TY - JOUR A1 - Schibalski, Anett A1 - Körner, Katrin A1 - Maier, Martin A1 - Jeltsch, Florian A1 - Schröder, Boris T1 - Novel model coupling approach for resilience analysis of coastal plant communities JF - Ecological applications : a publication of the Ecological Society of America N2 - 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. KW - Baltic Sea KW - hybrid model KW - Lolium perenne KW - model coupling KW - Scirpus maritimus KW - transient dynamics Y1 - 2018 U6 - https://doi.org/10.1002/eap.1758 SN - 1051-0761 SN - 1939-5582 VL - 28 IS - 6 SP - 1640 EP - 1654 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Tuominen, Sakari A1 - Eerikäinen, Kalle A1 - Schibalski, Anett A1 - Haakana, Markus A1 - Lehtonen, Aleksi T1 - Mapping Biomass Variables with a Multi-Source Forest Inventory Technique N2 - 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. Y1 - 2010 UR - http://www.metla.fi/silvafennica/ SN - 0037-5330 ER - TY - JOUR A1 - Schibalski, Anett A1 - Lehtonen, Aleksi A1 - Schroeder, Boris T1 - Climate change shifts environmental space and limits transferability of treeline models JF - Ecography : pattern and diversity in ecology ; research papers forum N2 - 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. Y1 - 2014 U6 - https://doi.org/10.1111/j.1600-0587.2013.00368.x SN - 0906-7590 SN - 1600-0587 VL - 37 IS - 4 SP - 321 EP - 335 PB - Wiley-Blackwell CY - Hoboken ER - TY - JOUR A1 - Eberhard, Julius A1 - Schaik, N. Loes M. B. A1 - Schibalski, Anett A1 - Gräff, Thomas T1 - Simulating future salinity dynamics in a coastal marshland under different climate scenarios JF - Vadose zone journal N2 - 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. Y1 - 2020 U6 - https://doi.org/10.1002/vzj2.20008 SN - 1539-1663 VL - 19 IS - 1 PB - Wiley CY - Hoboken ER - TY - THES A1 - Schibalski, Anett T1 - Statistical and process-based models for understanding species distributions in changing environments T1 - Statistische und prozessbasierte Modelle für die Verbreitung von Arten unter Umweltänderungen N2 - 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. N2 - Wissen über die Verbreitung von Arten ist fundamental für die Erhaltung von Biodiversität, das Management von Ökosystemen und zunehmend auch für die Abschätzung der Folgen des Klimawandels. Das Vorkommen einer Art an einem Standort hängt ab von: physiologischen Grenzwerten (abiotischen Faktoren), Interaktionen mit anderen Arten (biotischen Faktoren), Ausbreitungsprozessen (Erreichbarkeit des Standorts) sowie Nachwirkungen vergangener Ereignisse, z.B. Störungen (Standortgeschichte). Modellansätze zur Vorhersage von Artverbreitungen (i) korrelieren entweder beobachtete Artvorkommen mit abiotischen Umweltvariablen und ignorieren damit biotische Interaktionen, Ausbreitung und Nachwirkungen (statistische Artverbreitungsmodelle, SDM); oder (ii) sie modellieren mechanistisch, wie sich die verschiedenen Prozesse auf Arten auswirken (prozessbasierte Modelle, PBM). SDMs sind weitverbreitet, da sie einfach anzuwenden sind und verschiedenste Datenqualitäten akzeptieren. Aber sie beschreiben nicht korrekt, wie Arten dynamisch auf Umweltänderungen reagieren. PBMs sind ihnen in dieser Hinsicht überlegen. Allerdings benötigen diese sehr spezifische Daten, welche für viele Arten nicht verfügbar sind. Zudem sind sie oft komplexer und benötigen mehr Rechenkapazität. Relativ neu ist der Ansatz des Hybridmodells, welches statistische und prozessbasierte Modelle verknüpft und so ihre jeweiligen Stärken vereint. In dieser Arbeit, nutze ich sowohl statistische als auch prozessbasierte Modelle, um die Verbreitung von Arten vorherzusagen, und ich diskutiere ihre jeweiligen Schwächen, besonders für die Anwendung im Klimawandelkontext. Eine detaillierte Analyse der SDMs für boreale Baumarten in Finnland