Predicting tree distributions in an East African biodiversity hotspot: model selection, data bias and envelope uncertainty

The Eastern Arc Mountains (EAMs) of Tanzania and Kenya support some of the most ancient tropical rainforest on Earth. The forests are a global priority for biodiversity conservation and provide vital resources to the Tanzanian population. Here, we make a first attempt to predict the spatial distribution of 40 EAM tree species, using generalised additive models, plot data and environmental predictor maps at sub 1 km resolution. The results of three modelling experiments are presented, investigating predictions obtained by (1) two different procedures for the stepwise selection of predictors, (2) down-weighting absence data, and (3) incorporating an autocovariate term to describe fine-scale spatial aggregation. In response to recent concerns regarding the extrapolation of model predictions beyond the restricted environmental range of training data, we also demonstrate a novel graphical tool for quantifying envelope uncertainty in restricted range niche-based models (envelope uncertainty maps). We find that even for species with very few documented occurrences useful estimates of distribution can be achieved. Initiating selection with a null model is found to be useful for explanatory purposes, while beginning with a full predictor set can over-fit the data. We show that a simple multimodel average of these two best-model predictions yields a superior compromise between generality and precision (parsimony). Down-weighting absences shifts the balance of errors in favour of higher sensitivity, reducing the number of serious mistakes (i.e., falsely predicted absences); however, response functions are more complex, exacerbating uncertainty in larger models. Spatial autocovariates help describe fine-scale patterns of occurrence and significantly improve explained deviance, though if important environmental constraints are omitted then model stability and explanatory power can be compromised. We conclude that the best modelling practice is contingent both on the intentions of the analyst (explanation or prediction) and on the quality of distribution data; generalised additive models have potential to provide valuable information for conservation in the EAMs, but methods must be carefully considered, particularly if occurrence data are scarce. Full results and details of all species models are supplied in an online Appendix.     

Analysis of the number of flux chamber samples and study area size on the accuracy of emission rate measurements

Monte Carlo simulations were conducted on a set of flux chamber measurements at a landfill to estimate the relationship between the number of flux chamber samples and study area size on the emission rate measurement accuracy. The spatial variability of flux was addressed in the study by utilizing an existing flux chamber measurement data set that is one of the most dense flux chamber sampling arrays published to date for a landfill. At a probability of 95%, the Monte Carlo simulations indicated that achieving an accuracy within 10% with the flux chamber method is highly unlikely. An accuracy within 20% was achieved for small areas of less than about 0.2 hectares using 220 flux chamber measurements, but achieving this level of accuracy for area emission sources, of similar or greater variability, that are larger than this is highly unlikely. An accuracy within 30% was achieved up to the Full Area of about 0.4 hectares if more than approximately 120 samples were obtained. Even for an accuracy within 50%, at least 40 flux chamber measurements were needed for the Full Area of about 0.4 hectares. Available methods of estimating the number of samples required were compared to the Monte Carlo simulation results. The Monte Carlo simulations indicate that, in general, more samples are required than determined from an existing statistical method, which is a function of the mean and standard deviation of the population. Specifying the number of samples based on a regulatory method results in very poor accuracy. A modification to the statistical method for estimating the number of samples, or for estimating an accuracy for a given probability and number of samples, is proposed.

Implications: The flux chamber method is the most widely used method of measuring fugitive emission rates from area sources. However, extrapolation of a set of individual flux chamber samples to a larger area results in area flux measurement values of unknown accuracy. Quantification of the accuracy of the extrapolation of a set of flux chamber measurements would be beneficial for understanding the confidence that can be placed on the measurement results. Guidance as to the appropriate number of flux chamber measurements to achieve a desired level of accuracy would benefit flux chamber method practitioners.     

Demonstration and evaluation of commercial and municipal equipment for urban biological decontamination

Large-scale biological contamination incidents pose unique yet significant challenges to remediation operations. Previous incidents have demonstrated the utility of readily available commercial and municipal equipment for conducting remediation tasks. Preidentification and evaluation of such equipment could reduce lag time for response initiation and enhance overall response effectiveness and efficiency. The current study aimed to identify commercial and municipal equipment that could be beneficial in wide-area biological remediation operations. Equipment were identified by market research, their utility was assessed by a group of subject matter experts, and a subset of those equipment was observed under operational conditions in a realistic urban environment. Observations and feedback from demonstration participants are presented within the article. This information is intended to support rapid decision-making following large-scale biological incidents, broaden the universe of potentially useful equipment to support the response, enhance response operations, and reduce the impact on the public.     

Comparing carbon substrates for denitrification of subsurface drainage water

Nitrate in water from tile drained corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] fields in the U.S. Midwest contributes to nitrate contamination of surface waters. Denitrification-based biofilters are a promising strategy for reducing nitrate concentrations, but these systems require an external carbon supply to sustain denitrification. The ability of four organic materials to serve as carbon substrates for denitrification biofilters was evaluated in this laboratory study. Wood chips, wood chips amended with soybean oil, cornstalks, and cardboard fibers were mixed with subsoil (oxidized till) and incubated anaerobically for 180 d. Periodically, 15NO3-N was added to maintain nitrate N concentrations between 10 and 100 mg L-1. All of the materials stimulated NO3-N removal and the degree of removal from highest to lowest was: cornstalks, cardboard fibers, wood chips with oil, and wood chips alone. Analysis of 15N showed that immobilization and dissimilatory nitrate reduction to ammonium accounted for <4% of NO3-N removal in all treatments, therefore denitrification was the dominant NO3-N removal process. Cardboard fibers, wood chips and oil, and wood chips alone did not support as much denitrification as cornstalks, but their rates of NO3-N removal were steady and would probably continue longer than cornstalks. The addition of soybean oil to wood chips significantly increased denitrification over wood chips alone.     

