A multivariate approach to the feeding habits of small fishes in the Guadalquivir Estuary

In the analysis of the feeding habits of the 11 most abundant fish species in the Guadalquivir Estuary, collected monthly (February 1998 to January 1999) at two different sampling sites, a total of 46 prey taxa were identified. Classifications (based on Bray–Curtis similarities derived from occurrence, number and mass data) of the different fish categories (postlarvae and juvenile–adults of each species) revealed two main trophic guilds, whose preferential prey (SIMPER analysis) were mysids and copepods, respectively. The similarity matrices derived from occurrence, number and mass data were always significantly correlated (RELATE: r >0.636; P <0.01), indicating that a good agreement in feeding patterns emerged from these variables. The seasonal coincidence of maximal fish and key-prey species densities suggests that food availability may be a principal factor influencing the nursery function of the Guadalquivir Estuary.     

Experimental evidence for root competition effects on community evenness in one of two phytometer species

Aims Plant–plant interactions, being positive or negative, are recognized to be key factors in structuring plant communities. However, it is thought that root competition may be less important than shoot competition due to greater size symmetry belowground. Because direct experimental tests on the importance of root competition are scarce, we aim at elucidating whether root competition may have direct or indirect effects on community structure. Indirect effects may occur by altering the overall size asymmetry of competition through root–shoot competitive interactions.     

A review of automatic recognition technology for bird vocalizations in the deep learning era

Birds are considered critical indicators of ecosystem condition. Automatic recording devices have emerged as a trending tool to assist field observations, contributing to biodiversity monitoring on large spatio-temporal scales. However, manually processing huge volumes of recordings is challenging. Consequently, there has been a growing interest in automatic bird vocalization recognition in recent years. Automatic bird vocalization recognition technology has advanced from classical pattern recognition to deep learning (DL), with significantly improved recognition performance. This paper reviews related works on DL-based automatic bird vocalization recognition technology in the last decade. In this review, we present the current state of research in the three key areas of pre-processing, feature extraction and recognition methods involved in automatic bird vocalization recognition. The related datasets, evaluation metrics and software are also summarized. Finally, existing challenges along with opportunities for future work are highlighted. We conclude that, while DL-based automatic bird vocalization recognition has made recent advances in specific species, more robust denoising approaches, larger public datasets, and stronger generalization capabilities of feature extraction and recognition are required to achieve reliable and general bird recognition in the wild. We expect that this review will serve as a firm foundation for new researchers working in the field of DL-based automatic bird vocalization recognition technologies, as well as become an insightful guide for computer science and ecology experts.     

Incomplete partitioning of pollinators by Linum suffruticosum and its coflowering congeners

Premise

Linum suffruticosum shows variations in pollinator fit, pollen pickup, and local pollinators that predict pollen deposition rates. The species often coflowers with other Linum species using the same pollinators. We investigated whether L. suffruticosum trait variation could be explained by local patterns of pollinator sharing and associated evolution to reduce interspecific pollen transfer.

Methods

Pollinator observations were made in different localities (single species, coflowering with other congeners). Floral traits were measured to detect differences across populations and from coflowering species. Reproductive costs were quantified using interspecific hand pollinations and measures of pollen-tube formation, combined with observations of pollen arrival on stigmas and pollen-tube formation after natural pollination in allopatric and sympatric localities.

Results

The size and identity of the most important pollinator of L. suffruticosum and whether there was pollinator sharing with coflowering species appeared to explain floral trait variation related to pollinator fit. The morphological overlap of the flowers of L. suffruticosum with those of coflowering species varied, depending on coflowering species identity. A post-pollination incompatibility system maintains reproductive isolation, but conspecific pollen-tube formation was lower after heterospecific pollination. Under natural pollination at sites of coflowering with congeners, conspecific pollen-tube formation was lower than at single-species localities.

Conclusions

Trait variation in L. suffruticosum appears to respond to the most important local pollinator. Locally, incomplete pollinator partitioning might cause interspecific pollination, imposing reproductive costs. These reproductive costs may generate selection on floral traits for reduced morphological overlap with coflowering congeners, leading to the evolution of pollination ecotypes.     

Quality Assessment of Tandem Mass Spectra by Using a Weighted K-Means

Introduction  The tandem mass spectrometer is a powerful tool with which to generate peptide (tandem) mass spectrum data for the analysis of complex biological protein mixtures in genomic-related disease cell lines. However, the majority of experimental tandem mass spectra cannot be interpreted by any database search engines. One of the main reasons this happens is that majority of experimental spectra are of quality too poor to be interpretable. Interpreting these “un-interpretable” spectra is a waste of time. Therefore, it is worthwhile to determine the quality of mass spectra before any interpretation. Objectives  This paper proposes an approach to classifying tandem spectra into two groups: one with high quality and one with poor quality. Methods  The proposed approach has two steps. First, each spectrum is mapped to a feature vector which describes the quality of the spectrum. Then, a weighted K-means clustering method is applied in order to classify the tandem mass spectra. Results and Conclusion  Computational experiments illustrate that one cluster contains the majority of the high-quality spectra, while the other contains the majority of the poor-quality spectra. This result indicates that if we just search the spectra in the high-quality cluster, we can save the time for searching the majority of poor-quality spectra while losing a minimal amount of high-quality spectra. The software created for this work is available upon request.