All insects possess homologous segments, but segment specification differs radically among insect orders. In Drosophila, maternal morphogens control the patterned activation of gap genes, which encode transcriptional regulators that shape the patterned expression of pair-rule genes. This patterning cascade takes place before cellularization. Pair-rule gene products subsequently 'imprint' segment polarity genes with reiterated patterns, thus defining the primordial segments. This mechanism must be greatly modified in insect groups in which many segments emerge only after cellularization. In beetles and parasitic wasps, for instance, pair-rule homologues are expressed in patterns consistent with roles during segmentation, but these patterns emerge within cellular fields. In contrast, although in locusts pair-rule homologues may not control segmentation, some segment polarity genes and their interactions are conserved. Perhaps segmentation is modular, with each module autonomously expressing a characteristic intrinsic behaviour in response to transient stimuli. If so, evolution could rearrange inputs to modules without changing their intrinsic behaviours. Here we suggest, using computer simulations, that the Drosophila segment polarity genes constitute such a module, and that this module is resistant to variations in the kinetic constants that govern its behaviour.

Loss of biodiversity is one of the world's overriding environmental challenges. Reducing those losses by creating reserve networks is a cornerstone of global conservation and resource management. Historically, assembly of reserve networks has been ad hoc, but recently the focus has shifted to identifying optimal reserve networks. We show that while comprehensive reserve network design is best when the entire network can be implemented immediately, when conservation investments must be staged over years, such solutions actually may be sub-optimal in the context of biodiversity loss and uncertainty. Simple decision rules, such as protecting the available site with the highest irreplaceability or with the highest species richness, may be more effective when implementation occurs over many years.

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The neurogenic network is robust to changes in parameter values, which gives it the flexibility to make new patterns. Our model also offers a possible resolution of a debate on the role of lateral inhibition in cell fate specification.

Evolutionary adaptation affects demographic resilience to climate change but few studies have attempted to project changes in selective pressures or quantify impacts of trait responses on population dynamics and extinction risk. We used a novel individual-based model to explore potential evolutionary changes in migration timing and the consequences for population persistence in sockeye salmon Oncorhynchus nerka in the Fraser River, Canada, under scenarios of future climate warming. Adult sockeye salmon are highly sensitive to increases in water temperature during their arduous upriver migration, raising concerns about the fate of these ecologically, culturally, and commercially important fish in a warmer future. Our results suggest that evolution of upriver migration timing could allow these salmon to avoid increasingly frequent stressful temperatures, with the odds of population persistence increasing in proportion to the trait heritability and phenotypic variance. With a simulated 2°C increase in average summer river temperatures by 2100, adult migration timing from the ocean to the river advanced by ∼10 days when the heritability was 0.5, while the risk of quasi-extinction was only 17% of that faced by populations with zero evolutionary potential (i.e., heritability fixed at zero). The rates of evolution required to maintain persistence under simulated scenarios of moderate to rapid warming are plausible based on estimated heritabilities and rates of microevolution of timing traits in salmon and related species, although further empirical work is required to assess potential genetic and ecophysiological constraints on phenological adaptation. These results highlight the benefits to salmon management of maintaining evolutionary potential within populations, in addition to conserving key habitats and minimizing additional stressors where possible, as a means to build resilience to ongoing climate change. More generally, they demonstrate the importance and feasibility of considering evolutionary processes, in addition to ecology and demography, when projecting population responses to environmental change.

Diffusion and osmosis are central concepts in biology, both at the cellular and organ levels. They are presented several times throughout most introductory biology textbooks (e.g., Freeman, 2002), yet both processes are often difficult for students to understand (Odom, 1995;Zuckerman, 1994;Sanger et al., 2001; and results herein). Students have deep-rooted misconceptions about how diffusion and osmosis work, especially at the molecular level. We hypothesized that this might be in part due to the inability to see and explore these processes at the molecular level. In order to investigate this, we developed new software, OsmoBeaker, which allows students to perform inquiry-based experiments at the molecular level. Here we show that these simulated laboratories do indeed teach diffusion and osmosis and help overcome some, but not all, student misconceptions.

