| 1. |
- Griffin, M. J., et al.
(författare)
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The Herschel-SPIRE instrument and its in-flight performance
- 2010
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 518, s. L3-
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Tidskriftsartikel (refereegranskat)abstract
- The Spectral and Photometric Imaging REceiver (SPIRE), is the Herschel Space Observatory`s submillimetre camera and spectrometer. It contains a three-band imaging photometer operating at 250, 350 and 500 mu m, and an imaging Fourier-transform spectrometer (FTS) which covers simultaneously its whole operating range of 194-671 mu m (447-1550 GHz). The SPIRE detectors are arrays of feedhorn-coupled bolometers cooled to 0.3 K. The photometer has a field of view of 4' x 8', observed simultaneously in the three spectral bands. Its main operating mode is scan-mapping, whereby the field of view is scanned across the sky to achieve full spatial sampling and to cover large areas if desired. The spectrometer has an approximately circular field of view with a diameter of 2.6'. The spectral resolution can be adjusted between 1.2 and 25 GHz by changing the stroke length of the FTS scan mirror. Its main operating mode involves a fixed telescope pointing with multiple scans of the FTS mirror to acquire spectral data. For extended source measurements, multiple position offsets are implemented by means of an internal beam steering mirror to achieve the desired spatial sampling and by rastering of the telescope pointing to map areas larger than the field of view. The SPIRE instrument consists of a cold focal plane unit located inside the Herschel cryostat and warm electronics units, located on the spacecraft Service Module, for instrument control and data handling. Science data are transmitted to Earth with no on-board data compression, and processed by automatic pipelines to produce calibrated science products. The in-flight performance of the instrument matches or exceeds predictions based on pre-launch testing and modelling: the photometer sensitivity is comparable to or slightly better than estimated pre-launch, and the spectrometer sensitivity is also better by a factor of 1.5-2.
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| 2. |
- Brose, Ulrich, et al.
(författare)
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Spatial aspects of food webs
- 2005
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Ingår i: Dynamic Food Webs. - London, UK : Elsevier. - 9780120884582 - 0120884585 ; , s. 463-469
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Konferensbidrag (refereegranskat)abstract
- Aspects of spatial scale have until recently been largely ignored in empirical and theoretical food web studies (e.g., Cohen & Briand 1984, Martinez 1992, but see Bengtsson et al. 2002, Bengtsson & Berg, this book). Most ecologists tend to conceptualize and represent food webs as static representations of communities, depicting a community assemblage as sampled at a particular point in time, or highly aggregated trophic group composites over broader scales of time and space (Polis et al. 1996). Moreover, most researchers depict potential food webs, which contain all species sampled and all potential trophic links based on literature reviews, several sampling events, or laboratory feeding trials. In reality, however, not all these potential feeding links are realized as not all species co-occur, and not all samples in space or time can contain all species (Schoenly & Cohen 1991), hence, yielding a variance of food web architecture in space (Brose et al. 2004). In recent years, food web ecologists have recognized that food webs are open systems – that are influence by processes in adjacent systems – and spatially heterogeneous (Polis et al. 1996). This influence of adjacent systems can be bottom-up, due to allochthonous inputs of resources (Polis & Strong 1996, Huxel & McCann 1998, Mulder & De Zwart 2003), or top-down due to the regular or irregular presence of top predators (e.g., Post et al. 2000, Scheu 2001). However, without a clear understanding of the size of a system and a definition of its boundaries it is not possible to judge if flows are internal or driven by adjacent systems. Similarly, the importance of allochthony is only assessable when the balance of inputs and outputs are known relative to the scale and throughputs within the system itself. At the largest scale of the food web – the home range of a predator such as wolf, lion, shark or eagle of roughly 50 km2 to 300 km2 –the balance of inputs and outputs caused by wind and movement of water may be small compared to the total trophic flows within the home range of the large predator (Cousins 1990). Acknowledging these issues of space, Polis et al (1996) argued that progress toward the next phase of food web studies would require addressing spatial and temporal processes. Here, we present a conceptual framework with some nuclei about the role of space in food web ecology. Although we primarily address spatial aspects, this framework is linked to a more general concept of spatio-temporal scales of ecological research.
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| 4. |
- Rönnegård, Lars, et al.
(författare)
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Genetic heterogeneity of residual variance - estimation of variance components using double hierarchical generalized linear models
- 2010
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Ingår i: Genetics Selection Evolution. - : Springer Science and Business Media LLC. - 0999-193X .- 1297-9686. ; 42
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Tidskriftsartikel (refereegranskat)abstract
- Background: The sensitivity to microenvironmental changes varies among animals and may be under genetic control. It is essential to take this element into account when aiming at breeding robust farm animals. Here, linear mixed models with genetic effects in the residual variance part of the model can be used. Such models have previously been fitted using EM and MCMC algorithms.Results: We propose the use of double hierarchical generalized linear models (DHGLM), where the squared residuals are assumed to be gamma distributed and the residual variance is fitted using a generalized linear model. The algorithm iterates between two sets of mixed model equations, one on the level of observations and one on the level of variances. The method was validated using simulations and also by re-analyzing a data set on pig litter size that was previously analyzed using a Bayesian approach. The pig litter size data contained 10,060 records from 4,149 sows. The DHGLM was implemented using the ASReml software and the algorithm converged within three minutes on a Linux server. The estimates were similar to those previously obtained using Bayesian methodology, especially the variance components in the residual variance part of the model.Conclusions: We have shown that variance components in the residual variance part of a linear mixed model can be estimated using a DHGLM approach. The method enables analyses of animal models with large numbers of observations. An important future development of the DHGLM methodology is to include the genetic correlation between the random effects in the mean and residual variance parts of the model as a parameter of the DHGLM.
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