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Breeding and global population sizes of the Critically Endangered Red-fronted Macaw Ara rubrogenys revisited
- Sebastian K. Herzog, Tjalle Boorsma, Guido Saldaña-Covarrubias, Tomás Calahuma-Arispe, Teodoro Camacho-Reyes, Dirk Dekker, Suzanne Edwards de Vargas, Máximo García-Cárdenas, Víctor Hugo García-Solíz, Jazmín M. Quiroz-Calizaya, Sayda Quispe-Solíz de Dekker, Marcia M. Salvatierra-Gómez, Ramón Vargas, Rodrigo W. Soria-Auza
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- Journal:
- Bird Conservation International / Volume 33 / 2023
- Published online by Cambridge University Press:
- 09 August 2022, e14
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The ‘Critically Endangered’ Red-fronted Macaw is endemic to seasonally dry, rain-shadowed valleys in the south-central Andes of Bolivia. The remoteness and inaccessibility of most of this region have hampered the rigorous collection of reliable range-wide data on the species’ global, local and breeding population sizes. Such data are imperative, however, for effective conservation and management. Estimated to number up to 5,000 birds in the early 1980s, the most recent and thorough survey to date reported a total of only 807 macaws and a breeding population fraction of about 20% in 2011, disjunctly distributed across eight breeding and six foraging areas and divided into four genetic clusters. Ten years later, we reassessed the species’ population sizes and breeding distribution with increased survey effort and geographic coverage. Six teams simultaneously surveyed different sections of the species’ entire known breeding range in four watersheds focusing on nesting sites. We estimated a global population size of 1,160 macaws, a breeding population fraction of 23.8–27.4% (138–159 nesting pairs) and discovered four new breeding areas. Watersheds and breeding areas differed widely in nesting pair and total macaw numbers. The Mizque watershed held 53% of the species’ breeding and 41.5% of its global population and had the highest breeding population fraction of 30.7–34.9%; the Pilcomayo watershed obtained the lowest values (6%, 8.5% and 14.1–18.2%, respectively). Two of the four documented genetic clusters (subpopulations) each held well over 50 breeding individuals. Two of the eight breeding areas documented in 2011 were found unoccupied in 2021. Numbers of nesting pairs per breeding area in 2011 were poorly correlated with those in 2021, and timing of breeding activities also differed between years. Our new data indicate that the Red-fronted Macaw no longer meets IUCN Red List criteria for ‘Critically Endangered’ species and that it should be downlisted to ‘Endangered.’
Excess mortality in the United States during the first three months of the COVID-19 pandemic
- R. Rivera, J. E. Rosenbaum, W. Quispe
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- Journal:
- Epidemiology & Infection / Volume 148 / 2020
- Published online by Cambridge University Press:
- 29 October 2020, e264
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Deaths are frequently under-estimated during emergencies, times when accurate mortality estimates are crucial for emergency response. This study estimates excess all-cause, pneumonia and influenza mortality during the coronavirus disease 2019 (COVID-19) pandemic using the 11 September 2020 release of weekly mortality data from the United States (U.S.) Mortality Surveillance System (MSS) from 27 September 2015 to 9 May 2020, using semiparametric and conventional time-series models in 13 states with high reported COVID-19 deaths and apparently complete mortality data: California, Colorado, Connecticut, Florida, Illinois, Indiana, Louisiana, Massachusetts, Michigan, New Jersey, New York, Pennsylvania and Washington. We estimated greater excess mortality than official COVID-19 mortality in the U.S. (excess mortality 95% confidence interval (CI) 100 013–127 501 vs. 78 834 COVID-19 deaths) and 9 states: California (excess mortality 95% CI 3338–6344) vs. 2849 COVID-19 deaths); Connecticut (excess mortality 95% CI 3095–3952) vs. 2932 COVID-19 deaths); Illinois (95% CI 4646–6111) vs. 3525 COVID-19 deaths); Louisiana (excess mortality 95% CI 2341–3183 vs. 2267 COVID-19 deaths); Massachusetts (95% CI 5562–7201 vs. 5050 COVID-19 deaths); New Jersey (95% CI 13 170–16 058 vs. 10 465 COVID-19 deaths); New York (95% CI 32 538–39 960 vs. 26 584 COVID-19 deaths); and Pennsylvania (95% CI 5125–6560 vs. 3793 COVID-19 deaths). Conventional model results were consistent with semiparametric results but less precise. Significant excess pneumonia deaths were also found for all locations and we estimated hundreds of excess influenza deaths in New York. We find that official COVID-19 mortality substantially understates actual mortality, excess deaths cannot be explained entirely by official COVID-19 death counts. Mortality reporting lags appeared to worsen during the pandemic, when timeliness in surveillance systems was most crucial for improving pandemic response.
Prediction of alpaca fibre quality by near-infrared reflectance spectroscopy
- A. W. Canaza-Cayo, D. Alomar, E. Quispe
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Rapid and efficient methods to evaluate variables associated with fibre quality are essential in animal breeding programs and fibre trade. Near-infrared reflectance spectroscopy (NIRS) combined with multivariate analysis was evaluated to predict textile quality attributes of alpaca fibre. Raw samples of fibres taken from male and female Huacaya alpacas (n = 291) of different ages and colours were scanned and their visible–near-infrared (NIR; 400 to 2500 nm) reflectance spectra were collected and analysed. Reference analysis of the samples included mean fibre diameter (MFD), standard deviation of fibre diameter (SDFD), coefficient of variation of fibre diameter (CVFD), mean fibre curvature (MFC), standard deviation of fibre curvature (SDFC), comfort factor (CF), spinning fineness (SF) and staple length (SL). Patterns of spectral variation (loadings) were explored by principal component analysis (PCA), where the first four PC's explained 99.97% and the first PC alone 95.58% of spectral variability. Calibration models were developed by modified partial least squares regression, testing different mathematical treatments (derivative order, subtraction gap, smoothing segment) of the spectra, with or without applying spectral correction algorithms (standard normal variate and detrend). Equations were selected through one-out cross-validation according to the proportion of explained variance (R2CV), root mean square error in cross-validation (RMSECV) and the residual predictive deviation (RPD), which relates the standard deviation of the reference data to RMSECV. The best calibration models were accomplished when using the NIR region (1100 to 2500 nm) for the prediction of MFD and SF, with R2CV = 0.90 and 0.87; RMSECV = 1.01 and 1.08 μm and RPD = 3.13 and 2.73, respectively. Models for SDFD, CVFD, MFC, SDFC, CF and SL had lower predictive quality with R2CV < 0.65 and RPD < 1.5. External validation performed for MFD and SF on 91 samples was slightly poorer than cross-validation, with R2 of 0.86 and 0.82, and standard error of prediction of 1.21 and 1.33 μm, for MFD and SF, respectively. It is concluded that NIRS can be used as an effective technique to select alpacas according to some important textile quality traits such as MFD and SF.