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Spring mowing in May and June is one of the main causes of mortality of roe deer fawns in agricultural regions. Knowing the exact birth distribution of fawns is important to guide farmers in their pre‐mowing precautions to avoid fawn deaths. Wildlife volunteers searching fields prior to mowing can act as citizen scientists by producing data sets of rescued fawns and their approximate age at find. However, due to weather‐dependent searches, the corresponding birth distributions can be highly skewed. We simulated virtual field data to examine the shortcomings of such data sources and introduced two algorithms for reconstructing reliable birth distribution parameters (mean and standard deviation) based on skewed samples. We found that weather‐dependent search data biased the calculated means and standard deviations by up to 14 and 5 days, respectively. However, the use of the proposed advanced algorithms (Grid Search and Machine Learning) resulted in better estimates of the sample means and standard deviations by reducing the root‐mean‐square error by 65% and 80% respectively. Furthermore, the Grid Search algorithm was able to capture birth distribution parameters based on real citizen science data in Bavaria, Germany, from 2021, which are close to the results of more systematic samples of the same year. The simulation exercise highlighted the shortcomings and discrepancies of using non‐probabilistic samples, for example on the occasion of mowing activities, to study demographic parameters compared to the true simulated distribution. Yet, the proposed algorithms can address these drawbacks and potentially turn citizen science data into an important data source for wildlife studies. This could ultimately help reduce wildlife losses during the mowing season by better knowing the distribution of births in a region. Zusammenfassung Die Frühjahrsmahd im Mai und Juni ist eine der Hauptursachen für die Rehkitzsterblichkeit in landwirtschaftlichen Gebieten. Für den effektiven Einsatz von Kitzrettungsmaßnahmen ist es wichtig, die genaue Geburtenverteilung der Kitze zu kennen, um die Landwirt:innen entsprechend anzuleiten. Freiwillige Wildtierretter:innen, die Felder vor dem Mähen absuchen, können gleichzeitig als Bürgerwissenschaftler:innen Datensätze zu geretteten Kitzen und deren ungefährem Alter beim Fund sammeln. Aufgrund der wetterabhängigen Mahd und der damit verbundenen Kitzsuche können die entsprechenden Geburtenverteilungen jedoch stark verzerrt sein. Wir haben deshalb anhand virtueller (simulierter) Rehkitzfunddaten die Unzulänglichkeiten solcher Datenquellen evaluiert. Des Weiteren haben wir zwei Algorithmen entwickelt, um zuverlässige Parameter der Geburtenverteilung (Mittelwert und Standardabweichung) auf der Grundlage solcher verzerrten Stichproben zu rekonstruieren. Die Simulationen zeigten, dass wetterabhängige Suchdaten die berechneten Mittelwerte und Standardabweichungen um bis zu 14 bzw. 5 Tage verzerrten. Die Anwendung der vorgeschlagenen Algorithmen (Grid Search und Machine Learning) führte jedoch zu besseren Schätzungen der Mittelwerte und Standardabweichungen der Stichprobe, sodass der RMSE auf 65 % bzw. 80 % reduziert wurde. Darüber hinaus konnte der Grid‐Search‐Algorithmus die Parameter der Geburtenverteilung auf der Grundlage realer Citizen‐Science‐Daten aus Bayern im Jahr 2021 so abschätzen, dass sie den Ergebnissen systematischerer Stichproben desselben Jahres nahekamen. Die Simulationsstudie zeigte deutliche Mängel und Diskrepanzen, wenn ‐ im Vergleich zu den simulierten „wahren“ Verteilungen ‐ nicht‐probabilistische Stichproben zur Ableitung demographischer Parameter verwendet werden. Die vorgestellten Algorithmen können diese Nachteile jedoch beheben und damit bürgerwissenschaftliche Daten zu einer wichtigen Datenquelle für Wildtierstudien machen. Dies könnte letztendlich dazu beitragen, Wildtierverluste während der Frühjahrsmahd zu verringern, da die Verteilung der Geburten in einer Region besser bekannt ist. Spring mowing is one of the main causes of mortality in roe deer Capreolus capreolus L. fawns. Wildlife volunteers searching fields before mowing can function as citizen scientists producing data sets of saved fawns and their approximate age at find. Yet, searches and thus samples are confined to suitable weather conditions for mowing. Here, we characterized the error of approximating the population's mean and standard deviation of the breeding distribution from such sporadically sampled data. We further developed two algorithms to retrieve better estimates for the mean and standard deviation of roe deer's breeding distribution. This knowledge about the exact birth distributions of fawns can be an essential part to guide farmers on their precautionary measures before mowing.

