Andrew C. Parnell

Bio: Andrew C. Parnell is an academic researcher from Maynooth University. The author has contributed to research in topics: Mathematics & Computer science. The author has an hindex of 33, co-authored 138 publications receiving 8677 citations. Previous affiliations of Andrew C. Parnell include University College Dublin & Dublin City University.

Source partitioning using stable isotopes: coping with too much variation.

Abstract: Background Stable isotope analysis is increasingly being utilised across broad areas of ecology and biology. Key to much of this work is the use of mixing models to estimate the proportion of sources contributing to a mixture such as in diet estimation. Methodology By accurately reflecting natural variation and uncertainty to generate robust probability estimates of source proportions, the application of Bayesian methods to stable isotope mixing models promises to enable researchers to address an array of new questions, and approach current questions with greater insight and honesty. Conclusions We outline a framework that builds on recently published Bayesian isotopic mixing models and present a new open source R package, SIAR. The formulation in R will allow for continued and rapid development of this core model into an all-encompassing single analysis suite for stable isotope research.

Comparing isotopic niche widths among and within communities: SIBER - Stable Isotope Bayesian Ellipses in R.

Abstract: 1. The use of stable isotope data to infer characteristics of community structure and niche width of community members has become increasingly common. Although these developments have provided ecologists with new perspectives, their full impact has been hampered by an inability to statistically compare individual communities using descriptive metrics. 2. We solve these issues by reformulating the metrics in a Bayesian framework. This reformulation takes account of uncertainty in the sampled data and naturally incorporates error arising from the sampling process, propagating it through to the derived metrics. 3. Furthermore, we develop novel multivariate ellipse-based metrics as an alternative to the currently employed Convex Hull methods when applied to single community members. We show that unlike Convex Hulls, the ellipses are unbiased with respect to sample size, and their estimation via Bayesian inference allows robust comparison to be made among data sets comprising different sample sizes. 4. These new metrics, which we call SIBER (Stable Isotope Bayesian Ellipses in R), open up more avenues for direct comparison of isotopic niches across communities. The computational code to calculate the new metrics is implemented in the free-to-download package Stable Isotope Analysis for the R statistical environment.

Best practices for use of stable isotope mixing models in food-web studies

Abstract: Stable isotope mixing models are increasingly used to quantify consumer diets, but may be misused and misinter- preted. We address major challenges to their effective application. Mixing models have increased rapidly in sophistication. Current models estimate probability distributions of source contributions, have user-friendly interfaces, and incorporate com- plexities such as variability in isotope signatures, discrimination factors, hierarchical variance structure, covariates, and con- centration dependence. For proper implementation of mixing models, we offer the following suggestions. First, mixing models can only be as good as the study and data. Studies should have clear questions, be informed by knowledge of the system, and have strong sampling designs to effectively characterize isotope variability of consumers and resources on proper spatio-temporal scales. Second, studies should use models appropriate for the question and recognize their assumptions and limitations. Decisions about source grouping or incorporation of concentration dependence can influence results. Third, studies should be careful about interpretation of model outputs. Mixing models generally estimate proportions of assimilated resources with substantial uncertainty distributions. Last, common sense, such as graphing data before analyzing, is essential to maximize usefulness of these tools. We hope these suggestions for effective implementation of stable isotope mixing models will aid continued development and application of this field.

Bayesian Stable Isotope Mixing Models

Abstract: In this paper we review recent advances in Stable Isotope Mixing Models (SIMMs) and place them into an over-arching Bayesian statistical framework which allows for several useful extensions. SIMMs are used to quantify the proportional contributions of various sources to a mixture. The most widely used application is quantifying the diet of organisms based on the food sources they have been observed to consume. At the centre of the multivariate statistical model we propose is a compositional mixture of the food sources corrected for various metabolic factors. The compositional component of our model is based on the isometric log ratio (ilr) transform of Egozcue (2003). Through this transform we can apply a range of time series and non-parametric smoothing relationships. We illustrate our models with 3 case studies based on real animal dietary behaviour.

Analyzing mixing systems using a new generation of Bayesian tracer mixing models.

Abstract: The ongoing evolution of tracer mixing models has resulted in a confusing array of software tools that differ in terms of data inputs, model assumptions, and associated analytic products. Here we introduce MixSIAR, an inclusive, rich, and flexible Bayesian tracer (e.g., stable isotope) mixing model framework implemented as an open-source R package. Using MixSIAR as a foundation, we provide guidance for the implementation of mixing model analyses. We begin by outlining the practical differences between mixture data error structure formulations and relate these error structures to common mixing model study designs in ecology. Because Bayesian mixing models afford the option to specify informative priors on source proportion contributions, we outline methods for establishing prior distributions and discuss the influence of prior specification on model outputs. We also discuss the options available for source data inputs (raw data versus summary statistics) and provide guidance for combining sources. We then describe a key advantage of MixSIAR over previous mixing model software-the ability to include fixed and random effects as covariates explaining variability in mixture proportions and calculate relative support for multiple models via information criteria. We present a case study of Alligator mississippiensis diet partitioning to demonstrate the power of this approach. Finally, we conclude with a discussion of limitations to mixing model applications. Through MixSIAR, we have consolidated the disparate array of mixing model tools into a single platform, diversified the set of available parameterizations, and provided developers a platform upon which to continue improving mixing model analyses in the future.

