Spatial models of carbon, nitrogen, and sulfur stable isotope distributions (isoscapes) across a shelf sea: an INLA approach

Spatial models of variation in the isotopic composition of structural nutrients across habitats (isoscapes) offer information on physical, biogeochemical and anthropogenic processes occurring across space, and provide a tool for retrospective assignment of animals or animal products to their foraging area or geographic origin. The isotopic differences among reference samples used to construct isoscapes may vary spatially and according to non‐spatial terms (e.g. sampling date, or among individual or species effects). Partitioning variance between spatially dependent and spatially independent terms is a critical but overlooked aspect of isoscape creation with important consequences for the design of studies collecting reference data for isoscape creation and the accuracy and precision of isoscape models. We introduce the use of integrated nested Laplace approximation (INLA) to construct isoscape models. Integrated nested Laplace approximation provides a computationally efficient framework to construct spatial models of isotopic variability explicitly addressing additional variation introduced by including multiple reference species (or other recognized sources of variance). We present carbon, nitrogen and sulphur isoscape models extending over c. 1 million km2 of the UK shelf seas. Models were built using seven different species of jellyfish as spatial reference data and a suite of environmental correlates. Compared to alternative isoscape prediction methods, INLA‐spatial isotope models show high spatial precision and reduced variance. We briefly discuss the likely biogeochemical explanations for the observed spatial isotope distributions. We show for the first time that sulphur isotopes display systematic spatial variation across open marine shelf seas and may therefore be a useful additional tool for marine spatial ecology. The INLA technique provides a promising tool for generating isoscape models and associated uncertainty surfaces where reference data are accompanied by multiple, quantifiable sources of uncertainty

Planning tiger recovery: Understanding intraspecific variation for effective conservation

Although significantly more money is spent on the conservation of tigers than on any other threatened species, today only 3200 to 3600 tigers roam the forests of Asia, occupying only 7% of their historical range. Despite the global significance of and interest in tiger conservation, global approaches to plan tiger recovery are partly impeded by the lack of a consensus on the number of tiger subspecies or management units, because a comprehensive analysis of tiger variation is lacking. We analyzed variation among all nine putative tiger subspecies, using extensive data sets of several traits [morphological (craniodental and pelage), ecological, molecular]. Our analyses revealed little variation and large overlaps in each trait among putative subspecies, and molecular data showed extremely low diversity because of a severe Late Pleistocene population decline. Our results support recognition of only two subspecies: the Sunda tiger, Panthera tigris sondaica, and the continental tiger, Panthera tigris tigris, which consists of two (northern and southern) management units. Conservation management programs, such as captive breeding, reintroduction initiatives, or trans-boundary projects, rely on a durable, consistent characterization of subspecies as taxonomic units, defined by robust multiple lines of scientific evidence rather than single traits or ad hoc descriptions of one or few specimens. Our multiple-trait data set supports a fundamental rethinking of the conventional tiger taxonomy paradigm, which will have profound implications for the management of in situ and ex situ tiger populations and boost conservation efforts by facilitating a pragmatic approach to tiger conservation management worldwid

A Weakly Supervised Deep Learning Approach for Detecting Malaria and Sickle Cells in Blood Films

Machine vision analysis of blood films imaged under a brightfield microscope could provide scalable malaria diagnosis solutions in resource constrained endemic urban settings. The major bottleneck in successfully analyzing blood films with deep learning vision techniques is a lack of object-level annotations of disease markers such as parasites or abnormal red blood cells. To overcome this challenge, this work proposes a novel deep learning supervised approach that leverages weak labels readily available from routine clinical microscopy to diagnose malaria in thick blood film microscopy. This approach is based on aggregating the convolutional features of multiple objects present in one hundred high resolution image fields. We show that this method not only achieves expert-level malaria diagnostic accuracy without any hard object-level labels but can also identify individual malaria parasites in digitized thick blood films, which is useful in assessing disease severity and response to treatment. We demonstrate another application scenario where our approach is able to detect sickle cells in thin blood films. We discuss the wider applicability of the approach in automated analysis of thick blood films for the diagnosis of other blood disorders

Reshaping of Bulbar Odor Response by Nasal Flow Rate in the Rat

The impact of respiratory dynamics on odor response has been poorly studied at the olfactory bulb level. However, it has been shown that sniffing in the behaving rodent is highly dynamic and varies both in frequency and flow rate. Bulbar odor response could vary with these sniffing parameter variations. Consequently, it is necessary to understand how nasal airflow can modify and shape odor response at the olfactory bulb level.To assess this question, we used a double cannulation and simulated nasal airflow protocol on anesthetized rats to uncouple nasal airflow from animal respiration. Both mitral/tufted cell extracellular unit activity and local field potentials (LFPs) were recorded. We found that airflow changes in the normal range were sufficient to substantially reorganize the response of the olfactory bulb. In particular, cellular odor-evoked activities, LFP oscillations and spike phase-locking to LFPs were strongly modified by nasal flow rate.Our results indicate the importance of reconsidering the notion of odor coding as odor response at the bulbar level is ceaselessly modified by respiratory dynamics

