@article{EberhardSchaikSchibalskietal.2020, author = {Eberhard, Julius and Schaik, N. Loes M. B. and Schibalski, Anett and Gr{\"a}ff, Thomas}, title = {Simulating future salinity dynamics in a coastal marshland under different climate scenarios}, series = {Vadose zone journal}, volume = {19}, journal = {Vadose zone journal}, number = {1}, publisher = {Wiley}, address = {Hoboken}, issn = {1539-1663}, doi = {10.1002/vzj2.20008}, pages = {15}, year = {2020}, abstract = {Salinization is a well-known problem in agricultural areas worldwide. In the last 20-30 yr, rising salinity in the upper, unconfined aquifer has been observed in the Freepsumer Meer, a grassland near the German North Sea coast. For investigating long-term development of salinity and water balance during 1961-2099, the one-dimensional Soil-Water-Atmosphere-Plant (SWAP) model was set up and calibrated for a soil column in the area. The model setup involves a deep aquifer as the source of salt through upward seepage. In the vertical salt transport equation, dispersion and advection are included. Six different regional outputs of statistical downscaling methods were used as climate scenarios. These comprise different rates of increasing surface temperature and different trends in seasonal rainfall. The simulation results exhibit opposing salinity trends for topsoil and deeper layers. Although projections of some scenarios entail decreasing salinities near the surface, most of them project a rise in subsoil salinity, with the strongest trends of up to +0.9 mg cm(-3) 100 yr(-1) at -65 cm. The results suggest that topsoil salinity trends in the study area are affected by the magnitude of winter rainfall trends, whereas high subsoil salinities correspond to low winter rainfall and high summer temperature. How these projected trends affect the vegetation and thereby future land use will depend on the future management of groundwater levels in the area.}, language = {en} } @article{SchibalskiKoernerMaieretal.2018, author = {Schibalski, Anett and K{\"o}rner, Katrin and Maier, Martin and Jeltsch, Florian and Schr{\"o}der, Boris}, title = {Novel model coupling approach for resilience analysis of coastal plant communities}, series = {Ecological applications : a publication of the Ecological Society of America}, volume = {28}, journal = {Ecological applications : a publication of the Ecological Society of America}, number = {6}, publisher = {Wiley}, address = {Hoboken}, issn = {1051-0761}, doi = {10.1002/eap.1758}, pages = {1640 -- 1654}, year = {2018}, abstract = {Resilience is a major research focus covering a wide range of topics from biodiversity conservation to ecosystem (service) management. Model simulations can assess the resilience of, for example, plant species, measured as the return time to conditions prior to a disturbance. This requires process-based models (PBM) that implement relevant processes such as regeneration and reproduction and thus successfully reproduce transient dynamics after disturbances. Such models are often complex and thus limited to either short-term or small-scale applications, whereas many research questions require species predictions across larger spatial and temporal scales. We suggest a framework to couple a PBM and a statistical species distribution model (SDM), which transfers the results of a resilience analysis by the PBM to SDM predictions. The resulting hybrid model combines the advantages of both approaches: the convenient applicability of SDMs and the relevant process detail of PBMs in abrupt environmental change situations. First, we simulate dynamic responses of species communities to a disturbance event with a PBM. We aggregate the response behavior in two resilience metrics: return time and amplitude of the response peak. These metrics are then used to complement long-term SDM projections with dynamic short-term responses to disturbance. To illustrate our framework, we investigate the effect of abrupt short-term groundwater level and salinity changes on coastal vegetation at the German Baltic Sea. We found two example species to be largely resilient, and, consequently, modifications of SDM predictions consisted mostly of smoothing out peaks in the occurrence probability that were not confirmed by the PBM. Discrepancies between SDM- and PBM-predicted species responses were caused by community dynamics simulated in the PBM and absent from the SDM. Although demonstrated with boosted regression trees (SDM) and an existing individual-based model, IBC-grass (PBM), our flexible framework can easily be applied to other PBM and SDM types, as well as other definitions of short-term disturbances or long-term trends of environmental change. Thus, our framework allows accounting for biological feedbacks in the response to short- and long-term environmental changes as a major advancement in predictive vegetation modeling.