Mesocosm experiments enable researchers to study animal dynamics, but determining accurate estimates of survival and development rates of different life stages can be difficult, especially as the...
Mesocosm experiments enable researchers to study animal dynamics, but determining accurate estimates of survival and development rates of different life stages can be difficult, especially as the subjects may be hard to sample and mortality rates can be high. We propose a new methodology for estimating such parameters.
We used an experimental set-up with 48 aquatic mesocosms, each with 20 first instar mosquito larvae and under 1 of 12 treatments with varying temperatures and nutrient concentrations. We took daily subsamples of the aquatic life stages as well as counting the emerging adults. We developed a method to estimate the survival and development probabilities at each life stage, based on optimising a matrix population model. We used two different approaches, one assuming the difference between predictions and observations was normally distributed, and the other using a combination of a normal and a multinomial distribution. For each approach, the resulting optimisation problem had around 100 parameters, making conventional gradient descent ineffective with our limited number of data points. We solved this by computing the formal derivatives of our matrix model.
Both approaches proved effective in predicting mosquito populations over time, also when compared against a separate validation dataset, and the two approaches produced similar results. They also both predicted similar trends in the survival and development probabilities for each life stage, although there were some differences in the actual values. The approach which only used the normal distribution was considerably more computationally efficient than the mixed distribution approach.
This is an effective approach for determining the survival and development rates of small animals in mesocosm experiments. We have not found any other reliable methodology for estimating these parameters, especially not from incomplete data or when there are many different experimental treatments. This methodology enables researchers to gain a much more detailed understanding of the life cycles of small animals, potentially leading to advances in a wide range of areas, for example in mosquito-borne disease risk or in considering the effects of biodiversity loss or climate change on different species.
Over the last 20 years large efforts have been made in developing and optimising modelling techniques for DoE usage in engine calibration. A prerequisite for optimally applying DoE test designs... Show moreOver the last 20 years large efforts have been made in developing and optimising modelling techniques for DoE usage in engine calibration. A prerequisite for optimally applying DoE test designs is the detailed knowledge of the engine’s operating boundaries enclosing the ‘design space’. Known boundaries can be implemented in the DoE test plan such that no test points are planned in a region where the engine cannot or should not be operated. Four mathematical approaches have been analysed and compared on the problem statement ‘Which design space description method is most suitable for combustion engine calibration test applications?’: - Convex hull method (CH) - Prediction error variance (PEV) - Support vector machine (SVM) - Support vector machine with a leave-one-out optimisation (SVM-LOO) It can be concluded that the two SVM-based methods are the most suitable methods to describe a boundary. The quality of the assessment will be the primary selection criterion and therefore the SVM-LOO shows the most potential. Since the initial application of a design space description method will be offline, a longer boundary training time and new test point allocation time, e.g., required for the SVM and SVM-LOO methods, are acceptable for the current calibration problems. Show less