Spatial Prediction of N2O Emissions in Pasture: A Bayesian Model Averaging Analysis

Nitrous oxide (N₂O) is one of the greenhouse gases that can contribute to global warming. Spatial variability of N₂O can lead to large uncertainties in prediction. However, previous studies have often ignored the spatial dependency to quantify the N₂O – environmental factors relationships. Few researches have examined the impacts of various spatial correlation structures (e.g. independence, distance-based and neighbourhood based) on spatial prediction of N₂O emissions. This study aimed to assess the impact of three spatial correlation structures on spatial predictions and calibrate the spatial prediction using Bayesian model averaging (BMA) based on replicated, irregular point-referenced data. The data were measured in 17 chambers randomly placed across a 271 m² field between October 2007 and September 2008 in the southeast of Australia. We used a Bayesian geostatistical model and a Bayesian spatial conditional autoregressive (CAR) model to investigate and accommodate spatial dependency, and to estimate the effects of environmental variables on N₂O emissions across the study site. We compared these with a Bayesian regression model with independent errors. The three approaches resulted in different derived maps of spatial prediction of N₂O emissions. We found that incorporating spatial dependency in the model not only substantially improved predictions of N₂O emission from soil, but also better quantified uncertainties of soil parameters in the study. The hybrid model structure obtained by BMA improved the accuracy of spatial prediction of N₂O emissions across this study region.     

Delaying conservation actions for improved knowledge: how long should we wait?

Decisions about where conservation actions are implemented are based on incomplete knowledge about biodiversity. The Protea Atlas is a comprehensive database, containing information collated over a decade. Using this data set in a series of retrospective simulations, we compared the outcome from different scenarios of information gain, and habitat protection and loss, over a 20-year period. We assumed that there was no information on proteas at the beginning of the simulation but knowledge improved each year. Our aim was to find out how much time we should spend collecting data before protecting habitat when there is ongoing loss of habitat. We found that, in this case, surveying for more than 2 years rarely increased the effectiveness of conservation decisions in terms of representation of proteas in protected areas and retention within the landscape. If the delay is too long, it can sometimes be more effective just using a readily available habitat map. These results reveal the opportunity costs of delaying conservation action to improve knowledge.     

Light energy partitioning,photosynthetic efficiency and biomass allocation in invasive <Emphasis Type="Italic">Prunus serotina</Emphasis> and native <Emphasis Type="Italic">Quercus petraea</Emphasis> in relation to light environment,competition and allelopathy

This study addressed whether competition under different light environments was reflected by changes in leaf absorbed light energy partitioning, photosynthetic efficiency, relative growth rate and biomass allocation in invasive and native competitors. Additionally, a potential allelopathic effect of mulching with invasive Prunus serotina leaves on native Quercus petraea growth and photosynthesis was tested. The effect of light environment on leaf absorbed light energy partitioning and photosynthetic characteristics was more pronounced than the effects of interspecific competition and allelopathy. The quantum yield of PSII of invasive P. serotina increased in the presence of a competitor, indicating a higher plasticity in energy partitioning for the invasive over the native Q. petraea, giving it a competitive advantage. The most striking difference between the two study species was the higher crown-level net CO₂ assimilation rates (A_crown) of P. serotina compared with Q. petraea. At the juvenile life stage, higher relative growth rate and higher biomass allocation to foliage allowed P. serotina to absorb and use light energy for photosynthesis more efficiently than Q. petraea. Species-specific strategies of growth, biomass allocation, light energy partitioning and photosynthetic efficiency varied with the light environment and gave an advantage to the invader over its native competitor in competition for light. However, higher biomass allocation to roots in Q. petraea allows for greater belowground competition for water and nutrients as compared to P. serotina. This niche differentiation may compensate for the lower aboveground competitiveness of the native species and explain its ability to co-occur with the invasive competitor in natural forest settings.     

A variable undecad repeat domain in cavin1 regulates caveola formation and stability

Caveolae are plasma membrane invaginations involved in transport, signalling and mechanical membrane sensing in metazoans. Their formation depends upon multiple interactions between membrane‐embedded caveolins, lipids and cytosolic cavin proteins. Of the four cavin family members, only cavin1 is strictly required for caveola formation. Here, we demonstrate that an eleven residue (undecad) repeat sequence (UC1) exclusive to cavin1 is essential for caveolar localization and promotes membrane remodelling through binding to phosphatidylserine. In the notochord of mechanically stimulated zebrafish embryos, the UC1 domain is required for caveolar stability and resistance to membrane stress. The number of undecad repeats in the cavin1 UC1 domain varies throughout evolution, and we find that an increased number also correlates with increased caveolar stability. Lastly, we show that the cavin1 UC1 domain induces dramatic remodelling of the plasma membrane when grafted into cavin2 suggesting an important role in membrane sculpting. Overall, our work defines a novel conserved cavin1 modular domain that controls caveolar assembly and stability.     

