Sensory complementation and the acquisition of predator recognition by salmonid fishes

Following disturbance, some aquatic prey species release chemicals that act as a warning cue and increase vigilance in nearby conspecifics. Such disturbance cues evoke consistent low intensity anti-predator responses. In contrast, alarm cues from injured conspecifics often evoke stronger intensity responses in prey animals. In this study, we test the sensory complement hypothesis, which suggests that multiple cues act in an additive or synergistic fashion to provide additional information for risk assessment by prey. In the first experiment, we showed that juvenile rainbow trout pre-exposed to disturbance cues respond to a given concentration of damage-released alarm cues with a higher intensity of response than the trout that were pre-exposed to cues from undisturbed conspecifics. The two cues acted in an additive fashion. In the second experiment, we demonstrated that disturbance cues alone were not enough to elicit a conditioned response to the odour of a novel predator. We also showed that while disturbance cues elicit an increase in the response of trout to alarm cues, this increase does not translate into a stronger learned response to the predator when the predator odour is paired with alarm cues. Future studies should take into account sensory complementation to avoid underestimating the responses of prey to predators.     

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.     

Improved oxidative stability of biodiesel via alternative processing methods using cottonseed oil

Biodiesel from waste cooking oil (WCO) requires antioxidants to meet oxidation stability specifications set forth in ASTM D6751 or EN 14214. In contrast, unrefined cottonseed oil (CSO), containing tocopherols and gossypol, produces biodiesel of higher oxidation stability. However, only a portion of these CSO endogenous antioxidants are suspected to be retained in biodiesel. Because the economics of biodiesel manufacturing rely upon inexpensive sources of triglycerides, emphasis was placed on developing improved alternative processing methods where WCO was the main source of methyl esters (WCOME) and CSO was used as a supplemental source of triglycerides and antioxidants in a 4:1 ratio. This study compared four processing methods for their ability to produce biodiesel of increased oxidative stability prepared from a 4:1 ratio of WCO:CSO. Two novel processing methods developed for this study utilise solvent properties of fatty acid methyl esters and glycerol to avoid additional chemical inventory for biodiesel processors. This study concludes that the two new processing methods resulted in biodiesel that had statistically significant improved oxidation stability when compared to two common industrial processing methods. Another significant finding is that high-shear homogenisation during transesterification reduced reaction time from the published one hour to 16 minutes.     

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.     

Cross-correlation bias in lag analysis of aquatic time series

Cross-correlation analysis is the most valuable and widely used statistical tool for evaluating the strength and direction of time-lagged relationships between ecological variables. Although it is well understood that temporal autocorrelation can inflate estimates of cross correlations and cause high rates of incorrectly concluding that lags exist among time series (i.e. type I error), in this study we show that a problem we term intra-multiplicity can cause substantial bias in cross-correlation analysis even in the absence of autocorrelation. Intra-multiplicity refers to the numerous time lags examined and cross-correlation coefficients computed within a pair of time series during cross-correlation analysis. We show using Monte Carlo simulations that intra-multiplicity can spuriously inflate estimates of cross correlations by identifying incorrect time lags. Further, unlike autocorrelation, which generally identifies lags close to the true lag, intra-multiplicity can erroneously identify lags anywhere in the time series and commonly results in a direction change of the correlation (i.e. positive or negative). Using Monte Carlo simulations we develop formulas that quantify the bias introduced by intra-multiplicity as a function of sample size, true cross correlation between the series, and the number of time lags examined. A priori these formulas enable researchers to determine the sample size needed to minimize the biases introduced by intra-multiplicity. A posteriori the formulas can be used to predict the expected bias and type I error rate associated with the data at hand, as well as the maximum number of time lags that can be analyzed to minimize the effects of intra-multiplicity. We examine the relationship between commercial catch of chum salmon and surface temperatures of the North Pacific (1925–1992) to illustrate the problems of intra-multiplicity in fisheries studies and the application of our formulas. These analyses provide a more robust framework to assess the temporal relationships between ecological variables. Received: 28 July 2000 / Accepted: 6 December 2000