scholarly journals Estimates of caribou herd size using post-calving surveys in the Northwest Territories and Nunavut, Canada: A meta-analysis

Rangifer ◽  
2018 ◽  
Vol 38 (1) ◽  
pp. 39-78
Author(s):  
John Boulanger ◽  
Jan Adamczewski ◽  
Tracy Davison

Post-calving surveys to estimate herd size of barren-ground caribou (Rangifer tarandus groenlandicus, R. t. granti, and R. t. caribou) have been used for caribou herds in Alaska, Yukon, Northwest Territories, Nunavut, and Québec/Labrador. The main field procedure uses relocation of collared caribou to locate aggregated groups of hundreds or thousands of caribou during times of high insect harassment that usually occur in July. These groups are then pho­tographed to obtain a count of the caribou in the aggregated groups. Often some caribou are missed, and the count of caribou may be a negatively biased estimate of total herd size, unless a high proportion of the herd is found and photographed. To address this, some previous studies have used the Lincoln-Petersen estimator, which estimates the proportion of the herd counted based on the percentage of available collared caribou found during the survey. However, this estimator assumes equal probabilities of all groups of caribou being found, regardless of group size and the numbers of collared caribou in the group. These assumptions may not be valid, as larger groups are more likely to be found than smaller groups, particularly if there are several collared caribou present. This may lead to estimates that are biased low, along with an estimate of variance that may also be biased low. A two phase estimator developed by Rivest et al., in 1998 became available in R statistical software in 2012. We analyzed 20 data sets from post-calving surveys in the NWT and NU carried out between 2000 and 2015 using the Rivest estimator to explore working characteristics of this estimator. We compared the Rivest estimates with Lincoln-Petersen estimates and total counts on each survey. We considered factors that influence precision of the Rivest estimator with a focus on sampling factors such as the proportion of collars found, the number of collars available, and natural factors such as the degree of aggregation of caribou in each survey (as indexed by the negative binomial dispersion parameter). In general, the Rivest estimator displayed acceptable preci­sion when high proportions of caribou groups with collars were detected and counted, collar numbers were sufficient, and aggregation was adequate. Notable exceptions occurred in years of lower aggregation which resulted in many small groups with 0 or few collared caribou, and in these cases herd estimates had large variances and low precision. Estimates from the Rivest estimator, Lincoln-Petersen estimator, and total counts converged when sampling effort was high, collar numbers relative to herd size were high, and caribou were well aggregated in a limited number of groups. In other cases, estimates of the Rivest estimator were generally higher than Lincoln-Petersen estimates, presumably due to negative bias with the Lincoln-Petersen estimator. We provide a set of working recommendations to optimize field sampling to ensure reliable estimates of herd size using post-calving methods.

2021 ◽  
pp. 1-10
Author(s):  
Wei Qin ◽  
Wenwen Li ◽  
Qi Wang ◽  
Min Gong ◽  
Tingting Li ◽  
...  

Background: The global race-dependent association of Alzheimer’s disease (AD) and apolipoprotein E (APOE) genotype is not well understood. Transethnic analysis of APOE could clarify the role of genetics in AD risk across populations. Objective: This study aims to determine how race and APOE genotype affect the risks for AD. Methods: We performed a systematic search of PubMed, Embase, Web of Science, and the Cochrane Library since 1993 to Aug 25, 2020. A total of 10,395 reports were identified, and 133 were eligible for analysis with data on 77,402 participants. Studies contained AD clinical diagnostic and APOE genotype data. Homogeneous data sets were pooled in case-control analyses. Odds ratios and 95% confidence intervals for developing AD were calculated for populations of different races and APOE genotypes. Results: The proportion of APOE genotypes and alleles differed between populations of different races. Results showed that APOE ɛ4 was a risk factor for AD, whereas APOE ɛ2 protected against it. The effects of APOE ɛ4 and ɛ2 on AD risk were distinct in various races, they were substantially attenuated among Black people. Sub-group analysis found a higher frequency of APOE ɛ4/ɛ4 and lower frequency of APOE ɛ3/ɛ3 among early-onset AD than late-onset AD in a combined group and different races. Conclusion: Our meta-analysis suggests that the association of APOE genotypes and AD differ between races. These results enhance our understanding of APOE-related risk for AD across race backgrounds and provide new insights into precision medicine for AD.


2021 ◽  
pp. 027112142110327
Author(s):  
Esther R. Lindström ◽  
Jason C. Chow ◽  
Kathleen N. Zimmerman ◽  
Hongyang Zhao ◽  
Elise Settanni ◽  
...  

