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Ecologically meaningful predictors are often neglected in plant distribution studies, resulting in incomplete niche quantification and low predictive power of species distribution models (SDMs). Because environmental data are rare and expensive to collect, and because their relationship with local climatic and topographic conditions are complex, mapping them over large geographic extents and at high spatial resolution remains a major challenge.

Here, we propose to derive environmental data layers by mapping ecological indicator values in space. We combined ~6 million plant occurrences with expert-based plant ecological indicator values (EIVs) of 3600 species in Switzerland. EIVs representing local soil properties (pH, moisture, moisture variability, aeration, humus and nutrients) and climatic conditions (continentality, light) were modelled at 93 m spatial resolution with the Random Forest algorithm and 16 predictors representing mesoclimate, land use, topography and geology. Models were evaluated and predictions of EIVs were compared with soil inventory data. We mapped each EIV separately and evaluated EIV importance in explaining the distribution of 500 plant species using SDMs with a set of 30 environmental predictors. Finally, we tested how they improve an ensemble of SDMs compared to a standard set of predictors for ca 60 plant species.

All EIV models showed excellent performance (|r| > 0.9) and predictions were correlated reasonably (|r| > 0.4) to soil properties measured in the field. Resulting EIV maps were among the most important predictors in SDMs. Also, in ensemble SDMs overall predictive performance increased, mainly through improved model specificity reducing species range overestimation.

Combining large citizen science databases to expert-based EIVs is a powerful and cost–effective approach for generalizing local edaphic and climatic conditions over large areas. Producing ecologically meaningful predictors is a first step for generating better predictions of species distribution which is of main importance for decision makers in conservation and environmental management projects.

Descombes, P., Walthert, L., Baltensweiler, A., Meuli, R. G., Karger, D. N., Ginzler, C., Zurell, D., & Zimmermann, N. E. (2020). Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes. Ecography, 43(10), 1448–1463. https://doi.org/10.1111/ecog.05117

Species distribution models (SDMs) are often criticised for lacking explicit linkage to ecological concepts. We aim to improve the ecological basis of SDMs by integrating prior knowledge about ecological preferences of organisms. Additionally, we aim to support a systematic, data-driven review of such prior knowledge by confronting it with independent monitoring data using Bayesian inference. We developed a series of multi-species distribution models (MSDMs) with increasing complexity to predict the probability of occurrence of taxa at sampling sites based on habitat suitability functions that are parameterized with prior ecological knowledge. We subsequently assessed the models` predictive performance with 3-fold cross-validation. So far, if ecological preferences or functional traits have been used in SDMs, they were mainly used as fixed inputs without considering their uncertainty. We take the additional step of considering uncertainty about preference parameters by including them as uncertain prior information that is subsequently updated with Bayesian inference. We apply the series of models in a case study on macroinvertebrates in Swiss streams. We analyse differences in the quality of fit, changes in predictive performance, and the potential to learn about the parameters from the data. We consider ecological preferences for natural and human modified environmental factors including temperature, flow velocity, organic matter concentration, insecticide pollution, and substratum. Results indicate that updating prior knowledge on ecological preferences with Bayesian inference, rather than using it as fixed input, improves model fit and predictive performance. For example, the predictive performance measured by the deviance for validation data improves by 17 % and the explanatory power increases 3.8 times from a model that treats ecological preferences as fixed scores to a model that treats them as uncertain parameters. The spatial distribution of many taxa, including rare taxa with frequencies of occurrence down to about 5 %, which are difficult to model with SDMs that do not consider prior information, can be captured by the new models. Integrating prior knowledge as uncertain parameters in a Bayesian framework establishes ecological interpretable links between taxa and their environment and supports a systematic revision and complementation of databases on ecological preferences, even in case of poor or missing prior knowledge. Model outputs need to be carefully interpreted by modellers and experts on ecological preferences. Increased exchange between these research fields will benefit further integration of ecological preferences into SDMs.

