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Plant communities are impacted by local factors (related to environmental filtering) and landscape factors (related to dispersal limitation). While many studies have shown that the relative importance of these factors in understanding plant community dynamics due to urbanization, little is known about how they are altered by urbanization—a significant threat to biodiversity. This study evaluates the relative importance of local environmental (local factors), landscape, and spatial (landscape factors) variables that influence plant communities in 34 urban green spaces comprising two different habitats (forests and grasslands) along the urban–rural gradients in the Tokyo megacity, Japan. To continuously assess the relative importance of each factor along the urban–rural gradients, we extracted 1000 landscapes within a certain range that contained several sites. Subsequently, the relative importance of each factor and urbanization rate (proportion of artificial built‐up area) were estimated for each landscape. Our study found that the relative importance of both local and landscape factors decreased, while that of local factor for native species in forest habitats and that of landscape factors for native species in grassland habitats increased. Collectively, these findings suggest that city size and habitat characteristics must be considered when predicting changes in plant communities caused by urbanization. We investigated local and landscape factors driving plant communities in the Tokyo metropolitan area. The results revealed that the drivers of plant communities change along the urban–rural gradient.

In metacommunity ecology, a major focus has been on combining observational and analytical approaches to identify the role of critical assembly processes, such as dispersal limitation and environmental filtering, but this work has largely ignored temporal community dynamics. Here, we develop a \"virtual ecologist\" approach to evaluate assembly processes by simulating metacommunities varying in three main processes: density-independent responses to abiotic conditions, density-dependent biotic interactions, and dispersal. We then calculate a number of commonly used summary statistics of community structure in space and time and use random forests to evaluate their utility for inferring the strength of these three processes. We find that (i) both spatial and temporal data are necessary to disentangle metacommunity processes based on the summary statistics we test, and including statistics that are measured through time increases the explanatory power of random forests by up to 59% compared to cases where only spatial variation is considered; (ii) the three studied processes can be distinguished with different descriptors; and (iii) each summary statistic is differently sensitive to temporal and spatial sampling effort. Including repeated observations of metacommunities over time was essential for inferring the metacommunity processes, particularly dispersal. Some of the most useful statistics include the coefficient of variation of species abundances through time and metrics that incorporate variation in the relative abundances (evenness) of species. We conclude that a combination of methods and summary statistics is probably necessary to understand the processes that underlie metacommunity assembly through space and time, but we recognize that these results will be modified when other processes or summary statistics are used.

The methods of direct gradient analysis and variation partitioning are the most widely used frameworks to evaluate the contributions of species sorting to metacommunity structure. In many cases, however, species are also driven by spatial processes that are independent of environmental heterogeneity (e.g., neutral dynamics). As such, spatial autocorrelation can occur independently in both species (due to limited dispersal) and the environmental data, leading to spurious correlations between species distributions and the spatialized (i.e., spatially autocorrelated) environment. In these cases, the method of variation partitioning may present high Type I error rates (i.e., reject the null hypothesis more often than the pre-established critical level) and inflated estimates regarding the environmental component that is used to estimate the importance of species sorting. In this paper, we (1) demonstrate that metacommunities driven by neutral dynamics (via limited dispersal) alone or in combination with species sorting leads to inflated estimates and Type I error rates when testing for the importance of species sorting; and (2) propose a general and flexible new variation partitioning procedure to adjust for spurious contributions due to spatial autocorrelation from the environmental fraction. We used simulated metacommunity data driven by pure neutral, pure species sorting, and mixed (i.e., neutral + species sorting dynamics) processes to evaluate the performances of our new methodological framework. We also demonstrate the utility of the proposed framework with an empirical plant dataset in which we show that half of the variation initially due to the environment by the standard variation partitioning framework was due to spurious correlations.

Eigenvector mapping techniques are widely used by ecologists and evolutionary biologists to describe and control for spatial and/or phylogenetic patterns in their data. The selection of an appropriate subset of eigenvectors is a critical step (misspecification can lead to highly biased results and interpretations), and there is no consensus yet on how to proceed. We conducted a ten-year review of the practices of eigenvector selection and highlighted three main procedures: selecting the subset of descriptors minimising the Akaike information criterion (AIC), using a forward selection with double stopping criterion after testing the global model significance (FWD), and selecting the subset minimising the autocorrelation in the model residuals (MIR). We compared the type I error rates, statistical power, and R ² estimation accuracy of these methods using simulated data. Finally, a real dataset was analysed using variation partitioning analysis to illustrate to what extent the different selection approaches affected the ecological interpretation of the results. We show that, while the FWD and MIR approaches presented a correct type I error rate and were accurate, the AIC approach displayed extreme type I error rates (100%), and strongly overestimated the R ². Moreover, the AIC approach resulted in wrong ecological interpretations, as it overestimated the pure spatial fraction (and the joint spatial-environmental fraction to a lesser extent) of the variation partitioning. Both the FWD and MIR methods performed well at broad and medium scales but had a very low power to detect fine-scale patterns. The FWD approach selected more eigenvectors than the MIR approach but also returned more accurate R ² estimates. Hence, we discourage any future use of the AIC approach, and advocate choosing between the MIR and FWD approaches depending on the objective of the study: controlling for spatial or phylogenetic autocorrelation (MIR) or describing the patterns as accurately as possible (FWD).

