How much should one sample to accurately predict the distribution of species assemblages? A virtual community approach

Correlative species distribution models (SDMs) are widely used to predict species distributions and assemblages, with many fundamental and applied uses. Different factors were shown to affect SDM prediction accuracy. However, real data cannot give unambiguous answers on these issues, and for this reason, artificial data have been increasingly used in recent years. Here, we move one step further by assessing how different factors can affect the prediction accuracy of virtual assemblages obtained by stacking individual SDM predictions (stacked SDMs, S-SDM). We modeled 100 virtual species in a real study area, testing five different factors: sample size (200-800-3200), sampling method (nested, non-nested), sampling prevalence (25%, 50%, 75% and species true prevalence), modelling technique (GAM, GLM, BRT and RF) and thresholding method (ROC, MaxTSS, and MaxKappa). We showed that the accuracy of S-SDM predictions is mostly affected by modelling technique followed by sample size. Models fitted by GAM/GLM had a higher accuracy and lower variance than BRT/RF. Model accuracy increased with sample size and a sampling strategy reflecting the true prevalence of the species was most successful. However, even with sample sizes as high as >3000 sites, residual uncertainty remained in the predictions, potentially reflecting a bias introduced by creating and/or resampling the virtual species. Therefore, when evaluating the accuracy of predictions from S-SDMs fitted with real field data, one can hardly expect reaching perfect accuracy, and reasonably high values of similarity or predictive success can already be seen as valuable predictions. We recommend the use of a ‘plot-like’ sampling method (best approximation of the species’ true prevalence) and not simply increasing the number of presences-absences of species. As presented here, virtual simulations might be used more systematically in future studies to inform about the best accuracy level that one could expect given the characteristics of the data and the methods used to fit and stack SDMs

Flore alpine et réchauffement climatique: observation de trois sommets valaisans à travers le XXe siècle

Alpine flora and climate change: monitoring of three summits in Valais (Switzerland) during the 20th century Climate change might trigger an upward shift of the flora in the Swiss Alps, especially since these experienced higher change in average than observed on a global scale. Previous investigations in the canton des Grisons (Switzerland) and Austria have revealed an increase of floristic diversity on alpine summits since the beginning of the 20th century. Three summits in Valais were revisited in this study: the Gornergrat (first inventory in 1919), the Torrenthorn (about in 1885) and the Beaufort (about in 1920). Every summit was newly inventoried in 2003 in the framework of the PERMANENT.PLOT.CH project. All showed a strong increase in species richness. On the Gornergrat (3135 m), 16 species were not found anymore, but 35 new ones were observed. The number of species on this exceptionally rich summit rose from 102 to 121. In comparison, the floristic richness increased from 24 to 63 species on the Torrenthorn (2924 m) and from 16 to 48 species on the Beaufort (3048 m). As in previous studies, this increase seems likely to be associated with climate change: the new species prefer, in average, higher temperature conditions than those previously prevailing on the summits. On the Gornergrat and Beaufort, our observations reveal a development of alpine meadows, whereas species typical of rocks and raw soils are predominantly colonising the Torrenthorn. This difference might be related to the important damage caused by wanderers on the vegetation of the Torrenthorn

How to best threshold and validate stacked species assemblages? Community optimisation might hold the answer

1. The popularity of species distribution models (SDMs) and the associated stacked species distribution models (S-SDMs), as tools for community ecologists, largely increased in recent years. However, while some consensus was reached about the best methods to threshold and evaluate individual SDMs, little agreement exists on how to best assemble individual SDMs into communities, i.e. how to build and assess S-SDM predictions. 2. Here, we used published data of insects and plants collected within the same study region to test (1) if the most established thresholding methods to optimize single species prediction are also the best choice for predicting species assemblage composition, or if community-based thresholding can be a better alternative, and (2) whether the optimal thresholding method depends on taxa, prevalence distribution and/or species richness. Based on a comparison of different evaluation approaches we provide guidelines for a robust community cross-validation framework, to use if spatial or temporal independent data are unavailable. 3. Our results showed that the selection of the “optimal” assembly strategy mostly depends on the evaluation approach rather than taxa, prevalence distribution, regional species pool or species richness. If evaluated with independent data or reliable cross-validation, community-based thresholding seems superior compared to single species optimisation. However, many published studies did not evaluate community projections with independent data, often leading to overoptimistic community evaluation metrics based on single species optimisation. 4. The fact that most of the reviewed S-SDM studies reported over-fitted community evaluation metrics highlights the importance of developing clear evaluation guidelines for community models. Here, we move a first step in this direction, providing a framework for cross-validation at the community level

