent) states and modified this model to incorporate the additional data from non-survey years. For this model we incorporated 3 size-age states (juvenile, immature, adult), sex (female, male), two location states (inside and outside the fenced boundary) and 3 survival states (not-yet-entered, entered/alive, and dead/removed). We calculated population densities and estimated probabilities of growth of the tortoises from one size-age state to a larger size-age state, survival after 1 year and 5 years, and detection. Our results show a declining population with low estimates for survival after 1 year and 5 years. The probability for tortoises to move from outside to inside the boundary fence was greater than for tortoises to move from inside the fence to outside. The probability for detecting tortoises differed by size-age state and was lowest for the smallest tortoises and highest for the adult tortoises. The framework for the model can be used to analyze other animal populations where vital rates are expected to vary depending on multiple individual states. The model was incorporated into the manuscript that included several other databases for publication in Wildlife Monographs in 2020 by Berry et al.

Demographics

## Overview

Demographics is a dataset for object detection tasks - it contains Demographics annotations for 1,016 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

the file contains the demographic and clinical scores to provide further information to the uploaded data set

Aerial imagery of savanna wildlife counts used to automatically detect elephant, giraffe, and zebra with a deep learning algorithm

Description of the data The data described here were produced from the ANR projects ADAPTOM (ANR-13-ADAP-0013) and TomEpiSet (ANR-16-CE20-0014). An 8-way tomato MAGIC population was phenotyped over 12 environments including three geographical location (France, Israel and Morocco) and four conditions (control, and water-deficit, high-temperature and salinity stress). A set of 397 MAGIC lines were genotyped for 1345 markers, used together with the phenotypic traits for linkage mapping analysis. Genotype-by-environment interaction (GxE) was evaluated and phenotypic plasticity computed through different statistical models. Each file in the dataset has its own description below. • Phenotype files The Phenotypes files contain the 10 phenotypic traits that were evaluated. Phenotypic data averaged per genotype and environment are in the file “Phenotype_per_Environment”. The input phenotypes for the linkage mapping analysis are in the file “Pheno_Input_QTL_detection”. They represent for each trait the estimated average performance, slope and variance from the Finlay & Wilkinson regression model and sensitivity to environmental covariates from the factorial regression model, respectively. • MAGIC Genotyping information This file presents the genetic map with 1345 SNP markers used in linkage mapping analyses. The genotypic information of the eight founders and 397 MAGIC lines are also presented • Daily recorded climactic parameters This file presents the daily climatic parameters recorded within the greenhouses. The different parameters were computed over 24 hours. • Custom R script for the two-stage analysis of GxE The file “Two-stage-analysis_magicMET.txt” contains the custom R script used for analysis of factorial regression and Finlay-Wilkinson regression models. Average performance and plasticity parameters were derived from these analyses. Example have been given for fruit weight phenotype averaged per genotype and environment. The input file “Var_environment_P2P3” presents the average climatic parameters used particularly for the factorial regression model. • Custom R script for QEI modelling The files “QEI_Glbal_marker_effect_model5.txt” and “QEI_main_plus_interactive_effect_model6.txt” describe the custom R script used for the detection of interactive QTLs (QEI). Example of fruit weight phenotype have been developed. The input files for the script are “FW_pheno_GxE.csv”, the average phenotypic data per genotype and environment for fruit weight example and the parental haplotype probabilities “Proba_parents.txt” that were computed from R/qtl2 package with the function calc_genoprob. The “Geno_ID.csv” file gives the correspondence between genotype name and ID.

