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The attached document is the author(’s’) final accepted/submitted version of the journal article. You are advised to consult the publisher’s version if you wish to cite from it

AIM: We test hypotheses on the environmental control of elevational richness patterns of sphingid moths for their global applicability and generality. Specifically, we compare effects of area with climate‐related drivers, such as primary productivity and temperature, while also considering direct effects of precipitation. MAJOR TAXA: Sphingid moths (Lepidoptera). LOCATION: Eighty‐six mountain ranges of the Old World and the Australia/Pacific region, from Scandinavia and Siberia through the African and Australasian tropics to South Africa and Southern Australia. METHODS: We used a large compilation of point locality records for 744 species, in addition to fine‐grained range maps derived from species distribution modelling of these records, to characterize the elevational pattern of species richness in 86 custom‐delineated mountain regions. For both types of data, we compared the effects of environmental drivers on richness by comparing standardized coefficients of multivariate models for pooled data after accounting for between‐region variation in richness. RESULTS: We observed varying patterns of elevational richness across the research region, with a higher prevalence of midpeaks in arid regions. We found overwhelming support for area as a main determinant of richness, modulated by temperature and productivity, whereas we detected no effect of precipitation. MAIN CONCLUSIONS: Area, productivity and temperature are the main environmental predictors explaining a large proportion of variability in sphingid richness. This is consistent not only with other elevational studies, but also with empirical and theoretical biodiversity research in a non‐elevational context (with the caveat of some unresolved issues in elevational area effects). However, distinct differences in elevational patterns remain even within the same mountain ranges when comparing with other Lepidoptera, that is, geometrid moths, which highlights the importance of understanding higher clade differentiation in ecological responses, within insects and in other groups.

Aim To map the spatial variation of range sizes within sphingid moths, and to test hypotheses on its environmental control. In particular, we investigate effects of climate change velocity since the Pleistocene and the mid-Holocene, temperature and precipitation seasonality, topography, Pleistocene ice cover, and available land area. Location Old World and Australasia, excluding smaller islands. Methods We used fine-grained range maps (based on expert-edited distribution modelling) for all 972 sphingid moth species in the research region and calculated, at a grain size of 100 km, the median of range sizes of all species that co-occur in a pixel. Climate, topography and Pleistocene ice cover data were taken from publicly available sources. We calculated climate change velocities (CCV) for the last 21 kyr as well as 6 kyr. We compared the effects of seasonality and CCV on median range sizes with spatially explicit models while accounting for effects of elevation range, glaciation history and available land area. Results Range sizes show a clear spatial pattern, with highest median values in deserts and arctic regions and lowest values in isolated tropical regions. Range sizes were only weakly related to absolute latitude (predicted by Rapoport's effect), but there was a strong north-south pattern of range size decline. Temperature seasonality emerged as the strongest environmental correlate of median range size, in univariate as well as multivariate models, whereas effects of CCV were weak and unstable for both time periods. These results were robust to variations in the parameters in alternative analyses, among them multivariate CCV. Main conclusions Temperature seasonality is a strong correlate of spatial range size variation, while effects of longer-term temperature change, as captured by CCV, received much weaker support.

Aim To investigate the contribution to range filling, range extent and climatic niche space of species of information contained in the largest databank of digitized biodiversity data: the global biodiversity information facility (GBIF). We compared such information with a compilation of independent distributional data from natural history collections and other sources. Location Europe. Methods We used data for the hawkmoths (Lepidoptera, family Sphingidae) to assess three aspects of range information: (1) observed range filling in 100 km × 100 km grid cell squares, (2) observed European range extent and (3) observed climatic niche. Range extents were calculated as products of latitudinal and longitudinal extents. Areas derived from minimum convex polygons drawn onto a 2-dimensional niche space representing the two main axes of a principal component analysis (PCA) were used to calculate climatic niche space. Additionally, record-based permutation tests for niche differences were carried out. Results We found that GBIF provided many more distribution records than independent compilation efforts, but contributed less information on range filling, range extent and climatic niches of species. Main conclusions Although GBIF contributed relevant additional information, it is not yet an effective alternative to manual compilation and databasing of distributional records from collections and literature sources, at least in lesser-known taxa such as invertebrates. We discuss possible reasons for our findings, which may help shape GBIF strategies for providing more informative data.

