SWECO25: a cross-thematic raster database for ecological research in Switzerland.

Standard and easily accessible cross-thematic spatial databases are key resources in ecological research. In Switzerland, as in many other countries, available data are scattered across computer servers of research institutions and are rarely provided in standard formats (e.g., different extents or projections systems, inconsistent naming conventions). Consequently, their joint use can require heavy data management and geomatic operations. Here, we introduce SWECO25, a Swiss-wide raster database at 25-meter resolution gathering 5,265 layers. The 10 environmental categories included in SWECO25 are: geologic, topographic, bioclimatic, hydrologic, edaphic, land use and cover, population, transportation, vegetation, and remote sensing. SWECO25 layers were standardized to a common grid sharing the same resolution, extent, and geographic coordinate system. SWECO25 includes the standardized source data and newly calculated layers, such as those obtained by computing focal or distance statistics. SWECO25 layers were validated by a data integrity check, and we verified that the standardization procedure had a negligible effect on the output values. SWECO25 is available on Zenodo and is intended to be updated and extended regularly.

Assessment of the stream invertebrate ß -diversity along an elevation gradient using a bidimensional null model analysis.

ß -Diversity, commonly defined as the compositional variation among localities that links local diversity (α-diversity) and regional diversity (γ-diversity), can arise from two different ecological phenomena, namely the spatial species turnover (i.e., species replacement) and the nestedness of assemblages (i.e., species loss). However, any assessment that does not account for stochasticity in community assembly could be biased and misinform conservation management. In this study, we aimed to provide a better understanding of the overall ecological phenomena underlying stream ß -diversity along elevation gradients and to contribute to the rich debate on null model approaches to identify nonrandom patterns in the distribution of taxa. Based on presence-absence data of 78 stream invertebrate families from 309 sites located in the Swiss Alpine region, we analyzed the effect size of nonrandom spatial distribution of stream invertebrates on the ß -diversity and its two components (i.e., turnover and nestedness). We used a modeling framework that allows exploring the complete range of existing algorithms used in null model analysis and assessing how distribution patterns vary according to an array of possible ecological assumptions. Overall, the turnover of stream invertebrates and the nestedness of assemblages were significantly lower and higher, respectively, than the ones expected by chance. This pattern increased with elevation, and the consistent trend observed along the altitudinal gradient, even in the most conservative analysis, strengthened our findings. Our study suggests that deterministic distribution of stream invertebrates in the Swiss Alpine region is significantly driven by differential dispersal capacity and environmental stress gradients. As long as the ecological assumptions for constructing the null models and their implications are acknowledged, we believe that they still represent useful tools to measure the effect size of nonrandom spatial distribution of taxa on ß -diversity.

Impacts of climate change on aquatic insects in temperate alpine regions: Complementary modeling approaches applied to Swiss rivers.

Freshwater biodiversity loss is a major concern, and global warming is already playing a significant role in species extinctions. Our main goal was to predict climate change impacts on aquatic insect species distribution and richness in Swiss running waters according to two climate change scenarios (RCP2.6 and RCP8.5), using different modeling approaches, that is, species distribution models (SDMs), stacked-SDMs (S-SDMs) and a macroecological model (MEM). We analyzed 10,808 reaches, used as spatial units for model predictions, for a total river network length of 20,610 km. Results were assessed at both the countrywide and the biogeographic regional scales. We used incidence data of 41 species of Ephemeroptera, Plecoptera and Trichoptera (EPT) from 259 sites distributed across Switzerland. We integrated a coupled model for hydrology and glacier retreat to simulate monthly time-step discharge from which we derived hydrological variables. These, along with thermal, land-cover, topographic and spatially explicit data, served as predictors for our ecological models. Predictions of occurrence probabilities and EPT richness were compared among the different regions, periods and scenarios. A Shiny web application was developed to interactively explore all the models' details, to ensure transparency and promote the sharing of information. MEM and S-SDMs approaches consistently showed that overall, climate change is likely to reduce EPT richness. Decrease could be around 10% in the least conservative scenario, depending on the region. Global warming was shown to represent a threat to species from high elevation, but in terms of species richness, running waters from lowlands and medium elevation seemed more vulnerable. Finally, our results suggested that the effects of anthropogenic activities could overweight natural factors in shaping the future of river biodiversity.

A web platform for landuse, climate, demography, hydrology and beach erosion in the Black Sea catchment.

The Black Sea catchment (BSC) is facing important demographic, climatic and landuse changes that may increase pollution, vulnerability and scarcity of water resources, as well as beach erosion through sea level rise. Limited access to reliable time-series monitoring data from environmental, statistical, and socio-economical sources is a major barrier to policy development and decision-making. To address these issues, a web-based platform was developed to enable discovery and access to key environmental information for the region. This platform covers: landuse, climate, and demographic scenarios; hydrology and related water vulnerability and scarcity; as well as beach erosion. Each data set has been obtained with state-of-the-art modelling tools from available monitoring data using appropriate validation methods. These analyses were conducted using global and regional data sets. The data sets are intended for national to regional assessments, for instance for prioritizing environmental protection projects and investments. Together they form a unique set of information, which lay out future plausible change scenarios for the BSC, both for scientific and policy purposes.

Effect of classification procedure on the performance of numerically defined ecological regions.