The article presents the results of a research on electoral polls disseminated by the mass media in Italy and published on the institutional website www.sondaggipoliticoelettorali.it. All the electoral polls published on the institutional website from 1 January 2017 to 9 September 2022 were analyzed. In the period considered, 1.537 polls were published. The article examines their sample size, their response rates in relation to the different interviewing techniques, and the related sampling and weighting schemes. It proposes some solutions to improve the methodological transparency of the polls and make the results provided by the various polling agencies more usable. In summary, the results of our analyzes show that in Italy the electoral polls in the 21st century are going through a difficult transition period compared to the previous century. Among the various factors that in recent years make it particularly difficult to conduct polls, the spread of new communication tool and the general spread of the internet and the increase in subjects who refuse to respond to an electoral poll take in particular importance.

Web-based respondent-driven sampling is a novel sampling method for the recruitment of participants for generating population estimates, studying social network characteristics, and delivering health interventions. However, the application, barriers and facilitators, and recruitment performance of web-based respondent-driven sampling have not yet been systematically investigated. Our objectives were to provide an overview of published research using web-based respondent-driven sampling and to investigate factors related to the recruitment performance of web-based respondent-driven sampling. We conducted a scoping review on web-based respondent-driven sampling studies published between 2000 and 2019. We used the process evaluation of complex interventions framework to gain insights into how web-based respondent-driven sampling was implemented, what mechanisms of impact drove recruitment, what the role of context was in the study, and how these components together influenced the recruitment performance of web-based respondent-driven sampling. We included 18 studies from 8 countries (high- and low-middle income countries), in which web-based respondent-driven sampling was used for making population estimates (n=12), studying social network characteristics (n=3), and delivering health-related interventions (n=3). Studies used web-based respondent-driven sampling to recruit between 19 and 3448 participants from a variety of target populations. Studies differed greatly in the number of seeds recruited, the proportion of successfully recruiting participants, the number of recruitment waves, the type of incentives offered to participants, and the duration of data collection. Studies that recruited relatively more seeds, through online platforms, and with less rigorous selection procedures reported relatively low percentages of successfully recruiting seeds. Studies that did not offer at least one guaranteed material incentive reported relatively fewer waves and lower percentages of successfully recruiting participants. The time of data collection was shortest in studies with university students. Web-based respondent-driven sampling can be successfully applied to recruit individuals for making population estimates, studying social network characteristics, and delivering health interventions. In general, seed and peer recruitment may be enhanced by rigorously selecting and motivating seeds, offering at least one guaranteed material incentive, and facilitating adequate recruitment options regarding the target population's online connectedness and communication behavior. Potential trade-offs should be taken into account when implementing web-based respondent-driven sampling, such as having less opportunities to implement rigorous seed selection procedures when recruiting many seeds, as well as issues around online rather than physical participation, such as the risk of cheaters participating repeatedly.

Some environmental studies use non-probabilistic sampling designs to draw samples from spatially distributed populations. Unfortunately, these samples can be difficult to analyse statistically and can give biased estimates of population characteristics. Spatially balanced sampling designs are probabilistic designs that spread the sampling effort evenly over the resource. These designs are particularly useful for environmental sampling because they produce good-sample coverage over the resource, they have precise design-based estimators and they can potentially reduce the sampling cost. The most popular spatially balanced design is Generalized Random Tessellation Stratified (GRTS), which has many desirable features including a spatially balanced sample, design-based estimators and the ability to select spatially balanced oversamples. This article considers the popularity of spatially balanced sampling, reviews several spatially balanced sampling designs and shows how these designs can be implemented in the statistical programming language R. We hope to increase the visibility of spatially balanced sampling and encourage environmental scientists to use these designs.

The literature frequently recommends purposive sampling of elites based on the assumptions that random sampling negatively affects the response rate and that it induces bias. I test these assumptions drawing on metadata from 282 samples of political, economic, and social elites, and on microdata from 2,658 elites. First I use permutations to calculate confidence intervals for the expected response rate following each sampling method. Second, I estimate the effect of random sampling on the final response rate using a range of regression models. Finally, I compare the distributions of the estimators for the average age, the share of male elites, and elites’ ideology by simulating repeated random and purposive samples. Results indicate that both random and purposive sampling of elites generate sufficiently large samples, as well as consistent and unbiased estimators of population parameters. Contradicting methodological guidelines in the field, the conclusion is that random sampling of elites is efficient.

Commonly recognized effects of sexual reproduction include increased diversity, improved adaptability, enabling of DNA repair, constrained accumulation of deleterious mutations, and species genotype homogenization. Additionally, there are studies that show how sexual reproduction slows down certain evolutionary responses, offering advantages in population cumulative growth and stability over time and other metrics. Here, we contribute an observation of another distinct effect of sexual reproduction, focusing on retaining a species’s key traits. In an initial mathematical analysis and simulation, we show that in an environment where copying is prone to error, quantitative polygenic traits that are shared within a parents’ generation are transmitted to future generations under sexual reproduction with less deviation than under asexual reproduction. Furthermore, the model shows that this retention of common traits (abbr. RoCT), is driven by the very nature of mixing of parental traits, and occurs even before adding effects like trait-specific reproductive advantages, DNA repair, or the raising of reproductive barriers. Since survival of ecosystems depends on the ability of individuals to replace the networked interactions and interdependencies associated with failing, dying, or absent members of the same species, RoCT helps sustain species and ecosystems.

