Selection of climate variables in ant species distribution models: case study in South Korea.

The selection of explanatory variables is important in modeling prediction of changes in species distribution in response to climate change. In this study, we evaluated the importance of variable selection in species distribution models. We compared two different types of models for predicting the distribution of ant species: temperature-only and both temperature and precipitation. Ants were collected at 343 forest sites across South Korea from 2006 through 2009. We used a generalized additive model (GAM) to predict the future distribution of 16 species that showed significant responses to changes in climatic factors (temperature and/or precipitation). Four types of GAMs were constructed: temperature, temperature with interaction of precipitation, temperature and precipitation without interaction, and temperature and precipitation with interaction. Most species displayed similar results between the temperatureonly and the temperature and precipitation models. The results for predicted changes in species richness were different from the temperature-only model. This indicates higher uncertainty in the prediction of species richness, which is obtained by combining the prediction results of distribution change for each species, than in the prediction of distribution change. The turnover rate of the ant assemblages was predicted to increase with decreases in temperature and increases in elevation, which was consistent with other studies. Finally, our results showed that the prediction of the distribution or diversity of organisms responding to climate change is uncertain because of the high variability of the model outputs induced by the variables used in the models.

An interpretable machine learning method for supporting ecosystem management: Application to species distribution models of freshwater macroinvertebrates.

Species distribution models (SDMs), in which species occurrences are related to a suite of environmental variables, have been used as a decision-making tool in ecosystem management. Complex machine learning (ML) algorithms that lack interpretability may hinder the use of SDMs for ecological explanations, possibly limiting the role of SDMs as a decision-support tool. To meet the growing demand of explainable MLs, several interpretable ML methods have recently been proposed. Among these methods, SHaply Additive exPlanation (SHAP) has drawn attention for its robust theoretical justification and analytical gains. In this study, the utility of SHAP was demonstrated by the application of SDMs of four benthic macroinvertebrate species. In addition to species responses, the dataset contained 22 environmental variables monitored at 436 sites across five major rivers of South Korea. A range of ML algorithms was employed for model development. Each ML model was trained and optimized using 10-fold cross-validation. Model evaluation based on the test dataset indicated strong model performance, with an accuracy of ≥0.7 in all evaluation metrics for all MLs and species. However, only the random forest algorithm showed a behavior consistent with the known ecology of the investigated species. SHAP presents an integrated framework in which local interpretations that incorporate local interaction effects are combined to represent the global model structure. Consequently, this framework offered a novel opportunity to assess the importance of variables in predicting species occurrence, not only across sites, but also for individual sites. Furthermore, removing interaction effects from variable importance values (SHAP values) clearly revealed non-linear species responses to variations in environmental variables, indicating the existence of ecological thresholds. This study provides guidelines for the use of a new interpretable method supporting ecosystem management.

Potential impacts of global warming on the diversity and distribution of stream insects in South Korea.

Globally, the East Asian monsoon region is one of the richest environments in terms of biodiversity. The region is undergoing rapid human development, yet its river ecosystems have not been well studied. Global warming represents a major challenge to the survival of species in this region and makes it necessary to assess and reduce the potential consequences of warming on species of conservation concern. We projected the effects of global warming on stream insect (Ephemeroptera, Odonata, Plecoptera, and Trichoptera [EOPT]) diversity and predicted the changes of geographical ranges for 121 species throughout South Korea. Plecoptera was the most sensitive (decrease of 71.4% in number of species from the 2000s through the 2080s) order, whereas Odonata benefited (increase of 66.7% in number of species from the 2000s through the 2080s) from the effects of global warming. The impact of global warming on stream insects was predicted to be minimal prior to the 2060s; however, by the 2080s, species extirpation of up to 20% in the highland areas and 2% in the lowland areas were predicted. The projected responses of stream insects under global warming indicated that species occupying specific habitats could undergo major reductions in habitat. Nevertheless, habitat of 33% of EOPT (including two-thirds of Odonata and one-third of Ephemeroptera, Plecoptera, and Trichoptera) was predicted to increase due to global warming. The community compositions predicted by generalized additive models varied over this century, and a large difference in community structure in the highland areas was predicted between the 2000s and the 2080s. However, stream insect communities, especially Odonata, Plecoptera, and Trichoptera, were predicted to become more homogenous under global warming.

