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A rapidly expanding range of Pectinatella magnifica has been reported globally. Although many studies have been conducted to manage this invasive species, surveying methods have mainly been limited to visual observation of bryozoan colonies. For efficient monitoring and early detection of bryozoan species, we compared three different monitoring methods: visually detecting bryozoan colonies (visual observation of colonies, VOC), floating statoblasts (statoblasts in filtered water, SFW), and attached statoblasts (statoblasts attached to artificial substrate, SAS). Using these sampling methods, we compared the occurrences of P. magnifica in the littoral zones of lotic ecosystems from 117 sites in South Korea from 2015 to 2019. Occurrence frequency of P. magnifica was higher using both SFW (37.3%) and SAS (72.5%) than the traditional VOC (28.0%) method. The agreement of occurrence detection of P. magnifica between VOC, SFW and SAS was more consistent in tributaries than in main channels. Density estimations using colony (VOC) and statoblast detection methods (SFW, SAS) also showed a higher positive correlation in tributaries. To identify early P. magnifica invasion in an unidentified area, collecting statoblasts is a more efficient alternative to determine the occurrence of bryozoan species in field conditions.

Nutrias (Myocastor coypus) were imported to South Korea for farming in 1985, individuals escaped captivity and established wild populations in natural ecosystems in the late 1990s. Numerous studies have focused on their monitoring and management, however, information on the continuous movement of individuals is not available. In this study, telemetry data from field conditions were used to identify the nearest-neighbor distances of individuals in association with environmental factors, including plant type, land cover, and biological parameters. The minimum nearest-neighbor distances for the different sexes were, overall, according to the minimum distances for the same sex. Local co-occurrences of individuals, either of the same or different sex, were seasonal. Tall grasslands, followed by herbaceous vegetation, were associated with the co-occurrence of different sexes. Conversely, floating-leaved hydrophytes, followed by xeric herbaceous vegetation, were correlated with the co-occurrence of the same sex. Local female–male co-occurrences were negatively associated with male–male co-occurrences but not with female–female co-occurrences, suggesting male dominance in group formations. Movement and co-occurrence information extracted using Geo-self-organizing maps furthers our understanding of population dispersal and helps formulate management strategies for nutria populations.

In the 1970s, a theoretical ecologist deduced that a biological community tends to be unstable with increasing numbers of species and interactions. Because many species coexist and have complex interactions in natural communities, ecologists have investigated the characteristics and underlying mechanisms of species diversity. In this chapter, we review theoretical studies that investigate mechanisms of coexistence in complex biological networks. A variety of hypotheses have been proposed to explain the mechanism of species diversity, each of which is focused on specific processes of species interactions and/or community dynamics. We categorize these theoretical models, describing types of focal mechanisms as either “fitness dependence” or “fitness independence.” Despite the diverse hypotheses, no key factor of species diversity has been clarified, mainly due to difficulties in testing the hypotheses. We conclude that more interdisciplinary and integrative studies are needed to understand species diversity in depth.

Secreted proteins (secretomes) play crucial roles during bacterial pathogenesis in both plant and human hosts. The identification and characterization of secretomes in the two plant pathogens Burkholderia glumae BGR1 and B. gladioli BSR3, which cause diseases in rice such as seedling blight, panicle blight, and grain rot, are important steps to not only understand the disease-causing mechanisms but also find remedies for the diseases. Here, we identified two datasets of secretomes in B. glumae BGR1 and B. gladioli BSR3, which consist of 118 and 111 proteins, respectively, using mass spectrometry approach and literature curation. Next, we characterized the functional properties, potential secretion pathways and sequence information properties of secretomes of two plant pathogens in a comparative analysis by various computational approaches. The ratio of potential non-classically secreted proteins (NCSPs) to classically secreted proteins (CSPs) in B. glumae BGR1 was greater than that in B. gladioli BSR3. For CSPs, the putative hydrophobic regions (PHRs) which are essential for secretion process of CSPs were screened in detail at their N-terminal sequences using hidden Markov model (HMM)-based method. Total 31 pairs of homologous proteins in two bacterial secretomes were indicated based on the global alignment (identity ≥ 70%). Our results may facilitate the understanding of the species-specific features of secretomes in two plant pathogenic Burkholderia species.

Pine wilt disease is a serious pest for pine trees in many countries, especially in Asia (e.g., South Korea, Japan, China and Taiwan). In this study, we characterised dispersal patterns (e.g., direction and area) of pine wilt disease in the southern part of Korea in four-year period based on the cross-correlation function. The most likely distance of the highest infestation probability after one year is around 1.2 km from the disease source according to the distribution of pairwise distance. Subsequently, a spatially explicit model was developed in two dimensions by incorporating biological and environmental events, including influence of infested neighbourhoods, short- and long-distance dispersal, asymptomatic carriers and typhoon. The model results were in good agreement with field data when evaluated using the receiver operating characteristics and pair-correlation functions. Asymptomatic carrier played an important role in the spread of PWD. The infestation probabilities based on the spatially explicit model provided the map of spatial conformations that would be effective in addressing disease occurrence in both local and global aspects.

Species abundance distributions (SADs) are an efficient means of assessing community structure to define macroecological states and ecological integrity in responding to environmental impacts. Rank abundance distributions of benthic macroinvertebrate communities collected from streams with different levels of pollution in Korea were obtained and used to confirm water quality classification based on empirical assessment. The pollution states were broadly divided into weak and strong pollution groups according to SAD models, log-normal distribution and geometric series, respectively. A lower γ value in the log-normal distribution also differentiated weak pollution from strong pollution. Lower coefficient variation in the SAD slopes was further suitable in dividing less and slightly polluted states within the weak pollution group. Within the strong pollution group, a mixture of log-normal distribution to geometric series separated, polluted and severely polluted states. Higher correlation coefficients were observed between γ and other water quality parameters, as well as community and biological water quality indices. Similar patterns in SADs were presented between spring and fall; however, γ values of the less polluted state differed between seasons. Overall, developing SADs of related parameters is an efficient method of addressing ecological integrity that could serve as a reference system describing anthropogenic impact in streams.

