| 1. |
- Crone, Elizabeth E., et al.
(författare)
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Ability of Matrix Models to Explain the Past and Predict the Future of Plant Populations
- 2013
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Ingår i: Conservation Biology. - : Wiley. - 0888-8892 .- 1523-1739. ; 27:5, s. 968-978
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Tidskriftsartikel (refereegranskat)abstract
- Uncertainty associated with ecological forecasts has long been recognized, but forecast accuracy is rarely quantified. We evaluated how well data on 82 populations of 20 species of plants spanning 3 continents explained and predicted plant population dynamics. We parameterized stage-based matrix models with demographic data from individually marked plants and determined how well these models forecast population sizes observed at least 5 years into the future. Simple demographic models forecasted population dynamics poorly; only 40% of observed population sizes fell within our forecasts' 95% confidence limits. However, these models explained population dynamics during the years in which data were collected; observed changes in population size during the data-collection period were strongly positively correlated with population growth rate. Thus, these models are at least a sound way to quantify population status. Poor forecasts were not associated with the number of individual plants or years of data. We tested whether vital rates were density dependent and found both positive and negative density dependence. However, density dependence was not associated with forecast error. Forecast error was significantly associated with environmental differences between the data collection and forecast periods. To forecast population fates, more detailed models, such as those that project how environments are likely to change and how these changes will affect population dynamics, may be needed. Such detailed models are not always feasible. Thus, it may be wiser to make risk-averse decisions than to expect precise forecasts from models. Habilidad de los Modelos Matriciales para Explicar el Pasado y Predecir el Futuro de las Poblaciones de Plantas Resumen La incertidumbre asociada con el pronostico ecologico ha sido reconocida durante un largo tiempo pero rara vez se cuantifica su seguridad. Evaluamos que tan bien la informacion de 82 poblaciones de 20 especies de plantas a lo largo de 3 continentes explica y predice la dinamica de poblacion de las plantas. Realizamos parametros con modelos matriciales con base en estadios con datos demograficos a partir de plantas marcadas individualmente y determinamos que tan bien estos modelos pronostican el tamano de las poblaciones al menos 5 anos en el futuro. Los modelos demograficos simples pronosticaron pobremente las dinamicas de poblacion; solamente el 40% de las poblaciones observadas cayo dentro de los limites de confianza de 85% de nuestros pronosticos. Estos modelos sin embargo explicaron la dinamica de poblacion a lo largo de los anos en los que se colectaron datos; los cambios observados en el tamano de la poblacion durante el periodo de colecta de datos estuvieron positivamente correlacionados con la tasa de crecimiento de la poblacion. Asi, estos modelos son por lo menos una manera segura de cuantificar el estado de la poblacion. Los pronosticos debiles no estuvieron asociados con el numero de plantas individuales o con los anos de datos. Probamos si las tasas vitales dependian de la densidad y encontramos que existe dependencia hacia la densidad tanto positiva como negativa, sin embargo la dependencia de densidad no se asocio con el error de pronostico. El error de pronostico estuvo significativamente asociado con diferencias ambientales entre la recoleccion de datos y los periodos de pronostico. Para predecir el destino de las poblaciones se necesitan modelos mas detallados, como aquellos que proyectan los cambios probables en el ambiente y como estos cambios afectaran a la dinamica de las poblaciones. Tales modelos tan detallados no siempre son factibles. Por ello puede ser mejor tomar decisiones aversas a riesgos que esperar pronosticos precisos de los modelos.
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| 2. |
- Crone, Elizabeth E., et al.
(författare)
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How do plant ecologists use matrix population models?
- 2011
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Ingår i: Ecology Letters. - : Wiley. - 1461-023X .- 1461-0248. ; 14:1, s. 1-8
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Tidskriftsartikel (refereegranskat)abstract
- P>Matrix projection models are among the most widely used tools in plant ecology. However, the way in which plant ecologists use and interpret these models differs from the way in which they are presented in the broader academic literature. In contrast to calls from earlier reviews, most studies of plant populations are based on < 5 matrices and present simple metrics such as deterministic population growth rates. However, plant ecologists also cautioned against literal interpretation of model predictions. Although academic studies have emphasized testing quantitative model predictions, such forecasts are not the way in which plant ecologists find matrix models to be most useful. Improving forecasting ability would necessitate increased model complexity and longer studies. Therefore, in addition to longer term studies with better links to environmental drivers, priorities for research include critically evaluating relative/comparative uses of matrix models and asking how we can use many short-term studies to understand long-term population dynamics.
