Rare and common vertebrates span a wide spectrum of population trends

Abstract

The Earth's biota is changing over time in complex ways. A critical challenge is to test whether specific biomes, taxa or types of species benefit or suffer in a time of accelerating global change. We analysed nearly 10,000 abundance time series from over 2000 vertebrate species part of the Living Planet Database. We integrated abundance data with information on geographic range, habitat preference, taxonomic and phylogenetic relationships, and IUCN Red List Categories and threats. We find that 15% of populations declined, 18% increased, and 67% showed no net changes over time. Against a backdrop of no biogeographic and phylogenetic patterning in population change, we uncover a distinct taxonomic signal. Amphibians were the only taxa that experienced net declines in the analysed data, while birds, mammals and reptiles experienced net increases. Population trends were poorly captured by species' rarity and global-scale threats. Incorporation of the full spectrum of population change will improve conservation efforts to protect global biodiversity.

Accounting for year effects and sampling error in temporal analyses of invertebrate population and biodiversity change: a comment on Seibold et al. 2019

“…Additionally, longer-term studies, which should better capture the mean trend, did not present the dramatic declines reported in shorter term studies (Fig. 2, and similar to the effects found in other longer-term studies like Macgregor et al, 2019;Saunders et al, 2019;van Klink et al, 2020 and also found in vertebrate studies like Daskalova et al, 2020;Leung et al, 2020). Overall, we detected considerable variation across realms and among sites, with some individual locations exhibiting both substantial increases and decreases (Supporting Information Table S3 and Fig.…”

“…Not accounting for year pseudoreplication in time series analyses in ecology is far from an issue specific to Seibold et al (2019) (e.g., see Møller, 2019). As we work toward a more comprehensive understanding of change over time across invertebrates and other taxa (Saunders et al, 2019;Thomas et al, 2019;Daskalova et al, 2020;Leung et al, 2020;van Klink et al, 2020), scientists need to use statistical methods that incorporate the pronounced spatial and temporal structure of population and biodiversity data. Points show effect sizes from time series from terrestrial and freshwater taxa, as well as effect sizes from published studies (Hallmann et al, 2017;Macgregor et al, 2019;Seibold et al, 2019; red points, statistical significance of the literature-reported effect sizes not presented).…”

MOSAIC: A Unified Trait Database to Complement Structured Population Models

“…These initiatives address emerging and scaling challenges of ecoinformatics, such as the protocols by which we share data, search data, and preserve provenance within hierarchical data storage structures. These protocols will be essential in centralising datasets as diverse as government monitoring datasets (e.g., those stored in U.S. Data clearing houses [https://www.data.gov; https://www.dataone.org/]; National Biodiversity Atlas [https://nbnatlas.org/]); centralised monitoring and experimental networks (e.g., LTER and NEON), raw or reanalysed remote sensing datasets (e.g., Landsat data, NASA EarthData datasets, ERA-5 data), and private datasets (https://www.natureserve.org/) that will demand versatile and navigationally efficient data structures.phylogenies (e.g.,Daskalova, Myers-Smith, & Godlee, 2020;Levin, Crandall, Pokoski, Stein, & Knight, 2020;Roper, Capdevila, & Salguero-Gómez, 2021), adult bodymass (e.g.,Capdevila et al, 2020;Healy et al, 2014; Gómez, Violle, Gimenez, & Childs, 2018). Not all traits predict demographic outcomes and functional traits may exercise influence on only a few demographic pathways(Pistón et al, 2019;…”

“…are distributed across a wide range of databases. Comparative and macroecological researchers use phylogenies (e.g.,Daskalova, Myers-Smith, & Godlee, 2020;Levin, Crandall, Pokoski, Stein, & Knight, 2020;Roper, Capdevila, & Salguero-Gómez, 2021), adult bodymass (e.g.,Capdevila et al, 2020;Healy et al, 2014; Terry, O'Sullivan, & Rossberg, 2022; Williams, McRae, Freeman, Capdevila, & Clements, 2021), and high-resolution, global climate information (e.g.,Daskalova, Bowler, Myers-Smith, & Dornelas, 2021;Paniw, Maag, Cozzi, Clutton-Brock, & Ozgul, 2019; Stenseth & Mysterud, 2002) to answer relevant biological, evolutionary and ecological questions and to contextualise their findings. Population ecologists frequently examine a subset of physiological, morphological, and behavioural attributes associated with demographic outcomes ("functional traits", sensuViolle et al, 2007).…”

“…[https://nbnatlas.org/]); centralised monitoring and experimental networks (e.g., LTER and NEON), raw or reanalysed remote sensing datasets (e.g., Landsat data, NASA EarthData datasets, ERA-5 data), and private datasets (https://www.natureserve.org/) that will demand versatile and navigationally efficient datastructures. al., 2014;Chalmandrier et al, 2021;Laughlin et al, 2020;Yang, Cao, & Swenson, 2018).Population ecologists routinely use data that are distributed across a wide range of databases.Comparative and macroecological researchers use phylogenies (e.g.,Daskalova, Myers-Smith, & Godlee, 2020;Levin, Crandall, Pokoski, Stein, & Knight, 2020;Roper, Capdevila, & Salguero- Gómez, 2021), adult bodymass (e.g.,Capdevila et al, 2020;Healy et al, 2014;Terry, O'Sullivan, & Rossberg, 2022;Williams, McRae, Freeman, Capdevila, & Clements, 2021), and highresolution, global climate information (e.g.,Daskalova, Bowler, Myers-Smith, & Dornelas, 2021; …”