Depending on the life cycles of trophic biota, cascades of biological transition are established between top-down and bottom-up relationship. In South Korea, flows of the large rivers have been regulated with large weirs and estuary barrages construction. The Eurasian otter Lutra lutra is a semi-aquatic carnivore and top predator in freshwater systems. In order to understand the aquatic community variability under the impact of river modifications, we assessed the strengths of otter populations in relation to limnological data monitored between 2014 and 2016 at 250 sites of the Nakdong River, 92 sites of the Yeongsan River and 81 sites of the Seumjin River. The habitat preference and resource use of otter populations have been measured for each of the 423 river sites by the number of spraint (= otters' feces) per 600 m. The limnological data included water quality parameters, abundances of Mollusca, Anthropoda, Annelida, Nematomorpha, and Platyhelminthes as well as abundances of benthivore, herbivore, planktivore and piscivore fish. The hybrid evolutionary algorithm HEA has been applied to model relationships of otter with water quality, the benthic invertebrates and fish to determine habitat and food preferences of otter across the three rivers. Despite the stochastic noise of the data, the modelling results addressed well relationships between spraint densities and densities of fish and macroinvertebrate communities. Threshold conditions of the IF-THEN-ELSE-models discovered by HEA revealed otter responses to changing habitats and aquatic communities across the river sites but also between the three rivers. In addition, sensitivity analysis suggested possible relationships between the otter and aquatic communities. According to the results of sensitivity analysis, otter populations seemed not to control the piscivorous fish densities by showing a negative relationship, but preyed preferably on benthivorous and planktivorous fishes. Water quality parameters and benthic macro-invertebrates correlated well with otter populations suggesting favorable habitat and food-web structures. There is evidence that river flow regulation measures have a negative impact on otter populations, and that growing otter populations will alter future food web structures of the three rivers.

Point detection (e.g., the centroid of the body) of species has been conducted in numerous studies. However, line detection (i.e., the line body shape) of elongated species has rarely been investigated under stressful conditions. We analyzed the line movements of an Oligochaeta Lumbriculus variegatus in response to treatments with a toxic chemical, copper sulfate, at low concentrations (0.01 mg/L and 0.1 mg/L). The automatic line-tracking system was devised to identify the movement of body segments (body length) and the movements of segments (i.e., the speed and angles between segments) were recorded before and after treatment. Total body length was shortened from 31.22 (&plusmn, 5.18) mm to 20.91 (&plusmn, 4.65) mm after the 0.1 mg/L treatment. The Shannon entropy index decreased from 0.44 (&plusmn, 0.1) to 0.28 (&plusmn, 0.08) after treatment. On the other hand, the body and movement segments did not significantly change after the 0.01 mg/L treatment. Sequential movements of test organisms were further analyzed with a recurrent self-organizing map (RSOM) to determine the pattern of time-series line movements. The RSOM made it feasible to classify sequential behaviors of indicator organisms and identify various continuous body movements under stressful conditions.

We propose an automatic pest identification method suitable for large scale, long term monitoring for mobile or embedded devices in situ with less computational cost. A procedure of segmentation and image separation was devised to identify common greenhouse pests, whiteflies, aphid and thrips. Initially, the watershed algorithm was used to segment insects from the background (i.e., sticky trap) images. Color feature of the insects were subsequently extracted by Mahalanobis distance for identification of pest species. Accuracy and computational costs were evaluated across different image resolutions. The correlation of determination (R-2) between the proposed identification scheme and manual identification were high, showing 0.934 for whitefly, 0.925 for thrips, and 0.945 for aphids even with low resolution images. Comparing with the conventional methods, pests were efficiently identified with low computational cost. Optimal image resolution for species identification regarding long-term survey was discussed in practical aspect with less computational complexity. (C) 2014 Elsevier B.V. All rights reserved.

We observed zebrafish ( Danio rerio ) movement continuously in response to conflicting stimuli (food and predator). Linear parameters (i.e., speed, acceleration, locomotory rate) were lower when food was provided to the arena, whereas angular parameters (i.e., turning rate) decreased in response to predator. Self-organizing map (SOM) trained with parameters revealed that stop duration, stop number, and x - and y -coordinates played major roles in determining variations in movement. Behavior patterns (e.g., rightward movement to food) were identifiable in different clusters in association with parameters responding to sources and order of stimuli. Distribution shapes obtained from the SOM were suitable for identifying responses of test organisms. Acceleration intermittency in movement data in different distribution shapes addressed data structure variation caused by sources and order of stimuli. The order of stimuli was influential in determining the behavioral states in the following courses of stimulus provision (i.e., addition of secondary stimulus and removal of the initial stimulus). Predator-first stimuli caused stronger response behaviors than food-first stimuli. Computational analysis of response behavior to stimuli could be an additional indicator of stress in animals.

Water velocity is one of the most important abiotic factors influencing the survival of aquatic insects in rivers and streams. The unidirectional water flow shaping their habitat and characteristically dividing it into alternating zones of high and low water velocity (riffles and pools) also imposes on them the special necessity to adapt to continual downstream drift. Here, we analyze an individual-based eco-evolutionary model parameterized with field data, and show how species adapted to riffles and pools, respectively, emerge if three basic processes are considered: density-dependent local competition, drifting, and adult flight. We also find that evolutionary branching in velocity adaptation is accompanied by an differentiation of drifting behavior. Generally, individuals drift either frequently and for only a short duration, or infrequently and for a longer duration. While riffle and pool species each exhibit both drifting strategies, a third species that can stably establish at intermediate water velocities (runs) exhibits exclusively the former. As the run species is therefore particularly susceptible to drift loss, long-range adult flight turns out to be crucial for its persistence. These insights highlight the ability of process-based eco-evolutionary models to generate testable hypotheses and stimulate empirical research.

