Browsing by Subject "Bryozoans"

Now showing 1 - 1 of 1

The paradox of the bryozoans
unravelling the relation between structure and stability in benthic competition networks
(2025) Koch, Franziska; Allhoff, Korinna T.
Classical ecological models struggle to explain the persistence of diverse communities, where many species compete for the same resources. For example, traditional stability theory based on large, random matrices predicts that diverse communities should generally be unstable. Furthermore, according to the principle of competitive exclusion, two species competing for one resource should not be able to coexist. Parametrising network models with empirical data has revealed that real networks tend to contain specific non-random patterns of weak and strong links, which enable their stability. However, such patterns have been studied in food webs as well as mutualistic networks, while potential stabilising patterns of interaction strengths in competitive systems remain largely unexplored. Instead, simplified competition networks are often used, in which interactions are binary, so that potential stabilising effects due to specific patterns in weak and strong links cannot be considered. In such studies, intransitive competition, where competitive links are arranged as in a rock-papers-scissors game, is considered the only mechanism that can avoid competitive exclusion. In this thesis, I studied the role of interaction strengths in the stability of competition networks. To achieve this, I used data on encrusting bryozoan assemblages in order to parametrise weighted interaction networks. Bryozoans are small aquatic animals that grow in colonies on the sea bed. Individual colonies compete for space by overgrowing each other. My approach allowed me to obtain Jacobian matrices from species abundances and recorded overgrowths. I could thus connect this abstract mathematical modelling approach with empirical data. Based on the Jacobian matrices, I assessed network stability, as well as the strengths of amplifying and dampening feedback loops in the systems, which allowed me to draw connections between structure and stability. In Chapter 1, I analysed a data set of 30 polar bryozoan assemblages. These assemblages were quite hierarchical, meaning that species could be sorted from strongest to weakest competitors. I showed that as a result of this hierarchy, the Jacobian matrices contained asymmetric patterns of interaction strengths and that these patterns had a stabilising effect. While all empirical networks were unstable, destroying asymmetry by randomising the matrix elements increased the level of instability. This is because the asymmetric patterns keep feedback loops of all lengths weak. This applies to short, positive 2-link loops which can destabilise the system by amplifying perturbations, as well as to longer, negative loops which can cause unstable oscillations. Positive 2-link loops that are formed between each pair of competing species played a key role, and I found that the strongest 2-link loop in a matrix could be used as a predictor of network instability. I could thus identify hierarchy to have a stabilising effect in weighted competition networks, which contrasts with the common idea that intransitive competition stabilised complex systems. In Chapter 2, I additionally looked at the role of distributions of interaction strengths. I showed that the interaction strengths in my empirical data sets were very skewed, with few strong and many weak links. Similar patterns have also been found in the link strengths of both food webs and mutualistic networks, pointing towards a general pattern. I tested whether this skewed distribution of link strengths influenced the stabilising effect of asymmetry by building theoretical community matrices with asymmetric patterns and various distributions of link strengths. My results indicated that the full stabilising effect of asymmetry could only be reproduced when a skewed distribution of interaction strengths was used. This has important implications for many theoretical studies, where normal and uniform distributions of link strengths are often used, meaning that some stabilising patterns might be overlooked. Finally, in Chapter 3, I contrasted networks from polar regions to additional data sets collected at temperate and tropical locations. By comparing several network indices, I identified latitudinal differences in the strength of asymmetric patterns, which were generally stronger in polar networks. Due to the stabilising effect of asymmetry, this also leads to differences in network stability, with tropical networks having stronger positive 2-link feedback loops and thus being more unstable. Across all data sets, I found that again, the strongest 2-link loop closely predicted stability, emphasising the generality of this stability predictor. However, the strength of asymmetry varied significantly within some regions, which could potentially be linked to assemblage age and disturbance history. To summarise, my work extends our understanding of stabilising patterns in interaction strengths to competitive systems. A key result is that positive, amplifying 2-link feedback loops determine the stability of competition networks, and that asymmetric arrangement of link strengths reduce their amplifying effect. I identified the maximum 2-link loop weight as a predictor of network stability, which is in line with previous results on food webs, where the maximum 3-link loop determines stability. Finally, I was able to show that some insights derived from random matrix models and models of intransitive competition, which are both commonly used in theoretical ecology, might not be transferable to real systems. This highlights a further need to combine mathematical modelling approaches with empirical research.