Sutherland C.,University of Aberdeen |
Elston D.A.,Biomathematics and Statistics Scotland |
Lambin X.,University of Aberdeen
Ecology | Year: 2012
Metapopulations function and persist through a combination of processes acting at a variety of spatial scales. Although the contributions of stage structure, spatially correlated processes, and the rescue effect to metapopulation dynamics have been investigated in isolation, there is no empirical demonstration of all of these processes shaping dynamics in a single system. Dispersal and settlement differ according to the life stage involved; therefore, stage-specific population size may outperform total population size when predicting colonization-extinction dynamics. Synchrony in patch dynamics can lead to accelerated metapopulation extinction, although empirical evidence of the interplay between correlated colonization events and correlated extinctions is lacking. Likewise, few empirical examples exist that provide compelling evidence of migration acting to reduce extinction risk (the rescue effect). We parameterized a hierarchy of metapopulation models to investigate these predictions using a seven-year study of a naturally occurring water vole (Arvicola amphibius) metapopulation. Specifically, we demonstrated the importance of local stage structure in predicting both colonization and extinction events using juvenile and adult population sizes, respectively. Using a novel approach for quantifying correlation in extinction events, we compared the scale of synchrony in colonization and extinction. Strikingly, the scale of dispersal acting to synchronize colonization was an order of magnitude larger than that of correlated extinctions (halving distance of the effect: 12.40 km and 0.89 km, respectively). Additionally, we found compelling evidence for the existence of a nontrivial rescue effect. Here we provide a novel empirical demonstration of a variety of metapopulation processes operating at multiple spatial scales, further emphasizing the need to consider stage structure and local synchrony in the dynamics of spatially dependent, stage-structured (meta) populations. © 2012 by the Ecological Society of America.
Wallace J.M.,University of Aberdeen |
Bhattacharya S.,Aberdeen Maternity Hospital |
Horgan G.W.,Biomathematics and Statistics Scotland
Placenta | Year: 2013
Introduction: The weight of the placenta is a crude but useful proxy for its function in vivo. Accordingly extremes of placental weight are associated with adverse pregnancy outcomes while even normal variations in placental size may impact lifelong health. Centile charts of placental weight for gestational age and gender are used to identify placental weight extremes but none report the effect of parity. Thus the objective was to produce gender and gestational age specific centile charts for placental weight in nulliparous and multiparous women. Methods: Data was extracted from the Aberdeen Maternity and Neonatal Databank for all women delivering singleton babies in Aberdeen city and district after 24 weeks gestation. Gestational age specific centile charts for placental weight by gender and parity grouping (n = 88,649 deliveries over a 30 year period) were constructed using the LMS method after exclusion of outliers (0.63% of deliveries meeting study inclusion criteria). Results: Tables and figures are presented for placental weight centiles according to gestational age, gender and parity grouping. Tables are additionally presented for the birth weight to placental weight ratio by gender. Placental weight and the fetal:placental weight ratio were higher in male versus female deliveries. Placental weight was greater in multiparous compared with nulliparous women. Discussion: We present strong evidence that both gender and parity grouping influence placental weight centiles. The differences at any given gestational age are small and the effects of parity are greater overall than those of gender. In contrast the birth weight to placental weight ratio differs by gender only. Conclusion: These UK population specific centile charts may be useful in studies investigating the role of the placenta in mediating pregnancy outcome and lifelong health. © 2012 Elsevier Ltd. All rights reserved.
Wallace J.M.,University of Aberdeen |
Horgan G.W.,Biomathematics and Statistics Scotland |
Bhattacharya S.,Aberdeen Maternity Hospital
Placenta | Year: 2012
Herein we report placental weight and efficiency in relation to maternal BMI and the risk of pregnancy complications in 55,105 pregnancies. Adjusted placental weight increased with increasing BMI through underweight, normal, overweight, obese and morbidly obese categories and accordingly underweight women were more likely to experience placental growth restriction [OR 1.69 (95% CI 1.46-1.95)], while placental hypertrophy was more common in overweight, obese and morbidly obese groups [OR 1.59 (95% CI 1.50-1.69), OR 1.97 (95% CI 1.81-2.15) and OR 2.34 (95% CI 2.08-2.63), respectively]. In contrast the ratio of fetal to placental weight (a proxy for placental efficiency) was lower (P < 0.001) in overweight, obese and morbidly obese than in both normal and underweight women which were equivalent. Relative to the middle tertile reference group (mean 622 g), placental weight in the lower tertile (mean 484 g) was associated with a higher risk of pre-eclampsia, induced labour, spontaneous preterm delivery, stillbirth and low birth weight (P < 0.001). Conversely placental weight in the upper tertile (mean 788 g) was associated with a higher risk of caesarean section, post-term delivery and high birth weight (P < 0.001). With respect to assumed placental efficiency a ratio in the lower tertile was associated with an increased risk of pre-eclampsia, induced labour, caesarean section and spontaneous preterm delivery (P < 0.001) and a ratio in both the lower and higher tertiles was associated with an increased risk of low birth weight (P < 0.001). Placental efficiency was not related to the risk of stillbirth or high birth weight. No interactions between maternal BMI and placental weight tertile were detected suggesting that both abnormal BMI and placental growth are independent risk factors for a range of pregnancy complications. © 2012 Elsevier Ltd. All rights reserved.
