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Individual-based modeling includes the explicit representation of the environment, to which individual organisms interact. From the perspective of spatial representation, spatially extended environment is different from discrete individual organisms, thus the success of object-orientation in the representation of individual organisms may not be applicable to the representation of the environment. Over the past few years, the attempt to adopt object-orientation in the representation of the environment has stimulated interesting discussions over what space is and how it may be represented in ecological modeling. The objective of this paper is to evaluate the merit of two traditional approaches used to represent the environment, i.e., the grid model and the patch model, and, in particular, the object-oriented versions of the two approaches in the representation of the environment. Two case studies are provided in support of the discussions of how the environment may be represented. One case study concerns individual fish growth and movement in an aquatic environment and the other concerns the movement of calving elk in a short-grass prairie. The discussion stresses the importance of two issues in the context of individual-based modeling: (1) the distinction between object-orientation used as a programming technique and as a representation means, and (2) the conceptual compatibility between a perceived phenomenon and the approach used to represent the phenomenon. It is suggested that caution be exercised in the practice of treating cells as objects. The paper concludes that two approaches may be appropriate for individual-based modeling. One is a hybrid approach that incorporates the traditional grid model of the environment and an object-oriented model of individual organisms. The other is the all-object approach that combines the object-oriented patches of the environment and the object-oriented individual organisms. [Copyright &y& Elsevier]

Bayesian methods are experiencing increased use for probabilistic ecological modelling. Most Bayesian inference requires the numerical approximation of analytically intractable integrals. Two methods based on Monte Carlo simulation have appeared in the ecological/environmental modelling literature. Though they sound similar, the Bayesian Monte Carlo (BMC) and Markov Chain Monte Carlo (MCMC) methods are very different in their efficiency and effectiveness in providing useful approximations for accurate inference in Bayesian applications. We compare these two methods using a low-dimensional biochemical oxygen demand decay model as an example. We demonstrate that the BMC is extremely inefficient because the prior parameter distribution, from which the Monte Carlo sample is drawn, is often a poor surrogate for the posterior parameter distribution, particularly if the parameters are highly correlated. In contrast, MCMC generates a chain that converges, in distribution, on the posterior parameter distribution, that can be regarded as a sample from the posterior distribution. The inefficiency of the BMC can lead to marginal posterior parameter distributions that appear irregular and may be highly misleading because the important region of the posterior distribution may never be sampled. We also point out that a priori specification of the model error variance can strongly influence the estimation of the principal model parameters. Although the BMC does not require that the model error variance be specified, most published applications have treated this variance as a known constant. Finally, we note that most published BMC applications have chosen a uniform prior distribution, making the BMC more similar to a likelihood-based inference rather than a Bayesian method because the posterior is unaffected by the prior. Though other prior distributions could be applied, the treatment of Monte Carlo samples with any other choice of prior distribution has not been discussed in the BMC literature. [Copyright &y& Elsevier]

Heterogeneous usage of space by individual animals or animal populations is partly due to their preference for particular resources that are, themselves, heterogeneously distributed. When all points in the environment are equally accessible, a direct relationship between usage and preference can be assumed. However, when accessibility is restricted, spatial variations in usage can no longer be attributed entirely to preference. In such cases, it is necessary to control for the effects of accessibility on observed usage before conclusions about preference can be drawn. In this paper, I develop a modelling framework that treats the use of space by animals as a joint function of preference and accessibility. I specify a null version of the framework that assumes no preference and propose that its output can be used to control for the effect of accessibility on the observed, spatial distribution of usage. I briefly discuss how the framework can subsequently be used to provide insights about the animals’ preference for different resources and types of movement, and to predict usage in areas where no usage data exist. I explore the properties of the methodology using data from a population of simulated animals and present the first results of its application to a sub-set of the British population of grey seals (Halichoerus grypus). [Copyright &y& Elsevier]

Null models have proven to be an important quantitative tool in the search for ecological processes driving local diversity and species distribution. However, there remains an important concern that different processes, such as environmental conditions and biotic interactions may produce similar patterns in species distributions. In this paper we present an analytical protocol for incorporating habitat suitability as an occupancy criterion in null models. Our approach involves modeling species presence or absence as a function of environmental conditions, and using the estimated site-specific probabilities of occurrence as the likelihood of species occupancy of a site during the generation of “null communities”. We validated this approach by showing that type I error is not affected by the use of probabilities as a site occupancy criterion and is robust against a variety of predictive performances of the species-environmental models. We describe the expected differences when contrasting classical and the environmentally constrained null models, and illustrate our approach with a data set of Dutch dune hunting spider assemblages. Together, an environmentally constrained approach to null models will provide a more robust evaluation of species associations by facilitating the distinction between mutually exclusive processes that may shape species distributions and community assembly. [ABSTRACT FROM AUTHOR]

