A Primer of Ecology with R

Abstracts not available for BookReview

Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME

Mathematical simulation models are commonly applied to analyze experimental or environmental data and eventually to acquire predictive capabilities. Typically these models depend on poorly defined, unmeasurable parameters that need to be given a value. Fitting a model to data, so-called inverse modelling, is often the sole way of finding reasonable values for these parameters. There are many challenges involved in inverse model applications, e.g., the existence of non-identifiable parameters, the estimation of parameter uncertainties and the quantification of the implications of these uncertainties on model predictions. The R package FME is a modeling package designed to confront a mathematical model with data. It includes algorithms for sensitivity and Monte Carlo analysis, parameter identifiability, model fitting and provides a Markov-chain based method to estimate parameter confidence intervals. Although its main focus is on mathematical systems that consist of differential equations, FME can deal with other types of models. In this paper, FME is applied to a model describing the dynamics of the HIV virus.

R Package ecolMod: figures and examples from Soetaert and Herman (2009)

Abstract This document contains some examples of the demos from package ecolMod. This package has, in its demo's all the figures of the book: Soetaert K. and P.M.J. Herman (2009). A Practical Guide to Ecological Modelling. Using R as a Simulation Platform. Springer, 372 pp

Solving Differential Equations in R: Package deSolve

In this paper we present the R package deSolve to solve initial value problems (IVP) written as ordinary differential equations (ODE), differential algebraic equations (DAE) of index 0 or 1 and partial differential equations (PDE), the latter solved using the method of lines approach. The differential equations can be represented in R code or as compiled code. In the latter case, R is used as a tool to trigger the integration and post-process the results, which facilitates model development and application, whilst the compiled code significantly increases simulation speed. The methods implemented are efficient, robust, and well documented public-domain Fortran routines. They include four integrators from the ODEPACK package (LSODE, LSODES, LSODA, LSODAR), DVODE and DASPK2.0. In addition, a suite of Runge-Kutta integrators and special-purpose solvers to efficiently integrate 1-, 2- and 3-dimensional partial differential equations are available. The routines solve both stiff and non-stiff systems, and include many options, e.g., to deal in an efficient way with the sparsity of the Jacobian matrix, or finding the root of equations. In this article, our objectives are threefold: (1) to demonstrate the potential of using R for dynamic modeling, (2) to highlight typical uses of the different methods implemented and (3) to compare the performance of models specified in R code and in compiled code for a number of test cases. These comparisons demonstrate that, if the use of loops is avoided, R code can efficiently integrate problems comprising several thousands of state variables. Nevertheless, the same problem may be solved from 2 to more than 50 times faster by using compiled code compared to an implementation using only R code. Still, amongst the benefits of R are a more flexible and interactive implementation, better readability of the code, and access to RâÂÂs high-level procedures. deSolve is the successor of package odesolve which will be deprecated in the future; it is free software and distributed under the GNU General Public License, as part of the R software project.

The predation impact of juvenile herring Clupea harengus and sprat Sprattus sprattus on estuarine zooplankton

The consumption of estuarine copepods by juvenile herring and sprat during estuarine residency was estimated using fish biomass data and daily rations calculated from two models of feeding in fish: a bioenergetic model and a gastric evacuation model. The bioenergetic model predicted daily rations that were, on average, three times higher than those estimated by a model based on field records of stomach contents. The biomass of herring and sprat in the estuary was negatively correlated with the daily ration suggesting that the clupeid fish populations were resource-limited. Copepod production decreased towards the winter and peaked in spring and summer. The relative importance of predation changed seasonally in function of the migration pattern of herring and sprat. In the spring and the summer, in situ production of␣copepod biomass was higher than the in situ consumption by fish. During the fall and the winter, consumption exceeded production. This suggests that top–down control exerted by marine pelagic fish may be an important force structuring estuarine copepod populations

Nutritional importance of benthic bacteria for deep-sea nematodes from the Arctic ice margin: Results of an isotope tracer experiment

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