Please note: This is a Synthetic data file, also known as a Dummy file - it is not real data. This synthetic file should not be used for purposes other than to develop an test computer programs that are to be submitted by remote access. Each record in the synthetic file matches the format and content parameters of the real Statistics Canada Master File with which it is associated, but the data themselves have been 'made up'. They do NOT represent responses from real individuals and should NOT be used for actual analysis. These data are provided solely for the purpose of testing statistical package 'code' (e.g. SPSS syntax, SAS programs, etc.) in preperation for analysis using the associated Master File in a Research Data Centre, by Remote Job Submission, or by some other means of secure access. If statistical analysis 'code' works with the synthetic data, researchers can have some confidence that the same code will run successfully against the Master File data in the Resource Data Centres. In the fall of 1991, the National Health Information Council recommended that an ongoing national survey of population health be conducted. This recommendation was based on consideration of the economic and fiscal pressures on the health care systems and the requirement for information with which to improve the health status of the population in Canada. Commencing in April 1992, Statistics Canada received funding for development of a National Population Health Survey (NPHS). The NPHS collects information related to the health of the Canadian population and related socio-demographic information to: aid in the development of public policy by providing measures of the level, trend and distribution of the health status of the population, provide data for analytic studies that will assist in understanding the determinants of health, and collect data on the economic, social, demographic, occupational and environmental correlates of health. In addition the NPHS seeks to increase the understanding of the relationship between health status and health care utilization, including alternative as well as traditional services, and also to allow the possibility of linking survey data to routinely collected administrative data such as vital statistics, environmental measures, community variables, and health services utilization. The NPHS collects information related to the health of the Canadian population and related socio-demographic information. It is composed of three components: the Households, the Health Institutions, and the North components. The Household component started in 1994/1995 and is conducted every two years. The first two cycles (1994/1995, 1996/1997) were both cross-sectional and longitudinal. The NPHS longitudinal sample includes 17,276 persons from all ages in 1994/1995 and these same persons are to be interviewed every two years. Each cycle, a common set of health questions is asked to the respondents. This allows for the analysis of changes in the health of the respondents over time. In addition to the common set of questions, the questionnaire does include focus content and supplements that change from cycle to cycle. Health Canada, Public Health Agency of Canada and provincial ministries of health use NPHS longitudinal data to plan, implement and evaluate programs and health policies to improve health and the efficiency of health services. Non-profit health organizations and researchers in the academic fields use the information to move research ahead and to improve health.

Several different unsupervised anomaly detection algorithms have been applied to Space Shuttle Main Engine (SSME) data to serve the purpose of developing a comprehensive suite of Integrated Systems Health Management (ISHM) tools. As the theoretical bases for these methods vary considerably, it is reasonable to conjecture that the resulting anomalies detected by them may differ quite significantly as well. As such, it would be useful to apply a common metric with which to compare the results. However, for such a quantitative analysis to be statistically significant, a sufficient number of examples of both nominally categorized and anomalous data must be available. Due to the lack of sufficient examples of anomalous data, use of any statistics that rely upon a statistically significant sample of anomalous data is infeasible. Therefore, the main focus of this paper will be to compare actual examples of anomalies detected by the algorithms via the sensors in which they appear, as well the times at which they appear. We find that there is enough overlap in detection of the anomalies among all of the different algorithms tested in order for them to corroborate the severity of these anomalies. In certain cases, the severity of these anomalies is supported by their categorization as failures by experts, with realistic physical explanations. For those anomalies that can not be corroborated by at least one other method, this overlap says less about the severity of the anomaly, and more about their technical nuances, which will also be discussed.

Fall Detection Alert System Market Outlook

The global fall detection alert system market size was valued at approximately $520 million in 2023 and is projected to reach around $1.24 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.1% during the forecast period. This significant growth is driven by an increasing aging population, rising awareness about the importance of senior safety, and technological advancements in medical alert systems.

One of the primary drivers of the fall detection alert system market is the burgeoning elderly population worldwide. With advancements in healthcare leading to increased life expectancy, there is a growing demographic of older adults who are more susceptible to falls. This demographic shift necessitates reliable and efficient fall detection systems to ensure the safety and well-being of the elderly, prompting healthcare providers and senior living facilities to adopt these systems. Additionally, the high costs associated with fall-related injuries further underscore the economic imperative for effective fall detection solutions.

Technological innovations are also significantly propelling market growth. Integration of advanced technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) into fall detection systems has enhanced their accuracy and reliability. These technologies enable real-time monitoring and immediate alert generation, thereby reducing response times and potentially saving lives. Furthermore, the development of compact, user-friendly, and highly sensitive devices has made fall detection systems more accessible to a broader audience, including those living independently.

