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© 2019 The Authors. Ecological Monographs published by Wiley Periodicals, Inc. on behalf of Ecological Society of America This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

A key focus in ecology is to search for community assembly rules. Here we compare two community modelling frameworks that integrate a combination of environmental and spatial data to identify positive and negative species associations from presence–absence matrices, and incorporate an additional comparison using joint species distribution models (JSDM). The frameworks use a dichotomous logic tree that distinguishes dispersal limitation, environmental requirements, and interspecific interactions as causes of segregated or aggregated species pairs. The first framework is based on a classical null model analysis complemented by tests of spatial arrangement and environmental characteristics of the sites occupied by the members of each species pair (Classic framework). The second framework, (SDM framework) implemented here for the first time, builds on the application of environmentally-constrained null models (or JSDMs) to partial out the influence of the environment, and includes an analysis of the geographical configuration of species ranges to account for dispersal effects. We applied these approaches to examine plot-level species co-occurrence in plant communities sampled along a wide elevation gradient in the Swiss Alps. According to the frameworks, the majority of species pairs were randomly associated, and most of the non-random positive and negative species associations could be attributed to environmental filtering and/or dispersal limitation. These patterns were partly detected also with JSDM. Biotic interactions were detected more frequently in the SDM framework, and by JSDM, than in the Classic framework. All approaches detected species aggregation more often than segregation, perhaps reflecting the important role of facilitation in stressful high-elevation environments. Differences between the frameworks may reflect the explicit incorporation of elevational segregation in the SDM framework and the sensitivity of JSDM to the environmental data. Nevertheless, all methods have the potential to reveal general patterns of species co-occurrence for different taxa, spatial scales, and environmental conditions.

Climate change has been observed to expand distributions of woody plants in many areas of arctic and alpine environments-a phenomenon called shrubification. New spatial arrangements of shrubs cause further changes in vegetation via changing dynamics of biotic interactions. However, the mediating influence of shrubification is rarely acknowledged in predictions of tundra vegetation change. Here, we examine possible warming-induced landscape-level vegetation changes in a high-latitude environment using species distribution modelling (SDM), specifically concentrating on the impacts of shrubification on ambient vegetation. First, we produced estimates of current shrub and tree cover and forecasts of their expansion under climate change scenarios to be incorporated to SDMs of 116 vascular plants. Second, the predictions of vegetation change based on the models including only abiotic predictors and the models including abiotic, shrub and tree predictors were compared in a representative test area. Based on our model predictions, abundance of woody plants will expand, thus decreasing predicted species richness, amplifying species turnover and increasing the local extinction risk for ambient vegetation. However, the spatial variation demonstrated in our predictions highlights that tundra vegetation can be expected to show a wide variety of different responses to the combined effects of warming and shrubification, depending on the original plant species pool and environmental conditions. We conclude that realistic forecasts of the future require acknowledging the role of shrubification in warming-induced tundra vegetation change.

Biotic interactions are known to aff ect the composition of species assemblages via several mechanisms, such as competition and facilitation. However, most spatial models of species richness do not explicitly consider inter-specifi c interactions. Here, we test whether incorporating biotic interactions into high-resolution models alters predictions of species richness as hypothesised. We included key biotic variables (cover of three dominant arctic-alpine plant species) into two methodologically divergent species richness modelling frameworks – stacked species distribution models (SSDM) and macroecological models (MEM) – for three ecologically and evolutionary distinct taxonomic groups (vascular plants, bryophytes and lichens). Predictions from models including biotic interactions were compared to the predictions of models based on climatic and abiotic data only. Including plant – plant interactions consistently and signifi cantly lowered bias in species richness predictions and increased predictive power for independent evaluation data when compared to the conventional climatic and abiotic data based models. Improvements in predictions were constant irrespective of the modelling framework or taxonomic group used. Th e global biodiversity crisis necessitates accurate predictions of how changes in biotic and abiotic conditions will potentially aff ect species richness patterns. Here, we demonstrate that models of the spatial distribution of species richness can be improved by incorporating biotic interactions, and thus that these key predictor factors must be accounted for in biodiversity forecasts.

