Data-Mining Discovery of Pattern and Process in Ecological Systems

ABSTRACT Most ecologists use statistical methods as their main analytical tools when analyzing data to identify relationships between a response and a set of predictors; thus, they treat all analyses as hypothesis tests or exercises in parameter estimation. However, little or no prior knowledge about a system can lead to creation of a statistical model or models that do not accurately describe major sources of variation in the response variable. We suggest that under such circumstances data mining is more appropriate for analysis. In this paper we 1) present the distinctions between data-mining (usually exploratory) analyses and parametric statistical (confirmatory) analyses, 2) illustrate 3 strengths of data-mining tools for generating hypotheses from data, and 3) suggest useful ways in which data mining and statistical analyses can be integrated into a thorough analysis of data to facilitate rapid creation of accurate models and to guide further research.     

The Process of Loss: Exploring the Interactions Between Economic and Ecological Systems

SYNOPSIS. Though only a few naturalists have read much economictheory, current understandings of how biological diversity isbeing lost are largely framed by the models developed by economistsover the past two centuries. There is more than a touch of ironyhere. While conservation biologists are challenging the courseof economic development, their perception of the process ofbiodiversity loss is driven by historic patterns of economicreasoning that have become a part of popular consciousness.To be sure, the early economic models were designed to addressthe development of agriculture and the use of land. But agricultureis the most dependent on biodiversity. At the same time, thegeographic expansion of agricultural activities and the choiceof agricultural technologies have been the key driving forceof biodiversity loss. Laterm economic models addressed the limitsof markets to provide guiding signals for human interactionwith the complexities of ecosystems. Even the way we frame howwe should respond to the greatest long-term threat to biodiversity,the likelihood of climate change, is rooted in the economicsof more than half a century ago. This article elaborates these economic framings of the interactionof economic systems with the environment and discusses theirpolicy implications. One of the major problems is that evenexisting economic understandings of the processes of biodiversityloss are only accepted within a part of the economics professionbecause these understandings conflict with political ideologiesheld by most American economists. Thus processes of biodiversityloss are maintained, not for a lack of knowledge, but for adesire among people to maintain simple views of biological systems.Even the patterns of reasoning held by economists who do ponderbiological systems, however, are inadequate. The paper concludeswith suggestions of additional ways of modeling the interactionsbetween human activity and biological systems which may providefurther insight into how we might better maintain biologicaldiversity.     

Paleontological Patterns, Macroecological Dynamics and the Evolutionary Process

Here we consider evolutionary patterns writ large in the fossil record. We argue that Darwin recognized but downgraded or de-emphasized several of these important patterns, and we consider what a renewed emphasis on these patterns can tell us about the evolutionary process. In particular, one of the key patterns we focus on is the role geographic isolation plays in fomenting evolutionary divergence; another one of the key patterns is stasis of species; the final pattern is turnovers, which exist at several hierarchical scales, including regional ecosystem replacement and pulses of speciation and extinction. We consider how each one of these patterns are related to the dynamic of changing ecological and environmental conditions over time and also investigate their significance in light of other concepts including punctuated equilibria and hierarchy theory. Ultimately, we tie each of these patterns into a framework involving macroecological dynamics and the important role environmental change plays in shaping evolution from the micro- to macroscale.     

Understanding the Complexity of Economic, Ecological, and Social Systems

Hierarchies and adaptive cycles comprise the basis of ecosystems and social-ecological systems across scales. Together they form a panarchy. The panarchy describes how a healthy system can invent and experiment, benefiting from inventions that create opportunity while being kept safe from those that destabilize because of their nature or excessive exuberance. Each level is allowed to operate at its own pace, protected from above by slower, larger levels but invigorated from below by faster, smaller cycles of innovation. The whole panarchy is therefore both creative and conserving. The interactions between cycles in a panarchy combine learning with continuity. An analysis of this process helps to clarify the meaning of “sustainable development.” Sustainability is the capacity to create, test, and maintain adaptive capability. Development is the process of creating, testing, and maintaining opportunity. The phrase that combines the two, “sustainable development,” thus refers to the goal of fostering adaptive capabilities and creating opportunities. It is therefore not an oxymoron but a term that describes a logical partnership. Received 7 March 2001; accepted 16 March 2001.     

Using Moment Equations to Understand Stochastically Driven Spatial Pattern Formation in Ecological Systems

Spatial patterns in biological populations and the effect of spatial patterns on ecological interactions are central topics in mathematical ecology. Various approaches to modeling have been developed to enable us to understand spatial patterns ranging from plant distributions to plankton aggregation. We present a new approach to modeling spatial interactions by deriving approximations for the time evolution of the moments (mean and spatial covariance) of ensembles of distributions of organisms; the analysis is made possible by “moment closure,” neglecting higher-order spatial structure in the population. We use the growth and competition of plants in an explicitly spatial environment as a starting point for exploring the properties of second-order moment equations and comparing them to realizations of spatial stochastic models. We find that for a wide range of effective neighborhood sizes (each plant interacting with several to dozens of neighbors), the mean-covariance model provides a useful and analytically tractable approximation to the stochastic spatial model, and combines useful features of stochastic models and traditional reaction-diffusion-like models.     