Influence of Diet On the Distribtion of Nitrogen Isotopes in Animals

Abstract: The influence of diet on the distribution of nitrogen isotopes in animals was investigated by analyzing animals grown in the laboratory on diets of constant nitrogen isotopic composition. The isotopic composition of the nitrogen in an animal reflects the nitrogen isotopic composition of its diet. The δ^(15)N values of the whole bodies of animals are usually more positive than those of their diets. Different individuals of a species raised on the same diet can have significantly different δ^(15)N values. The variability of the relationship between the δ^(15)N values of animals and their diets is greater for different species raised on the same diet than for the same species raised on different diets. Different tissues of mice are also enriched in ^(15)N relative to the diet, with the difference between the δ^(15)N values of a tissue and the diet depending on both the kind of tissue and the diet involved. The δ^(15)N values of collagen and chitin, biochemical components that are often preserved in fossil animal remains, are also related to the δ^(15)N value of the diet. The dependence of the δ^(15)N values of whole animals and their tissues and biochemical components on the δ^(15)N value of diet indicates that the isotopic composition of animal nitrogen can be used to obtain information about an animal's diet if its potential food sources had different δ^(15)N values. The nitrogen isotopic method of dietary analysis probably can be used to estimate the relative use of legumes vs non-legumes or of aquatic vs terrestrial organisms as food sources for extant and fossil animals. However, the method probably will not be applicable in those modern ecosystems in which the use of chemical fertilizers has influenced the distribution of nitrogen isotopes in food sources. The isotopic method of dietary analysis was used to reconstruct changes in the diet of the human population that occupied the Tehuacan Valley of Mexico over a 7000 yr span. Variations in the δ^(15)C and δ^(15)N values of bone collagen suggest that C_4 and/or CAM plants (presumably mostly corn) and legumes (presumably mostly beans) were introduced into the diet much earlier than suggested by conventional archaeological analysis.

Climate change 2014 - Mitigation of climate change

Abstract: The talk with present the key results of the IPCC Working Group III 5th assessment report. Concluding four years of intense scientific collaboration by hundreds of authors from around the world, the report responds to the request of the world's governments for a comprehensive, objective and policy neutral assessment of the current scientific knowledge on mitigating climate change. The report has been extensively reviewed by experts and governments to ensure quality and comprehensiveness.

Flexible paleoclimate age-depth models using an autoregressive gamma process

Abstract: Radiocarbon dating is routinely used in paleoecology to build chronolo- gies of lake and peat sediments, aiming at inferring a model that would relate the sediment depth with its age. We present a new approach for chronology building (called \Bacon") that has received enthusiastic attention by paleoecologists. Our methodology is based on controlling core accumulation rates using a gamma au- toregressive semiparametric model with an arbitrary number of subdivisions along the sediment. Using prior knowledge about accumulation rates is crucial and in- formative priors are routinely used. Since many sediment cores are currently ana- lyzed, using difierent data sets and prior distributions, a robust (adaptive) MCMC is very useful. We use the t-walk (Christen and Fox, 2010), a self adjusting, robust MCMC sampling algorithm, that works acceptably well in many situations. Out- liers are also addressed using a recent approach that considers a Student-t model for radiocarbon data. Two examples are presented here, that of a peat core and a core from a lake, and our results are compared with other approaches. Past climates and environments can be reconstructed from deposits such as ocean or lake sediments, ice sheets and peat bogs. Within a vertical sediment proflle (core), mea- surements of microfossils, macrofossils, isotopes and other variables at a range of depths serve as proxy estimates or \proxies" of climate and environmental conditions when the sediment of those depths was deposited. It is crucial to establish reliable relationships between these depths and their ages. Age-depth relationships are used to study the evolution of climate/environmental proxies along sediment depth and therefore through time (e.g., Lowe and Walker 1997). Age-depth models are constructed in various ways. For sediment depths containing organic matter, and for ages younger than c. 50,000 years, radiocarbon dating is often used to create an age-depth model. Cores are divided into slices and some of these are radiocarbon dated. A curve is fltted to the radiocarbon data and interpolated to obtain an age estimate for every depth of the core. The flrst restriction to be considered is that age should be increasing monotonically with depth, because sediment can never have accumulated backwards in time (extraordinary events leading to mixed or reversed sediments are, most of the time, noticeable in the stratigraphy and therefore such cores are ruled out from further analyses). Moreover, cores may have missing sections, leading to ∞at parts in the age depth models.