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1. For managed temperate forests, conservationists and policymakers favour finegrained uneven-aged (UEA) management over more traditional coarse-grained even-aged (EA) management, based on the assumption that within-stand habitat heterogeneity enhances biodiversity. There is, however, little empirical evidence to support this assumption. We investigated for the first time how differently grained forest management systems affect the biodiversity of multiple above- and below-ground taxa across spatial scales. 2. We sampled 15 taxa of animals, plants, fungi and bacteria within the largest contiguous beech forest landscape of Germany and classified them into functional groups. Selected forest stands have been managed for more than a century at different spatial grains. The EA (coarse-grained management) and UEA (fine-grained) forests are comparable in spatial arrangement, climate and soil conditions. These were compared to forests of a nearby national park that have been unmanaged for at least 20 years. We used diversity accumulation curves to compare γ-diversity for Hill numbers ⁰D (species richness), ¹D (Shannon diversity) and ²D (Simpson diversity) between the management systems. Beta diversity was quantified as multiplesite dissimilarity. 3. Gamma diversity was higher in EA than in UEA forests for at least one of the three Hill numbers for six taxa (up to 77%), while eight showed no difference. Only bacteria showed the opposite pattern. Higher γ-diversity in EA forests was also found for forest specialists and saproxylic beetles. 4. Between-stand β-diversity was higher in EA than in UEA forests for one-third (all species) and half (forest specialists) of all taxa, driven by environmental heterogeneity between age-classes, while α-diversity showed no directional response across taxa or for forest specialists. 5. Synthesis and applications. Comparing EA and uneven-aged forest management in Central European beech forests, our results show that a mosaic of different ageclasses is more important for regional biodiversity than high within-stand heterogeneity. We suggest reconsidering the current trend of replacing even-aged management in temperate forests. Instead, the variability of stages and stand structures should be increased to promote landscape-scale biodiversity.

Questions: The co-existence of high numbers of species has always fascinated ecologists, but what and where are the communities with the world records for plant species richness? The species—area relationship is among the best-known patterns in community ecology, but does it give a consistent global pattern for the most saturated communities, the global maxima? Location: The world. Methods: We assembled the maximum values recorded for vascular plant species richness for contiguous areas from 1 mm2 up to 1 ha. We applied the power function to relate maximal richness to area and to make extrapolations to the whole Earth. Results: Only two community types contain global plant species maxima. The maxima at smaller spatial grain were from oligo- to meso-trophic, managed, semi-natural, temperate grasslands (e.g. 89 species on 1 m 2 ), those at larger grains were from tropical rain forests (e.g. 942 species on 1 ha). The maximum richness values closely followed a power function with z = 0.250: close to Preston's 'canonical' value of 0.262. There was no discernable difference between maxima using rooted presence (i.e. including only plants rooted in the plot) vs shoot presence (i.e. including any plant with physical cover over the plot). However, shoot presence values must logically be greater, with the curves flattening out at very small grain, and there is evidence of this from point quadrats. Extrapolating the curve to the terrestrial surface of the Earth gave a prediction of 219 204 vascular plant species, surprisingly close to a recent estimate of 275 000 actual species. Conclusions: Very high richness at any spatial grain is found only in two particular habitat/community types. Nevertheless, these high richness values form a very strong, consistent pattern, not greatly affected by the method of sampling, and this pattern extrapolates amazingly well. The records challenge ecologists to consider mechanisms of species co-existence, answers to the 'Paradox of the Plankton'.