Modeling the distribution of coprophagous beetle species in the Western Swiss Alps

Coprophagous beetles are essential for fecal matter removal and are thus considered key ecosystem services providers. Yet, our knowledge of these beetles’ distribution and ecology remains very limited. Here, we used Species Distribution Models (SDM) to investigate the species-environment relationships (i.e. their niche) and predict the geographic distribution of coprophagous beetles in the Western Swiss Alps. We used our own sampled data and existing national data from the Swiss faunal database to calibrate, for each species, a regional and a national SDM respectively. In both models, the best predictors were temperature and rock cover proportion, while a soil characteristic (∂13C) indicating its organic content and texture was important in the regional models and precipitations in the Swiss models. The model performed better for species specialized on low or high altitudes than for generalist species occurring in a large altitudinal range. The model performances were neither influenced by the size, nor by the nesting behavior (laying eggs inside or below the excrements) of the species. We also showed that species richness decreased with altitude. This study opens new perspective for a better knowledge of coprophagous beetle’s ecology and a useful tool for their conservation in mountain regions

More than range exposure: global otters’ vulnerability to climate change

Climate change impact on species is commonly assessed by predicting species’ range change, a measure of a species’ extrinsic exposure. However, this is only one dimension of species’ vulnerability to climate change. Spatial arrangement of suitable habitats (e.g., fragmentation), their degree of protection or human disturbance, as well as species’ intrinsic sensitivity, such as climatic tolerances, are often neglected. Here, we consider components of species’ intrinsic sensitivity to climate change (climatic niche specialization and marginality) together with components of extrinsic exposure (changes in range extent, fragmentation, coverage of protected areas, and human footprint) to develop an integrated vulnerability index to climate change for world’s freshwater otters. As top freshwater predators, otters are among the most vulnerable mammals, with most species being threatened by habitat loss and degradation. All dimensions of climate change exposure were based on present and future predictions of species distributions. Annual mean temperature, mean diurnal temperature range, mean temperature of the wettest quarter, precipitation during the wettest quarter, and precipitation seasonality prove the most important variables for otters. All species are vulnerable to climate change, with global vulnerability index ranging from -0,19 for Lontra longicaudis to -36,9 for Aonyx congicus. However, we found that, for a given species, climate change can have both positive and negative effects on different components of extrinsic exposure, and that measures of species’ sensitivity are not necessarily congruent with measures of exposure. For instance, the range of all African species would be negatively affected by climate change, but their different sensitivity offers a more (Hydrictis maculicollis, Aonyx capensis) or less (Aonyx congicus) pessimistic perspective on their ability to cope with climate change. Also, highly sensitive species like the South-American Pteronura brasiliensis, Lontra provocax, and Lutra perspicillata might face no exposure to climate change. For the Asian Lutra sumatrana, climate change would instead lead to an increased, less fragmented, and more protected range extent, but the range extent would also be shifted into areas with higher human disturbances. Our study represents a balanced example of how to develop an index aimed at comparatively evaluating vulnerability to climate change of different species by combining different aspects of sensitivity and exposure, providing additional information on which to base more efficient conservation strategies

Human Capital, Industry, Tourism and Economic Development of EU25 Regions

The role of human capital, industry and tourism in regional development is analysed by means of econometric models with data of both EU15 and the ten countries of the 2004's Enlargement. The study points to the need to improve economic policies at EU level, in order to increase production in the less developed regions and to get a higher degree of socio-economic convergence among EU regions. We analyse the main measures that have shown a positive impact on regional development during the last years

Optimizing ensembles of small models for predicting the distribution of species with few occurrences