Ecological regionalizations define geographic regions exhibiting relative homogeneity in ecological (i.e., environmental and biotic) characteristics. Multivariate clustering methods have been used to define ecological regions based on subjectively chosen environmental variables. We developed and tested three procedures for defining ecological regions based on spatial modeling of a multivariate target pattern that is represented by compositional dissimilarities between locations (e.g., taxonomic dissimilarities). The procedures use a "training dataset" representing the target pattern and models this as a function of environmental variables. The model is then extrapolated to the entire domain of interest. Environmental data for our analysis were drawn from a 400 m grid covering all of Switzerland and consisted of 12 variables describing climate, topography and lithology. Our target patterns comprised land cover composition of each grid cell that was derived from interpretation of aerial photographs. For Regionalization 1 we used conventional cluster analysis of the environmental variables to define 60 hierarchically organized levels comprising from 5 to 300 regions. Regionalization 1 provided a base-case for comparison with the model-based regionalizations. Regionalization 2, 3 and 4 also comprised 60 hierarchically organized levels and were derived by modeling land cover composition for 4000 randomly selected "training" cells. Regionalization 2 was based on cluster analysis of environmental variables that were transformed based on a Generalized Dissimilarity Model (GDM). Regionalization 3 and 4 were defined by clustering the training cells based on their land cover composition followed by predictive modeling of the distribution of the land cover clusters using Classification and Regression Tree (CART) and Random Forest (RF) models. Independent test data (i.e. not used to train the models) were used to test the discrimination of land cover composition at all hierarchical levels of the regionalizations using the classification strength (CS) statistic. CS for all the model-based regionalizations was significantly higher than for Regionalization 1. Regionalization 3 and 4 performed significantly better than Regionalization 2 at finer hierarchical levels (many regions) and Regionalization 4 performed significantly better than Regionalization 3 for coarse levels of detail (few regions). Compositional modeling can significantly increase the performance of numerically defined ecological regionalizations. CART and RF-based models appear to produce stronger regionalizations because discriminating variables are able to change at each hierarchic level.

Strong influence of variable treatment on the performance of numerically defined ecological regions.

Numerical clustering has frequently been used to define hierarchically organized ecological regionalizations, but there has been little robust evaluation of their performance (i.e., the degree to which regions discriminate areas with similar ecological character). In this study we investigated the effect of the weighting and treatment of input variables on the performance of regionalizations defined by agglomerative clustering across a range of hierarchical levels. For this purpose, we developed three ecological regionalizations of Switzerland of increasing complexity using agglomerative clustering. Environmental data for our analysis were drawn from a 400 m grid and consisted of estimates of 11 environmental variables for each grid cell describing climate, topography and lithology. Regionalization 1 was defined from the environmental variables which were given equal weights. We used the same variables in Regionalization 2 but weighted and transformed them on the basis of a dissimilarity model that was fitted to land cover composition data derived for a random sample of cells from interpretation of aerial photographs. Regionalization 3 was a further two-stage development of Regionalization 2 where specific classifications, also weighted and transformed using dissimilarity models, were applied to 25 small scale "sub-domains" defined by Regionalization 2. Performance was assessed in terms of the discrimination of land cover composition for an independent set of sites using classification strength (CS), which measured the similarity of land cover composition within classes and the dissimilarity between classes. Regionalization 2 performed significantly better than Regionalization 1, but the largest gains in performance, compared to Regionalization 1, occurred at coarse hierarchical levels (i.e., CS did not increase significantly beyond the 25-region level). Regionalization 3 performed better than Regionalization 2 beyond the 25-region level and CS values continued to increase to the 95-region level. The results show that the performance of regionalizations defined by agglomerative clustering are sensitive to variable weighting and transformation. We conclude that large gains in performance can be achieved by training classifications using dissimilarity models. However, these gains are restricted to a narrow range of hierarchical levels because agglomerative clustering is unable to represent the variation in importance of variables at different spatial scales. We suggest that further advances in the numerical definition of hierarchically organized ecological regionalizations will be possible with techniques developed in the field of statistical modeling of the distribution of community composition.

Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data.

Most methods for modeling species distributions from occurrence records require additional data representing the range of environmental conditions in the modeled region. These data, called background or pseudo-absence data, are usually drawn at random from the entire region, whereas occurrence collection is often spatially biased toward easily accessed areas. Since the spatial bias generally results in environmental bias, the difference between occurrence collection and background sampling may lead to inaccurate models. To correct the estimation, we propose choosing background data with the same bias as occurrence data. We investigate theoretical and practical implications of this approach. Accurate information about spatial bias is usually lacking, so explicit biased sampling of background sites may not be possible. However, it is likely that an entire target group of species observed by similar methods will share similar bias. We therefore explore the use of all occurrences within a target group as biased background data. We compare model performance using target-group background and randomly sampled background on a comprehensive collection of data for 226 species from diverse regions of the world. We find that target-group background improves average performance for all the modeling methods we consider, with the choice of background data having as large an effect on predictive performance as the choice of modeling method. The performance improvement due to target-group background is greatest when there is strong bias in the target-group presence records. Our approach applies to regression-based modeling methods that have been adapted for use with occurrence data, such as generalized linear or additive models and boosted regression trees, and to Maxent, a probability density estimation method. We argue that increased awareness of the implications of spatial bias in surveys, and possible modeling remedies, will substantially improve predictions of species distributions.

Using niche-based models to improve the sampling of rare species.

Because data on rare species usually are sparse, it is important to have efficient ways to sample additional data. Traditional sampling approaches are of limited value for rare species because a very large proportion of randomly chosen sampling sites are unlikely to shelter the species. For these species, spatial predictions from niche-based distribution models can be used to stratify the sampling and increase sampling efficiency. New data sampled are then used to improve the initial model. Applying this approach repeatedly is an adaptive process that may allow increasing the number of new occurrences found. We illustrate the approach with a case study of a rare and endangered plant species in Switzerland and a simulation experiment. Our field survey confirmed that the method helps in the discovery of new populations of the target species in remote areas where the predicted habitat suitability is high. In our simulations the model-based approach provided a significant improvement (by a factor of 1.8 to 4 times, depending on the measure) over simple random sampling. In terms of cost this approach may save up to 70% of the time spent in the field.