Joint care can outweigh costs of nonkin competition in communal breeders

Competition between offspring can greatly influence offspring fitness and parental investment decisions, especially in communal breeders where unrelated competitors have less incentive to concede resources. Given the potential for escalated conflict, it remains unclear what mechanisms facilitate the evolution of communal breeding among unrelated females. Resolving this question requires simultaneous consideration of offspring in noncommunal and communal nurseries, but such comparisons are missing. In the Seychelles warbler Acrocephalus sechellensis, we compare nestling pairs from communal nests (2 mothers) and noncommunal nests (1 mother) with singleton nestlings. Our results indicate that increased provisioning rate can act as a mechanism to mitigate the costs of offspring rivalry among nonkin. Increased provisioning in communal broods, as a consequence of having 2 female parents, mitigates any elevated costs of offspring rivalry among nonkin: per-capita provisioning and survival was equal in communal broods and singletons, but lower in noncommunal broods. Individual offspring costs were also more divergent in noncommunal broods, likely because resource limitation exacerbates differences in competitive ability between nestlings. It is typically assumed that offspring rivalry among nonkin will be more costly because offspring are not driven by kin selection to concede resources to their competitors. Our findings are correlational and require further corroboration, but may help explain the evolutionary maintenance of communal breeding by providing a mechanism by which communal breeders can avoid these costs

Aircraft predictive maintenance modeling using a hybrid imbalance learning approach

The continued development of the industrial internet of things (IIoT) has caused an increase in the availability of industrial datasets. The massive availability of assets operational dataset has prompted more research interest in the area of condition-based maintenance, towards the API-lead integration for assets predictive maintenance modelling. The large data generated by industrial processes inherently comes along with different analytical challenges. Data imbalance is one of such problems that exist in datasets. It affects the performance of machine learning algorithms, which yields imprecise prediction. In this paper, we propose an advanced approach to handling imbalance classification problems in equipment heterogeneous datasets. The technique is based on a hybrid of soft mixed Gaussian processes with the EM method to improves the prediction of the minority class during learning. The algorithm is then used to develop a prognostic model for predicting aircraft component replacement. We validate the feasibility and effectiveness of our approach using real-time aircraft operation and maintenance datasets. The dataset spans over seven years. Our approach shows better performance compared to other similar methods

A review of diagnostic methods for hydraulically powered flight control actuation systems

Aircraft systems are designed to perform functions that will aid the various missions of the aircraft. Their performance, when subjected to an unfamiliar condition of operation, imposes stress on them. The system components experience degradation due to fault which ultimately results in failure. Maintenance and monitoring mechanisms are put in place to ensure these systems are readily available when required. Thus, the sensing of parameters assists in providing conditions under which healthy and faulty scenarios can be indicated. To obtain parameter values, sensor data is processed, and the results are displayed so that the presence of faults may be known. Some faults are intermittent and incipient in nature. These are not discovered easily and can only be known through a display of unusual system performance by error code indication. Therefore, the assessed faults are transmitted to a maintenance crew by error codes. The results may be fault found (FF), no fault found (NFF), or cannot display (CND). However, the main classification of the faults and their origins may not be known in the system. This continues throughout the life cycle of the system or equipment. This paper reviews the diagnostic methods used for the hydraulically powered flight control actuation system (HPFCAS) of an aircraft and its interaction with other aircraft systems. The complexities of the subsystem’s integration are discussed, and different subsystems are identified. Approaches used for the diagnostics of faults, such as model-based, statistical mapping and classification, the use of algorithms, as well as parity checks are reviewed. These are integrated vehicle health management (IVHM) tools for systems diagnostics. The review shows that when a system is made up of several subsystems on the aircraft with dissimilar functions, the probability of fault existing in the system increases, as the subsystems are interconnected for resource sharing, space, and weight savings. Additionally, this review demonstrates that data-driven approaches for the fault diagnostics of components are good. However, they require large amounts of data for feature extraction. For a system such as the HPFCAS, flight-management data or aircraft maintenance records hold information on performance, health monitoring, diagnostics, and time scales during operation. These are needed for analysis. Here, a knowledge of training algorithms is used to interpret different fault scenarios from the record. Thus, such specific data are not readily available for use in a data-driven approach, since manufacturers, producers, and the end users of the system components or equipment do not readily distribute these verifiable data. This makes it difficult to perform diagnostics using a data-driven approach. In conclusion, this paper exposes the areas of interest, which constitute opportunities and challenges in the diagnostics and health monitoring of flight-control actuation systems on aircraft

