After massive proliferation over the last decade, distribution modelling (DM) – research with the purpose of modelling the distribution of observable objects of a specific type – has grown into an independent branch of ecological science. There is consensus that this new discipline needs a stronger theoretical foundation. I describe DM as an inductive scientific process with 12 steps, organised into three composite steps: ecological model, data model, and statistical model. Step 8, modelling of the overall ecological response, places DM unambiguously among gradient analysis techniques and motivates for a gradient analytic (GA) perspective on DM. DM terminology is reviewed and revised accordingly. Three fundamental insights of the GA perspective are described: (1) that external ‘factors’ do not influence the species one by one, but act on the species in concert; (2) that a few major complex-gradients normally account for most of the variation in species composition that can be explained environmentally; and (3) that species occur within a restricted interval along each major complex-gradient. These insights are developed into a theoretical platform for DM. General patterns of species performance variation along environmental complex-gradients and the structuring processes responsible for these patterns are reviewed. Three categories of ecoclines, i.e., gradients of variation in species composition and the environment, are recognised: regional ecoclines, local ecoclines, and condition or impact ecoclines. Causes and implications of the unimodal shape of species’ responses to environmental complex-gradients are reviewed. Structuring processes are divided into three categories: limited physiological tolerance, interspecific interactions, and demographic processes. Relationships between categories of ecoclines, the processes responsible for variation in species performance along them, and the spatial and temporal scale intervals in which variation is large, are reviewed. The GA perspective forms the basis for discussions of important steps in the DM process. Initially, the controversial concepts of the habitat and the niche are reviewed and their role in the ecological model (Step 1) discussed. I conclude that neither of these concepts are necessary, nor useful, for DM. As an alternative to conceptual models based upon the niche concept, I propose a new conceptual modelling framework for DM, the HED framework, which is rooted in the gradient analytic perspective. I show how this new framework can be used, in initial phases of a DM study to formulate a meta-model for factors that influence distributions, and in the analytic phase to guide important choices of methods and options and to assist interpretation of modelling results. Important data model issues are: collection of data for the modelled target and preparation of raw response variables (Steps 2 and 6); collection of explanatory data (Step 3); conceptualisation of the study area (Step 4); collection of data for calibration and evaluation (Step 9); and transformation of explanatory variables to derived variables subjected to DM (Step 5,ii). Important statistical model issues are: statistical model formulation, i.e. choice of method (Step 7,i) and model specification (Step 7,ii); model selection and internal assessment of model performance (Steps 8,i and 8,ii); and model evaluation (Step 10). Two points are emphasised: (1) that modelling purpose should inform choice of methods and options; and (2) the importance of an independently collected presence/absence data set, which can be used to calibrate, evaluate and iteratively improve models. Finally I list seven challenges of particular importance for progress in DM: (1) that more knowledge of patterns of natural variation is needed; (2) that a better mechanistic understanding of causes of patterns of natural variation is needed; (3) that the availability of relevant rasterised explanatory variables needs to be improved; (4) that more studies of patterns at local and micro spatial scales, in addition to multiple-scale studies using DM methods, are needed; (5) that evaluation by independent data should be established as a standard in DM; (6) that further insights into statistical modelling methods and their options, with particular reference to appropriateness for different types of data and DM purposes, are needed; and (7) that DM methods should be incorporated in studies with a broader scope. I conclude that there are considerable potentials for improvement of DM methods and practice. Increased return from DM in terms of contributions that improve our understanding of patterns of natural variation and their causes, should be expected.

We investigated changes in the root-associated fungal communities associated with the ectomycorrhizal herb Bistorta vivipara along a primary succession gradient using 454 amplicon sequencing. Our main objective was to assess the degree of variation in fungal richness and community composition as vegetation cover increases along the chronosequence. Sixty root systems of B. vivipara were sampled in vegetation zones delimited by dated moraines in front of a retreating glacier in Norway. We extracted DNA from rinsed root systems, amplified the ITS1 region using fungal-specific primers and analysed the amplicons using 454 sequencing. Between 437 and 5063 sequences were obtained from each root system. Clustering analyses using a 98.5% sequence similarity cut-off yielded a total of 470 operational taxonomic units (OTUs), excluding singletons. Between eight and 41 fungal OTUs were detected within each root system. Already in the first stage of succession, a high fungal diversity was present in the B. vivipara root systems. Total number of OTUs increased significantly along the gradient towards climax vegetation, but the average number of OTUs per root system stayed unchanged. There was a high patchiness in distribution of fungal OTUs across root systems, indicating that stochastic processes to a large extent structure the fungal communities. However, time since deglaciation had impact on the fungal community structure, as a systematic shift in the community composition was observed along the chronosequence. Ectomycorrhizal basidiomycetes were the dominant fungi in the roots of B. vivipara, when it comes to both number of OTUs and number of sequences.