}, language = {en} } @phdthesis{Schibalski2017, author = {Schibalski, Anett}, title = {Statistical and process-based models for understanding species distributions in changing environments}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-401482}, school = {Universit{\"a}t Potsdam}, pages = {ix, 129}, year = {2017}, abstract = {Understanding the distribution of species is fundamental for biodiversity conservation, ecosystem management, and increasingly also for climate impact assessment. The presence of a species in a given site depends on physiological limitations (abiotic factors), interactions with other species (biotic factors), migratory or dispersal processes (site accessibility) as well as the continuing effects of past events, e.g. disturbances (site legacy). Existing approaches to predict species distributions either (i) correlate observed species occurrences with environmental variables describing abiotic limitations, thus ignoring biotic interactions, dispersal and legacy effects (statistical species distribution model, SDM); or (ii) mechanistically model the variety of processes determining species distributions (process-based model, PBM). SDMs are widely used due to their easy applicability and ability to handle varied data qualities. But they fail to reproduce the dynamic response of species distributions to changing conditions. PBMs are expected to be superior in this respect, but they need very specific data unavailable for many species, and are often more complex and require more computational effort. More recently, hybrid models link the two approaches to combine their respective strengths. In this thesis, I apply and compare statistical and process-based approaches to predict species distributions, and I discuss their respective limitations, specifically for applications in changing environments. Detailed analyses of SDMs for boreal tree species in Finland reveal that nonclimatic predictors - edaphic properties and biotic interactions - are important limitations at the treeline, contesting the assumption of unrestricted, climatically induced range expansion. While the estimated SDMs are successful within their training data range, spatial and temporal model transfer fails. Mapping and comparing sampled predictor space among data subsets identifies spurious extrapolation as the plausible explanation for limited model transferability. Using these findings, I analyze the limited success of an established PBM (LPJ-GUESS) applied to the same problem. Examination of process representation and parameterization in the PBM identifies implemented processes to adjust (competition between species, disturbance) and missing processes that are crucial in boreal forests (nutrient limitation, forest management). Based on climatic correlations shifting over time, I stress the restricted temporal transferability of bioclimatic limits used in LPJ-GUESS and similar PBMs. By critically assessing the performance of SDM and PBM in this application, I demonstrate the importance of understanding the limitations of the applied methods. As a potential solution, I add a novel approach to the repertoire of existing hybrid models. By simulation experiments with an individual-based PBM which reproduces community dynamics resulting from biotic factors, dispersal and legacy effects, I assess the resilience of coastal vegetation to abrupt hydrological changes. According to the results of the resilience analysis, I then modify temporal SDM predictions, thereby transferring relevant process detail from PBM to SDM. The direction of knowledge transfer from PBM to SDM avoids disadvantages of current hybrid models and increases the applicability of the resulting model in long-term, large-scale applications. A further advantage of the proposed framework is its flexibility, as it is readily extended to other model types, disturbance definitions and response characteristics. Concluding, I argue that we already have a diverse range of promising modelling tools at hand, which can be refined further. But most importantly, they need to be applied more thoughtfully. Bearing their limitations in mind, combining their strengths and openly reporting underlying assumptions and uncertainties is the way forward.}, language = {en} } @article{SchibalskiLehtonenSchroeder2014, author = {Schibalski, Anett and Lehtonen, Aleksi and Schroeder, Boris}, title = {Climate change shifts environmental space and limits transferability of treeline models}, series = {Ecography : pattern and diversity in ecology ; research papers forum}, volume = {37}, journal = {Ecography : pattern and diversity in ecology ; research papers forum}, number = {4}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {0906-7590}, doi = {10.1111/j.1600-0587.2013.00368.x}, pages = {321 -- 335}, year = {2014}, abstract = {Our study aims at gaining insights into the processes determining the current treeline dynamics in Finnish Lapland. Using forest surveys conducted in 1978 and 2003 we modelled the occurrence and abundance of three dominant tree species in Finnish Lapland, i.e. Pinus sylvestris, Picea abies and Betula pubescens, with