庞泉沟自然保护区华北落叶松林的自组织特征映射网络分类与排序

自组织特征映射网络（SOM）是新近引入植物生态学的分析方法,对复杂问题和非线性问题具有较强的分析和求解功能。本研究应用SOM分类和排序研究了庞泉沟自然保护区华北落叶松林。研究结果表明,SOM将120个样方分为7个植物群落类型,分类结果具有明确的生态意义;样方和物种在SOM训练图上呈现一定规律的分布;7个群落类型各有其分布范围和界限,揭示了群落间的生态关系。在此基础上,通过引入一种在SOM训练图上可视化环境因子梯度的方法,能够较好地完成样方、物种和环境因子相互关系的分析,揭示了海拔是影响该区华北落叶松林生长和分布的最主要因子。生态分析表明SOM分类和排序是一种有效的梯度分析方法,适用于表征生态特征和探索群落和环境相互关系的研究。     

基于SOM的野生秤锤树群落的空间分布和环境解释

本文采用自组织特征映射网络(self-organizing map, SOM)对南京老山野生秤锤树(Sinojackia xylocarpa)群落进行数量分类和排序, 分析了其与环境因子之间的关系。结果表明: (1) SOM将秤锤树野生群落的100个样方划分为5个群丛类型, 分类结果在空间上反映了秤锤树野生群落的演替变化趋势, 各群丛的群落结构和物种组成存在差异且群丛界限明显, 可较好地进行排序与分类的环境解释。(2)通过环境因子梯度的可视化方法, 确定了海拔、坡位和土壤厚度是影响该地区秤锤树生长和分布的主要因子, 同时也揭示了以不同优势种为代表的各群丛和环境因子的关系。(3) SOM可以摆脱许多定量技术的限制性假设, 使得神经网络对于群落生态特征及探索群落和环境相互关系具有良好展现力; SOM群落生态数据具有更高的映射能力, 进行群落分类以及较少程度的排序的潜力, 将有利于不同群落类型的分类和管理, 对于濒危植物保护具有重要意义。     

Predicting invasive fungal pathogens using invasive pest assemblages: testing model predictions in a virtual world

Predicting future species invasions presents significant challenges to researchers and government agencies. Simply considering the vast number of potential species that could invade an area can be insurmountable. One method, recently suggested, which can analyse large datasets of invasive species simultaneously is that of a self organising map (SOM), a form of artificial neural network which can rank species by establishment likelihood. We used this method to analyse the worldwide distribution of 486 fungal pathogens and then validated the method by creating a virtual world of invasive species in which to test the SOM. This novel validation method allowed us to test SOM's ability to rank those species that can establish above those that can't. Overall, we found the SOM highly effective, having on average, a 96-98% success rate (depending on the virtual world parameters). We also found that regions with fewer species present (i.e. 1-10 species) were more difficult for the SOM to generate an accurately ranked list, with success rates varying from 100% correct down to 0% correct. However, we were able to combine the numbers of species present in a region with clustering patterns in the SOM, to further refine confidence in lists generated from these sparsely populated regions. We then used the results from the virtual world to determine confidences for lists generated from the fungal pathogen dataset. Specifically, for lists generated for Australia and its states and territories, the reliability scores were between 84-98%. We conclude that a SOM analysis is a reliable method for analysing a large dataset of potential invasive species and could be used by biosecurity agencies around the world resulting in a better overall assessment of invasion risk.     

funrar: An R package to characterize functional rarity

Emphasis has been put in recent ecological research on investigating phylogenetic, functional and taxonomic facets of biological diversity. While a flourishing number of indices have been proposed for assessing functional diversity, surprisingly few options are available to characterize functional rarity. Functional rarity can play a key role in community and ecosystem dynamics. We introduce here the funrar R package to quantify functional rarity based on species trait differences and species frequencies at local and regional scales. Because of the increasing availability of big datasets in macroecology and biogeography, we optimized funrar to work with large datasets of thousands of species and sites. We illustrate the use of the package to investigate the functional rarity of North and Central American mammals.     