Variance and invariance residing in multi-individual movement of zebrafish under chemical stress were addressed concurrently before and after treatment with formaldehyde. Variance was observed in movement parameters. Without treatment, linear speed was highest in groups with two-individuals, followed by those with one- and four-individuals. Following treatment, linear speed decreased significantly in groups with one- and two-individuals, but not in those with four-individuals. Inter-distances between two-individuals in groups with two-individuals decreased markedly after treatment, whereas inter-distances between two randomly selected individuals in the groups with four-individuals were not affected. The zero- and peak-values in the time-lag of autocorrelation of inter-distances decreased further after treatment in groups of two individuals relative to those in groups of four individuals. However, the empirical transition probability matrix between predefined behavioral patterns remained invariant among groups with different numbers of individuals as well as before and after treatment. Variance and invariance in multi-individual behaviors are suitable in expressing complex behaviors under chemical stress and could be a reference for detecting contaminants in the environment using indicator species.

Zebrafish behavior was monitored based on continuous movement tracking of individuals in response to two conflicting stimuli, food and predators. The shape parameters describing positional distribution along the x -axis (mean, standard deviation (SD) and skewness) were differentiated according to source and time order (i.e., food first or predator first) of stimuli. Behavioral states were expressed by primary (mean and SD of x -coordinates) and secondary (skewness of x -coordinates) affectedness, and were addressed by specificity of individual response to stimuli. Even after adjusting spatial location and movement range in primary affectedness, secondary affectedness still occurred owing to a residual effect of the stimulus according to asymmetry of the positional distribution (e.g., away from the predator). Predator (as initial or secondary stimulus) produced stronger affectedness than food given as initial stimulus. Even after removal of initial stimulus, it still influenced affectedness of test organisms in the subsequent phases, implying memory effect of initial stimulus. Quantification of stress based on expression of behavioral state changes is feasible and could provide insight for monitoring based on indicator organism behavior under stressful conditions.

The golden apple snail (Pomacea canaliculata) is a detrimental invasive pest of rice in Asia. Temperature is a critical factor to determine their invasiveness including the behavior and distribution of snails. In this study, the behavioral responses of golden apple snails acclimated to different water temperatures (15 °C, 20 °C, 25 °C, and 30 °C) were examined based on Markov chain analysis, Shannon entropy, and Random forest modeling. Markov chains revealed that most snails maintained their previous behavior at low temperatures, while behavior transition tended to be higher at high temperature. Shannon entropy was also dependent on temperature (low at low temperature and high at high temperature), indicating that snails maintained their previous behavior for a long time at low temperature regardless having motion and motionless behavior, whereas they changed continuously their behavioral types at high temperature. The Random forest model showed that Shannon entropy at low temperature was influenced by crawling at the bottom or at side of the aquarium (motion behavior), while clinging to the side of the aquarium or clinging to the side of the aquarium with stretching out antennae were important behaviors determining Shannon entropy at high temperature. Our results showed that snails controlled their behavior to reduce their thermal stress and maintain a stable internal state in harsh environment. With this mechanism, they are able to overwinter in the open fields with low temperature in Korea, resulting in the increase of potential damage in agricultural ecosystems. Therefore, further study on the development of adequate management system is required to avoid their invasiveness to open water systems and ecological impacts. Finally, Shannon entropy, Markov chain, and Random forest model are useful computation methods to quantify the effects of temperature changes on the behavior of golden apple snails.

Online monitoring systems provided a significant evidence for feasibility of the stepwise behavioral response model in detecting the effects of organophosphorus pesticides on movements of medaka (Oryzias latipes), being able to determine the state of indicator organisms, "no effect," "stimulation," "acclimation," "adjustment (readjustment)," and "toxic effect." Though the stepwise behavioral response model postulated that an organism displays a time-dependent sequence of compensatory stepwise behavioral response during exposure to pollutants above their respective thresholds of resistance, it was still a conceptual model based on tendency only in analysis. In this study, the phenomenon of bacterial persistence was used to interpret the relationship between the stepwise behavioral response model and the environmental stress caused by both exposure time and different treatments. Quantitative measurements of the stepwise behavioral response model led to a simple mathematical description of the threshold switch, which evaluated the effects of environmental stress on behavioral responses to decide the tendency. The adjustment ability correlated to "persisters (p)" is very important for test individuals to overcome the "threshold" from the outside environmental stress. The computational modeling results suggested that "persister (p)," as described in the general equations of bacterial persistence model in changing environments, illustrated behavior acclimation and adjustment (or readjustment) clearly. Consequently, the persistence parameter, p, was critical in addressing for medaka to be adapted to fluctuating environments under different environmental stress.

An autonomous robot (Cabbot) was built to control insect pests in a plastic greenhouse and utilized to sample adult whiteflies (Bemisia tabaci) on paprika plants (Capsicum annum var. angulosum). To accomplish this, a sampling device consisting of an air compressor for pest agitation, a sticky trap (100 mm x 150 mm) and an image processing system for pest identification were installed on the Cabbot The sampling precision of the Cabbot (D = 0.16) was higher than that of the sticky trap (D = 0.19) when sampling of adult white flies was conducted in caged pots. The Cabbot could also collect a substantial number of individuals within a short duration (3 minutes). The collection efficiency (i.e., percent of samples collected at least n (I in this case) individuals to the total sample number) of insects by the Cabbot was markedly high at low population size, showing approximately 30% when the population size was