Analyzing retraction responses of Tubifex tubifex (Oligochaeta: Naididae) colonies to copper treatments through a digital image analysis approach for developing early biological warning systems.

Biological monitoring and assessments are commonly used for sustainable ecosystem management. Oligochaetes are found in various freshwater ecosystems and have been used as indicators of water quality and for the biological assessment of aquatic ecosystems. Among aquatic oligochaetes, the sludge worm Tubifex tubifex (Oligochaeta, Naididae) is tolerant to organic pollution and has been used as a biomonitoring indicator of toxicity and organic pollution. In this study, we investigated the response of worm colonies to copper (CuSO4) treatments (0.01, 0.05, 0.1, 0.5, and 1.0 mg/L) in an observation cage (100 mL beaker) for 30 min. Using a digital image analysis approach, we measured the changes in the colony image area between pre- and post-copper treatment. After copper treatment, the colony image area tended to decrease, even at low copper concentrations. In addition, the colony areas did not recover to their original levels at high concentrations, although those at low concentrations did. Area decreased proportional to the logarithm of the copper concentration. Finally, our results present the possible use of the retraction responses of Tubifex tubifex colonies to chemical disturbances as early biological warning systems.

Interpretable machine learning approach to analyze the effects of landscape and meteorological factors on mosquito occurrences in Seoul, South Korea.

Mosquitoes are the underlying cause of various public health and economic problems. In this study, patterns of mosquito occurrence were analyzed based on landscape and meteorological factors in the metropolitan city of Seoul. We evaluated the influence of environmental factors on mosquito occurrence through the interpretation of prediction models with a machine learning algorithm. Through hierarchical cluster analysis, the study areas were classified into waterside and non-waterside areas, according to the landscape patterns. The mosquito occurrence was higher in the waterside area, and mosquito abundance was negatively affected by rainfall at the waterside. The mosquito occurrence was predicted in each cluster area based on the landscape and cumulative meteorological variables using a random forest algorithm. Both models exhibited good performance (both accuracy and AUROC > 0.8) in predicting the level of mosquito occurrence. The embedded relationship between the mosquito occurrence and the environmental factors in the models was explained using the Shapley additive explanation method. According to the variable importance and the partial dependence plots for each model, the waterside area was more influenced by the meteorological and land cover variables than the non-waterside area. Therefore, mosquito control strategies should consider the effects of landscape and meteorological conditions, including the temperature, rainfall, and the landscape heterogeneity. The present findings can contribute to the development of mosquito forecasting systems in metropolitan cities for the promotion of public health.

Functional trait dataset of benthic macroinvertebrates in South Korean streams.

Functional traits are the result of evolution and adaptation, providing important ecological insights into how organisms interact with their environment. Benthic macroinvertebrates, in particular, have garnered attention as biomonitoring indicators for freshwater ecosystems. This study presents a functional trait dataset for benthic macroinvertebrates, comprising 447 taxa (393 at genus level, 53 at family level and one at class level) from five phyla (Annelida, Arthropoda, Mollusca, Nematomorpha, and Platyhelmenthes), categorized into nine traits related to life history, morphology, and habit. To account for variation in available trait information, we assigned confidence levels to each taxon and functional trait based on the level of evidence using fuzzy coding. Our dataset provides an important resource for understanding the ecology of benthic macroinvertebrates in South Korea, serving as a valuable baseline dataset for studying their biodiversity, conservation, and biomonitoring in freshwater ecosystems.

Understanding the patterns and processes underlying water quality and pollution risk in West-Africa River using self-organizing maps and multivariate analyses.

