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Mountains are among the most biodiverse places on Earth, and plant lineages that inhabit them have some of the highest speciation rates ever recorded. Plant diversity within the alpine zone - the elevation above which trees cannot grow—contributes significantly to overall diversity within mountain systems, but the origins of alpine plant diversity are poorly understood. Here, we quantify the processes that generate alpine plant diversity and their changing dynamics through time in Saxifraga (Saxifragaceae), an angiosperm genus that occurs predominantly in mountain systems. We present a time-calibrated molecular phylogenetic tree for the genus that is inferred from 329 low-copy nuclear loci and incorporates 73% (407) of known species. We show that upslope biome shifts into the alpine zone are considerably more prevalent than dispersal of alpine specialists between regions, and that the rate of upslope biome shifts increased markedly in the last 5 Myr, a timeframe concordant with a cooling and fluctuating climate that is likely to have increased the extent of the alpine zone. Furthermore, alpine zone specialists have lower speciation rates than generalists that occur inside and outside the alpine zone, and major speciation rate increases within Saxifraga significantly pre-date increased rates of upslope biome shifts. Specialisation to the alpine zone is not therefore associated with speciation rate increases. Taken together, this study presents a quantified and broad scale perspective of processes underpinning alpine plant diversity. The origins of alpine plant diversity are unclear. Here, the authors provide a time-calibrated molecular phylogenetic tree for Saxifraga , a diverse alpine plant clade, and show that upslope biome shifts into the alpine zone occurred more often than dispersal between alpine regions.

Taxonomic monographs have the potential to make a unique contribution to the understanding of global biodiversity. However, such studies, now rare, are often considered too daunting to undertake within a realistic time frame, especially as the world’s collections have doubled in size in recent times. Here, we report a global-scale monographic study of morning glories ( Ipomoea ) that integrated DNA barcodes and high-throughput sequencing with the morphological study of herbarium specimens. Our approach overhauled the taxonomy of this megadiverse group, described 63 new species and uncovered significant increases in net diversification rates comparable to the most iconic evolutionary radiations in the plant kingdom. Finally, we show that more than 60 species of Ipomoea , including sweet potato, independently evolved storage roots in pre-human times, indicating that the storage root is not solely a product of human domestication but a trait that predisposed the species for cultivation. This study demonstrates how the world’s natural history collections can contribute to global challenges in the Anthropocene. Taxonomic monographs have been considered too vast and daunting as a source for studying biodiversity, but this novel study of morning glories combines herbarium specimens with DNA barcodes and high-throughput sequencing to describe new species and discover hidden traits.

We present a method of divergence time estimation (exTREEmaTIME) that aims to effectively account for uncertainty in divergence time estimates. The method requires a minimal set of assumptions, and, based on these assumptions, estimates the oldest possible divergence times and youngest possible divergence times that are consistent with the assumptions. We use a series of simulations and empirical analyses to illustrate that exTREEmaTIME is effective at representing uncertainty. We then describe how exTREEmaTIME can act as a basis to determine the implications of the more stringent assumptions that are incorporated into other methods of divergence time estimation that produce more precise estimates. This is critically important given that many of the assumptions that are incorporated into these methods are highly complex, difficult to justify biologically, and as such can lead to estimates that are highly inaccurate. This article has an associated First Person interview with the first author of the paper.

Atlantic bluefin tuna (Thunnus thynnus) is considered to be overfished, but the status of its populations has been debated, partly because of uncertainties regarding the effects of mixing on fishing grounds. A better understanding of spatial structure and mixing may help fisheries managers to successfully rebuild populations to sustainable levels while maximizing catches. We formulate a new seasonally and spatially explicit fisheries model that is fitted to conventional and electronic tag data, historic catch-at-age reconstructions, and otolith microchemistry stock-composition data to improve the capacity to assess past, current, and future population sizes of Atlantic bluefin tuna. We apply the model to estimate spatial and temporal mixing of the eastern (Mediterranean) and western (Gulf of Mexico) populations, and to reconstruct abundances from 1950 to 2008. We show that western and eastern populations have been reduced to 17% and 33%, respectively, of 1950 spawning stock biomass levels. Overfishing to below the biomass that produces maximum sustainable yield occurred in the 1960s and the late 1990s for western and eastern populations, respectively. The model predicts that mixing depends on season, ontogeny, and location, and is highest in the western Atlantic. Assuming that future catches are zero, western and eastern populations are predicted to recover to levels at maximum sustainable yield by 2025 and 2015, respectively. However, the western population will not recover with catches of 1750 and 12,900 tonnes (the \"rebuilding quotas\") in the western and eastern Atlantic, respectively, with or without closures in the Gulf of Mexico. If future catches are double the rebuilding quotas, then rebuilding of both populations will be compromised. If fishing were to continue in the eastern Atlantic at the unregulated levels of 2007, both stocks would continue to decline. Since populations mix on North Atlantic foraging grounds, successful rebuilding policies will benefit from trans-Atlantic cooperation.

