AIC model selection and multimodel inference in behavioral ecology: some background,observations, and comparisons

We briefly outline the information-theoretic (I-T) approaches to valid inference including a review of some simple methods for making formal inference from all the hypotheses in the model set (multimodel inference). The I-T approaches can replace the usual t tests and ANOVA tables that are so inferentially limited, but still commonly used. The I-T methods are easy to compute and understand and provide formal measures of the strength of evidence for both the null and alternative hypotheses, given the data. We give an example to highlight the importance of deriving alternative hypotheses and representing these as probability models. Fifteen technical issues are addressed to clarify various points that have appeared incorrectly in the recent literature. We offer several remarks regarding the future of empirical science and data analysis under an I-T framework.     

Graphical spatial models: a new view on interpreting spatial pattern

Graphical models provide an important tool for facilitating communication between scientists, decision-makers, and statisticians—many complicated ecological processes can be described in terms of “box-and-arrow” conceptual diagrams (e.g., Shipley in Cause and correlation in biology: a user’s guide to path analysis, structural equations and causal inferences, Cambridge Universtiy Press, Cambridge, 2000; Clark and Gelfand TRENDS in Ecology and Evolution 21:375–380, 2006). In particular, problems in landscape ecology often involve modeling relationships among multiple physical and/or biological variables that may operate on differing spatial scales (e.g., Rossi et al. in Ecol Monographs 62:277–314, 1992; Legendre et al. in Ecography 25:601–615, 2002; Overmars et al. in Ecol Model 164:257–270, 2003; Brown and Spector in J Appl Ecol 45:1639–1648, 2008; Koniak and Noy-Meir in Ecol Model 220:1148–1158, 2008). These problems are inherently multivariate, though researchers commonly rely on univariate methods, such as spatial regression models, to address them. In this paper, we introduce a multivariate method—graphical spatial models—that extends path analysis to incorporate spatial autocorrelation in one or more variables in a directed graph. We show how both exogenous and endogenous ecological processes as defined by Legendre et al. (Ecography 25:601–615, 2002) and Lichstein et al. (Ecol Monographs 72:445–463, 2002) can be represented in a graph. Most importantly, we show how to translate graphs representing these ecological processes into statistically estimable models. We motivate our theoretical results using an example of stream health data from the Willamette Valley, Oregon. For these data we are interested in the spatial pattern within both riparian land use and an index of stream health, and whether there is an association between land use and stream health, after accounting for these spatial patterns. We use a graphical spatial model to address these ecological questions simultaneously. We find that the health of a stream decreases as the percent of developed land within a 120-m riparian buffer increases; interestingly, there is only evidence of spatial pattern within land use.     

On-road and simulated driving: concurrent and discriminant validation

Introduction

A converging pair of studies investigated the validity of a simulator for measuring driving performance/skill.

Study 1

A concurrent validity study compared novice driver performance during an on-road driving test with their performance on a comparable simulated driving test.

Results

Results showed a reasonable degree of concordance in terms of the distribution of driving errors on-road and errors on the simulator. Moreover, there was a significant relationship between the two when driver performance was rank ordered according to errors, further establishing the relative validity of the simulator. However, specific driving errors on the two tasks were not closely related suggesting that absolute validity could not be established and that overall performance is needed to establish the level of skill.

Study 2

A discriminant validity study compared driving performance on the simulator across three groups of drivers who differ in their level of experience - a group of true beginners who had no driving experience, a group of novice drivers who had completed driver education and had a learner's permit, and a group of fully licensed, experienced drivers.

Results

The findings showed significant differences among the groups in the expected direction -- the various measures of driving errors showed that beginners performed worse than novice drivers and that experienced drivers had the fewest errors. Collectively, the results of the concurrent and discriminant validity studies support the use of the simulator as a valid measure of driving performance for research purposes.

Impact on industry

These findings support the use of a driving simulator as a valid measure of driving performance for research purposes. Future research should continue to examine validity between on-road driving performance and performance on a driving simulator and the use of simulated driving tests in the evaluation of driver education/training programs.     

Ecological and physiological controls of species composition in green macroalgal blooms

Green macroalgal blooms have substantially altered marine community structure and function, specifically by smothering seagrasses and other primary producers that are critical to commercial fisheries and by creating anoxic conditions in enclosed embayments. Bottom-up factors are viewed as the primary drivers of these blooms, but increasing attention has been paid to biotic controls of species composition. In Washington State, USA, blooms are often dominated by Ulva spp. intertidally and Ulvaria obscura subtidally. Factors that could cause this spatial difference were examined, including competition, grazer preferences, salinity, photoacclimation, nutrient requirements, and responses to nutrient enrichment. Ulva specimens grew faster than Ulvaria in intertidal chambers but not significantly faster in subtidal chambers. Ulva was better able to acclimate to a high-light environment and was more tolerant of low salinity than Ulvaria. Ulvaria had higher tissue N content, chlorophyll, chlorophyll b: chlorophyll a, and protein content than Ulva. These differences suggest that nitrogen availability could affect species composition. A suite of five grazers preferred Ulva to Ulvaria in choice experiments. Thus, bottom-up factors allow Ulva to dominate the intertidal zone while resistance to grazers appears to allow Ulvaria to dominate the subtidal zone. While ulvoid algae are in the same functional-form group, they are not functionally redundant.     