We conducted an inhalation toxicity test on the alternative animal model, Drosophila melanogaster, to investigate potential hazards of indoor air pollution. The inhalation toxicity of toluene and formaldehyde was investigated using comprehensive transcriptomics and computational behavior analyses. The ingenuity pathway analysis (IPA) based on microarray data suggests the involvement of pathways related to immune response, stress response, and metabolism in formaldehyde and toluene exposure based on hub molecules. We conducted a toxicity test using mutants of the representative genes in these pathways to explore the toxicological consequences of alterations of these pathways. Furthermore, extensive computational behavior analysis showed that exposure to either toluene or formaldehyde reduced most of the behavioral parameters of both wild-type and mutants. Interestingly, behavioral alteration caused by toluene or formaldehyde exposure was most severe in the p38b mutant, suggesting that the defects in the p38 pathway underlie behavioral alteration. Overall, the results indicate that exposure to toluene and formaldehyde via inhalation causes severe toxicity in Drosophila, by inducing significant alterations in gene expression and behavior, suggesting that Drosophila can be used as a potential alternative model in inhalation toxicity screening.

Body-mass index, abbreviated as BMI and given by M/H2 with the mass M and the height H, has been widely used as a useful proxy to measure a general health status of a human individual. We generalise BMI in the form of M/H p and pursue to answer the question of the value of p for populations of animal species including human. We compare values of p for several different datasets for human populations with the ones obtained for other animal populations of fish, whales, and land mammals. All animal populations but humans analyzed in our work are shown to have p ≈ 3 unanimously. In contrast, human populations are different: As young infants grow to become toddlers and keep growing, the sudden change of p is observed at about one year after birth. Infants younger than one year old exhibit significantly larger value of p than two, while children between one and five years old show p ≈ 2, sharply different from other animal species. The observation implies the importance of the upright posture of human individuals. We also propose a simple mechanical model for a human body and suggest that standing and walking upright should put a clear division between bipedal human (p ≈ 2) and other animals (p ≈ 3).

Even though subsampling can minimize the research efforts needed to deal with numerous specimens, the accuracy of bioassessment and community analysis can be affected according to subsampled individuals. In this study, the effects of subsampling on community indices, the parameters of the species abundance distributions (SADs) and ordination based on communities were evaluated using benthic macroinvertebrates from three different stream pollution levels: less, intermediately, and heavily polluted. The values of the community indices and the parameters of the SADs were clearly different according to the differences in the pollution levels, regardless of subsampling efforts. Detrended correspondence analysis was applied to characterize similarities between the subsamples with different individuals based on community indices and the parameters of the SADs. A self-organizing map was used to characterize the community similarities and properties of the subsamples, differentiating the subsamples from their original datasets. Finally, our results revealed that the effects of subsampling were reflected differently in different indices, parameters, and ordinations of communities at different pollution levels, suggesting that subsample size should be determined differently at different pollution levels depending on the objectives of the study. However, if only one subsample size is to be used for simplicity and to reduce the cost of aquatic ecosystem health monitoring and assessment, then we recommend at least 400 individuals as the subsample size. At least 800 individuals should be subsampled for biodiversity studies.

The study utilized the Electroantennogram (EAG) technique as biological acoustic stimulus to Liriomyza trifolii (Diptera: Agromyzidae). Pure tone sounds and courtship songs were tested in the excised head of test specimens. Significant EAG stimuli were detected when released at 20 Hz pure tone sound together with the courtship song having long pulse train patterns of Cryptonevra inquilina and Lipara japonica (Chloropidae). For their biological effectiveness, movement behaviors were analyzed with stimulus taken from EAG recordings. No attraction effect was observed even with and without acoustic stimulus. However, movement activities were transformed by information entropy and observed to be clearly different when selected acoustic stimuli were broadcast. The combination of response to EAG and behavioral observation would be useful to determine effective movement modification through acoustic stimulus in insect pests that are necessary toward the development of an acoustic pest management tool in agriculture.

Benthic communities of macroinvertebrates, algae, and microorganisms were concurrently collected using a Surber sampler (30 × 30 m 2 ; 300 μm mesh), brush (5 × 5 cm 2 ), and syringe (100 mL; Denaturing Gradient Gel Electrophoresis), respectively, to determine the ecological integrity of streams with different levels of pollution. Macroinvertebrates provided a clearer representation of the gradient of pollution, while a broader scope of species distribution was observed for algae and microorganisms, including sites severely polluted with heavy metals. Species associations among different taxa were presented on the Self-Organizing Map (SOM) and Nonmetric Multidimensional Scaling (NMDS) based on environmental factors. After screening, indicator species visualized on the SOM represented a wider range of environmental impacts and were more illustrative with benthic macroinvertebrates in least polluted sites. In contrast NMDS presented species more closely associated with overall variance of communities with severe pollution, mainly in microorganisms and algae. Multi-taxa community analysis using SOM and NMDS in combination would provide a comprehensive assessment for addressing ecological integrity in streams.

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.

As a characteristic in bacterial colony, persistence model described the dynamics of two subpopulations (normal (n) and persister (p)). In order to illustrate the switch of "Threshold" in the stepwise behavior responses of organisms, it is hypothesized that total behavior (Bt) of organisms consists of two types in behavior tendency, intoxication (Bp) and normal/recovery behavior (Bn). Both Bp and Bn could be concurrently affected by environmental stress E, and behavior response modes (M) are decided by the relationship between E and toxicity threshold of test organisms (Ti). The results suggested stress constant λ was decided by the constant rates gnE,gpE, an and ap. Due to different stress constant λ, the behavior responses of indicators showed great difference in different M, which included 'safe mode' (Ms), 'acclimation mode' (Mac), 'adjustment mode' (Maj) and 'toxic effect' (Mte). Usually, Bt during Ms could maintain around 0.8, and Mte would happen once it is lower than 0.2. According to the relationship between Bt values and E changes in 7 Majs, behavior persistence relying on adjustment could reflect the behavior homeostasis of organisms under environmental stress and be regarded as a threshold switch for the stepwise behavior responses. The mathematical analysis of behavior persistence allows making a quantitative prediction on environment assessment that would promote the emergence of persistence, as well as evaluating its ecological implications.