An integrated machine learning model for aircraft components rare failure prognostics with log-based dataset

Predictive maintenance is increasingly advancing into the aerospace industry, and it comes with diverse prognostic health management solutions. This type of maintenance can unlock several benefits for aerospace organizations. Such as preventing unexpected equipment downtime and improving service quality. In developing data-driven predictive modelling, one of the challenges that cause model performance degradation is the data-imbalanced distribution. The extreme data imbalanced problem arises when the distribution of the classes present in the datasets is not uniform. Such that the total number of instances in a class far outnumber those of the other classes. Extremely skew data distribution can lead to irregular patterns and trends, which affects the learning of temporal features. This paper proposes a hybrid machine learning approach that blends natural language processing techniques and ensemble learning for predicting extremely rare aircraft component failure. The proposed approach is tested using a real aircraft central maintenance system log-based dataset. The dataset is characterized by extremely rare occurrences of known unscheduled component replacements. The results suggest that the proposed approach outperformed the existing imbalanced and ensemble learning methods in terms of precision, recall, and f1-score. The proposed approach is approximately 10% better than the synthetic minority oversampling technique. It was also found that by searching for patterns in the minority class exclusively, the class imbalance problem could be overcome. Hence, the model classification performance is improve

A rare failure detection model for aircraft predictive maintenance using a deep hybrid learning approach

The use of aircraft operation logs to develop a data-driven model to predict probable failures that could cause interruption poses many challenges and has yet to be fully explored. Given that aircraft is high-integrity assets, failures are exceedingly rare. Hence, the distribution of relevant log data containing prior signs will be heavily skewed towards the typical (healthy) scenario. Thus, this study presents a novel deep learning technique based on the auto-encoder and bidirectional gated recurrent unit networks to handle extremely rare failure predictions in aircraft predictive maintenance modelling. The auto-encoder is modified and trained to detect rare failures, and the result from the auto-encoder is fed into the convolutional bidirectional gated recurrent unit network to predict the next occurrence of failure. The proposed network architecture with the rescaled focal loss addresses the imbalance problem during model training. The effectiveness of the proposed method is evaluated using real-world test cases of log-based warning and failure messages obtained from the fleet database of aircraft central maintenance system records. The proposed model is compared to other similar deep learning approaches. The results indicated an 18% increase in precision, a 5% increase in recall, and a 10% increase in G-mean values. It also demonstrates reliability in anticipating rare failures within a predetermined, meaningful time frame

Does personality affect premating isolation between locally-adapted populations?

Background: One aspect of premating isolation between diverging, locally-adapted population pairs is female mate choice for resident over alien male phenotypes. Mating preferences often show considerable individual variation, and whether or not certain individuals are more likely to contribute to population interbreeding remains to be studied. In the Poecilia mexicana-species complex different ecotypes have adapted to hydrogen sulfide (H2S)-toxic springs, and females from adjacent non-sulfidic habitats prefer resident over sulfide-adapted males. We asked if consistent individual differences in behavioral tendencies (animal personality) predict the strength and direction of the mate choice component of premating isolation in this system. Results: We characterized focal females for their personality and found behavioral measures of ‘novel object exploration’, ‘boldness’ and ‘activity in an unknown area’ to be highly repeatable. Furthermore, the interaction term between our measures of exploration and boldness affected focal females’ strength of preference (SOP) for the resident male phenotype in dichotomous association preference tests. High exploration tendencies were coupled with stronger SOPs for resident over alien mating partners in bold, but not shy, females. Shy and/or little explorative females had an increased likelihood of preferring the non-resident phenotype and thus, are more likely to contribute to rare population hybridization. When we offered large vs. small conspecific stimulus males instead, less explorative females showed stronger preferences for large male body size. However, this effect disappeared when the size difference between the stimulus males was small. Conclusions: Our results suggest that personality affects female mate choice in a very nuanced fashion. Hence, population differences in the distribution of personality types could be facilitating or impeding reproductive isolation between diverging populations depending on the study system and the male trait(s) upon which females base their mating decisions, respectively