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Distribution modelling - research with the purpose of modelling the distribution of observable objects of a specific type - has become established as an independent branch of ecological science, with strong proliferation of approaches and methods in recent years. Since it was first made available to distribution modellers in 2004, the maximum entropy modelling method (MaxEnt) has established itself as a state-of-the-art method for distribution modelling. Default options and settings in the user-friendly Maxent software has become established as a standard practice for distribution modelling by MaxEnt.A mini-review of 87 recent publications in which MaxEnt was used with empirical data to model distributions showed that the ‘standard MaxEnt practice’ is followed by a large majority of users and questioned by few. However, the review also provides indications that MaxEnt models obtained by the standard practice are sometimes overfitted to the data used to parameterise the model; examples of cases in which simpler MaxEnt models with predictive performance do exist. Results of the review motivate strongly for a better understanding of the ecological implications of the maximum entropy principle, as a basis for choosing MaxEnt options and settings.This paper provides a thorough explanation of MaxEnt for ecologists, ending with a set of suggestions for improvements to the current practice of distribution modelling by MaxEnt. The explanation for MaxEnt given in the paper differs from previous explanations by being based on the maximum likelihood principle and by being based upon a gradient analytic perspective on distribution modelling. Four new findings are particularly emphasised: (1) that a strict maximum likelihood explanation of MaxEnt is possible, which places MaxEnt among regression methods in the widest sense; (2) that the true degrees of freedom for the residuals of a Max- Ent null model is N - n, the difference between the number of background and the number of presence observations used in the modelling; (3) that likelihood-ratio and F-ratio tests can be used to compare nested MaxEnt models; and (4) that subset selection methods are likely to be preferential to shrinkage methods for model selection in MaxEnt. Methods for internal model performance assessment, model comparison, and interpretation of MaxEnt model predictions (MaxEnt output), are described and discussed. Two simulated data sets are used to explore and illustrate important issues relating to MaxEnt methodology.Arguments for development of a generally applicable ‘consensus MaxEnt practice’ for spatial prediction modelling are given, and elements of such a practice discussed. Five main additions or amendments to the ʻstandard MaxEnt practiceʼ are suggested: (1) flexible, interactive tools to assist deriving of variables from raw explanatory variables; (2) interactive tools to allow the user freely to combine model selection methods, methods and approaches for internal model performance assessment, and model improvement criteria, into a data-driven modelling procedure, (3) integration of independent presence/absence data into the modelling process, for external model performance assessment, for model calibration, and for model evaluation; (4) new output formats, notably a probability-ratio output format which directly expresses the ʻrelative suitability of one place vs. anotherʼ for the modelled target; and (5) development of options for discriminative use of MaxEnt, i.e., use of with presence/absence data. The most important research needs are considered to be: (1) comparative studies of strategies for construction of parsimonious sets of derived variables for use in MaxEnt modelling; and (2) comparative tests on independent presence/absence data of the predictive performance of MaxEnt models obtained with different model selection strategies, different approaches for internal model performance assessment, and different model improvement criteria.

