Dynamic resource allocation between pre- and postcopulatory episodes of sexual selection determines competitive fertilization success

In polyandrous mating systems, male reproductive success depends on both mate-acquisition traits (precopulatory) and sperm competitive abilities (postcopulatory). Empirical data on the interaction between these traits are inconsistent; revealing positive, negative or no relationships. It is generally expected that the investment in pre- and postcopulatory traits is mediated by environmental conditions. To test how dietary resource availability affects sexual ornamentation, sperm quality and their interrelationship in three-spined sticklebacks (Gasterosteus aculeatus), full-sibling groups were raised under three conditions differing in food quantity and/or quality (i.e. carotenoid content): (i) high-quantity/high-quality, (ii) high-quantity/low-quality or (iii) low-quantity/low-quality. After 1 year of feeding, food-restricted males developed a more intense breeding coloration and faster sperm compared with their well-fed brothers, indicating that they allocated relatively more in pre- and postcopulatory traits. Moreover, they outcompeted their well-fed, carotenoid-supplemented brothers in sperm competition trials with equal numbers of competing sperm, suggesting that food-restricted males maximize their present reproductive success. This may result in reduced future reproductive opportunities as food-restricted males suffered from a higher mortality, had an overall reduced body size, and sperm number available for fertilization. In accordance with theory, a trade-off between the investment in pre- and postcopulatory traits was observed in food-restricted males, whereas well-fed males were able to allocate to both traits resulting in a significantly positive relationship.     

Enhancing the structural diversity between forest patches—A concept and real-world experiment to study biodiversity,multifunctionality and forest resilience across spatial scales

Intensification of land use by humans has led to a homogenization of landscapes and decreasing resilience of ecosystems globally due to a loss of biodiversity, including the majority of forests. Biodiversity–ecosystem functioning (BEF) research has provided compelling evidence for a positive effect of biodiversity on ecosystem functions and services at the local (α-diversity) scale, but we largely lack empirical evidence on how the loss of between-patch β-diversity affects biodiversity and multifunctionality at the landscape scale (γ-diversity). Here, we present a novel concept and experimental framework for elucidating BEF patterns at α-, β-, and γ-scales in real landscapes at a forest management-relevant scale. We examine this framework using 22 temperate broadleaf production forests, dominated by Fagus sylvatica. In 11 of these forests, we manipulated the structure between forest patches by increasing variation in canopy cover and deadwood. We hypothesized that an increase in landscape heterogeneity would enhance the β-diversity of different trophic levels, as well as the β-functionality of various ecosystem functions. We will develop a new statistical framework for BEF studies extending across scales and incorporating biodiversity measures from taxonomic to functional to phylogenetic diversity using Hill numbers. We will further expand the Hill number concept to multifunctionality allowing the decomposition of γ-multifunctionality into α- and β-components. Combining this analytic framework with our experimental data will allow us to test how an increase in between patch heterogeneity affects biodiversity and multifunctionality across spatial scales and trophic levels to help inform and improve forest resilience under climate change. Such an integrative concept for biodiversity and functionality, including spatial scales and multiple aspects of diversity and multifunctionality as well as physical and environmental structure in forests, will go far beyond the current widely applied approach in forestry to increase resilience of future forests through the manipulation of tree species composition.     

Order of bovine DRB3, DYA,and PRL determined by sperm typing

The order and recombination fractions () between the bovine major histocompatibility complex DRB3, DYA, and prolactin (PRL) genes were determined by typing of 254 sperm from a triply heterozygous bull. A recently developed method, primer extension preamplification (PEP), was used to amplify the bovine sperm genome prior to amplification of specific loci by the polymerase chain reaction (PCR). At least 28 copies of the DRB3, PRL, or DYA gene were obtained from 50 cycles of PEP. For sperm typing, alleles of each locus were discriminated by restriction endonuclease cleavage of PCR products and polyacrylamide gel electrophoresis of the restriction fragments. The most likely gene order is PRL-DRB3-DYA, with =0.025 (±0.012) and =0.150 (±0.024), respectively. The odds are 128:1 in favor of this order in comparison with the second most likely order DRB3-PRL-DYA. Our results demonstrate the power of sperm typing in concert with PEP for multilocus gene mapping.     

