Statistics Research
http://hdl.handle.net/10023/100
2021-09-22T20:32:32ZSurveying abundance and stand type associations of Formica aquilonia and F. lugubris (Hymenoptera: Formicidae) nest mounds over an extensive area : Trialing a novel method
http://hdl.handle.net/10023/16260
Red wood ants are ecologically important members of woodland communities, and some species are of conservation concern. They occur commonly only in certain habitats in Britain, but there is limited knowledge of their numbers and distribution. This study provided baseline information at a key locality (Abernethy Forest, 37 km2) in the central Highlands of Scotland and trialed a new method of surveying red wood ant density and stand type associations: a distance sampling line transect survey of nests. This method is efficient because it allows an observer to quickly survey a large area either side of transect lines, without having to assume that all nests are detected. Instead, data collected on the distance of nests from the line are used to estimate probability of detection and the effective transect width, using the free software "Distance". Surveys took place in August and September 2003 along a total of 71.2 km of parallel, equally-spaced transects. One hundred and forty-four red wood ant nests were located, comprising 89 F. aquilonia (Yarrow, 1955) and 55 F. lugubris (Zetterstedt, 1838) nests. Estimated densities were 1.13 nests per hectare (95% CI 0.74-1.73) for F. aquilonia and 0.83 nests per hectare (95% CI 0.32-2.17) for F. lugubris. These translated to total estimated nest numbers of 4,200 (95% CI 2,700-6,400) and 3,100 (95% CI 1,200-8,100), respectively, for the whole forest. Indices of stand selection indicated that F. aquilonia had some positive association with old-growth and F. lugubris with younger stands (stem exclusion stage). No nests were found in areas that had been clear-felled, and ploughed and planted in the 1970s-1990s. The pattern of stand type association and hence distribution of F. aquilonia and F. lugubris may be due to the differing ability to disperse (F. lugubris is the faster disperser) and compete (F. aquilonia is competitively superior). We recommend using line transect sampling for extensive surveys of ants that construct nest mounds to estimate abundance and stand type association.
2012-01-03T00:00:00ZBorkin, KerrySummers, RonThomas, LenRed wood ants are ecologically important members of woodland communities, and some species are of conservation concern. They occur commonly only in certain habitats in Britain, but there is limited knowledge of their numbers and distribution. This study provided baseline information at a key locality (Abernethy Forest, 37 km2) in the central Highlands of Scotland and trialed a new method of surveying red wood ant density and stand type associations: a distance sampling line transect survey of nests. This method is efficient because it allows an observer to quickly survey a large area either side of transect lines, without having to assume that all nests are detected. Instead, data collected on the distance of nests from the line are used to estimate probability of detection and the effective transect width, using the free software "Distance". Surveys took place in August and September 2003 along a total of 71.2 km of parallel, equally-spaced transects. One hundred and forty-four red wood ant nests were located, comprising 89 F. aquilonia (Yarrow, 1955) and 55 F. lugubris (Zetterstedt, 1838) nests. Estimated densities were 1.13 nests per hectare (95% CI 0.74-1.73) for F. aquilonia and 0.83 nests per hectare (95% CI 0.32-2.17) for F. lugubris. These translated to total estimated nest numbers of 4,200 (95% CI 2,700-6,400) and 3,100 (95% CI 1,200-8,100), respectively, for the whole forest. Indices of stand selection indicated that F. aquilonia had some positive association with old-growth and F. lugubris with younger stands (stem exclusion stage). No nests were found in areas that had been clear-felled, and ploughed and planted in the 1970s-1990s. The pattern of stand type association and hence distribution of F. aquilonia and F. lugubris may be due to the differing ability to disperse (F. lugubris is the faster disperser) and compete (F. aquilonia is competitively superior). We recommend using line transect sampling for extensive surveys of ants that construct nest mounds to estimate abundance and stand type association.Crambled : a Shiny application to enable intuitive resolution of conflicting cellularity estimates
http://hdl.handle.net/10023/16248
It is now commonplace to investigate tumour samples using whole-genome sequencing, and some commonly performed tasks are the estimation of cellularity (or sample purity), the genome-wide profiling of copy numbers, and the assessment of sub-clonal behaviours. Several tools are available to undertake these tasks, but often give conflicting results - not least because there is often genuine uncertainty due to a lack of model identifiability. Presented here is a tool, "Crambled", that allows for an intuitive visual comparison of the conflicting solutions. Crambled is implemented as a Shiny application within R, and is accompanied by example images from two use cases (one tumour sample with matched normal sequencing, and one standalone cell line example) as well as functions to generate the necessary images from any sequencing data set. Through the use of Crambled, a user may gain insight into why each tool has offered its given solution and combined with a knowledge of the disease being studied can choose between the competing solutions in an informed manner.
