Modelling habitat-suitability using museum collections: an example with three sympatric Apodemus species from the Alps

Aim, Location Although the alpine mouse Apodemus alpicola has been given species status since 1989, no distribution map has ever been constructed for this endemic alpine rodent in Switzerland. Based on redetermined museum material and using the Ecological-Niche Factor Analysis (ENFA), habitat-suitability maps were computed for A. alpicola , and also for the co-occurring A. flavicollis and A. sylvaticus .
Methods In the particular case of habitat suitability models, classical approaches (GLMs, GAMs, discriminant analysis, etc.) generally require presence and absence data. The presence records provided by museums can clearly give useful information about species distribution and ecology and have already been used for knowledge-based mapping. In this paper, we apply the ENFA which requires only presence data, to build a habitat-suitability map of three species of Apodemus on the basis of museum skull collections.
Results Interspecific niche comparisons showed that A. alpicola is very specialized concerning habitat selection, meaning that its habitat differs unequivocally from the average conditions in Switzerland, while both A. flavicollis and A. sylvaticus could be considered as 'generalists' in the study area.
Main conclusions Although an adequate sampling design is the best way to collect ecological data for predictive modelling, this is a time and money consuming process and there are cases where time is simply not available, as for instance with endangered species conservation. On the other hand, museums, herbariums and other similar institutions are treasuring huge presence data sets. By applying the ENFA to such data it is possible to rapidly construct a habitat suitability model. The ENFA method not only provides two key measurements regarding the niche of a species (i.e. marginality and specialization), but also has ecological meaning, and allows the scientist to compare directly the niches of different species.     

Construction of genetic maps for some Eurasian coniferous species using allozyme genes

In an analysis of allozyme genes in three pine and one spruce species distributed in Eurasia, 45 of 87 loci were mapped. Four linkage groups inPinus sylvestris andPicea abies, three inPinus pallasiana, and two inPinus pumila were determined. The order and the locations of homologous genes in the linkage groups in the different species were similar. The data suggest that during the separate development of thePinus andPicea genera that has lasted for millions of years, there was not any large inversion, translocation, or other significant chromosomal change, at least in the gene blocks analyzed.     

Analysis of metabolite profile data using batch-learning self-organizing maps

Novel tools are needed for efficient analysis and visualization of the massive data sets associated with metabolomics. Here, we describe a batch-learning self-organizing map (BL-SOM) for metabolome informatics that makes the learning process and resulting map independent of the order of data input. This approach was successfully used in analyzing and organizing the metabolome data forArabidopsis thaliana cells cultured under salt stress. Our 6 × 4 matrix presented patterns of metabolite levels at different time periods. A negative correlation was found between the levels of amino acids and metabolites related to glycolysis metabolism in response to this stress. Therefore, BL-SOM could be an excellent tool for clustering and visualizing high dimensional, complex metabolome data in a single map.     

Analysis of gene expression data using self-organizing maps.

DNA microarray technologies together with rapidly increasing genomic sequence information is leading to an explosion in available gene expression data. Currently there is a great need for efficient methods to analyze and visualize these massive data sets. A self-organizing map (SOM) is an unsupervised neural network learning algorithm which has been successfully used for the analysis and organization of large data files. We have here applied the SOM algorithm to analyze published data of yeast gene expression and show that SOM is an excellent tool for the analysis and visualization of gene expression profiles.     

Constructing linkage maps in autotetraploid species using simulated annealing

In this paper we demonstrate how molecular markers segregating in a full-sib autotetraploid mapping population can be ordered to form a linkage map using simulated annealing. This approach facilitates the examination of orders close to the optimum to see which marker placings are fixed and identify the markers whose position is less certain. A simulation study investigates the effects of population size, marker spacing, ratio of dominant to codominant markers, typing errors and missing values. The method is applied to map 30 amplified fragment length polymorphism and microsatellite markers on linkage group IV of potato.     