Water quality maintenance should be considered from an ecological perspective since water is a substrate ingredient in the biogeochemical cycle and is closely linked with ecosystem functioning and services. Addressing the status of live organisms in aquatic ecosystems is a critical issue for appropriate prediction and water quality management. Recently, genetic changes in biological organisms have garnered more attention due to their in-depth expression of environmental stress on aquatic ecosystems in an integrative manner. We demonstrate that genetic diversity would adaptively respond to environmental constraints in this study. We applied a self-organizing map (SOM) to characterize complex Amplified Fragment Length Polymorphisms (AFLP) of aquatic insects in six streams in Japan with natural and anthropogenic variability. After SOM training, the loci compositions of aquatic insects effectively responded to environmental selection pressure. To measure how important the role of loci compositions was in the population division, we altered the AFLP data by flipping the existence of given loci individual by individual. Subsequently we recognized the cluster change of the individuals with altered data using the trained SOM. Based on SOM recognition of these altered data, we determined the outlier loci (over 90th percentile) that showed drastic changes in their belonging clusters (D). Subsequently environmental responsiveness (Ek’) was also calculated to address relationships with outliers in different species. Outlier loci were sensitive to slightly polluted conditions including Chl-a, NH4-N, NOX-N, PO4-P, and SS, and the food material, epilithon. Natural environmental factors such as altitude and sediment additionally showed relationships with outliers in somewhat lower levels. Poly-loci like responsiveness was detected in adapting to environmental constraints. SOM training followed by recognition shed light on developing algorithms de novo to characterize loci information without a priori knowledge of population genetics.

We examined the occurrence patterns of insect pests in paddy fields using a self-organizing map (SOM) and the influence of meteorological factors on these patterns. Data for nine insect pests and three meteorological factors were collected on the Korean nationwide scale from 1992 to 2008. The SOM analysis was based on annual mean densities of each pest species in eight regions. We classified the samples into five clusters (I–V) to display the spatial and temporal changes in the insect pests' occurrences. Clusters I and II included samples collected before 2000; clusters III and IV included samples collected after 2000. The density of Scotinophara lurida was in the lowest range in clusters I and II, and Lissorhoptrus oryzophilus exhibited higher densities in clusters III and IV. Seasonal temperature, humidity, and precipitation were significantly different among clusters, indicating that these factors play a key role in population dynamics and the occurrence of insect pests. The occurrence area of the insect pests was expected to increase with elevated air temperature influenced by global warming based on the IPCC (Intergovernmental Panel on Climate Change) A1B scenario. This study suggests that gathering data on temporal and spatial variations in pest populations and changes in occurrence rate under the effects of global warming can provide insight into the factors governing the population-environment relationships, as well as the information needed for future pest management.

The locomotion behavior of Caenorhabditis elegans has been studied extensively to understand the respective roles of neural control and biomechanics as well as the interaction between them. In the present study, we suggest a new approach to characterize the temporal patterns in the swimming behavior of the organism. The approach is based on the branching length similarity (BLS) entropy defined on a simple branching network consisting of a single node and branches. The organism’s swimming activity is recorded using a charge-coupled device (CCD) camera for 3 h at a rate of 4 frames per second. In each frame, we place 13 points as nodes, those points being distributed at equal intervals along the organism’s length. Thus, the organism is represented by 13 nodes and 12 edges between nodes. By using the nodes and edges, we construct two simple networks. One is formed by connecting the center point to all other points, and the other is generated from the angles between edges. The BLS entropy values are calculated as SL for the former network and Sθ for the latter. We investigate the distributions of the SL and the Sθ values in the phase space of SL — Sθ and compare those with the values obtained from a simulated C. elegans generated by using randomly-moving chained particles along a certain angle. The comparison revealed distinctive features of the movement patterns of C. elegans during swimming activity. In addition, we briefly discuss the application of our method to bio-monitoring systems to capture behavioral changes of test organisms before and after chemical treatment at low concentrations.

Environmental perturbations occur in ecosystems as the result of disturbance, which is closely related to ecosystem stability and resilience. To understand how perturbations can affect ecosystems, we constructed a spatially explicit lattice model to simulate the integrative predator–prey–grass relationships. In this model, a predator (or prey) gives birth to offspring, according to a specific birth probability, when it is able to feed on prey (or grass). When a predator or prey animal was initially introduced or newly born, its health state was set at a given high value. This state decreased by 1 with each time step. When the state of an animal decreased to zero, the animal was considered dead and was removed from the system. In this model, the perturbation was defined as the sudden death of some portion of the population. The heterogeneous landscape was characterized by a parameter, H, which controlled the degree of heterogeneity. When H ≥ 0.6, the predator population size was positively influenced by the perturbation. However, the perturbation had little effect upon the population sizes of prey or grass, regardless of the value of H.

Response behaviors of medaka (Oryzias latipes) were computationally analyzed after exposure to a temperature increase and toxic treatments. The temperature was increased from 25 °C to 35 °C while individual organisms of medaka were treated with copper at a low concentration (1.0 mg/L). Movement tracks were continuously recorded for 6 hours before and for 6 hours after the treatments. Parameters (e.g., speed and stop number) and entropy values (e.g., Shannon entropy and Reyni entropy) characterizing the movement tracks were calculated before and after the treatments. After the temperature increase, parameters and entropy values remained in the same range. Test organisms appeared to adapt to a gradual temperature increase to maintain the same behavioral state. After the treatments with copper, however, parameters such as speed, acceleration and stop number substantially decreased while the turning rate increased. Entropy values such as Shannon and Reyni entropies were lower while Simpson index was higher after the chemical treatments. Complexity residing in the movement tracks appeared to decrease after the treatment with a chemical. According to the self-organizing map results, entropy values were associated with speed and acceleration. The parameter (speed) and the entropy value (Shannon entropy) changed in different time periods after the chemical treatment. The periodicity in entropy was not different between the early and the late periods after the treatments according to fast Fourier transform. The feasibility of computational monitoring and the structure property in the behavioral data were further discussed regarding ecological assessment of environmental stressors.

