Fire severity and ecosytem responses following crown fires in California shrublands

Chaparral shrublands burn in large high-intensity crown fires. Managers interested in how these wildfires affect ecosystem processes generally rely on surrogate measures of fire intensity known as fire severity metrics. In shrublands burned in the autumn of 2003, a study of 250 sites investigated factors determining fire severity and ecosystem responses. Using structural equation modeling we show that stand age, prefire shrub density, and the shortest interval of the prior fire history had significant direct effects on fire severity, explaining > 50% of the variation in severity. Fire severity per se is of interest to resource managers primarily because it is presumed to be an indicator of important ecosystem processes such as vegetative regeneration, community recovery, and erosion. Fire severity contributed relatively little to explaining patterns of regeneration after fire. Two generalizations can be drawn: fire severity effects are mostly shortlived, i.e., by the second year they are greatly diminished, and fire severity may have opposite effects on different functional types. Species richness exhibited a negative relationship to fire severity in the first year, but fire severity impacts were substantially less in the second postfire year and varied by functional type. Much of this relationship was due to alien plants that are sensitive to high fire severity; at all scales from 1 to 1000 m2, the percentage of alien species in the postfire flora declined with increased fire severity. Other aspects of disturbance history are also important determinants of alien cover and richness as both increased with the number of times the site had burned and decreased with time since last fire. A substantial number of studies have shown that remote-sensing indices are correlated with field measurements of fire severity. Across our sites, absolute differenced normalized burn ratio (dNBR) was strongly correlated with field measures of fire severity and with fire history at a site but relative dNBR was not. Despite being correlated with fire severity, absolute dNBR showed little or no relationship with important ecosystem responses to wildfire such as shrub resprouting or total vegetative regeneration. These findings point to a critical need for further research on interpreting remote sensing indices as applied to postfire management of these shrublands.     

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.     

Analyzing animal movements using Brownian bridges

By studying animal movements, researchers can gain insight into many of the ecological characteristics and processes important for understanding population-level dynamics. We developed a Brownian bridge movement model (BBMM) for estimating the expected movement path of an animal, using discrete location data obtained at relatively short time intervals. The BBMM is based on the properties of a conditional random walk between successive pairs of locations, dependent on the time between locations, the distance between locations, and the Brownian motion variance that is related to the animal's mobility. We describe two critical developments that enable widespread use of the BBMM, including a derivation of the model when location data are measured with error and a maximum likelihood approach for estimating the Brownian motion variance. After the BBMM is fitted to location data, an estimate of the animal's probability of occurrence can be generated for an area during the time of observation. To illustrate potential applications, we provide three examples: estimating animal home ranges, estimating animal migration routes, and evaluating the influence of fine-scale resource selection on animal movement patterns.     

Amphibian recovery after a decrease in acidic precipitation

We here report the first sign of amphibian recovery after a strong decline due to acidic precipitation over many decades and peaking around 1980–90. In 2010, the pH level of ponds and small lakes in two heavily acidified areas in southwestern Scandinavia (Aust-Agder and Østfold in Norway) had risen significantly at an (arithmetic) average of 0.14 since 1988–89. Parallel with the general rise in pH, amphibians (Rana temporaria, R. arvalis, Bufo bufo, Lissotriton vulgaris, and Triturus cristatus) had become significantly more common: the frequency of amphibian localities rose from 33% to 49% (n = 115), and the average number of amphibian species per locality had risen from 0.51 to 0.88. In two other (reference) areas, one with better buffering capacity (Telemark, n = 21) and the other with much less input of acidic precipitation (Nord-Trøndelag, n = 106), there were no significant changes in pH or amphibians.     

Leachate migration from spent mushroom substrate through intact and repacked subsurface soil columns

Field weathering of spent mushroom substrate (SMS) produces soluble compost leachate that percolates into underlying soils and may adversely impact groundwater. Laboratory experiments were conducted to investigate movement and retention of SMS leachate solutes in subsurface soil columns. Spent mushroom substrate leachate with high concentrations of dissolved organic matter (DOM) and inorganic salts was passively loaded to intact and repacked columns of Bt1 soil (fine-loamy, mixed, semiactive, mesic Typic Hapludults) and effluents were monitored for changes in chemical composition. Transport of SMS leachate in undisturbed soil cores was mainly via preferential flow, whereas matrix flow was predominant in repacked soil columns. Leachate DOM and phosphate were sorbed by soil minerals while Cl-, SO4(2-), Na+ and NH4+ were eluted. Leachate K+ displaced exchangeable native cations and was retained. Biodegradation of leachate DOM resulted in reduction and elution of soil Mn and Fe, especially in repacked columns. Persistent anoxia also inhibited nitrification. Precipitation of gypsum and CaCO3 blocked preferential flow channels, and movement of SMS leachate was subsequently reduced. The results demonstrate that SMS leachate migrates via rapid preferential flow initially, followed by matrix flow at a lower rate. Leachate solutes may transport to depth in soil profiles through preferential channels. To protect water resources, weathering of deep SMS piles should be conducted on compact surfaces or in fields with a condensed soil layer (no structural cracks) above the groundwater table, and measures controlling leachate runoff be imposed.     