Prioritising plant-parasitic nematode species biosecurity risks using self organising maps

The biosecurity risks from many plant-parasitic nematode (PPN) species are poorly known and remain a major challenge for identifying potentially invasive species. A self organising map (SOM) was used to prioritise biosecurity risks from PPN to the whole of continental Australia as well as each of the states and the Northern Territory separately. The SOM used the recorded worldwide distributions of 250 systematically selected species from 43 genera, and identified 18 different countries spanning Asia, Africa, North and Central America, Europe and the Pacific with very similar PPN assemblages to Australia as a whole. Many of the species in these countries are not recorded in Australia, and therefore pose a biosecurity risk. Analysed separately, the states and territories were identified as forming five separate clusters, each with a different region of the world, and with different characteristic PPN. Many of the PPN found in the regions clustered with an Australian state have not been recorded from anywhere in Australia, and others have very restricted distributions within Australia, thus posing different biosecurity risks. The SOM analysis ranked the risks of the different PPN based on likelihoods of establishment. The rankings confirmed the risks from frequently quarantined PPN, but more importantly identified species, which upon further investigation could be new threats. This method can be used to identify previously overlooked species for more detailed risk assessments.     

Methods for estimating understory light conditions using crown projection maps and topographic data

Four types of indices of canopy characteristics were compared to estimate understory light conditions using crown projection maps and topographic data. Crown area (CA) was formulated from the crowns in a focal sub-block (20 m×20 m). Canopy shade index (CSI) was formulated from the crowns in a focal sub-block and the surrounding sub-blocks with topographic effect. Site shade index (SSI) incorporated the shade of the slopes into CSI and represented the total physical closure of the site. Site light index (SLI) incorporated the azimuth of the site into SSI and evaluated the light condition of the understory. We tested the methods using the data on steep old-growth Cryptomeria japonica forests on Yakushima Island and the three-dimensional canopy structures of the forests were digitized on a geographic information system. The results were validated by the basic indices obtained by hemispherical photographs: canopy openness (CO) and gap light index (GLI). The three-dimensional structure of the surrounding canopies and topography had significant effects on the understory light conditions. CA had no significant correlation with CO, whereas CSI and SSI had significant negative correlations with CO. Although SLI had the highest negative correlation with GLI (R²=0.69), CSI had practical utility in the estimation of GLI (R²= 0.65). Our method is applicable to past crown projection maps that have been recorded at many forest research sites, thus making it possible to reconstruct past understory light conditions and to compare them with present conditions.     

Clustering of ant communities and indicator species analysis using self-organizing maps

To understand the complex relationships that exist between ant assemblages and their habitats, we performed a self-organizing map (SOM) analysis to clarify the interactions among ant diversity, spatial distribution, and land use types in Fukuoka City, Japan. A total of 52 species from 12 study sites with nine land use types were collected from 1998 to 2012. A SOM was used to classify the collected data into three clusters based on the similarities between the ant communities. Consequently, each cluster reflected both the species composition and habitat characteristics in the study area. A detrended correspondence analysis (DCA) corroborated these findings, but removal of unique and duplicate species from the dataset in order to avoid sampling errors had a marked effect on the results; specifically, the clusters produced by DCA before and after the exclusion of specific data points were very different, while the clusters produced by the SOM were consistent. In addition, while the indicator value associated with SOMs clearly illustrated the importance of individual species in each cluster, the DCA scatterplot generated for species was not clear. The results suggested that SOM analysis was better suited for understanding the relationships between ant communities and species and habitat characteristics.     

Prioritizing regionally rare plant species for conservation using herbarium data

Because the conservation of biodiversity occurs under time and resource constraints, it is necessary to prioritize species most deserving of attention. Natural history collections have been identified as a valuable source of information in applied conservation practice, particularly for species-rich taxa like plants. Here, online herbarium information was combined with a novel, straightforward priority setting approach to screen a large list of rare vascular plant species (n = 418) in Saskatchewan, Canada. Data was quantified to develop priority scores (for a given species) using three key criteria: (1) provincial responsibility in species survival, (2) species local population characteristics, and (3) the anthropogenic threats causing species to be rare. The use of a hierarchy of the three criteria, wherein provincial responsibility was assigned the most weight, resulted in the highest ranking for 13 species that exist only in Saskatchewan and no other Canadian province or territory. The list is a first step in identifying species deserving of conservation attention and/or further study, while the method itself was deemed to be highly relevant to conservation managers and decision makers due to its scale adaptability and fairly minimal resource requirements.     