In recent decades, the behavior of Caenorhabditis elegans (C. elegans) has been extensively studied to understand the respective roles of neural control and biomechanics. Thus far, however, only a few studies on the simulation modeling of C. elegans swimming behavior have been conducted because it is mathematically difficult to describe its complicated behavior. In this study, we built two hidden Markov models (HMMs), corresponding to the movements of C. elegans in a controlled environment with no chemical treatment and in a formaldehyde-treated environment (0.1 ppm), respectively. The movement was characterized by a series of shape patterns of the organism, taken every 0.25 s for 40 min. All shape patterns were quantified by branch length similarity (BLS) entropy and classified into seven patterns by using the self-organizing map (SOM) and the k-means clustering algorithm. The HMM coupled with the SOM was successful in accurately explaining the organism’s behavior. In addition, we briefly discussed the possibility of using the HMM together with BLS entropy to develop bio-monitoring systems for real-time applications to determine water quality.

The stepwise behavioral responses (SBR) of medaka (Oryzias latipes) were continuously monitored by an online monitoring system (OMS) after exposure at low concentrations of Trichlorfon (T), Parathion (P) and Malathion (M). The Self-organizing map (SOM) was used to train behavior strength (BS) measured by OMS according to different chemicals and concentrations. Behavioral difference was observed according to different levels of concentrations, and some differences were observed according to chemicals and concentrations. Differences in photo- and scotophases were also observed in control and treatments with lower concentrations and short exposure at high concentrations. Meanwhile, the changes of BS demonstrated SBR model correctly. SBR of medaka would be useful for presenting behavioral states in responses to treatments. The SBR model supported and developed the Stepwise Stress Model (SSM), which included No effect, Stimulation, Acclimation, Adjustment (Readjustment) and Toxic effect.

Two computational methods were applied to classification of movement patterns of zebrafish (Danio rerio) to elucidate Markov processes in behavioral changes before and after treatment of formaldehyde (0.1 mg/L) in semi-natural conditions. The complex data of the movement tracks were initially classified by the Self-organizing map (SOM) to present different behavioral states of test individuals. Transition probabilities between behavioral states were further evaluated to fit Markov processes by using the hidden Markov model (HMM). Emission transition probability was also obtained from the observed variables (i.e., speed) for training with the HMM. Experimental transition and emission probability matrices were successfully estimated with the HMM for recognizing sequences of behavioral states with accuracy rates in acceptable ranges at central and boundary zones before (77.3–81.2%) and after (70.1–76.5%) treatment. A heuristic algorithm and a Markov model were efficiently combined to analyze movement patterns and could be a means of in situ behavioral monitoring tool.

Movement of different strains in Drosophila melanogaster was continuously observed by using computer interfacing techniques and was analyzed by permutation entropy (PE) after exposure to toxic chemicals, toluene (0.1 mg/m3) and formaldehyde (0.01 mg/m3). The PE values based on one-dimensional time series position (vertical) data were variable according to internal constraint (i.e. strains) and accordingly increased in response to external constraint (i.e. chemicals) by reflecting diversity in movement patterns from both normal and intoxicated states. Cross-correlation function revealed temporal associations between the PE values and between the component movement patterns in different chemicals and strains through the period of intoxication. The entropy based on the order of position data could be a useful means for complexity measure in behavioral changes and for monitoring the impact of stressors in environment.

Revealing biological responses of organisms in responding to environmental stressors is the critical issue in contemporary ecological sciences. Markov processes in behavioral data were unraveled by utilizing the hidden Markov model (HMM). Individual organisms of daphnia (Daphnia magna) and zebrafish (Danio rerio) were exposed to diazinon at low concentrations. The transition probability matrix (TPM) and the emission probability matrix (EPM) were accordingly estimated by training with the HMM and were verified before and after the treatments with 10-6 tolerance in 103 iterations. Structured property in behavioral changes was accordingly revealed to characterize dynamic processes in movement patterns. Parameters and sequences produced through the HMM training could be a suitable means of monitoring toxic chemicals in environment.

The genotypic and phenotypic processes were incorporated into one system in the gene–individual–population relationships under the framework of Individual based models (IBMs). The gene types addressing different degrees of metabolic efficiency and toxin susceptibility were provided as attributes in the individuals. Subsequently ecological processes such as food competition and movement were allowed concurrently on the 2-D space to determine the suitable species adapted to the system. The integrative gene–individual–population model accordingly responded to gene exchanges between the neighboring individuals through conjugation. At a substantially low level of gene exchange, system heterogeneity increased to produce high levels of eco-exergy, being presented by species diversity and total population size in the system. The issues related to genetic and ecological effects in the integrative gene–individual–population relationships were further discussed.

This study investigated the relationships of three major aquatic assemblages (diatom, macro- invertebrate, and fish) and environmental variables, including sub-basin, hydrology, land cover, and water quality variables on multiple scales. Samples were collected at 720 sampling sites on the Korean nationwide scale. Geological variables, including altitude and slope, showed a strong positive correlation with propor- tions of forest in land cover types and cobbles in substrates, while they were negatively correlated with water quality variables, including conductivity and total phosphorus. Considering the concordance of the different assemblages, species richness of fish and macroinvertebrates displayed significant correlation, and diatoms were significantly correlated with fish. However, diatoms did not show significant correlation with macro- invertebrates. Altitude and slope showed significant correlation with all biological variables of the three as- semblages. Macroinvertebrates and fish showed positive relations with large substrate sizes. Indices of diatoms and macroinvertebrates well reflected the perturbation of water quality variables. However, fish indices showed a relatively low association with water quality variables, compared with those of diatoms and macro- invertebrates. These patterns were also confirmed by the ordination and prediction of biological indices with environmental variables through the learning process of a self-organizing map as well as random forest. Overall, our study supports the concept of multi-scale habitat filters and functional organization in streams, and is consistent with the recommended use of multiple biological indices with more than one assemblage for the assessment of the biotic integrity of aquatic ecosystems.