Global Establishment Risk of Economically Important Fruit Fly Species (Tephritidae)

The global invasion of Tephritidae (fruit flies) attracts a great deal of attention in the field of plant quarantine and invasion biology because of their economic importance. Predicting which one in hundreds of potential invasive fruit fly species is most likely to establish in a region presents a significant challenge, but can be facilitated using a self organising map (SOM), which is able to analyse species associations to rank large numbers of species simultaneously with an index of establishment. A global presence/absence dataset including 180 economically significant fruit fly species in 118 countries was analysed using a SOM. We compare and contrast ranked lists from six countries selected from each continent, and also show that those countries geographically close were clustered together by the SOM analysis because they have similar fruit fly assemblages. These closely clustered countries therefore represent greater threats to each other as sources of invasive fruit fly species. Finally, we indicate how this SOM method could be utilized as an initial screen to support prioritizing fruit fly species for further research into their potential to invade a region.     

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.     

宜昌百里荒草山草坡群落物种分布的空间趋势分析

通过基于CCA的趋势面分析和空间插值方法,研究了宜昌百里荒山地草场的群落结构空间变化,以及群落结构空间趋势与主要环境因子的相关性。结果表明,该群落物种空间中的群落结构面和物理空间中的空间趋势面可以很好地吻合,说明该群落的结构由一种具有强烈空间结构化特征的机制控制。对群落结构和空间趋势影响最显著的环境因素是土壤有效磷。     

Classifying habitat characteristics of wetlands using a self-organizing map

Wetlands are nutrient-rich and biodiverse ecosystems that provide habitats for various animals and plants and protect against flooding. Classification of wetlands provides information to conservation planners and resource managers for ecosystem service determination. Many ecological case studies illuminate the self-organizing map (SOM) as a robust and powerful data classification and visualization tool. In this study, we use the SOM to analyze the habitat characteristics of inland wetlands in South Korea. We surveyed the plants, benthic macroinvertebrates, and bird species inhabiting 530 nationwide wetlands for four years from 2016 to 2019. Nine environmental features, including the proportion of urban area, farmland, grassland, a forest within a 1 km buffer zone, distance from the river and nearest wetland, area, perimeter, and average slope of wetland polygons, were used to train the SOM and examine the habitat characteristics of the surveyed living components. A map size of 10 × 11 pixels was considered for SOM training, and the output data were classified into eight clusters. Based on the occurrence frequency of the surveyed species group, most species were distributed in all clusters, whereas some dominated in specific clusters. We believe that our study contributes significantly to the literature because it highlights the significance of the SOM approach to cluster wetlands with dependent habitats and provides ecological information to build sustainable wetland conservation policies.     

Self-organizing map and species abundance distribution of stream benthic macroinvertebrates in revealing community patterns in different seasons

Benthic macroinvertebrates are considered to be one of the most representative taxa in assessing the ecological integrity of aquatic ecosystems. Data for benthic macroinvertebrates collected using the Surber sampler were used for analysis at different sampling sites across different levels of pollution. Species Abundance Distribution (SAD) and Self-Organizing Map (SOM) were utilized in combination to reveal both consistency and variability in community compositions under natural and anthropogenic conditions. According to the SOM benthic macroinvertebrates were clustered in different season groups (e.g., “summer”, “autumn–winter”) at the less polluted site. SADs of the sampled communities, however, were overall stable across different seasons except the period from late spring to summer (i.e., low level of abundance for the mid-ranked species in SADs) due to heavy rainfall in the Monsoon climate. Along with increase in degree of pollution, seasonality deceased for both SOMs and SADs. In all seasons, the SAD curves were fitted to a lognormal distribution for the less polluted site while the polluted site was in accordance with a geometric series. The parameters in the SAD models were not significantly different across different seasons. Species in the highest ranks in the SADs were persistently dominant regardless of seasons, while densities of the mid-ranked species were variable in different seasons at the less and intermediately polluted sites. At the severely polluted site a few selected tolerant species showed high densities persistently and variability of densities in different seasons was minimized. Species groups clustered using the SOM also presented stronger persistence in SADs, and were feasible in addressing diverse patterns of species composition and in outlining species associations presented in different sampling sites through ordination and clustering. The combined use of SOM and SAD is highly be suitable in presenting community properties and ecological integrity in stream ecosystems in response to natural variability and anthropogenic disturbances.     

