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Aim: Recent studies suggest insect declines in parts of Europe; however, the generality of these trends across different taxa and regions remains unclear. Standardized data are not available to assess large‐scale, long‐term changes for most insect groups but opportunistic citizen science data are widespread for some. Here, we took advantage of citizen science data to investigate distributional changes of Odonata. Location: Germany. Methods: We compiled over 1 million occurrence records from different regional databases. We used occupancy‐detection models to account for imperfect detection and estimate annual distributions for each species during 1980–2016 within 5 × 5 km quadrants. We also compiled data on species attributes that were hypothesized to affect species' sensitivity to different drivers and related them to the changes in species' distributions. We further developed a novel approach to cluster groups of species with similar patterns of distributional change to represent multispecies indicators. Results: More species increased (45%) than decreased (29%) or remained stable (26%) in their distribution (i.e. number of occupied quadrants). Species showing increases were generally warm‐adapted species and/or running water species, while species showing decreases were cold‐adapted species using standing water habitats such as bogs. Time series clustering defined five main patterns of change—each associated with a specific combination of species attributes, and confirming the key roles of species' temperature and habitat preferences. Overall, our analysis predicted that mean quadrant‐level species richness has increased over most of the time period. Main conclusions: Trends in Odonata provide mixed news—improved water quality, coupled with positive impacts of climate change, could explain the positive trends of many species. At the same time, declining species point to conservation challenges associated with habitat loss and degradation. Our study demonstrates the great value of citizen science and the work of natural history societies for assessing large‐scale distributional change. [ABSTRACT FROM AUTHOR]

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Plot‐level redundancy or alpha redundancy is usually defined as the fraction of species diversity not expressed by functional or phylogenetic diversity. Redundancy is zero when all species in one plot are maximally dissimilar from each other. In contrast, redundancy tends to its maximum if the functional or phylogenetic differences between species tend to be minimal.To explore the ecological drivers of community assembly, ecologists also use dissimilarity measures between pairs of plots (a component of beta diversity). Traditional dissimilarity measures summarize compositional differences between pairs of plots based either on species presence and absence data or on species abundances, thus attributing equal distinctiveness between any two species.In the last decades a number of dissimilarity measures which incorporate information on functional or phylogenetic differences among species have been proposed. On the basis of such improved measures, we can define an index of beta redundancy for a pair of plots as the fraction of species dissimilarity not expressed by functional or phylogenetic dissimilarity.A necessary condition to get a meaningful index of beta redundancy is that for a given pair of plots, the functional or phylogenetic dissimilarity is always lower or equal to the corresponding species dissimilarity. However, many of the existing indices of functional or phylogenetic dissimilarity can lead to values greater than for species dissimilarity.The aim of this paper was thus to introduce a new family of tree‐based measures of phylogenetic and functional dissimilarity that conform to this requirement. To show the behaviour of the proposed measures, a worked example with data on Alpine vegetation is used. [ABSTRACT FROM AUTHOR]

Aim: We test whether urbanization drives biotic homogenization. We hypothesize that declines in abundance and species diversity of aerial insects are exacerbated by the urbanization‐driven loss of species with low habitat generalism, mobility and warm‐adaptedness. We predict this homogenization to be more pronounced for nocturnal taxa, and at wider scales for mobile taxa. Location: Belgium. Time period: Summers 2014–2015. Major taxa studied: Lepidoptera. Methods: We compare communities along urbanization gradients using a shared, replicated and nested sampling design, in which butterflies were counted within 81 grassland and macro‐moths light‐trapped in 12 woodland sites. We quantify taxonomic and functional community composition, the latter via community‐weighted means and variation of species‐specific traits related to specialization, mobility and thermophily. Using linear regression models, variables are analysed in relation to site‐specific urbanization values quantified at seven scales (50–3,200 m radii). At best‐fitting scales, we test for taxonomic homogenization. Results: With increasing urbanization, abundance, species richness and Shannon diversity severely declined, with butterfly and macro‐moth declines due to local‐ versus landscape‐scale urbanization (200 vs. 800–3,200 m radii, respectively). While taxonomic homogenization was absent for butterflies, urban macro‐moth communities displayed higher nestedness than non‐urban communities. Overall, communities showed mean shifts towards generalist, mobile and thermophilous species, displaying trait convergence too. These functional trait models consistently fit best with urbanization quantified at local scales (100–200 m radii) for butterfly communities, and at local to wider landscape scales (200–800 m radii) for macro‐moth communities. Main conclusions: Urban communities display functional homogenization that follows urbanization at scales linked to taxon‐specific mobility. Light pollution may explain why homogenization was more pronounced for the nocturnal taxon. We discuss that urbanization is likely to impact flying insect communities across the globe, but also that impacts on their ecosystem functions and services could be mitigated via multi‐scale implementation of urban green infrastructure. [ABSTRACT FROM AUTHOR]