E-mail: gjjoo@pusan.ac.krTel: +82-51-510-2258This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://cre-ativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

movement states. Subsequently, a hidden Markov model (HMM) was applied to sequence the movement states identified by the SOM. The transition probability matrix (TPM) and the emission probability matrix (EPM) were eciently estimated by using the HMM. Markov processes were separately observed before and after the treatments, and the changes in the response behaviors of the indicator organisms were demonstrated under stressful conditions.

Chironomid assemblages collected from seven different streams in South Korea were investigated. The subfamily composition of chironomids was clearly differentiated accross different levels of organic pollu- tion. Species abundance distributions (SADs) of chironomid communities were compared with the total macro- invertebrate communities across different levels of pollution. The number of species with minimal range of abundance was lower in SADs for chironomid communities compared with total communities. The log normal distribution was accepted for both total and chironomid communities, while the geometric series was relatively more suitable for chironomids and the log series were more fitted to total communities. The a values in the log normal distribution increased in chironomid communities across different levels of pollution, while c values increased at the polluted sites for both chironomid and total communities. In the Power law analysis, the parameter decreased in chironomid communities. The dominance decay model was more fitted to chiro- nomid communities in clean conditions while random fraction and assortment models were more suitable for the polluted sites.

A Gram-negative, rod-shaped bacterium, designated strain YC6274(T), was isolated from a stream (Sasang) carrying wastewater polluted with heavy metals in Busan, Korea. Growth was observed at 10-35 degrees C (optimum, 30 degrees C) and pH 6.0-9.5 (optimum, pH 7.5-8.0). Comparative 16S rRNA gene sequence analyses showed that the strain was most closely related to Flavobacterium cucumis R2A45-3(T) (96.6 % similarity), F. aquatile ATCC 11947(T) (93.7 %), F. croceum EMB47(T) (93.3 %), F. indicum GPTSA100-9(T) (93.3 %) and F. terrigena DS-20(T) (93.2 %). Sequence similarities with strains of other Flavobacterium species with validly published names were lower than 93.0 %. Phylogenetic analysis based on the 16S rRNA gene sequence showed that strain YC6274(T) formed a distinct phyletic lineage within the genus Flavobacterium. The predominant fatty acids of strain YC6274(T) were iso-C(15 : 0), iso-C(16 : 0), iso-C(15 : 1) G, iso-C(17 : 0) 3-OH, iso-C(16 : 0) 3-OH and iso-C(15 : 0) 3-OH. The G+C content of the genomic DNA was 32.5 mol% and the major quinone was MK-6. On the basis of phenotypic, chemotaxonomic and molecular data, it is clear that strain YC6274(T) represents a novel species within the genus Flavobacterium, for which the name Flavobacterium sasangense sp. nov. is proposed. The type strain is YC6274(T) (=KCTC 22246(T) =DSM 21067(T)).

Accurate forecasting of pest density is essential for effective pest management. In this study, a simple image processing system that automatically estimated the density of whiteflies on sticky traps was developed. The estimated densities of samples in a laboratory and a greenhouse were in accordance with the actual values. The detection system was especially efficient when the whitefly densities were at medium to high levels, however, the development of identification algorithms and more appropriate collection methods may improve the image processing system. Taken together, the results of this study indicate that the image processing system described here may be useful for evaluating the population dynamics.

Benthic macroinvertebrates collected at seven different streams displaying different pollution levels were used to investigate species abundance patterns in polluted streams. Community response to disturbances in streams was analysed using species abundance distribution (SAD) for benthic macroinvertebrates across different levels of pollution. The slopes of rank abundance were characteristically steeper with decreasing species richness at the polluted sample sites, while the slopes were less steep with higher species richness at the clean or slightly disturbed sample sites. The SADs were broadly fitted to the log normal distribution in benthic macroinvertebrate communities across different levels of pollution. A geometric series was partly accepted for the communities at the severely polluted sites where new species could not be readily introduced. A power law was applied to the SADs, and the parameters reflected the states pertaining to the sample sites. The SADs were also efficient in revealing ecological state of communities where physico-chemical indicators could not be easily differentiated for stressful conditions in streams.

The water quality and phytoplankton communities in the Daechung Reservoir, Korea, were monitored from summer to autumn in 1999, 2001, and 2003. The temporal patterns of cyanobacterial blooming caused by Microcystis were then elucidated using a combination of two artificial neural networks (ANNs): self-organizing map (SOM) and multilayer perceptron (MLP). The SOM was initially used to cluster the phytoplankton communities, then the MLP was applied to identify the major environmental factors causing the abundance of phytoplankton in the clustered communities. The SOM divided the phytoplankton communities into four clusters based on their algal composition (Cyanophyceae, Chlorophyceae, Bacillariophyceae, and others). In particular, cluster II was mostly composed of sampling times in August and September, and closely matched the period of severe cyanobacterial bloom dominated by Cyanophyceae. Meanwhile, cluster IV was mainly composed of the samples collected in the other periods, covering April, May, June, and October, and was mostly dominated by Bacillariophyceae. Cyanophyceae was the main component of the total algae, and its variation among the clusters showed a similar pattern to that of the changes in the chlorophyll-a concentration. Based on the MLP model, the water temperature, total particulate nitrogen, daily irradiance, and total nitrogen were highlighted as the four most important environmental variables predicting cyanobacterial abundance, yet quite different environmental variables were found to affect the chlorophyll-a concentration. The usage of sampled data and analyses by ANNs are also discussed with reference to an early alert system for algal bloom.