Fisheries researchers have focused on the value of information (VOI) in fisheries management and trade-offs since scientists and managers realized that information from different resources has different contribution in the management process. We picked seven indicators, which are log-normal annual catch observation error (Cobs), annual catch observation bias (Cbias), log-normal annual index observation error (Iobs), maximum length observation bias (Linfbias), observed natural mortality rate bias (Mbias), observed von Bertalanffy growth parameter K bias (Kbias), and catch-at-age sample size (CAA_nsamp), and built operating models (OMs) to simulate fisheries dynamics, and then applied management strategy evaluation (MSE). Relative yield is chosen as the result to evaluate the contribution of the seven indicators. Within the parameter range, there was not much information value reflected from fisheries-dependent parameters including Cobs, Cbias, and Iobs. On the other hand, for fisheries-independent parameters such as Kbias, Mbias, and Linfbias, similar tendency of the information value was showed in the results, in which the relative yield goes down from the upper bound to the lower bound of the interval. CAA_nsamp had no impact on the yield after over 134 individuals. The VOI analysis contributes to the trade-offs in the decision-making process. Information with more value is more worthy to collect in case of waste of time and money so that we could make the best use of scientific effort. But we still need to improve the simulation process such as enhancing the diversity and predictability in an OM. More parameters are on the way to be tested in order to collect optimum information for management and decision-making.

First Person is a series of interviews with the first authors of a selection of papers published in Biology Open, helping early-career researchers promote themselves alongside their papers. Tom Carruthers is first author on ‘ exTREEmaTIME: a method for incorporating uncertainty into divergence time estimates’, published in BiO. Tom conducted the research described in this article while a PhD student in Professor Robert Scotland's lab in the Department of Plant Sciences, University of Oxford. He is now a postdoc in the lab of Dr William Baker at the Royal Botanic Gardens, Kew, working on determining the extent to which large molecular phylogenies provide information about evolutionary history.

Background: Many scholars focus their research efforts on the entrepreneurial intention of students and non-entrepreneurs, yet most of these scholars found empirical evidence that intention does not necessarily lead these individuals to start businesses (entrepreneurial action). Possible explanations for this could be that: (1) previous studies focused on the wrong samples; (2) they measured entrepreneurial intention as a single construct; and (3) there is a missing link between intention and action. Aim: To address these gaps, we determine the relationship between recurring entrepreneurial intention attitudes and action as well as entrepreneurial intention behaviours and action of 154 existing entrepreneurs in South Africa. By focusing on a sample of existing entrepreneurs who have already started a business, we shed light on the set of entrepreneurial competencies as a missing link between intention and action. This article is of academic importance as it focuses on the recurring process that entrepreneurs follow instead of the initial intention that is often overemphasised in literature. As far as could be determined, no other studies have investigated the relationships between entrepreneurial competencies, recurring entrepreneurial intention attitudes, recurring entrepreneurial intention behaviours and recurring entrepreneurial action. Setting: The research was conducted on 154 existing entrepreneurs in South Africa. Methods: A self-administered survey was used and the findings indicate that entrepreneurial competencies have a positive relationship with recurring entrepreneurial action, recurring entrepreneurial intention behaviours and recurring entrepreneurial intention attitudes. Results: There was no significant relationship between entrepreneurial action and recurring entrepreneurial intention behaviours. This is an unexpected finding as a positive relationship was expected for a sample that had prior entrepreneurial experience and already engaged in prior behaviours. However, this study contributes to the entrepreneurial intention–action literature by suggesting that existing entrepreneurs with recurring intention should also be measured in these relationships, in comparison to other research that mainly focused on the intentions of students and non-entrepreneurs. Conclusion: The practical contribution of this article is in the identification of specific entrepreneurial competencies, such as creative problem-solving, opportunity recognition and value creation that existing entrepreneurs relied on the most when engaging in entrepreneurial action. Potential, nascent, existing and serial entrepreneurs could focus on these competencies if they wish to engage in entrepreneurial action as well as recurring entrepreneurship.

