Cryptic female choice favours sperm from major histocompatibility complex-dissimilar males

Cryptic female choice may enable polyandrous females to avoid inbreeding or bias offspring variability at key loci after mating. However, the role of these genetic benefits in cryptic female choice remains poorly understood. Female red junglefowl, Gallus gallus, bias sperm use in favour of unrelated males. Here, we experimentally investigate whether this bias is driven by relatedness per se, or by similarity at the major histocompatibility complex (MHC), genes central to vertebrate acquired immunity, where polymorphism is critical to an individual's ability to combat pathogens. Through experimentally controlled natural matings, we confirm that selection against related males' sperm occurs within the female reproductive tract but demonstrate that this is more accurately predicted by MHC similarity: controlling for relatedness per se, more sperm reached the eggs when partners were MHC-dissimilar. Importantly, this effect appeared largely owing to similarity at a single MHC locus (class I minor). Further, the effect of MHC similarity was lost following artificial insemination, suggesting that male phenotypic cues might be required for females to select sperm differentially. These results indicate that postmating mechanisms that reduce inbreeding may do so as a consequence of more specific strategies of cryptic female choice promoting MHC diversity in offspring

Shiny app to predict agricultural tire dimensions

The main objective of this project, carried out in an industrial context, was to apply a multivariate analysis to variables related to the specifications required for the production of an agricultural tire and the dimensional test results. With the exploratory data analysis, it was possible to identify strong correlations between predictor variables and with the response variables of each test. In this project, the principal component analysis (PCA) serves to eliminate the effects of multicollinearity. The use of regression analysis was intended to predict the behavior of the agricultural tire considering the selected variables of each test. In the case of Test 1, when applying the Stepwise methods to select the variables, the model with the lowest value of Akaike Information Criterion (AIC) was achieved with the technique “Both”. However, the lowest value of AIC for Test 2 was achieved with “Backward”. Regarding the validation of assumptions, both Test 1 and Test 2 were validated. Therefore, all the quantitative variables are important, both in Test 1 and Test 2, because they are a linear combination that determines the principal components. In order to make it easier to compute predictions for future agricultural tires, an application that was developed in Shiny allows the company to know the behavior of the tire before it was produced. Using the application, it is possible to reduce the industrialization time, materials and resources, thus increasing efficiency and profits.This work has been supported by FCT – Fundação para a Ciˆencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

Social learning in otters

The use of information provided by others to tackle life's challenges is widespread, but should not be employed indiscriminately if it is to be adaptive. Evidence is accumulating that animals are indeed selective and adopt ‘social learning strategies’. However, studies have generally focused on fish, bird and primate species. Here we extend research on social learning strategies to a taxonomic group that has been neglected until now: otters (subfamily Lutrinae). We collected social association data on captive groups of two gregarious species: smooth-coated otters (Lutrogale perspicillata), known to hunt fish cooperatively in the wild, and Asian short-clawed otters (Aonyx cinereus), which feed individually on prey requiring extractive foraging behaviours. We then presented otter groups with a series of novel foraging tasks, and inferred social transmission of task solutions with network-based diffusion analysis. We show that smooth-coated otters can socially learn how to exploit novel food sources and may adopt a ‘copy when young’ strategy. We found no evidence for social learning in the Asian short-clawed otters. Otters are thus a promising model system for comparative research into social learning strategies, while conservation reintroduction programmes may benefit from facilitating the social transmission of survival skills in these vulnerable specie

Timescale Dependence in River Channel Migration Measurements

Accurately measuring river meander migration over time is critical for sediment budgets and understanding how rivers respond to changes in hydrology or sediment supply. However, estimates of meander migration rates or streambank contributions to sediment budgets using repeat aerial imagery, maps, or topographic data will be underestimated without proper accounting for channel reversal. Furthermore, comparing channel planform adjustment measured over dissimilar timescales are biased because short‐ and long‐term measurements are disproportionately affected by temporary rate variability, long‐term hiatuses, and channel reversals. We evaluate the role of timescale dependence for the Root River, a single threaded meandering sand‐ and gravel‐bedded river in southeastern Minnesota, USA, with 76 years of aerial photographs spanning an era of landscape changes that have drastically altered flows. Empirical data and results from a statistical river migration model both confirm a temporal measurement‐scale dependence, illustrated by systematic underestimations (2–15% at 50 years) and convergence of migration rates measured over sufficiently long timescales (\u3e 40 years). Frequency of channel reversals exerts primary control on measurement bias for longer time intervals by erasing the record of observable migration. We conclude that using long‐term measurements of channel migration for sediment remobilization projections, streambank contributions to sediment budgets, sediment flux estimates, and perceptions of fluvial change will necessarily underestimate such calculations. © 2019 John Wiley & Sons, Ltd

Latitudinal diversity gradients in Mesozoic non-marine turtles

© 2016 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. The attached file is the published version of the article

Predicting population change from models based on habitat availability and utilization.