2015-12-07T00:00:00ZLynch, AndyIt is now commonplace to investigate tumour samples using whole-genome sequencing, and some commonly performed tasks are the estimation of cellularity (or sample purity), the genome-wide profiling of copy numbers, and the assessment of sub-clonal behaviours. Several tools are available to undertake these tasks, but often give conflicting results - not least because there is often genuine uncertainty due to a lack of model identifiability. Presented here is a tool, "Crambled", that allows for an intuitive visual comparison of the conflicting solutions. Crambled is implemented as a Shiny application within R, and is accompanied by example images from two use cases (one tumour sample with matched normal sequencing, and one standalone cell line example) as well as functions to generate the necessary images from any sequencing data set. Through the use of Crambled, a user may gain insight into why each tool has offered its given solution and combined with a knowledge of the disease being studied can choose between the competing solutions in an informed manner.Adaptive multivariate global testing
http://hdl.handle.net/10023/15760
We present a methodology for dealing with recent challenges in testing global hypotheses using multivariate observations. The proposed tests target situations, often arising in emerging applications of neuroimaging, where the sample size n is relatively small compared with the observations' dimension K. We employ adaptive designs allowing for sequential modifications of the test statistics adapting to accumulated data. The adaptations are optimal in the sense of maximizing the predictive power of the test at each interim analysis while still controlling the Type I error. Optimality is obtained by a general result applicable to typical adaptive design settings. Further, we prove that the potentially high-dimensional design space of the tests can be reduced to a low-dimensional projection space enabling us to perform simpler power analysis studies, including comparisons to alternative tests. We illustrate the substantial improvement in efficiency that the proposed tests can make over standard tests, especially in the case of n smaller or slightly larger than K. The methods are also studied empirically using both simulated data and data from an EEG study, where the use of prior knowledge substantially increases the power of the test. Supplementary materials for this article are available online.
2014-06-01T00:00:00ZMinas, GiorgosAston, John A DStallard, NigelWe present a methodology for dealing with recent challenges in testing global hypotheses using multivariate observations. The proposed tests target situations, often arising in emerging applications of neuroimaging, where the sample size n is relatively small compared with the observations' dimension K. We employ adaptive designs allowing for sequential modifications of the test statistics adapting to accumulated data. The adaptations are optimal in the sense of maximizing the predictive power of the test at each interim analysis while still controlling the Type I error. Optimality is obtained by a general result applicable to typical adaptive design settings. Further, we prove that the potentially high-dimensional design space of the tests can be reduced to a low-dimensional projection space enabling us to perform simpler power analysis studies, including comparisons to alternative tests. We illustrate the substantial improvement in efficiency that the proposed tests can make over standard tests, especially in the case of n smaller or slightly larger than K. The methods are also studied empirically using both simulated data and data from an EEG study, where the use of prior knowledge substantially increases the power of the test. Supplementary materials for this article are available online.ReTrOS : a MATLAB toolbox for reconstructing transcriptional activity from gene and protein expression data
http://hdl.handle.net/10023/15759
BACKGROUND: Given the development of high-throughput experimental techniques, an increasing number of whole genome transcription profiling time series data sets, with good temporal resolution, are becoming available to researchers. The ReTrOS toolbox (Reconstructing Transcription Open Software) provides MATLAB-based implementations of two related methods, namely ReTrOS-Smooth and ReTrOS-Switch, for reconstructing the temporal transcriptional activity profile of a gene from given mRNA expression time series or protein reporter time series. The methods are based on fitting a differential equation model incorporating the processes of transcription, translation and degradation. RESULTS: The toolbox provides a framework for model fitting along with statistical analyses of the model with a graphical interface and model visualisation. We highlight several applications of the toolbox, including the reconstruction of the temporal cascade of transcriptional activity inferred from mRNA expression data and protein reporter data in the core circadian clock in Arabidopsis thaliana, and how such reconstructed transcription profiles can be used to study the effects of different cell lines and conditions. CONCLUSIONS: The ReTrOS toolbox allows users to analyse gene and/or protein expression time series where, with appropriate formulation of prior information about a minimum of kinetic parameters, in particular rates of degradation, users are able to infer timings of changes in transcriptional activity. Data from any organism and obtained from a range of technologies can be used as input due to the flexible and generic nature of the model and implementation. The output from this software provides a useful analysis of time series data and can be incorporated into further modelling approaches or in hypothesis generation.
