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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.

The regional origin of a food product commonly affects its value. To this, DNA-based identification of tissue remains could offer fine resolution. For honey, this would allow the usage of not only pollen but all plant tissue, and also that of microbes in the product, for discerning the origin. Here we examined how plant, bacterial and fungal taxa identified by DNA metabarcoding and metagenomics differentiate between honey samples from three neighbouring countries. To establish how the taxonomic contents of honey reflect the country of origin, we used joint species distribution modelling. At the lowest taxonomic level by metabarcoding, with operational taxonomic units, the country of origin explained the majority of variation in the data (70–79%), with plant and fungal gene regions providing the clearest distinction between countries. At the taxonomic level of genera, plants provided the most separation between countries with both metabarcoding and metagenomics. The DNA-based methods distinguish the countries more than the morphological pollen identification and the removal of pollen has only a minor effect on taxonomic recovery by DNA. As we find good resolution among honeys from regions with similar biota, DNA-based methods hold great promise for resolving honey origins among more different regions.

When comparing multiple models of species distribution, models yielding higher predictive performance are clearly to be favored. A more difficult question is how to decide whether even the best model is “good enough”. Here, we clarify key choices and metrics related to evaluating the predictive performance of presence–absence models. We use a hierarchical case study to evaluate how four metrics of predictive performance (AUC, Tjur's R2, max-Kappa, and max-TSS) relate to each other, the random and fixed effects parts of the model, the spatial scale at which predictive performance is measured, and the cross-validation strategy chosen. We demonstrate that the very same metric can achieve different values for the very same model, even when similar cross-validation strategies are followed, depending on the spatial scale at which predictive performance is measured. Among metrics, Tjur's R2 and max-Kappa generally increase with species' prevalence, whereas AUC and max-TSS are largely independent of prevalence. Thus, Tjur's R2 and max-Kappa often reach lower values when measured at the smallest scales considered in the study, while AUC and max-TSS reaching similar values across the different spatial levels included in the study. However, they provide complementary insights on predictive performance. The very same model may appear excellent or poor not only due to the applied metric, but also how predictive performance is exactly calculated, calling for great caution on the interpretation of predictive performance. The most comprehensive evaluation of predictive performance can be obtained by evaluating predictive performance through the combination of measures providing complementary insights. Instead of following simple rules of thumb or focusing on absolute values, we recommend comparing the achieved predictive performance to the researcher's own a priori expectations on how easy it is to make predictions related to the same question that the model is used for.

© 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.

Estimation of intra- and interspecific interactions from time-series on species-rich communities is challenging due to the high number of potentially interacting species pairs. The previously proposed sparse interactions model overcomes this challenge by assuming that most species pairs do not interact. We propose an alternative model that does not assume that any of the interactions are necessarily zero, but summarizes the influences of individual species by a small number of community-level drivers. The community-level drivers are defined as linear combinations of species abundances, and they may thus represent e.g. the total abundance of all species or the relative proportions of different functional groups. We show with simulated and real data how our approach can be used to compare different hypotheses on community structure. In an empirical example using aquatic microorganisms, the community-level drivers model clearly outperformed the sparse interactions model in predicting independent validation data.

Incompleteness of reference sequence databases and unresolved taxonomic relationships complicates taxonomic placement of fungal sequences. We developed Protax-fungi, a general tool for taxonomic placement of fungal internal transcribed spacer (ITS) sequences, and implemented it into the PlutoF platform of the UNITE database for molecular identification of fungi. With empirical data on root- and wood-associated fungi, Protax-fungi reliably identified (with at least 90% identification probability) the majority of sequences to the order level but only around one-fifth of them to the species level, reflecting the current limited coverage of the databases. Protax-fungi outperformed the Sintax and Rdb classifiers in terms of increased accuracy and decreased calibration error when applied to data on mock communities representing species groups with poor sequence database coverage. We applied Protax-fungi to examine the internal consistencies of the Index Fungorum and UNITE databases. This revealed inconsistencies in the taxonomy database as well as mislabelling and sequence quality problems in the reference database. The according improvements were implemented in both databases. Protax-fungi provides a robust tool for performing statistically reliable identifications of fungi in spite of the incompleteness of extant reference sequence databases and unresolved taxonomic relationships.

