Sampling bias and the robustness of ecological metrics for plant–damage‐type association networks

Abstract

Plants and their insect herbivores have been a dominant component of the terrestrial ecological landscape for the past 410 million years and feature intricate evolutionary patterns and co-dependencies. A complex systems perspective allows for both detailed resolution of these evolutionary relationships as well as comparison and synthesis across systems. Using proxy data of insect herbivore damage (denoted by the damage type or DT) preserved on fossil leaves, functional bipartite network representations provide insights into how plant-insect associations depend on geological time, paleogeographical space, and environmental variables such as temperature and precipitation. However, the metrics measured from such networks are prone to sampling bias. Such sensitivity is of special concern for plant-DT association networks in paleontological settings where sampling effort is often severely limited. Here, we explore the sensitivity of functional bipartite network metrics to sampling intensity and identify sampling thresholds above which metrics appear robust to sampling effort. Across a broad range of sampling efforts, we find network metrics to be less affected by sampling bias and/or sample size than richness metrics, which are routinely used in studies of fossil plant-DT interactions.

Identification of qualitative characteristics of immunosuppression in sepsis based on immune-related genes and immune infiltration features

“…The inherent variability in biological samples arises from the stochastic nature of biological processes. Random fluctuations in gene expression, cell populations, and molecular interactions contribute to this variability, making it challenging to discern genuine biological changes from natural fluctuations [ 25 ]. Furthermore, inconsistencies in data preprocessing steps, like data normalization and transformation, can introduce additional variability if not uniformly applied across datasets.…”

Sampling bias and the robustness of ecological metrics for plant–damage-type association networks: Comment

“… Abstract Bipartite network metrics, which link taxa at two trophic levels, are notoriously biased when sampling is incomplete or uneven (Blüthgen et al, 2008; Dormann and Blüthgen, 2017; Fründ et al, 2016). Yet a new contribution (Swain et al, 2023, henceforth SEA) claims the opposite: that bipartite network metrics are minimally sensitive to incomplete sampling and, in fact, perform better at low sample sizes than traditional richness metrics. Here I show that SEA achieved this extraordinary finding by abandoning accepted practices, including practices from the authors’ previous papers. …”

“…Currano et al did not follow these conventions, however, in the Swain et al (2023) paper they coauthored. SEA examined the sensitivity of the abovementioned measures—richness, and bipartite network metrics—to sampling incompleteness.…”

Drivers of herbivore diversity decoupled by leveraging the fossil record

“…( 14 ). These DTs can therefore be seen as functional traits of herbivory, which have remained consistent across geographical space and evolutionary time, due to repeated convergences in mouthparts and feeding strategies among phytophagous insects ( 16 , 17 ).…”

“…While these studies have yielded invaluable insights into their diversity, they have all focused on a taxonomic, or species-based, perspective ( 4 ). Using a functional (or trait-based) perspective, in place of a purely taxonomic one, has shown the importance of ecological drivers on interactions in both the modern and paleo-ecological literature and provides a complementary perspective to species-based studies ( 16 , 18 – 20 ). In the case of DTs and their host plants, the rich fossil record has allowed the exploration of important and longstanding ecological questions across large spatiotemporal scales ( 10 , 21 – 24 ).…”