Moreover, increasing awareness and proactive measures by governments and healthcare organizations are contributing to market growth. Campaigns and programs aimed at educating the public about the risks of falls and the benefits of early detection have led to higher adoption rates of fall detection systems. Regulatory support and funding initiatives for elderly care and safety technologies further drive market expansion, providing a conducive environment for the proliferation of these systems.

Regionally, the market outlook varies, with North America and Europe being the leading regions in terms of market share. These regions have well-established healthcare infrastructures and higher adoption rates of advanced medical technologies. The Asia Pacific region, however, is expected to witness the highest growth rate, driven by a rapidly aging population, increasing healthcare expenditure, and growing awareness about elderly care solutions. Latin America, the Middle East, and Africa are also showing promising growth, albeit at a slower pace, due to improving healthcare systems and rising awareness.

In addition to fall detection systems, the integration of a Drowning Detection System in various environments is gaining attention. This system is particularly crucial in settings such as swimming pools, recreational water parks, and residential areas with water features, where the risk of drowning incidents is significant. The Drowning Detection System utilizes advanced technologies like computer vision and AI to monitor water bodies and detect signs of distress or submersion. By providing real-time alerts to caregivers and lifeguards, these systems enhance safety and enable rapid response to potential drowning incidents. As awareness of water safety grows, the demand for such systems is expected to rise, complementing the market for fall detection solutions and expanding the scope of safety technologies.

Product Type Analysis

The fall detection alert system market can be segmented by product type into wearable devices and non-wearable devices. Wearable devices, such as smartwatches, pendants, and belts equipped with fall detection technology, dominate the market. These devices are favored for their portability, ease of use, and continuous monitoring capabilities. The integration of fall detection systems with wearable fitness trackers and health monitoring devices further boosts their appeal, as users can benefit from a wide range of health metrics in a single device. The wearable segment is expected to continue its dominance, driven by ongoing innovations and increasing adoption among tech-savvy older adults.

Non-wearable devices, which include floor sensors, wall-mounted units, and cameras, also play a crucial role in

Large-scale disturbances, such as megafires, motivate restoration at equally large extents. Measuring the survival and growth of individual plants plays a key role in current efforts to monitor restoration success. However, the scale of modern restoration (e.g., >10,000 ha) challenges measurements of demographic rates with field data. In this study, we demonstrate how unoccupied aerial system (UAS) flights can provide an efficient solution to the tradeoff of precision and spatial extent in detecting demographic rates from the air. We flew two, sequential UAS flights at two sagebrush (Artemisia tridentata) common gardens to measure the survival and growth of individual plants. The accuracy of Bayesian-optimized segmentation of individual shrub canopies was high (73–95%, depending on the year and site), and remotely sensed survival estimates were within 10% of ground-truthed survival censuses. Stand age structure affected remotely sensed estimates of growth; growth was overestimated relative to field-based estimates by 57% in the first garden with older stands, but agreement was high in the second garden with younger stands. Further, younger stands (similar to those just after disturbance) with shorter, smaller plants were sometimes confused with other shrub species and bunchgrasses, demonstrating a need for integrating spectral classification approaches that are increasingly available on affordable UAS platforms. The older stand had several merged canopies, which led to an underestimation of abundance but did not bias remotely sensed survival estimates. Advances in segmentation and UAS structure from motion photogrammetry will enable demographic rate measurements at management-relevant extents.

The zip files contains 12338 datasets for outlier detection investigated in the following papers:(1) Instance space analysis for unsupervised outlier detection Authors : Sevvandi Kandanaarachchi, Mario A. Munoz, Kate Smith-Miles (2) On normalization and algorithm selection for unsupervised outlier detection Authors : Sevvandi Kandanaarachchi, Mario A. Munoz, Rob J. Hyndman, Kate Smith-MilesSome of these datasets were originally discussed in the paper: On the evaluation of unsupervised outlier detection:measures, datasets and an empirical studyAuthors : G. O. Campos, A, Zimek, J. Sander, R. J.G.B. Campello, B. Micenkova, E. Schubert, I. Assent, M.E. Houle.

