Notas sobre neonatos de Lepidochelys Olivacea (Testudines: Cheloniidae) en playa nueva esperanza, Tumbes, Peru

Informa sobre la nidada y neonatos de "tortuga pico de loro" observados en marzo de 2008 en la playa de Nueva Esperanza.7 p

Amino acid-specific isotopes reveal changing five-dimensional niche segregation in Pacific seabirds over 50 years

Abstract Hutchison’s niche theory suggests that coexisting competing species occupy non-overlapping hypervolumes, which are theoretical spaces encompassing more than three dimensions, within an n-dimensional space. The analysis of multiple stable isotopes can be used to test these ideas where each isotope can be considered a dimension of niche space. These hypervolumes may change over time in response to variation in behaviour or habitat, within or among species, consequently changing the niche space itself. Here, we use isotopic values of carbon and nitrogen of ten amino acids, as well as sulphur isotopic values, to produce multi-isotope models to examine niche segregation among an assemblage of five coexisting seabird species (ancient murrelet Synthliboramphus antiquus, double-crested cormorant Phalacrocorax auritus, Leach’s storm-petrel Oceanodrama leucorhoa, rhinoceros auklet Cerorhinca monocerata, pelagic cormorant Phalacrocorax pelagicus) that inhabit coastal British Columbia. When only one or two isotope dimensions were considered, the five species overlapped considerably, but segregation increased in more dimensions, but often in complex ways. Thus, each of the five species occupied their own isotopic hypervolume (niche), but that became apparent only when factoring the increased information from sulphur and amino acid specific isotope values, rather than just relying on proxies of δ 15N and δ 13C alone. For cormorants, there was reduction of niche size for both species consistent with a decline in their dominant prey, Pacific herring Clupea pallasii, from 1970 to 2006. Consistent with niche theory, cormorant species showed segregation across time, with the double-crested demonstrating a marked change in diet in response to prey shifts in a higher dimensional space. In brief, incorporating multiple isotopes (sulfur, PC1 of δ 15N [baselines], PC2 of δ 15N [trophic position], PC1 and PC2 of δ 13C) metrics allowed us to infer changes and differences in food web topology that were not apparent from classic carbon–nitrogen biplots

Understanding alternative splicing: towards a cellular code

© 2009 Nature Publishing Group, a division of Macmillan Publishers Limited.In violation of the 'one gene, one polypeptide' rule, alternative splicing allows individual genes to produce multiple protein isoforms - thereby playing a central part in generating complex proteomes. Alternative splicing also has a largely hidden function in quantitative gene control, by targeting RNAs for nonsense-mediated decay. Traditional gene-by-gene investigations of alternative splicing mechanisms are now being complemented by global approaches. These promise to reveal details of the nature and operation of cellular codes that are constituted by combinations of regulatory elements in pre-mRNA substrates and by cellular complements of splicing regulators, which together determine regulated splicing pathways

Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology

Gould E, Fraser H, Parker T, et al. Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology. 2023.Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different (mostly social science) fields, and has found substantial variability among results, despite analysts having the same data and research question. We implemented an analogous study in ecology and evolutionary biology, fields in which there have been no empirical exploration of the variation in effect sizes or model predictions generated by the analytical decisions of different researchers. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment), and the project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future