Abundance of introduced species at home predicts abundance away in herbaceous communities

Many ecosystems worldwide are dominated by introduced plant species, leading to loss of biodiversity and ecosystem function. A common but rarely tested assumption is that these plants are more abundant in introduced vs. native communities, because ecological or evolutionary-based shifts in populations underlie invasion success. Here, data for 26 herbaceous species at 39 sites, within eight countries, revealed that species abundances were similar at native (home) and introduced (away) sites - grass species were generally abundant home and away, while forbs were low in abundance, but more abundant at home. Sites with six or more of these species had similar community abundance hierarchies, suggesting that suites of introduced species are assembling similarly on different continents. Overall, we found that substantial changes to populations are not necessarily a pre-condition for invasion success and that increases in species abundance are unusual. Instead, abundance at home predicts abundance away, a potentially useful additional criterion for biosecurity programmes.     

Spatio-temporal pattern of the Chironomidae community: toward the use of non-biting midges in bioassessment programs

We employed the self-organizing map (SOM) method to investigate the spatio-temporal pattern of the Chironomidae community in the Southern Morava River basin (Serbia) and to examine to what extent the Chironomidae community is affected by environmental factors. Additionally, this study explores the problems of utilizing chironomids in bioassessment programs. The SOM analysis of the chironomid community data produced 3 groups of sites. The indicator species analysis presented indicator taxa for two groups. Twenty taxa (at species, species group and genus level), according to the Kruskal–Wallis test, showed the most pronounced differences among the temporal units. Out of 15 measured environmental parameters, one-way ANOVA pointed out that 10 significantly differ between the groups. Elevation had the most important influence on the chironomid community, also affecting other environmental parameters. According to our findings, the winter season and the periods with high water level are the main sources of natural variability. To avoid such variability and to successfully incorporate Chironomidae in bioassessment programs, we suggest exclusion of the arguable months from monitoring programs.     

Community differentiation on landscapes: drift, migration and speciation

Theories of the differentiation of ecological communities on landscapes have typically not considered evolutionary dynamics. Here we analytically study the expected differentiation among local communities in a large metacommunity, undergoing speciation, ecological drift and intercommunity dispersal, in the context of neutral theory. We demonstrate that heterogeneity in species diversity and abundance arises among communities when local communities are small and intercommunity migration is infrequent. We propose a new measure to describe community differentiation, defined as the average correlation or the average probability (C_st) that two randomly sampled individuals of the same species within local communities are from the same ancestor. The effects of driving forces (migration, mutation, and ecological drift) are incorporated into the two-level hierarchical community structure in a finite island model of neutral communities. Community differentiation can increase the effective metacommunity size or the Hubbell's fundamental species diversity in the metacommunity by a factor (1−C_st)⁻¹. Significant community differentiation arises when C_st≠0. Intercommunity migration promotes species diversity in local communities but reduce species diversity in the metacommunity. In either the finite or infinite island case, one can estimate the number of intercommunity migrants by using multiple local community datasets when the speciation is negligible in the neutral local communities, or by using the metacommunity dataset when the speciation is included in the local neutral communities. These results highlight the significance of the evolutionary mechanisms in generating heterogeneous communities in the absence of complicated ecological processes on large landscapes.     