zeigt, dass nicht-klimatische Variablen - Bodeneigenschaften und biotische Interaktionen - wichtige Faktoren an der Baumgrenze sind und daher die Reaktion von Arten auf Klimaänderungen beeinflussen. Während die SDMs innerhalb der Wertebereiche ihrer Trainingsdatensätze erfolgreich sind, scheitern Versuche, die Modelle auf andere Regionen oder in die Zukunft zu übertragen. Die Visualisierung und der Vergleich des abgedeckten Umweltraums zwischen den Teildatensätzen liefert eine plausible Erklärung: Extrapolation. Basierend auf diesen Ergebnissen, analysiere ich den bedingten Erfolg eines etablierten PBMs (LPJ-GUESS), das ich auf dieselbe Fragestellung anwende. Die Untersuchung der Prozessbeschreibungen im Modell sowie der Parametrisierung zeigen, dass bereits implementierte Prozesse angepasst werden müssen (Konkurrenz, Störungen) und dass für boreale Wälder entscheidende Prozesse fehlen (Nährstoffe, Bewirtschaftung). Mithilfe von klimatischen Schwellenwerten, die sich über die Zeit verschieben, betone ich die eingeschränkte Übertragbarkeit von bioklimatischen Grenzwerten in LPJ-GUESS und ähnlichen PBMs. Indem ich die Performance beider Methoden in dieser Anwendung kritisch beleuchte, zeige ich, wie wichtig es ist, sich der Grenzen jedes Modellansatzes bewusst zu sein. Als Lösungsmöglichkeit füge ich dem bestehenden Repertoire der Hybridmodelle einen neuen Ansatz hinzu. Mithilfe von Simulationsexperimenten mit einem individuenbasierten PBM, das erfolgreich die Dynamik von Artgemeinschaften beschreibt (resultierend aus biotischen Faktoren, Ausbreitung und Nachwirkungen), untersuche ich die Resilienz von Küstenvegetation auf abrupte Änderungen der Hydrologie. Entsprechend der Ergebnisse dieser Resilienzanalyse passe ich die zeitlichen Vorhersagen eines SDMs an und übertrage so das nötige Prozesswissen von PBM zu SDM. Die Übertragungsrichtung von PBM zu SDM umgeht die Nachteile bestehender Hybridmodelle und verbessert die Anwendbarkeit für langfristige, großflächige Berechnungen. Ein weiterer Vorteil des vorgestellten Konzepts ist seine Flexibilität, denn es lässt sich einfach auf andere Modellarten, andere Definitionen von Umweltstörungen sowie andere Vorhersagegrößen anwenden. Zusammenfassend argumentiere ich, dass uns bereits vielfältige, erfolgversprechende Modellansätze zur Verfügung stehen, die noch weiterentwickelt werden können. Vor allem aber müssen sie mit mehr Bedacht angewendet werden. Voran kommen wir, indem wir die Schwächen der Ansätze berücksichtigen, ihre Stärken in Hybridmodellen kombinieren und die zugrunde liegenden Annahmen und damit verbundene Unsicherheiten deutlich machen. KW - species distribution KW - Artverbreitung KW - climate change KW - Klimawandel KW - hybrid model KW - Hybridmodell Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-401482 ER - TY - JOUR A1 - Müller, Eva Nora A1 - van Schaik, Loes A1 - Blume, Theresa A1 - Bronstert, Axel A1 - Carus, Jana A1 - Fleckenstein, Jan H. A1 - Fohrer, Nicola A1 - Geissler, Katja A1 - Gerke, Horst H. A1 - Gräff, Thomas A1 - Hesse, Cornelia A1 - Hildebrandt, Anke A1 - Hölker, Franz A1 - Hunke, Philip A1 - Körner, Katrin A1 - Lewandowski, Jörg A1 - Lohmann, Dirk A1 - Meinikmann, Karin A1 - Schibalski, Anett A1 - Schmalz, Britta A1 - Schröder-Esselbach, Boris A1 - Tietjen, Britta T1 - Scales, key aspects, feedbacks and challenges of ecohydrological research in Germany JF - Hydrologie und Wasserbewirtschaftung N2 - 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. KW - Coastal regions KW - drylands KW - ecohydrological modelling KW - feedback KW - hyporheic zone KW - meso-scale ecosystems KW - plant-animal-soil-system KW - river networks Y1 - 2014 U6 - https://doi.org/10.5675/HyWa_2014,4_2 SN - 1439-1783 VL - 58 IS - 4 SP - 221 EP - 240 PB - Bundesanst. für Gewässerkunde CY - Koblenz ER -