Biology subjects

Hildenbrandt, H.

Publications and source records attributed to Hildenbrandt, H..

5 recordsLinked to original sources

bioRxiv2025

The phylogenetic Janzen-Connell effect can explain multiple macroecological and macroevolutionary patterns

The classical Janzen-Connell (J-C) hypothesis states that species-specific natural enemies induce local-density dependence which explains high diversity observed in tropical tree communities. Recent studies have extended the hypothesis to include the fact that these natural enemies often attack phylogenetically related species as well, which reduces diversity and hence the explanatory power of the hypothesis. However, these studies consider ecological time scales at which the phylogenetic relatedness between species is constant, and thus ignore that at evolutionary time scales the population dynamics change the phylogeny. We use a spatially explicit eco-evolutionary model of the interplay between the dynamics of the phylogeny and the species abundances to study the predictions for common macroevolutionary and macroecological patterns. We confirm that hyperdiversity can be maintained by the J-C effect but the phylogenetic relatedness effect weakens the rarity advantage, reducing diversity. Incorporating protracted speciation further improves the result. Our model predicts a triphasic species-area relationship at much shorter time scales than in neutral scenarios. The species-abundance distribution can have one or two modes depending on the dispersal distance. Phylogenetic trees show diversification slowdowns and imbalance, consistent with empirical patterns. We study a new pattern of phylogenetic relatedness through space and find that small dispersal distance causes clusters of species with large phylogenetic distance to the community while large dispersal distance makes species distribute uniformly. As an illustration of how our models predictions can be compared to empirical data we study macroecological and phylogenetic patterns of the Barro Colorado Island (BCI) tree community. We conclude that the spatially explicit eco-evolutionary phylogenetic J-C effect can explain commonly observed macroevolutionary and macroecological patterns, providing an alternative way, while the traditional methods are prone, to demonstrate the role of J-C effect on species community assembly.

evolutionary biology↗

bioRxiv2024

Starling murmurations under predation

Large flocks of European starlings change shape, size, and internal structure continuously and rapidly when hunted by aerial predators. How their diverse patterns of collective escape emerge is still unknown. Here, we disentangle the collective behavior of starlings combining video footage of flocks pursued by a robotic predator and a data-driven 3-dimentional agent-based model. In vivo, we show that flock members often differ in their evasive maneuvers and that several collective patterns arise simultaneously across a flock. In silica, we identify individual-level rules that lead to dynamics of collective motion and escape similar to real starlings. Our results suggest that the mechanisms underlying starling murmurations depend on how fast escape information propagates through the flock, the relative positions of the escaping individuals to the predator, and the previous state of the flock (hysteresis). Our study highlights the importance of investigating fine-scale dynamics and micro-macro feedback when studying self-organized adaptive systems.

animal behavior and cognition↗

bioRxiv2021

Self-organization of collective escape in pigeon flocks

Bird flocks under predation demonstrate complex patterns of collective escape. These patterns may emerge by self-organization from simple interactions among group-members. Computational models have been shown to be valuable for identifying the behavioral rules that may govern these interactions among individuals during collective motion. However, our knowledge of such rules for collective escape is limited by the lack of quantitative data on bird flocks under predation in the field. In the present study, we analyze the first dataset of GPS trajectories of pigeons in airborne flocks attacked by a robotic falcon in order to build a species-specific model of collective escape. We use our model to examine a recently identified distance-dependent pattern of collective behavior that shows an increase in the escape frequency of pigeons when the predator is closer. We first extract from the empirical data the characteristics of pigeon flocks regarding their shape and internal structure (bearing angle and distance to nearest neighbours). Combining these with information on their coordination from the literature, we build an agent-based model tuned to pigeons collective escape. We show that the pattern of increased escape frequency closer to the predator arises without flock-members prioritizing escape when the predator is near. Instead, it emerges through self-organization from an individual rule of predator-avoidance that is independent of predator-prey distance. During this self-organization process, we uncover a role of hysteresis and show that flock members increase their consensus over the escape direction and turn collectively as the predator gets closer. Our results suggest that coordination among flock-members, combined with simple escape rules, reduces the cognitive costs of tracking the predator. Such rules that are independent of predator-prey distance can now be examined in other species. Finally, we emphasize on the important role of computational models in the interpretation of empirical findings of collective behavior. Author summaryBird flocks show fascinating patterns of collective motion, particularly when escaping a predator. Little is however known about their underlying mechanisms. We fill this gap by firstly analyzing GPS data of pigeon flocks under attack by a robotic-predator and secondly, studying their collective escape in a computer simulation. Previous research on pigeons has revealed that flock-members turn away from the predator more the closer the predator gets. Using computer simulations that are based on pigeon-specific characteristics of motion and coordination among individuals, we study what escape rules at the individual level may underlie this distance-dependent pattern. We show that even if individuals do not intend to escape more when the predator is closer, their escape frequency still increases the closer they get to the predator. This happens by self-organization from the coordination among individuals and despite their tendency to turn away from the predator being constant. A key aspect of this process is the increasing consensus among flock members over the escape direction when the predator gets closer.