The lists of species obtained by purposive sampling by field ecologists can be used to improve the sample-based estimation of species richness. A new estimator is here proposed as a modification of the difference estimator in which the species inclusion probabilities are estimated by means of the species frequencies from incidence data. If the species list used to support the estimation is complete the estimator guesses the true richness without error. In the case of incomplete lists, the estimator provides values invariably greater than the number of species detected by the combination of sample-based and purposive surveys. An asymptotically conservative estimator of the mean squared error is also provided. A simulation study based on two artificial communities is carried out in order to check the obvious increase in accuracy and precision with respect to the widely applied estimators based on the sole sample information. Finally, the proposed estimator is adopted to estimate species richness in the Maremma Regional Park, Italy.

In the short evolution from 2019 to the present, the application domains of ChatGPT have experienced exponential growth, ranging from editing, consulting, banking, healthcare, journalism, and mass media to entertainment, education, and remote technical assistance in industrial processes. This innovative tool, specialised in generating and understanding texts, is capable of meet the needs of employees anywhere and at any time. The article investigates employees' perceptions regarding the use and utility of ChatGPT and identifies the factors underlying hedonic motivation and the intention to use ChatGPT in office work. Starting from this premise, the results obtained serve as a basis for building a new structural model. The online survey used aims to model data collected from 402 Romanian employees. PLS structural equation modelling (PLS-SEM) and multigroup analysis (PLS-MGA) helped test statistical hypotheses and validate the construct model. SmartPLS 4 and SPSS 28 software was used to process and analyse the data collected from employees. The results highlight the significant influence of factors (ease of use, utility, and hedonic motivators) on the behaviour of Romanian employees regarding the use of ChatGPT in office work.

Background As the global aging population expands, understanding older adults’ preferences for place of death becomes pivotal in ensuring person-centered end-of-life care. Objective This study aimed to investigate the influence of sociodemographic, health, and lifestyle-related factors on end-of-life care preferences of older adults in South Tyrol, Italy. Methods Employing a cross-sectional design, a population-based survey was conducted with a stratified probabilistic sample of adults aged ≥ 75 years in South Tyrol (Autonomous Province of Bolzano/Bozen, Italy). From a randomly selected sample of 3,600 older adults, participants were invited to respond to a questionnaire that included items on older adults’ preferences for place of death and socio-demographic and health- and lifestyle-related factors, including frailty (e.g., PRISMA-7). Descriptive and multinomial logistic regression analyses were performed. Results The majority (55.3%) of the 1,695 older adults (participation rate: 47%) expressed a preference for dying at home and 12.7% indicated a desire for specialized end-of-life care in a healthcare facility. However, 27.9% refrained from disclosing their end-of-life care preferences. The factors influencing these preferences concerning the place of death included age, native language, educational level, living situation, and community. Compared to the preference of dying at own home or home of family or friends, older adults aged ≥ 85 years (OR = 0.57, P  = 0.002) and living in an urban area (OR = 0.40, P  < 0.001) were less likely to prefer dying at a hospital, palliative care unit, or hospice. Older adults living alone (OR = 1.90, P  < 0.001), Italian-speaking (OR = 1.46, P  = 0.03), and those with an educational level above high school (OR = 1.69, P  = 0.002) were more likely to prefer dying at a hospital, palliative care unit, or hospice. Conclusions End-of-life care preferences among older adults in South Tyrol were associated with socio-demographic, yet not health- and lifestyle-related factors. Recognizing and integrating these preferences is essential for developing, implementing, and evaluating interventions to promote advance care planning and provide effective, patient-centered end-of-life care.

This paper presents a mathematical reinterpretation of virtual multi-vector synthesis defined over finite vector sets. Unlike conventional approaches that treat multi-vector synthesis as an algorithmic technique, the proposed framework characterizes it as a structured problem combining convex geometry, combinatorial selection, and probabilistic averaging. First, it is shown that the set of all realizable virtual vectors coincides with the convex hull of a finite vector set, providing a geometric interpretation of the synthesis process. Based on this observation, a subset-based formulation is introduced, in which virtual vectors are constructed as averages over selected subsets. This formulation allows the synthesis problem to be interpreted as a combinatorial selection problem. Under a uniform subset selection model, closed-form expressions for the expectation and variance of the synthesized vectors are derived. In particular, it is demonstrated that the approximation behavior can be interpreted through the variance structure of subset-averaged vectors, and that increasing the subset size leads to a systematic reduction in variance. Furthermore, the trade-off between approximation accuracy and combinatorial complexity is analyzed, and the existence of an optimal subset size is established. The proposed framework provides a theoretical foundation for understanding multi-vector synthesis as a structured mathematical process, and offers a general perspective applicable to a wide class of approximation problems over finite vector sets.