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Waldökosysteme unterliegen vielfältigen Einflüssen wie forstlicher Bewirtschaftung, Stickstoffdeposition, Veränderung des Grundwasserspiegels oder der Einwanderung invasiver Arten. Die Wiederholung historischer Vegetationsaufnahmen ist ein wichtiges Mittel, um Veränderungen der Pflanzengesellschaften zu dokumentieren und mögliche Hauptursachen (Treiber) zu bestimmen. Wir haben 2015 den Vegetationswandel auf 140 semi-permanenten Plots in Wirtschaftswäldern der Elbtalniederung im Nordostdeutschen Tiefland (Sachsen-Anhalt, Brandenburg) untersucht. Die Erstaufnahme erfolgte von 1956 bis 1963. Die Vegetationsaufnahmen decken ein fast einzigartig breites Spektrum unterschiedlicher Waldstandorte ab, das von Feuchtwäldern (Au-, Bruch- und Moorwäldern des Alnion incanae, Alnion glutinosae und Betulion pubescentis) über bodensaure Eichen-Mischwälder (Quercion roboris) bis hin zu bodensauren, meist trockenen Kiefernwäldern mit unterschiedlicher Nährstoffausstattung (Dicrano-Pinion) reicht.
Die Veränderungen der Vegetation haben wir mit Hilfe von Bestandesdaten, Gewinner- und Verliererarten, der α- und β -Diversität sowie der Ellenberg-Zeigerwerte für Stickstoff, Reaktion, Feuchte und Licht analysiert. Dabei wurden, anders als in den meisten bisherigen Wiederholungsuntersuchungen, auch Flächen berücksichtigt, auf denen bis zur Zweitaufnahme ein vollständiger Bestandeswechsel stattgefunden hatte.
Insbesondere in den Feuchtwäldern und den bodensauren Wäldern mit mäßig guter Nährstoffversorgung sind Wechsel der Hauptbaumarten zu verzeichnen; außerdem wurden viele Kiefernbestände zwischenzeitlich neu begründet. Die Artenzahl hat insgesamt und in fast allen Waldtypen abgenommen, die β-Diversität ist jedoch unverändert geblieben bzw. hat sich erhöht. Die Zeigerwerte deuten auf eine Abnahme der Bodenfeuchte in den Au-, Bruch-, und Moorwäldern hin, während insbesondere die bodensauren Kiefernwälder dunkler, nährstoffreicher und feuchter geworden sind. Die Anzahl der Verlierer-Arten ist mehr als doppelt so hoch wie die der Gewinner-Arten, jedoch mit unterschiedlicher Entwicklung in den einzelnen Waldtypen. Insbesondere die nassen und feuchten Wälder, die bodensauren Eichen-Mischwälder und die Flechten-Kiefernwälder haben die meisten ihrer charakteristischen Arten verloren.
Veränderungen der Vegetation in den Feuchtwäldern gehen v. a. auf lokal gesunkene Grundwasserspiegel und eine dadurch gestiegene Nährstoffverfügbarkeit zurück; die Artenzusammensetzung der Auwälder wurde zudem sehr stark durch forstliche Eingriffe beeinflusst. Ursachen für den Trend zu feuchteren und nährstoffreicheren Bedingungen in ehemals trockenen bodensauren Kiefern- und Eichenwäldern sind Stickstoffeinträge sowie eine Sukzession nach Aufgabe historischer Waldnutzungs-formen (Streunutzung, Waldweide). Obwohl sich die einzelnen Waldtypen unterschiedlich entwickelt haben, sind Eutrophierung, sinkende Grundwasserspiegel und Waldbaumaßnahmen insgesamt die wichtigsten Ursachen für die beobachteten Vegetationsveränderungen. Forstliche Eingriffe wie Kahlschlag und Bestandesumbau mit Baumartenwechsel sind zugleich die Hauptursache dafür, dass es trotz Nivellierung des Standortsgradienten, gemessen an der β-Diversität, nicht zu einer Homogenisierung der Vegetation gekommen ist.
A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.
A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.
Using individual-based modeling to understand grassland diversity and resilience in the Anthropocene
(2020)
The world’s grassland systems are increasingly threatened by anthropogenic change. Susceptible to a variety of different stressors, from land-use intensification to climate change, understanding the mechanisms driving the maintenance of these systems’ biodiversity and stability, and how these mechanisms may shift under human-mediated disturbance, is thus critical for successfully navigating the next century. Within this dissertation, I use an individual-based and spatially-explicit model of grassland community assembly (IBC-grass) to examine several processes, thought key to understanding their biodiversity and stability and how it changes under stress. In the first chapter of my thesis, I examine the conditions under which intraspecific trait variation influences the diversity of simulated grassland communities. In the second and third chapters of my thesis, I shift focus towards understanding how belowground herbivores influence the stability of these grassland systems to either a disturbance that results in increased, stochastic, plant mortality, or eutrophication.
