International audienceThe depth of equatorial Pacific thermocline is diagnostic of the main modes of tropical climates. Past estimates of Pacific thermocline dynamics have been reconstructed either for the Last Glacial Maximum or on longer timescales at low resolution. Here we document a new high-resolution set of reconstructed past sea surface and subsurface waters temperatures from the southwestern subequatorial Pacific, core MD05-2930, in the Gulf of Papua, over the last 800ka. We used two morphotypes of Globigerinoides ruber known to live at different water depths to reconstruct past stratification. We estimated calcification temperature of each morphotypes by Mg/Ca paleothermometry. Our subequatorial Pacific thermocline paleotemperature record indicates a response of the thermocline to both direct orbital forcing and glacial-interglacial changes. Our stratification record shows a systematic shallower glacial thermocline, whereas sea surface temperatures are characterized by precessional forcing. The record is indicative of a progressive long-term shoaling of the thermocline during the glacial stages during the late Pleistocene. The shoaling of the subequatorial Pacific thermocline is consistent with regional estimates. An enhanced South Pacific shallow overturning wind-driven circulation could have driven this progressive shoaling. We speculate that this late Pleistocene glacial shoaling of the thermocline could be related to an increase in the amplitude of the obliquity

Abstract. Geophysical time series are sometimes sampled irregularly along the time axis. The situation is particularly frequent in palaeoclimatology. Yet, there is so far no general framework for handling the continuous wavelet transform when the time sampling is irregular.Here we provide such a framework. To this end, we define the scalogram as the continuous-wavelet-transform equivalent of the extended Lomb-Scargle periodogram defined in Part 1 of this study (Lenoir and Crucifix, 2018). The signal being analysed is modelled as the sum of a locally periodic component in the time-frequency plane, a polynomial trend, and a background noise. The mother wavelet adopted here is the Morlet wavelet classically used in geophysical applications. The background noise model is a stationary Gaussian continuous autoregressive-moving-average (CARMA) process, which is more general than the traditional Gaussian white and red noise processes. The scalogram is smoothed by averaging over neighbouring times in order to reduce its variance. The Shannon-Nyquist exclusion zone is however defined as the area corrupted by local aliasing issues. The local amplitude in the time-frequency plane is then estimated with least-squares methods. We also derive an approximate formula linking the squared amplitude and the scalogram. Based on this property, we define a new analysis tool: the weighted smoothed scalogram, which we recommend for most analyses. The estimated signal amplitude also gives access to band and ridge filtering. Finally, we design a test of significance for the weighted smoothed scalogram against the stationary Gaussian CARMA background noise, and provide algorithms for computing confidence levels, either analytically or with Monte Carlo Markov chain methods. All the analysis tools presented in this article are available to the reader in the Python package WAVEPAL.

The objective was to determine operational proxies for robustness based on data collected routinely on farm that allow phenotyping of these traits in fattening pigs, and to estimate their genetic parameters. A total of 7256 pigs, from two Piétrain paternal lines (Pie and Pie NN), were tested at the AXIOM boar testing station (Azay-sur-Indre, France) in 2019-2021. During the fattening period (from 75 to 150 days of age), individual performance indicators were recorded (growth, backfat, loin depth, feed intake, feed conversion ratio) together with indicators such as insufficient growth, observable defect, symptoms of diseases and antibiotic and anti-inflammatory injections. These indicators were combined into three categorical robustness scores: R1, R2 and R3. Genetic parameters were estimated using an animal linear model. The robustness score R2 (selectable or not selectable animal) that combined information from status at testing and mortality had the highest heritability estimates of 0.08 ±0.03 for Pie NN line and a value of 0.09 ±0.02 for Pie line, compared to traits R1 and R3. The score R3 that combines information from the score R2 with antibiotic and anti-inflammatory injections presented slightly lower heritability estimates (0.05 ±0.02 to 0.07±0.03). Genetic correlations between R2 and R3 were high and favourable (0.93 ±0.04 to 0.95 ±0.03) and R2 and R3 can be considered as identical with regard to the confidence interval. These two robustness scores were also highly and favourably genetically correlated with initial body weight and average daily gain, and unfavourably correlated with daily feed intake (ranging from 0.73 ±0.06 to 0.90 ±0.08). Estimates of genetic correlations of R2 and R3 with backfat depth and raw feed conversion ratio (not standardized between starting and finishing weights) were moderate and unfavorable (0.20 ±0.13 to 0.46±0.20). A part of these genetic correlations, that are of low precision due to the number of data available, have to be confirmed on larger datasets. The results showed the interest of using routine phenotypes collected on farm to build simple robustness indicators that can be applied in breeding.