The need to understand the impacts of land management for conservation, agriculture and disease prevention are driving demand for new predictive ecology approaches that can reliably forecast future changes in population size. Currently, although the link between habitat composition and animal population dynamics is undisputed, its function has not been quantified in a way that enables accurate prediction of population change in nature. Here, using 12 house sparrow colonies as a proof-of-concept, we apply recent theoretical advances to predict population growth or decline from detailed data on habitat composition and habitat selection. We show, for the first time, that statistical population models using derived covariates constructed from parametric descriptions of habitat composition and habitat selection can explain an impressive 92% of observed population variation. More importantly, they provide excellent predictive power under cross-validation, anticipating 81% of variability in population change. These models may be embedded in readily available generalized linear modelling frameworks, allowing their rapid application to field systems. Furthermore, we use optimization on our sample of sparrow colonies to demonstrate how such models, linking populations to their habitats, permit the design of practical and environmentally sound habitat manipulations for managing populations

Behavioural homogenisation with spillovers in a normative domain

The importance of culture for human social evolution hinges largely on the extent to which culture supports outcomes that would not otherwise occur. An especially controversial claim is that social learning leads groups to coalesce around group-typical behaviours and associated social norms that spill over to shape choices in asocial settings. To test this, we conducted an experiment with 878 groups of participants in 116 communities in Sudan. Participants watched a short film and evaluated the appropriate way to behave in the situation dramatized in the film. Each session consisted of an asocial condition in which participants provided private evaluations and a social condition in which they provided public evaluations. Public evaluations allowed for social learning. Across sessions, we randomized the order of the two conditions. Public choices dramatically increased the homogeneity of normative evaluations. When the social condition was first, this homogenizing effect spilled over to subsequent asocial conditions. The asocial condition when first was thus alone in producing distinctly heterogeneous groups. Altogether, information about the choices of others led participants to converge rapidly on similar normative evaluations that continued to hold sway in subsequent asocial settings. These spillovers were at least partly owing to the combined effects of conformity and self-consistency. Conformity dominated self-consistency when the two mechanisms were in conflict, but self-consistency otherwise produced choices that persisted through time. Additionally, the tendency to conform was heterogeneous. Females conformed more than males, and conformity increased with the number of other people a decision-maker observed before making her own choice

Climate and Dispersal: Black-Winged Stilts Disperse Further in Dry Springs

Climate affects the abundance and distribution of many species of wildlife. Nevertheless, the potential effects of climate on dispersive behaviour remain unstudied. Here, I combine data from (i) a long-term Black-winged Stilt (Himantopus himantopus) monitoring program, (ii) a capture-recapture marking program in Doñana, and (iii) reports from the Rare Birds Committee in the United Kingdom to analyse at different geographical scales the relationship between climate, survival, philopatry, and dispersive behaviour. Black-winged Stilt populations varied in size in consonance with changes in both the North Atlantic Oscillation (NAO) and local rainfall during the breeding season. Changes in population size are related to changes in philopatry and increases in dispersal beyond the traditional range of the species. The results indicate that climatic conditions influence the dispersive behaviour of individual birds, explaining rapid changes in the local population of this species breeding in unstable Mediterranean wetlands

Standardising terminology and notation for the analysis of demographic processes in marked populations

The development of statistical methods for the analysis of demographic processes in marked animal populations has brought with it the challenges of communication between the disciplines of statistics, ecology, evolutionary biology and computer science. In order to aid communication and comprehension, we sought to root out a number of cases of ambiguity, redundancy and inaccuracy in notation and terminology that have developed in the literature. We invited all working in this field to submit topics for resolution and to express their own views. In the ensuing discussion forum it was then possible to establish a series of general principles which were, almost without exception, unanimously accepted. Here we set out the background to the areas of confusion, how these were debated and the conclusions which were reached in each case. We hope that the resulting guidelines will be widely adopted as standard terminology in publications and in software for the analysis of demographic processes in marked animal populationspostprin

Automated multi-objective calibration of biological agent-based simulations

Computational agent-based simulation (ABS) is increasingly used to complement laboratory techniques in advancing our understanding of biological systems. Calibration, the identification of parameter values that align simulation with biological behaviours, becomes challenging as increasingly complex biological domains are simulated. Complex domains cannot be characterized by single metrics alone, rendering simulation calibration a fundamentally multi-metric optimization problem that typical calibration techniques cannot handle. Yet calibration is an essential activity in simulation-based science; the baseline calibration forms a control for subsequent experimentation and hence is fundamental in the interpretation of results. Here, we develop and showcase a method, built around multi-objective optimization, for calibrating ABSs against complex target behaviours requiring several metrics (termed objectives) to characterize. Multi-objective calibration (MOC) delivers those sets of parameter values representing optimal trade-offs in simulation performance against each metric, in the form of a Pareto front. We use MOC to calibrate a well-understood immunological simulation against both established a priori and previously unestablished target behaviours. Furthermore, we show that simulation-borne conclusions are broadly, but not entirely, robust to adopting baseline parameter values from different extremes of the Pareto front, highlighting the importance of MOC's identification of numerous calibration solutions. We devise a method for detecting overfitting in a multi-objective context, not previously possible, used to save computational effort by terminating MOC when no improved solutions will be found. MOC can significantly impact biological simulation, adding rigour to and speeding up an otherwise time-consuming calibration process and highlighting inappropriate biological capture by simulations that cannot be well calibrated. As such, it produces more accurate simulations that generate more informative biological predictions