The enigmatic Placozoa part 2: Exploring evolutionary controversies and promising questions on earth and in space.

The placozoan Trichoplax adhaerens has been bridging gaps between research disciplines like no other animal. As outlined in part 1, placozoans have been subject of hot evolutionary debates and placozoans have challenged some fundamental evolutionary concepts. Here in part 2 we discuss the exceptional genetics of the phylum Placozoa and point out some challenging model system applications for the best known species, Trichoplax adhaerens.

The enigmatic Placozoa part 1: Exploring evolutionary controversies and poor ecological knowledge.

The placozoan Trichoplax adhaerens is a tiny hairy plate and more simply organized than any other living metazoan. After its original description by F.E. Schulze in 1883, it attracted attention as a potential model for the ancestral state of metazoan organization, the "Urmetazoon". Trichoplax lacks any kind of symmetry, organs, nerve cells, muscle cells, basal lamina, and extracellular matrix. Furthermore, the placozoan genome is the smallest (not secondarily reduced) genome of all metazoan genomes. It harbors a remarkably rich diversity of genes and has been considered the best living surrogate for a metazoan ancestor genome. The phylum Placozoa presently harbors three formally described species, while several dozen "cryptic" species are yet awaiting their description. The phylogenetic position of placozoans has recently become a contested arena for modern phylogenetic analyses and view-driven claims. Trichoplax offers unique prospects for understanding the minimal requirements of metazoan animal organization and their corresponding malfunctions.

DNA barcoding a unique avifauna: an important tool for evolution, systematics and conservation.

BACKGROUND: DNA barcoding utilises a standardised region of the cytochrome c oxidase I (COI) gene to identify specimens to the species level. It has proven to be an effective tool for identification of avian samples. The unique island avifauna of New Zealand is taxonomically and evolutionarily distinct. We analysed COI sequence data in order to determine if DNA barcoding could accurately identify New Zealand birds. RESULTS: We sequenced 928 specimens from 180 species. Additional Genbank sequences expanded the dataset to 1416 sequences from 211 of the estimated 236 New Zealand species. Furthermore, to improve the assessment of genetic variation in non-endemic species, and to assess the overall accuracy of our approach, sequences from 404 specimens collected outside of New Zealand were also included in our analyses. Of the 191 species represented by multiple sequences, 88.5% could be successfully identified by their DNA barcodes. This is likely a conservative estimate of the power of DNA barcoding in New Zealand, given our extensive geographic sampling. The majority of the 13 groups that could not be distinguished contain recently diverged taxa, indicating incomplete lineage sorting and in some cases hybridisation. In contrast, 16 species showed evidence of distinct intra-species lineages, some of these corresponding to recognised subspecies. For species identification purposes a character-based method was more successful than distance and phylogenetic tree-based methods. CONCLUSIONS: DNA barcodes accurately identify most New Zealand bird species. However, low levels of COI sequence divergence in some recently diverged taxa limit the identification power of DNA barcoding. A small number of currently recognised species would benefit from further systematic investigations. The reference database and analysis presented will provide valuable insights into the evolution, systematics and conservation of New Zealand birds.

CAOS-R: Character-Based Barcoding.

CAOS-Barcoding is a culmination of traditional taxonomy and modern DNA barcoding. CAOS identifies taxa by diagnostic characters as is done in traditional taxonomy and produces an identification matrix for taxon discrimination similar to DNA barcoding distance matrices. Here, I describe how to set up the CAOS-Barcoder and CAOS-Classifier software, which input data is needed, and how to interpret the output data. With the CAOS-Barcoder, single marker or concatenated data can be processed into diagnostic barcodes for taxon discrimination. The CAOS-Classifier can use the diagnostic barcodes for specimen identification.

Detecting tail biters by monitoring pig screams in weaning pigs.