The dynamics of ant mosaics in tropical rainforests characterized using the Self‐Organizing Map algorithm

Ants, the most abundant taxa among canopy‐dwelling animals in tropical rainforests, are mostly represented by territorially dominant arboreal ants (TDAs) whose territories are distributed in a mosaic pattern (arboreal ant mosaics). Large TDA colonies regulate insect herbivores, with implications for forestry and agronomy. What generates these mosaics in vegetal formations, which are dynamic, still needs to be better understood. So, from empirical research based on 3 Cameroonian tree species (Lophira alata, Ochnaceae; Anthocleista vogelii, Gentianaceae; and Barteria fistulosa, Passifloraceae), we used the Self‐Organizing Map (SOM, neural network) to illustrate the succession of TDAs as their host trees grow and age. The SOM separated the trees by species and by size for L. alata, which can reach 60 m in height and live several centuries. An ontogenic succession of TDAs from sapling to mature trees is shown, and some ecological traits are highlighted for certain TDAs. Also, because the SOM permits the analysis of data with many zeroes with no effect of outliers on the overall scatterplot distributions, we obtained ecological information on rare species. Finally, the SOM permitted us to show that functional groups cannot be selected at the genus level as congeneric species can have very different ecological niches, something particularly true for Crematogaster spp., which include a species specifically associated with B. fistulosa, nondominant species and TDAs. Therefore, the SOM permitted the complex relationships between TDAs and their growing host trees to be analyzed, while also providing new information on the ecological traits of the ant species involved.     

Mite (Acari) colonization of vegetated mine tailings near Sudbury,Ontario, Canada

INCO Ltd. near Sudbury, Ontario is rehabilitating nickel- and copper-mine tailings using vegetating techniques that have resulted in habitats of varying age and complexity. Mite (Acari) communities and soil quality characteristics were used to evaluate soil ecosystem development on four tailings sites, 0, 8, 20 and 40 years post rehabilitation in comparison with that on four control sites. Abundance, species richness, diversity (H) and community structure of soil mites were sensitive indicators of tailings rehabilitation and soil formation. Mite density on older and botanically diverse tailings sites was comparable to that on control sites; however, species richness of oribatids and mesostigmatics was lower than on control sites. Mite species richness and diversity on tailings were lower at less botanically diverse sites regardless of age. Similarity of tailings-mite communities to control-site communities generally increased with age; however, similarities were all less than 60% (Sorenson's quantitative index, C _N). Mite assemblages on tailings were dominated by a few colonizing species, whereas control sites had a diverse assemblage of species. Soil pH and organic matter (SOM) of rehabilitated tailings improved with age and degree of botanical complexity. Mite density and diversity was positively related to SOM particularily for the Oribatida. SOM and mites were mainly restricted to the 0–5-cm horizon.     

Assessing the functional turnover of species assemblages with tailored dissimilarity matrices

It is often suggested that community functional diversity is an appropriate predictive measure of ecosystem functioning, particularly if relevant species traits for the ecological property of interest are carefully selected. However, methods for selecting traits are often based on expert knowledge or on theoretical models of ecosystem functioning, but usually do not include explicitly developed quantitative procedures. Here we propose to construct a so‐called ‘tailored dissimilarity matrix’ between species assemblages to emphasize their functional turnover in response to some user‐defined ecological property. First, a subset of community weighted mean trait values (CWM) is selected by stepwise regression on the ecological process of interest. The selected CWM values are then replaced by the residuals of the least‐squares regressions of each single CWM on the ecological process of interest and pairwise Euclidean distances between the residual values at each sampling site are calculated. We illustrate the advantages of the tailored approach using two distinct plant and bee communities under contrasting fire regimes in temperate forests of southern Switzerland. Our results demonstrated that, unlike for the original CWM values, the tailored approach optimized the degree of functional differentiation among bee and plant species assemblages, i.e. the species functional turnover, with respect to different fire regimes.     

