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Record W4303647279 · doi:10.1002/ecy.3894

Demographic consequences of changes in environmental periodicity

2022· article· en· W4303647279 on OpenAlexaff
Eva Conquet, Arpat Özgül, Daniel T. Blumstein, Kenneth B. Armitage, Madan K. Oli, Julien G. A. Martin, Tim Clutton‐Brock, Maria Paniw

Bibliographic record

VenueEcology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversity of Ottawa
FundersSchool of Life Sciences and Biotechnology Division of Life Sciences, Korea UniversityHorizon 2020 Framework ProgrammeUniversidad de CádizSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungUniversity of California, Los AngelesMAVA FoundationEuropean CommissionUniversity of PretoriaNational Geographic SocietyMinisterio de Asuntos Económicos y Transformación Digital, Gobierno de EspañaH2020 Marie Skłodowska-Curie ActionsMinisterio de Economía y CompetitividadNational Science Foundation
KeywordsVital ratesEcologySeasonalityPopulationEcological successionHabitatDensity dependencePatch dynamicsPopulation growthOccupancyBiologyGeographyEnvironmental scienceDemography

Abstract

fetched live from OpenAlex

Abstract The fate of natural populations is mediated by complex interactions among vital rates, which can vary within and among years. Although the effects of random, among‐year variation in vital rates have been studied extensively, relatively little is known about how periodic, nonrandom variation in vital rates affects populations. This knowledge gap is potentially alarming as global environmental change is projected to alter common periodic variations, such as seasonality. We investigated the effects of changes in vital‐rate periodicity on populations of three species representing different forms of adaptation to periodic environments: the yellow‐bellied marmot (Marmota flaviventer), adapted to strong seasonality in snowfall; the meerkat (Suricata suricatta), adapted to inter‐annual stochasticity as well as seasonal patterns in rainfall; and the dewy pine (Drosophyllum lusitanicum), adapted to fire regimes and periodic post‐fire habitat succession. To assess how changes in periodicity affect population growth, we parameterized periodic matrix population models and projected population dynamics under different scenarios of perturbations in the strength of vital‐rate periodicity. We assessed the effects of such perturbations on various metrics describing population dynamics, including the stochastic growth rate, log λS. Overall, perturbing the strength of periodicity had strong effects on population dynamics in all three study species. For the marmots, log λS decreased with increased seasonal differences in adult survival. For the meerkats, density dependence buffered the effects of perturbations of periodicity on log λS. Finally, dewy pines were negatively affected by changes in natural post‐fire succession under stochastic or periodic fire regimes with fires occurring every 30 years, but were buffered by density dependence from such changes under presumed more frequent fires or large‐scale disturbances. We show that changes in the strength of vital‐rate periodicity can have diverse but strong effects on population dynamics across different life histories. Populations buffered from inter‐annual vital‐rate variation can be affected substantially by changes in environmentally driven vital‐rate periodic patterns; however, the effects of such changes can be masked in analyses focusing on inter‐annual variation. As most ecosystems are affected by periodic variations in the environment such as seasonality, assessing their contributions to population viability for future global‐change research is crucial.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.222
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations18
Published2022
Admission routes1
Has abstractyes

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