Although evolutionary theory is considered to be a unifying foundation for biological education, misconceptions about basic evolutionary processes such as natural selection inhibit student understanding. Even after instruction, students harbor misconceptions about natural selection, suggesting that traditional teaching methods are insufficient for correcting these confusions. This has spurred an effort to develop new teaching methods and tools that effectively confront student misconceptions. In this study, we designed an interactive computer-based simulated laboratory to teach the principles of evolution through natural selection and to correct common student misconceptions about this process. We quantified undergraduate student misconceptions and understanding of natural selection before and after instruction with multiple-choice and open-response test questions and compared student performance across gender and academic levels. While our lab appeared to be effective at dispelling some common misconceptions about natural selection, we did not find evidence that it was as successful at increasing student mastery of the major principles of natural selection. Student performance varied across student academic level and question type, but students performed equally across gender. Beginner students were more likely to use misconceptions before instruction. Advanced students showed greater improvement than beginners on multiple-choice questions, while beginner students reduced their use of misconceptions in the open-response questions to a greater extent. These results suggest that misconceptions can be effectively addressed through computer-based simulated laboratories. Given the level of misconception use by beginner and advanced undergraduates and the gains in performance recorded after instruction at both academic levels, natural selection should continue to be reviewed through upper-level biology courses.

Estimating the risk of extinction for populations of endangered species is an important component of conservation biology. These estimates must be made from data that contain both environmental noise in the year-to-year transitions in population size (so-called "process error"), random errors in sampling, and possible biases in sampling (both forms of observation errors). To determine how much faith to place in estimated extinction rates, it is important to know how sensitive they are to observation error. We used three simple, commonly employed models of population dynamics to generate simulated population time series. We then combined random observation error or systematic biases with those data, fit models to the time series data, and observed how close the extinction dynamics of the fitted models compared with the dynamics of the underlying models. We found that systematic biases in sampling rarely affected estimates of extinction risk. We also found that even moderate levels of random observation error do not significantly affect extinction estimates except over a small range of process errors, corresponding to the region where extinction risk is most uncertain. With more substantial sampling error, estimates of extinction risk degraded rapidly. Field census techniques for a variety of taxa often involve observation errors within Ϯ 32% of actual population sizes. For typical time series used in conservation, therefore, we often may not need to be overly concerned about observation errors as an extra source of imperfection in our estimated extinction rates.¿Se Arruinará el Uso de Modelos Simples de Extinción por Errores y Sesgos de Observación?Resumen: La estimación del riesgo de extinción de poblaciones amendazadas es un componente importante de la biologia de la consevación. Estas estimaciones se hacen con datos que contienen ruido ambiental derivado de las transiciones anuales en el tamaño de la población (el llamado "error de proceso"), errores aleatorios de muestreo y posibles sesgos en el muestreo (ambas de errores de observación). Para determinar la confianza de las tasas de extinción estimadas es importante conocer su sensibilidad a errores de observación. Utilizamos tres models simples de dinámica poblacional para generar series de tiempo simuladas. Combinamos errores aleatorios de observación o sesgos sistemáticos con esos datos, ajustamos modelos a los datos de las series de tiempo y observamos que tanto se acercaba la dinámica de extinción de los modelos ajustados en comparación con la dinámica de los modelos originales. Encontramos que los sesgos sistemáticos de muestreo raramente afectan las estimaciones del riesgo de extinción. También encontramos que aún con niveles moderados de errores aleatorios de observación no se afectan significativamente las estimaciones de extinción excepto en un rango pequeño de errores de proceso, que corresponde a la región en la que el riesgo de extinción es más incierto. Con más errores de muestreo, las estimaciones de riesgo de extinción se degradan rápidamente. ...