Selenium distribution in eggs of avian species

We studied the effect of egg mass of eight different avian species on Se distribution between egg components and the effect of incubation on Se accumulation by chicken eggshell and shell membrane. Eight groups of birds received a diet without Se supplementation. Unfertile eggs were collected after 35 days of feeding; yolk, albumen, shell and shell membrane were assayed separately for Se. All avian species studied showed identical Se concentration in yolk–albumen complex equal to 38.7 μg Se/100 g, reflecting a linear correlation between yolk–albumen mass and Se content. Shells and shell membrane Se accumulation showed quadratic correlation with the appropriate mass thus explaining unusually high Se concentration in ostrich shell and shell membrane, that reached values 1785 and 1904 μg Se/kg respectively. Incubation of fertile eggs decreased eggshell Se content, the effect being more expressed in eggs from hens fed sodium selenite compared to organic Se utilization (Sel-Plex). It was concluded that shell might be an additional Se source for an embryo.     

Correlation and process in species distribution models: bridging a dichotomy

Within the field of species distribution modelling an apparent dichotomy exists between process‐based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlative–process spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the process–correlation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process‐based approaches to species distribution modelling lags far behind more correlative (process‐implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process‐explicit species distribution models and how they may complement current approaches to study species distributions.     

Emerging concepts of self-organization and the living state. Special issue

This special issue present papers that examine the concept of self-organization in the origin of life. Concepts explored include chaos and order in open systems, the origin of biochemical organization, non-cellular phases of life on clay, biodynamics necessary for the emergence of energy consumers, molecular evolution, order in self-oscillators, self-organization of semi-conductor physics, self-organizing behavior of microtubules in the cytoskeleton, biogenesis and physics, perceptive funcion, and an overview of the experimental realization of artificial intelligence.     

Cartogramming uncertainty in species distribution models: A Bayesian approach

Predicting the geographical distribution of a species is a central topic in ecology, conservation and management of natural resources especially for invasive organisms. Invasive species can modify the structure and function of invaded ecosystems, altering their biodiversity, and causing significant economic losses locally and globally. Therefore, measuring and visualizing the uncertainty inherent in species’ potential distributions is fundamental for effective biodiversity monitoring and planning conservation interventions. This paper discusses a new Bayesian approach to mapping this uncertainty using cartograms, previously published knowledge, and presence/absence data.     

Incorporating microclimate into species distribution models

Species distribution models (SDMs) have rapidly evolved into one of the most widely used tools to answer a broad range of ecological questions, from the effects of climate change to challenges for species management. Current SDMs and their predictions under anthropogenic climate change are, however, often based on free‐air or synoptic temperature conditions with a coarse resolution, and thus fail to capture apparent temperature (cf. microclimate) experienced by living organisms within their habitats. Yet microclimate operates as soon as a habitat can be characterized by a vertical component (e.g. forests, mountains, or cities) or by horizontal variation in surface cover. The mismatch between how we usually express climate (cf. coarse‐grained free‐air conditions) and the apparent microclimatic conditions that living organisms experience has only recently been acknowledged in SDMs, yet several studies have already made considerable progress in tackling this problem from different angles. In this review, we summarize the currently available methods to obtain meaningful microclimatic data for use in distribution modelling. We discuss the issue of extent and resolution, and propose an integrated framework using a selection of appropriately‐placed sensors in combination with both the detailed measurements of the habitat 3D structure, for example derived from digital elevation models or airborne laser scanning, and the long‐term records of free‐air conditions from weather stations. As such, we can obtain microclimatic data with a relevant spatiotemporal resolution and extent to dynamically model current and future species distributions.