Early identification of tail biting and intervention are necessary to reduce tail lesions and their impact on animal health and welfare. Removal of biters has become an effective intervention strategy, but finding them can be difficult and time-consuming. The aim of this study was to investigate whether tail biting and, in particular, individual biters could be identified by detecting pig screams in audio recordings. The study included 288 undocked weaner pigs housed in six pens in two batches. Once a tail biter (n = 7) was identified by visual inspection in the stable and removed by the farm staff, the previous days of video and audio recordings were analyzed for pig screams (sudden increase in loudness with frequencies above 1 kHz) and tail biting events until no biting before the removal was observed anymore. In total, 2893 screams were detected in four pens where tail biting occurred. Of these screams, 52.9% were caused by tail biting in the observed pen, 25.6% originated from other pens, 8.8% were not assignable, and 12.7% occurred due to other reasons. In case of a tail biting event, screams were assigned individually to biter and victim pigs. Based on the audio analysis, biters were identified between one and nine days prior to their removal from the pen after visual inspection. Screams were detected earlier than the increase in hanging tails and could therefore be favored as an early warning indicator. Analyzing animal vocalization has potential for monitoring and early detection of tail biting events. In combination with individual marks and automatic analysis algorithms, biters could be identified and tail biting efficiently reduced. In this way, biters can be removed earlier to increase animal health and welfare.

Features of animal babbling in the vocal ontogeny of the gray mouse lemur (Microcebus murinus).

In human infants babbling is an important developmental stage of vocal plasticity to acquire maternal language. To investigate parallels in the vocal development of human infants and non-human mammals, seven key features of human babbling were defined, which are up to date only shown in bats and marmosets. This study will explore whether these features can also be found in gray mouse lemurs by investigating how infant vocal streams gradually resemble the structure of the adult trill call, which is not present at birth. Using unsupervised clustering, we distinguished six syllable types, whose sequential order gradually reflected the adult trill. A subset of adult syllable types was produced by several infants, with the syllable production being rhythmic, repetitive, and independent of the social context. The temporal structure of the calling bouts and the tempo-spectral features of syllable types became adult-like at the age of weaning. The age-dependent changes in the acoustic parameters differed between syllable types, suggesting that they cannot solely be explained by physical maturation of the vocal apparatus. Since gray mouse lemurs exhibit five features of animal babbling, they show parallels to the vocal development of human infants, bats, and marmosets.

Utilizing DeepSqueak for automatic detection and classification of mammalian vocalizations: a case study on primate vocalizations.

Bioacoustic analyses of animal vocalizations are predominantly accomplished through manual scanning, a highly subjective and time-consuming process. Thus, validated automated analyses are needed that are usable for a variety of animal species and easy to handle by non-programing specialists. This study tested and validated whether DeepSqueak, a user-friendly software, developed for rodent ultrasonic vocalizations, can be generalized to automate the detection/segmentation, clustering and classification of high-frequency/ultrasonic vocalizations of a primate species. Our validation procedure showed that the trained detectors for vocalizations of the gray mouse lemur (Microcebus murinus) can deal with different call types, individual variation and different recording quality. Implementing additional filters drastically reduced noise signals (4225 events) and call fragments (637 events), resulting in 91% correct detections (Ntotal = 3040). Additionally, the detectors could be used to detect the vocalizations of an evolutionary closely related species, the Goodman's mouse lemur (M. lehilahytsara). An integrated supervised classifier classified 93% of the 2683 calls correctly to the respective call type, and the unsupervised clustering model grouped the calls into clusters matching the published human-made categories. This study shows that DeepSqueak can be successfully utilized to detect, cluster and classify high-frequency/ultrasonic vocalizations of other taxa than rodents, and suggests a validation procedure usable to evaluate further bioacoustics software.

The marker choice: Unexpected resolving power of an unexplored CO1 region for layered DNA barcoding approaches.