Direct molecular analysis of the fragile X syndrome in a sample of Egyptian and German patients using non-radioactive PCR and Southern blot followed by chemiluminescent detection

Molecular genetic analysis of individuals from 6 Egyptian and 33 German families with fragile X syndrome and 240 further patients with mental retardation was performed applying a completely non-radioactive system. The aim of our study was the development of a non-radioactive detection method and its implementation in molecular diagnosis of the fragile X syndrome. Furthermore, we wanted to assess differences in the mutation sizes between Egyptian and German patients and between Egyptian and German carriers of a premutation. Using non-radioactive polymerase chain reaction (PCR), agarose gel electrophoresis and blotting of the PCR products, followed by hybridisation with a digoxigenin-labelled oligonucleotide probe (CGG)₅ and chemiluminescent detection, we identified the fragile X full mutation (amplification of a CGG repeat in the FMR-1 gene ranging from several hundred to several thousand repeat units) in all patients. We observed no differences in the length of the CGG repeat between the Egyptian and German patients and carriers, respectively. However, in one prenatal diagnosis, we detected only one normal sized allele in a female fetus using the PCR-agarose assay, whereas Southern blot analysis with the digoxigenin labelled probe StB 12.3 revealed presence of a full mutation. Our newly established nonradioactive genomic blotting method is based on the conventional radioactive Southern blot analysis. Labelling of the probe StB 12.3 with digoxigenin via PCR allowed the detection of normal, premutated and fully mutated alleles. For exact sizing of small premutated or large normal alleles, we separated digoxigenin labelled PCR products through denaturing poly-acrylamide gelelectrophoresis (PAGE) and transfered them to a nylon membrane using a gel dryer. The blotted PCR-fragments can easily be detected with alkaline phosphate-labelled anti-digoxigenin antibody. The number of trinucleotide repeat units can be determined by scoring the detected bands against a digoxigenated M13 sequencing ladder. Our newly developed digoxigenin/chemiluminescence approach using PCR and Southern blot analysis provides reliable results for routine detection of full fragile X mutations and premutations.     

Chick Nutrition and Mortality in Captive Willow Ptarmigan (Lagopus L. Lagopus)

Willow ptarmigan chicks were reared during 8 years on concentrates supplemented with blueberry plants. Mortality during the first 3 weeks after hatching ranged between 33 and 65 %, and was mainly caused by enteritis and digestive tract obstructions. The annual variations in chick survival seemed to be caused by the variations in plant phenology. The survival was highest when spring and blueberry plant development was late, and lowest when spring was early and warm, leading to early lignification of blueberry plant leaves.     

How does landscape context contribute to effects of habitat fragmentation on diversity and population density of butterflies?

Aim Studies on habitat fragmentation of insect communities mostly ignore the impact of the surrounding landscape matrix and treat all species equally. In our study, on habitat fragmentation and the importance of landscape context, we expected that habitat specialists are more affected by area and isolation, and habitat generalists more by landscape context. Location and methods The study was conducted in the vicinity of the city of Göttingen in Germany in the year 2000. We analysed butterfly communities by transect counts on thirty‐two calcareous grasslands differing in size (0.03–5.14 ha), isolation index (2100–86,000/edge‐to‐edge distance 55–1894 m), and landscape diversity (Shannon–Wiener: 0.09–1.56), which is correlated to percentage grassland in the landscape. Results A total of 15,185 butterfly specimens belonging to fifty‐four species are recorded. In multiple regression analysis, the number of habitat specialist (n = 20) and habitat generalist (n = 34) butterfly species increased with habitat area, but z‐values (slopes) of the species–area relationships for specialists (z = 0.399) were significantly steeper compared with generalists (z = 0.096). Generalists, but not specialists, showed a marginally significant increase with landscape diversity. Effects of landscape diversity were scale‐dependent and significant only at the smallest scale (landscape context within a 250 m radius around the habitat). Habitat isolation was not related to specialist and generalist species numbers. In multiple regression analysis the density of specialists increased significantly with habitat area, whereas generalist density increased only marginally. Habitat isolation and landscape diversity did not show any effects. Main conclusions Habitat area was the most important predictor of butterfly community structure and influenced habitat specialists more than habitat generalists. In contrast to our expectations, habitat isolation had no effect as most butterflies could cope with the degree of isolation in our study region. Landscape diversity appeared to be important for generalist butterflies only.     

Understanding extinction debts: spatio–temporal scales,mechanisms and a roadmap for future research

Extinction debt refers to delayed species extinctions expected as a consequence of ecosystem perturbation. Quantifying such extinctions and investigating long‐term consequences of perturbations has proven challenging, because perturbations are not isolated and occur across various spatial and temporal scales, from local habitat losses to global warming. Additionally, the relative importance of eco‐evolutionary processes varies across scales, because levels of ecological organization, i.e. individuals, (meta)populations and (meta)communities, respond hierarchically to perturbations. To summarize our current knowledge of the scales and mechanisms influencing extinction debts, we reviewed recent empirical, theoretical and methodological studies addressing either the spatio–temporal scales of extinction debts or the eco‐evolutionary mechanisms delaying extinctions. Extinction debts were detected across a range of ecosystems and taxonomic groups, with estimates ranging from 9 to 90% of current species richness. The duration over which debts have been sustained varies from 5 to 570 yr, and projections of the total period required to settle a debt can extend to 1000 yr. Reported causes of delayed extinctions are 1) life‐history traits that prolong individual survival, and 2) population and metapopulation dynamics that maintain populations under deteriorated conditions. Other potential factors that may extend survival time such as microevolutionary dynamics, or delayed extinctions of interaction partners, have rarely been analyzed. Therefore, we propose a roadmap for future research with three key avenues: 1) the microevolutionary dynamics of extinction processes, 2) the disjunctive loss of interacting species and 3) the impact of multiple regimes of perturbation on the payment of debts. For their ability to integrate processes occurring at different levels of ecological organization, we highlight mechanistic simulation models as tools to address these knowledge gaps and to deepen our understanding of extinction dynamics.     