Genetic Approaches To The Analysis of Body Colouration in Nile Tilapia (Oreochromis niloticus)

Body colouration in tilapia is an important trait affecting consumer preference. In the Nile tilapia (Oreochromis niloticus), there are three colour variants which are normal (wild type), red and blond. In some countries, the red variant is important and reaches higher prices in the market. However, one major problem regarding red tilapia culture is their body colouration which is often associated with blotching (mainly black but also red) which is undesirable for the consumer. The overall aim of this work was to expand knowledge on various aspects of body colouration in Nile tilapia using genetic approaches. The results of this research are presented as four different manuscripts. The manuscripts (here referred as Papers) have either been published (Paper IV) or are to be submitted (Paper I, II and III) in relevant peer reviewed journals. Paper I and II investigated the inheritance of black blotching and other body colour components of the red body colour. Specifically, Paper I consisted of two preliminary trials (Trial 1 and 2), to look at the ontogeny of black blotching and body colour components over a period of six months. Trial 1 investigated the effect of tank background colour (light vs dark) on black blotching and other body colour components and was carried out using a fully inbred (all female) clonal red line. Trial 2 was carried out using mixed sex fish and was aimed to investigate the association of black blotching with the sex of the fish. The results from this study were used to guide the experiment described in Paper II. Sixteen red sires with various levels of black and red blotching were crossed to clonal females and the inheritance of blotching and other body colour components were investigated using parent-offspring regressions. The results showed no significant heritability for black blotching and body redness, but a significant correlation for body redness and black blotching was found in female offspring at one sampling point suggesting that attempts to increase body redness may increase black blotching, as had been hypothesized. Paper III was divided into two parts. The first objective was to map the blond locus onto the tilapia linkage map and the second was to investigate the interaction of the blond and red genes on black blotching using the blond-linked markers to distinguish different blond genotypes in heterozygous red fish (i.e. RrBlbl or Rrblbl). In the blond fish, the formation of melanin is almost blocked via much reduced melanophores and this feature may be able to help reducing the black blotching in red tilapia. Two intraspecific families (O. niloticus) and one interspecific family (O. aureus and O. niloticus) were used as mapping families and the blond locus was located in LG5. Four out of eight markers were successfully used to assess the interaction of blond on red blotched fish. The blond gene did not significantly reduce the area of blotching but did reduce the saturation (paler blotching) and enhanced the redness of body colour in the Rrblbl fish compared to the RrBlbl group. Finally, Paper IV aimed to find out the effect of male colouration on reproductive success in Nile tilapia. A choice of one wild type male and one red male was presented to red or wild type females and these fish were allowed to spawn under semi-natural spawning conditions. Eggs were collected from the female’s mouth after spawning and paternity was assessed using microsatellite genotyping and phenotype scoring. No significant departures from equal mating success were observed between the red and wild type males, however there was a significant difference between the red and wild type females in the frequency of secondary paternal contribution to egg batches. The results suggest that mating success of wild type and red tilapia is approximately equal. The results from this research help to broaden our knowledge and understanding on the aspects of body colouration in Nile tilapia and provide fundamental information for further research

Preferred reporting items for systematic reviews and meta‐analyses in ecology and evolutionary biology: a PRISMA extension