This work was supported through providing funds by the Biotechnology and Biological Sciences Research Council [BB/F005806/1, BB/F005237/1]; and the Engineering and Physical Sciences Research Council [EP/C544587/1 to DAR].
2017-06-26T00:00:00ZMinas, GiorgosMomiji, HiroshiJenkins, Dafyd JCosta, Maria JRand, David AFinkenstädt, BärbelBACKGROUND: Given the development of high-throughput experimental techniques, an increasing number of whole genome transcription profiling time series data sets, with good temporal resolution, are becoming available to researchers. The ReTrOS toolbox (Reconstructing Transcription Open Software) provides MATLAB-based implementations of two related methods, namely ReTrOS-Smooth and ReTrOS-Switch, for reconstructing the temporal transcriptional activity profile of a gene from given mRNA expression time series or protein reporter time series. The methods are based on fitting a differential equation model incorporating the processes of transcription, translation and degradation. RESULTS: The toolbox provides a framework for model fitting along with statistical analyses of the model with a graphical interface and model visualisation. We highlight several applications of the toolbox, including the reconstruction of the temporal cascade of transcriptional activity inferred from mRNA expression data and protein reporter data in the core circadian clock in Arabidopsis thaliana, and how such reconstructed transcription profiles can be used to study the effects of different cell lines and conditions. CONCLUSIONS: The ReTrOS toolbox allows users to analyse gene and/or protein expression time series where, with appropriate formulation of prior information about a minimum of kinetic parameters, in particular rates of degradation, users are able to infer timings of changes in transcriptional activity. Data from any organism and obtained from a range of technologies can be used as input due to the flexible and generic nature of the model and implementation. The output from this software provides a useful analysis of time series data and can be incorporated into further modelling approaches or in hypothesis generation.Epigenetic and oncogenic regulation of SLC16A7 (MCT2) results in protein over-expression, impacting on signalling and cellular phenotypes in prostate cancer
http://hdl.handle.net/10023/11445
Monocarboxylate Transporter 2 (MCT2) is a major pyruvate transporter encoded by the SLC16A7 gene. Recent studies pointed to a consistent overexpression of MCT2 in prostate cancer (PCa) suggesting MCT2 as a putative biomarker and molecular target. Despite the importance of this observation the mechanisms involved in MCT2 regulation are unknown. Through an integrative analysis we have discovered that selective demethylation of an internal SLC16A7/MCT2 promoter is a recurrent event in independent PCa cohorts. This demethylation is associated with expression of isoforms differing only in 5'-UTR translational control motifs, providing one contributing mechanism for MCT2 protein overexpression in PCa. Genes co-expressed with SLC16A7/MCT2 also clustered in oncogenic-related pathways and effectors of these signalling pathways were found to bind at the SLC16A7/MCT2 gene locus. Finally, MCT2 knock-down attenuated the growth of PCa cells. The present study unveils an unexpected epigenetic regulation of SLC16A7/MCT2 isoforms and identifies a link between SLC16A7/MCT2, Androgen Receptor (AR), ETS-related gene (ERG) and other oncogenic pathways in PCa. These results underscore the importance of combining data from epigenetic, transcriptomic and protein level changes to allow more comprehensive insights into the mechanisms underlying protein expression, that in our case provide additional weight to MCT2 as a candidate biomarker and molecular target in PCa.
Felisbino S. received a fellowship from the Sao Paulo Research Foundation (FAPESP) ref. 2013/08830-2 and 2013/06802-1. Anne Y Warren research time funded by: Cambridge Biomedical Research Centre.