Abstract. We develop a general framework for the frequency analysis of irregularly sampled time series. It is based on the Lomb–Scargle periodogram, but extended to algebraic operators accounting for the presence of a polynomial trend in the model for the data, in addition to a periodic component and a background noise. Special care is devoted to the correlation between the trend and the periodic component. This new periodogram is then cast into the Welch overlapping segment averaging (WOSA) method in order to reduce its variance. We also design a test of significance for the WOSA periodogram, against the background noise. The model for the background noise is a stationary Gaussian continuous autoregressive-moving-average (CARMA) process, more general than the classical Gaussian white or red noise processes. CARMA parameters are estimated following a Bayesian framework. We provide algorithms that compute the confidence levels for the WOSA periodogram and fully take into account the uncertainty in the CARMA noise parameters. Alternatively, a theory using point estimates of CARMA parameters provides analytical confidence levels for the WOSA periodogram, which are more accurate than Markov chain Monte Carlo (MCMC) confidence levels and, below some threshold for the number of data points, less costly in computing time. We then estimate the amplitude of the periodic component with least-squares methods, and derive an approximate proportionality between the squared amplitude and the periodogram. This proportionality leads to a new extension for the periodogram: the weighted WOSA periodogram, which we recommend for most frequency analyses with irregularly sampled data. The estimated signal amplitude also permits filtering in a frequency band. Our results generalise and unify methods developed in the fields of geosciences, engineering, astronomy and astrophysics. They also constitute the starting point for an extension to the continuous wavelet transform developed in a companion article (Lenoir and Crucifix, 2018). All the methods presented in this paper are available to the reader in the Python package WAVEPAL.

Quantifying growth perturbations over the fattening period in swine via mathematical modelling, PeerCommunity Journal, 2: e9.

Background: Pasteurellosis (Pasteurella infection) is one of the most common bacterial infections in rabbits on commercial farms and in laboratory facilities. Curative treatments using antibiotics are only partly efficient, with frequent relapses. Breeding rabbits for improved genetic resistance to pasteurellosis is a sustainable alternative approach. In this study, we infected 964 crossbred rabbits from six sire lines experimentally with Pasteurella multocida. After post-mortem examination and bacteriological analyses, abscess, bacteria, and resistance scores were derived for each rabbit based on the extent of lesions and bacterial dissemination in the body. This is the first study to use such an experimental design and response traits to measure resistance to pasteurellosis in a rabbit population. We investigated the genetic variation of these traits in order to identify potential selection criteria. We also estimated genetic correlations of resistance to pasteurellosis in the experimental population with traits that are under selection in the breeding populations (number of kits born alive and weaning weight). Results: Heritability estimates for the novel response traits, abscess, bacteria, and resistance scores, ranged from 0.08 (± 0.05) to 0.16 (± 0.06). The resistance score showed very strong negative genetic correlation estimates with abscess (− 0.99 ± 0.05) and bacteria scores (− 0.98 ± 0.07). A very high positive genetic correlation of 0.99 ± 0.16 was estimated between abscess and bacteria scores. Estimates of genetic correlations of the resistance score with average daily gain traits for the first and second week after inoculation were 0.98 (± 0.06) and 0.70 (± 0.14), respectively. Estimates of genetic correlations of the disease-related traits with average daily gain pre-inoculation were favorable but with high standard errors. Estimates of genetic and phenotypic correlations of the disease-related traits with commercial selection traits were not significantly different from zero. Conclusions: Disease response traits are heritable and are highly correlated with each other, but do not show any significant genetic correlations with commercial selection traits. Thus, the prevalence of pasteurellosis could be decreased by selecting more resistant rabbits on any one of the disease response traits with a limited impact on the selection traits, which would allow implementation of a breeding program to improve resistance to pasteurellosis in rabbits.

This chapter is dedicated to the slow dynamics of the climate system, at time scales of one thousand to one million years. We focus specifically on the phenomenon of ice ages that has characterised the slow evolution of climate over the Quaternary. Ice ages are a form of variability featuring interactions between different large-scale components and processes in the climate system, including ice sheet, deep-ocean and carbon cycle dynamics. This variability 1 arXiv:1512.03557v1 [physics.ao-ph]

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