Beale C.M.,Macaulay Institute |
Lennon J.J.,Macaulay Institute |
Yearsley J.M.,University of Lausanne |
Yearsley J.M.,Science Center |
And 2 more authors.
Ecology Letters | Year: 2010
Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future. © 2010 Blackwell Publishing Ltd/CNRS.
Agency: GTR | Branch: NERC | Program: | Phase: Training Grant | Award Amount: 78.19K | Year: 2011
Ensuring future food security in the UK will rely on increasing crop production by c. 70% by 2070. Meeting these production goals will depend on reducing crop losses to insect pests at a time when new pesticide legislation makes pest management increasingly challenging. Plant derived resistance, and particularly multi-species interactions that enhance such crop resistance, could therefore be invaluable. The aim of this project is to develop a sustainable biological control system for vine weevil (Otiorhynchus sulcatus) in a model perennial crop (red raspberry; Rubus idaeus) using a combination of arbuscular mycorrhizal fungi (AMF) and entomopathogenic nematodes. Increasing evidence points to the importance of root defences against belowground attack, including directly detrimental effects on the attacker and the recruitment of antagonists (i.e. indirect defence). Root defence signalling is only now being characterised in some plant species, but nothing is known about how AMF communities influence these processes despite good evidence for AMF generally increasing resistance to root herbivory. In particular, we hypothesise that some combinations of AMF species will facilitate root defences either directly or indirectly. The proposed research will identify key AMF species that influence root defence signalling and examine the responses of root-feeding weevil larvae to various combinations of these species. The collaboration between RHUL (Prof. Gange) and SCRI-MLURI (Drs Johnson, Bennett and Dawson) will allow the student to perform state of the art metabolomic profiling and measurement of in situ root exudation patterns, to gain a mechanistic understanding of the resistance. The most promising combinations of AMF will then be tested in conjunction with nematodes, to identify that which provides maximum levels of weevil control. This project is strongly aligned with the NERC CASE Priority area of Food and Agriculture (AGRIFOOD) and involves one of Europes main plant breeders (SCRI, a charitable company limited by guarantee) as the CASE partner. In particular, the project combines fundamental environmental research at one of the UKs leading biology departments (RHUL) with applied expertise at one of the world leaders in soft fruit production, ecology and protection (SCRI).
Beale C.M.,University of York |
Baker N.E.,Tanzania Bird Atlas |
Brewer M.J.,Biomathematics and Statistics Scotland |
Lennon J.J.,Queen's University of Belfast
Ecology Letters | Year: 2013
The extent to which climate change might diminish the efficacy of protected areas is one of the most pressing conservation questions. Many projections suggest that climate-driven species distribution shifts will leave protected areas impoverished and species inadequately protected while other evidence suggests that intact ecosystems within protected areas will be resilient to change. Here, we tackle this problem empirically. We show how recent changes in distribution of 139 Tanzanian savannah bird species are linked to climate change, protected area status and land degradation. We provide the first evidence of climate-driven range shifts for an African bird community. Our results suggest that the continued maintenance of existing protected areas is an appropriate conservation response to the challenge of climate and environmental change. © 2013 John Wiley & Sons Ltd/CNRS.
Agency: GTR | Branch: BBSRC | Program: | Phase: Research Grant | Award Amount: 608.33K | Year: 2012
In an ever changing climate, we are constantly trying to improve crops to achieve more sustainable agricultural practices. To do this, we need to understand in great detail how plants grow and work. Genetics is a powerful tool for helping us to obtain such an understanding. Using it we are able to analyse the whole genetic make-up (the genome) of plants to discover all the genes required for them to develop and operate correctly. We can use this information to study variants that have a defect in a particular gene (or genes) in which we are interested. This helps us to understand how the gene works when it is operating normally and its role in the plant. Many species of plant have had their whole genome sequenced already, but there are still thousands of their genes whose role in the plant is not understood. To obtain plants that bear a defective gene we can treat seeds with a chemical or radiation that damages the DNA coding for that gene. The offspring from these treated seeds will bear a large number of defects in their genomes. By producing a large population of offspring we can make every gene in the genome bear a defect. The problem then is to find the plant in the population that has a defect in the gene in which you are interested. We have developed methods that can sort out defective genes of interest and find the plants that contain them, so we can find out the role of the gene. This whole process is known as reverse genetics. No one chemical or physical means can induce all the defects we need to study particular genes so we need to use several different methods. In a previous project we set up a resource for the plant science research community so that they can discover the function of their particular genes of interest. We did this for legumes and brassicas (treated with chemicals) that contain plants bearing different forms of defective genes, but now we would like to do it for major UK cereal crops to help both scientists and plant breeders. We have developed special methods based on high throughput machines (sequencers) that can detect the defects when compared to the normal gene. Now we wish to develop methods that will do this in a more efficient and cost-effective manner. Scientists can then send us information about the gene of interest, for example, a gene from oats or barley, and we can then look for defects in their specific gene in our populations of thousands of plants. We then send them seeds from the plant that they can grow to study the action of the defective gene in that plant. All the information that we gather about our plants and their thousand upon thousands of genes will be stored in a computer database that we have constructed especially for this project, although it will be written in such a way that others can use it as well. It will also be available to use on the worldwide web so that a scientist anywhere in the UK or the World can come and browse to see if the database contains information about their gene of interest. The reason for wanting to do this is to improve the ability of crop plants to grow in different environments, especially adverse ones, to improve the quality of our food, and to help the farmer work in a sustainable way using less added fertiliser and fewer herbicides and pesticides.