2015-06-02T00:00:00ZPértega-Gomes, NelmaVizcaino, Jose R.Felisbino, SergioWarren, Anne Y.Shaw, GregKay, JonathanWhitaker, HayleyLynch, Andy G.Fryer, LeeNeal, David E.Massie, Charles E.Monocarboxylate Transporter 2 (MCT2) is a major pyruvate transporter encoded by the SLC16A7 gene. Recent studies pointed to a consistent overexpression of MCT2 in prostate cancer (PCa) suggesting MCT2 as a putative biomarker and molecular target. Despite the importance of this observation the mechanisms involved in MCT2 regulation are unknown. Through an integrative analysis we have discovered that selective demethylation of an internal SLC16A7/MCT2 promoter is a recurrent event in independent PCa cohorts. This demethylation is associated with expression of isoforms differing only in 5'-UTR translational control motifs, providing one contributing mechanism for MCT2 protein overexpression in PCa. Genes co-expressed with SLC16A7/MCT2 also clustered in oncogenic-related pathways and effectors of these signalling pathways were found to bind at the SLC16A7/MCT2 gene locus. Finally, MCT2 knock-down attenuated the growth of PCa cells. The present study unveils an unexpected epigenetic regulation of SLC16A7/MCT2 isoforms and identifies a link between SLC16A7/MCT2, Androgen Receptor (AR), ETS-related gene (ERG) and other oncogenic pathways in PCa. These results underscore the importance of combining data from epigenetic, transcriptomic and protein level changes to allow more comprehensive insights into the mechanisms underlying protein expression, that in our case provide additional weight to MCT2 as a candidate biomarker and molecular target in PCa.Distance software: design and analysis of distance sampling surveys for estimating population size
http://hdl.handle.net/10023/817
1. Distance sampling is a widely used technique for estimating the size or density of biological populations. Many distance sampling designs and most analyses use the software Distance. 2. We briefly review distance sampling and its assumptions, outline the history, structure and capabilities of Distance, and provide hints on its use. 3. Good survey design is a crucial pre-requisite for obtaining reliable results. Distance has a survey design engine, with a built-in geographic information system, that allows properties of different proposed designs to be examined via simulation, and survey plans to be generated. 4. A first step in analysis of distance sampling data is modelling the probability of detection. Distance contains three increasingly sophisticated analysis engines for this: CDS (conventional distance sampling), which models detection probability as a function of distance from the transect and assumes all objects at zero distance are detected; MCDS (multiple covariate distance sampling), which allows covariates in addition to distance; and MRDS (mark-recapture distance sampling), which relaxes the assumption of certain detection at zero distance. 5. All three engines allow estimation of density or abundance, stratified if required, with associated measures of precision calculated either analytically or via the bootstrap. 6. Advanced analysis topics covered include the use of multipliers to allow analysis of indirect surveys (such as dung or nest surveys), the DSM (density surface modelling) analysis engine for spatial and habitat modelling, and information about accessing the analysis engines directly from other software. 7. Synthesis and applications. Distance sampling is a key method for producing abundance and density estimates in challenging field conditions. The theory underlying the methods continues to expand to cope with realistic estimation situations. In step with theoretical developments, state-of-the-art software that implements these methods is described that makes the methods accessible to practicing ecologists.
2010-01-01T00:00:00ZThomas, LenBuckland, Stephen TerrenceRexstad, EricLaake, J LStrindberg, SHedley, S LBishop, J R BMarques, Tiago A.1. Distance sampling is a widely used technique for estimating the size or density of biological populations. Many distance sampling designs and most analyses use the software Distance. 2. We briefly review distance sampling and its assumptions, outline the history, structure and capabilities of Distance, and provide hints on its use. 3. Good survey design is a crucial pre-requisite for obtaining reliable results. Distance has a survey design engine, with a built-in geographic information system, that allows properties of different proposed designs to be examined via simulation, and survey plans to be generated. 4. A first step in analysis of distance sampling data is modelling the probability of detection. Distance contains three increasingly sophisticated analysis engines for this: CDS (conventional distance sampling), which models detection probability as a function of distance from the transect and assumes all objects at zero distance are detected; MCDS (multiple covariate distance sampling), which allows covariates in addition to distance; and MRDS (mark-recapture distance sampling), which relaxes the assumption of certain detection at zero distance. 5. All three engines allow estimation of density or abundance, stratified if required, with associated measures of precision calculated either analytically or via the bootstrap. 6. Advanced analysis topics covered include the use of multipliers to allow analysis of indirect surveys (such as dung or nest surveys), the DSM (density surface modelling) analysis engine for spatial and habitat modelling, and information about accessing the analysis engines directly from other software. 7. Synthesis and applications. Distance sampling is a key method for producing abundance and density estimates in challenging field conditions. The theory underlying the methods continues to expand to cope with realistic estimation situations. In step with theoretical developments, state-of-the-art software that implements these methods is described that makes the methods accessible to practicing ecologists.The importance of analysis method for breeding bird survey population trend estimates