Rivers are dynamic systems in complex interactions with their surrounding environments. Reliable and fast interpretation of water quality is therefore needed for sustainable river management. Unfortunately, water quality and environmental status interactions have not yet been documented sufficiently in West-Africa. This study explored the spatial-latitudinal and seasonal features of water quality along the Sô River Basin (SRB, West Africa) using self-organizing map (SOM) and principal component analysis. Twenty-two water quality variables were measured in the surface layer at 12 different sampling sites during a twenty-four-month period from July 2016 to June 2018. The results revealed three water quality groups, following an upstream-downstream pollution gradient: (1) upstream and middle reach sites with high dissolved oxygen and Secchi disk depth values, which are more suitable for the aquatic biota; (2) downstream sites with high concentrations of ammonium, biochemical oxygen demand, and heavy metals especially in flood period, reflecting both high organic and heavy metal pollution; and (3) brackish downstream sites characterized by less heavy metal and organic pollutions. No significant variation was observed between seasons. However, the SRB relatively suffered from higher risks of heavy metal contamination and organic pollution in wet seasons. Although hydroclimatic processes affect the water quality, anthropogenic inputs of point and non-point sources were identified and discussed as a more prominent factor contributing to variation in the water quality condition. These results offer insights into the water quality dynamics in river-estuary system as well as potential pollution sources, crucial for defining sanitation, and management measures.

Ant Mortality with Food Competition in Forests along a Temperature Gradient.

The authors elucidated the relationship between temperature and mortality due to food competition in ant communities in forests. A field experiment was conducted using four bait types at six different oak forest sites with different mean annual temperatures in South Korea. The mortality rate due to food competition showed a hump-shaped trend, with temperature distribution being higher at study sites with intermediate temperatures and a linear trend increasing or decreasing with temperature along the temperature gradient. In most species, the mortality rate due to interspecific competition was higher than that due to intraspecific competition, but the dominant species, which were less affected by other species, had a higher mortality rate due to intraspecific competition. In subordinate species that are highly affected by other species, the mortality rate due to intraspecific competition increased as the mortality rate due to interspecific competition decreased. The results indicated that mortality due to inter- or intraspecific competition for food was associated by temperature, density of other species, and species characteristics (body size, dominance, feeding strategy, and aggressiveness). Given the relationship between temperature and mortality due to food competition, the authors expect that changes in competition due to climate warming will affect the fitness of ant species.

Taxonomic and Functional Diversity of Benthic Macroinvertebrate Assemblages in Reservoirs of South Korea.

Numerous community indices have been developed to quantify the various aspects of communities. However, indices including functional aspects have been less focused on. Here, we examined how community composition varies in response to the environment and discovered the relationship between taxonomic diversity and functional diversity while considering the environment. Macroinvertebrate communities were collected from 20 reservoirs in South Korea. To characterize functional diversity, functional traits in four categories were considered: generation per year, adult lifespan, adult size, and functional feeding groups. Based on their community composition, we classified the reservoirs using hierarchical cluster analysis. Physicochemical and land use variables varied considerably between clusters. Non-metric multidimensional scaling indicated differences between reservoirs and clusters in terms of structure, functional diversity, and environmental variables. A self-organizing map was used to categorize functional traits, and network association analysis was used to unravel relationships between functional traits. Our results support the characteristics of species' survival strategies such as r- and K-selection. Functional richness exhibited a relationship with taxonomic diversity. Our findings suggest that different types of diversity could play complementary roles in identifying biodiversity. Our findings should prove useful in developing new criteria for assessing freshwater ecosystem health, as well as in evaluating and predicting future alteration of benthic macroinvertebrate communities facing anthropogenic disturbances.

Simulating Pine Wilt Disease Dispersal With an Individual-Based Model Incorporating Individual Movement Patterns of Vector Beetles.

