A theoretical framework for resource translocation during sexual reproduction in modular organisms

An individual of modular organisms, such as plants and fungi, consists of more than one module that is sometimes physically and physiologically connected with each other. We examined effects of translocation costs, resource–fitness relationships and original resource conditions for modules on the optimal resource translocation strategy for reproductive success in modular organisms with simple models. We considered two types of translocation cost: amount-dependent and ratio-dependent costs. Three optimal resource translocation strategies were recognized: all resource translocation (ART), partial resource translocation (PRT), and no resource translocation (NRT). These strategies depended on the translocation cost, shape of resource–fitness curve, and original resource condition for each module. Generally, a large translocation cost and a concave resource–fitness relationship promoted NRT or PRT. Meanwhile, a small translocation cost and convex resource–fitness relationship facilitated ART. The type of translocation cost did not strongly affect the optimal resource translocation patterns, although ART was never an optimal strategy when the cost was ratio-dependent. Resource translocation patterns found in modular plants were discussed in the light of our model results.     

Plant species’ coexistence relationships may shift according to life history traits and seasons

Although several studies have demonstrated that disturbance contributes to species’ diversity, little emphasis has been placed on the identification of species’ coexistence mechanisms related to life history traits. In this study, we compared species’ richness and components of plant communities around river confluences to explore how disturbance promotes the coexistence of species with different life history traits. Sites upstream and downstream of confluences are ideal for such comparisons because they draw on the same species’ pools and have similar ambient conditions, but differ markedly in the extents of flooding disturbance. We compared sites upstream and downstream of confluences by calculating species’ richness and community similarity indices for several life history traits in both summer and spring. In summer, the combined richness of all the species, of annual- and summer-flowering species, was higher downstream from confluences than upstream, but this was not the case for perennials. Similarity analyses suggested that plant communities are constructed according to a neutral process, whereby interactions between the coexisting species are neutral. However, in spring, species’ richness was similar upstream and downstream of confluences for all life history traits. Similarity analyses suggested that under these circumstances, the communities were constructed through a species-sorting process; i.e., each life history trait had a distinct habitat preference. Thus, the relative strengths of different community assembly processes may change seasonally. We concluded that species groups differing in their responses to disturbance may coexist in a single community. Thus, community structuring following disturbance may involve two processes: a neutral and a species-sorting process. The relative importance of each may vary between species’ life history traits and between seasons, and the interaction may account for current community structures.     

Bagging GLM: Improved generalized linear model for the analysis of zero-inflated data

Species-occurrence data sets tend to contain a large proportion of zero values, i.e., absence values (zero-inflated). Statistical inference using such data sets is likely to be inefficient or lead to incorrect conclusions unless the data are treated carefully. In this study, we propose a new modeling method to overcome the problems caused by zero-inflated data sets that involves a regression model and a machine-learning technique. We combined a generalized liner model (GLM), which is widely used in ecology, and bootstrap aggregation (bagging), a machine-learning technique. We established distribution models of Vincetoxicum pycnostelma (a vascular plant) and Ninox scutulata (an owl), both of which are endangered and have zero-inflated distribution patterns, using our new method and traditional GLM and compared model performances. At the same time we modeled four theoretical data sets that contained different ratios of presence/absence values using new and traditional methods and also compared model performances. For distribution models, our new method showed good performance compared to traditional GLMs. After bagging, area under the curve (AUC) values were almost the same as with traditional methods, but sensitivity values were higher. Additionally, our new method showed high sensitivity values compared to the traditional GLM when modeling a theoretical data set containing a large proportion of zero values. These results indicate that our new method has high predictive ability with presence data when analyzing zero-inflated data sets. Generally, predicting presence data is more difficult than predicting absence data. Our new modeling method has potential for advancing species distribution modeling.