animal behavior and cognition↗

bioRxiv2021

DETECTING PHYLODIVERSITY-DEPENDENT DIVERSIFICATION WITH A GENERAL PHYLOGENETIC INFERENCE FRAMEWORK

AO_SCPLOWBSTRACTC_SCPLOWDiversity-dependent diversification models have been extensively used to study the effect of ecological limits and feedback of community structure on species diversification processes, such as speciation and extinction. Current diversity-dependent diversification models characterise ecological limits by carrying capacities for species richness. Such ecological limits have been justified by niche filling arguments: as species diversity increases, the number of available niches for diversification decreases. However, as species diversify they may diverge from one another phenotypically, which may open new niches for new species. Alternatively, this phenotypic divergence may not affect the species diversification process or even inhibit further diversification. Hence, it seems natural to explore the consequences of phylogenetic diversity-dependent (or phylodiversity-dependent) diversification. Current likelihood methods for estimating diversity-dependent diversification parameters cannot be used for this, as phylodiversity is continuously changing as time progresses and species form and become extinct. Here, we present a new method based on Monte Carlo Expectation-Maximization (MCEM), designed to perform statistical inference on a general class of species diversification models and implemented in the R package emphasis. We use the method to fit phylodiversity-dependent diversification models to 14 phylogenies, and compare the results to the fit of a richness-dependent diversification model. We find that in a number of phylogenies, phylogenetic divergence indeed spurs speciation even though species richness reduces it. Not only do we thus shine a new light on diversity-dependent diversification, we also argue that our inference framework can handle a large class of diversification models for which currently no inference method exists.

evolutionary biology↗

bioRxiv2020

Complex eco-evolutionary dynamics induced by the coevolution of predator-prey movement strategies

The coevolution of predators and prey has been the subject of much empirical and theoretical research, which has produced intriguing insights into the intricacies of eco-evolutionary dynamics. Mechanistically detailed models are rare, however, because the simultaneous consideration of individual-level behaviour (on which natural selection is acting) and the resulting ecological patterns is challenging and typically prevents mathematical analysis. Here we present an individual-based simulation model for the coevolution of predators and prey on a fine-grained resource landscape. Throughout their lifetime, predators and prey make repeated movement decisions on the basis of heritable and evolvable movement strategies. We show that these strategies evolve rapidly, inducing diverse ecological patterns like spiral waves and static spots. Transitions between these patterns occur frequently, induced by coevolution rather than by external events. Regularly, evolution leads to the emergence and stable coexistence of qualitatively different movement strategies. Although the strategy space considered is continuous, we often observe discrete variation. Accordingly, our model includes features of both population genetic and quantitative genetic approaches to coevolution. The model demonstrates that the inclusion of a richer ecological structure and higher number of evolutionary degrees of freedom results in even richer eco-evolutionary dynamics than anticipated previously.

evolutionary biology↗