Regional avian species declines estimated from volunteer-collected long-term data using List Length Analysis

Long-term systematic population monitoring data sets are rare but are essential in identifying changes in species abundance. In contrast, community groups and natural history organizations have collected many species lists. These represent a large, untapped source of information on changes in abundance but are generally considered of little value. The major problem with using species lists to detect population changes is that the amount of effort used to obtain the list is often uncontrolled and usually unknown. It has been suggested that using the number of species on the list, the "list length," can be a measure of effort. This paper significantly extends the utility of Franklin's approach using Bayesian logistic regression. We demonstrate the value of List Length Analysis to model changes in species prevalence (i.e., the proportion of lists on which the species occurs) using bird lists collected by a local bird club over 40 years around Brisbane, southeast Queensland, Australia. We estimate the magnitude and certainty of change for 269 bird species and calculate the probabilities that there have been declines and increases of given magnitudes. List Length Analysis confirmed suspected species declines and increases. This method is an important complement to systematically designed intensive monitoring schemes and provides a means of utilizing data that may otherwise be deemed useless. The results of List Length Analysis can be used for targeting species of conservation concern for listing purposes or for more intensive monitoring. While Bayesian methods are not essential for List Length Analysis, they can offer more flexibility in interrogating the data and are able to provide a range of parameters that are easy to interpret and can facilitate conservation listing and prioritization. © 2010 by the Ecological Society of America.

Mass spectrometric study of the dissociation of Group XI metal complexes with fatty acids and glycerolipids : Ag2H+ and Cu2H+ ion formation in the presence of a double bond

Results of mass spectrometric studies are reported for the collisional dissociation of Group XI (Cu, Ag, Au) metal ion complexes with fatty acids (palmitic, oleic, linoleic and a-linolenic) and glycerolipids. Remarkably, the formation of M2H+ ions (M = Cu, Ag) is observed as a dissociation product of the ion complexes containing more than one metal cation and only if the lipid in the complex contains a double bond. Ag2H+ is formed as the main dissociation channel for all three of the fatty acids containing double bonds that were investigated while Cu2H+ is formed with one of the fatty acids and, although abundant, is not the dominant dissociation channel. Also. Cu(I) and Ag(I) ion complexes were observed with glycerolipids (including triacylglycerols and glycerophospholipids) containing either saturated or unsaturated fatty acid substituents. Interestingly. Ag2H+ ion is formed in a major fragmentation channel with the lipids that are able to form the complex with two metal cations (triacylglycerols and glycerophosphoglycerols), while lipids containing a fixed positive charge (glycerophospocholines) complex only with a single metal cation. The formation of Ag2H+ ion is a significant dissociation channel from the complex ion Ag-2(L-H)(+) where L = Glycerophospholipid (GP) (18:1/18:1). Cu(I) also forms complexes of two metal cations with glycerophospholipids but these do not produce Cu2H+ upon dissociation. Rather organic fragments, not containing Cu(I), are formed, perhaps due to different interactions of these metal cations with lipids resulting from the much smaller ionic radius of Cu(I) compared to Ag(I) (C).

Effects of wireless sensor network uncertainties on output-only modal analysis employing merged data of multiple tests

The use of Wireless Sensor Networks (WSNs) for vibration-based Structural Health Monitoring (SHM) has become a promising approach due to many advantages such as low cost, fast and flexible deployment. However, inherent technical issues such as data asynchronicity and data loss have prevented these distinct systems from being extensively used. Recently, several SHM-oriented WSNs have been proposed and believed to be able to overcome a large number of technical uncertainties. Nevertheless, there is limited research verifying the applicability of those WSNs with respect to demanding SHM applications like modal analysis and damage identification. Based on a brief review, this paper first reveals that Data Synchronization Error (DSE) is the most inherent factor amongst uncertainties of SHM-oriented WSNs. Effects of this factor are then investigated on outcomes and performance of the most robust Output-only Modal Analysis (OMA) techniques when merging data from multiple sensor setups. The two OMA families selected for this investigation are Frequency Domain Decomposition (FDD) and data-driven Stochastic Subspace Identification (SSI-data) due to the fact that they both have been widely applied in the past decade. Accelerations collected by a wired sensory system on a large-scale laboratory bridge model are initially used as benchmark data after being added with a certain level of noise to account for the higher presence of this factor in SHM-oriented WSNs. From this source, a large number of simulations have been made to generate multiple DSE-corrupted datasets to facilitate statistical analyses. The results of this study show the robustness of FDD and the precautions needed for SSI-data family when dealing with DSE at a relaxed level. Finally, the combination of preferred OMA techniques and the use of the channel projection for the time-domain OMA technique to cope with DSE are recommended.

The dilemma of international capital tax competition in the presence of public capital and endogenous growth

Using an OLG-model with endogenous growth and public capital we show, that an international capital tax competition leads to inefficiently low tax rates, and as a consequence to lower welfare levels and growth rates. Each national government has an incentive to reduce the capital income tax rates in its effort to ensure that this policy measure increases the domestic private capital stock, domestic income and domestic economic growth. This effort is justified as long as only one country applies this policy. However, if all countries follow this path then all of them will be made worse off in the long run.

A simple method for estimating growth parameters from multiple length-frequency data in presence of continuous recruitment

The extended recruitment season for short-lived species such as prawns biases the estimation of growth parameters from length-frequency data when conventional methods are used. We propose a simple method for overcoming this bias given a time series of length-frequency data. The difficulties arising from extended recruitment are eliminated by predicting the growth of the succeeding samples and the length increments of the recruits in previous samples. This method requires that some maximum size at recruitment can be specified. The advantages of this multiple length-frequency method are: it is simple to use; it requires only three parameters; no specific distributions need to be assumed; and the actual seasonal recruitment pattern does not have to be specified. We illustrate the new method with length-frequency data on the tiger prawn Penaeus esculentus from the north-western Gulf of Carpentaria, Australia.

Towards automated content analysis of discussion transcripts: A cognitive presence case

In this paper, we present the results of an exploratory study that examined the problem of automating content analysis of student online discussion transcripts. We looked at the problem of coding discussion transcripts for the levels of cognitive presence, one of the three main constructs in the Community of Inquiry (CoI) model of distance education. Using Coh-Metrix and LIWC features, together with a set of custom features developed to capture discussion context, we developed a random forest classification system that achieved 70.3% classification accuracy and 0.63 Cohen's kappa, which is significantly higher than values reported in the previous studies. Besides improvement in classification accuracy, the developed system is also less sensitive to overfitting as it uses only 205 classification features, which is around 100 times less features than in similar systems based on bag-of-words features. We also provide an overview of the classification features most indicative of the different phases of cognitive presence that gives an additional insights into the nature of cognitive presence learning cycle. Overall, our results show great potential of the proposed approach, with an added benefit of providing further characterization of the cognitive presence coding scheme.