Since the early 1990s, ecologists and evolutionary biologists have aggregated primary research using meta‐analytic methods to understand ecological and evolutionary phenomena. Meta‐analyses can resolve long‐standing disputes, dispel spurious claims, and generate new research questions. At their worst, however, meta‐analysis publications are wolves in sheep's clothing: subjective with biased conclusions, hidden under coats of objective authority. Conclusions can be rendered unreliable by inappropriate statistical methods, problems with the methods used to select primary research, or problems within the primary research itself. Because of these risks, meta‐analyses are increasingly conducted as part of systematic reviews, which use structured, transparent, and reproducible methods to collate and summarise evidence. For readers to determine whether the conclusions from a systematic review or meta‐analysis should be trusted – and to be able to build upon the review – authors need to report what they did, why they did it, and what they found. Complete, transparent, and reproducible reporting is measured by ‘reporting quality’. To assess perceptions and standards of reporting quality of systematic reviews and meta‐analyses published in ecology and evolutionary biology, we surveyed 208 researchers with relevant experience (as authors, reviewers, or editors), and conducted detailed evaluations of 102 systematic review and meta‐analysis papers published between 2010 and 2019. Reporting quality was far below optimal and approximately normally distributed. Measured reporting quality was lower than what the community perceived, particularly for the systematic review methods required to measure trustworthiness. The minority of assessed papers that referenced a guideline (~16%) showed substantially higher reporting quality than average, and surveyed researchers showed interest in using a reporting guideline to improve reporting quality. The leading guideline for improving reporting quality of systematic reviews is the Preferred Reporting Items for Systematic reviews and Meta‐Analyses (PRISMA) statement. Here we unveil an extension of PRISMA to serve the meta‐analysis community in ecology and evolutionary biology: PRISMA‐EcoEvo (version 1.0). PRISMA‐EcoEvo is a checklist of 27 main items that, when applicable, should be reported in systematic review and meta‐analysis publications summarising primary research in ecology and evolutionary biology. In this explanation and elaboration document, we provide guidance for authors, reviewers, and editors, with explanations for each item on the checklist, including supplementary examples from published papers. Authors can consult this PRISMA‐EcoEvo guideline both in the planning and writing stages of a systematic review and meta‐analysis, to increase reporting quality of submitted manuscripts. Reviewers and editors can use the checklist to assess reporting quality in the manuscripts they review. Overall, PRISMA‐EcoEvo is a resource for the ecology and evolutionary biology community to facilitate transparent and comprehensively reported systematic reviews and meta‐analyses.Data collection for this project was funded through AustraliaResearch Council Discovery Grants: DP180100818 to S.N.,and DP190100297 to M.D.J. We are grateful to 208 anony-mous members of the ecology and evolution meta-analysis com-munity for providing feedback on PRISMA-EcoEvo during itsdevelopment. We thank Alison Bell and Bob Wong for provid-ing feedback on earlier drafts of both the main text and support-ing information, and two anonymous reviewers for theirconstructive comments on the submitted manuscript. Finally,we are grateful to the Cambridge Philosophical Society formaking this article open access

Environmental condition-dependent effects on a heritable, preferred male trait

Models for the evolution of female mating preferences suggest that preferred male traits may be condition dependent. In the field cricket, Gryllus integer, a preferred male trait (calling-bout duration) shows high additive genetic variance. I found that this preferred trait is also condition dependent. Under food deprivation, males lose body mass and correspondingly shorten the durations of their calling bouts. This result implies that females might be able to gain cues from calling-bout durations about a male's body condition. (c) 2005 The Association for the Study of Animal Behaviour Published by Elsevier Ltd. All rights reserved

Publication Bias in Reports of Animal Stroke Studies Leads to Major Overstatement of Efficacy

Publication bias confounds attempts to use systematic reviews to assess the efficacy of various interventions tested in experiments modelling acute ischaemic stroke, leading to a 30% overstatement of efficacy of interventions tested in animals