boosted regression trees. We assessed the importance of climatic, biotic and topographic variables in predicting tree occurrence and abundance based on their relative importance and response curves. We compared temporal and spatial transferability by using an extended transferability index. Site fertility, the abundance of co-occurring species and growing degree days were generally the most important predictors for both occurrence and abundance across all species and datasets. Climatic predictors were more important for modelling occurrences than for modelling abundances. Occurrence models were able to reproduce the observed treeline pattern within one time period or region. Abundance models underestimated basal area but captured the general pattern of low and high values. Model performance as well as transferability differed considerably between species and datasets. Pinus sylvestris was modelled more successfully than P. abies and B. pubescens. Generally, spatial transferability was greater than temporal transferability. Comparing the environmental space between datasets revealed that transferring models means extrapolating to novel environments, providing a plausible explanation for limited transferability. Our study illustrates how climate change can shift the environmental space and lead to limited model transferability. We identified non-climatic factors to be important in predicting the distribution of dominant tree species, contesting the widespread assumption of climatically induced range expansion.}, language = {en} } @article{MuellervanSchaikBlumeetal.2014, author = {M{\"u}ller, Eva Nora and van Schaik, Loes and Blume, Theresa and Bronstert, Axel and Carus, Jana and Fleckenstein, Jan H. and Fohrer, Nicola and Geissler, Katja and Gerke, Horst H. and Gr{\"a}ff, Thomas and Hesse, Cornelia and Hildebrandt, Anke and H{\"o}lker, Franz and Hunke, Philip and K{\"o}rner, Katrin and Lewandowski, J{\"o}rg and Lohmann, Dirk and Meinikmann, Karin and Schibalski, Anett and Schmalz, Britta and Schr{\"o}der-Esselbach, Boris and Tietjen, Britta}, title = {Scales, key aspects, feedbacks and challenges of ecohydrological research in Germany}, series = {Hydrologie und Wasserbewirtschaftung}, volume = {58}, journal = {Hydrologie und Wasserbewirtschaftung}, number = {4}, publisher = {Bundesanst. f{\"u}r Gew{\"a}sserkunde}, address = {Koblenz}, issn = {1439-1783}, doi = {10.5675/HyWa_2014,4_2}, pages = {221 -- 240}, year = {2014}, abstract = {Ecohydrology analyses the interactions of biotic and abiotic aspects of our ecosystems and landscapes. It is a highly diverse discipline in terms of its thematic and methodical research foci. This article gives an overview of current German ecohydrological research approaches within plant-animal-soil-systems, meso-scale catchments and their river networks, lake systems, coastal areas and tidal rivers. It discusses their relevant spatial and temporal process scales and different types of interactions and feedback dynamics between hydrological and biotic processes and patterns. The following topics are considered key challenges: innovative analysis of the interdisciplinary scale continuum, development of dynamically coupled model systems, integrated monitoring of coupled processes at the interface and transition from basic to applied ecohydrological science to develop sustainable water and land resource management strategies under regional and global change.}, language = {de} } @article{TuominenEerikaeinenSchibalskietal.2010, author = {Tuominen, Sakari and Eerik{\"a}inen, Kalle and Schibalski, Anett and Haakana, Markus and Lehtonen, Aleksi}, title = {Mapping Biomass Variables with a Multi-Source Forest Inventory Technique}, issn = {0037-5330}, year = {2010}, abstract = {Map form information on forest biomass is required for estimating bioenergy potentials and monitoring carbon stocks. In Finland, the growing stock of forests is monitored using multi-source forest inventory, where variables are estimated in the form of thematic maps and area statistics by combining information of field measurements, satellite images and other digital map data. In this study, we used the multi-source forest inventory methodology for estimating forest biomass characteristics. The biomass variables were estimated for national forest inventory field plots on the basis of measured tree variables. The plot-level biomass estimates were used as reference data for satellite image interpretation. The estimates produced by satellite image interpretation were tested by cross-validation. The results indicate that the method for producing biomass maps on the basis of biomass models and satellite image interpretation is operationally feasible. Furthermore, the accuracy of the estimates of biomass variables is similar or even higher than that of traditional growing stock volume estimates. The technique presented here can be applied, for example, in estimating biomass resources or in the inventory of greenhouse gases.}, language = {en} }