Understanding species’ responses to environmental conditions, and how these species–environment associations shape spatial distributions, are longstanding goals in ecology and biogeography. However, an essential component of species–environment relationships – the spatial unit, or grain, at which they operate – remains unresolved. We identify three components of scale-dependence in analyses of species–environment associations: 1) response grain, the grain at which species respond most strongly to their environment; 2) environment spatial structure, the pattern of spatial autocorrelation intrinsic to an environmental factor; and 3) analysis grain, the grain at which analyses are conducted and ecological inferences are made. We introduce a novel conceptual framework that defines these scale components in the context of analyzing species–environment relationships, and provide theoretical examples of their interactions for species with various ecological attributes. We then use a virtual species approach to investigate the impacts of each component on common methods of measuring and predicting species–environment relationships. We find that environment spatial structure has a substantial impact on the ability of even simple, univariate species distribution models (SDMs) to recover known species–environment associations at coarse analysis grains. For simulated environments with ‘fine’ and ‘intermediate’ spatial structure, model explanatory power, and the frequency with which simple SDMs correctly estimated a virtual species’ response to the simulated environment, dramatically declined as analysis grain increased. Informed by these results, we use a scaling analysis to identify maximum analysis grains for individual environmental factors, and a scale optimization procedure to determine the grain of maximum predictive accuracy. Implementing these analysis grain thresholds and model performance standards in an example east African study system yields more accurate distribution predictions, compared to SDMs independently constructed at arbitrary analysis grains. Finally, we integrate our conceptual framework with virtual and empirical results to provide practical recommendations for researchers asking common questions about species–environment relationships.

1. Assembly of grassland communities has long been scrutinized through the lens of functional diversity. Studies generally point to an overwhelming influence of climate on observed patterns of functional diversity, despite experimental evidence demonstrating the importance of biotic interactions. We postulate that this is because most observational studies neglect both scale dependencies of assembly processes and phenotypic variation between individuals. Here, we test for changes in the importance of abiotic filtering and biotic interactions along a stress gradient by explicitly accounting for different scales. In addition to quantifying intraspecific trait variability (ITV), we also vary the two components of spatial scale, including grain (i.e. community size) and extent (i.e. the geographical area that defines the species pool). 2. We sampled 20 grassland communities in ten sites distributed along a 975-m elevation gradient. At each site, we measured seven functional traits for a total of 2020 individuals at different spatial grains. We related community functional diversity metrics to the main environmental gradient of our study area, growing season length (GSL), and assessed the dependence of these relationships on spatial grain, spatial extent and ITV. 3. At large spatial grain and extent, the imprint of environmental filtering on functional diversity became more important with increasing stress (i.e. functional diversity decreased with shorter GSL). At small spatial grain and extent, we found a convex relationship between functional diversity and GSL congruent with the hypothesis that competition is dominant at low-stress levels while facilitative interactions are dominant at high-stress levels (i. e. high functional diversity at both extremes of the stress gradient). Importantly, the effect of intraspecific variability on assembly rules was noticeable only at small spatial grain and extent. 4. Synthesis. Our study reveals how the combination of abiotic stress and biotic interactions shapes the functional diversity of alpine grasslands at different spatial scales, and highlights the importance of phenotype variation between individuals for community assembly processes at fine spatial scale. Our results suggest that studies analysing trait-based assembly rules but ignoring ITV and focusing on a single spatial scale are likely to miss essential features of community diversity patterns.

Beta diversity is an important concept used to describe turnover in species composition across a wide range of spatial and temporal scales, and it underpins much of conservation theory and practice. Although substantial progress has been made in the mathematical and terminological treatment of different measures of beta diversity, there has been little conceptual synthesis of potential scale dependence of beta diversity with increasing spatial grain and geographic extent of sampling. Here, we evaluate different conceptual approaches to the spatial scaling of beta diversity, interpreted from ‘fixed’ and ‘varying’ perspectives of spatial grain and extent. We argue that a ‘sliding window’ perspective, in which spatial grain and extent covary, is an informative way to conceptualize community differentiation across scales. This concept more realistically reflects the varying empirical approaches that researchers adopt in field sampling and the varying scales of landscape perception by different organisms. Scale dependence in beta diversity has broad implications for emerging fields in ecology and biogeography, such as the integration of fine‐resolution ecogenomic data with large‐scale macroecological studies, as well as for guiding appropriate management responses to threats to biodiversity operating at different spatial scales.