Invasion processes of pest insects in forest were elucidated through simulation by the lattice model. Population size was regarded as the state of the model, and the local rules governing movement and reproduction were accordingly incorporated with the weights for calculating the state. The constraints of population growth with the upper limit and the long distance movement contributed to rapid increase in the speed of range expansion. The spatially explicit model was feasible in integrating local development of population and provided a comprehensive illustration on the range expansion of pest populations on the lattice space. The results from the models were comparable with the field data. Advantages in the usage of the lattice model were further discussed in managing populations with high densities.

The two dimensional movement tracks of STAT92E 06346 mutant and two control strains ( Oregon red ( OR ) and TM3 ) of Drosophila melonogaster were continuously observed with image processors. Subsequently Self-Organizing Map (SOM) was implemented to patterning of responding behaviors of the tested specimens. Movement behaviors were accordingly revealed in different strains and sex. SOM showed difference in degree of grouping in behaviors in different genotypes. Visualization through SOM further characterized the clusters of specimens with the variables regarding activities and spatial information. The study demonstrated that techniques in data mining in artificial neural networks could be a useful tool for analyzing complex behaviors induced by changes in genetic information.

This study demonstrated prediction of breeding success of Black-tailed Gulls in relation to the selected environmental factors through evaluation of relative importance in determining breeding success. The data were obtained from the 258 selected and 120 non-selected sites for breeding of the gulls during the breeding periods in 2002–2003. Breeding success at the selected sites, and environmental factors such as vegetation cover, vegetation height, rock cover, nest-wall, nearest distance between neighbors and slope, were measured at each sampling site. For predicting breeding success of Black-tailed Gulls, we used two different artificial neural networks in this study: self-organizing map (SOM) and multilayer perceptron (MLP). SOM was used to classify the sampling sites based on the environmental factors, whereas MLP was implemented to prediction of breeding success of the gulls at the non-selected sites based on environmental conditions. In our results, SOM discriminated clearly the sampling sites and presented differences in environmental factors between the selected and non-selected sites. Subsequently, the breeding success was accordingly predicted by MLP. Nest-wall was considered the most important environmental factor in determining survival status of the gulls. An increase in nest-wall and vegetation cover was required to support breeding of the specimens for managing the habitats for Black-tailed Gulls.

Movement behavior of Chironomus samoensis larvae was observed in response to the treatments of carbofuran, an anticholinesterase insecticide, at a low concentration (0.1 mg/l) in semi-natural conditions. Two typical movement patterns were selected before and after the treatments, and the variables characterizing movement tracks in two dimensions were analyzed by discrete wavelet transform (DWT) with Daubechie's 4 functions. The variables were selected based on the feature coefficients of DWT and were subsequently used as input for training with the multi-layer perceptron network. The trained network efficiently detected changes in movement patterns before and after the treatments. We demonstrated that the combined use of the wavelets and artificial neural networks would be a useful tool for automatic behavioral monitoring for water quality assessment.

The dynamic behavior of prey-flock in response to predator's attack was investigated by using molecular dynamics (MD) simulations in a two-dimensional (2D) continuum model. By locally applying interactive forces between prey individuals (e.g. attraction, repulsion, and alignment), a coherently moving state in the same direction was obtained among individuals in prey-flock. When a single predator was introduced to the prey population, the prey-flock was correspondingly deformed by the predator's continuous attacks towards the prey-flock's center. In response to the predator's attack, three regimes in the flock size (compression (Regime I), expansion (Regime II), compression (Regime III)) were revealed if the predator's attack speed (kappa) was comparatively low to the escape speed of prey-flock. If noise was added to the predator's attacking course, a higher degree of variation was observed in the patterns of compression and expansion in the prey-flock size. However, the scaling behavior in the changes in prey-flock size was present in different levels of noise with the increase in predation risk (R) when kappa takes an appropriately low value. During the procedure of escaping, order breaking in alignment (phi) of prey population was observed, while the degree of alignment was dependent upon the changes in parameters of kappa and R.

The factors affecting nest-site selection and breeding success of Black-tailed Gulls (Larus crassirostris) were studied in Hongdo Island in Korea during the breeding seasons in 2002 and 2003. Two analyzing methods, Principal Component Analysis (PCA) and Self-Organizing Map (SOM) – an unsupervised learning method in artificial neural networks, were applied to multivariable datasets characterizing nest-sites of the gulls. Both methods provided insights on the major trends in nest-site selection by Black-tailed Gulls. PCA showed that the variables regarding the “wall” effect such as rock cover and nest-wall (positively), and the nearest distance between neighbors (negatively) were related to breeding success of Black-tailed Gulls. SOM confirmed ordination of the sample sites by PCA and efficiently classified nest-sites according to environmental condition for breeding. Grouping based on the “wall” effect on PCA was more finely revealed in subdivision on SOM regarding the variables of slope and the nearest distance between neighbors. The use of techniques in ecological informatics such as SOM would be an efficient tool in analyzing data for breeding behavior of birds.

This paper considers the advantage of Machine Learning (ML) implemented to freshwater ecosystem research. Currently, many studies have been carried out to find the patterns of environmental impact on dynamics of communities in aquatic ecosystems. Ecological models popularly adapted by many researchers have been a means of information processing in dealing with dynamics in various ecosystems. The up-to-date trend in ecological modelling partially turns to the application of ML to explain specific ecological events in complex ecosystems and to overcome the necessity of complicated data manipulation. This paper briefly introduces ML techniques applied to freshwater ecosystems in Korea. The manuscript provides promising information for the ecologists who utilize ML for elucidating complex ecological patterns and undertaking modelling of spatial and temporal dynamics of communities.