Individual movements of the insect vector pine sawyer beetles were incorporated into an individual-based model (IBM) to elucidate the dispersal of pine wilt disease (PWD) and demonstrate the effects of control practices. The model results were compared with the spatial data of infested pine trees in the Gijang-gun area of Busan, Republic of Korea. Step functions with long- and middle-distance movements of individual beetles effectively established symptomatic and asymptomatic trees for the dispersal of PWD. Pair correlations and pairwise distances were suitable for evaluating PWD dispersal between model results and field data at short and long scales, respectively. The accordance between model and field data was observed in infestation rates at 0.08 and 0.09 and asymptomatic rates at 0.16-0.17 for disease dispersal. Eradication radii longer than 20 m would effectively control PWD dispersal for symptomatic transmission and 20-40 m for asymptomatic transmission. However, the longer eradication radii were more effective at controlling PWD. Therefore, to maximize control effects, a longer radius of at least 40 m is recommended for clear-cutting eradication. The IBM of individual movement patterns provided practical information on interlinking the levels of individuals and populations and could contribute to the monitoring and management of forest pests where individual movement is important for population dispersal.

EVALUATION OF ENVIRONMENTAL FACTORS ON CYANOBACTERIAL BLOOM IN EUTROPHIC RESERVOIR USING ARTIFICIAL NEURAL NETWORKS1.

Cyanobacterial blooms are a common issue in eutrophic freshwaters, and some cyanobacteria produce toxins, threatening the health of humans and livestock. Microcystin, a representative cyanobacterial hepatotoxin, is frequently detected in most Korean lakes and reservoirs. This study developed predictive models for cyanobacterial bloom using artificial neural networks (ANNs; self-organizing map [SOM] and multilayer perceptron [MLP]), including an evaluation of related environmental factors. Fourteen environmental factors, as independent variables for predicting the cyanobacteria density, were measured weekly in the Daechung Reservoir from spring to autumn over 5 years (2001, 2003-2006). Cyanobacterial density was highly associated with environmental factors measured 3 weeks earlier. The SOM model was efficient in visualizing the relationships between cyanobacteria and environmental factors, and also for tracing temporal change patterns in the environmental condition of the reservoir. And the MLP model exhibited a good predictive power for the cyanobacterial density, based on the environmental factors of 3 weeks earlier. The water temperature and total dissolved nitrogen were the major determinants for cyanobacteria. The water temperature had a stronger influence on cyanobacterial growth than the nutrient concentrations in eutrophic waters. Contrary to general expectations, the nitrogen compounds played a more important role in bloom formation than the phosphorus compounds.

Predicting the Global Distribution of Solenopsis geminata (Hymenoptera: Formicidae) under Climate Change Using the MaxEnt Model.

The tropical fire ant Solenopsis geminata (Hymenoptera: Formicidae) is a serious invasive species that causes a decline in agricultural production, damages infrastructure, and harms human health. This study was aimed to develop a model using the maximum entropy (MaxEnt) algorithm to predict the current and future distribution of S. geminata on a global scale for effective monitoring and management. In total, 669 occurrence sites of S. geminata and six bioclimatic variables of current and future climate change scenarios for 2050 and 2100 were used for the modeling. The annual mean temperature, annual precipitation, and precipitation in the driest quarter were the key influential factors for determining the distribution of S. geminata. Although the potential global distribution area of S. geminata is predicted to decrease slightly under global warming, the distribution of favorable habitats is predicted to expand to high latitudes under climate scenarios. In addition, some countries in America and East Asia, such as Brazil, China, South Korea, the USA, and Uruguay, are predicted to be threatened by S. geminata invasion under future climate change. These findings can facilitate the proactive management of S. geminata through monitoring, surveillance, and quarantine measures.

Differences of Gut Microbiota in the Freshwater Blackworm (Lumbriculus variegatus: Oligochaeta) in Two Different Habitat Conditions.

