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.

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.

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.

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.

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.

Mitigation of scintillation in FSO communication using a temporally and spatially incoherent spectrum-sliced light source.

We propose and demonstrate a temporally and spatially incoherent spectrum-sliced light source for scintillation mitigation in free-space optical (FSO) communications. For the temporal incoherence, we utilize the spectrum-sliced incoherent light (SSIL) modulated by gain-saturated reflective semiconductor optical amplifiers for data modulation and the suppression of excess intensity noise inherent in a narrowband incoherent light source. We then employ offset launching from a single-mode fiber to a multi-mode fiber to add the spatial incoherence to the SSIL source.

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.

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.

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

The tropical fire ant (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 on a global scale for effective monitoring and management. In total, 669 occurrence sites of and six bioclimatic variables of current and future climate change scenarios for 2050 and 2100 were used for the modeling.

Thermo-optic control of the longitudinal radiation angle in a silicon-based optical phased array.

We demonstrate longitudinal beam-steering with a 1×16 silicon optical phased array (OPA) using a monochromatic light source and thermo-optic control of the refractive index in the grating radiator region. The refractive index is controlled by forming a series of n-i-n heaters, placing i-regions in each radiator of the OPA. When the biased voltage in the heaters is increased, the refractive index of the radiator region is increased by the thermo-optic effect, and the longitudinal radiation angle is changed according to the Bragg condition.