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Modern Applied Statistics With S

The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

Beginning with Emlen (1966) and MacArthur and Pianka (1966) and extending through the last ten years, several authors have sought to predict the foraging behavior of animals by means of mathematical models. These models are very similar,in that they all assume that the fitness of a foraging animal is a function of the efficiency of foraging measured in terms of some "currency" (Schoener, 1971) -usually energy- and that natural selection has resulted in animals that forage so as to maximize this fitness. As a result of these similarities, the models have become known as "optimal foraging models"; and the theory that embodies them, "optimal foraging theory." The situations to which optimal foraging theory has been applied, with the exception of a few recent studies, can be divided into the following four categories: (1) choice by an animal of which food types to eat (i.e., optimal diet); (2) choice of which patch type to feed in (i.e., optimal patch choice); (3) optimal allocation of time to different patches; and (4) optimal patterns and speed of movements. In this review we discuss each of these categories separately, dealing with both the theoretical developments and the data that permit tests of the predictions. The review is selective in the sense that we emphasize studies that either develop testable predictions or that attempt to test predictions in a precise quantitative manner. We also discuss what we see to be some of the future developments in the area of optimal foraging theory and how this theory can be related to other areas of biology. Our general conclusion is that the simple models so far formulated are supported are supported reasonably well by available data and that we are optimistic about the value both now and in the future of optimal foraging theory. We argue, however, that these simple models will requre much modification, espicially to deal with situations that either cannot easily be put into one or another of the above four categories or entail currencies more complicated that just energy.

Citation classic - optimal foraging - a selective review of theory and tests

https://pubag.nal.usda.gov/download/10780/pdf

rosetta: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions

https://core.ac.uk/download/pdf/374223.pdf

A manifesto for the equifinality thesis

Stop early to travel fast: modelling risk-averse scheduling among nocturnally migrating birds

The lack of high-resolution measurements of 3D ecosystem structure across broad spatial extents impedes major advancements in animal ecology and biodiversity science. We aim to fill this gap by using Light Detection and Ranging (LiDAR) technology to characterize the vertical and horizontal complexity of vegetation and landscapes at high resolution across regional to continental scales. The newly LiDAR-derived 3D ecosystem structures will be applied in species distribution models for breeding birds in forests and marshlands, for insect pollinators in agricultural landscapes, and songbirds at stopover sites during migration. This will allow novel insights into the hierarchical structure of animal-habitat associations, into why animal populations decline, and how they respond to habitat fragmentation and ongoing land use change. The processing of these massive amounts of LiDAR point cloud data will be achieved by developing a generic interactive eScience environment with multi-scale object-based image analysis (OBIA) and interpretation of LiDAR point clouds, including data storage, scalable computing, tools for machine learning and visualisation (feature selection, annotation/segmentation, object classification, and evaluation), and a PostGIS spatial database. The classified objects will include trees, forests, vegetation strata, edges, bushes, hedges, reedbeds etc. with their related metrics, attributes and summary statistics (e.g. vegetation openness, height, density, vertical biomass distribution etc.). The newly developed eScience tools and data will be available to other disciplines and applications in ecology and the Earth sciences, thereby achieving high impact. The project will foster new multi-disciplinary collaborations between ecologists and eScientists and contribute to training a new generation of geo-ecologists.

/pdf/eecolidar-escience-infrastructure-for-ecological-p5zlu0otgx.pdf

eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales

This paper is the second in a series of three papers, dealing with the hydrology of a forested lowland catchment, within the context of soil acidification research. the hydrological behaviour of the unsaturated soil zone is described with the numerical simulation model SWIF. the first paper presents a site specific model calibration and discusses the implications of the hydrological behaviour for soil acidification (Bouten et al., 1992). the present paper deals with the extension of the model results from one specific site to a larger research area. a third paper gives a model description and discusses its numerical behaviour (Tiktak and Bouten, 1992).

In order to evaluate the effects of the field variability of soil horizon thicknesses, the model SWIF is validated by using measured groundwater table dynamics of three sites, each having different soil horizon thicknesses. Subsequently, a sensitivity analysis is carried out in order to select the location-dependent model parameters that cause the main variation in hydrological behaviour. Finally, the spatial patterns of model results and of the location-dependent model parameters are compared.

The drainage depth and the depth of the boundary between the sandy top soil and the underlying boulder clay appear to be the key parameters that cause large differences in transpiration and in the vertical distribution of root water uptake and soil water fluxes. Spatial patterns of model results, therefore, also show resemblance with the spatial patterns of these location-dependent model parameters.

Simulation results for a reference location with averaged soil horizon thicknesses turn out to be beyond the 90 per cent confidence interval of the areal mean model results. This emphasizes the necessity to simulate first and then average.

Willem Bouten

Papers

Stop early to travel fast: modelling risk-averse scheduling among nocturnally migrating birds

eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales

Modelling soil water dynamics in a forest ecosystem. II: Evaluation of spatial variation of soil profiles

Soil water content measurements at different scales

Transpiration dynamics of an Austrian Pine stand and its forest floor: identifying controlling conditions using artificial neural networks