Variation in Birth Mass and Mass Gain During Lactation for a Long-Lived Mammal: A Case Study Using the Weddell Seal

Reproduction in mammals is costly, with implications for trade-offs between current reproduction and survival.  Life history theory suggests the amount of energy allocated to reproduction should change as a function of increasing maternal age, and empirical work supports both within-individual decreases (consistent with senescence) and increases (consistent with constraint and restraint).  The Weddell seal (Leptonychotes weddellii) is an ideal organism with which to study patterns of maternal energy allocation due to the life history.  Here, we used a long-term mark-recapture database of individually marked mothers and pups in Erebus Bay, Antarctica, to develop a stratified sample of masses from pups and known-age females across maternal ages in 11 different years.  Hierarchical modeling of potential sources of variation in:  1) pup masses at parturition, 2) pup mass gains from parturition to mid-lactation (~20 days), and 3) pup mass gains from mid-lactation to late-lactation (~20-days to ~35-days) was used to 1) evaluate the relative support for increases/decreases in reproductive allocation as a function of maternal age, 2) assess how patterns of reproductive allocation may reflect pre-parturition resource acquisition and/or post-parturition resource allocation, and 3) estimate the magnitude of individual heterogeneity in maternal effects after accounting for maternal and offspring characteristics. Our results provide strong evidence that reproductive investment at parturition and pup mass gains from parturition through late-lactation vary with maternal age and breeding history and result in important differences in late-lactation pup masses.   Such variation may have consequences for the early-life success of offspring, and thus implications at the population level

Imperfect Tests, Pervasive Pathogens, and Variable Demographic Performance: Thoughts on Managing Bighorn Sheep Respiratory Disease

Respiratory disease (pneumonia) has been a persistent challenge for bighorn sheep (Ovis canadensis) conservation and its cause has been attributed to numerous bacteria including Mycoplasma ovipneumoniae and several Pasteurellaceae family species. This study sought to investigate efficacy of diagnostic protocols in detecting Pasteurellaceae and Mycoplasma ovipneumoniae, generate sampling recommendations for different protocols, assess the distribution of these disease agents among 17 bighorn sheep populations in Montana and Wyoming, and evaluate what associations existed between detection of these agents and demographic performance of bighorn sheep populations. Analysis of replicate samples from individual bighorn sheep revealed that detection probability for regularlyused diagnostic protocols was generally low (<50%) for Pasteurellaceae and was high (>70%) for Mycoplasma ovipneumoniae, suggesting that routine pathogen sampling likely mischaracterizes respiratory pathogen communities. Power analyses found that most pathogen species could be detected with 80% confidence at the population-level by conducting regularly-used protocols multiple times per animal. Each pathogen species was detected in over half of the study populations, but after accounting for detection probability there was low confidence in negative test results for populations where Pasteurellaceae species were not detected. Seventy-six percent of study populations hosted both Mycoplasma ovipneumoniae and Pasteurellaceae pathogens, yet a number of these populations were estimated to have positive population growth rates and recruitment rates greater than 30%. Overall, the results of this work suggest that bighorn sheep respiratory disease may be mitigated by manipulating population characteristics and respiratory disease epizootics could be caused by pathogens already resident in bighorn sheep population

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Evaluación geologica-geotecnica para la estabilidad de taludes en la carretera Muñani-Saytococha tramo km. 14+700 al 30+00