Aim: The species abundance distribution (SAD) is a fundamental pattern in macroecology. Understanding how SADs vary spatially, and identifying the variables that drive any change, is important from a theoretical perspective because it enables greater understanding of the factors that underpin the relative abundance of species. However, precise knowledge on how the form of SADs varies across large (continental) scales is limited. Here, we use the shape parameter of the gambin distribution to assess how meta‐community‐scale SAD shape varies spatially as a function of various climatic variables and dataset characteristics. Location: Eastern North America (ENA). Time period: Present day. Major taxa studied: Trees. Methods: Using an extensive continental‐scale dataset of 863,930 individual trees in plots across ENA sampled using a standardized method, we use a spatial regression framework to examine the effect of temperature and precipitation on the form of the SAD. We also assess whether the prevalence of multimodality in the SAD varies spatially across ENA as a function of temperature and precipitation, in addition to other sample characteristics. Results: We found that temperature, precipitation and species richness can explain two‐thirds of the variation in tree SAD form across ENA. Temperature had the largest effect on SAD shape, and it was found that increasing temperature resulted in more logseries‐like SAD shapes (i.e. SADs with a relatively higher proportion of rarer species). We also found spatial variation in SAD multimodality as a function of temperature and species richness. Main conclusions: Our results indicate that temperature is a key environmental driver governing the form of ENA tree meta‐community‐scale SADs. This finding has implications for our understanding of local‐scale variation in tree abundance and suggests that niche factors and environmental filtering are important in the structuring of ENA tree communities at larger scales. [ABSTRACT FROM AUTHOR]

Modelling spatio-temporal changes in species abundance and attributing those changes to potential drivers such as climate, is an important but difficult problem. The standard approach for incorporating climatic variables into such models is to include each weather variable as a single covariate, whose effect is expressed through a low-order polynomial or smoother in an additive model. This, however, confounds the spatial and temporal effects of the covariates., We developed a novel approach to distinguish between three types of change in any particular weather covariate. We decomposed the weather covariate into three new covariates by separating out temporal variation in weather (averaging over space), spatial variation in weather (averaging over years) and a space-time anomaly term (residual variation). These three covariates were each fitted separately in the models. We illustrate the approach using generalized additive models applied to count data for a selection of species from the UK's Breeding Bird Survey, 1994-2013. The weather covariates considered were the mean temperatures during the preceding winter and temperatures and rainfall during the preceding breeding season. We compare models that include these covariates directly with models including decomposed components of the same covariates, considering both linear and smooth relationships., The lowest QAIC values were always associated with a decomposed weather covariate model. Different relationships between counts and the three new covariates provided strong evidence that the effects of changes in covariate values depended on whether changes took place in space, in time, or in the space-time anomaly. These results promote caution in predicting species distribution and abundance in future climate, based on relationships that are largely determined by environmental variation over space., Our methods estimate the effect of temporal changes in weather, while accounting for spatial effects of long-term climate, improving inference on overall and/or localized effects of climate change. With increasing availability of large-scale datasets, need is growing for appropriate analytical tools. The proposed decomposition of the weather variables represents an important advance by eliminating the confounding issue often inherent in analyses of large-scale datasets. [ABSTRACT FROM AUTHOR]