Pitfalls and windfalls of detecting demographic declines using population genetics in long-lived species

https://doi.org/10.5061/dryad.w0vt4b91p

This repository details the generation and analysis of simulated data for exploring the application of age-aware sampling to detecting demographic declines. There is no empirical data associated with this study, but simulated datafiles are uploaded and detailed below. All code required to reproduce analyses in the paper are below. Please reach out to Meaghan with questions at meaghaniclark (at) gmail.com.

Data

pWF_slim_output.tar.gz
nWF_slim_output_2.tar.gz
nWF_slim_output_5.tar.gz
nWF_slim_output_10.tar.gz
nWF_slim_output_20.tar.gz

These directories contain simulated data output from slim. Perennial model outputs ("nWF") are split by average age. File names denote the average age, bottleneck severity, and replicate number in that order for the...

Introduction

Gas Sensors Statistics: Gas sensors detect and measure specific gases in diverse environments using principles like chemical reactions, physical adsorption, and optical absorption.

Types include electrochemical, catalytic, infrared, semiconductor, and photoionization detectors, each tailored to specific gas types and applications.

Electrochemical sensors offer sensitivity and low power usage for portability, while catalytic sensors excel in industrial settings detecting combustible gases.

Infrared sensors identify gases like carbon dioxide, semiconductor sensors are versatile and cost-effective, and photoionization detectors specialize in volatile organic compound detection.

Widely used in industrial safety, environmental monitoring, automotive emissions control, healthcare, and indoor air quality assessment.

Gas sensors significantly contribute to safety, efficiency, and environmental preservation. Key selection factors include target gases, detection range, environmental conditions, response time, and cost-effectiveness.

Please note: This is a Synthetic data file, also known as a Dummy file - it is not real data. This synthetic file should not be used for purposes other than to develop an test computer programs that are to be submitted by remote access. Each record in the synthetic file matches the format and content parameters of the real Statistics Canada Master File with which it is associated, but the data themselves have been 'made up'. They do NOT represent responses from real individuals and should NOT be used for actual analysis. These data are provided solely for the purpose of testing statistical package 'code' (e.g. SPSS syntax, SAS programs, etc.) in preperation for analysis using the associated Master File in a Research Data Centre, by Remote Job Submission, or by some other means of secure access. If statistical analysis 'code' works with the synthetic data, researchers can have some confidence that the same code will run successfully against the Master File data in the Resource Data Centres. In the fall of 1991, the National Health Information Council recommended that an ongoing national survey of population health be conducted. This recommendation was based on consideration of the economic and fiscal pressures on the health care systems and the requirement for information with which to improve the health status of the population in Canada. Commencing in April 1992, Statistics Canada received funding for development of a National Population Health Survey (NPHS). The NPHS collects information related to the health of the Canadian population and related socio-demographic information to: aid in the development of public policy by providing measures of the level, trend and distribution of the health status of the population, provide data for analytic studies that will assist in understanding the determinants of health, and collect data on the economic, social, demographic, occupational and environmental correlates of health. In addition the NPHS seeks to increase the understanding of the relationship between health status and health care utilization, including alternative as well as traditional services, and also to allow the possibility of linking survey data to routinely collected administrative data such as vital statistics, environmental measures, community variables, and health services utilization. The NPHS collects information related to the health of the Canadian population and related socio-demographic information. It is composed of three components: the Households, the Health Institutions, and the North components. The Household component started in 1994/1995 and is conducted every two years. The first three cycles (1994/1995, 1996/1997, 1997/1998) were both cross-sectional and longitudinal. The NPHS longitudinal sample includes 17,276 persons from all ages in 1994/1995 and these same persons are to be interviewed every two years. Beginning in Cycle 4 (2000/2001) the survey became strictly longitudinal (collecting health information from the same individuals each cycle). The cross-sectional and longitudinal documentation of the Household component is presented separately as well as the documentation for the Health Institutions and North components. The cross-sectional component of the Population Health Survey Program has been taken over by the Canadian Community Health Survey (CCHS). With the introduction of the Canadian Community Health Survey (CCHS), there were many changes to the 2000-2001 National Population Health Survey - Household questionnaire. Since NPHS is strictly a longitudinal survey, some content was migrated to the CCHS (such as the two-week disability section and certain questions on place where health care was provided) or was dropped (e.g. certain chronic conditions), while the order of the questionnaire changed. As only the longitudinal respondent is now surveyed, it was no longer necessary to distinguish between the General questionnaire and the Health component. Health Canada, Public Health Agency of Canada and provincial ministries of health use NPHS longitudinal data to plan, implement and evaluate programs and health policies to improve health and the efficiency of health services. Non-profit health organizations and researchers in the academic fields use the information to move research ahead and to improve health.