TesisEl presente trabajo es el resultado de una investigación, con la finalidad de realizar un análisis, en la estabilidad de taludes, de suelos y macizos rocosos de la Carretera Muñani- Saytococha tramo Km.14+700 al Km.30+000, Distrito de Muñani, Provincia de Azángaro. Por lo cual se contempla demostrar las causas que se presentan en los deslizamientos de taludes; por tal razón que se ha pretendido determinar cuáles son las razones de esta inestabilidad. Es bastante complejo por la presencia de estructuras fuertemente disturbadas, dando lugar a fracturamiento en los macizos rocosos, los cuales por estar constituidos por areniscas, cuarzosas así como la cobertura de depósitos cuaternarios aluviales que son propensas a un fuerte proceso de erosión y meteorización, produciéndose alteraciones profundas en la estructura rocosa. Las fuertes precipitaciones, la infiltración, la erosión hidrogeológica y los cambios bruscos de temperaturas, están alterando la estructura de las rocas, produciendo alteraciones en las propiedades físicas mecánicas de la roca y de los suelos la disminución de los esfuerzos de resistencia como la cohesión, la fricción interna y las fuerzas resistentes, por lo cual el talud de la prog. 16+100 al 16+200 es un talud inestable. Se realizó un mapeo lineal, para determinar sus propiedades físicas mecánicas de los taludes de rocas de las prog. 17+010 al 17+080, 20+080 al 20+160, 26+480 al 26+540.son taludes estables de modo de falla de cuña. Determinados en campo y laboratorio, así mismo empleado: Rack Mass Rating (RMR - Bieniawski 1989), Rack Quality Designation (RQD - Deere 1967) y Geological Strength lndex (GSI- Hoek 1994); y la clasificación de suelos en base del sistema SUCS Los resultados que se obtuvieron son de gran importancia para analizar el comportamiento de los taludes

How sure are you? A web-based application to confront imperfect detection of respiratory pathogens in bighorn sheep.

The relationships between host-pathogen population dynamics in wildlife are poorly understood. An impediment to progress in understanding these relationships is imperfect detection of diagnostic tests used to detect pathogens. If ignored, imperfect detection precludes accurate assessment of pathogen presence and prevalence, foundational parameters for deciphering host-pathogen dynamics and disease etiology. Respiratory disease in bighorn sheep (Ovis canadensis) is a significant impediment to their conservation and restoration, and effective management requires a better understanding of the structure of the pathogen communities. Our primary objective was to develop an easy-to-use and accessible web-based Shiny application that estimates the probability (with associated uncertainty) that a respiratory pathogen is present in a herd and its prevalence given imperfect detection. Our application combines the best-available information on the probabilities of detection for various respiratory pathogen diagnostic protocols with a hierarchical Bayesian model of pathogen prevalence. We demonstrated this application using four examples of diagnostic tests from three herds of bighorn sheep in Montana. For instance, one population with no detections of Mycoplasma ovipneumoniae (PCR assay) still had an 6% probability of the pathogen being present in the herd. Similarly, the apparent prevalence (0.32) of M. ovipneumoniae in another herd was a substantial underestimate of estimated true prevalence (0.46: 95% CI = [0.25, 0.71]). The negative bias of naïve prevalence increased as the probability of detection of testing protocols worsened such that the apparent prevalence of Mannheimia haemolytica (culture assay) in a herd (0.24) was less than one third that of estimated true prevalence (0.78: 95% CI = [0.43, 0.99]). We found a small difference in the estimates of the probability that Mannheimia spp. (culture assay) was present in one herd between the binomial sampling approach (0.24) and the hypergeometric approach (0.22). Ignoring the implications of imperfect detection and sampling variation for assessing pathogen communities in bighorn sheep can result in spurious inference on pathogen presence and prevalence, and potentially poorly informed management decisions. Our Shiny application makes the rigorous assessment of pathogen presence, prevalence and uncertainty straightforward, and we suggest it should be incorporated into a new paradigm of disease monitoring

An improved understanding of ungulate population dynamics using count data: Insights from western Montana.

