Bayesian belief networks (BBNs) are useful tools for modeling ecological predictions and aiding resource-management decision-making. We provide practical guidelines for developing, testing, and revising BBNs. Primary steps in this process include creating influence diagrams of the hypothesized "causal web" of key factors affecting a species or ecological outcome of interest; developing a first, alpha-level BBN model from the influence diagram; revising the model after expert review; testing and calibrating the model with case files to create a beta-level model; and updating the model structure and conditional probabilities with new validation data, creating the final-application gamma-level model. We illustrate and discuss these steps with an empirically based BBN model of factors influencing probability of capture of northern flying squirrels (Glaucomys sabrinus (Shaw)). Testing and updating BBNs, especially with peer review and calibration, are essential to ensure their credibility and reduce bias. Our guidelines provide modelers with insights that allow them to avoid potentially spurious or unreliable models.
The intestinal microbiota is a microbial ecosystem of crucial importance to human health. Understanding how the microbiota confers resistance against enteric pathogens and how antibiotics disrupt that resistance is key to the prevention and cure of intestinal infections. We present a novel method to infer microbial community ecology directly from time-resolved metagenomics. This method extends generalized Lotka-Volterra dynamics to account for external perturbations. Data from recent experiments on antibiotic-mediated Clostridium difficile infection is analyzed to quantify microbial interactions, commensal-pathogen interactions, and the effect of the antibiotic on the community. Stability analysis reveals that the microbiota is intrinsically stable, explaining how antibiotic perturbations and C. difficile inoculation can produce catastrophic shifts that persist even after removal of the perturbations. Importantly, the analysis suggests a subnetwork of bacterial groups implicated in protection against C. difficile. Due to its generality, our method can be applied to any high-resolution ecological time-series data to infer community structure and response to external stimuli.
Accurate prediction and explanation are fundamental objectives of statistical analysis, yet they seldom coincide. Boosted trees are a statistical learning method that attains both of these objectives for regression and classification analyses. They can deal with many types of response variables (numeric, categorical, and censored), loss functions (Gaussian, binomial, Poisson, and robust), and predictors (numeric, categorical). Interactions between predictors can also be quantified and visualized. The theory underpinning boosted trees is presented, together with interpretive techniques. A new form of boosted trees, namely, "aggregated boosted trees" (ABT), is proposed and, in a simulation study, is shown to reduce prediction error relative to boosted trees. A regression data set is analyzed using ABT to illustrate the technique and to compare it with other methods, including boosted trees, bagged trees, random forests, and generalized additive models. A software package for ABT analysis using the R software environment is included in the Appendices together with worked examples.
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Ecological niche modeling?that is, estimation of the dimensions of fundamental ecological niches of species?to predict their geographic distributions is increasingly being employed in systematics, ecology, conservation, public health, etc. This technique is often (of necessity) based on data comprising records of presences only. In recent years, many modeling approaches have been devised to estimate these interrelated expressions of a species’ ecology, distributional biology, and evolutionary history?nevertheless, in many cases, a formal basis in ecological and evolutionary theory has been lacking. In this paper, we outline such a formal basis for the suite of techniques that can be termed ‘ecological niche modeling,’ analyze example situations that can be modeled using these techniques, and clarify the interpretation of results.
Maxent, one of the most commonly used methods for inferring species distributions and environmental tolerances from occurrence data, allows users to fit models of arbitrary complexity. Model complexity is typically constrained via a process known as L1 regularization, but at present little guidance is available for setting the appropriate level of regularization, and the effects of inappropriately complex or simple models are largely unknown. In this study, we demonstrate the use of information criterion approaches to setting regularization in Maxent, and we compare models selected using information criteria to models selected using other criteria that are common in the literature. We evaluate model performance using occurrence data generated from a known "true" initial Maxent model, using several different metrics for model quality and transferability. We demonstrate that models that are inappropriately complex or inappropriately simple show reduced ability to infer habitat quality, reduced ability to infer the relative importance of variables in constraining species' distributions, and reduced transferability to other time periods. We also demonstrate that information criteria may offer significant advantages over the methods commonly used in the literature.