This dataset provides a detailed look into transactional behavior and financial activity patterns, ideal for exploring fraud detection and anomaly identification. It contains 2,512 samples of transaction data, covering various transaction attributes, customer demographics, and usage patterns. Each entry offers comprehensive insights into transaction behavior, enabling analysis for financial security and fraud detection applications.

Key Features:

• TransactionID: Unique alphanumeric identifier for each transaction.
• AccountID: Unique identifier for each account, with multiple transactions per account.
• TransactionAmount: Monetary value of each transaction, ranging from small everyday expenses to larger purchases.
• TransactionDate: Timestamp of each transaction, capturing date and time.
• TransactionType: Categorical field indicating 'Credit' or 'Debit' transactions.
• Location: Geographic location of the transaction, represented by U.S. city names.
• DeviceID: Alphanumeric identifier for devices used to perform the transaction.
• IP Address: IPv4 address associated with the transaction, with occasional changes for some accounts.
• MerchantID: Unique identifier for merchants, showing preferred and outlier merchants for each account.
• AccountBalance: Balance in the account post-transaction, with logical correlations based on transaction type and amount.
• PreviousTransactionDate: Timestamp of the last transaction for the account, aiding in calculating transaction frequency.
• Channel: Channel through which the transaction was performed (e.g., Online, ATM, Branch).
• CustomerAge: Age of the account holder, with logical groupings based on occupation.
• CustomerOccupation: Occupation of the account holder (e.g., Doctor, Engineer, Student, Retired), reflecting income patterns.
• TransactionDuration: Duration of the transaction in seconds, varying by transaction type.
• LoginAttempts: Number of login attempts before the transaction, with higher values indicating potential anomalies.

This dataset is ideal for data scientists, financial analysts, and researchers looking to analyze transactional patterns, detect fraud, and build predictive models for financial security applications. The dataset was designed for machine learning and pattern analysis tasks and is not intended as a primary data source for academic publications.

According to a survey conducted in 2022, the majority of public K-12 teachers reported that their school had procedures to keep exterior doors locked during school hours, and had locks or other measures for interior doors, at 91 percent and 90 percent respectively. However, only eight percent indicated that their schools had metal detectors or other screening at school entrances in place. In comparison, teachers in higher-poverty schools, in schools located in urban areas, and in schools which primarily served students of color were more likely to report that their schools had metal detectors or other screening in place.

Population Screening Market Outlook

The global population screening market size was valued at approximately USD 22 billion in 2023 and is projected to reach around USD 42 billion by 2032, growing at a CAGR of 7.5% during the forecast period. The market's growth is primarily driven by increasing awareness about early disease detection, advancements in screening technologies, and a growing emphasis on preventive healthcare.

One of the key growth factors in the population screening market is the rising prevalence of chronic diseases. With an aging global population and lifestyle changes, there is an increasing incidence of diseases such as cancer, cardiovascular conditions, and diabetes. Early detection through screening programs can significantly improve treatment outcomes and reduce healthcare costs. Governments and healthcare organizations are thus investing heavily in screening programs to manage and mitigate these diseases effectively.

Technological advancements in screening methods are also playing a vital role in market expansion. Innovations in genetic testing, imaging technologies, and biomarker identification have made screenings more accurate, less invasive, and more accessible. These advancements not only enhance the early detection of diseases but also expand the range of conditions that can be screened. For instance, liquid biopsy and next-generation sequencing (NGS) are revolutionizing cancer screening by enabling the detection of genetic mutations and biomarkers from a simple blood sample.