The characterization of species’ environmental niches and spatial distribution predictions based on them are now central to much of ecology and conservation, but implicitly requires decisions about the appropriate spatial scale (i.e., grain) of analysis. Ecological theory and empirical evidence suggest that range-resident species respond to their environment at two characteristic, hierarchical spatial grains: (1) response grain, the (relatively fine) grain at which an individual uses environmental resources, and (2) occupancy grain, the (relatively coarse) grain equivalent to a typical home range. We use a multi-grain (MG) occupancy model, aided by fine-grain remotely sensed imagery, to simultaneously estimate species–environment associations at both grains, conduct grain optimization to measure response grain, and apply this analysis framework to an example species: a medium-sized bird (Tockus deckeni) in a heterogeneous East African landscape. Based on home range analysis of movement data, we calculate an occupancy grain of 1 km for T. deckeni. Using a grain optimization procedure across 32 grains from 10 to 500 m, we identify 60 m as the most strongly supported response grain for a suite of environmental variables, slightly coarser than opportunistic behavioral observations would have suggested. Validation confirms that the accuracy of the optimized MG occupancy model substantially exceeds that of equivalent single-grain (SG) occupancy models. We further use a simulation approach to assess the potential impacts of accounting for the multi-scale structure of species’ environmental requirements on estimates of population size. We find that the more strongly supported MG approach consistently predicts a minimum population size in the study landscape that is much lower than that provided by the SG model. This suggests that SG approaches commonly used in conservation applications could lead to overly optimistic abundance and population estimates, and that the MG approach may be more appropriate for supporting species conservation goals. More generally, we conclude that multi-grain approaches of the sort presented, and increasingly enabled by growing high-resolution remotely sensed data, hold great promise for offering a more mechanistic framework for assessing the appropriate grain(s) for population monitoring and management and enable more reliable estimates of abundances and species’ distributions.

Alien or non-native species are defined as species living outside their natural distributional ranges. The spread of alien species is increasing globally as a result of rapid technological advances and globalization. Recent investigations have estimated global hotspots of alien established species on the basis of geopolitical boundaries, including Dawson et al. (in: Nat Ecol Evol 1:186. https://doi.org/10.1038/s41559-017-0186, 2017). In particular, these investigations do not consider Intra-Country Established Alien Species, i.e., successful introductions that occur among regions within the same country. In continental countries such as Brazil, the USA and China, studies excluding Intra-Country Established Alien Species (IEAS) waste essential information. Here, we argue that researchers should also consider intra-country introductions when estimating and addressing the risks of alien introductions. By using detailed data for freshwater fish including IEAS in large countries, we demonstrate that novel hotspots for IEAS have arisen worldwide. We illustrate emblematic examples of IEAS, as well as their vectors and negative impacts, to demonstrate the range of impacts that might be missed when excluding IEAS data from analysis. We recognize the need for generalizations, but generalizations based on incomplete data can misinform conservation efforts, particularly in megadiverse regions. Ignores IEAS influences how we count non-native species, invasions and perceive invisibility and impacts. Consequently, upcoming records and analysis of invasion patterns and management of aliens and EAS global hotspots must account for such biases in quantifying the IEAS portion.

The question of whether biodiversity conservation and carbon conservation can be synergistic hinges on the form of the biodiversity–productivity relationship (BPR), a fundamental ecological pattern. The stakes are particularly high when it comes to forests, which at a global level comprises a large fraction of both biodiversity and carbon. And yet, in forests, the BPR is relatively poorly understood. In this review, we critically evaluate research on forest BPRs, focussing on the experimental and observational studies of the last 2 decades. We find general support for a positive forest BPR, suggesting that biodiversity and carbon conservation are synergistic to a degree. However, we identify several major caveats: (i) although, on average, productivity may increase with biodiversity, the highest-yielding forests are often monocultures of very productive species; (ii) productivity typically saturates at fewer than ten species; (iii) positive BPRs can be driven by some third variable, in particular stem density, instead of a causal arrow from biodiversity to productivity; (iv) the BPR’s sign and magnitude varies across spatial grains and extents, and it may be weak at scales relevant to conservation; and (v) most productivity estimates in forests are associated with large errors. We conclude by explaining the importance of these caveats for both conservation programmes focussed on protection of existing forests and conservation programmes focussed on restoring or replanting forests.

