Upward shift in elevational plant species ranges in Sikkilsdalen,central Norway

Phytosociological studies can be an important tool to detect temporal vegetation changes in response to global climate change. In this study, we present the results of a resurvey of a plot‐based phytosociological study from Sikkilsdalen, central Norway, originally executed between 1922 and 1932. By using a detailed phytosociological study we are able to investigate several aspects of elevational shifts in species ranges. Here we tested for upward and downward shifts in observed upper and lower distribution limits of species, as well as changes in species optima along an elevational gradient, and related the observed range shifts to species traits that could explain the observed trends. More species shifted upwards than downwards, independently of whether we were investigating shifts in species’ upper or lower distribution ranges or in species optima. However, shifts in species upper range margins changed independently of their lower range margins. Linking different species traits to the magnitude of shifts we found that species with a higher preference for prolonged snow cover shifted upwards more in their upper elevational limits and in their optima than species that prefer a shorter snow cover, whereas no species traits were correlated with the magnitude of changes in lower limits. The observed change in species ranges concord both with studies on other mountains in the region and with studies from other alpine areas. Furthermore, our study indicates that different factors are influencing species ranges at the upper and lower range limits. Increased precipitation rates and increased temperatures are considered the most important factors for the observed changes, probably mainly through altering the pattern in snow cover dynamics in the area.     

The relative importance of positive and negative interactions for pollinator attraction in a plant community

Plant–pollinator interactions provide ideal frameworks for studying interactions in plant communities. Despite the large potential influence of such interactions on plant community structure, biodiversity and evolutionary processes, we know surprisingly little about the relative importance of positive and negative interactions among plant species for pollinator attraction. Therefore, we explored the relationships between conspecific and heterospecific floral densities and the flower visitation rates of nine plant species mainly visited by bumble bees, and six plant species mainly visited by flies, in a temperate grassland, through stepwise multiple regressions. Significant relationships were interpreted as interactions for pollinator attraction. Our results revealed that positive intra- and interspecific interactions for pollinator attraction were far more frequent than negative ones. Seventeen interspecific interactions were revealed of which 14 were significantly positive, whereas three of four significant intraspecific interactions were positive. Seven species experienced only positive interactions and two species experienced only negative interactions. The results presented here indicate that negative interactions are not necessarily the dominant ecological interaction for pollination among plants within a community, and the study represents a straightforward approach to study intra- and interspecific interactions among multiple species within a community. We discuss which mechanisms may drive the positive interactions for pollinator attraction and whether this may result in facilitative effects on reproductive success. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling

Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km² to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere.     

The taxonomic distribution of rare and common species among families in the vascular plant flora of Fennoscandia

Many plant traits are not randomly distributed among families. The question considered here is ‘are rarity and commonness of vascular plants in Fennoscandia randomly distributed among families?’ If more rare or more common species are found within a family, this may give some initial indications about which traits may predict rarity and commonness of species. A species was defined as rare or common based on its abundance and on the number of grid squares it occupies. 1521 naturally occurring species in 229 75×75 km grid squares were used. Permutation tests were performed to assess statistically if rarity and commonness are randomly distributed among families. Several families can be identified as having more rare or more common species than would be expected under a random allocation model. However, there are little deviations from what would be expected if rarity and commonness were randomly distributed among families in the whole Fennoscandian flora. It is proposed that the arbitrary geographical limits of the study area may account for the lack of any clear patterns of rarity and commonness among and between families.     

A guide to the processing and standardization of global palaeoecological data for large-scale syntheses using fossil pollen

Aim

Palaeoecological data are crucial for comprehending large-scale biodiversity patterns and the natural and anthropogenic drivers that influence them over time. Over the last decade, the availability of open-access research databases of palaeoecological proxies has substantially increased. These databases open the door to research questions needing advanced numerical analyses and modelling based on big-data compilations. However, compiling and analysing palaeoecological data pose unique challenges that require a guide for producing standardized and reproducible compilations.

Innovation

We present a step-by-step guide of how to process fossil pollen data into a standardized dataset compilation ready for macroecological and palaeoecological analyses. We describe successive criteria that will enhance the quality of the compilations. Though these criteria are project and research question-dependent, we discuss the most important assumptions that should be considered and adjusted accordingly. Our guide is accompanied by an R-workflow—called FOSSILPOL—and corresponding R-package—called R-Fossilpol—that provide a detailed protocol ready for interdisciplinary users. We illustrate the workflow by sourcing and processing Scandinavian fossil pollen datasets and show the reproducibility of continental-scale data processing.