The potential of DNA barcoding approaches to identify single species and characterize species compositions strongly depends on the marker choice. The prominent "Folmer region", a 648 basepair fragment at the 5' end of the mitochondrial CO1 gene, has been traditionally applied as a universal DNA barcoding region for metazoans. In order to find a suitable marker for biomonitoring odonates (dragonflies and damselflies), we here explore a new region of the CO1 gene (CO1B) for DNA barcoding in 51 populations of 23 dragonfly and damselfly species. We compare the "Folmer region", the mitochondrial ND1 gene (NADH dehydrogenase 1) and the new CO1 region with regard to (i) speed and reproducibility of sequence generation, (ii) levels of homoplasy and (iii) numbers of diagnostic characters for discriminating closely related sister taxa and populations. The performances of the gene regions regarding these criteria were quite different. Both, the amplification of CO1B and ND1 was highly reproducible and CO1B showed the highest potential for discriminating sister taxa at different taxonomic levels. In contrast, the amplification of the "Folmer region" using the universal primers was difficult and the third codon positions of this fragment have experienced nucleotide substitution saturation. Most important, exploring this new barcode region of the CO1 gene identified a higher discriminating power between closely related sister taxa. Together with the design of layered barcode approaches adapted to the specific taxonomic "environment", this new marker will further enhance the discrimination power at the species level.

Some 'ant'swers: Application of a layered barcode approach to problems in ant taxonomy.

DNA barcoding has emerged as a routine tool in modern taxonomy. Although straightforward, this approach faces new challenges, when applied to difficult situation such as defining cryptic biodiversity. Ants are prime examples for high degrees of cryptic biodiversity due to complex population differentiation, hybridization and speciation processes. Here, we test the DNA barcoding region, cytochrome c oxidase 1 and two supplementary markers, 28S ribosomal DNA and long-wavelength rhodopsin, commonly used in ant taxonomy, for their potential in a layered, character-based barcoding approach across different taxonomic levels. Furthermore, we assess performance of the character-based barcoding approach to determine cryptic species diversity in ants. We found (i) that the barcode potential of a specific genetic marker varied widely among taxonomic levels in ants; (ii) that application of a layered, character-based barcode for identification of specimens can be a solution to taxonomical challenging groups; (iii) that the character-based barcoding approach allows us to differentiate specimens even within locations based on pure characters. In summary, (layered) character-based barcoding offers a reliable alternative for problematic species identification in ants and can be used as a fast and cost-efficient approach to estimate presence, absence or frequency of cryptic species.

Character-based DNA barcoding: a superior tool for species classification.

In zoonosis research only correct assigned host-agent-vector associations can lead to success. If most biological species on Earth, from agent to host and from procaryotes to vertebrates, are still undetected, the development of a reliable and universal diversity detection tool becomes a conditio sine qua non. In this context, in breathtaking speed, modern molecular-genetic techniques have become acknowledged tools for the classification of life forms at all taxonomic levels. While previous DNA-barcoding techniques were criticised for several reasons (Moritz and Cicero, 2004; Rubinoff et al., 2006a, b; Rubinoff, 2006; Rubinoff and Haines, 2006) a new approach, the so called CAOS-barcoding (Character Attribute Organisation System), avoids most of the weak points. Traditional DNA-barcoding approaches are based on distances, i. e. they use genetic distances and tree construction algorithms for the classification of species or lineages. The definition of limit values is enforced and prohibits a discrete or clear assignment. In comparison, the new character-based barcoding (CAOS-barcoding; DeSalle et al., 2005; DeSalle, 2006; Rach et al., 2008) works with discrete single characters and character combinations which permits a clear, unambiguous classification. In Hannover (Germany) we are optimising this system and developing a semiautomatic high-throughput procedure for hosts, agents and vectors being studied within the Zoonosis Centre of the "Stiftung Tierärztliche Hochschule Hannover". Our primary research is concentrated on insects, the most successful and species-rich animal group on Earth (every fourth animal is a bug). One subgroup, the winged insects (Pterygota), represents the outstanding majority of all zoonosis relevant animal vectors.