Intraspecific trait variation (ITV), or variation in trait values between individuals of the same species, is fundamental to the structure of ecological communities. However, because it has historically been difficult to incorporate into theoretical and statistical models, it has remained largely overlooked in community-level analyses. This reality is quickly shifting, however, as a consensus of research suggests that it may compose a sizeable proportion of the total variation within an ecological community and that it may play a critical role in determining if species coexist. Despite this increasing awareness that ITV matters, there is little consensus of the magnitude and direction of its influence. Therefore, to better understand how ITV changes the assembly of grassland communities, in the first chapter of my thesis, I incorporate it into an established, individual-based grassland community model, simulating both pairwise invasion experiments as well as the assembly of communities with varying initial diversities. By varying the amount of ITV in these species’ functional traits, I examine the magnitude and direction of ITV’s influence on pairwise invasibility and community coexistence. During pairwise invasion, ITV enables the weakest species to more frequently invade the competitively superior species, however, this influence does not generally scale to the community level. Indeed, unless the community has low alpha- and beta- diversity, there will be little effect of ITV in bolstering diversity. In these situations, since the trait axis is sparsely filled, the competitively inferior may suffer less competition and therefore ITV may buffer the persistence and abundance of these species for some time.
In the second and third chapters of my thesis, I model how one of the most ubiquitous trophic interactions within grasslands, herbivory belowground, influences their diversity and stability. Until recently, the fundamental difficulty in studying a process within the soil has left belowground herbivory “out of sight, out of mind.” This dilemma presents an opportunity for simulation models to explore how this understudied process may alter community dynamics. In the second chapter of my thesis, I implement belowground herbivory – represented by the weekly removal of plant biomass – into IBC-grass. Then, by introducing a pulse disturbance, modelled as the stochastic mortality of some percentage of the plant community, I observe how the presence of belowground herbivores influences the resistance and recovery of Shannon diversity in these communities. I find that high resource, low diversity, communities are significantly more destabilized by the presence of belowground herbivores after disturbance. Depending on the timing of the disturbance and whether the grassland’s seed bank persists for more than one season, the impact of the disturbance – and subsequently the influence of the herbivores – can be greatly reduced. However, because human-mediated eutrophication increases the amount of resources in the soil, thus pressuring grassland systems, our results suggest that the influence of these herbivores may become more important over time.
In the third chapter of my thesis, I delve further into understanding the mechanistic underpinnings of belowground herbivores on the diversity of grasslands by replicating an empirical mesocosm experiment that crosses the presence of herbivores above- and below-ground with eutrophication. I show that while aboveground herbivory, as predicted by theory and frequently observed in experiments, mitigates the impact of eutrophication on species diversity, belowground herbivores counterintuitively reduce biodiversity. Indeed, this influence positively interacts with the eutrophication process, amplifying its negative impact on diversity. I discovered the mechanism underlying this surprising pattern to be that, as the herbivores consume roots, they increase the proportion of root resources to root biomass. Because root competition is often symmetric, herbivory fails to mitigate any asymmetries in the plants’ competitive dynamics. However, since the remaining roots have more abundant access to resources, the plants’ competition shifts aboveground, towards asymmetric competition for light. This leads the community towards a low-diversity state, composed of mostly high-performance, large plant species. We further argue that this pattern will emerge unless the plants’ root competition is asymmetric, in which case, like its counterpart aboveground, belowground herbivory may buffer diversity by reducing this asymmetry between the competitively superior and inferior plants.
I conclude my dissertation by discussing the implications of my research on the state of the art in intraspecific trait variation and belowground herbivory, with emphasis on the necessity of more diverse theory development in the study of these fundamental interactions. My results suggest that the influence of these processes on the biodiversity and stability of grassland systems is underappreciated and multidimensional, and must be thoroughly explored if researchers wish to predict how the world’s grasslands will respond to anthropogenic change. Further, should researchers myopically focus on understanding central ecological interactions through only mathematically tractable analyses, they may miss entire suites of potential coexistence mechanisms that can increase the coviability of species, potentially leading to coexistence over ecologically-significant timespans. Individual-based modelling, therefore, with its focus on individual interactions, will prove a critical tool in the coming decades for understanding how local interactions scale to larger contexts, and how these interactions shape ecological communities and further predicting how these systems will change under human-mediated stress.