Aim There is little consensus about the relative roles of biotic versus abiotic factors in setting limits to species distributions or in generating geographical patterns of species richness. However, despite the probable importance of host availability in governing the distribution and diversity of consumers, few studies have simultaneously tested the effects of resource distribution and diversity on consumer ranges and richness patterns. Location Sierra de Guadarrama, central Spain. Methods We examined the effects of biotic resources, consumer attributes and climate on the ranges and species richness patterns of 43 specialist butterflies at 40 sites over a 1800-m elevational gradient. Evidence for resource use was based on comprehensive field records of oviposition and larval feeding on host plants. Results We show that limitation by either biotic interactions with resources (the distributions and parts eaten of the larval host plants) or intrinsic dispersal ability was stronger at upper than lower elevational range limits for butterflies. Both resource and consumer richness followed a unimodal, humped pattern over the elevational gradient, but host plant richness peaked 300 m lower than butterfly richness. In addition, whereas changes in butterfly species richness were roughly symmetrical around peak richness over the gradient studied, the host plants showed markedly lower species richness at high elevations (> 1750 m). Butterfly species richness increased with host plant resource diversity and relative humidity, with a steeper response to host plant richness in cooler sites (at higher elevations). Main conclusions The results demonstrate the role of bottom-up control by resource availability in limiting consumer distributions and richness. Importantly, resource limitation had increasing relevance towards the coolest parts of environmental gradients and those poorest in resource species, with potential consequences for ecological responses to environmental change. [ABSTRACT FROM AUTHOR]

Aim Whether the gradients of global diversity conform to equilibrium or non-equilibrium dynamics remains an unresolved question in ecology and evolution. Here, we evaluate four prominent hypotheses which invoke either equilibrium (more individuals, niche diversity) or non-equilibrium dynamics (diversification rate, evolutionary time) to explain species richness and functional diversity of mammals worldwide. Location Global. Methods We combine structural equation modelling with simulations to examine whether species richness and functional diversity are in equilibrium with environmental conditions (climate, productivity) or whether they vary with non-equilibrium factors (diversification rates, evolutionary time). We use the newest and most inclusive phylogenetic, distributional and trait data for mammals. Results We find that species richness and functional diversity are decoupled across multiple regions of the world. While species richness correlates closely with environmental conditions, functional diversity depends mostly on non-equilibrium factors (evolutionary time to overcome niche conservatism). Moreover, functional diversity plateaus with species richness, such that species-rich regions (especially the Neotropics) host many species that are apparently functionally redundant. Main conclusions We conclude that species richness depends on environmental factors while functional diversity depends on the evolutionary history of the region. Our work further challenges the classic notion that highly productive regions host more species because they offer a great diversity of ecological niches. Instead, they suggest that productive regions offer more resources, which allow more individuals, populations and species to coexist within a region, even when the species are apparently functionally redundant (the more individuals hypothesis). Together these findings demonstrate how ecological (the total amount of resources) and evolutionary factors (time to overcome niche conservatism) might have interacted to generate the striking diversity of mammals and their life histories. [ABSTRACT FROM AUTHOR]

An enduring challenge in ecology is to understand what drives spatial variation in the size and structure of communities. The ability to count the number of species present at a location is hindered by the fact that not all species are equally detectable, and invariably some go completely undetected. This makes comparing species richness across distinct spatial units (or regions) problematic as sources of error are usually unaccounted for in simple enumerations of species., Multi-species occupancy models explicitly incorporate a model for this observation uncertainty and provide a framework for estimating community size when detection is imperfect. Currently, however, the model is restricted to estimating the number of species at only a single region of interest. In this paper we extend the multi-species occupancy model to accommodate data collected across multiple regions of interest (e.g., reserves or biomes)., We report improved model performance of the joint multiregion approach when compared to the more traditional two-stage approach of modelling spatial variation in species richness using simulations. Then, applying the model to data collected from eight avian communities in northern Italy, we demonstrate how species richness can be modeled as a spatially varying function of habitat complexity., Extending the multi-species occupancy model to accommodate data collected across multiple regions of interest (e.g., reserves or biomes) allows for joint estimation of region-specific community size and permits species richness to be modeled as a function of region-specific covariates. Our approach provides a mechanism for testing hypotheses about why and how species richness varies across space. [ABSTRACT FROM AUTHOR]

