Using a robust multi‐settings inference framework on published datasets still reveals limited support for the abundant centre hypothesis: More testing needed on other datasets

Aim: The abundant centre hypothesis (ACH) predicts a negative relationship between species abundance and the distance to the geographical range centre. Since its formulation, empirical tests of the ACH have involved different settings (e.g. the distance to the ecological niche or to the geographical range centre), but studies found contrasting support for this hypothesis. Here, we evaluate whether these discrepancies might stem from differences regarding the context in which the ACH is tested (geographical or environmental), how distances are measured, how species envelopes are delineated, how the relationship is evaluated and which data are used. Location: The Americas. Time period: 1800–2017. Major taxa studied: Mammals, birds, fish, and tree seedlings. Methods: Using published abundance data for 801 species, together with species range maps, we tested the ACH using three distance metrics in both environmental and geographical spaces with range and niche envelopes delineated using two different algorithms, totalling 12 different settings. We then evaluated the distance–abundance relationship using correlation coefficients (traditional approach) and mixed-effect models to reduce the effect of sampling noise on parameter estimates. Results: Similar to previous studies, correlation coefficients indicated an absence of effect of distance on abundance for all taxonomic groups and settings. In contrast, mixed-effect models highlighted relationships of various strengths and shapes, with a tendency for more theoretically supported settings to provide stronger support for the ACH. The relationships were however not consistent across taxonomic groups and settings, and were sometimes even opposite to ACH expectations. Main conclusions: We found mixed and inconclusive results regarding the ACH. These results corroborate recent findings, and suggest either that our ability to predict abundances from the location of populations within geographical or environmental spaces is low, or that the data used here have a poor signal-to-noise-ratio. The latter calls for further testing on other datasets using the same range of settings and methodological framework

Rarity types among plant species with high conservation priority in Switzerland

Abstract.: Broennimann O., Vittoz P., Moser D. and Guisan A. 2005. Rarity types among plant species with high conservation priority in Switzerland. Bot. Helv. 115: 95-108. We investigated the ecogeographic characteristics of 118 Swiss plant species listed as those deserving highest conservation priority in a national conservation guide and classified them into the seven Rabinowitz' rarity types, taking geographic distribution, habitat rarity and local population size into account. Our analysis revealed that species with high conservation priority in Switzerland mostly have a very restricted geographic distribution in Switzerland and generally occur in rare habitats, but do not necessarily constitute small populations and are generally not endemics on a global scale. Moreover, species that are geographically very restricted on a regional scale are not generally restricted on a global scale. By analysing relationships between rarity and IUCN extinction risks for Switzerland, we demonstrated that species with the highest risk of extinction are those with the most restricted geographic distribution; whereas species with lower risk of extinction (but still high conservation priority) include many regional endemics. Habitat rarity and local population size appeared to be of minor importance for the assessment of extinction risk in Switzerland, but the total number of fulfilled rarity criteria still correlated positively with the severity of extinction risk. Our classification is the first preliminary assessment of the relative importance of each rarity type among endangered plant species of the Swiss flora and our results underline the need to distinguish between a regional and a global responsibility for the conservation of rare and endangered specie

Snow cover persistence as a useful predictor of alpine plant distributions

Aim: We examine whether the addition of snow cover persistence in plant species distribution models (SDMs) improves their predictive power. We investigate the link between species’ ecology and SDM improvements by the addition of various snow cover persistence predictors. Location: Western Swiss Alps. Taxon: 206 alpine flowering plants (Angiospermes). Methods: We produced three maps of landsat satellite-based snow cover persistence indices over an entire mountain region, one of them using an online open access platform allowing quick and easy replication and used them as a predictor in plant SDMs alongside commonly used predictors. We tested whether this improved the predictive performance of plant SDMs. Results: All three snow cover persistence indices improved the overall SDM predictive accuracy, but the overall improvement was potentially limited by their correlation with other climatic predictors. Alpine plant species known for their dependence on snow benefited more from the additional snow information. Main conclusions: Snow cover persistence should be used for predicting at least the distribution of alpine, snow related plant species. Given that adding snow cover improves SDMs and that snow duration decreases as climate warms, future predictions of alpine plant distributions should account for both snow predictor and associated snow change scenarios