AIM: Many taxa, especially invertebrates, remain biogeographically highly understudied and even baseline assessments are missing, with too limited and heterogeneous sampling being key reasons. Here we set out to assess the human geographic and associated environmental factors behind inventory completeness for the hawkmoths of Africa. We aim to separate the causes of differential sampling from those affecting gradients of species richness to illustrate a potential general avenue for advancing knowledge about diversity in understudied groups. Location Sub-Saharan Africa. METHODS: Using a database of distributional records of hawkmoths, we computed rarefaction curves and estimated total species richness across 200 km × 200 km grid cells. We fitted multivariate models to identify environmental predictors of species richness and used environmental co-kriging to map region-wide diversity patterns. We estimated cell-wide inventory completeness from observed and estimated data, and related these to human geographic factors. RESULTS: Observed patterns of hawkmoths species richness are strongly determined by the number of available records in grid cells. Both show spatially structured distributions. Variables describing vegetation type, emerge as important predictors of estimated total richness, and variables capturing heat, energy availability and topographic heterogeneity all show a strong positive relationship. Patterns of interpolated richness identify three centres of diversity: Cameroon coastal mountains, and the northern and southern East African montane areas. Inventory completeness is positively influenced by population density, accessibility, protected areas and colonial history. Species richness is still under-recorded in the western Congo Basin and southern Tanzania/Mozambique. MAIN CONCLUSIONS: Sampling effort is highly biased and controlling for it in large-scale compilations of presence-only data is critical for drawing inferences from our still limited knowledge of invertebrate distributions. Our study shows that a baseline of estimate of broad-scale diversity patterns in understudied taxa can be derived from combining numerical estimators of richness, models of main environmental effects and spatial interpolation. Inventory completeness can be partly predicted from human geographic features and such models may offer fruitful guidance for prioritization of future sampling to further refine and validate estimated patterns of species richness.

Aim: Conservation studies often investigate the biodiversity of one taxonomic group with the expectation that it reflects biodiversity of other taxa as well. However, previous studies have found that biodiversity patterns are often only weakly correlated across ecologically very different taxa. Using three arthropod taxa that share the same habitat, utilize similar resources and are sampled with identical technique, we investigate the applicability of two levels of biodiversity indication: (1) prediction of biodiversity patterns, and (2) inference of environment–biodiversity relationships. The second aspect is of high relevance to applied conservation management yet mostly neglected, at least in terrestrial systems, when discussing the indicator concept. Location: Northern Switzerland Methods: We used a very large data set of pitfall trap samples from different habitats for three taxonomic groups (Carabidae, Staphylinidae and Araneae). We quantified biodiversity by different metrics of local diversity (species richness, effective number of species) and of pairwise faunal dissimilarities (Sorensen, Bray–Curtis). We investigated the congruence of (1) biodiversity patterns by cross-taxa regressions, and (2) environmental models of biodiversity by comparing fitted coefficients, and resulting extrapolations across the research region. Results: We found positive yet not very strong correlations in biodiversity patterns, while environmental models differed considerably between taxa as well as between diversity metrics. Main conclusions: Inferences of environment–biodiversity relationships can differ between taxonomic groups even if biodiversity patterns alone show significant correlation. This may be either because species indeed respond differently to environmental variation or because of misspecifications inherent in ecological modelling. Both possibilities suggest a need for caution in selecting and applying biodiversity indicators. Furthermore, the choice of diversity metric can strongly affect results, and therefore, decisions about which metric to use in any given situation need to be made carefully.

Over the last two decades, macroecology – the analysis of large-scale, multi-species ecological patterns and processes – has established itself as a major line of biological research. Analyses of statistical links between environmental variables and biotic responses have long and successfully been employed as a main approach, but new developments are due to be utilized. Scanning the horizon of macroecology, we identifi ed four challenges that will probably play a major role in the future. We support our claims by examples and bibliographic analyses. 1) Integrating the past into macroecological analyses, e.g. by using paleontological or phylogenetic information or by applying methods from historical biogeography, will sharpen our understanding of the underlying reasons for contemporary patterns. 2) Explicit consideration of the local processes that lead to the observed larger-scale patterns is necessary to understand the fi ne-grain variability found in nature, and will enable better prediction of future patterns (e.g. under environmental change conditions). 3) Macroecology is dependent on large-scale, high quality data from a broad spectrum of taxa and regions. More available data sources need to be tapped and new, small-grain large-extent data need to be collected. 4) Although macroecology already lead to mainstreaming cuttingedge statistical analysis techniques, we fi nd that more sophisticated methods are needed to account for the biases inherent to sampling at large scale. Bayesian methods may be particularly suitable to address these challenges. To continue the vigorous development of the macroecological research agenda, it is time to address these challenges and to avoid becoming too complacent with current achievements.