The distribution of organisms is governed by their habitat condition. We analyzed bacterial communities in the gut of the blackworm Lumbriculus variegatus by pyrosequencing of the extracted intestinal metagenomic DNA. Blackworms were collected from two sampling sites with differences in irradiance and riparian vegetation, where site GP7 was covered by riparian vegetation and site GP8 was exposed to sunlight. We obtained the filtered 6414 reads from three samples of each site. At GP7, 271 OTUs were identified, including 32 OTUs unique to the site, whereas at GP8, 238 OTUs were identified, including 22 unique OTUs. Among them, 18 OTUs were shared between both sites. The phylum Proteobacteria was a major component contributing 67.84% and 64.05% of sequences at sites GP7 and GP8, respectively, while each remaining phylum contributed less than 10% at both sites. The two sites differed in microbial community composition and KEGG-indicated biochemical pathways. Community indices such as species richness and Shannon diversity were higher at site GP7 than at GP8. Meanwhile, the abundance of Cyanobacteria was significantly higher at site GP8, while site GP7 showed a greater proportion of genes for membrane transport and carbohydrate metabolism, reflecting differences in food resources.

Spatial heterogeneities of human-mediated dispersal vectors accelerate the range expansion of invaders with source-destination-mediated dispersal.

Rapid range expansions of invasive species are a major threat to ecosystems. Understanding how invasive species increase their habitat ranges and how environmental factors, including intensity of human activities, influence dispersal processes is an important issue in invasion biology, especially for invasive species management. We have investigated how spatially heterogeneous factors influence range expansion of an invasive species by focusing on long-distance dispersal, which is frequently assisted by human activities. We have developed models varying two underlying processes of a dispersal event. These events are described by source and destination functions that determine spatial variations in dispersal frequency and the probability of being a dispersal destination. Using these models, we investigated how spatially heterogeneous long-distance dispersal influences range expansion. We found that: (1) spatial variations in the destination function slow down late population dynamics, (2) spatial variations in the source function increase the stochasticity of early population dynamics, and (3) the speed of early population dynamics changes when both the source and the destination functions are spatially heterogeneous and positively correlated. These results suggest an importance of spatial heterogeneity factors in controlling long-distance dispersal when predicting the future spread of invasive species.

Analyzing the Response Behavior of Lumbriculus variegatus (Oligochaeta: Lumbriculidae) to Different Concentrations of Copper Sulfate Based on Line Body Shape Detection and a Recurrent Self-Organizing Map.

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 (±5.18) mm to 20.91 (±4.65) mm after the 0.1 mg/L treatment. The Shannon entropy index decreased from 0.44 (±0.1) to 0.28 (±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.

Abundance and Distribution of Korean Flower Flies (Diptera: Syrphidae): Dominant Influence of Latitude on Regional Distribution.

Studies on abundance and distribution at different scales are rare. We examined whether the abundance of flower flies at a site in South Korea was related to the national occupancy and global distribution (distributional extent or range size) and whether the national occupancy was related to global distribution. In global distribution, the influence of two dimensions (latitude and longitude) was analyzed separately. Flower flies were collected by malaise and pitfall traps at a forest gap in South Korea. Data regarding national occupancy and global distribution were obtained from a Korean Flower Fly Atlas. We collected 46 species from the field survey and obtained a list of 119 species from the Korean Flower Fly Atlas. Our results showed that abundance at a site was positively correlated with national occupancy, but not global distribution, and the national occupancy was positively correlated with global distribution, mainly by the latitudinal range size. Finally, our results indicated that the regional distribution of flower flies was influenced by its one-dimensional global distribution.

What do artificial neural networks tell us about the genetic structure of populations? The example of European pig populations.