1. Ensembles of Small Models (ESM) represent a novel strategy for species distribution modelling with few observations. ESMs are built by calibrating many small models and then averaging them into an ensemble model where the small models are weighted by their cross-validated scores of predictive performance. In a previous paper (Breiner, Guisan, Bergamini, & Nobis, Methods in Ecology and Evolution, 6, 1210-1218, 2015), we reported two major findings. First, ESMs proved largely superior to standard models in terms of model performance and transferability. Second, ESMs including different modelling techniques did not clearly improve model performance compared to single-technique ESMs. However, ESMs often require a large computation effort, which can become problematic when modelling large numbers of species. Given the appealing new perspectives offered by ESMs, it is especially important to investigate if some techniques yield increased performance while saving computation time and thus could be predominantly used for building ESMs. 2. Here, we present results from a reanalysis of a subset of the data used in Breiner etal. (2015). More specifically, we ran ESMs: (1) fitted with 10 modelling techniques separately (in Breiner etal., 2015 we used only three techniques); and (2) using various parameter options for each modelling technique (i.e., model tuning). 3. We show that ESMs vary in model performance and computation time across techniques, and some techniques are advantageous in terms of optimizing model performance and computation time (i.e., GLM, CTA and ANN). Including one of these modelling techniques could thus optimize computation time compared to using more computing-intensive techniques like GBM. Next, we show that parameter tuning can improve performance and transferability of ESMs, but often at the cost of computation time. Parameter tuning could therefore be used when computing resources are not a limiting factor. 4. These findings help improve the applicability and performance of ESMs when applied to large numbers of species

Contrasted host specificity of gut and endosymbiont bacterial communities in alpine grasshoppers and crickets.

Bacteria colonize the body of macroorganisms to form associations ranging from parasitic to mutualistic. Endosymbiont and gut symbiont communities are distinct microbiomes whose compositions are influenced by host ecology and evolution. Although the composition of horizontally acquired symbiont communities can correlate to host species identity (i.e. harbor host specificity) and host phylogeny (i.e. harbor phylosymbiosis), we hypothesize that the microbiota structure of vertically inherited symbionts (e.g. endosymbionts like Wolbachia) is more strongly associated with the host species identity and phylogeny than horizontally acquired symbionts (e.g. most gut symbionts). Here, using 16S metabarcoding on 336 guts from 24 orthopteran species (grasshoppers and crickets) in the Alps, we observed that microbiota correlated to host species identity, i.e. hosts from the same species had more similar microbiota than hosts from different species. This effect was ~5 times stronger for endosymbionts than for putative gut symbionts. Although elevation correlated with microbiome composition, we did not detect phylosymbiosis for endosymbionts and putative gut symbionts: closely related host species did not harbor more similar microbiota than distantly related species. Our findings indicate that gut microbiota of studied orthopteran species is more correlated to host identity and habitat than to the host phylogeny. The higher host specificity in endosymbionts corroborates the idea that-everything else being equal-vertically transmitted microbes harbor stronger host specificity signal, but the absence of phylosymbiosis suggests that host specificity changes quickly on evolutionary time scales

Uneven rate of plant turnover along elevation in grasslands

Plant taxonomic and phylogenetic composition of assemblages are known to shift along environmental gradients, but whether the rate of species turnover is regular or not (e.g., accelerations in particular sections of the gradient) remains poorly documented. Understanding how rates of assemblage turnover vary along gradients is crucial to forecast where climate change could promote the fastest changes within extant communities. Here we analysed turnover rates of plant assemblages along a 2500 m elevation gradient in the Swiss Western Alps. We found a peak of turnover rate between 1800 and 2200 m indicating an acceleration of grassland compositional changes at the transition between subalpine and alpine belts. In parallel, we found a peak in phylogenetic turnover rate in Poales between 1700 m and 1900 and Super-Rosids between 1900 and 2300 m. Our results suggest that changes in abiotic or biotic conditions near the human-modified treeline constitute a strong barrier for many grassland plant species, which share analogous elevation range limits. We propose that this vegetation zone of high ecological transitions over short geographical distances should show the fastest community responses to climate change from the breakdown of barrier across ecotones