Understanding and representing uncertainty is crucial in academic research because it enables studies to build on the conclusions of previous studies, leading to robust advances in a particular field. Here, we evaluate the nature of uncertainty and the manner by which it is represented in divergence time estimation, a field that is fundamental to many aspects of macroevolutionary research, and where there is evidence that uncertainty has been seriously underestimated. We address this issue in the context of methods used in divergence time estimation, and with respect to the manner by which time-calibrated phylogenies are interpreted. With respect to methods, we discuss how the assumptions underlying different methods may not adequately reflect uncertainty about molecular evolution, the fossil record, or diversification rates. Therefore, divergence time estimates may not adequately reflect uncertainty and may be directly contradicted by subsequent findings. For the interpretation of time-calibrated phylogenies, we discuss how the use of time-calibrated phylogenies for reconstructing general evolutionary timescales leads to inferences about macroevolution that are highly sensitive to methodological limitations in how uncertainty is accounted for. By contrast, we discuss how the use of time-calibrated phylogenies to test specific hypotheses leads to inferences about macroevolution that are less sensitive to methodological limitations. Given that many biologists wish to use time-calibrated phylogenies to reconstruct general evolutionary timescales, we conclude that the development of methods of divergence time estimation that adequately account for uncertainty is necessary.

Phylogenies are increasingly being used as a basis to provide insight into macroevolutionary history. Here, we use simulation experiments and empirical analyses to evaluate methods that use phylogenies as a basis to make estimates of divergence times and rates of diversification. This is the first study to present a comprehensive assessment of the key variables that underpin analyses in this field—including substitution rates, speciation rates, and extinction, plus character sampling and taxon sampling. We show that in unrealistically simplistic cases (where substitution rates and speciation rates are constant, and where there is no extinction), increased character and taxon sampling lead to more accurate and precise parameter estimates. By contrast, in more complex but realistic cases (where substitution rates, speciation rates, and extinction rates vary), gains in accuracy and precision from increased character and taxon sampling are far more limited. The lack of accuracy and precision even occurs when using methods that are designed to account for more complex cases, such as relaxed clocks, fossil calibrations, and models that allow speciation rates and extinction rates to vary. The problem also persists when analyzing genomic scale data sets. These results suggest two interrelated problems that occur when the processes that generated the data are more complex. First, methodological assumptions are more likely to be violated. Second, limitations in the information content of the data become more important.

Relaxed clock methods account for among-branch-rate-variation when estimating divergence times by inferring different rates for individual branches. In order to infer different rates for individual branches, important assumptions are required. This is because molecular sequence data do not provide direct information about rates but instead provide direct information about the total number of substitutions along any branch, which is a product of the rate and time for that branch. Often, the assumptions required for estimating rates for individual branches depend heavily on the implementation of multiple fossil calibrations in a single phylogeny. Here, we show that the basis of these assumptions is often critically undermined. First, we highlight that the temporal distribution of the fossil record often violates key assumptions of methods that use multiple fossil calibrations with relaxed clocks. With respect to “node calibration” methods, this conclusion is based on our inference that different fossil calibrations are unlikely to reflect the relative ages of different clades. With respect to the fossilized birth–death process, this conclusion is based on our inference that the fossil recovery rate is often highly heterogeneous. We then demonstrate that methods of divergence time estimation that use multiple fossil calibrations are highly sensitive to assumptions about the fossil record and among-branch-rate-variation. Given the problems associated with these assumptions, our results highlight that using multiple fossil calibrations with relaxed clocks often does little to improve the accuracy of divergence time estimates.