Main Conclusions

The study of biodiversity and macroecological patterns through time and space requires large-scale syntheses of palaeoecological datasets. The data preparation for such syntheses must be transparent and reproducible. With our FOSSILPOL workflow and R-package, we provide a protocol for optimal handling of large compilations of fossil pollen datasets and workflow reproducibility. Our workflow is also relevant for the compilation and synthesis of other palaeoecological proxies and as such offers a guide for synthetic and cross-disciplinary analyses with macroecological, biogeographical and palaeoecological perspectives. However, we emphasize that expertise and informed decisions based on palaeoecological knowledge remain crucial for high-quality data syntheses and should be strongly embedded in studies that rely on the increasing amount of open-access palaeoecological data.     

Patterns and causes of species richness: a general simulation model for macroecology

Understanding the causes of spatial variation in species richness is a major research focus of biogeography and macroecology. Gridded environmental data and species richness maps have been used in increasingly sophisticated curve‐fitting analyses, but these methods have not brought us much closer to a mechanistic understanding of the patterns. During the past two decades, macroecologists have successfully addressed technical problems posed by spatial autocorrelation, intercorrelation of predictor variables and non‐linearity. However, curve‐fitting approaches are problematic because most theoretical models in macroecology do not make quantitative predictions, and they do not incorporate interactions among multiple forces. As an alternative, we propose a mechanistic modelling approach. We describe computer simulation models of the stochastic origin, spread, and extinction of species’ geographical ranges in an environmentally heterogeneous, gridded domain and describe progress to date regarding their implementation. The output from such a general simulation model (GSM) would, at a minimum, consist of the simulated distribution of species ranges on a map, yielding the predicted number of species in each grid cell of the domain. In contrast to curve‐fitting analysis, simulation modelling explicitly incorporates the processes believed to be affecting the geographical ranges of species and generates a number of quantitative predictions that can be compared to empirical patterns. We describe three of the ‘control knobs’ for a GSM that specify simple rules for dispersal, evolutionary origins and environmental gradients. Binary combinations of different knob settings correspond to eight distinct simulation models, five of which are already represented in the literature of macroecology. The output from such a GSM will include the predicted species richness per grid cell, the range size frequency distribution, the simulated phylogeny and simulated geographical ranges of the component species, all of which can be compared to empirical patterns. Challenges to the development of the GSM include the measurement of goodness of fit (GOF) between observed data and model predictions, as well as the estimation, optimization and interpretation of the model parameters. The simulation approach offers new insights into the origin and maintenance of species richness patterns, and may provide a common framework for investigating the effects of contemporary climate, evolutionary history and geometric constraints on global biodiversity gradients. With further development, the GSM has the potential to provide a conceptual bridge between macroecology and historical biogeography.     

The influence of scale and species pool on the relationship between vascular plant species richness and cover in an alpine area in Norway

Recent studies emphasise the potential importance of scale and species pool on the humped-back or unimodal relationship between species richness and productivity. We use a classic phytosociological data-set from Rondane, central south Norway, to evaluate the relative importance of these factors in an alpine area. The effect of species pool is assessed using plot scores from a Correspondence Analysis (CA) of the data. Generalised Additive Models (GAM) are used to relate vascular plant species richness to cover of vascular plants, CA plot scores, and plot area in different combinations. Species richness of vascular plants is unimodally related to total vascular plant cover. Plot scores of the first three CA axes (representing the effect of species pool) have a complex relationship with species richness, but explain a large fraction of the total deviance in richness. A humped relationship between richness and cover remains after accounting for CA plot scores in the model, i.e. the relationship is independent of species pool. The results suggest that the relationship between richness and cover changes from one vegetation type to another, as evaluated statistically through the importance of the interaction between cover and CA scores in explaining variation in richness. Plot area also influences the relationship. A unimodal relationship is only evident when small plot sizes are used, whereas a monotonically increasing relationship is found at large plot sizes. Plot area has the strongest effect on the unimodal relationship between richness and cover, whereas vegetation type has only a minor effect on this relationship. This revised version was published online in June 2006 with corrections to the Cover Date.