The beech bark necrosis (BBN) infestation severity of European beech (Fagus sylvatica L.) was assessed in regions of Central (CE) and South-Eastern Europe (SE). Altogether more than 10,000 trees were sampled at 114 sites. Using variation partitioning method, we examined the pure and shared effects of stand, site, climate and spatial sets of variables on mean BBN severity. Our rating included (i) the whole stand, (ii) tree social status classes, (iii) canopy (C) and (iv) understory (U) trees separately. We found that C trees were less affected by BBN than sub-canopy and U trees in both regions. There were found inter-regional differences in amount of explained variability (25.4–73.9%) for whole stand BBN and in the sensitivity of C and U trees to the environmental gradients. The analysis revealed that the climate and spatial variables followed by stand variables had the largest marginal effects on mean BBN severity in all models, while the site set of variables had the weakest one. More than half of the explained variation was shared among four sets of variables in SE, contrary to CE. Except to U trees in SE, the effect of climate – pure or spatially structured – remained the highest also after partitioning of variance; more in SE than in CE. Taking into account positive association between mean annual temperature and mean BBN severity in C trees in SE, reinforced negative effect of climate change on the necrosis might be expected to be more serious mainly in low situated beech forests there. Promoting the tree species diversity in forested areas with higher incidence of beech bark necrosis, i.e. in low altitudes in SE, could reduce the susceptibility of forests to the necrosis at regional level in the future. For better understanding of the relative importance of environmental and spatial variables on BBN severity, further research performed on finer spatial scale (extent and grain) is necessary, along with accounting for pathogens involved in the infestation.

Aim: We aimed to assess the contribution of marginal habitats to the tree species richness of the Mata Atlântica (Atlantic Forest) biodiversity hotspot In addition, we aimed to determine which environmental factors drive the occurrence and distribution of these marginal habitats. Location: The whole extension of the South American Atlantic Forest Domain plus forest intrusions into the neighbouring Cerrado and Pampa Domains, which comprises rain forests (\"core\" habitat) and five marginal habitats, namely high elevation forests, rock outcrop dwarf-forests, riverine forests, semideciduous forests and restinga (coastal white-sand woodlands). Methods: We compiled a dataset containing 366,875 occurrence records of 4,431 tree species from 1,753 site-checklists, which were a priori classified into 10 main vegetation types. We then performed ordination analyses of the species-by-site matrix to assess the floristic consistency of this classification. In order to assess the relative contribution of environmental predictors to the community turnover, we produced models using 26 climate and substrate-related variables as environmental predictors. Results: Ordination diagrams supported the floristic segregation of vegetation types, with those considered as marginal habitats placed at the extremes of ordination axes. These marginal habitats are associated with the harshest extremes of five limiting factors: temperature seasonality (high elevation and subtropical riverine forests), flammability (rock outcrop dwarf-forests), high salinity (restinga), water deficit severity (semideciduous forests) and waterlogged soils (tropical riverine forests). Importantly, 45% of all species endemic to the Atlantic Domain only occur in marginal habitats. Main conclusions: Our results showed the key role of the poorly protected marginal habitats in contributing to the high species richness of the Atlantic Domain. Various types of environmental harshness operate as environmental filters determining the distribution of the Atlantic Domain habitats. Our findings also stressed the importance of fire, a previously neglected environmental factor.

A key challenge for community ecology is to understand to what extent observational data can be used to infer the underlying community assembly processes. As different processes can lead to similar or even identical patterns, statistical analyses of non‐manipulative observational data never yield undisputable causal inference on the underlying processes. Still, most empirical studies in community ecology are based on observational data, and hence understanding under which circumstances such data can shed light on assembly processes is a central concern for community ecologists. We simulated a spatial agent‐based model that generates variation in metacommunity dynamics across multiple axes, including the four classic metacommunity paradigms as special cases. We further simulated a virtual ecologist who analysed snapshot data sampled from the simulations using eighteen output metrics derived from beta‐diversity and habitat variation indices, variation partitioning and joint species distribution modelling. Our results indicated two main axes of variation in the output metrics. The first axis of variation described whether the landscape has patchy or continuous variation, and thus was essentially independent of the properties of the species community. The second axis of variation related to the level of predictability of the metacommunity. The most predictable communities were niche‐based metacommunities inhabiting static landscapes with marked environmental heterogeneity, such as metacommunities following the species sorting paradigm or the mass effects paradigm. The most unpredictable communities were neutral‐based metacommunities inhabiting dynamics landscapes with little spatial heterogeneity, such as metacommunities following the neutral or patch sorting paradigms. The output metrics from joint species distribution modelling yielded generally the highest resolution to disentangle among the simulated scenarios. Yet, the different types of statistical approaches utilized in this study carried complementary information, and thus our results suggest that the most comprehensive evaluation of metacommunity structure can be obtained by combining them.

1. Statistical tests partitioning community variation into environmental and spatial components have been widely used to test ecological theories and explore the determinants of community structure for applied conservation questions. Despite the wide use of these tests, there is considerable debate about their relative effectiveness. 2. We used simulated communities to evaluate the most commonly employed tests that partition community variation: regression on distance matrices and canonical ordination using a third-order polynomial, principal components of neighbour matrices (PCNM) or Moran's eigenvector maps (MEM) to model spatial components. Each test was evaluated under a variety of realistic sampling scenarios. 3. All tests failed to correctly model spatial and environmental components of variation, and in some cases produced biased estimates of the relative importance of components. Regression on distance matrices under-fit the spatial component, and ordination models consistently under-fit the environmental component. The PCNM and MEM approaches often produced inflated R² statistics, apparently as a result of statistical artefacts involving selection of superfluous axes. This problem occurred regardless of the forward-selection technique used. 4. Both sample configuration and the underlying linear model used to analyse species-environment relationships also revealed strong potential to bias results. 5. Synthesis and applications. Several common applications of variation partitioning in ecology now appear inappropriate. These potentially include decisions for community conservation based on inferred relative strengths of niche and dispersal processes, inferred community responses to climate change, and numerous additional analyses that depend on precise results from multivariate variation-partitioning techniques. We clarify the appropriate uses of these analyses in research programmes, and outline potential steps to improve them.