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Since the 1980s, ecological models of health promotion have generated a great deal of enthusiasm among researchers and interventionists. These models emerged from conceptual developments in other fields, and only selected elements of the ecological approach have been integrated into them. In this article, we describe the tenets of the ecological approach and highlight those aspects that have been integrated into ecological models used in health promotion. We also analyze how ecological models have been applied to the study of two important public health issues, namely physical activity promotion and the increased consumption of fruits and vegetables, by conducting an archival study of published research. Finally, we make a statement regarding the usefulness of ecological models for research and practice and propose recommendations for future research, program planning, and evaluation.
Species distribution models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental estimates. They are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time. SDMs are now widely used across terrestrial, freshwater, and marine realms. Differences in methods between disciplines reflect both differences in species mobility and in “established use.” Model realism and robustness is influenced by selection of relevant predictors and modeling method, consideration of scale, how the interplay between environmental and geographic factors is handled, and the extent of extrapolation. Current linkages between SDM practice and ecological theory are often weak, hindering progress. Remaining challenges include: improvement of methods for modeling presence-only data and for model selection and evaluation; accounting for biotic interactions; and assessing model uncertainty.
Many analyses of ecological networks in recent years have introduced new indices to describe network properties. As a consequence, tens of indices are available to address similar questions, differing in specific detail, sensitivity in detecting the property in question, and robustness with respect to network size and sampling intensity. Furthermore, some indices merely reflect the number of species participating in a network, but not their interrelationship, requiring a null model approach. Here we introduce a new, free software calculating a large spectrum of network indices, visualizing bipartite networks and generating null models. We use this tool to explore the sensitivity of 26 network indices to network dimensions, sampling intensity and singleton observations. Based on observed data, we investigate the interrelationship of these indices, and show that they are highly correlated, and heavily influenced by network dimensions and connectance. Finally, we re-evaluate five common hypotheses about network properties, comparing 19 pollination networks with three differently complex null models: 1. The number of links per species ("degree") follow (truncated) power law distributions. 2. Generalist pollinators interact with specialist plants, and vice versa (dependence asymmetry). 3. Ecological networks are nested. 4. Pollinators display complementarity, owing to specialization within the network. 5. Plant-pollinator networks are more robust to extinction than random networks. Our results indicate that while some hypotheses hold up against our null models, others are to a large extent understandable on the basis of network size, rather than ecological interrelationships. In particular, null model pattern of dependence asymmetry and robustness to extinction are opposite to what current network paradigms suggest. Our analysis, and the tools we provide, enables ecologists to readily contrast their findings with null model expectations for many different questions, thus separating statistical inevitability from ecological process.
Ecologists and environmental managers rely on mathematical models, both to understand ecological systems and to predict future system behavior. In turn, models rely on appropriate estimates of their parameters. This book brings together a diverse and scattered literature, to provide clear guidance on how to estimate parameters for models of animal populations. It is not a recipe book of statistical procedures. Instead, it concentrates on how to select the best approach to parameter estimation for a particular problem, and how to ensure that the quality estimated is the appropriate one for the specific purpose of the modelling exercise. Commencing with a toolbox of useful generic approaches to parameter estimation, the book deals with methods for estimating parameters for single populations. These parameters include population size, birth and death rates, and the population growth rate. For such parameters, rigorous statistical theory has been developed, and software is readily available. The problem is to select the optimal sampling design and method of analysis. The second part of the book deals with parameters that describe spatial dynamics, and ecological interactions such as competition, predation and parasitism. Here the principle problems are designing appropriate experiments and ensuring that the quantities measured by the experiments are relevant to the ecological models in which they will be used. This book will be essential reading for ecological researchers, postgraduate students and environmental managers who need to address an ecological problem through a population model. It is accessible to anyone with an understanding of basic statistical methods and population ecology.
The authors conducted a qualitative study of the motivations and contextual factors that induce corporate ecological responsiveness. Analytic induction applied to data collected from 53 firms in the United Kingdom and Japan revealed three motivations: competitiveness, legitimation, and ecological responsibility. These motivations were influenced by three contextual conditions: field cohesion, issue salience, and individual concern. In this article, the authors also identify the conditions that likely lead to high corporate ecological responsiveness.