Species absent from a community but with the potential to establish (dark diversity) are an important, yet rarely considered component of habitat-specific species pools. Quantifying this component remains a challenge as dark diversity cannot be observed directly and must be estimated. Here, we empirically test whether species ecological requirements or species co-occurrences provide accurate estimates of dark diversity., We used two spatially nested independent datasets, one comprising 3033 samples of coastal grassland vegetation from 4 m2 and 200 m2 plots from Scotland, UK, and another comprising 780 samples of forest vegetation plots from 30 m2 and 500 m2 plots from Switzerland. Dark diversity for each of the smaller scaled plots was estimated through investigating the degree of (i) similarity in ecological requirements (measured as Ellenberg values); and (ii) co-occurrence likelihood. Estimates were validated using species from the larger spatial scales. Estimates were further validated using observations from all larger scale plots surrounding a focal assemblage within a 2 km (Scottish grassland) and 10 km (Swiss forest) radius., The co-occurrence method was shown to be more accurate resulting in far fewer negative mismatches (i.e. species observed but not predicted), as well as higher proportions of observed and predicted species, relative to the Ellenberg method. Of the species observed in the large-scale samples, 18% were estimated as part of the smaller scale dark diversity via the co-occurrence approach relative to 8% for the Ellenberg method for both the Scottish and Swiss data, respectively. These values increased to 67% & 60% and 32% & 35%, respectively, across all observations within a 2 km (Scottish grasslands) and 10 km (Swiss forests) radius., The study demonstrates that dark diversity for a community can be successfully estimated using readily available data, through exploring species co-occurrence patterns. This work substantiates that habitat-specific species pools can be accurately quantified and should prove valuable for understanding underlying community processes and improving our knowledge of the mechanisms governing species co-existence. [ABSTRACT FROM AUTHOR]

1. Understanding the drivers of population abundance across species and sites is crucial for effective conservation management. At present, we lack a framework for predicting which sites are likely to support abundant butterfly communities. 2. We address this problem by exploring the determinants of abundance among 1111 populations of butterflies in the UK, spanning 27 species on 54 sites. Our general hypothesis is that the availability of food resources is a strong predictor of population abundance both within and between species, but that the relationship varies systematically with species' traits. 3. We found strong positive correlations between butterfly abundance and the availability of food resources. Our indices of host plant and nectar are both significant predictors of butterfly population density, but the relationship is strongest for host plants, which explain up to 36% of the inter-site variance in abundance for some species. 4. Among species, the host plant-abundance relationship is mediated by butterfly species traits. It is strongest among those species with narrow diet breadths, low mobility and habitat specialists. Abundance for species with generalist diet and habitat associations is uncorrelated with our host plant index. 5. The host plant-abundance relationship is more pronounced on sites with predominantly north-facing slopes, suggesting a role for microclimate in mediating resource availability. 6. Synthesis and applications. We have shown that simple measures can be used to help understand patterns in abundance at large spatial scales. For some butterfly species, population carrying capacity on occupied sites is predictable from information about the vegetation composition. These results suggest that targeted management to increase host plant availability will translate into higher carrying capacity. Among UK butterflies, the species that would benefit most from such intervention have recently experienced steep declines in both abundance and distribution. The host plant-abundance relationship we have identified is likely to be transferrable to other systems characterized by strong interspecific interactions across trophic levels. This raises the possibility that the quality of habitat patches for specialist species is estimable from rapid assessment of the host plant resource. [ABSTRACT FROM AUTHOR]

In 2003, 24 presence-absence β-diversity metrics were reviewed and a number of trade-offs and redundancies identified. We present a parallel investigation into the performance of abundance-based metrics of β-diversity., β-diversity is a multi-faceted concept, central to spatial ecology. There are multiple metrics available to quantify it: the choice of metric is an important decision., We test 16 conceptual properties and two sampling properties of a β-diversity metric: metrics should be 1) independent of α -diversity and 2) cumulative along a gradient of species turnover. Similarity should be 3) probabilistic when assemblages are independently and identically distributed. Metrics should have 4) a minimum of zero and increase monotonically with the degree of 5) species turnover, 6) decoupling of species ranks and 7) evenness differences. However, complete species turnover should always generate greater values of β than extreme 8) rank shifts or 9) evenness differences. Metrics should 10) have a fixed upper limit, 11) symmetry (β A, B = β B, A), 12) double-zero asymmetry for double absences and double presences and 13) not decrease in a series of nested assemblages. Additionally, metrics should be independent of 14) species replication 15) the units of abundance and 16) differences in total abundance between sampling units. When samples are used to infer β-diversity, metrics should be 1) independent of sample sizes and 2) independent of unequal sample sizes. We test 29 metrics for these properties and five 'personality' properties., Thirteen metrics were outperformed or equalled across all conceptual and sampling properties. Differences in sensitivity to species' abundance lead to a performance trade-off between sample size bias and the ability to detect turnover among rare species. In general, abundance-based metrics are substantially less biased in the face of undersampling, although the presence-absence metric, βsim, performed well overall. Only βBaselga R turn, βBaselga B-C turn and βsim measured purely species turnover and were independent of nestedness. Among the other metrics, sensitivity to nestedness varied >4-fold., Our results indicate large amounts of redundancy among existing β-diversity metrics, whilst the estimation of unseen shared and unshared species is lacking and should be addressed in the design of new abundance-based metrics. [ABSTRACT FROM AUTHOR]

