Clade-wide variation in bite-force performance is determined primarily by size, not ecology

Performance traits are tightly linked to the fitness of organisms. However, because studies of variation in performance traits generally focus on just one or several closely related species, we are unable to draw broader conclusions about how and why these traits vary across clades. One important performance trait related to many aspects of an animal's life history is bite-force. Here, we use a clade-wide phylogenetic comparative approach to investigate relationships between size, head dimensions and bite-force among lizards and tuatara (lepidosaurs), using the largest bite-force dataset collated to date for any taxonomic group. We test four predictions: that bite-force will be greater in larger species, and for a given body size, bite-force will be greatest in species with acrodont tooth attachment, herbivorous diets, and non-burrowing habits. We show that bite-force is strongly related to body and head size across lepidosaurs and, as predicted, larger species have the greatest bite-forces. Contrary to our other predictions, tooth attachment, diet and habit have little predictive power when accounting for size. Herbivores bite more forcefully simply because they are larger. Our results also highlight priorities for future sampling to further enhance our understanding of broader evolutionary patterns

ChatGPT is likely reducing opportunity for support, friendship and learned kindness in research

1. Large language models (LLM) have proved to be highly popular since the release of ChatGPT, leading many researchers to explore their potential across multiple fields of scientific research. In a recent Perspective, Cooper et al. (2024) highlight a set of benefits and challenges for the use of LLMs in ecology, emphasising their value to coding in research and education. 2. While we agree that the ability of LLMs to assist in the coding process is remarkable, researchers should be conscious that this capability is likely changing the lived experience of primarily computational researchers, especially early career ecologists between Masters and Postdoctoral career stages. 3. In particular, since the release of ChatGPT, the authors of this paper have noticed a marked reduction in the frequency of social interactions emergent from coding and statistics queries. These questions are highly likely still being asked, but now often exclusively to a LLM. 4. Further research is needed to fully understand the effect of LLMs on the lived-experience of researchers and students. For primarily computational researchers, ChatGPT is likely reducing emergent opportunity for support, friendship and learned kindness. Group leaders should recognise this and foster deliberate within-group communication and collaboration

Clade-wide variation in bite-force 1 performance is determined primarily by 2 size not ecology.

Performance traits are tightly linked to the fitness of organisms. However, because studies of variation in performance traits generally focus on just one or several closely-related species, we are unable to draw broader conclusions about how and why these traits vary across clades. One important performance trait related to many aspects of an animal’s life history is bite-force. Here we use a clade-wide phylogenetic comparative approach to investigate relationships between size, head dimensions and bite-force among lizards and tuatara (lepidosaurs), using the largest bite-force dataset collated to date for any taxonomic group. We test four predictions: that bite-force will be greater in larger species, and for a given body size, bite-force will be greatest in species with acrodont tooth attachment, herbivorous diets, and non-burrowing habits. We show that bite-force is strongly related to body and head size across lepidosaurs and, as predicted, larger species have the greatest bite-forces. Contrary to our other predictions, tooth attachment, diet and habit have little predictive power when accounting for size. Herbivores bite more forcefully simply because they are larger. Our results also highlight priorities for future sampling to further enhance our understanding of broader evolutionary patterns.Copyright © The Authors 2021. This document is the authors' final accepted version of the journal article. You are advised to consult the published version if you wish to cite from it.NHM Repositor

Supplementary methods, figures and tables from Clade-wide variation in bite-force performance is determined primarily by size, not ecology

Supplementary methods, figures and table

ChatGPT is likely reducing opportunity for support, friendship and learned kindness in research

Publication status: PublishedAbstract Large language models (LLM) have proved to be highly popular since the release of ChatGPT, leading many researchers to explore their potential across multiple fields of scientific research. In a recent Perspective, Cooper et al. (2024) highlight a set of benefits and challenges for the use of LLMs in ecology, emphasising their value to coding in research and education. While we agree that the ability of LLMs to assist in the coding process is remarkable, researchers should be conscious that this capability is likely changing the lived experience of primarily computational researchers, especially early career ecologists between Masters and Postdoctoral career stages. In particular, since the release of ChatGPT, the authors of this paper have noticed a marked reduction in the frequency of social interactions emergent from coding and statistics queries. These questions are highly likely still being asked, but now often exclusively to a LLM. Further research is needed to fully understand the effect of LLMs on the lived‐experience of researchers and students. For primarily computational researchers, ChatGPT is likely reducing emergent opportunity for support, friendship and learned kindness. Group leaders should recognise this and foster deliberate within‐group communication and collaboration. </jats:p

A multithreat meta-analytic database for understanding insect biodiversity change

Aim: Widespread declines in insect biodiversity have been attributed to a diverse set of anthropogenic drivers, but the relative importance of these drivers remains unclear. A key reason for this uncertainty is that their effects depend on many factors, such as taxonomy, geography, sampling method and the biodiversity metric considered. To better understand the relative impact of different drivers on insect biodiversity, effect sizes need to be anchored to major sources of heterogeneity and collected reproducibly through a structured and consistent protocol. This standardised approach will allow a quantitative synthesis of relative threats to insects, enabling more robust predictions of changes in insect biodiversity. Innovation: Here we publish a global database of effect sizes that quantify the effect of 5 anthropogenic drivers on insect abundance, species richness, biomass and fecundity within the framework of the IUCN threat classification. While we only present results for a subset of major anthropogenic drivers and insect Orders, the database structure allows the addition of new studies for all major IUCN threats and insect Orders. Our current set of effect sizes was collated from 7 meta-analyses, including 6308 effect sizes from 317 studies, focusing on threats ranked highly in an initial expert elicitation process. Data collection followed an overall meta-protocol and a set of individual protocols tailored to each meta-analysis. Our database provides a framework for the first global meta-analytic overview of the response of insects to a range of major anthropogenic drivers. Main Conclusions: Structured collation of both experimental and quasi-experimental effect sizes, together with metadata that capture the main sources of heterogeneity, is needed to understand the effect of anthropogenic activity on insects. In turn, this understanding opens the way to predicting how we might expect insect biodiversity to have changed in the past and into the future