Sampling Scale Effects in Random Fields and Implications for Environmental Monitoring

The concept of a sampling scale triplet of spacing, extent and support is used to define the spatial dimensions of a monitoring network or a field study. The spacing is the average distance between samples, the extent is the size of the domain sampled and the support is the averaging area of one sample. The aim of this paper is to examine what is the bias and the random error (uncertainty) introduced by the sampling scale triplet into estimates of the mean, the spatial variance and the integral scale of a variable in a landscape. The integral scale is a measure of the average distance over which a variable is correlated in space. A large number of two dimensional random fields are generated from which hypothetical samples, conforming to a certain sampling scale triplet, are drawn which in turn are used to estimate the sample mean, spatial variance and integral scale. The results indicate that the biases can be up to two orders of magnitude. The bias of the integral scale is positively related to the magnitude of any of the components of the scale triplet while the bias of the spatial variance is different for different components of the scale triplet. All sampling scale effects are relative to the underlying correlation length of the variable of interest which is closely related to the integral scale. The integral scale can hence be used for sampling design and data interpretation. Suggestions are given on how to adjust a monitoring network to the scales of the variables of interest and how to interpret sampling scale effects in environmental data.     

Evaluating portable infrared spectrometers for measuring the silica content of coal dust

Miners face a variety of respiratory hazards while on the job, including exposure to silica dust which can lead to silicosis, a potentially fatal lung disease. Currently, field-collected filter samples of silica are sent for laboratory analysis and the results take weeks to be reported. Since the mining workplace is constantly moving into new and often different geological strata with changing silica levels, more timely data on silica levels in mining workplaces could help reduce exposures. Improvements in infrared (IR) spectroscopy open the prospect for end-of-shift silica measurements at mine sites. Two field-portable IR spectrometers were evaluated for their ability to quantify the mass of silica on filter samples loaded with known amounts of either silica or silica-bearing coal dust (silica content ranging from 10-200 μg/filter). Analyses included a scheme to correct for the presence of kaolin, which is a confounder for IR analysis of silica. IR measurements of the samples were compared to parallel measurements derived using the laboratory-based U.S. Mine Safety and Health Administration P7 analytical method. Linear correlations between Fourier transform infrared (FTIR) and P7 data yielded slopes in the range of 0.90-0.97 with minimal bias. Data from a variable filter array spectrometer did not correlate as well, mainly due to poor wavelength resolution compared to the FTIR instrument. This work has shown that FTIR spectrometry has the potential to reasonably estimate the silica exposure of miners if employed in an end-of-shift method.     

Implementing infrared determination of quartz particulates on novel filters for a prototype dust monitor

Research by the National Institute for Occupational Safety and Health (NIOSH) has pursued quartz analysis for the specialized filter assemblies of a new worker-wearable personal dust monitor (PDM). The PDM is a real-time instrument utilizing a tapered element oscillating microbalance (TEOM). Standard fiberglass TEOM filters cannot accommodate the desired P-7 infrared analytical method used by the Mine Safety and Health Administration (MSHA). Novel filter materials were tested with the objective of demonstrating this type of analysis. Low temperature ashing and spectrometric examination were employed, revealing that nylon fiber candidate filters left minimal residual ash and produced no significant spectral interference. Avoiding titanium dioxide in all filter materials proved to be a key requirement. Fine quartz particulates were collected on prototype filters in a Marple chamber, either open-faced or through PDMs during test runs. The filters were then subjected to MSHA P-7 analysis and the spectrometrically based analytical results for quartz mass were compared to reference measurements. Also, PDM instrumental mass readings were compared to filter gravimetric measurements. Results suggest that the P-7 method is adaptable to variations in filter materials and that quartz dust analysis by the P-7 method when utilizing the new ashable PDM filters can have accuracy and precision within 10% and 4%, respectively. This is within the declared 13% accuracy and 7-10% precision of the P-7 method itself. Instrument mass readings had modest positive bias but met NIOSH accuracy criteria. Continued work with specialized PDM filters is merited, as they are a new type of TEOM sample amenable to ashing analysis of particulates.     

Screening organic chemicals in commerce for emissions in the context of environmental and human exposure

Quantitative knowledge of organic chemical release into the environment is essential to understand and predict human exposure as well as to develop rational control strategies for any substances of concern. While significant efforts have been invested to characterize and screen organic chemicals for hazardous properties, relatively less effort has been directed toward estimating emissions and hence also risks. Here, a rapid throughput method to estimate emissions of discrete organic chemicals in commerce has been developed, applied and evaluated to support screening studies aimed at ranking and identifying chemicals of potential concern. The method builds upon information in the European Union Technical Guidance Document and utilizes information on quantities in commerce (production and/or import rates), chemical function (use patterns) and physical-chemical properties to estimate emissions to air, soil and water within the OECD for five stages of the chemical life-cycle. The method is applied to 16,029 discrete substances (identified by CAS numbers) from five national and international high production volume lists. As access to consistent input data remains fragmented or even impossible, particular attention is given to estimating, evaluating and discussing uncertainties in the resulting emission scenarios. The uncertainty for individual substances typically spans 3 to 4 orders of magnitude for this initial tier screening method. Information on uncertainties in emissions is useful as any screening or categorization methods which solely rely on threshold values are at risk of leading to a significant number of either false positives or false negatives. A limited evaluation of the screening method's estimates for a sub-set of about 100 substances, compared against independent and more detailed emission scenarios presented in various European Risk Assessment Reports, highlights that up-to-date and accurate information on quantities in commerce as well as a detailed breakdown on chemical function are critically needed for developing more realistic emission scenarios.     

The Application of IUCN Red List Criteria at Regional Levels

This paper represents a work in progress by the World Conservation Union (IUCN). Earlier drafts of the work have appeared in IUCN publications, but the "International Issues" section of Conservation Biology seems an appropriate and important place to publish the penultimate version of the document. Comments on this issue in general or on the approach taken by the IUCN should be sent to the authors directly.
Joshua Ginsburg