Expert knowledge in ecology is gaining momentum as a tool for conservation decision-making where data are lacking. Yet, little information is available to help a researcher decide whether expert opinion is useful for their model, how an elicitation should be conducted, what the most relevant method for elicitation is and how this can be translated into prior distributions for analysis in a Bayesian model. In this study, we provide guidance in using expert knowledge in a transparent and credible manner to inform ecological models and ultimately natural resource and conservation decision-making. We illustrate the decisions faced when considering the use of expert knowledge in a model with the help of two real ecological case studies. These examples are explored further to examine the impact of expert knowledge through 'priors' in Bayesian modeling and specifically how to minimize potential bias. Finally, we make recommendations on the use of expert opinion in ecology. We believe if expert knowledge is elicited and incorporated into ecological models with the same level of rigour provided in the collection and use of empirical data, expert knowledge can increase the precision of models and facilitate informed decision-making in a cost-effective manner.
Abstract Landscape‐scale ecological modelling has been hindered by suitable high‐resolution surface meteorological datasets. To overcome these limitations, desirable spatial attributes of gridded climate data are combined with desirable temporal attributes of regional‐scale reanalysis and daily gauge‐based precipitation to derive a spatially and temporally complete, high‐resolution (4‐km) gridded dataset of surface meteorological variables required in ecological modelling for the contiguous United States from 1979 to 2010. Validation of the resulting gridded surface meteorological data, using an extensive network of automated weather stations across the western United States, showed skill comparable to that derived from interpolation using station observations, suggesting it can serve as suitable surrogate for landscape‐scale ecological modelling across vast unmonitored areas of the United States. Copyright © 2011 Royal Meteorological Society
Preface: Beyond the Null Hypothesis1An Ecological Scenario and the Tools of the Ecological Detective32Alternative Views of the Scientific Method and of Modeling123Probability and Probability Models: Know Your Data394Incidental Catch in Fisheries: Seabirds in the New Zealand Squid Trawl Fishery945The Confrontation: Sum of Squares1066The Evolutionary Ecology of Insect Oviposition Behavior1187The Confrontation: Likelihood and Maximum Likelihood1318Conservation Biology of Wildebeest in the Serengeti1809The Confrontation: Bayesian Goodness of Fit20310Management of Hake Fisheries in Namibia Motivation23511The Confrontation: Understanding How the Best Fit Is Found263AppendixThe Method of Multiple Working Hypotheses281References295Index309
This review examines the psychological impact of adult sexual assault through an ecological theoretical perspective to understand how factors at multiple levels of the social ecology contribute to post-assault sequelae. Using Bronfenbrenner's (1979, 1986, 1995) ecological theory of human development, we examine how individual-level factors (e.g., sociodemographics, biological/genetic factors), assault characteristics (e.g., victim-offender relationship, injury, alcohol use), microsystem factors (e.g., informal support from family and friends), meso/ exosystem factors (e.g., contact with the legal, medical, and mental health systems, and rape crisis centers), macrosystem factors (e.g., societal rape myth acceptance), and chronosystem factors (e.g., sexual revictimization and history of other victimizations) affect adult sexual assault survivors' mental health outcomes (e.g., post-traumatic stress disorder, depression, suicidality, and substance use). Self-blame is conceptualized as meta-construct that stems from all levels of this ecological model. Implications for curbing and/or preventing the negative mental health effects of sexual assault are discussed.
Cloudflare built an AI agent workspace for its employees。 Now it’s open source
Scientists have uncovered new evidence that Venus may still be tearing itself apart from within。 Advanced 3D simulations indicate that some of the planet's giant rift valleys formed relatively recently and could still be expanding。 The results suggest Venus has a far more active interior than researchers once believed, challenging the long-held vie
NASA’s Curiosity rover has entered a Martian valley covered by an astonishing “sea” of tiny polygon-shaped fractures。 The honeycomb patterns, each only a few inches wide, stretch across the landscape and even wrap around a nearby 20-foot-tall butte