A model-based method for identifying species hybrids using multilocus genetic data

We present a statistical method for identifying species hybrids using data on multiple, unlinked markers. The method does not require that allele frequencies be known in the parental species nor that separate, pure samples of the parental species be available. The method is suitable for both markers with fixed allelic differences between the species and markers without fixed differences. The probability model used is one in which parentals and various classes of hybrids (F(1)'s, F(2)'s, and various backcrosses) form a mixture from which the sample is drawn. Using the framework of Bayesian model-based clustering allows us to compute, by Markov chain Monte Carlo, the posterior probability that each individual belongs to each of the distinct hybrid classes. We demonstrate the method on allozyme data from two species of hybridizing trout, as well as on two simulated data sets.     

Metabolite-based clustering and visualization of mass spectrometry data using one-dimensional self-organizing maps

Background  

One of the goals of global metabolomic analysis is to identify metabolic markers that are hidden within a large background of data originating from high-throughput analytical measurements. Metabolite-based clustering is an unsupervised approach for marker identification based on grouping similar concentration profiles of putative metabolites. A major problem of this approach is that in general there is no prior information about an adequate number of clusters.     

Messinian vegetation maps of the Mediterranean region using models and interpolated pollen data

This study proposes to compare the outputs from the CARAIB vegetation model forced by results from the LMD General Circulation Model with interpolated pollen data (Kriging method) from the Mediterranean region during the Messinian. The vegetation maps that have been obtained represent distinct phases of the salinity crisis: before the crisis and during the marginal evaporitic phase (interpolated map), and during the complete desiccation phase (simulated map). However, they are comparable in terms of vegetation density and agree on a strong contrast between the Northern (forest vegetation) and Southern (open vegetation) Mediterranean regions. Main differences concern the type of forests in the northern Mediterranean region, which are explained by discrepancies between precipitation amount predicted by the model and that calculated by a transfer function using pollen records. The interpolation method has been successfully tested in France using interpolated current pollen records by comparison with the present-day potential vegetation map. The resulting Messinian map is useful to validate or improve model simulation which does not take into account the depth of the Mediterranean Basin when it dried up. The Southern Mediterranean landscapes were open, with a steppe-like vegetation to the West and a savannah-like vegetation to the East. Forests prevailed to the North, organized in a mosaic system mainly controlled by relief. Such a contrast provides some explanation of the large number of deep fluvial canyons cut on the Northern margin at opposed to the South during the Mediterranean desiccation.     

Characterising neurological time series data using biologically motivated networks of coupled discrete maps

Artificial biochemical networks (ABNs) are a class of computational dynamical system whose architectures are motivated by the organisation of genetic and metabolic networks in biological cells. Using evolutionary algorithms to search for networks with diagnostic potential, we demonstrate how ABNs can be used to carry out classification when stimulated with time series data collected from human subjects with and without Parkinson's disease. Artificial metabolic networks, composed of coupled discrete maps, offer the best recognition of Parkinsonian behaviour, achieving accuracies in the region of 90%. This is comparable to the diagnostic accuracies found in clinical diagnosis, and is significantly higher than those found in primary and non-expert secondary care. We also illustrate how an evolved classifier is able to recognise diverse features of Parkinsonian behaviour and, using perturbation analysis, show that the evolved classifiers have interesting computational behaviours.     

Methods for delimiting species via population genetics and phylogenetics using genotype data

In species delimitation, a formidable goal in the discipline of systematic biology, we identify and describe species morphologically and ecologically based on phenotypic data. Efficient genotyping technologies produce genetic and genomic data with relative ease, which promotes species discovery and validation using genotype data. For the last two decades, we have seen the development of species delimitation methods based on genetic distances and phylogenetic trees using genotype data. However, speciation processes via evolutionary relationship among species were mostly divorced from species delimitation. Recent approaches to drawing species boundaries use multi-locus sequence data to account for evolutionary processes including speciation and gene flow. They allow us to learn of jointly speciation and species delimitation, leveraging computational and statistical techniques developed in population genetics and phylogenetics. Here, we review the recent progress in the development of species delimitation using genotype data and discuss the future outlook for the research of developing species delimitation methods.     