Engagement in early childhood has been linked with later achievement, but the relation between these variables and how they are measured in early childhood requires examination. We estimated the overall association between academic engagement and achievement in children prior to kindergarten entry. Our systematic literature search yielded 13,521 reports for structured eligibility screening; from this pool of studies, we identified 21 unique data sets, with 199 effect sizes for analysis. We coded eligible studies, extracted effect sizes, accounted for effect size dependency, and used random-effects models to synthesize findings. The overall correlation between academic engagement and achievement was r = .24 (range: −.08 to −.71), and moderator analyses did not significantly predict the relation between the two constructs. This study aligns with previous research on this topic and examines issues related to these measures, their constraints, and applications as they pertain to early childhood research.


2021 ◽  
pp. 263208432199622
Author(s):  
Tim Mathes ◽  
Oliver Kuss

Background Meta-analysis of systematically reviewed studies on interventions is the cornerstone of evidence based medicine. In the following, we will introduce the common-beta beta-binomial (BB) model for meta-analysis with binary outcomes and elucidate its equivalence to panel count data models. Methods We present a variation of the standard “common-rho” BB (BBST model) for meta-analysis, namely a “common-beta” BB model. This model has an interesting connection to fixed-effect negative binomial regression models (FE-NegBin) for panel count data. Using this equivalence, it is possible to estimate an extension of the FE-NegBin with an additional multiplicative overdispersion term (RE-NegBin), while preserving a closed form likelihood. An advantage due to the connection to econometric models is, that the models can be easily implemented because “standard” statistical software for panel count data can be used. We illustrate the methods with two real-world example datasets. Furthermore, we show the results of a small-scale simulation study that compares the new models to the BBST. The input parameters of the simulation were informed by actually performed meta-analysis. Results In both example data sets, the NegBin, in particular the RE-NegBin showed a smaller effect and had narrower 95%-confidence intervals. In our simulation study, median bias was negligible for all methods, but the upper quartile for median bias suggested that BBST is most affected by positive bias. Regarding coverage probability, BBST and the RE-NegBin model outperformed the FE-NegBin model. Conclusion For meta-analyses with binary outcomes, the considered common-beta BB models may be valuable extensions to the family of BB models.


Author(s):  
Hayley A Thompson ◽  
Andria Mousa ◽  
Amy Dighe ◽  
Han Fu ◽  
Alberto Arnedo-Pena ◽  
...  

Abstract Background Understanding the drivers of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission is crucial for control policies, but evidence of transmission rates in different settings remains limited. Methods We conducted a systematic review to estimate secondary attack rates (SARs) and observed reproduction numbers (Robs) in different settings exploring differences by age, symptom status, and duration of exposure. To account for additional study heterogeneity, we employed a beta-binomial model to pool SARs across studies and a negative-binomial model to estimate Robs. Results Households showed the highest transmission rates, with a pooled SAR of 21.1% (95% confidence interval [CI]:17.4–24.8). SARs were significantly higher where the duration of household exposure exceeded 5 days compared with exposure of ≤5 days. SARs related to contacts at social events with family and friends were higher than those for low-risk casual contacts (5.9% vs 1.2%). Estimates of SARs and Robs for asymptomatic index cases were approximately one-seventh, and for presymptomatic two-thirds of those for symptomatic index cases. We found some evidence for reduced transmission potential both from and to individuals younger than 20 years of age in the household context, which is more limited when examining all settings. Conclusions Our results suggest that exposure in settings with familiar contacts increases SARS-CoV-2 transmission potential. Additionally, the differences observed in transmissibility by index case symptom status and duration of exposure have important implications for control strategies, such as contact tracing, testing, and rapid isolation of cases. There were limited data to explore transmission patterns in workplaces, schools, and care homes, highlighting the need for further research in such settings.


2012 ◽  
Vol 132 (2) ◽  
pp. 485-487 ◽  
Author(s):  
Matthew H. Law ◽  
Grant W. Montgomery ◽  
Kevin M. Brown ◽  
Nicholas G. Martin ◽  
Graham J. Mann ◽  
...  