Aims: The choice of environmental predictor variables in correlative models of plant species distributions (hereafter SDMs) is crucial to ensure predictive accuracy and model realism, as highlighted in multiple earlier studies. Because variable selection is directly related to a model's capacity to capture important species' environmental requirements, one would expect an explicit prior consideration of all ecophysiologically meaningful variables. For plants, these include temperature, water, soil nutrients, light, and in some cases, disturbances and biotic interactions. However, the set of predictors used in published correlative plant SDM studies varies considerably. No comprehensive review exists of what environmental predictors are meaningful, available (or missing) and used in practice to predict plant distributions. Contributing to answer these questions is the aim of this review. Methods: We carried out an extensive, systematic review of recently published plant SDM studies (years 2010-2015; n = 200) to determine the predictors used (and not used) in the models. We additionally conducted an in-depth review of SDM studies in selected journals to identify temporal trends in the use of predictors (years 2000-2015; n = 40). Results: A large majority of plant SDM studies neglected several ecophysiologically meaningful environmental variables, and the number of relevant predictors used in models has stagnated or even declined over the last 15 yr. Conclusions: Neglecting ecophysiologically meaningful predictors can result in incomplete niche quantification and can thus limit the predictive power of plant SDMs. Some of these missing predictors are already available spatially or may soon become available (e.g. soil moisture). However, others are not yet easily obtainable across whole study extents (e.g. soil pH and nutrients), and their development should receive increased attention. We conclude that more effort should be made to build ecologically more sound plant SDMs. This requires a more thorough rationale for the choice of environmental predictors needed to meet the study goal, and the development of missing ones. The latter calls for increased collaborative effort between ecological and geo-environmental sciences.

Issue: Approaches to predicting species assemblages through stacking individual niche-based species distribution models (S-SDMs) need to account for community processes other than abiotic filtering. Such constraints have been introduced by implementing ecological assembly rules (EARs) into S-SDMs, and can be based on patterns of functional traits in communities. Despite being logically valid, this approach has led to a limited improvement in prediction, possibly because of mismatches between the scales of measurement of niche and trait data.Evidence: S-SDM studies have often related single values of a species' traits to environmental niches that are captured by abiotic conditions measured at a much finer spatial scale, without accounting for intraspecific trait variation along environmental gradients. Many pieces of evidence show that omitting intraspecific trait variation can hinder the proper inference of EARs from trait patterns, and we further argue that it can therefore also affect our capacity to spatially predict functional properties of communities. In addition, estimates of environmental niches and trait envelopes may vary depending on the scale at which environmental and trait measurements are made.Conclusion: We suggest that to overcome these limitations, surveys sampling both niche and trait measurements should be conducted at fine scales along wide environmental gradients, and integrated at the same scale to test and improve a new generation of spatial community models and their functional properties. K E Y W O R D S ecological assembly rules, environmental gradients, fine-scale sampling, microhabitat, multivariate envelope, stacked species distribution models

Soil bacteria are largely missing from future biodiversity assessments hindering comprehensive forecasts of ecosystem changes. Soil bacterial communities are expected to be more strongly driven by pH and less by other edaphic and climatic factors. Thus, alkalinisation or acidification along with climate change may influence soil bacteria, with subsequent influences for example on nutrient cycling and vegetation. Future forecasts of soil bacteria are therefore needed. We applied species distribution modelling (SDM) to quantify the roles of environmental factors in governing spatial abundance distribution of soil bacterial OTUs and to predict how future changes in these factors may change bacterial communities in a temperate mountain area. Models indicated that factors related to soil (especially pH), climate and/or topography explain and predict part of the abundance distribution of most OTUs. This supports the expectations that microorganisms have specific environmental requirements (i.e., niches/envelopes) and that they should accordingly respond to environmental changes. Our predictions indicate a stronger role of pH over other predictors (e.g. climate) in governing distributions of bacteria, yet the predicted future changes in bacteria communities are smaller than their current variation across space. The extent of bacterial community change predictions varies as a function of elevation, but in general, deviations from neutral soil pH are expected to decrease abundances and diversity of bacteria. Our findings highlight the need to account for edaphic changes, along with climate changes, in future forecasts of soil bacteria.

