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.

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.

The selection of explanatory variables is very important in a species distribution model for predicting changes in the distribution of organisms caused by climate change. In this study, a two-dimensional prediction of the abundance and distribution of beetles and spiders using temperature and precipitation was compared with the results of previous studies that employed a one-dimensional prediction using temperature. This study used the data from previous surveys for 366 forest sites in South Korea between 2006 and 2009 using pitfall traps. Species distribution models were created for 51 species with a high occurrence (collected from more than 10% of the total sites). A generalized additive model (GAM) was used for the distribution models. The future abundance and distribution based on climate scenarios RCP 4.5 and 8.5 were predicted by selecting 35 species from 51 common species for which climatic factors (temperature and precipitation) had a significant effect on abundance and distribution. In a two-dimensional prediction using temperature and precipitation, the range of change was larger compared with a one-dimensional prediction using temperature, and precipitation had a significant effect on decreasing species.

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.

Climate Envelope Models (CEMs) commonly employ 19 bioclimatic variables to predict species distributions, yet selecting which variables to include remains a critical challenge. Although it seems logical to select ecologically relevant variables, the biological responses of many target species are poorly understood. Random Forest (RF), a popular method in CEMs, can effectively handle correlated and nonlinear variables. In light of these strengths, this study explores the full model hypothesis, which involves using all 19 bioclimatic variables in an RF model, using Crustulina guttata (Theridiidae: Araneae) as a test case. Four model variants—a simplified model with two variables, an ecologically selected model with seven variables, a statistically selected model with ten variables, and a full model with nineteen variables—were compared against a thousand randomly assembled models with matching variable counts. All models achieved high performance, though results varied based on the number of variables employed. Notably, the full model consistently produced stronger predictions than models with fewer variables. Moreover, specifying particular variables did not yield a significant advantage over random selections of equally sized sets, indicating that omitting variables may risk the loss of important information. Although the final model suggests that C. guttata may have dispersed beyond its native European range through artificial means, this study examined only a single species. Thus, caution is warranted in generalizing these findings, and additional research is needed to determine whether the full model hypothesis extends to other taxa and environmental contexts. In scenarios where ecological knowledge is limited, however, using all available variables in an RF model may preserve potentially significant predictors and enhance predictive accuracy.

Climate change is rapidly altering the distribution and abundance of species, with significant impacts on regional ecosystems, including reduced ecosystem services and the loss of biodiversity. Accurately predicting changes in the distribution and abundance of taxa under future climate scenarios is, therefore, crucial. In South Korea, beetle data collected via pitfall traps from approximately 300 forest sites between 2007 and 2009 (30 families, 4 genera, and 150 species) were used to forecast changes in their abundance and distribution under climate change scenarios RCP 4.5 and 8.5. This study evaluated the accuracy of those predictions using data from a subsequent survey conducted between 2017 and 2019. We compared species richness, abundance, changes in abundance (i.e., number of individuals), and occurrence (i.e., number of occupied sites) using data from 273 sites that were surveyed in both the initial (2007–2009) and follow-up (2017–2019) periods. All four parameters were found to be significantly influenced by the identifiers. This identifier bias was attributed to the omission of morphologically similar species in the initial survey or the loss of individuals during the preparation process of dry specimens. As a result, increases in abundance and distribution appear to have been affected by identification errors, whereas decreases more closely reflect actual ecological changes. When the comparison between predicted and observed results was restricted to taxa with reduced abundance and distribution, the number of taxa that matched the predictions was significantly higher than that of those that did not. Based on ease of identification, abundance, and sensitivity to climate change, we selected a set of indicator taxa (four families, two genera, and seven species) for climate change monitoring.