Areas of endemism are conservation priorities and indicators of biogeographical processes, representing concentrations of range-restricted biodiversity. While the term endemism describes geographical range restriction, the technical definition has often been re-worked. Categorical definitions are increasingly replaced by weighting of species according to range sizes. In various contexts, range sizes have been estimated by the number of occupied map grid cells, the latitudinal or longitudinal range of point locations, or the area of a polygon surrounding point locations. Existing implementations of weighted endemism use the number of occupied grid cells to estimate range. However, this represents area of occupancy ( AOO), not extent of occurrence ( EOO)., We developed endemism calculations that include strictly geographical (georeferenced) interpretations of range ('span' and 'area'), and compared them to cell frequency-based measures. We compared species weights and endemism scores for 330 004 records of 3083 vascular plant species in 14 328 plots (Biological Survey of South Australia). We provide a self-contained R function that calculates endemism using alternative range weights and integrates point data with automatically generated rasters. A parallel function tests for deviance from the expected (null) distribution, given observed species richness (nonparametric significance test; outlier metrics)., Species range weights based on grid cell frequency ( AOO) versus georeferenced 'span' and 'area' ( EOO) are conceptually different and were poorly correlated for the same data set, resulting in differences in the classification of grid cells as outliers from expected endemism. For example, the correlation (Kendall's τ) between endemism based on geographical 'span' and cell frequency was just 0·57, when partialling out the influence of species richness, potentially leading to contrasting conservation priorities over 8% or 46 000 km2 of the state of South Australia., Weighting endemism by georeferenced range estimates provides an alternative approach that more explicitly highlights restriction in EOO, generates differences in endemism metrics and requires only georeferenced species records. This approach need not replace existing implementations, but provides an alternative measure of range-restricted biodiversity. The functions provide efficient calculation of species ranges (with or without geographical outlier exclusion) and weighted endemism with novel implementation of significance tests and outlier detection, in the R environment. [ABSTRACT FROM AUTHOR]

Silvicultural practices lead to changes in forest composition and structure and may impact species diversity from the overall regional species pool to stand-level species occurrence. We explored to what extent fine-scale occupancy patterns in differently managed forest stands are driven by environment and ecological traits in three regions in Germany using a multi-species hierarchical model. We tested for the possible impact of environmental variables and ecological traits on occupancy dynamics in a joint modelling exercise while taking possible variation in coefficient estimates over years and plots into account. Bird species richness differed across regions and years, and trends in species richness across years were different in the three regions. On the species level, forest management affected occupancy of species in all regions, but only 3-5% of the total assemblage-level variation in occurrence probability was explained by either forest type and successional stage and < 1% by forest edge. On the assemblage level, bird occurrence decreased with body mass in all regions. Species with smaller breeding ranges had lower occurrence probabilities in one region, while later spring arrival decreased occurrence probabilities in the two other regions. Spatial variation in the effect size of trait covariates such as species phylogeny and breeding strata showed that variation in patch occupancy due to fine-scale differences in forest management is, to some extent, predictable from ecological traits. Our results show that environmental factors and ecological traits jointly predict variation in bird occupancy patterns and their response to forest management. Observations at the fine scale of forest stands, at which conservation efforts can be arranged along with forest management practices in heterogeneous environments, have been shown to provide meaningful insights despite the difficulties involved in monitoring mobile organisms such as birds at the plot level. [ABSTRACT FROM AUTHOR]

Abstract.Studies of species richness have been hampered by the use of methods that fail to account for phylogenetic non-independence of character states. MacroCAIC is a computer program that extends the method of phylogenetically independent contrasts to encompass species-richness data. It examines user-selected characters for correlation with species richness, thus allowing clearer identification of the factors driving large-scale patterns of diversity. [ABSTRACT FROM AUTHOR]