Spatial Population Models in Spatiotemporally Structured Environments

Spatial effects, such as habitat fragmentation and the location and size of disturbance events, play a key role in the dynamics of populations. This is true in natural populations (such as herbs living under a forest canopy) as well as human-dominated systems (for example, crop pests in agricultural landscapes). Focusing on the development of spatial population models, the project seeks to better understand how and why spatially autocorrelated disturbances affect the dynamics of populations with mixtures of short- and long-distance dispersal. A variety of disturbances are considered, including (1) static disturbance, representing habitat heterogeneity across a landscape; (2) short-term disturbance whereby populations are removed from sites which may then be recolonized, e.g. representing short-term control of agricultural crop pests, and (3) landscapes with specified spatial and temporal autocorrelation, whereby blocks of sites become unsuitable and cannot be recolonized until some time has elapsed. The primary objectives of the computational side of the project are to improve the simulation methodology used for these types of spatial models, to enable more rapid/complete exploration of the parameter space and the use of simulation models in Monte Carlo parameter estimation techniques.Spatial disturbances and heterogeneities play a fundamental role in any ecological system. This project develops mathematical models and computational tools to be used in the study of various types of disturbances. Possible applications include the modeling of understory plant species in a forest where gap formation renders a group of sites unsuitable until the canopy regenerates, or the application of pesticides over a region which leaves those sites unsuitable for recolonization by pests for some time. The project will include collaboration with entomologists and other biologists to study the effects of different strategies for controlling pests across agricultural landscapes, such as maggot flies in commercial blueberry fields. The effects of changing habitat distributions on populations, due to factors such as changing land-use patterns and global climate change, will also be considered. Significant undergraduate research training will be included in the project, including participation in a summer research program primarily aimed at underrepresented minority groups

CAREER: Dynamics of Hierarchical HouseholdStructured Epidemiological Models

Mathematical and computational models will be used to study populations hierarchically segregated into groups referred to as households . These households may represent patches within an agricultural field, fields within a landscape, dorms within a school, schools within a city, cities within a region, or even subnetworks within larger computer networks. Population models and epidemiological models will be explored within this framework, complementing other work with lattice-structured populations. In the models, interactions within a household occur much more often than interactions between different households. Primary goals of the models are to better understand how and why spatially targeted and/or clustered treatments affect dynamics of infections, for example varying the rates of pesticide application to crop fields based on the levels of insect infestations among those fields. Factors such as spatially varying resistance (both long-term, due to e.g. mosaic planting of different crops in different fields; and short-term, e.g. as a result of pesticides) will be included in the models. Another application will be better understanding the spread of malicious computer software ( worms ) using biological dispersal strategies in clustered heterogeneous computer networks.Entomologists and other researchers will cooperate with the principal investigator and his students to develop applications of the project such as controlling maggot flies in commercial blueberry fields in Maine, planthoppers in rice fields in China, and other agricultural crop pests. Interdisciplinary courses on modeling and simulation will incorporate various topics from the project. Undergraduate research training will be a significant part of the work, including participation in a summer research program primarily aimed at underrepresented minority groups. Outreach to high school students and teachers will also be included, with the participation of current undergraduates studying to become K-12 teachers

Moment Closure - A Brief Review

Moment closure methods appear in myriad scientific disciplines in the modelling of complex systems. The goal is to achieve a closed form of a large, usually even infinite, set of coupled differential (or difference) equations. Each equation describes the evolution of one "moment", a suitable coarse-grained quantity computable from the full state space. If the system is too large for analytical and/or numerical methods, then one aims to reduce it by finding a moment closure relation expressing "higher-order moments" in terms of "lower-order moments". In this brief review, we focus on highlighting how moment closure methods occur in different contexts. We also conjecture via a geometric explanation why it has been difficult to rigorously justify many moment closure approximations although they work very well in practice.Comment: short survey paper (max 20 pages) for a broad audience in mathematics, physics, chemistry and quantitative biolog