Ecological and biological indicators of the accuracy of species distribution models: lessons from European bryophytes

peer reviewedThe predictive power of species distribution models (SDMs) varies substantially among species depending on their ecological and life-history traits, but which of these traits are the most relevant and how they influence species ‘predictability’ remains an area of debate. Here, we address these questions in bryophytes. SDMs employing macroclimatic, topographic and edaphic predictors were calibrated for 411 species in Europe and externally evaluated using an independent dataset. Regression models were implemented to determine whether species characteristics, including life-history traits, ecological preference and niche breadth, determine the accuracy of SDMs. Variation in SDM accuracy among species was significantly explained by species characteris-tics, supporting the hypothesis that the strength of species–environment correlations is affected by characteristics of the species themselves. The percent variance of SDM accuracy explained by species traits, however, substantially varied between 9 and 57% depending on the evaluation metrics used. The lower correlation observed between species traits and MaxKappa and the Boyce index than with area under the curve (AUC) and MaxTSS suggests that the former are less suitable than the latter for deter-mining species ‘predictability’ based on their traits. SDM accuracy decreased from species restricted to pristine habitats to species thriving in eutrophic habitats with high levels of human disturbance. The widespread distribution of man-made habitats in fact opens the door for the spread of now ubiquitous species, even in environments that would primarily not be suitable for them. Such species, likely to occur anywhere, reach very high to full occupancy rates, thereby decreasing the accuracy of models aiming at predicting their distributions. The fact that AUC and MaxTSS were higher for species from pristine habitats is important in a conservation context, as ubiquitous species from eutrophic, disturbed environments are precisely the ones of lower conservation relevance

Physicochemical space of synthetic and natural pesticides – a meta-analysis

The first commercial use of synthetic pesticides for crop protection dates back to the 1940s, followed by a fast spreading of their use and the development of a large number of compounds. In contrast to synthetic pesticides that are nowadays designed with the help of artificial intelligence that includes computational science and combinatorial chemistry, natural pesticides are the results of long evolutionary processes involving specific host-pathogens, predator-prey and competitor interactions. For these reasons, natural pesticides are often more specific and less harmful to the environment. Natural pesticides are very diverse and can be found in various living organisms. In the present study, we investigated differences in the physicochemical space occupied by synthetic and natural pesticides. In this respect, we measured the mean and breadth of synthetic and natural pesticides, as well as the overlap between these groups in a reduced physicochemical space derived from a set of 44 physicochemical variables. We showed that physicochemical space strongly differs between synthetic and natural pesticides and could be determined with 93-100% certainty, a result comparable to differences observed in drugs. Importantly, the physicochemical space occupied by synthetic pesticides was 2.6 fold smaller than the one of natural pesticides and toxicity potential was lower in the latter. In conclusion, our work showed that the design of commercialized synthetic pesticides is underexploiting the possible physicochemical space of known natural pesticides, likely due to specific constraints. Such limitations should trigger the development of efficient natural pesticides avoiding as much as possible detrimental effects on non-target organism

Multiple introductions boosted genetic diversity in the invasive range of black cherry (Prunus serotina; Rosaceae)

Background and Aims Black cherry (Prunus serotina) is a North American tree that is rapidly invading European forests. This species was introduced first as an ornamental plant then it was massively planted by foresters in many countries but its origins and the process of invasion remain poorly documented. Based on a genetic survey of both native and invasive ranges, the invasion history of black cherry was investigated by identifying putative source populations and then assessing the importance of multiple introductions on the maintenance of gene diversity. Methods Genetic variability and structure of 23 populations from the invasive range and 22 populations from the native range were analysed using eight nuclear microsatellite loci and five chloroplast DNA regions. Key Results Chloroplast DNA diversity suggests there were multiple introductions from a single geographic region (the north-eastern United States). A low reduction of genetic diversity was observed in the invasive range for both nuclear and plastid genomes. High propagule pressure including both the size and number of introductions shaped the genetic structure in Europe and boosted genetic diversity. Populations from Denmark, The Netherlands, Belgium and Germany showed high genetic diversity and low differentiation among populations, supporting the hypothesis that numerous introduction events, including multiple individuals and exchanges between sites, have taken place during two centuries of plantation. Conclusions This study postulates that the invasive black cherry has originated from east of the Appalachian Mountains (mainly the Allegheny plateau) and its invasiveness in north-western Europe is mainly due to multiple introductions containing high numbers of individual

Anthropogenic disturbance as a driver of microspatial and microhabitat segregation of cytotypes of Centaurea stoebe and cytotype interactions in secondary contact zones

