Using time-dependent models to investigate body condition and growth rate of the giant gartersnake

Identifying links between phenotypic attributes and fitness is a primary goal of reproductive ecology. Differences in within-year patterns of body condition between sexes of gartersnakes in relation to reproduction and growth are not fully understood. We conducted an 11-year field study of body condition and growth rate of the giant gartersnake Thamnophis gigas across 13 study areas in the Central Valley of California, USA. We developed a priori mixed effects models of body condition index (BCI), which included covariates of time, sex and snout–vent length and reported the best-approximating models using an information theoretic approach. Also, we developed models of growth rate index (GRI) using covariates of sex and periods based on reproductive behavior. The largest difference in BCI between sexes, as predicted by a non-linear (cubic) time model, occurred during the mating period when female body condition (0.014±0.001  se ) was substantially greater than males (−0.027±0.002  se ). Males likely allocated energy to search for mates, while females likely stored energy for embryonic development. We also provided evidence that males use more body energy reserves than females during hibernation, perhaps because of different body temperatures between sexes. We found GRI of male snakes was substantially lower during the mating period than during a non-mating period, which indicated that a trade-off existed between searching for mates and growth. These findings contribute to our understanding of snake ecology in a Mediterranean climate.     

Decision Analysis: A Method for Taking Uncertainties into Account in Risk-Based Decision Making

The assessment of human and ecological risks and associated risk-management decisions are characterized by only partial knowledge of the relevant systems. Typically, large variability and measurement errors in data create challenges for estimating risks and identifying appropriate management strategies. The formal quantitative method of decision analysis can help deal with these challenges because it takes uncertainties into account explicitly and quantitatively. In recent years, research in several areas of natural resource management has demonstrated that decision analysis can identify policies that are appropriate in the presence of uncertainties. More importantly, the resulting optimal decision is often different from the one that would have been chosen had the uncertainties not been taken into account quantitatively. However, challenges still exist to effective implementation of decision analysis.     

Meta-analysis in applied ecology

This overview examines research synthesis in applied ecology and conservation. Vote counting and pooling unweighted averages are widespread despite the superiority of syntheses based on weighted combination of effects. Such analyses allow exploration of methodological uncertainty in addition to consistency of effects across species, space and time, but exploring heterogeneity remains controversial. Meta-analyses are required to generalize in ecology, and to inform evidence-based decision-making, but the more sophisticated statistical techniques and registers of research used in other disciplines must be employed in ecology to fully realize their benefits.     

Phylogeny of the genus Peperomia (Piperaceae) inferred from the trnK/matK region (cpDNA)

The genus Peperomia is one of the largest genera of basal angiosperms, comprising about 1500-1700 pantropically distributed species. The currently accepted infrageneric classification divides Peperomia into nine subgenera and seven sections. This classification is based on some 200 species, primarily using fruit morphology. The monophyly of these infrageneric taxa has never been tested and molecular phylogenetic studies of a representative sampling within Peperomia do not exist. This paper provides the first molecular phylogeny for the genus Peperomia. Monophyletic clades within Peperomia are identified and previously used morphological characters are critically reviewed. We show that the importance of some morphological characters has been overestimated and that some of these characters presumably have evolved several times independently. Only one previously described subgenus has been confirmed to be monophyletic.     

The Advantages of Theft over Toil: The Design Inference and Arguing from Ignorance

Intelligent design theorist William Dembski hasproposed an ``explanatory filter' fordistinguishing between events due to chance,lawful regularity or design. We show that ifDembski's filter were adopted as a scientificheuristic, some classical developments inscience would not be rational, and thatDembski's assertion that the filter reliablyidentifies rarefied design requires ignoringthe state of background knowledge. Ifbackground information changes even slightly,the filter's conclusion will vary wildly.Dembski fails to overcome Hume's objections toarguments from design.     

Markov chain Monte Carlo fitting of single-channel data from inositol trisphosphate receptors

In many cell types, the inositol trisphosphate receptor (IPR) is one of the important components that control intracellular calcium dynamics, and an understanding of this receptor (which is also a calcium channel) is necessary for an understanding of calcium oscillations and waves. Recent advances in experimental techniques now allow for the measurement of single-channel activity of the IPR in conditions similar to its native environment, and these data can be used to determine the rate constants in Markov models of the IPR. We illustrate a parameter estimation method based on Markov chain Monte Carlo, which can be used to fit directly to single-channel data, and determining, as an intrinsic part of the fit, the times at which the IPR is opening and closing. We show, using simulated data, the most complex Markov model that can be unambiguously determined from steady-state data and show that non-steady-state data is required to determine more complex models.     

Quantifying Uncertainty in Predictions of Invasiveness

Using the Australian weed risk assessment (WRA) model as an example, we applied a combination of bootstrapping and Bayesian techniques as a means for explicitly estimating the posterior probability of weediness as a function of an import risk assessment model screening score. Our approach provides estimates of uncertainty around model predictions, after correcting for verification bias arising from the original training dataset having a higher proportion of weed species than would be the norm, and incorporates uncertainty in current knowledge of the prior (base-rate) probability of weediness. The results confirm the high sensitivity of the posterior probability of weediness to the base-rate probability of weediness of plants proposed for importation, and demonstrate how uncertainty in this base-rate probability manifests itself in uncertainty surrounding predicted probabilities of weediness. This quantitative estimate of the weediness probability posed by taxa classified using the WRA model, including estimates of uncertainty around this probability for a given WRA score, would enable bio-economic modelling to contribute to the decision process, should this avenue be pursued. Regardless of whether or not this avenue is explored, the explicit estimates of uncertainty around weed classifications will enable managers to make better informed decisions regarding risk. When viewed in terms of likelihood of weed introduction, the current WRA model outcomes of ‘accept’, ‘further evaluate’, or ‘reject’, whilst not always accurate in terms of weed classification, appear consistent with a high expected cost of mistakenly introducing a weed. The methods presented have wider application to the quantitative prediction of invasive species for situations where the base-rate probability of invasiveness is subject to uncertainty, and the accuracy of the screening test imperfect     

Quantifying uncertainty in predictions of invasiveness, with emphasis on weed risk assessment

Using the Australian Weed Risk Assessment (WRA) model as an example, we applied a combination of bootstrapping and Bayesian techniques as a means of explicitly estimating the posterior probability of weediness as a function of an import risk assessment model screening score. Our approach provides estimates of uncertainty around model predictions, after correcting for verification bias arising from the original training dataset having a higher proportion of weed species than would be the norm, and incorporates uncertainty in current knowledge of the prior (base-rate) probability of weediness. The results confirm the high sensitivity of the posterior probability of weediness to the base-rate probability of weediness of plants proposed for importation, and demonstrate how uncertainty in this base-rate probability manifests itself in uncertainty surrounding predicted probabilities of weediness. This quantitative estimate of the weediness probability posed by taxa classified using the WRA model, including estimates of uncertainty around this probability for a given WRA score, would enable bio-economic modelling to contribute to the decision process, should this avenue be pursued. Regardless of whether or not this avenue is explored, the explicit estimates of uncertainty around weed classifications will enable managers to make better informed decisions regarding risk. When viewed in terms of likelihood of weed introduction, the current WRA model outcomes of ‘accept’, ‘further evaluate’ or ‘reject’, whilst not always accurate in terms of weed classification, appear consistent with a high-expected cost of mistakenly introducing a weed. The methods presented have wider application to the quantitative prediction of invasive species for situations where the base-rate probability of invasiveness is subject to uncertainty, and the accuracy of the screening test imperfect.