2002 ◽  
Vol 2 ◽  
pp. 169-189 ◽  
Author(s):  
Lawrence W. Barnthouse ◽  
Douglas G. Heimbuch ◽  
Vaughn C. Anthony ◽  
Ray W. Hilborn ◽  
Ransom A. Myers

We evaluated the impacts of entrainment and impingement at the Salem Generating Station on fish populations and communities in the Delaware Estuary. In the absence of an agreed-upon regulatory definition of “adverse environmental impact” (AEI), we developed three independent benchmarks of AEI based on observed or predicted changes that could threaten the sustainability of a population or the integrity of a community.Our benchmarks of AEI included: (1) disruption of the balanced indigenous community of fish in the vicinity of Salem (the “BIC” analysis); (2) a continued downward trend in the abundance of one or more susceptible fish species (the “Trends” analysis); and (3) occurrence of entrainment/impingement mortality sufficient, in combination with fishing mortality, to jeopardize the future sustainability of one or more populations (the “Stock Jeopardy” analysis).The BIC analysis utilized nearly 30 years of species presence/absence data collected in the immediate vicinity of Salem. The Trends analysis examined three independent data sets that document trends in the abundance of juvenile fish throughout the estuary over the past 20 years. The Stock Jeopardy analysis used two different assessment models to quantify potential long-term impacts of entrainment and impingement on susceptible fish populations. For one of these models, the compensatory capacities of the modeled species were quantified through meta-analysis of spawner-recruit data available for several hundred fish stocks.All three analyses indicated that the fish populations and communities of the Delaware Estuary are healthy and show no evidence of an adverse impact due to Salem. Although the specific models and analyses used at Salem are not applicable to every facility, we believe that a weight of evidence approach that evaluates multiple benchmarks of AEI using both retrospective and predictive methods is the best approach for assessing entrainment and impingement impacts at existing facilities.


2015 ◽  
Vol 135 (10) ◽  
pp. 2455-2463 ◽  
Author(s):  
Lanlan Yin ◽  
Sergio G. Coelho ◽  
Julio C. Valencia ◽  
Dominik Ebsen ◽  
Andre Mahns ◽  
...  

2017 ◽  
Vol 92 (2) ◽  
pp. 197-202 ◽  
Author(s):  
G. Pérez-Ponce de León ◽  
R. Poulin

AbstractCryptic parasite diversity is a major issue for taxonomy and systematics, and for attempts to control diseases of humans, domestic animals and wildlife. Here, we re-examine an earlier report that, after correcting for sampling effort, more cryptic species of trematodes are found per published study than for other helminth taxa. We performed a meta-analysis of 110 studies that used DNA sequences to search for cryptic species in parasitic helminth taxa. After correcting for study effort and accounting for the biogeographical region of origins, we found that more cryptic species tend to be uncovered among trematodes, and fewer among cestodes and animal-parasitic nematodes, than in other helminth groups. However, this pattern was only apparent when we included only studies using nuclear markers in the analysis; it was not seen in a separate analysis based only on mitochondrial markers. We propose that the greater occurrence of cryptic diversity among trematodes may be due to some of their unique features, such as their mode of reproduction or frequent lack of hard morphological structures, or to the way in which trematode species are described. Whatever the reason, the high frequency of cryptic species among trematodes has huge implications for estimates of parasite diversity and for future taxonomic research.


2013 ◽  
Vol 1 (5) ◽  
pp. 5453-5498 ◽  
Author(s):  
A. Merino ◽  
L. López ◽  
J. L. Sánchez ◽  
E. García-Ortega ◽  
E. Cattani ◽  
...  

Abstract. Identifying deep convection is of paramount importance, as it may be associated with extreme weather that has significant impact on the environment, property and the population. A new method, the Hail Detection Tool (HDT), is described for identifying hail-bearing storms using multi-spectral Meteosat Second Generation (MSG) data. HDT was conceived as a two-phase method, in which the first step is the Convective Mask (CM) algorithm devised for detection of deep convection, and the second a Hail Detection algorithm (HD) for the identification of hail-bearing clouds among cumulonimbus systems detected by CM. Both CM and HD are based on logistic regression models trained with multi-spectral MSG data-sets comprised of summer convective events in the middle Ebro Valley between 2006–2010, and detected by the RGB visualization technique (CM) or C-band weather radar system of the University of León. By means of the logistic regression approach, the probability of identifying a cumulonimbus event with CM or a hail event with HD are computed by exploiting a proper selection of MSG wavelengths or their combination. A number of cloud physical properties (liquid water path, optical thickness and effective cloud drop radius) were used to physically interpret results of statistical models from a meteorological perspective, using a method based on these "ingredients." Finally, the HDT was applied to a new validation sample consisting of events during summer 2011. The overall Probability of Detection (POD) was 76.9% and False Alarm Ratio 16.7%.


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