Determinants of species evenness in a neotropical bat ensemble

Evenness is an important property of communities. Species richness alone does not capture the fact that one or a few species may dominate total abundance and biomass of a community. This in turn has important consequences for ecosystem functioning and species interactions. Evenness has been observed to vary systematically along environmental and productivity gradients. However, a truly general theory about which factors control evenness in a community has yet to emerge. Prior research on evenness has suggested that high richness, biomass and abundance should lead to lower community evenness in our study system of bats in Panama. However, only few empirical studies examine the simultaneous effects of species richness, biomass or abundance on evenness. For the first time, we applied path analysis in the study of evenness to tease apart the relative importance and direction (positive or negative) of causality among these three factors. As predicted, we found that evenness decreases with increasing species richness, abundance and biomass. The negative effect of abundance was mediated by the positive joint effect of biomass and richness. The selected models varied in the strength of the correlation between the three variables with evenness but their direction was consistent. Overall, we argue that rarity, high mobility and differences in resource availability at sites with lower environmental stress can explain the negative effects of richness on evenness.     

Cool gleaners: Thermoregulation in sympatric bat species

Thermoregulatory behavior in temperate bats is influenced by gender, food availability, ambient temperature and reproduction. Ecologically and morphologically similar bat species (Myotis bechsteinii, M. nattereri, and Plecotus auritus; Vespertilionidae) facing similar diurnal conditions should therefore not differ in their thermoregulatory behavior. Identified day roosts (n = 23) of radio-tagged bats (n = 30) were spread over an area of 33.1 ha, but ambient temperature did not differ between roosting sites. Furthermore, there was no significant difference in cardinal direction, roost height, canopy coverage, and breast height diameter between day roosts used by the three species. Minimum roost temperatures and isolation values, however, differed significantly between our species with lowest values in P. auritus. The range of skin temperatures (min–max) recorded by temperature-sensitive transmitters was not species-specific with the lowest ranges in late pregnancy (mean ± SD: 7.1 ± 1.1 °C) and highest in post-lactation (mean ± SD: 13.1 ± 1.1 °C). The minimum skin temperature, however, was species-specific with the lowest values in P. auritus (mean ± SD: 20.2 ± 1.1 °C), intermediate in M. nattereri (mean ± SD: 23.4 ± 1.0 °C), and the highest in M. bechsteinii (mean ± SD: 26.8 ± 1.0 °C). Species-specific usage of energy-saving mechanisms might represent an important niche differentiation of species. Different mechanisms might allow, e.g. one species to occupy colder roosts with higher temperature variations or to shorten foraging times due to distinct thermoregulatory behavior.     

Identification of sympatric bat species by the echolocation calls

One hundred and thirty-eight echolocation calls of 63 free-flying individuals of five bat species (Rhinolophus ferrumequinum, Myotis formosus, Myotis ikonnikovi, Myotis daubentoni and Murina leucogaster) were recorded (by ultrasonic bat detector (D980)) in Zhi’an village of Jilin Province, China. According to the frequency-time spectra, these calls were categorized into two types: FM/CF (constant frequency) / FM (R. ferrumequinum) and FM (frequency modulated) (M. formosus, M. ikonnikovi, M. daubentoni and M. leucogaster). Sonograms of the calls of R. ferrumequinum could easily be distinguished from those of the other four species. For the calls of the remaining four species, six echolocation call parameters, including starting frequency, ending frequency, peak frequency duration, longest inter-pulse interval and shortest inter-pulse interval, were examined by stepwise discriminant analysis. The results show that 84.1% of calls were correctly classified, which indicates that these parameters of echolocation calls play an important role in identifying bat species. These parameters can be used to test the accuracy of general predictions based on bats’ morphology in the same forest and can provide essential information for assessing patterns of bat habitat use. __________ Translated from Journal of Northeast Normal University (Natural Science Edition), 2006, 38 (3): 109–114 [译自: 东北师范大学学报(自然科学版)]