Why most conservation monitoring is, but need not be, a waste of time

Ecological conservation monitoring programmes abound at various organisational and spatial levels from species to ecosystem. Many of them suffer, however, from the lack of details of goal and hypothesis formulation, survey design, data quality and statistical power at the start. As a result, most programmes are likely to fail to reach the necessary standard of being capable of rejecting a false null hypothesis with reasonable power. Results from inadequate monitoring are misleading for their information quality and are dangerous because they create the illusion that something useful has been done. We propose that conservation agencies and those funding monitoring work should require the demonstration of adequate power at the outset of any new monitoring scheme.     

Linking groundwaters of high CO2 to aluminium levels in rivers: the case for the upper Severn in mid-Wales

Al is a critical ecotoxicant in surface waters impacted by acidic deposition. Apart from the most acidic surface waters, Al concentrations are often considered to be controlled by Al(OH)(3) or aluminosilicate (clay) solubility for modelling studies. For many UK rivers there is no clear evidence for such solubility controls even though there is the potential under moderately acidic/alkaline conditions. Here, Al solubility in ground and river water is compared for acid sensitive catchments in mid-Wales. The results reveal that there may be a solubility control within the groundwater but a more complex state of affairs within the river. The groundwater is of high CO(2) content and once in the river it degasses to raise pH. However, there is limited change in Al concentration and hence the solubility relationship is lost. The results flag the potential importance of groundwater solubility controls for Al and the potential for the groundwater zone to act as an Al filter. For positive alkalinity groundwaters, the high CO(2) levels depress the pH to near the value for minimum Al solubility. However, there is no simple groundwater end-member. Examining Al solubility controls solely within the rivers provides cryptic and misleading clues to the hydrogeological controls for Al within catchments. Assessing the within-catchment processes requires direct measurement with full consideration of both inorganic and organic attenuation.     

Determination of the compositional change (1999–2006) in the pine forests of British Columbia due to mountain pine beetle infestation

The current mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreak in British Columbia and Alberta is the largest recorded forest pest infestation in Canadian history. We integrate a spatial hierarchy of mountain pine beetle and forest health monitoring data, collected between 1999 and 2006, with provincial forest inventory data, and generate three information products representing 2006 forest conditions in British Columbia: cumulative percentage of pine infested by mountain pine beetle, percentage of pine uninfested, and the change in the percentage of pine on the landscape. All input data were formatted to a standardized spatial representation (1 ha minimum mapping unit), with preference given to the most detailed monitoring data available at a given location for characterizing mountain pine beetle infestation conditions. The presence or absence of mountain pine beetle attack was validated using field data (n?=?2054). The true positive rate for locations of red attack damage over all years was 92%. Classification of attack severity was validated using the Kruskal gamma statistic (γ?=?0.49). Error between the survey data and field data was explored using spatial autoregressive (SAR) models, which indicated that percentage pine and year of infestation were significant predictors of survey error at α?=?0.05. Through the integration of forest inventory and infestation survey data, the total area of pine infested is estimated to be between 2.89 and 4.14 million hectares. The generated outputs add value to existing monitoring data and provide information to support management and modeling applications.     

Development of the Coastal Intensive Site Network (CISNET)

The U.S. Environmental Protection Agency (EPA), National Oceanic and Atmospheric Administration (NOAA), and National Aeronautics and Space Administration (NASA) have formed a partnership to establish pilot sites for the development of a network known as the Coastal Intensive Site Network (CISNet). CISNet is composed of intensive, long-term monitoring and research sites around the U.S. marine and Great Lakes coasts. In this partnership, EPA and NOAA are funding research and monitoring programs at pilot sites that utilize ecological indicators and investigate the ecological effects of environmental stressors. NASA is funding research aimed at developing a remote sensing capability that will augment or enhance in situresearch and monitoring programs selected by EPA and NOAA. CISNet has three objectives: 1) to develop a sound scientific basis for understanding ecological responses to anthropogenic stresses in coastal environments, including the interaction of exposure, environment/climate, and biological/ecological factors in the response, and the spatial and temporal nature of these interactions, 2) to demonstrate the value of developing data from selected sites intensively monitored to examine the relationships between changes in environmental stressors, including anthropogenic and natural stresses, and ecological response, and 3) to provide intensively monitored sites for development and evaluation of indicators of change in coastal systems.     

Arrival time and magnitude of airborne fission products from the Fukushima, Japan, reactor incident as measured in Seattle, WA, USA

We report results of air monitoring started due to the recent natural catastrophe on 11 March 2011 in Japan and the severe ensuing damage to the Fukushima Dai-ichi nuclear reactor complex. On 17-18 March 2011, we registered the first arrival of the airborne fission products ¹³¹I, ¹³²I, ¹³²Te, ¹³⁴Cs, and ¹³⁷Cs in Seattle, WA, USA, by identifying their characteristic gamma rays using a germanium detector. We measured the evolution of the activities over a period of 23 days at the end of which the activities had mostly fallen below our detection limit. The highest detected activity from radionuclides attached to particulate matter amounted to 4.4 ± 1.3 mBq m⁻³ of ¹³¹I on 19-20 March.