The problem of investigating compatibility of an assumed model with the data is investigated in the situation when the assumed model has unknown parameters. The most frequently used measures of compatibility are p values, based on statistics T for which large values are deemed to indicate incompatibility of the data and the model. When the null model has unknown parameters, p values are not uniquely defined. The proposals for computing a p value in such a situation include the plug-in and similar p values on the frequentist side, and the predictive and posterior predictive p values on the Bayesian side. We propose two alternatives, the conditional predictive p value and the partial posterior predictive p value, and indicate their advantages from both Bayesian and frequentist perspectives. [ABSTRACT FROM AUTHOR]

Compares the performance of nine null model algorithms and four co-occurrence indices with respect to Type I and Type II errors in the analysis of presence-absence matrices in community ecology and biogeography. Estimation of errors; Comparison of different indices; Simulation procedures; Selection of the right algorithm; Index of nonrandomness.

It can be argued that purely statistical models, that make no assumptions about biological interactions among species provide an adequate explanation of the data. But it is often very difficult to decide what constitutes a truly null, purely statistical, model. This article aims at surveying the controversies surrounding the construction and use of null models in several areas of community ecology. It identifies some biological questions at issue and the way null models can shed light on them, and then focuses on various technical problems involved in attempting to find an appropriate null model.

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The article focuses on the conclusion of the research "Why are diversity and endemism linked on islands?," by C. C. Witt and S. Maliakal-Witt (2007). They showed with a null model that the diversity and endemism link on islands can arise by chance alone through stochastic colonizations and extinctions. It investigates whether the conclusion still holds when the processes of speciation, extinction and inter-island colonization, are included. It concludes that, chance alone explains the relationship between the proportion of endemics and species diversity.

The author reflects on the importance for ecologists to use established names when publishing probability distribution to prevent duplication of their research. He cites the effort of several ecologists in investing substantial time on spatial aggregation only to find out that they are published in the probability literature under an established name. The author encourages the need for ecologists to make connections among ecological models to avoid the duplication of their efforts.

The article provides information on the new tools to be used in gathering models and data for ecological research purposes. It mentions that researchers have used the traditional models in gathering data. In addition, it cites the three traditions used in research including empirical, theoretical, and a tradition that deal with detailed systems. However, it notes that the high dimension of the three traditions have not yet adopted the complexness of scientific approaches including mathematical analysis, symbols, and simulation.

The article discusses various reports within the issue, including one by Andrew R. Kanarek, Roland H. Lamberson and Jeffrey M. Black on the study of the importance of learned behavior for successful animal feeding through an individual-based foraging model and another one by John Goss-Custard on his study of a shorebird model with real, on-the-ground management implications.

Reserve establishment and strategic harvest planning are two longstanding but often separate approaches to conserving biodiversity in working landscapes. Our paper unites these fields and explores how ecological characteristics of landscapes influence conservation outcomes, with a particular consideration of tropical forests. We used an integer programming model to compare the performance of different management designs on simulated landscapes with different species diversity values and degrees of conspecific spatial aggregation. We explored three classes of reserve and harvest plans: optimal, random, and fixed-pattern (the last of which is most common in tropical forest management). Optimal designs (and performance criteria) were rooted in the Optimized Floating Refugia strategy, a new approach to landscape-level forest management that assumes local extinctions will occur and seeks to facilitate recolonization for as many species as possible via strategic spatiotemporal planning. We found several interesting interactions between harvest planning and reserve establishment. On landscapes with ecological characteristics resembling those of tropical forests (high species diversity and high conspecific aggregation), strategic harvest plans with no reserves saved more species than fixed-pattern, aggregated harvest plans with over 20 percent of stands set aside as reserves. Our findings also suggest an important rule of thumb: less aggregated harvest plans lead to fewer extinctions than more aggregated harvest plans. Overall, we found that the integration of harvest planning and reserve design led to novel insights, and that the divergence in absolute performance between different management regimes (but not the ordinal ranking) was highly dependent on the ecological characteristics of the landscape. [ABSTRACT FROM AUTHOR]

Presents information on science-related Web sites. Features of the online DNA database created by the Biomolecular Interaction Network Database; Simulation of ecological activities on the Web site www.gingerbooth.com; Creation of a Web site for women scientists.

Abstract One of the assumptions of analysis of variance (anova) is that the variances of the groups being compared are approximately equal. This assumption is routinely checked before doing an analysis, although some workers consider anova robust and do not bother and others avoid parametric procedures entirely. Two of the more commonly used heterogeneity tests are Bartlett's and Cochran's, although, as for most of these tests, they may well be more sensitive to violations of the anova assumptions than is anova itself. Simulations were used to examine how well these two tests protected anova against the problems created by variance heterogeneity. Although Cochran's test performed a little better than Bartlett's, both tests performed poorly, frequently disallowing perfectly valid analyses. Recommendations are made about how to proceed, given these results. [ABSTRACT FROM AUTHOR]