Close to the same: Similarity influences remembered distance between stimuli

Six preregistered studies show that the degree of similarity of two objects biases people's spatial memory of these objects. When objects are high in similarity, people remember having seen them closer together in space than when they are low in similarity. All six studies provide correlational evidence, showing that the more similar participants rated two stimuli, the smaller they remembered the distance between them. This was true for both conceptual and perceptual similarity (Study 3). Furthermore, Studies 2, 4A, and 4B provide evidence of causality by manipulating similarity experimentally. Replicating the correlational findings, highly similar stimuli were remembered as closer together than stimuli low in similarity. This pattern was found across different stimulus categories and similarity dimensions. Overall, these findings show that the similarity of stimuli influences perceivers' reconstruction of their spatial locations.

Directional spatial processes override non-directional ones in structuring communities of lotic macroinvertebrates differing in dispersal ability

Dispersal is an essential natural process that influences community assembly, yet directional dispersal through wind and water may have distinctive effects. Environmental and spatial factors jointly influence community structure, but their relative importance is anticipated to vary with spatial distance, dispersal mode, and season. Accordingly, a systemic survey was conducted in subtropical Chinese mountain lotic systems to distinguish the relative contributions of environmental control and spatial structuring upon communities of macroinvertebrates with different dispersal ability. Macroinvertebrate samples were collected from the upper reaches and five tributaries of the Hanjiang River in October 2017 (autumn) and April 2018 (spring). These macroinvertebrates were identified and classified into three dispersal groups: aquatic passive (AqPa), terrestrial passive (TePa), and terrestrial active (TeAc). Variation partitioning analyses were performed on environmental factors and different sets of spatial factors (overland dispersal: Overland, directional downwind dispersal: AEM_Wind, along watercourse dispersal: Watercourse, and directional downstream dispersal: AEM_Water). Findings showed that both environmental filtering and spatial structuring influenced the structure of macroinvertebrate metacommunities. For AqPa and TePa groups, pure environmental effects were stronger than pure spatial effects based on most distance matrices; however, in AEM_Water, the effects of spatial processes surpassed those of environmental filtering. For TeAc group, the role of environmental control and spatial structuring varied depending on different spatial models. The results also highlighted seasonal shifts in metacommunity structuring processes. Spatial structures featuring direction, especially AEM_Water, were predominant in explaining the construction of macroinvertebrate communities. This work suggests that directional dispersal should be explicitly considered when examining the structure of ecological communities.

Bayesian Spatial Modeling for Housing Data in South Africa

Spatial process models are being increasingly employed for analyzing data available at geocoded locations. In this article, we build a hierarchical framework with multivariate spatial processes, where the outcomes are "mixed" in the sense that some may be continuous, some binary and others may be counts. The underlying idea is to build a joint model by hierarchically building conditional distributions with different spatial processes embedded in each conditional distribution. The idea is simple and the resulting models can be fitted to multivariate spatial data using straightforward Bayesian computing methods such as Markov chain Monte Carlo methods. Bayesian inference is carried out for parameter estimation and spatial interpolation. The proposed models are illustrated using housing data collected in the Walmer district of Port Elizabeth, South Africa. Inferential interest resides in modeling spatial dependencies of dependent outcomes and associations accounting for independent explanatory variables. Comparisons across different models confirm that the selling price of a house in our data set is relatively better modeled by incorporating spatial processes.

The effect of spatial distance between objects on categorization level

We show that spatial distance between two objects influences how people categorize these objects. We report three (two pre-registered) experiments that show that when objects are presented close together (proximal), they are more likely to be categorized in a superordinate category than when they are presented further apart (distant). In Experiments 1A and 1B, participants provided spontaneous category labels in an open response format. In Experiment 2, we asked participants to indicate their preference for either of two category labels. We found that when objects were close together, they were categorized more often into superordinate categories than when objects were far apart (Experiments 1A and 2). Our findings demonstrate that the categorization of objects is, in part, determined by where they are in relation to other objects.

Seasonal changes in metacommunity assembly mechanisms of benthic macroinvertebrates in a subtropical river basin

Unraveling the ecological factors that control variation in local community structure in space and time is fundamental to metacommunity ecology. In this scenario, environmental filtering and spatial processes are recognized as important drivers of community assembly, yet their relative importance is anticipated to vary for biological communities in different seasons, network positions and organisms with distinct dispersal modes. In this study, we used a dataset (macroinvertebrate communities and environmental variables) collected in different seasons from the Ganjiang River in China to test the above ideas. We divided the whole metacommunity in each season into mainstream communities, tributary communities, strictly aquatic dispersers and aquatic/aerial dispersers, and subsequently used variation partitioning to examine the relative contribution of environmental and spatial factors separately for the overall and decomposed components of the metacommunity. Our results showed that both environmental filtering and spatial processes were important drivers of variation in community structure, yet their explanatory powers varied considerably among seasons. Environmental filtering was the primary driver of metacommunity organization in most scenarios, while the effects of spatial processes surpassing environmental filtering occurred only sporadically. For communities in different network positions, tributary communities were structured by both strong environmental filtering and profound effects of spatial processes via dispersal limitation. However, communities in mainstream sites were mainly determined by environmental filtering, and the effects of spatial processes were almost negligible. Moreover, environmental filtering was clearly more important for aquatic/aerial dispersers, while spatial processes were more influential for strictly aquatic dispersers. We thus concluded that environmental filtering, spatial processes, network position and dispersal mode can interact to regulate metacommunity organization of riverine macroinvertebrates. Considering that the relative contribution of these factors varied among seasons, we strongly uphold the idea that community ecology research should go beyond one-season snapshot surveys in river networks.

Global patterns in the metacommunity structuring of lake macrophytes: regional variations and driving factors

We studied community-environment relationships of lake macrophytes at two metacommunity scales using data from 16 regions across the world. More specifically, we examined (a) whether the lake macrophyte communities respond similar to key local environmental factors, major climate variables and lake spatial locations in each of the regions (i.e., within-region approach) and (b) how well can explained variability in the community-environment relationships across multiple lake macrophyte metacommunities be accounted for by elevation range, spatial extent, latitude, longitude, and age of the oldest lake within each metacommunity (i.e., across-region approach). In the within-region approach, we employed partial redundancy analyses together with variation partitioning to investigate the relative importance of local variables, climate variables, and spatial location on lake macrophytes among the study regions. In the across-region approach, we used adjusted R2 values of the variation partitioning to model the community-environment relationships across multiple metacommunities using linear regression and commonality analysis. We found that niche filtering related to local lake-level environmental conditions was the dominant force structuring macrophytes within metacommunities. However, our results also revealed that elevation range associated with climate (increasing temperature amplitude affecting macrophytes) and spatial location (likely due to dispersal limitation) was important for macrophytes based on the findings of the across-metacommunities analysis. These findings suggest that different determinants influence macrophyte metacommunities within different regions, thus showing context dependency. Moreover, our study emphasized that the use of a single metacommunity scale gives incomplete information on the environmental features explaining variation in macrophyte communities.