Aim To investigate how changing grid size can alter model predictions of the distribution of mesophotic taxa and how it affects different modelling methods. Location Ningaloo Marine Park, Western Australia. Taxon Benthic mesophotic taxa: corals, macroalgae and sponges. Methods We determined the distributions of the major benthic taxonomic groups: corals, macroalgae and sponges, using a number of modelling techniques and an ensemble using the ‘sdm’ R package. A range of grid sizes were used (10, 50, 100 and 250 m) to identify how model predictions were altered. Models were evaluated using the area under the curve of a receiver operator characteristic plot (AUC) and the true skill statistic (TSS) using a spatially independent dataset. Results Grid size had a large effect on model performance across the taxonomic groups. Model outputs were compared to null surfaces and 88.8% of models performed significantly better than null. Distribution of corals was best predicted using the finest grid size (10 m) regardless of modelling method, although a model ensemble produced the best results (AUC = 0.80, TSS = 0.52). Macroalgae and sponges were better predicted at coaster grids sizes (250 m). Again, ensembles performed well for both macroalgae (AUC = 0.83, TSS = 0.63) and sponges (AUC = 0.88, TSS = 0.66). Model ensembles maintained high accuracy across grid sizes and were consistently the best, or second‐best, performing method. Main conclusions This study has shown how grid size should be considered when producing distribution models. Identifying the most relevant grid size and being aware of the influence it may have will provide more accurate predictions of the distributions of taxa. Ensemble methods maintained good performance across scenarios and thus provide a useful tool for conservation and management especially where single modelling methods showed high levels of variability.

AIM: Emerging polyploids may depend on environmental niche shifts for successful establishment. Using the alpine plant Ranunculus kuepferi as a model system, we explore the niche shift hypothesis at different spatial resolutions and in contrasting parts of the species range. LOCATION: European Alps. METHODS: We sampled 12 individuals from each of 102 populations of R. kuepferi across the Alps, determined their ploidy levels, derived coarse‐grain (100 × 100 m) environmental descriptors for all sampling sites by downscaling WorldClim maps, and calculated fine‐scale environmental descriptors (2 × 2 m) from indicator values of the vegetation accompanying the sampled individuals. Both coarse and fine‐scale variables were further computed for 8239 vegetation plots from across the Alps. Subsequently, we compared niche optima and breadths of diploid and tetraploid cytotypes by combining principal components analysis and kernel smoothing procedures. Comparisons were done separately for coarse and fine‐grain data sets and for sympatric, allopatric and the total set of populations. RESULTS: All comparisons indicate that the niches of the two cytotypes differ in optima and/or breadths, but results vary in important details. The whole‐range analysis suggests differentiation along the temperature gradient to be most important. However, sympatric comparisons indicate that this climatic shift was not a direct response to competition with diploid ancestors. Moreover, fine‐grained analyses demonstrate niche contraction of tetraploids, especially in the sympatric range, that goes undetected with coarse‐grained data. MAIN CONCLUSIONS: Although the niche optima of the two cytotypes differ, separation along ecological gradients was probably less decisive for polyploid establishment than a shift towards facultative apomixis, a particularly effective strategy to avoid minority cytotype exclusion. In addition, our results suggest that coarse‐grained analyses overestimate niche breadths of widely distributed taxa. Niche comparison analyses should hence be conducted at environmental data resolutions appropriate for the organism and question under study.