RESEARCH: Land Use Characteristics and Water Quality: A Methodology for Valuing of Forested Buffers

In this study, water quality and basin characteristics data from different basins of the Fish River basin, Baldwin County, Alabama, were used to develop a valuation model. This valuation model is based on the effectiveness of “contributing zones” identified and delineated using methods described by Basnyat and others (Environmental Management]1999] 23(4):539–549). The “contributing zone” delineation model suggests that depending on soil permeability, soil moisture, depth to water table, slope, and vegetation, buffer widths varying from 16 m to 104 m must be maintained to assimilate or detain more than 90% of the nitrate passing through the buffers. The economic model suggests the value of retiring lands (to create the buffers) varies from $0 to $3067 per ha, depending on the types of crops currently grown. The total value of retiring all areas identified by the contributing zone model is $1,125,639 for the study area. This land value will then form the basis for estimates of the costs of land management options for improving (or maintaining) water quality throughout the study area.     

Preferences for different nitrogen forms by coexisting plant species and soil microbes

The growing awareness that plants might use a variety of nitrogen (N) forms, both organic and inorganic, has raised questions about the role of resource partitioning in plant communities. It has been proposed that coexisting plant species might be able to partition a limited N pool, thereby avoiding competition for resources, through the uptake of different chemical forms of N. In this study, we used in situ stable isotope labeling techniques to assess whether coexisting plant species of a temperate grassland (England, UK) display preferences for different chemical forms of N, including inorganic N and a range of amino acids of varying complexity. We also tested whether plants and soil microbes differ in their preference for different N forms, thereby relaxing competition for this limiting resource. We examined preferential uptake of a range of 13C15N-labeled amino acids (glycine, serine, and phenylalanine) and 15N-labeled inorganic N by coexisting grass species and soil microbes in the field. Our data show that while coexisting plant species simultaneously take up a variety of N forms, including inorganic N and amino acids, they all showed a preference for inorganic N over organic N and for simple over the more complex amino acids. Soil microbes outcompeted plants for added N after 50 hours, but in the long-term (33 days) the proportion of added 15N contained in the plant pool increased for all N forms except for phenylalanine, while the proportion in the microbial biomass declined relative to the first harvest. These findings suggest that in the longer-term plants become more effective competitors for added 15N. This might be due to microbial turnover releasing 15N back into the plant-soil system or to the mineralization and subsequent plant uptake of 15N transferred initially to the organic matter pool. We found no evidence that soil microbes preferentially utilize any of the N forms added, despite previous studies showing that microbial preferences for N forms vary over time. Our data suggest that coexisting plants can outcompete microbes for a variety of N forms, but that such plant species show similar preferences for inorganic over organic N.     

UV-C as an efficient means to combat biofilm formation in show caves: evidence from the La Glacière Cave (France) and laboratory experiments

Environmental Science and Pollution Research - Ultra-violet C (UV-C) treatment is commonly used in sterilization processes in industry, laboratories, and hospitals, showing its efficacy against...     

Blowing policy bubbles: rethinking emissions targets and low-carbon energy policies in the U.K.

The issue of proportionality is central to climate policy debates about setting targets for the reduction of greenhouse gas emissions and the development of low-carbon energy. In effect, these debates centre on whether the perceived social costs outweigh the benefits of policies and, if not, whether this may lead to over-investment or ‘policy bubbles’. Political attention, agenda setting and policy image are all crucial drivers of ambitious policy formation, as seen in the case of the United Kingdom's (U.K.) Climate Change Act (2008). However, as political salience waned and economic depression dragged on, the cost of long-term climate targets has been reconsidered. Based on documentary analysis and 33 interviews with central political actors, this article presents a detailed account of how economic arguments have been used to reinterpret and challenge policies, using the heuristics of ‘over-investment’ and ‘policy bubbles’. Ultimately, arguments about proportionality hinge on which costs and benefits are considered. In the U.K., economic and technical framings are typically prioritised, but they do not explain contradictory and politically motivated policy decisions. We discuss these dynamics within the context of maturing renewable energy technologies, high energy prices and the U.K.’s cross-party consensus approach to climate politics.     

Analysis of a Farquhar-von Caemmerer-Berry leaf-level photosynthetic rate model for Populus tremuloides in the context of modeling and measurement limitations

The balance of mechanistic detail with mathematical simplicity contributes to the broad use of the Farquhar, von Caemmerer and Berry (FvCB) photosynthetic rate model. Here the FvCB model was coupled with a stomatal conductance model to form an [A,g_s] model, and parameterized for mature Populus tremuloides leaves under varying CO₂ and temperature levels. Data were selected to be within typical forest light, CO₂ and temperature ranges, reducing artifacts associated with data collected at extreme values. The error between model-predicted photosynthetic rate (A) and A data was measured in three ways and found to be up to three times greater for each of two independent data sets than for a base-line evaluation using parameterization data. The evaluation methods used here apply to comparisons of model validation results among data sets varying in number and distribution of data, as well as to performance comparisons of [A,g_s] models differing in internal-process components.