General and genetic statistical methods are commonly used to deal with microsatellite data (highly variable neutral genetic markers). In this paper, the self-organizing map (SOM) that belongs to the unsupervised artificial neural networks (ANNs) was applied to analyse the structure of 58 European and two Chinese pig populations (Sus scrofa) including commercial lines, local breeds and cosmopolitan breeds. Results were compared with other unsupervised classification or ordination methods such as factorial correspondence analysis, hierarchical clustering from an allele sharing distance and the Bayesian genetic model and with principal components analysis and neighbour joining from allelic frequencies and genetic distances between populations. Like other methods, SOMs were able to classify individuals according to their breed origin and to visualize similarities between breeds. They provided additional information on the within- and between-population diversity, allowed differences between similar populations to be highlighted and helped differentiate different groups of populations.

Distribution Patterns of Odonate Assemblages in Relation to Environmental Variables in Streams of South Korea.

Odonata species are sensitive to environmental changes, particularly those caused by humans, and provide valuable ecosystem services as intermediate predators in food webs. We aimed: (i) to investigate the distribution patterns of Odonata in streams on a nationwide scale across South Korea; (ii) to evaluate the relationships between the distribution patterns of odonates and their environmental conditions; and (iii) to identify indicator species and the most significant environmental factors affecting their distributions. Samples were collected from 965 sampling sites in streams across South Korea. We also measured 34 environmental variables grouped into six categories: geography, meteorology, land use, substrate composition, hydrology, and physicochemistry. A total of 83 taxa belonging to 10 families of Odonata were recorded in the dataset. Among them, eight species displayed high abundances and incidences. Self-organizing map (SOM) classified sampling sites into seven clusters (A⁻G) which could be divided into two distinct groups (A⁻C and D⁻G) according to the similarities of their odonate assemblages. Clusters A⁻C were characterized by members of the suborder Anisoptera, whereas clusters D⁻G were characterized by the suborder Zygoptera. Non-metric multidimensional scaling (NMDS) identified forest (%), altitude, and cobble (%) in substrata as the most influential environmental factors determining odonate assemblage compositions. Our results emphasize the importance of habitat heterogeneity by demonstrating its effect on odonate assemblages.

Structural dynamics in the host-parasitoid system of the pine needle gall midge (Thecodiplosis japonensis) during invasion.

The structural dynamics of host-parasitoid populations play a key role in the mechanism of natural community development with invasive species. Species invading new habitats experience coevolution with their newly acquired natural enemies, and their population dynamics are driven by a complex interaction between biological and environmental factors. We examined the biological and environmental factors which potentially influence a community of parasitoids throughout the 25-year invasion history of the pine needle gall midge (PNGM), Thecodiplosis japonensis, an important pest of pines in eastern Asia. We found that differences in establishment sequence and competitive ability among the parasitoids attacking this species determined the parasitoid community's structure and dynamics. In particular, the timing for the initial establishment of the host-parasitoid association, incomplete superiority in competition among parasitoids, and indirect competition by a combination of the parasitoids were important factors for determining community's structure and dynamics. Finally, the history of change in the community composition could be explained by the phenology differences in its member species, mediated by environmental factors.

Stream fish assemblages and basin land cover in a river network.

This study focused on characterizing fish assemblages in the Adour-Garonne basin and identifying the relative influences of landscape-scale features on observed patterns in stream fish assemblages. Two different artificial neural network algorithms were used: a self-organizing map (SOM) and a multilayer perceptron (MLP). A SOM was applied to determine fish assemblage types, and a MLP was used to predict the fish assemblage types defined by the SOM. Thirty four species were collected at 191 sampling sites in a major river-system, the Adour-Garonne basin, and topographical factors, namely altitude, distance from source and surface area of drainage basin were measured. Using GIS, land cover types (agricultural land, forests and urbanized artificial surface) were calculated for each site and expressed as percentage of the surface area of basin. These variables were introduced to the MLP and factorial discriminant analysis for the prediction of assemblage types. As a result, the SOM distinguished three fish assemblage types according to the differences of species composition, and the assemblage types were better predicted with landscape-scale features by MLP than discriminant analysis. The percentages of agricultural land and the surface area of a basin showed the greatest influence on assemblage types 1 and 2, and distance from source was the most important factor to determine assemblage type 3.