On the Estimation of Dispersal Kernels from Individual Mark-Recapture Data

We present a new method for estimating a distribution of dispersal displacements (a dispersal kernel) from mark-recapture data. One conventional method of calculating the dispersal kernel assumes that the distribution of displacements are Gaussian (e.g. resulting from a diffusion process) and that individuals remain within sampled areas. The first assumption prohibits an analysis of dispersal data that do not exhibit the Gaussian distribution (a common situation); the second assumption leads to underestimation of dispersal distance because individuals that disperse outside of sampling areas are never recaptured. Our method eliminates these two assumptions. In addition, the method can also accommodate mortality during a sampling period. This new method uses integrodifference equations to express the probability of spatial mark-recapture data; associated dispersal, survival, and recapture parameters are then estimated using a maximum likelihood method. We examined the accuracy of the estimators by applying the method to simulated data sets. Our method suggests designs for future mark-recapture experiments. Received: January 2004 / Revised: July 2005     

Probability distributions and statistical inference for axial data

Observations on axes which lack information on the direction of propagation are referred to as axial data. Such data are often encountered in enviromental sciences, e.g. observations on propagations of cracks or on faults in mining walls. Even though such observations are recorded as angles, circular probability models are inappropriate for such data since the constraint that observations lie only in [0, π) needs to be enforced. Probability models for such axial data are argued here to have a general structure stemming from that of wrapping a circular distribution on a semi-circle. In particular, we consider the most popular circular model, the von Mises or circular normal distribution, and derive the corresponding axial normal distribution. Certain properties of this distribution are established. Maximum likelihood estimation of its parameters are shown to be surprisingly, in contrast to trigonometric moment estimation, numerically quite appealing. Finally we illustrate our results by several real life axial data sets. Received: September 2004/ Revised: December 2004     

Efficient and sustainable management of complex forest ecosystems

A large range of models has been developed for the analysis of optimal forest management strategies, with the well-known Faustmann models dating back to the mid-19th century. To date, however, there has been relatively little attention for the implications of complex ecosystem dynamics for optimal forest management. This paper examines the implications of irreversible ecosystem responses for efficient and sustainable forest management. The paper is built around two forest models that comprise two ecosystem components, forest cover and topsoil, the interactions between these components, and the supply of the ecosystem services ‘wood’ and ‘erosion control’. The first model represents a forest that responds in a reversible way to overharvesting. In the second model, an additional ecological process has been included and the ecosystem irreversibly collapses below certain thresholds in forest cover and topsoil depth. The paper presents a general model, and demonstrates the implications of pursuing efficient as well as sustainable forest management for the two forest ecosystems. Both fixed and variable harvesting cycles are examined. Efficient and sustainable harvesting cycles are compared, and it is shown that irreversible ecosystem behaviour reduces the possibilities to reconcile efficient and sustainable forest management through a variable harvesting cycle.     

Studying crop sequences with CarrotAge,a HMM-based data mining software

We have developed a knowledge discovery system based on high-order hidden Markov models for analyzing spatio-temporal data bases. This system, named CarrotAge , takes as input an array of discrete data – the rows represent the spatial sites and the columns the time slots – and builds a partition together with its a posteriori probability. CarrotAge has been developed for studying the cropping patterns of a territory. It uses therefore an agricultural drench database, named Ter-Uti , which records every year the land-use category of a set of sites regularly spaced. The results of CarrotAge are interpreted by agronomists and used in research works linking agricultural land use and water management. Moreover, CarrotAge can be used to find out and study crop sequences in large territories, that is a main question for agricultural and environmental research, as discussed in this paper.     

Aphid population response to agricultural landscape change: A spatially explicit,individual-based model

A spatially explicit individual-based simulation model has been developed to represent aphid population dynamics in agricultural landscapes. The application of the model to Rhopalosiphum padi (L.) population dynamics is detailed, including an outline of the construction of the model, its parameterisation and validation. Over time, the aphids interact with the landscape and with one another. The landscape is modified by varying a simple pesticide regime, and the multi-scale spatial and temporal implications for a population of aphids is analysed. The results show that a spatial modelling approach that considers the effects on the individual of landscape properties and factors such as wind speed and wind direction provides novel insight into aphid population dynamics both spatially and temporally. This forms the basis for the development of further simulation models that can be used to analyse how changes in landscape structure impact upon important species distributions and population dynamics.     