Parental Height Differences Predict the Need for an Emergency Caesarean Section

More than 30% of all pregnancies in the UK require some form of assistance at delivery, with one of the more severe forms of assistance being an emergency Caesarean section (ECS). Previously it has been shown that the likelihood of a delivery via ECS is positively associated with the birth weight and size of the newborn and negatively with maternal height. Paternal height affects skeletal growth and mass of the fetus, and thus might also affect pregnancy outcomes. We hypothesized that the effect of newborn birth weight on the risk of ECS would decrease with increasing maternal height. Similarly, we predicted that there would be an increase in ECS risk as a function of paternal height, but that this effect would be relative to maternal height (i.e., parental height differences). We used data from the Millennium Cohort Study: a large-scale survey (N = 18,819 births) with data on babies born and their parents from the United Kingdom surveyed 9 to 12-months after birth. We found that in primiparous women, both maternal height and parental height differences interacted with birth weight and predicted the likelihood of an ECS. When carrying a heavy newborn, the risk of ECS was more than doubled for short women (46.3%) compared to tall women (21.7%), in agreement with earlier findings. For women of average height carrying a heavy newborn while having a relatively short compared to tall partner reduced the risk by 6.7%. In conclusion, the size of the baby, the height of the mother and parental height differences affect the likelihood of an ECS in primiparous women

Genetic variance in fitness indicates rapid contemporary adaptive evolution in wild animals

Funding: Hoge Veluwe great tits: the NIOO-KNAW, ERC, and numerous funding agencies; Wytham great tits: Biotechnology and Biological Sciences Research Council, ERC, and the UK Natural Environment Research Council (NERC).The rate of adaptive evolution, the contribution of selection to genetic changes that increase mean fitness, is determined by the additive genetic variance in individual relative fitness. To date, there are few robust estimates of this parameter for natural populations, and it is therefore unclear whether adaptive evolution can play a meaningful role in short-term population dynamics. We developed and applied quantitative genetic methods to long-term datasets from 19 wild bird and mammal populations and found that, while estimates vary between populations, additive genetic variance in relative fitness is often substantial and, on average, twice that of previous estimates. We show that these rates of contemporary adaptive evolution can affect population dynamics and hence that natural selection has the potential to partly mitigate effects of current environmental change.PostprintPeer reviewe

A curvilinear effect of height on reproductive success in human males

Human male height is associated with mate choice and intra-sexual competition, and therefore potentially with reproductive success. A literature review (n = 18) on the relationship between male height and reproductive success revealed a variety of relationships ranging from negative to curvilinear to positive. Some of the variation in results may stem from methodological issues, such as low power, including men in the sample who have not yet ended their reproductive career, or not controlling for important potential confounders (e.g. education and income). We investigated the associations between height, education, income and the number of surviving children in a large longitudinal sample of men (n = 3,578; Wisconsin Longitudinal Study), who likely had ended their reproductive careers (e.g. > 64 years). There was a curvilinear association between height and number of children, with men of average height attaining the highest reproductive success. This curvilinear relationship remained after controlling for education and income, which were associated with both reproductive success and height. Average height men also married at a younger age than shorter and taller men, and the effect of height diminished after controlling for this association. Thus, average height men partly achieved higher reproductive success by marrying at a younger age. On the basis of our literature review and our data, we conclude that men of average height most likely have higher reproductive success than either short or tall men

Adaptive responses of animals to climate change are most likely insufficient

Biological responses to climate change have been widely documented across taxa and regions, but it remains unclear whether species are maintaining a good match between phenotype and environment, i.e. whether observed trait changes are adaptive. Here we reviewed 10,090 abstracts and extracted data from 71 studies reported in 58 relevant publications, to assess quantitatively whether phenotypic trait changes associated with climate change are adaptive in animals. A meta-analysis focussing on birds, the taxon best represented in our dataset, suggests that global warming has not systematically affected morphological traits, but has advanced phenological traits. We demonstrate that these advances are adaptive for some species, but imperfect as evidenced by the observed consistent selection for earlier timing. Application of a theoretical model indicates that the evolutionary load imposed by incomplete adaptive responses to ongoing climate change may already be threatening the persistence of species. © 2019, The Author(s)

Genetic variance in fitness indicates rapid contemporary adaptive evolution in wild animals

The rate of adaptive evolution, the contribution of selection to genetic changes that increase mean fitness, is determined by the additive genetic variance in individual relative fitness. To date, there are few robust estimates of this parameter for natural populations, and it is therefore unclear whether adaptive evolution can play a meaningful role in short-term population dynamics. We developed and applied quantitative genetic methods to long-term datasets from 19 wild bird and mammal populations and found that, while estimates vary between populations, additive genetic variance in relative fitness is often substantial and, on average, twice that of previous estimates. We show that these rates of contemporary adaptive evolution can affect population dynamics and hence that natural selection has the potential to partly mitigate effects of current environmental change