Hackett C.A.,Biomathematics and Statistics Scotland |
McLean K.,James Hutton Institute |
Bryan G.J.,James Hutton Institute
PLoS ONE | Year: 2013
New sequencing and genotyping technologies have enabled researchers to generate high density SNP genotype data for mapping populations. In polyploid species, SNP data usually contain a new type of information, the allele dosage, which is not used by current methodologies for linkage analysis and QTL mapping. Here we extend existing methodology to use dosage data on SNPs in an autotetraploid mapping population. The SNP dosages are inferred from allele intensity ratios using normal mixture models. The steps of the linkage analysis (testing for distorted segregation, clustering SNPs, calculation of recombination fractions and LOD scores, ordering of SNPs and inference of parental phase) are extended to use the dosage information. For QTL analysis, the probability of each possible offspring genotype is inferred at a grid of locations along the chromosome from the ordered parental genotypes and phases and the offspring dosages. A normal mixture model is then used to relate trait values to the offspring genotypes and to identify the most likely locations for QTLs. These methods are applied to analyse a tetraploid potato mapping population of parents and 190 offspring, genotyped using an Infinium 8300 Potato SNP Array. Linkage maps for each of the 12 chromosomes are constructed. The allele intensity ratios are mapped as quantitative traits to check that their position and phase agrees with that of the corresponding SNP. This analysis confirms most SNP positions, and eliminates some problem SNPs to give high-density maps for each chromosome, with between 74 and 152 SNPs mapped and between 100 and 300 further SNPs allocated to approximate bins. Low numbers of double reduction products were detected. Overall 3839 of the 5378 polymorphic SNPs can be assigned putative genetic locations. This methodology can be applied to construct high-density linkage maps in any autotetraploid species, and could also be extended to higher autopolyploids. © 2013 Hackett et al.
Roberts A.M.I.,Biomathematics and Statistics Scotland
International Journal of Biometeorology | Year: 2012
Several methods exist for investigation of the relationship between records and weather data. These can be broadly classified into models that attempt to incorporate information about underlying biological processes, such as those based on the concept of thermal time, and linear regression methods. The latter are less driven by the biology but have the advantages of ease of use and flexibility. Regression can be used where there is no obvious mechanistic model or to suggest the form of a mechanistic or empirical model where there are several to choose from. Stepwise regression is commonly used in phenology. However, it requires aggregation of the weather records, resulting in loss of information. Penalised signal regression (PSR) was recently introduced to overcome this weakness. Here, we introduce a further method to the phenology context called fusion, which is a sparse version of PSR. In this paper, we compare the performance of these three regression methods based on simulations from two types of mechanistic models, the spring warming and sequential models. Given a suitable choice of temperature days as regression covariates, PSR and fusion performed better than stepwise regression for the spring warming model and PSR performed best for the sequential model. However, if a large number of redundant temperature days were included as covariates, the performance of PSR fell off whilst fusion was quite robust to this change. For this reason, it is best to use PSR and fusion methods in tandem, and to vary the number of covariates included. © 2011 ISB.
Palarea-Albaladejo J.,Biomathematics and Statistics Scotland |
Martin-Fernandez J.A.,University of Girona
Analytica Chimica Acta | Year: 2013
Samples representing part of a whole, usually called compositional data in statistics, are commonplace in analytical chemistry-say chemical data in percentage, ppm, or μgg-1. Their distinctive feature is that there is an inherent relationship between all the analytes constituting a chemical sample as they only convey relative information. Some compositional data analysis principles and the log-ratio based methodology are outlined here in practical terms. Besides, one often finds that some analytes are not present in sufficient concentration in a sample to allow the measuring instruments to effectively detect them. These non-detects are usually labelled as "