Model-based combination of spatial information for stream networks

Evolutionary improvements in Geographic Information Systems (GIS) now routinely allow the management and mapping of spatial-temporal information. In response, the development of statistical models to combine information of different types and spatial support is of vital importance to environmental science. In this paper we develop a hierarchical spatial statistical model for environmental indicators of stream and river systems in the United States Mid-Atlantic Region by combining information from separate monitoring surveys, available contextual information on hydrologic units and remote sensing information. These models are used to estimate the indicators throughout the riverine system based on information from multiple sources and aggregate scales. The analysis is based on information underlying the Landscape Atlas of the mid-Atlantic region produced by the US Environmental Monitoring and Assessment Program (EMAP). We also combine information from two overlapping separate monitoring surveys, the EMAP Stream and River Survey and the Maryland Biological Streams Survey. We present a general framework for comparative distributional analysis based on the concept of a relative spatial distribution. As an application, the spatial model is used to predict spatial distributions and relative spatial distributions for a watershed.     

Multistate models for monitoring individual trees in permanent observation plots

This interdisciplinary research on forest ecosystems begins with some characteristics of ecosystems which are the basis for the derivation of statistical models for the development and vitality of trees. Several ecological problems which could be solved by longitudinal studies are mentioned. Statistical methods for the evaluation of the crowns of spruce trees (Picea abies Karst) in three permanent observation plots in Switzerland are described. In particular, the time-dependent proportional odds model and a transitional model are used. Through application of these multistate models the data give information on the dependence of an ordered categorical response variable on covariates characterizing the ecosystem. The response variable is observed through infrared aerial photographs. This monitoring system gives insight into the dynamic behaviour of the forest ecosystem. The need for more eco-systematically motivated statistical research using longitudinal studies is identified.     

The People and Landscape Model (PALM): Towards full integration of human decision-making and biophysical simulation models

A model for simulating resource flows in a rural subsistence community is described. The People and Landscape Model (PALM) consists of a number of agents representing households, the landscape, and livestock. The landscape is made up of a number of homogeneous land units, or ‘fields’, each represented by an object containing data, methods and properties relevant to the field. Each field object consists of a number of soil layer objects, each of which contains routines to calculate its water balance and carbon and nitrogen dynamics. Organic matter decomposition is simulated by a version of the CENTURY model, while water and nitrogen dynamics are simulated by versions of the routines in the DSSAT crop models. The soil processes are simulated continuously, and vegetation types (crops, weeds, trees) can come and go in a field depending on its management. Crop growth and development are simulated by a generic model based on the DSSAT crop models, and which can be parameterised for different crops. Similarly, livestock growth and resource use is simulated by a generic model which can be parameterised for buffalo, cows, goats, sheep, chickens and pigs.     

Shifts in the spatio-temporal growth dynamics of shortleaf pine

Previous studies focusing on the growth history of Pinus echinata at the edge of its geographical range have suggested that changes in growth correspond to climatic and non-climatic (e.g., anthropogenic) factors. We employ a regime-dependent state-space model that allows us to detect and characterize the changes in tree growth dynamics over space and time using readily available dendrochronological and climatic data in the presence of various sources of uncertainty. We utilize methods common in atmospheric sciences but relatively unknown in ecology and forestry to develop a hierarchical model for tree growth and describe the growth dynamics. The utility of such methods for addressing ecological problems will grow as more high dimensional spatio-temporal processes are considered and datasets become more readily available.     

Roads and Landscape Pattern in Northern Wisconsin Based on a Comparison of Four Road Data Sources

Abstract:  Roads are important components of landscapes; they fragment habitat, facilitate invasive species spread, alter hydrology, and influence patterns of land use. Previous research on the ecological impacts of roads may have underestimated their effect because currently available sources of road data do not include the full road network. We compared differences in road density and landscape pattern among U.S. Census Bureau TIGER line files, U.S. Geological Survey 1:100,000-scale digital line graphs, and U.S. Geological Survey 1:24,000-scale digital raster graphics in northern Wisconsin to road data derived from 1:40,000-scale digital orthophotos. Road density measured from digital orthophotos (2.82 km/km²) was significantly greater than that of digital raster graphics (1.62 km/km²) and more than double that of digital line graphs (1.21 km/km²) and TIGER (1.27 km/km²) data. The increased road densities in raster graphics and orthophoto data were mainly due to the addition of minor roads. When all roads were used to define patch boundaries, landscape metrics produced with orthophoto data showed significantly greater levels of fragmentation than those based on line or raster graphics. For example, maximum patch size was 1074 ha and total edge was 109 km for line graphs, compared with 686 ha and 211 km for orthophoto data. Roads are missing in commonly used data, primarily because mapping standards systematically exclude minor roads. These standards are not ecologically based and may result in false assumptions about the ecological effects of roads. We recommend that future studies take special consideration of the completeness of road data and consider whether all ecologically relevant roads are included.