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| 3. |
- Hardisty, Alex R., et al.
(författare)
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BioVeL: A virtual laboratory for data analysis and modelling in biodiversity science and ecology
- 2016
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Ingår i: BMC Ecology. - : Springer Science and Business Media LLC. - 1472-6785. ; 16
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Tidskriftsartikel (refereegranskat)abstract
- © 2016 The Author(s).Background: Making forecasts about biodiversity and giving support to policy relies increasingly on large collections of data held electronically, and on substantial computational capability and capacity to analyse, model, simulate and predict using such data. However, the physically distributed nature of data resources and of expertise in advanced analytical tools creates many challenges for the modern scientist. Across the wider biological sciences, presenting such capabilities on the Internet (as "Web services") and using scientific workflow systems to compose them for particular tasks is a practical way to carry out robust "in silico" science. However, use of this approach in biodiversity science and ecology has thus far been quite limited. Results: BioVeL is a virtual laboratory for data analysis and modelling in biodiversity science and ecology, freely accessible via the Internet. BioVeL includes functions for accessing and analysing data through curated Web services; for performing complex in silico analysis through exposure of R programs, workflows, and batch processing functions; for on-line collaboration through sharing of workflows and workflow runs; for experiment documentation through reproducibility and repeatability; and for computational support via seamless connections to supporting computing infrastructures. We developed and improved more than 60 Web services with significant potential in many different kinds of data analysis and modelling tasks. We composed reusable workflows using these Web services, also incorporating R programs. Deploying these tools into an easy-to-use and accessible 'virtual laboratory', free via the Internet, we applied the workflows in several diverse case studies. We opened the virtual laboratory for public use and through a programme of external engagement we actively encouraged scientists and third party application and tool developers to try out the services and contribute to the activity. Conclusions: Our work shows we can deliver an operational, scalable and flexible Internet-based virtual laboratory to meet new demands for data processing and analysis in biodiversity science and ecology. In particular, we have successfully integrated existing and popular tools and practices from different scientific disciplines to be used in biodiversity and ecological research.
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| 4. |
- Hooftman, Danny A. P., et al.
(författare)
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Introgression potential of downy mildew resistance from lettuce to Lactuca serriola and its relevance for plant fitness
- 2007
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Ingår i: Basic and Applied Ecology. - : Elsevier BV. - 1439-1791 .- 1618-0089. ; 8:2, s. 135-146
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Tidskriftsartikel (refereegranskat)abstract
- Hybridization between plant species may cause ecological changes in species, enhancing their potential to become invasive. Increasing evidence suggests that the expanding distribution of Lactuca serriola could (partly) be caused by hybridization with its closest relative, the crop plant L. sativa (lettuce). Fitness advantages in hybrids may result from heterosis and epistasis but single added traits may enhance hybrid fitness as well. Here, we study the potential for introgression of an important crop trait, downy mildew (Bremia lactucae) resistance, into L. serriola hybrids. We monitored the abundance of B. lactucae on wild L. serriola in The Netherlands to test its impact potential. We found that B. lactucae is common in The Netherlands: B. lactucae occurred in 33 of the 35 surveyed populations of L. serriola. Subsequently, under regulated conditions, using two physiotypes of B. lactucae (BL-16 and BL-18), we recorded (i) quantitative conidiophore and (ii) haustoria development in seedlings. Furthermore, we explored its effects on plant fitness. Based on the (non-) occurrence of conidiophores, genomic segments from L. sativa that include Bremia resistance are present and expressing in these hybrids for at least two hybrid generations. However, haustoria density was lower in all hybrids, irrespective of physiotype or siring cultivar. We attributed this to heterosis. Furthermore, all plants shed infected leaves, but we observed no effects of infection on reproductive fitness. We therefore suggest that when L. serriola gains resistance to B. lactucae, the fitness effects are tow. The hypothesis that the gain of B. lactucae resistance is causally related to the recent spread of L. serriola has become less Likely. If hybridization is connected to this at all, it seems through heterosis. We conclude that it is important to study fitness impacts of introgressed traits along with other ecological factors. (c) 2006 Gesellschaft fur Okologie. Published by Elsevier GmbH. All rights reserved.
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