Spatially-Explicit Estimation of Geographical Representation in Large-Scale Species Distribution Datasets

Much ecological research relies on existing multispecies distribution datasets. Such datasets, however, can vary considerably in quality, extent, resolution or taxonomic coverage. We provide a framework for a spatially-explicit evaluation of geographical representation within large-scale species distribution datasets, using the comparison of an occurrence atlas with a range atlas dataset as a working example. Specifically, we compared occurrence maps for 3773 taxa from the widely-used Atlas Florae Europaeae (AFE) with digitised range maps for 2049 taxa of the lesser-known Atlas of North European Vascular Plants. We calculated the level of agreement at a 50-km spatial resolution using average latitudinal and longitudinal species range, and area of occupancy. Agreement in species distribution was calculated and mapped using Jaccard similarity index and a reduced major axis (RMA) regression analysis of species richness between the entire atlases (5221 taxa in total) and between co-occurring species (601 taxa). We found no difference in distribution ranges or in the area of occupancy frequency distribution, indicating that atlases were sufficiently overlapping for a valid comparison. The similarity index map showed high levels of agreement for central, western, and northern Europe. The RMA regression confirmed that geographical representation of AFE was low in areas with a sparse data recording history (e.g., Russia, Belarus and the Ukraine). For co-occurring species in south-eastern Europe, however, the Atlas of North European Vascular Plants showed remarkably higher richness estimations. Geographical representation of atlas data can be much more heterogeneous than often assumed. Level of agreement between datasets can be used to evaluate geographical representation within datasets. Merging atlases into a single dataset is worthwhile in spite of methodological differences, and helps to fill gaps in our knowledge of species distribution ranges. Species distribution dataset mergers, such as the one exemplified here, can serve as a baseline towards comprehensive species distribution datasets.     

Interpreting 16S rDNA T-RFLP Data: Application of Self-Organizing Maps and Principal Component Analysis to Describe Community Dynamics and Convergence

Interpreting the large amount of data generated by rapid profiling techniques, such as T-RFLP, DGGE, and DNA arrays, is a difficult problem facing microbial ecologists. This study compares the ability of two very different ordination methods, principal component analysis (PCA) and self-organizing map neural networks (SOMs), to analyze 16S-DNA terminal restriction-fragment length polymorphism (T-RFLP) profiles from microbial communities in glucose-fed methanogenic bioreactors during startup and changes in operational parameters. Our goal was not only to identify which samples were similar, but also to decipher community dynamics and describe specific phylotypes, i.e., phylogenetically similar organisms, that behaved similarly in different reactors. Fifteen samples were taken over 56 volume changes from each of two bioreactors inoculated from river sediment (S2) and anaerobic digester sludge (M3) and from a well-established control reactor (R1). PCA of bacterial T-RFLP profiles indicated that both the S2 and M3 communities changed rapidly during the first nine volume changes, and then became relatively stable. PCA also showed that an HRT of 8 or 6 days had no effect on either reactor communtity, while an HRT of 2 days changed community structure significantly in both reactors. The SOM clustered the terminal restriction fragments according to when each fragment was most abundant in a reactor community, resulting in four clearly discernible groups. Thirteen fragments behaved similarly in both reactors, eight of which composed a significant proportion of the microbial community as judged by the relative abundance of the fragment in the T-RFLP profiles. Six Bacteria terminal restriction fragments shared between the two communities matched cloned 16S rDNA sequences from the reactors related to Spirochaeta, Aminobacterium, Thermotoga, and Clostridium species. Convergence also occurred within the acetoclastic methanogen community, resulting in a predominance of Methanosarcina siciliae-related organisms. The results demonstrate that both PCA and SOM analysis are useful in the analysis of T-RFLP data; however, the SOM was better at resolving patterns in more complex and variable data than PCA ordination.     

自组织特征映射网络在北京松山自然保护区山地草甸数量分析中的应用

采用自组织特征映射网络(SOM)对松山自然保护区山地草甸群落进行了数量分类研究, 并用Kruskal-Wallis检验和Tukey多重比较方法分析了草甸类型的环境因子之间差异的显著性。结合完全连接法和SOM, 将松山自然保护区的山地草甸群落分为7个类型, 其群落结构、物种组成等特征明显。这7个山地草甸群落主要受海拔高度、坡度、枯枝落叶层厚度和土壤深度等环境因子的影响, 其差异极显著。生态学分析表明SOM是非常有效的植物群落分类方法, 适合于山地草甸植被的 研究。