A standard protocol to report discrete stage‐structured demographic information

Abstract

Stage‐based demographic methods, such as matrix population models (MPMs), are powerful tools used to address a broad range of fundamental questions in ecology, evolutionary biology and conservation science. Accordingly, MPMs now exist for over 3000 species worldwide. These data are being digitised as an ongoing process and periodically released into two large open‐access online repositories: the COMPADRE Plant Matrix Database and the COMADRE Animal Matrix Database. During the last decade, data archiving and curation of COMPADRE and COMADRE, and subsequent comparative research, have revealed pronounced variation in how MPMs are parameterized and reported. Here, we summarise current issues related to the parameterisation and reporting of MPMs that arise most frequently and outline how they affect MPM construction, analysis, and interpretation. To quantify variation in how MPMs are reported, we present results from a survey identifying key aspects of MPMs that are frequently unreported in manuscripts. We then screen COMPADRE and COMADRE to quantify how often key pieces of information are omitted from manuscripts using MPMs. Over 80% of surveyed researchers (n = 60) state a clear benefit to adopting more standardised methodologies for reporting MPMs. Furthermore, over 85% of the 300 MPMs assessed from COMPADRE and COMADRE omitted one or more elements that are key to their accurate interpretation. Based on these insights, we identify fundamental issues that can arise from MPM construction and communication and provide suggestions to improve clarity, reproducibility and future research utilising MPMs and their required metadata. To fortify reproducibility and empower researchers to take full advantage of their demographic data, we introduce a standardised protocol to present MPMs in publications. This standard is linked to www.compadre‐db.org, so that authors wishing to archive their MPMs can do so prior to submission of publications, following examples from other open‐access repositories such as DRYAD, Figshare and Zenodo. Combining and standardising MPMs parameterized from populations around the globe and across the tree of life opens up powerful research opportunities in evolutionary biology, ecology and conservation research. However, this potential can only be fully realised by adopting standardised methods to ensure reproducibility.

Uncertainty propagation in matrix population models: gaps, importance, and guidelines

“…Furthermore, several papers presented uncertainties only visually (Ali et al, 2018;Hanly & Haase, 2016;Rota et al, 2014), making it almost impossible to propagate this uncertainty to further studies. This inconsistent reporting mirrors recent findings about MPM reporting more broadly (Gascoigne et al, 2023) and makes it challenging to compare uncertainty across studies, therefore limiting the potential to use it in comparative analyses. Inconsistency also brings challenges for incorporating uncertainty into open-access databases, as if it is not reported in a standardised way, it is not straightforward to store it in a standardised and interpretable way.…”

“…Mirroring the guidelines of Gascoigne et al (2023), we emphasise (1) the need for uncertainty reporting for each matrix element of the MPM. This level of detail ensures both a transparent picture of the uncertainty associated with the MPM and lays a solid groundwork for future analyses.…”

“…We currently lack a standardised protocol for uncertainty quantification, propagation and reporting for MPMs. Recent efforts to develop a standard protocol for reporting MPMs (Gascoigne et al, 2023) are vitally important and stress the importance of accompanying demographic rate estimates with a metric of uncertainty. Although following this protocol will greatly aid interpretation and transparency, the protocol is insufficient for consistent propagation, comparison or easy storage of uncertainty data in online databases.…”

“…In this paper, we suggest best-practice guidelines for uncertainty reporting and propagation for MPMs, building on Gascoigne et al's (2023) protocol. To inform the development of these guidelines, we review MPM papers to identify any gaps/inconsistencies in the current consideration of uncertainty in MPMs.…”

Developmental synchrony and extraordinary multiplication rates in pathogenic organisms

“…The timing of maximal overestimates in population growth rates depends on the initial age distribution of the population and developmental waiting times, both largely uncharted. The number of individuals in each developmental age class (referred to as the initial population vector in matrix population models) remains one of the most poorly communicated quantities across diverse plant and animal species [26] and are rarely known beforehand when sampling infections. Indeed, the developmental sampling bias that makes it difficult to accurately estimate population growth also impedes efforts to characterize the age structure of populations of pathogenic organisms.…”