Assessing agronomic and environmental implications of different N fertilisation strategies in subtropical grain cropping systems on Oxisols

A multi-season 15N tracer recovery experiment was conducted on an Oxisol cropped with wheat, maize and sorghum to compare crop N recoveries of different fertilisation strategies and determine the main pathways of N losses that limit N recovery in these agroecosystems. In the wheat and maize seasons, 15N-labelled fertiliser was applied as conventional urea (CONV) and urea coated with a nitrification inhibitor (DMPP). In sorghum, the fate of 15N-labelled urea was monitored in this crop following a legume ley pasture (L70) or a grass ley pasture (G100). The fertiliser N applied to sorghum in the legume-cereal rotation was reduced (70 kg N ha−1) compared to the grass-cereal (100 kg N ha−1) to assess the availability of the N residual from the legume ley pasture. Average crop N recoveries were 73 % (CONV) and 77 % (DMPP) in wheat and 50 % (CONV) and 51 % (DMPP) in maize, while in sorghum were 71 % (L70) and 53 % (G100). Data gathered in this study indicate that the intrinsic physical and chemical conditions of Oxisols can be extremely effective in limiting N losses via deep leaching or denitrification. Elevated crop 15N recoveries can be therefore obtained in subtropical Oxisols using conventional urea while in these agroecosystems DMPP urea has no significant scope to increase fertiliser N recovery in the crop. Overall, introducing a legume phase to limit the fertiliser N requirements of the following cereal crop proved to be the most effective strategy to reduce N losses and increase fertiliser N recovery.

Human vs. machine : identification of bat species from their echolocation calls by humans and by artificial neural networks

Automated remote ultrasound detectors allow large amounts of data on bat presence and activity to be collected. Processing of such data involves identifying bat species from their echolocation calls. Automated species identification has the potential to provide more consistent, predictable, and potentially higher levels of accuracy than identification by humans. In contrast, identification by humans permits flexibility and intelligence in identification, as well as the incorporation of features and patterns that may be difficult to quantify. We compared humans with artificial neural networks (ANNs) in their ability to classify short recordings of bat echolocation calls of variable signal to noise ratios; these sequences are typical of those obtained from remote automated recording systems that are often used in large-scale ecological studies. We presented 45 recordings (1–4 calls) produced by known species of bats to ANNs and to 26 human participants with 1 month to 23 years of experience in acoustic identification of bats. Humans correctly classified 86% of recordings to genus and 56% to species; ANNs correctly identified 92% and 62%, respectively. There was no significant difference between the performance of ANNs and that of humans, but ANNs performed better than about 75% of humans. There was little relationship between the experience of the human participants and their classification rate. However, humans with <1 year of experience performed worse than others. Currently, identification of bat echolocation calls by humans is suitable for ecological research, after careful consideration of biases. However, improvements to ANNs and the data that they are trained on may in future increase their performance to beyond those demonstrated by humans.

Identification of New Zealand bats in flight from analysis of echolocation calls by artificial neural networks.

Time-expanded and heterodyned echolocation calls of the New Zealand long-tailed Chalinolobus tuberculatus and lesser short-tailed bat Mystacina tuberculata were recorded and digitally analysed. Temporal and spectral parameters were measured from time-expanded calls and power spectra generated for both time-expanded and heterodyned calls. Artificial neural networks were trained to classify the calls of both species using temporal and spectral parameters and power spectra as input data. Networks were then tested using data not previously seen. Calls could be unambiguously identified using parameters and power spectra from time-expanded calls. A neural network, trained and tested using power spectra of calls from both species recorded using a heterodyne detector set to 40 kHz (the frequency with the most energy of the fundamental of C. tuberculatus call), could identify 99% and 84% of calls of C. tuberculatus and M. tuberculata, respectively. A second network, trained and tested using power spectra of calls from both species recorded using a heterodyne detector set to 27 kHz (the frequency with the most energy of the fundamental of M. tuberculata call), could identify 34% and 100% of calls of C. tuberculatus and M. tuberculata, respectively. This study represents the first use of neural networks for the identification of bats from their echolocation calls. It is also the first study to use power spectra of time-expanded and heterodyned calls for identification of chiropteran species. The ability of neural networks to identify bats from their echolocation calls is discussed, as is the ecology of both species in relation to the design of their echolocation calls.

Acoustic identification of 12 species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

The long and short of it: branch lengths and the problem of placing the New Zealand short-tailed bat.

The taxonomic position of the endemic New Zealand bat genus Mystacina has vexed systematists ever since its erection in 1843. Over the years the genus has been linked with many microchiropteran families and superfamilies. Most recent classifications place it in the Vespertilionoidea, although some immunological evidence links it with the Noctilionoidea (=Phyllostomoidea). We have sequenced 402 bp of the mitochondrial cytochrome b gene for M. tuberculata (Gray in Dieffenbach, 1843), and using both our own and published DNA sequences for taxa in both superfamilies, we applied different tree reconstruction methods to find the appropriate phylogeny and different methods of estimating confidence in the parts of the tree. All methods strongly support the classification of Mystacina in the Noctilionoidea. Spectral analysis suggests that parsimony analysis may be misleading for Mystacina's precise placement within the Noctilionoidea because of its long terminal branch. Analyses not susceptible to long-branch attraction suggest that the Mystacinidae is a sister family to the Phyllostomidae. Dating the divergence times between the different taxa suggests that the extant chiropteran families radiated around and shortly after the Cretaceous–Tertiary boundary. We discuss the biogeographical implications of classifying Mystacina within the Noctilionoidea and contrast our result with those classifications placing Mystacina in the Vespertilionoidea, concluding that evidence for the latter is weak.

Identifying weak linear features with the "coalescing shortest path image transform"

The detection of line-like features in images finds many applications in microanalysis. Actin fibers, microtubules, neurites, pilis, DNA, and other biological structures all come up as tenuous curved lines in microscopy images. A reliable tracing method that preserves the integrity and details of these structures is particularly important for quantitative analyses. We have developed a new image transform called the "Coalescing Shortest Path Image Transform" with very encouraging properties. Our scheme efficiently combines information from an extensive collection of shortest paths in the image to delineate even very weak linear features. © Copyright Microscopy Society of America 2011.