http://hdl.handle.net/10023/685
Population trends from the Breeding Bird Survey are widely used to focus conservation efforts on species thought to be in decline and to test preliminary hypotheses regarding the causes of these declines. A number of statistical methods have been used to estimate population trends, but there is no consensus us to which is the most reliable. We quantified differences in trend estimates or different analysis methods applied to the same subset of Breeding Bird Survey data. We estimated trends for 115 species in British Columbia using three analysis methods: U.S. National Biological Service route regression, Canadian Wildlife Service route regression, and nonparametric rank-trends analysis. Overall, the number of species estimated to be declining was similar among the three methods, but the number of statistically significant declines was not similar (15, 8, and 29 respectively). In addition, many differences existed among methods in the trend estimates assigned to individual species. Comparing the two route regression methods, Canadian Wildlife Service estimates had a greater absolute magnitude on average than those of the U.S. National Biological Service method. U.S. National Biological Service estimates were on average more positive than the Canadian Wildlife Service estimates when the respective agency's data selection criteria were applied separately. These results imply that our ability to detect population declines and to prioritize species of conservation concern depend strongly upon the analysis method used. This highlights the need for further research to determine how best to accurately estimate trends from the data. We suggest a method for evaluating the performance of the analysis methods by using simulated Breeding Bird Survey data.
1996-01-01T00:00:00ZThomas, LenMartin, KathyPopulation trends from the Breeding Bird Survey are widely used to focus conservation efforts on species thought to be in decline and to test preliminary hypotheses regarding the causes of these declines. A number of statistical methods have been used to estimate population trends, but there is no consensus us to which is the most reliable. We quantified differences in trend estimates or different analysis methods applied to the same subset of Breeding Bird Survey data. We estimated trends for 115 species in British Columbia using three analysis methods: U.S. National Biological Service route regression, Canadian Wildlife Service route regression, and nonparametric rank-trends analysis. Overall, the number of species estimated to be declining was similar among the three methods, but the number of statistically significant declines was not similar (15, 8, and 29 respectively). In addition, many differences existed among methods in the trend estimates assigned to individual species. Comparing the two route regression methods, Canadian Wildlife Service estimates had a greater absolute magnitude on average than those of the U.S. National Biological Service method. U.S. National Biological Service estimates were on average more positive than the Canadian Wildlife Service estimates when the respective agency's data selection criteria were applied separately. These results imply that our ability to detect population declines and to prioritize species of conservation concern depend strongly upon the analysis method used. This highlights the need for further research to determine how best to accurately estimate trends from the data. We suggest a method for evaluating the performance of the analysis methods by using simulated Breeding Bird Survey data.Retrospective power analysis
http://hdl.handle.net/10023/679
Many papers have appeared in the recent biological literature encouraging us to incorporate statistical power analysis into our hypothesis testing protocol (Peterman 1990; Fairweather 1991; Muller & Benignus 1992; Taylor & Gerrodette 1993; Searcy-Bernal 1994; Thomas & Juanes 1996). The importance of doing a power analysis before beginning a study (prospective power analysis) is universally accepted: such analyses help us to decide how many samples are required to have a good chance of getting unambiguous results. In contrast, the role of power analysis after the data are collected and analyzed (retrospective power analysis) is controversial, as is evidenced by the papers of Reed and Blaustein (1995) and Hayes and Steidl (1997). The controversy is over the use of information from the sample data in retrospective power calculations. As I will show, the type of information used has fundamental implications for the value of such analyses. I compare the approaches to calculating retrospective power, noting the strengths and weaknesses of each, and make general recommendations as to how and when retrospective power analyses should be conducted.
The pdf contains the article; the ASCII file contains SAS code to calculate power and confidence limits for simple linear regression, as detailed in the article appendix.
