How to evaluate community predictions without thresholding?

Stacked species distribution models (S-SDM) provide a tool to make spatial predictions about communities by first modelling individual species and then stacking the modelled predictions to form assemblages. The evaluation of the predictive performance is usually based on a comparison of the observed and predicted community properties (e.g. species richness, composition). However, the most available and widely used evaluation metrics require the thresholding of single species' predicted probabilities of occurrence to obtain binary outcomes (i.e. presence/absence). This binarization can introduce unnecessary bias and error. Herein, we present and demonstrate the use of several groups of new or rarely used evaluation approaches and metrics for both species richness and community composition that do not require thresholding but instead directly compare the predicted probabilities of occurrences of species to the presence/absence observations in the assemblages. Community AUC, which is based on traditional AUC, measures the ability of a model to differentiate between species presences or absences at a given site according to their predicted probabilities of occurrence. Summing the probabilities gives the expected species richness and allows the estimation of the probability that the observed species richness is not different from the expected species richness based on the species' probabilities of occurrence. The traditional Sorensen and Jaccard similarity indices (which are based on presences/absences) were adapted to maxSorensen and maxJaccard and to probSorensen and probJaccard (which use probabilities directly). A further approach (improvement over null models) compares the predictions based on S-SDMs with the expectations from the null models to estimate the improvement in both species richness and composition predictions. Additionally, all metrics can be described against the environmental conditions of sites (e.g. elevation) to highlight the abilities of models to detect the variation in the strength of the community assembly processes in different environments. These metrics offer an unbiased view of the performance of community predictions compared to metrics that requiring thresholding. As such, they allow more straightforward comparisons of model performance among studies (i.e. they are not influenced by any subjective thresholding decisions).Peer reviewe

Arctic shrubification mediates the impacts of warming climate on changes to tundra vegetation

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Outcomes of biotic interactions are dependent on multiple environmental variables

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.Academy of Finland (Project Number 1140873), the Nordenskiöld Foundation and the Department of Geosciences and Geography (University of Helsinki)http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1654-1103hb201

Improving forecasts of arctic-alpine refugia persistence with landscape-scale variables

Refugia, the sites preserving conditions reminiscent of suitable climates, are projected to be crucial for species in a changing climate, particularly at high latitudes. However, the knowledge of current locations of high-latitude refugia and particularly their ability to retain suitable conditions under future climatic changes is limited. Occurrences of refugia have previously been mainly assessed and modelled based solely on climatic features, with insufficient attention being paid to potentially important landscape-scale factors. Here, climate-only models and full' models incorporating topo-edaphic landscape-scale variables (radiation, soil moisture and calcareousness) were developed and compared for 111 arctic-alpine plant species in Northern Fennoscandia. This was done for both current and future climates to determine cells with resilient climatic suitability harbouring refugia. Our results show that topographic and edaphic landscape-scale predictors both significantly improve models of arctic-alpine species distributions and alter projections of refugia occurrence. The predictions of species-climate models ignore landscape-scale ecological processes and may thus provide inaccurate estimates of extinction risk and forecasts of refugia where species can persist under a changing climate.Peer reviewe

Contrasting effects of biotic interactions on richness and distribution of vascular plants, bryophytes and lichens in an arctic–alpine landscape

Biotic interactions may strongly affect the distribution of individual species and the resulting patterns of species richness. However, the impacts can vary depending on the species or taxa examined, suggesting that the influences of interactions on species distributions and diversity are not always straightforward and can be taxon-contingent. The aim of this study was therefore to examine how the importance of biotic interactions varies within a community. We incorporated three biotic predictors (cover of the dominant vascular species) into two correlative species richness modelling frameworks to predict spatial variation in the number of vascular plants, bryophytes and lichens in arctic-alpine Fennoscandia, in N Europe. In addition, predictions based on single-species distribution models were used to determine the nature of the impact (negative vs. positive outcome) of the three dominant species on individual vascular plant, bryophyte and lichen species. Our results suggest that biotic variables can be as important as abiotic variables, but their relative contributions in explaining the richness of sub-dominant species varies among dominant species, species group and the modelling framework implemented. Similarly, the impacts of biotic interactions on individual species varied among the three species groups and dominant species, with the observed patterns partly reflecting species’ biogeographic range. Our study provides additional support for the importance of biotic interactions in modifying arctic-alpine biodiversity patterns, and highlights that the impacts of interactions are not constant across taxa or biotic drivers. The influence of biotic interactions, including the taxon-contingency and range-based impacts, should therefore be accounted for when developing biodiversity forecasts.Academy of Finland (Project Number 1140873). Research Foundation of the University of Helsinki.http://link.springer.com/journal/3002017-04-30hb2016Plant Production and Soil Scienc