Species interactions are known to structure ecological communities. Still, the influence of climate change on biodiversity has primarily been evaluated by correlating individual species distributions with local climatic descriptors, then extrapolating into future climate scenarios. We ask whether predictions on arctic arthropod response to climate change can be improved by accounting for species interactions. For this, we use a 14‐year‐long, weekly time series from Greenland, resolved to the species level by mitogenome mapping. During the study period, temperature increased by 2°C and arthropod species richness halved. We show that with abiotic variables alone, we are essentially unable to predict species responses, but with species interactions included, the predictive power of the models improves considerably. Cascading trophic effects thereby emerge as important in structuring biodiversity response to climate change. Given the need to scale up from species‐level to community‐level projections of biodiversity change, these results represent a major step forward for predictive ecology.

We investigated the interaction between fungal communities of soil and dead wood substrates. For this, we applied molecular species identification and stable isotope tracking to both soil and decaying wood in an unmanaged boreal Norway spruce-dominated stand. Altogether, we recorded 1990 operational taxonomic units, out of which more than 600 were shared by both substrates and 589 were found to exclusively inhabit wood. On average the soil was more species-rich than the decaying wood, but the species richness in dead wood increased monotonically along the decay gradient, reaching the same species richness and community composition as soil in the late stages. Decaying logs at all decay stages locally influenced the fungal communities from soil, some fungal species occurring in soil only under decaying wood. Stable isotope analyses suggest that mycorrhizal species colonising dead wood in the late decay stages actively transfer nitrogen and carbon between soil and host plants. Most importantly, Piloderma sphaerosporum and Tylospora sp. mycorrhizal species were highly abundant in decayed wood. Soil- and wood-inhabiting fungal communities interact at all decay phases of wood that has important implications in fungal community dynamics and thus nutrient transportation.

Through their ephemeral reproductive structures (fruiting bodies), ectomycorrhizal forest soil fungi provide a resource for a plethora of organisms. Thus, resolving what biotic and abiotic factors determine the occurrence and abundance of fruiting bodies is fundamental for understanding the dynamics of forest trophic networks. While the influence of abiotic factors such as moisture and temperature on fungal fruiting are relatively well established, little is known about how these processes interact with the evolutionary history of fungal species to determine when, where, and in which abundance fungal fruiting bodies will emerge. A specific knowledge gap relates to whether species' responses to their environment are phylogenetically structured. Here, we ask whether related fungal taxa respond similarly to climatic factors and forest habitat characteristics, and whether such correlated responses will affect the assembly of fungal fruiting communities. To resolve these questions, we fitted joint species distribution models combining data on the species composition and abundance of fungal fruiting bodies, environmental variation, and phylogenetic relationships among fungal taxa. Our results show that both site-level forest characteristics (dominant tree species and forest age) and climatic factors related to phenology (effective heat sum) greatly influence the occurrence and abundance of fruiting bodies. More importantly, while different fungal species responded unequally to their shared environment, there was a strong phylogenetic signal in their responses, so that related fungal species tended to fruit under similar environmental conditions. Thus, not only are fruiting bodies short-lived and patchily distributed, but the availability of similar resources will be further aggregated in time and space. These strong constraints on resource availability for fungus-associated taxa highlight the potential of fungus-based networks as a model system for studies on the ecology and evolution of resource–consumer relations in ephemeral systems of high spatiotemporal patchiness.

The general negative impact of forestry on wood-inhabiting fungal diversity is well recognized, yet the effect of forest naturalness is poorly disentangled among different fungal groups inhabiting dead wood of different tree species. We studied the relationship between forest naturalness, log characteristics and diversity of different fungal morpho-groups inhabiting large decaying logs of similar quality in spruce dominated boreal forests. We sampled all non-lichenized fruitbodies from birch, spruce, pine and aspen in 12 semi-natural forest sites of varying level of naturalness. The overall fungal community composition was mostly determined by host tree species. However, when assessing the relevance of the environmental variables separately for each tree species, the most important variable varied, naturalness being the most important explanatory variable for fungi inhabiting pine and aspen. More strikingly, the overall species richness increased as the forest naturalness increased, both at the site and log levels. At the site scale, the pattern was mostly driven by the discoid and pyrenoid morpho-groups inhabiting pine , whereas at the log scale, it was driven by pileate and resupinate morpho-groups inhabiting spruce. Although our study demonstrates that formerly managed protected forests serve as effective conservation areas for most wood-inhabiting fungal groups, it also shows that conservation planning and management should account for group- or host tree -specific responses.