1997-01-01T00:00:00ZThomas, LenMany papers have appeared in the recent biological literature encouraging us to incorporate statistical power analysis into our hypothesis testing protocol (Peterman 1990; Fairweather 1991; Muller & Benignus 1992; Taylor & Gerrodette 1993; Searcy-Bernal 1994; Thomas & Juanes 1996). The importance of doing a power analysis before beginning a study (prospective power analysis) is universally accepted: such analyses help us to decide how many samples are required to have a good chance of getting unambiguous results. In contrast, the role of power analysis after the data are collected and analyzed (retrospective power analysis) is controversial, as is evidenced by the papers of Reed and Blaustein (1995) and Hayes and Steidl (1997). The controversy is over the use of information from the sample data in retrospective power calculations. As I will show, the type of information used has fundamental implications for the value of such analyses. I compare the approaches to calculating retrospective power, noting the strengths and weaknesses of each, and make general recommendations as to how and when retrospective power analyses should be conducted.A unified framework for modelling wildlife population dynamics
http://hdl.handle.net/10023/678
This paper proposes a unified framework for defining and fitting stochastic, discrete-time, discrete-stage population dynamics models. The biological system is described by a state–space model, where the true but unknown state of the population is modelled by a state process, and this is linked to survey data by an observation process. All sources of uncertainty in the inputs, including uncertainty about model specification, are readily incorporated. The paper shows how the state process can be represented as a generalization of the standard Leslie or Lefkovitch matrix. By dividing the state process into subprocesses, complex models can be constructed from manageable building blocks. The paper illustrates the approach with a model of the British Grey Seal metapopulation, using sequential importance sampling with kernel smoothing to fit the model.
The pdf document contains the full article text; program code (in S-PLUS 6.1) for the example analysis is in the three text files; data is available from the Sea Mammal Research Unit (http://www.smru.st-and.ac.uk)
2005-01-01T00:00:00ZThomas, LenBuckland, Stephen T.Newman, KBHarwood, JohnThis paper proposes a unified framework for defining and fitting stochastic, discrete-time, discrete-stage population dynamics models. The biological system is described by a state–space model, where the true but unknown state of the population is modelled by a state process, and this is linked to survey data by an observation process. All sources of uncertainty in the inputs, including uncertainty about model specification, are readily incorporated. The paper shows how the state process can be represented as a generalization of the standard Leslie or Lefkovitch matrix. By dividing the state process into subprocesses, complex models can be constructed from manageable building blocks. The paper illustrates the approach with a model of the British Grey Seal metapopulation, using sequential importance sampling with kernel smoothing to fit the model.WinBUGS for population ecologists: Bayesian modeling using Markov Chain Monte Carlo methods.
http://hdl.handle.net/10023/677
The computer package WinBUGS is introduced. We first give a brief introduction to Bayesian theory and its implementation using Markov chain Monte Carlo (MCMC) algorithms. We then present three case studies showing how WinBUGS can be used when classical theory is difficult to implement. The first example uses data on white storks from Baden Württemberg, Germany, to demonstrate the use of mark-recapture models to estimate survival, and also how to cope with unexplained variance through random effects. Recent advances in methodology and also the WinBUGS software allow us to introduce (i) a flexible way of incorporating covariates using spline smoothing and (ii) a method to deal with missing values in covariates. The second example shows how to estimate population density while accounting for detectability, using distance sampling methods applied to a test dataset collected on a known population of wooden stakes. Finally, the third case study involves the use of state-space models of wildlife population dynamics to make inferences about density dependence in a North American duck species. Reversible Jump MCMC is used to calculate the probability of various candidate models. For all examples, data and WinBUGS code are provided.
This paper was presented at the EURING 2007 Technical Meeting, January 14-21, Dunedin, New Zealand. It has been submitted for publication in the conference proceedings, which will appear as a special issue of Environmental and Ecological Statistics.; The zip file contains accompanying code in WinBUGS
2008-01-01T00:00:00ZGiminez, OBonner, S JKing, RuthParker, R ABrooks, S PJamieson, L EGrosbois, VMorgan, B J TThomas, LenThe computer package WinBUGS is introduced. We first give a brief introduction to Bayesian theory and its implementation using Markov chain Monte Carlo (MCMC) algorithms. We then present three case studies showing how WinBUGS can be used when classical theory is difficult to implement. The first example uses data on white storks from Baden Württemberg, Germany, to demonstrate the use of mark-recapture models to estimate survival, and also how to cope with unexplained variance through random effects. Recent advances in methodology and also the WinBUGS software allow us to introduce (i) a flexible way of incorporating covariates using spline smoothing and (ii) a method to deal with missing values in covariates. The second example shows how to estimate population density while accounting for detectability, using distance sampling methods applied to a test dataset collected on a known population of wooden stakes. Finally, the third case study involves the use of state-space models of wildlife population dynamics to make inferences about density dependence in a North American duck species. Reversible Jump MCMC is used to calculate the probability of various candidate models. For all examples, data and WinBUGS code are provided.