Intense or Spatially Heterogeneous Predation Can Select against Prey Dispersal

Dispersal theory generally predicts kin competition, inbreeding, and temporal variation in habitat quality should select for dispersal, whereas spatial variation in habitat quality should select against dispersal. The effect of predation on the evolution of dispersal is currently not well-known: because predation can be variable in both space and time, it is not clear whether or when predation will promote dispersal within prey. Moreover, the evolution of prey dispersal affects strongly the encounter rate of predator and prey individuals, which greatly determines the ecological dynamics, and in turn changes the selection pressures for prey dispersal, in an eco-evolutionary feedback loop. When taken all together the effect of predation on prey dispersal is rather difficult to predict. We analyze a spatially explicit, individual-based predator-prey model and its mathematical approximation to investigate the evolution of prey dispersal. Competition and predation depend on local, rather than landscape-scale densities, and the spatial pattern of predation corresponds well to that of predators using restricted home ranges (e.g. central-place foragers). Analyses show the balance between the level of competition and predation pressure an individual is expected to experience determines whether prey should disperse or stay close to their parents and siblings, and more predation selects for less prey dispersal. Predators with smaller home ranges also select for less prey dispersal; more prey dispersal is favoured if predators have large home ranges, are very mobile, and/or are evenly distributed across the landscape

The Swarm Simulation System and Individual-Based Modeling

Swarm is a simulation environment which facilitates development and experimentation with simulations involving a large number of agents behaving and interacting within a dynamic environment. Many of the computer simulations performed by researchers in various disciplines rest upon a very similar computational foundation. These researchers often invest significant effort into developing the basic platform to support their simulations. A prototype of the Swarm simulation system was developed as an attempt to provide a general-purpose tool for building these simulations. The Swarm system provides a framework that will allow people to only write code describing the details of their specific problem, and then gain easy access to user-interface, simulation management, and analysis tools. This paper will provide an overview of the Swarm system and discuss some of the general advantages and disadvantages of using individual-based models

R and MATLAB

In today’s increasingly interdisciplinary world, R and MATLAB® users from different backgrounds must often work together and share code. R and MATLAB® is designed for users who already know R or MATLAB and now need to learn the other platform. The book makes the transition from one platform to the other as quick and painless as possible. The author covers essential tasks, such as working with matrices and vectors, writing functions and other programming concepts, graphics, numerical computing, and file input/output. He highlights important differences between the two platforms and explores common mistakes that are easy to make when transitioning from one platform to the other.https://digitalcommons.library.umaine.edu/fac_monographs/1266/thumbnail.jp

Supplement 1. A C program implementing the algorithm for generating landscapes with spatially structured heterogeneities.

<h2>File List</h2><blockquote> <p><a href="2x1gen.c"><b>2x1gen.c</b></a> -- c source code </p> </blockquote><h2>Description</h2><blockquote> <p>This file is the source code for a program (written in the C programming language) that generates rectangular arrays representing landscapes with two habitat types. The habitat types have local spatial clustering, as described in the "Landscapes" section of the paper, and as shown in Fig. 1.</p> <p>The landscape can be output in a couple of different formats. The default behavior is to output an ASCII text file (with a small header) containing a picture of the landscape, with the characters `.' and `X' representing the two habitat types. This is probably most useful when working with landscapes less than approximately 70 x 70 sites. The program can also output the landscape in a raw binary format (with a small header). Finally, it is also capable of storing the landscape in a PGM (Portable Graymap) file. There is a large collection of free utilities written by Jef Poskanzer which manipulate files of this format and convert them to many other image formats (such as GIF); these utilities are included with many distributions of Linux, or you can download them from the <a href="http://www.acme.com/software/pbmplus/">ACME Laboratories pbmplus page</a>.</p> <p>You will need some kind of standard ANSI C compiler to compile the code. There are comments at the beginning of the program indicating portability concerns. In particular, the program was developed using "gcc" under the Linux operating system. If you are using a different environment, the calls to the random number generator may need to be modified.</p> </blockquote

Implications of Creation

If the field of Artificial Life (&quot;ALife&quot;) is successful, we will be forced to confront some difficult moral and philosophical issues which we might otherwise have been able to avoid. The ability to create new life forms as well as destroy existing ones will place a greater responsibility upon us. In addition, the existence of living systems within computer-simulated environments will present some new and unusual moral issues, as a result of the nature of computers and our control over them. It is the purpose of this paper to stimulate some questions that we may be forced to directly confront in the future; this paper will not attempt to resolve these issues. It is the author&apos;s hope to encourage speculation about the moral role of scientists engaging in ALife endeavors, and to remind the ALife scientist that this research does not take place in a moral vacuum. 1 Introduction The study of systems with lifelike behavior has a long history; it seems very natural for people to wonder what ..