Another significant driver is the increase in government initiatives and public health campaigns promoting preventive healthcare. Many countries have implemented nationwide screening programs for diseases such as breast cancer, cervical cancer, and colorectal cancer. These initiatives are often supported by substantial funding and public awareness campaigns, which have led to higher participation rates and, consequently, a larger market for screening services. The integration of screening programs into routine healthcare check-ups has also contributed to market growth.

Medical Screen procedures are becoming increasingly integral to the healthcare landscape, offering a proactive approach to identifying potential health issues before they manifest into serious conditions. These screenings encompass a variety of tests and assessments designed to detect early signs of diseases, enabling timely intervention and management. The growing emphasis on preventive healthcare has led to the widespread adoption of medical screens, as they provide valuable insights into an individual's health status. With advancements in technology, medical screens have become more sophisticated, offering greater accuracy and reliability. This evolution in screening techniques is crucial for improving patient outcomes and reducing the burden on healthcare systems.

Regionally, North America holds the largest share in the population screening market, driven by advanced healthcare infrastructure, high healthcare expenditure, and robust government initiatives. Europe follows closely, with many countries implementing comprehensive screening programs. However, the Asia Pacific region is expected to exhibit the highest growth rate due to increasing healthcare investments, a growing middle-class population, and rising awareness about preventive healthcare. Countries like China and India are rapidly expanding their screening capabilities to manage their large and diverse populations.

Test Type Analysis

The population screening market by test type can be segmented into genetic screening, blood tests, imaging, and others. Genetic screening is gaining significant traction due to its ability to identify individuals at high risk for certain genetic disorders and cancers. Technological advancements in genetic testing, such as next-generation sequencing (NGS) and CRISPR, have made these tests more accurate and cost-effective. These advancements have also expanded the scope of genetic screening to include a wider range of conditions, thereby driving market growth.

Blood tests remain a cornerstone of population screening due to their non-invasive nature and ability to detect a variety of conditions, from metabolic disorders to infectious diseases. Innovations in biomarker discovery have enhanced the sensitivity and specificity of blood tests, making them more reliable for early disease detection. The adoption of liquid biopsy for cancer screenin

Detection of plant individuals is imperfect. Not accounting for this issue can result in biased estimates of demographic parameters as important as population growth rates. In mobile organisms, a common practice is to explicitly account for detection probability during the estimation of most demographic parameters, but no study in plant populations has examined the consequences of ignoring imperfect detectability on the estimation of population growth rates. The lack of accounting for detection probability occurs because plant demographers have frequently assumed that detection is perfect, and because there is a scarcity of studies that formally compare the performance of estimation methods that incorporate detection probabilities with respect to methods that ignore detectabilities. Based on field data of five plant species and data simulations, we compared the performance of three methods that estimate population growth rates, two that do not estimate detection probabilities (direct counts of individuals and the minimum-number-alive method) and the other that explicitly accounts for detection probabilities (temporal symmetry models). Our aims were (1) to estimate detection probabilities, and (2) to evaluate the performance of these three methods by calculating bias, accuracy, and precision in their estimates of population growth rates. Our five plant species had imperfect detection. Estimates of population growth rates that explicitly incorporate detectabilities had better performance (less biased estimates, with higher accuracy and precision) than those obtained with the two methods that do not calculate detection probabilities. In these latter methods, bias increases as detection probability decreases. Our findings highlight the importance of using robust analytical methods that account for detection probability of plants during the estimation of critical demographic parameters such as population growth rates. In this way, estimates of plant population parameters will reliably indicate their actual status and quantitative trends.

Introduction

Microfluidics Statistics: Microfluidics is the manipulation of small fluid volumes at the microscale. Offering advantages such as reduced reagent usage with enhanced control, with applications spanning biomedicine, drug delivery, chemistry, and materials science.

Microfluidic systems consist of microchannels, valves, pumps, and sensors fabricated using techniques like soft lithography.

Challenges include scaling for industrial use, standardization, and system robustness. Despite challenges, microfluidics has profoundly impacted research and industries. Revolutionizing fields like healthcare and materials science by exploiting unique microscale fluid behaviors.