Vermeiren, P., Reichert, P., & Schuwirth, N. (2020). Integrating uncertain prior knowledge regarding ecological preferences into multi-species distribution models: Effects of model complexity on predictive performance. Ecological Modelling, 420, 108956. https://doi.org/10.1016/j.ecolmodel.2020.108956

Biotic homogenization represents a major concern in ecology but relatively few studies have assessed climate change impacts on assemblage patterns of freshwater species. Our main goals were to predict the current and future (years 2035, 2060 and 2085) patterns of mayfly, stonefly and caddisfly (EPT) diversity across Switzerland from macroscale environmental variables, and to assess the impact of warming temperatures on β-diversity. The study area was the entire Swiss territory divided into 21 818 subcatchments (median area of 1.41 km²), used as spatial units for predicting patterns of EPT diversity. We assumed that the stream conditions were homogeneous within a subcatchment at this scale. Incidence of EPT larvae was derived from samplings carried out between 2010 and 2017 in 292 water-course sites as part of two national monitoring programs. We employed generalized dissimilarity modeling to analyze the spatial turnover of EPT assemblages. Climatic, topographic, geological and land-use variables were used as covariates, and different climate change scenarios were used for future predictions. We compared β-diversity among the different scenarios through distance-based tests of homogeneity of multivariate dispersions. Our findings showed the largest amount of EPT turnover occurred along the air temperature and slope gradients, considered as good proxies for water temperature and flow velocity. We predicted a biotic homogenization with increasing temperatures due to the upstream expansion of some species from the sub-montane level, which only stabilizes in the most conservative climate scenario. This study is the first countrywide prediction of EPT composition patterns in the context of global warming and provides insights into the vulnerability of assemblages occurring at high elevations.

Timoner, P., Marle, P., Castella, E., & Lehmann, A. (2020). Spatial patterns of mayfly, stonefly and caddisfly assemblages in Swiss running waters in the face of global warming. Ecography, 43(7), 1065–1078. https://doi.org/10.1111/ecog.04808

Individuals of large or dark-colored ectothermic species often have a higher reproduction and activity than small or light-colored ones. However, investments into body size or darker colors should negatively affect the fitness of individuals as they increase their growth and maintenance costs. Thus, it is unlikely that morphological traits directly affect species’ distribution and abundance. Yet, this simplification is frequently made in trait-based ecological analyses. Here, we integrated the energy allocation strategies of species into an ecophysiological framework to explore the mechanisms that link species’ morphological traits and population dynamics. We hypothesized that the effects of morphological traits on species’ distribution and abundance are not direct but mediated by components of the energy budget and that species can allocate more energy towards dispersal and reproduction if they compensate their energetic costs by reducing mobility costs or increasing energy uptake. To classify species’ energy allocation strategies, we used easily measured proxies for the mobility costs and energy uptake of butterflies that can be also applied to other taxa. We demonstrated that contrasting effects of morphological traits on distribution and abundance of butterfly species offset each other when species’ energy allocation strategies are not taken into account. Larger and darker butterfly species had wider distributions and were more abundant if they compensated the investment into body size and color darkness (i.e., melanin) by reducing their mobility costs or increasing energy uptake. Adults of darker species were more mobile and foraged less compared to lighter colored ones, if an investment into melanin was indirectly compensated via a size-dependent reduction of mobility costs or increase of energy uptake. Our results indicate that differences in the energy allocations strategies of species account for a considerable part of the variation in species’ distribution and abundance that is left unexplained by morphological traits alone and ignoring these differences can lead to false mechanistic conclusions. Therefore, our findings highlight the potential of integrating proxies for species’ energy allocation strategies into trait-based models not only for understanding the physiological mechanisms underlying variation in species’ distribution and abundance, but also for improving predictions of the population dynamics of species.

Pinkert, S., Friess, N., Zeuss, D., Gossner, M. M., Brandl, R., & Brunzel, S. (2020). Mobility costs and energy uptake mediate the effects of morphological traits on species’ distribution and abundance. Ecology, 101(10). https://doi.org/10.1002/ecy.3121