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Record W4309690600 · doi:10.1111/1365-2745.14038

Nutrient addition drives declines in grassland species richness primarily via enhanced species loss

2022· article· en· W4309690600 on OpenAlexaff
Andrew J. Muehleisen, Carmen R. E. Watkins, Gabriella R. Altmire, E. Ashley Shaw, Madelon F. Case, Lina Aoyama, Alejandro Brambila, Paul B. Reed, Marina LaForgia, Elizabeth T. Borer, Eric W. Seabloom, Jonathan D. Bakker, Carlos Alberto Arnillas, Lori Biederman, Qingqing Chen, Elsa E. Cleland, Anu Eskelinen, Philip A. Fay, Nicole Hagenah, W. Stanley Harpole, Yann Hautier, Jeremiah A. Henning, Johannes M. H. Knops, Kimberly J. Komatsu, Emma Ladouceur, Ramesh Laungani, Andrew MacDougall, Rebecca L. McCulley, Joslin L. Moore, Tim Ohlert, Sally A. Power, Xavier Raynaud, Carly Stevens, Risto Virtanen, Peter A. Wilfahrt, Lauren M. Hallett

Bibliographic record

VenueJournal of Ecology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of GuelphUniversity of Toronto
FundersResearch Committee, Aristotle University of ThessalonikiInstitute on the Environment, University of MinnesotaAgricultural Research ServiceUniversity of MinnesotaDivision of Environmental BiologyU.S. Department of AgricultureAgence Nationale de la RechercheNational Science Foundation
KeywordsSpecies richnessColonizationGrasslandEcologyExtinction (optical mineralogy)BiologyNutrientForbPerennial plantPlant communityBiodiversity

Abstract

fetched live from OpenAlex

Abstract Declines in grassland diversity in response to nutrient addition are a general consequence of global change. This decline in species richness may be driven by multiple underlying processes operating at different time‐scales. Nutrient addition can reduce diversity by enhancing the rate of local extinction via competitive exclusion, or by reducing the rate of colonization by constraining the pool of species able to colonize under new conditions. Partitioning net change into extinction and colonization rates will better delineate the long‐term effect of global change in grasslands. We synthesized changes in richness in response to experimental fertilization with nitrogen, phosphorus and potassium with micronutrients across 30 grasslands. We quantified changes in local richness, colonization, and extinction over 8–10 years of nutrient addition, and compared these rates against control conditions to isolate the effect of nutrient addition from background dynamics. Total richness at steady state in the control plots was the sum of equal, relatively high rates of local colonization and extinction. On aggregate, 30%–35% of initial species were lost and the same proportion of new species were gained at least once over a decade. Absolute turnover increased with site‐level richness but was proportionately greater at lower‐richness sites relative to starting richness. Loss of total richness with nutrient addition, especially N in combination with P or K, was driven by enhanced rates of extinction with a smaller contribution from reduced colonization. Enhanced extinction and reduced colonization were disproportionately among native species, perennials, and forbs. Reduced colonization plateaued after the first few (<5) years after nutrient addition, while enhanced extinction continued throughout the first decade. Synthesis . Our results indicate a high rate of colonizations and extinctions underlying the richness of ambient communities and that nutrient enhancement drives overall declines in diversity primarily by exclusion of previously established species. Moreover, enhanced extinction continues over long time‐scales, suggesting continuous, long‐term community responses and a need for long‐term study to fully realize the extinction impact of increased nutrients on grassland composition.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0070.000

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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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 teacher head, not a consensus.

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

Citations37
Published2022
Admission routes1
Has abstractyes

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