Realistic rates of nitrogen addition increase carbon flux rates but do not change soil carbon stocks in a temperate grassland.

Changes in the biosphere carbon (C) sink are of utmost importance given rising atmospheric CO2 levels. Concurrent global changes, such as increasing nitrogen (N) deposition, are affecting how much C can be stored in terrestrial ecosystems. Understanding the extent of these impacts will help in predicting the fate of the biosphere C sink. However, most N addition experiments add N in rates that greatly exceed ambient rates of N deposition, making inference from current knowledge difficult. Here, we leveraged data from a 13-year N addition gradient experiment with addition rates spanning realistic rates of N deposition (0, 1, 5, and 10 g N m-2  year-1 ) to assess the rates of N addition at which C uptake and storage were stimulated in a temperate grassland. Very low rates of N addition stimulated gross primary productivity and plant biomass, but also stimulated ecosystem respiration such that there was no net change in C uptake or storage. Furthermore, we found consistent, nonlinear relationships between N addition rate and plant responses such that intermediate rates of N addition induced the greatest ecosystem responses. Soil pH and microbial biomass and respiration all declined with increasing N addition indicating that negative consequences of N addition have direct effects on belowground processes, which could then affect whole ecosystem C uptake and storage. Our work demonstrates that experiments that add large amounts of N may be underestimating the effect of low to intermediate rates of N deposition on grassland C cycling. Furthermore, we show that plant biomass does not reliably indicate rates of C uptake or soil C storage, and that measuring rates of C loss (i.e., ecosystem and soil respiration) in conjunction with rates of C uptake and C pools are crucial for accurately understanding grassland C storage.

Adult-like neural representation of species-specific songs in the auditory forebrain of zebra finch nestlings.

Encoding of conspecific signals during development can reinforce species barriers as well as set the stage for learning and production of species-typical vocalizations. In altricial songbirds, the development of the auditory system is not complete at hatching, so it is unknown the degree to which recently hatched young can process auditory signals like birdsong. We measured in vivo extracellular responses to song stimuli in a zebra finch (Taeniopygia guttata) secondary auditory forebrain region, the caudomedial nidopallium (NCM). We recorded from three age groups between 13 days post-hatch and adult to identify possible shifts in stimulus encoding that occur before the opening of the sensitive period of song motor learning. We did not find differences in putative cell type composition, firing rate, response strength, and selectivity across ages. Across ages narrow-spiking units had higher firing rates, response strength, accuracy, and trial-by-trial reliability along with lower selectivity than broad-spiking units. In addition, we showed that stimulus-specific adaptation, a characteristic of adult NCM, was also present in nestlings and fledglings. These results indicate that most features of secondary auditory processing are already adult-like shortly after hatching. Furthermore, we showed that selectivity for species-specific stimuli is similar across all ages, with the greatest fidelity in temporal coding in response to conspecific song and domesticated Bengalese finch song, and reduced fidelity in response to owl finch song, a more ecologically relevant heterospecific, and white noise. Our study provides the first evidence that the electrophysiological properties of higher-order auditory neurons are already mature in nestling songbirds.

The variability of song variability in zebra finch (Taeniopygia guttata) populations.

Birdsong is a classic example of a learned social behaviour. Song behaviour is also influenced by genetic factors, and understanding the relative contributions of genetic and environmental influences remains a major goal. In this study, we take advantage of captive zebra finch populations to examine variation in a population-level song trait: song variability. Song variability is of particular interest in the context of individual recognition and in terms of the neuro-developmental mechanisms that generate song novelty. We find that the Australian zebra finch Taeniopygia guttata castanotis (TGC) maintains higher song diversity than the Timor zebra finch T. g. guttata (TGG) even after experimentally controlling for early life song exposure, suggesting a genetic basis to this trait. Although wild-derived TGC were intermediate in song variability between domesticated TGC populations and TGG, the difference between domesticated and wild TGC was not statistically significant. The observed variation in song behaviour among zebra finch populations represents a largely untapped opportunity for exploring the mechanisms of social behaviour.

Quantitative acoustic differentiation of cryptic species illustrated with King and Clapper rails.

Reliable species identification is vital for survey and monitoring programs. Recently, the development of digital technology for recording and analyzing vocalizations has assisted in acoustic surveying for cryptic, rare, or elusive species. However, the quantitative tools that exist for species differentiation are still being refined. Using vocalizations recorded in the course of ecological studies of a King Rail (Rallus elegans) and a Clapper Rail (Rallus crepitans) population, we assessed the accuracy and effectiveness of three parametric (logistic regression, discriminant function analysis, quadratic discriminant function analysis) and six nonparametric (support vector machine, CART, Random Forest, k-nearest neighbor, weighted k-nearest neighbor, and neural networks) statistical classification methods for differentiating these species by their kek mating call. We identified 480 kek notes of each species and quantitatively characterized them with five standardized acoustic parameters. Overall, nonparametric classification methods outperformed parametric classification methods for species differentiation (nonparametric tools were between 57% and 81% accurate, parametric tools were between 57% and 60% accurate). Of the nine classification methods, Random Forest was the most accurate and precise, resulting in 81.1% correct classification of kek notes to species. This suggests that the mating calls of these sister species are likely difficult for human observers to tell apart. However, it also implies that appropriate statistical tools may allow reasonable species-level classification accuracy of recorded calls and provide an alternative to species classification where other capture- or genotype-based survey techniques are not possible.