Comparative analysis of diversity and environmental niches of soil bacterial, archaeal, fungal and protist communities reveal niche divergences along environmental gradients in the Alps

Although widely used in ecology, comparative analyses of diversity and niche properties are still lacking for microorganisms, especially focusing on niche variations. Quantifying the niches of microbial taxa is necessary to then forecast how taxa and the communities they compose might respond to environmental changes. In this study, we first identified important topoclimatic, edaphic, spatial and biotic drivers of the alpha and beta di-versity of bacterial, archaeal, fungal and protist communities. Then, we calculated the niche breadth and position of each taxon along the important environmental gradients to determine how these vary within and among the taxonomic groups. We found that edaphic properties were the most important drivers of both, community di-versity and composition, for all microbial groups. Protists and bacteria presented the largest niche breadths on average, followed by archaea, with fungi displaying the smallest. Niche breadth generally decreased towards environmental extremes, especially along edaphic gradients, suggesting increased specialization of microbial taxa in highly selective environments. Overall, we showed that microorganisms have well defined niches, as do macro-organisms, likely driving part of the observed spatial patterns of community variations. Assessing niche variation more widely in microbial ecology should open new perspectives, especially to tackle global change effects on microbes.Peer reviewe

Predicting spatial patterns of soil bacteria under current and future environmental conditions

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.Peer reviewe

Soil protist function varies with elevation in the Swiss Alps

Protists are abundant and play key trophic functions in soil. Documenting how their trophic contributions vary across large environmental gradients is essential to understand and predict how biogeochemical cycles will be impacted by global changes. Here, using amplicon sequencing of environmental DNA in open habitat soil from 161 locations spanning 2600 m of elevation in the Swiss Alps (from 400 to 3000 m), we found that, over the whole study area, soils are dominated by consumers, followed by parasites and phototrophs. In contrast, the proportion of these groups in local communities shows large variations in relation to elevation. While there is, on average, three times more consumers than parasites at low elevation (400-1000 m), this ratio increases to 12 at high elevation (2000-3000 m). This suggests that the decrease in protist host biomass and diversity toward mountains tops impact protist functional composition. Furthermore, the taxonomic composition of protists that infect animals was related to elevation while that of protists that infect plants or of protist consumers was related to soil pH. This study provides a first step to document and understand how soil protist functions vary along the elevational gradient.Peer reviewe

Data from: Disentangling biotic interactions, environmental filters, and dispersal limitation as drivers of species co-occurrence

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

The relationships of plant species occupancy to niches and traits vary with spatial scale

Aim Support for different underlying mechanisms of species occupancy is inconsistent, yet this could be related to spatial scale. Since abiotic filtering typically acts at broader scales than biotic interactions, we hypothesise that occupancy could be more driven by species' abiotic niche (i.e. tolerance and preference of abiotic conditions) at broad scales, whereas species' traits affecting competitive ability could be more important at fine scales. Here, we test these hypotheses by assessing relationships of occupancy to niche and trait metrics across spatial scales. Location Four study areas located north of Arctic Circle. Taxon Vascular plants. Methods We derived occupancy for 106 species at four spatial scales (micro-scale with plot size of 0.04 m2 and extent of 2 km, local-scale with plot size of 4 m2 and extent of 40 km, regional-scale with plot size of 4 ha and extent of 800 km, and polar-scale with plot size of 4 km2 and extent of 5200 km). We then assessed using generalized additive models whether the relationships between occupancy and species' niche breadth, niche marginality, intraspecific trait variability (ITV) and trait distinctiveness vary across the scales. Results At the finer scales, ITV (especially of specific leaf area) had the highest contribution with positive relationship in explaining occupancy. At the broader scales, occupancy was better explained by niche metrics. Especially at the broadest scale, the occupancy had a positive relationship with species' climatic tolerance. Main Conclusions Abiotic filtering, especially related to macro-climate, drives species occupancy at broader spatial scales while biotic interactions are relatively more important at local scales. This scale-dependency of factors behind species occupancy should be accounted for when, for example, planning conservation of rare species, forecasting invasions or anticipating the effects of changing climate on biota at local versus global scales.peerReviewe