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PlantÁherbivore dynamics is a major topic in ecological research, but empirical knowledge on the ecological effects of different densities of large grazers from fully replicated experiments is rare. Previous studies have focused on grazing vs no grazing, and our understanding of the extent to which different levels of grazing alter vegetation composition, and how quickly such effects can be measured, is therefore limited. We performed a fully replicated, short-term (four-year) experiment using large enclosures (each Â0.3 km 2 ) with three different sheep densities (no grazing, low grazing and high grazing, respectively) in an alpine environment with summer grazing in southern Norway to address these issues. Sheep grazing mainly affected plant species at high densities of sheep as compared to no sheep after a four-year treatment; few effects of low sheep densities were detectable. Highly selected herbs, herbs suggested vulnerable to trampling, and woody species decreased, while most graminoids, one ruderal, one prostrate species and two bryophyte taxa increased at high sheep densities. We found contrasting responses within main functional groups highlighting that fine details of plant life histories need to be known for responses to grazing to be successfully predicted. Vascular plant cover and bare soil responded to sheep density after two years of treatment, but only for one of the species was frequency change observed at this stage. Overall, plants in low grazing plots were found to be almost unaffected. Changes in abundance were mainly found at the no grazing and high grazing treatments. Plant species that decreased at high grazing generally increased at no grazing and vice versa, suggesting a response to both cessation of grazing and enhanced grazing respectively. Our study demonstrates, beyond a simple comparison of heavily grazed and non-grazed sites, that herbivore effects on plants are typically non-linearly related to herbivore density, and that the speed of plant responses will depend both on the plant property examined and the grazing pressure.Herbivores can strongly affect plant community patterns by favouring resistant and tolerant plants to the detriment of less tolerant, highly selected species (Crawley 1997, Hester et al. 2006. Although the outcome of direct and indirect herbivore effects in terms of (change in) overall species composition appears more or less predictable for the alpine tundra (Van Der Wal 2006), these are mostly qualitative patterns for main growth-form groups. Exactly how high herbivore intensity is required for different species in alpine communities to trigger abundance change over time has rarely been examined experimentally. The effect of herbivory has mainly been studied by small-scale exclosure experiments (Mulder 1999) or by use of spatial contrasts in herbivore density (rather than following grazing over time in one area) in quasi-experiments at coarse scales (Bråthen et al. 2007, Ims et al. 2007. Empirical knowledge of the shape of the density-effect function of large g...

In terrestrial ecosystems, fungi are the major agents of decomposition processes and nutrient cycling and of plant nutrient uptake. Hence, they have a vital impact on ecosystem processes and the terrestrial carbon cycle. Changes in productivity and phenology of fungal fruit bodies can give clues to changes in fungal activity, but understanding these changes in relation to a changing climate is a pending challenge among ecologists. Here we report on phenological changes in fungal fruiting in Europe over the past four decades. Analyses of 746,297 dated and geo-referenced mushroom records of 486 autumnal fruiting species from Austria, Norway, Switzerland, and the United Kingdom revealed a widening of the annual fruiting season in all countries during the period 1970-2007. The mean annual day of fruiting has become later in all countries. However, the interspecific variation in phenological responses was high. Most species moved toward a later ending of their annual fruiting period, a trend that was particularly strong in the United Kingdom, which may reflect regional variation in climate change and its effects. Fruiting of both saprotrophic and mycorrhizal fungi now continues later in the year, but mycorrhizal fungi generally have a more compressed season than saprotrophs. This difference is probably due to the fruiting of mycorrhizal fungi partly depending on cues from the host plant. Extension of the European fungal fruiting season parallels an extended vegetation season in Europe. Changes in fruiting phenology imply changes in mycelia activity, with implications for ecosystem function.fungal ecology | Basidiomycetes | agarics | seasonality

Aim The main aims of this study are: (1) to test if temperature and related parameters are the primary determinants of the regional distribution of macrofungi (as is commonly recognized for plants) ; (2) to test if the success of modelling fungal distribution patterns depends on species and distribution characteristics; and (3) to explore the potential of using herbarium data for modelling and predicting fungal species' distributions.Location The study area, Norway, spans 58-71°N latitude and 4-32°E longitude, and embraces extensive ecological gradients in a small area. MethodsThe study is based on 1020 herbarium collections of nine selected species of macrofungi and a set of 75 environmental predictor variables, all recorded in a 5 · 5-km grid covering Norway. Primarily, generalized linear model (GLM; logistic regression) analyses were used to identify the environmental variables that best accounted for the species' recorded distributions in Norway. Second, Maxent analyses (using variables identified by GLM) were used to produce predictive potential distribution maps for these species.Results Variables relating to temperature and radiation were most frequently included in the GLMs, and between 24.8% and 59.8% of the variation in singlespecies occurrence was accounted for. The fraction of variation explained by the GLMs ranged from 41.6% to 59.8% for species with restricted distributions, and from 24.8% to 39.3% for species with widespread/scattered and intermediate distributions. The two-step procedure of GLM followed by Maxent gave predictions with very high values for the area under the curve (0.927-0.997), and maps of potential distribution were generally credible.Main conclusions We show that temperature is a key factor governing the distribution of macrofungi in Norway, indicating that fungi may respond strongly to global warming. We confirm that modelling success depends partly on species and distribution characteristics, notably on how the distribution relates to the extent of the study area. Our study demonstrates that the combination of GLM and Maxent may be a fruitful approach for biogeography. We conclude that herbarium data improve insight into factors that control the distributions of fungi, of particular value for research on fleshy fungi (mushrooms), which have largely cryptic life cycles.