Modelling habitat and planning surveillance using Landsat imagery: a case study using Imported Red Fire ants

This paper presents a Bayesian mixture model approach for detecting areas of habitat that are suitable for S. invicta infestation, aiding the ongoing surveillance for early detection of these exotic pests. We show that Landsat imagery is an affordable and valuable tool to assist in determining an informed surveillance strategy. In this paper, we use Landsat band 3 (visible red), Landsat band 6 (mid infrared) and a soil brightness index, in various combinations, to assess the probability that the area associated with each pixel is habitable terrain, either in a multivariate analysis, or as a univariate spatial temporal model. The multivariate analysis allows researchers to create meaningful clusters that reflect the sometimes complex combinations of conditions of conditions that form suitable habitat, rather then relying on single derived indices.     

Motivations,success, and cost of coral reef restoration

Coral reef restoration is an increasingly important part of tropical marine conservation. Information about what motivates coral reef restoration as well as its success and cost is not well understood but is needed to inform restoration decisions. We systematically review and synthesize data from mostly scientific studies published in peer‐reviewed and gray literature on the motivations for coral reef restoration, the variables measured, outcomes reported, the cost per hectare of the restoration project, the survival of restored corals, the duration of the project, and its overall spatial extent depending on the restoration technique employed. The main motivation to restore coral reefs for the projects assessed was to further our ecological knowledge and improve restoration techniques, with coral growth, productivity, and survival being the main variables measured. The median project cost was 400,000 US$/ha (2010 US$), ranging from 6,000 US$/ha for the nursery phase of coral gardening to 4,000,000 US$/ha for substrate addition to build an artificial reef. Restoration projects were mostly of short duration (1–2 years) and over small spatial extents (0.01 ha or 108 m²). Median reported survival of restored corals was 60.9%. Future research to survey practitioners who do not publish their discoveries would complement this work. Our findings and database provide critical data to inform future research in coral reef restoration.     

Concern about threatened species and ecosystem disservices underpin public willingness to pay for ecological restoration

Public preferences for ecological restoration can be revealed through environmental valuation studies that aim to measure willingness to pay. However, respondents' environmental views will often influence the conclusions drawn from such studies. We conducted a national survey of perceptions of the benefits and perverse outcomes arising from ecological restoration using a dichotomous choice payment card. Using interval regression to estimate willingness to pay, we find that there are respondents who will perceive mostly biodiversity benefits from restoration, with a particular interest in threatened species recovery. We find that this eco‐centric view of the benefits of restoration also increases the dollar amount that respondents are willing to pay to support restoration activities. A proportion of respondents also perceive restoration as having negative impacts, with concerns orientated towards increased fire, decreased farmland productivity, and groundwater availability. Perceptions of the potential effects of restoration on land productivity had a significant negative influence on the amount of money respondents were willing to pay. These findings are useful for targeting outreach in order to garner public support for ecological restoration.     

Classification based upon gene expression data: bias and precision of error rates

MOTIVATION: Gene expression data offer a large number of potentially useful predictors for the classification of tissue samples into classes, such as diseased and non-diseased. The predictive error rate of classifiers can be estimated using methods such as cross-validation. We have investigated issues of interpretation and potential bias in the reporting of error rate estimates. The issues considered here are optimization and selection biases, sampling effects, measures of misclassification rate, baseline error rates, two-level external cross-validation and a novel proposal for detection of bias using the permutation mean. RESULTS: Reporting an optimal estimated error rate incurs an optimization bias. Downward bias of 3-5% was found in an existing study of classification based on gene expression data and may be endemic in similar studies. Using a simulated non-informative dataset and two example datasets from existing studies, we show how bias can be detected through the use of label permutations and avoided using two-level external cross-validation. Some studies avoid optimization bias by using single-level cross-validation and a test set, but error rates can be more accurately estimated via two-level cross-validation. In addition to estimating the simple overall error rate, we recommend reporting class error rates plus where possible the conditional risk incorporating prior class probabilities and a misclassification cost matrix. We also describe baseline error rates derived from three trivial classifiers which ignore the predictors. AVAILABILITY: R code which implements two-level external cross-validation with the PAMR package, experiment code, dataset details and additional figures are freely available for non-commercial use from http://www.maths.qut.edu.au/profiles/wood/permr.jsp