Biology subjects

Lajaaiti, I.

Publications and source records attributed to Lajaaiti, I..

6 recordsLinked to original sources

bioRxiv2025

Beyond biomass: how interactions shape species' importance for ecosystem functioning

Assessing the functional role of species in a changing world is critical for effectively preserving ecosystems. Intuitively, this role should relate to how the loss of a species ultimately impacts a function. We introduce the notion of a species dynamic contribution, which takes into account biotic interactions as they develop in the community. Contrary to the static contribution --a measure of what a species directly does-- the dynamic contribution captures what the presence of the species causes. Using both model simulations and empirical data from a ciliate microcosm experiment, we demonstrate that dynamic and static contributions of species are generically unrelated. Our novel characterization of species functional contributions reveals that, due to biotic interactions, rare species that do not possess unique functional traits are just as likely as any other species to play an important role for ecosystem functioning.

ecology↗

bioRxiv2025

Revealing the organization of species stability in ecological communities

Ecological communities are often composed of many species, each interacting in complex ways. This complexity makes predictions of species responses to disturbances challenging. Here, we analyze dynamical community models and reveal an unexpectedly simple principle: species stability is governed by a single metric--self-regulation loss (SL). SL quantifies the importance of self-regulatory processes in species population dynamics. In effect, SL captures a collective outcome of species interactions, organizing how individual species respond to disturbances. When applied to data from protist community experiments, SL accurately forecasts species responses to temperature changes. Our work reveals that, despite the complexity of ecological systems, species stability follows a remarkably simple organizing principle.

ecology↗

bioRxiv2024

Ecological networks across interaction types are modular and highly driven by sampling intensity at biogeographical scales

Understanding how the structure of ecological communities varies across biotic and abiotic dimensions is a fundamental goal in ecology. This challenge is now approachable due to the increasing availability of data on community structure across the globe. Ecological communities are often defined with respect to the guilds considered and the interactions they engage in, but it is unclear whether interactions of different types respond similarly to large-scale environmental gradients. Therefore, we lack a deeper understanding of how the emergent structure of interaction networks varies across biogeographical gradients, and how this effect may change depending on their constituent interaction types. Here, using a unique dataset of 952 networks across the globe, we provide a first comparison of network structural metrics and their large-scale variability for five overarching interaction types (feeding, frugivory, herbivory, parasitism and pollination). We show that degree distribution, but not connectance alone, helps us understand the observed network structures, and this pattern is maintained across interaction types (with the partial exception of food webs). Moreover, degree distribution descriptors are generally explained by differences across studies, which represent a proxy for variability in sampling and network construction methods. Environ-mental factors show weaker but robust effects on network degree distribution, and food webs are generally more sensitive to changes in environmental factors than networks of other interaction types. By analysing common descriptors of the degree distributions of ecological networks, this study underscores for the first time generalities and differences across networks of different interaction types and their response to environmental and anthropogenic factors.

ecology↗

bioRxiv2024

Unpacking sublinear growth: diversity, stability and coexistence

How can many species coexist in natural ecosystems remains a fundamental question in ecology. Theory suggests that competition for space and resources should maintain the number of coexisting species far below the staggering diversity commonly found in nature. A recent model finds that, when sublinear growth rates of species are coupled with competition, species diversity can stabilize community dynamics. This, in turn, is suggested to explain the coexistence of many species in natural ecosystems. In this brief note we clarify why the sublinear growth (SG) model does not solve the long standing paradox of species coexistence. This is because in the SG model coexistence emerges from an unrealistic property, in which species per-capita growth rate diverges at low abundance, preventing species from ever going extinct. When infinite growth at low abundance is reconciled with more realistic assumptions, the SG model recovers the expected paradox: increasing diversity leads to competitive exclusion and species extinctions.

ecology↗

bioRxiv2024

EcologicalNetworksDynamics.jl: A Julia package to simulate the temporal dynamics of complex ecological networks

O_LISpecies interactions play a crucial role in shaping biodiversity, species coexistence, population dynamics, community stability and ecosystem functioning. Our understanding of the role of the diversity of species interactions driving these species, community and ecosystem features is limited because current approaches often focus only on trophic interactions. This is why a new modelling framework that includes a greater diversity of interactions between species is crucially needed. C_LIO_LIWe developed a modular, user-friendly, and extensible Julia package that delivers the core functionality of the bio-energetic food web model. Moreover, it embeds several ecological interaction types alongside the capacity to manipulate external drivers of ecological dynamics like temperature. These new features represent important processes known to influence biodiversity, coexistence, functioning and stability in natural communities. Specifically, they include: a) an explicit multiple nutrient intake model for producers, b) competition among producers, c) temperature dependence implemented via the Boltzmann-Arhennius rule, and d) the ability to model several non-trophic interactions including competition for space, plant facilitation, predator interference and refuge provisioning. C_LIO_LIThe inclusion of the various features provides users with the ability to ask questions about multiple simultaneous processes and stressor impacts, and thus develop theory relevant to real world scenarios facing complex ecological communities in the Anthropocene. It will allow researchers to quantify the relative importance of different mechanisms to stability and functioning of complex communities. C_LIO_LIThe package was build for theoreticians seeking to explore the effects of different types of species interactions on the dynamics of complex ecological communities, but also for empiricists seeking to confront their empirical findings with theoretical expectations. The package provides a straightforward framework to model explicitly complex ecological communities or provide tools to generate those communities from few parameters. C_LI

ecology↗

bioRxiv2023

A Comparison of Deep Learning Architectures for Inferring Parameters of Diversification Models from Extant Phylogenies

AO_SCPLOWBSTRACTC_SCPLOWTo infer the processes that gave rise to past speciation and extinction rates across taxa, space and time, we often formulate hypotheses in the form of stochastic diversification models and estimate their parameters from extant phylogenies using Maximum Likelihood or Bayesian inference. Unfortunately, however, likelihoods can easily become intractable, limiting our ability to consider more complicated diversification processes. Recently, it has been proposed that deep learning (DL) could be used in this case as a likelihood-free inference technique. Here, we explore this idea in more detail, with a particular focus on understanding the ideal network architecture and data representation for using DL in phylogenetic inference. We evaluate the performance of different neural network architectures (DNN, CNN, RNN, GNN) and phylogeny representations (summary statistics, Lineage Through Time or LTT, phylogeny encoding and phylogeny graph) for inferring rates of the Constant Rate Birth-Death (CRBD) and the Binary State Speciation and Extinction (BISSE) models. We find that deep learning methods can reach similar or even higher accuracy than Maximum Likelihood Estimation, provided that network architectures and phylogeny representations are appropriately tuned to the respective model. For example, for the CRBD model we find that CNNs and RNNs fed with LTTs outperform other combinations of network architecture and phylogeny representation, presumably because the LTT is a sufficient and therefore less redundant statistic for homogenous BD models. For the more complex BiSSE model, however, it was necessary to feed the network with both topology and tip states information to reach acceptable performance. Overall, our results suggest that deep learning provides a promising alternative for phylogenetic inference, but that data representation and architecture have strong effects on the inferential performance.

ecology↗