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Paper: Funder Restrictions on Application Numbers Lead to Chaos

Daniel Bearup with Dylan Childs and Robert Freckleton have written the paper Funder Restrictions on Application Numbers Lead to Chaos, which has been published by Trends in Ecology and Evolution.

Paper abstract: Restricting application rates is an attractive way for funders to reduce time and money wasted evaluating uncompetitive applications. However, mathematical models show that this could induce chaotic cycles in total application numbers, increasing uncertainty in the funding process. One emergent property is that smaller institutions spend disproportionally more time unfunded.

 

 

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International Statistical Ecology Conference

Rachel, Byron, Diana and Marina attended the International Statistical Ecology Conference in St Andrews from 2nd to 6th July.

Rachel was chair of the scientific committee.

On 2nd July Marina gave a talk on Integrated Population Modelling Incorporating Spatial Information.

On 3rd July Diana gave a talk entitled Is Bayesian Identifiability Really a Problem?

On 6th July Byron talked about Integrated population modelling of fuliginator beetle data with two temporal dynamic scales.

 

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Diana gave talk at the University of Nottingham

On 25th April Diana gave a talk on Parameter Redundancy and Identifiability at the University of Nottingham as a joint seminar of the  Algebra and Analysis group and the Statistics and Probability group.

The abstract for the talk was:

Mathematical and statistical models can be used to describe different systems or provide a representation of a process, which are defined by parameters that describe different characteristics that underlie the model. For example, a dynamic system could be used to describe a pharmacokinetic system with a parameter representing the infusion rate of a drug or a statistical model could be used to describe an ecological population, with a parameter describing the survival probability of an animal.  Determining or estimating these parameters will provide key information about the underlying process, though it is not always possible to estimate every parameter.  Such a problem stems from the inherent structure of a model; for example two parameters could be confounded and only ever appear as a product. This is known as parameter redundancy or the parameters are termed unidentifiable.

It may not be obvious whether parameters in a model are identifiable. A general method for checking identifiability involves forming a matrix of derivatives and calculating its rank, which can be executed in symbolic algebra packages such as Maple. However for more structurally complex models Maple can run out of memory trying to calculate the rank. This talk discusses two methods to solve this problem. The first uses reparameterisation to simplify the derivative matrix and allow the calculation of the rank, and the second involves calculating the rank numerically.

 

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Daniel gave talk at MPDEE’18

In April Daniel gave a talk on The emergence of mutualistic relationships in communities of competing ecosystem engineers at Models in Population Dynamics, Ecology, and Evolution in Leicester.

Talk Abstract:

Ecosystem engineers, species which significantly modify their habitats, play a disproportionate role in shaping the composition, and character, of the ecological communities of which they are a part. In particular, by creating and maintaining an atypical habitat (e.g. a coral reef), they support communities that are uniquely adapted to that habitat. Studies of these species have focused either on capturing the effect of ecosystem engineering activity on its own survival (or invasion) chances, or on interactions between ecosystem engineers with antithetical preferred habitats. Far less is known about how these species interact when they engineer compatible habitats.

In this study, we use a simple mathematical model, inspired by the classical competitive Lotka-Volterra system and the more recent work of Hastings and Cuddington, to investigate such interactions. While a species is always able to attain a higher population in a single species community, greater habitat improvements (and indeed higher total populations) can be attained in multi-species communities. Furthermore, species spread is often fastest in such communities. Thus ecosystem engineering facilitates a form of mutualism.

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Papers

Paper: A Test of Positive Association for Detecting Heterogeneity in Capture for Capture–Recapture Data

Anita and Rachel have had a paper accepted in Journal of Agricultural Biological and Environmental Statistics.

A Test of Positive Association for Detecting Heterogeneity in Capture for Capture–Recapture Data

Anita Jeyam, Rachel S. McCrea, Thomas Bregnballe, Morten Frederiksen and Roger Pradel

The Cormack–Jolly–Seber (CJS) model assumes that all marked animals have equal recapture probabilities at each sampling occasion, but heterogeneity in capture often occurs and should be taken into account to avoid biases in parameter estimates. Although diagnostic tests are generally used to detect trap-dependence or transience and assess the overall fit of the model, heterogeneity in capture is not routinely tested for. In order to detect and identify this phenomenon in a CJS framework, we propose a test of positive association between previous and future encounters using Goodman–Kruskal’s gamma. This test is based solely on the raw capture histories and makes no assumption on model structure. The development of the test is motivated by a dataset of Sandwich terns (Thalasseus sandvicensis), and we use the test to formally show that they exhibit heterogeneity in capture. We use simulation to assess the performance of the test in the detection of heterogeneity in capture, compared to existing and corrected diagnostic goodness-of-fit tests, Leslie’s test of equal catchability and Carothers’ extension of the Leslie test. The test of positive association is easy to use and produces good results, demonstrating high power to detect heterogeneity in capture. We recommend using this new test prior to model fitting as the outcome will guide the model-building process and help draw more accurate biological conclusions.

The full article is available as open access and can be found here:  https://link.springer.com/content/pdf/10.1007%2Fs13253-017-0315-4.pdf

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