Understanding the dynamics of ungulate populations is critical given their ecological and economic importance. In particular, the ability to evaluate the evidence for potential drivers of variation in population trajectories is important for informed management. However, the use of age ratio data (e.g., juveniles:adult females) as an index of variation in population dynamics is hindered by a lack of statistical power and difficult interpretation. Here, we show that the use of a population model based on count, classification and harvest data can dramatically improve the understanding of ungulate population dynamics by: 1) providing estimates of vital rates (e.g., per capita recruitment and population growth) that are easier to interpret and more useful to managers than age ratios and 2) increasing the power to assess potential sources of variation in key vital rates. We used a time series of elk (Cervus canadensis) spring count and classification data (2004 to 2016) and fall harvest data from hunting districts in western Montana to construct a population model to estimate vital rates and assess evidence for an association between a series of environmental covariates and indices of predator abundance on per capita recruitment rates of elk calves. Our results suggest that per capita recruitment rates were negatively associated with cold and wet springs, and severe winters, and positively associated with summer precipitation. In contrast, an analysis of the raw age ratio data failed to detect these relationships. Our approach based on a population model provided estimates of the region-wide mean per capita recruitment rate (mean = 0.25, 90% CI = 0.21, 0.29), temporal variation in hunting-district-specific recruitment rates (minimum = 0.09; 90% CI = [0.07, 0.11], maximum = 0.43; 90% CI = [0.38, 0.48]), and annual population growth rates (minimum = 0.83; 90% CI = [0.78, 0.87], maximum = 1.20; 90% CI = [1.11, 1.29]). We recommend using routinely collected population count and classification data and a population modeling approach rather than interpreting estimated age ratios as a substantial improvement in understanding population dynamics

WESE_births

csv file containing data on pup birth dates, pup sex, year of birth, and attributes of mothers for Weddell seals in Erebus Bay, Antarctica for 4.465 pups in 28 different years during 1993 through 2014

Data from: Simulation-based validation of spatial capture-recapture models: a case study using mountain lions

Spatial capture-recapture (SCR) models have improved the ability to estimate densities of rare and elusive animals. However, SCR models have seldom been validated even as model formulations diversify and expand to incorporate new sampling methods and/or additional sources of information on model parameters. Information on the relationship between encounter probabilities, sources of additional information, and the reliability of density estimates, is rare but crucial to assessing reliability of SCR-based estimates. We used a simulation-based approach that incorporated prior empirical work to assess the accuracy and precision of density estimates from SCR models using spatially unstructured sampling. To assess the consequences of sparse data and potential sources of bias, we simulated data under six scenarios corresponding to three different levels of search effort and two levels of correlation between search effort and animal density. We then estimated density for each scenario using four models that included increasing amounts of information from harvested individuals and telemetry to evaluate the impact of additional sources of information. Model results were sensitive to the quantity of available information: density estimates based on low search effort were biased high and imprecise, whereas estimates based on high search effort were unbiased and precise. A correlation between search effort and animal density resulted in a positive bias in density estimates, though the bias decreased with increasingly informative datasets. Adding information from harvested individuals and telemetered individuals improved density estimates based on low and moderate effort but had negligible impact for datasets resulting from high effort. We demonstrated that density estimates from SCR models using spatially unstructured sampling are reliable when sufficient information is provided. Accurate density estimates can result if empirical-based simulations such as those presented here are used to develop study designs with appropriate amounts of effort and information sources

Simulation-based validation of spatial capture-recapture models: A case study using mountain lions.

Spatial capture-recapture (SCR) models have improved the ability to estimate densities of rare and elusive animals. However, SCR models have seldom been validated even as model formulations diversify and expand to incorporate new sampling methods and/or additional sources of information on model parameters. Information on the relationship between encounter probabilities, sources of additional information, and the reliability of density estimates, is rare but crucial to assessing reliability of SCR-based estimates. We used a simulation-based approach that incorporated prior empirical work to assess the accuracy and precision of density estimates from SCR models using spatially unstructured sampling. To assess the consequences of sparse data and potential sources of bias, we simulated data under six scenarios corresponding to three different levels of search effort and two levels of correlation between search effort and animal density. We then estimated density for each scenario using four models that included increasing amounts of information from harvested individuals and telemetry to evaluate the impact of additional sources of information. Model results were sensitive to the quantity of available information: density estimates based on low search effort were biased high and imprecise, whereas estimates based on high search effort were unbiased and precise. A correlation between search effort and animal density resulted in a positive bias in density estimates, though the bias decreased with increasingly informative datasets. Adding information from harvested individuals and telemetered individuals improved density estimates based on low and moderate effort but had negligible impact for datasets resulting from high effort. We demonstrated that density estimates from SCR models using spatially unstructured sampling are reliable when sufficient information is provided. Accurate density estimates can result if empirical-based simulations such as those presented here are used to develop study designs with appropriate amounts of effort and information sources