Methods to account for spatial autocorrelation in the analysis of species distributional data: a review

Species distributional or trait data based on range map (extent‐of‐occurrence) or atlas survey data often display spatial autocorrelation, i.e. locations close to each other exhibit more similar values than those further apart. If this pattern remains present in the residuals of a statistical model based on such data, one of the key assumptions of standard statistical analyses, that residuals are independent and identically distributed (i.i.d), is violated. The violation of the assumption of i.i.d. residuals may bias parameter estimates and can increase type I error rates (falsely rejecting the null hypothesis of no effect). While this is increasingly recognised by researchers analysing species distribution data, there is, to our knowledge, no comprehensive overview of the many available spatial statistical methods to take spatial autocorrelation into account in tests of statistical significance. Here, we describe six different statistical approaches to infer correlates of species’ distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations. A comprehensive comparison of the relative merits of these methods is beyond the scope of this paper. To demonstrate each method's implementation, however, we undertook preliminary tests based on simulated data. These preliminary tests verified that most of the spatial modeling techniques we examined showed good type I error control and precise parameter estimates, at least when confronted with simplistic simulated data containing spatial autocorrelation in the errors. However, we found that for presence/absence data the results and conclusions were very variable between the different methods. This is likely due to the low information content of binary maps. Also, in contrast with previous studies, we found that autocovariate methods consistently underestimated the effects of environmental controls of species distributions. Given their widespread use, in particular for the modelling of species presence/absence data (e.g. climate envelope models), we argue that this warrants further study and caution in their use. To aid other ecologists in making use of the methods described, code to implement them in freely available software is provided in an electronic appendix.     

Multiple stressors on biotic interactions: how climate change and alien species interact to affect pollination

Global change may substantially affect biodiversity and ecosystem functioning but little is known about its effects on essential biotic interactions. Since different environmental drivers rarely act in isolation it is important to consider interactive effects. Here, we focus on how two key drivers of anthropogenic environmental change, climate change and the introduction of alien species, affect plant–pollinator interactions. Based on a literature survey we identify climatically sensitive aspects of species interactions, assess potential effects of climate change on these mechanisms, and derive hypotheses that may form the basis of future research. We find that both climate change and alien species will ultimately lead to the creation of novel communities. In these communities certain interactions may no longer occur while there will also be potential for the emergence of new relationships. Alien species can both partly compensate for the often negative effects of climate change but also amplify them in some cases. Since potential positive effects are often restricted to generalist interactions among species, climate change and alien species in combination can result in significant threats to more specialist interactions involving native species.     

Hipposin,a histone-derived antimicrobial peptide in Atlantic halibut (Hippoglossus hippoglossus L.)

A novel 51-residue antimicrobial peptide (AMP) from the skin mucus of Atlantic halibut (Hippoglossus hippoglossus L.) was isolated using acid extraction, and cationic exchange and reversed phase chromatography. The complete amino acid sequence of the AMP, termed hipposin, was determined by automated Edman degradation and mass spectrometry to be SGRGKTGGKARAKAKTRSSRAGLQFPVGRVHRLLRKGNYAHRVGAGAPVYL. The N-terminal amino group was acetylated. The theoretical mass of hipposin was calculated to be 5458.4 Da, which was in good agreement with the mass of 5459 Da determined by matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS). Hipposin was shown to be derived from histone H2A by PCR amplifying the encoding sequences from Atlantic halibut genomic DNA. The peptide showed sequence similarity with the 39-mer AMP buforin I of Asian toad and the 19-mer AMP parasin I of catfish. Fifty of the fifty-one residues in hipposin were identical to the N-terminal region of histone H2A from rainbow trout. Hipposin showed strong antimicrobial activity against several Gram-positive and Gram-negative bacteria and activity could be detected down to hipposin concentrations of 0.3 microM (1.6 microg/ml). Hipposin without N-terminal acetylation was prepared by solid-phase peptide synthesis and shown to have the same antimicrobial activity as the natural acetylated peptide. Thus, hipposin is a new broad-spectrum histone-derived AMP found in the skin mucus of Atlantic halibut.