Background and Aims In a mixed-ploidy population, strong frequency-dependent mating will lead to the elimination of the less common cytotype, unless prezygotic barriers enhance assortative mating. However, such barriers favouring cytotype coexistence have only rarely been explored. Here, an assessment is made of the mechanisms involved in formation of mixed-ploidy populations and coexistence of diploid plants and their closely related allotetraploid derivates from the Centaurea stoebe complex (Asteraceae). Methods An investigation was made of microspatial and microhabitat distribution, life-history and fitness traits, flowering phenology, genetic relatedness of cytotypes and intercytotype gene flow (cpDNA and microsatellites) in six mixed-ploidy populations in Central Europe. Key Results Diploids and tetraploids were genetically differentiated, thus corroborating the secondary origin of contact zones. The cytotypes were spatially segregated at all sites studied, with tetraploids colonizing preferentially drier and open microhabitats created by human-induced disturbances. Conversely, they were rare in more natural microsites and microsites with denser vegetation despite their superior persistence ability (polycarpic life cycle). The seed set of tetraploid plants was strongly influenced by their frequency in mixed-ploidy populations. Triploid hybrids originated from bidirectional hybridizations were extremely rare and almost completely sterile, indicating a strong postzygotic barrier between cytotypes. Conclusions The findings suggest that tetraploids are later immigrants into already established diploid populations and that anthropogenic activities creating open niches favouring propagule introductions were the major factor shaping the non-random distribution and habitat segregation of cytotypes at fine spatial scale. Establishment and spread of tetraploids was further facilitated by their superior persistence through the perennial life cycle. The results highlight the importance of non-adaptive spatio-temporal processes in explaining microhabitat and microspatial segregation of cytotype

Comparative analysis of diversity and environmental niches of soil bacterial, archaeal, fungal and protist communities reveal niche divergences along environmental gradients in the Alps

Although widely used in ecology, comparative analyses of diversity and niche properties are still lacking for microorganisms, especially focusing on niche variations. Quantifying the niches of microbial taxa is necessary to then forecast how taxa and the communities they compose might respond to environmental changes. In this study, we first identified important topoclimatic, edaphic, spatial and biotic drivers of the alpha and beta di-versity of bacterial, archaeal, fungal and protist communities. Then, we calculated the niche breadth and position of each taxon along the important environmental gradients to determine how these vary within and among the taxonomic groups. We found that edaphic properties were the most important drivers of both, community di-versity and composition, for all microbial groups. Protists and bacteria presented the largest niche breadths on average, followed by archaea, with fungi displaying the smallest. Niche breadth generally decreased towards environmental extremes, especially along edaphic gradients, suggesting increased specialization of microbial taxa in highly selective environments. Overall, we showed that microorganisms have well defined niches, as do macro-organisms, likely driving part of the observed spatial patterns of community variations. Assessing niche variation more widely in microbial ecology should open new perspectives, especially to tackle global change effects on microbes.Peer reviewe

Low spatial autocorrelation in mountain biodiversity data and model residuals

Spatial autocorrelation (SAC) is a common feature of ecological data where observations tend to be more similar at some geographic distance(s) than expected by chance. Despite the implications of SAC for data dependencies, its impact on the performance of species distribution models (SDMs) remains controversial, with reports of both strong and negligible impacts on inference. Yet, no study has comprehensively assessed the prevalence and the strength of SAC in the residuals of SDMs over entire geographic areas. Here, we used a large-scale spatial inventory in the western Swiss Alps to provide a thorough assessment of the importance of SAC for (1) 850 species belonging to nine taxonomic groups, (2) six predictors commonly used for modeling species distributions, and (3) residuals obtained from SDMs fitted with two algorithms with the six predictors included as covariates. We used various statistical tools to evaluate (1) the global level of SAC, (2) the spatial pattern and spatial extent of SAC, and (3) whether local clusters of SAC can be detected. We further investigated the effect of the sampling design on SAC levels. Overall, while environmental predictors expectedly displayed high SAC levels, SAC in biodiversity data was rather low overall and vanished rapidly at a distance of similar to 5-10 km. We found low evidence for the existence of local clusters of SAC. Most importantly, model residuals were not spatially autocorrelated, suggesting that inferences derived from SDMs are unlikely to be affected by SAC. Further, our results suggest that the influence of SAC can be reduced by a careful sampling design. Overall, our results suggest that SAC is not a major concern for rugged mountain landscapes.Peer reviewe

N‐SDM: a high‐performance computing pipeline for Nested Species Distribution Modelling

Predicting contemporary and future species distributions is relevant for science and decision making, yet the development of high-resolution spatial predictions for numerous taxonomic groups and regions is limited by the scalability of available modelling tools. Uniting species distribution modelling (SDM) techniques into one high-performance computing (HPC) pipeline, we developed N-SDM, an SDM platform aimed at delivering reproducible outputs for standard biodiversity assessments. N-SDM was built around a spatially-nested framework, intended at facilitating the combined use of species occurrence data retrieved from multiple sources and at various spatial scales. N-SDM allows combining two models fitted with species and covariate data retrieved from global to regional scales, which is useful for addressing the issue of spatial niche truncation. The set of state-of-the-art SDM features embodied in N-SDM includes a newly devised covariate selection procedure, five modelling algorithms, an algorithm-specific hyperparameter grid search, and the ensemble of small-models approach. N-SDM is designed to be run on HPC environments, allowing the parallel processing of thousands of species at the same time. All the information required for installing and running N-SDM is openly available on the GitHub repository https://github.com/N-SDM/N-SDM