Allozyme Diversity in Non-social Spiders: Pattern, Process and Conservation Implications

In this article we summarize estimates of genetic variation based on allozymes for 30 non-social spider species. Overall, these species show moderate levels of genetic variability (mean H_o = 6.8%) compared to other invertebrate species surveyed for allozymes, although a number of spiders possess only minimal variation. Fossorial spiders, especially those which are coastal dune dwellers, typically display less variation than other non-social arachnids. In general, differences in heterozygosity estimates between groups of non-social spiders in this article are not confounded by the varying mix of proteins that have been assayed by individual investigators. There is a significant positive relationship between genetic variability and gene flow (Nm), indicating that non-social spider populations which exhibit reduced variability are likely to be genetically isolated. Population bottlenecks, directional selection and environmental homogeneity have all been cited to account for reduced variability in particular non-social spiders. In addition, an analysis using the genus Lutica suggests that low genetic variation may be accompanied by decreased population fitness. Since the potential for evolutionary change is dependent on the existence of genetic variability, our findings indicate that a number of non-social spiders may be at risk in terms of long-term population viability. This conclusion should be verified/extended via a combination of more genetic surveys; genetic and ecological monitoring of populations and their fitnesses in the wild; and experimental studies of the mechanisms underlying fitness differences.     

Contagious Disturbance, Ecological Memory, and the Emergence of Landscape Pattern

Landscapes are strongly shaped by the degree of interaction between pattern and process. This paper examines how ecological memory, the degree to which an ecological process is shaped by its past modifications of a landscape, influences landscape dynamics. I use a simulation model to examine how ecological memory shapes the landscape dynamics produced by the interaction of vegetative regrowth and fire. The model illustrated that increased ecological memory increased the strength and spatial extent of landscape pattern. The extent of these changes depended upon the relative rates of vegetative recovery and fire initiation. When ecological memory is strong, landscape pattern is persistent; pattern tends to be maintained rather than destroyed by fire. The generality of the simulation model suggests that these results may also apply to disturbance processes other than fire. The existence of ecological memory in ecosystems may allow processes to produce ecological pattern that can entrain other ecosystem variables. The methods presented in this paper to analyze pattern in model ecosystems could be used to detect such pattern in actual ecosystems. Received 14 November 2000; accepted 21 September 2001.     

低丘红壤地区几种农林复合系统的生态经济效益

低丘红壤地区几种农林复合系统的生态经济效益傅庆林,罗永进（浙江省农科院土壤肥料研究所,杭州３１００２１）柴锡周（浙江省林业科学研究所,杭州３１００２３）ＥｃｏｌｏｇｉｃａｌＥｆｆｅｃｔｓａｎｄＥｃｏｎｏｍｉｃＢｅｎｃｆｉｔｏｆｔｈｅＣｏｍｐｌｅｘＡｇ...     

多米诺效应次生地质灾害的生态后果

接受我们采访的是中国科学院生态环境研究中心副主任、城市与区域生态国家重点实验室主任欧阳志云研究员,他是最早关注震区生态并进入现场调查的专家之一。他说：这次地震导致的生态破坏十分严重．这绝不只是一种学术上的讨论,因为这一地区是我国生态环境十分脆弱的地区,     

多米诺效应——次生地质灾害的生态后果

接受我们采访的是中国科学院生态环境研究中心副主任、城市与区域生态国家重点实验室主任欧阳志云研究员,他是最早关注震区生态并进入现场调查的专家之一。他说:这次地震导致的生态破坏十分严重,这绝不只是一种学术上的讨论,因为这一地区是我国生态环境十分脆弱的地区,又是具有重要生态服务功能的地区。我们的访谈集中在:这次地震在生态方面造成了哪些破坏?为什么需要我们对此给与足够的关注?     

Ecological Assessment and Geological Significance of Microbial Communities from Cesspool Cave,Virginia

Microbial mats from hydrogen sulfide-rich waters and cave-wall biofilms were investigated from Cesspool Cave, Virginia, to determine community composition and potential geomicrobiological functioning of acid-producing bacteria. Rates of microbial mat chemoautotrophic productivity were estimated using [ 14 C]-bicarbonate incorporations and microbial heterotrophy was determined using [ 14 C]-leucine incubations. Chemoautotrophic fixation was measured at 30.4 - 12.0 ng C mg dry wt -1 h -1 , whereas heterotrophic productivity was significantly less at 0.17 - 0.02 ng C mg dry wt -1 h -1 . The carbon to nitrogen ratios of the microbial mats averaged 13.5, indicating that the mats are not a high quality food source for higher trophic levels. Ribosomal RNA-based methods were used to examine bacterial diversity in the microbial mats, revealing the presence of at least five strains of bacteria. The identity of some of the strains could be resolved to the genus Thiothrix and the Flexibacter - Cytophaga - Bacteriodes phylum, and the identity of the remaining strains was to either the Helicobacter or Thiovulum group. Two of 10 sulfur-oxidizing, chemoautotrophic pure cultures of Thiobacillus spp. (syn. Thiomonas gen. nov.) demonstrated the ability to corrode calcium carbonate, suggesting that the colonization and metabolic activity of these bacteria may be enhancing cave enlargement.     

On Variograms in Point Process Statistics,II: Models of Markings and Ecological Interpretation

This paper presents new models for marked point processes for describing forestry data. In these models two factors play a role: Long‐range variability is modeled by a random field, which may describe environmental variability, while short‐range variability is caused by the interaction of points of two classes. These models may help in the interpretation of empirical mark variograms as is shown by two examples from forestry statistics.     

黄塘小集水区生态经济型防护林林种布局研究

１黄塘小集水区概况１．１社会经济概况集水区总面积３２７ｋｍ２,有村民小组１１个,村民２５０户,人口１０１８人,人均耕地００８７ｈａ,其中水田００６７ｈａ。区内共有劳动力４８６人,劳动力资源丰富,每年农闲时期外出打工的人数多达３００人左右,有９４...