1. Habitat filtering and limiting similarity are well-documented ecological assembly processes that hierarchically filter species across spatial scales, from a regional pool to local assemblages. However, information on the effects of fine-scale spatial partitioning of species, working as an additional mechanism of coexistence, on community patterns is much scarcer. 2. In this study, we quantified the importance of fine-scale spatial partitioning, relative to habitat filtering and limiting similarity in structuring grassland communities in the western Swiss Alps. To do so, 298 vegetation plots (2 mx2 m) each with five nested subplots (20 cmx20 cm) were used for trait-based assembly tests (i.e., comparisons with several alternative null expectations), examining the observed plot and subplot level alpha-diversity (indicating habitat filtering and limiting similarity) and the among-subplot beta-diversity of traits (indicating fine-scale spatial partitioning). We further assessed variations in the detected signatures of these assembly processes along a set of environmental gradients. 3. We found habitat filtering was the dominating assembly process at the plot level with diminished effect at the subplot level, whereas limiting similarity prevailed at the subplot level with weaker average effect at the plot level. Plot-level limiting similarity was positively correlated with fine-scale partitioning, suggesting that the trait divergence resulted from a combination of competitive exclusion between functionally similar species and environmental micro-heterogeneities. Overall, signatures of assembly processes only marginally changed along environmental gradients, but the observed trends were more prominent at the plot than at the subplot scale. 4. Synthesis. Our study emphasises the importance of considering multiple assembly processes and traits simultaneously across spatial scales and environmental gradients to understand the complex drivers of plant community composition.

Aim: Biotic interactions have a central role in defining species assemblages, realized both through negative and positive impacts. However, forecasts of how these interactions affect biodiversity across landscapes are challenging (and lacking) because the outcome of interactions depends not only on the identity of the interacting species but also on local environmental conditions. Thus, we study how biotic interactions manifest across a landscape. Location: High-latitude northern Finland and Norway (69° N, 21° E). Methods: We modelled the influence of a dominant shrub, Empetrum hermaphrodituniy on spatial patterns of species richness of vascular plants, bryophytes and lichens (using cover of the shrub as a proxy for the frequency and intensity of interaction with other species) across a topographically variable landscape. Results: The relationship between the cover of the dominant shrub and species richness differed between guilds, being strongest for vascular plant richness, and varied considerably along environmental gradients. The dominant shrub showed stronger negative impact on vascular plant richness under less abiotically extreme conditions. For lichens, the relationship was the opposite: under mild conditions, species richness increased with the cover of the shrub. Incorporating specific leaf area (SLA) data into analyses revealed that the dominant shrub affected species with high SLA (typically competitive species of resourcerich environments) most negatively. Main conclusions: Our findings show how the impact of a dominant species varies across a landscape, with distinctly different effects on competitive and stress-tolerant vascular plants, and on bryophytes and lichens. Spatial predictions of biodiversity trends under global environmental change could, therefore, critically benefit from accounting for context-dependent impacts of biotic interactions.

QUESTION : Can variation in the outcome of biotic interactions in relation to environmental severity bemore accurately predictedwhen consideringmultiple stress and/or disturbance variables? LOCATION : Arctic-alpine tundra in Kilpisj€arvi, North Finland. METHODS : To test the impact of including multiple environmental variables in analyses of the outcomes of biotic interactions, we modelled reproductive effort and cover of 17 arctic-alpine species as a function of Empetrum nigrum subsp. hermaphroditum cover, geomorphological disturbance and soil moisture with statistical interactions of the explanatory variables included.We implemented a best-subset approach using generalized linear models (GLM) and selected the bestmodel for each species based on Akaike’s information criterion (AIC). RESULTS : For the majority of species, models including multiple environmental variables were selected as best. Reproductive effort depended on one or both environmental variables for all species, and 14 species were additionally influenced by Empetrum,with the impact of Empetrum varyingwith abiotic conditions in all but one of those species. Moreover, the three-way interaction of three explanatory variables was included in the best-fit models for six species. The impact of Empetrum on species cover showed a similar pattern, with 11 species affected by Empetrum and its statistical interactions with one or both abiotic variables. CONCLUTIONS : Biotic interactions have an important role in arctic-alpine vegetation, but to fully understand variation in their effects multiple environmental factors should be explicitly considered. In this study, the outcome of biotic interactions was frequently dependent on two abiotic variables (and occasionally additionally on their statistical interaction). Therefore, we demonstrate that studies based on only one environmental factor may cause misleading interpretations of the nature of biotic interactions in plant communities where there are multiple independent variables underlying the habitat severity gradient.