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Record W2097919457 · doi:10.1093/jpe/rtr035

Large-scale spatial synchrony and the stability of forest biodiversity revisited

2012· article· en· W2097919457 on OpenAlexaboutno aff
Annette Ostling

Bibliographic record

VenueJournal of Plant Ecology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiological dispersalEcologyNull hypothesisNull modelNeutral theory of molecular evolutionBiodiversitySpatial ecologyScale (ratio)Stability (learning theory)Spatial distributionGeographyBiologyEconometricsComputer scienceMathematicsCartographyRemote sensingMachine learningPopulation

Abstract

fetched live from OpenAlex

AimsMechanisms contributing to species coexistence have at least one of two modes of action: (i) stabilization of populations through restoring forces and (ii) equalization of fitness across individuals of different species.Recently, ecologists have begun gleaning the relative roles of these by testing the predictions of neutral theory, which predicts the properties of communities under pure fitness equalization.This null hypothesis was rejected for forests of southern Ontario based on large-scale (;100 km) spatial synchrony evident in the fossil pollen record over the entire Holocene, and the argument that a species' relative abundance would instead vary independently at such distances in the absence of stabilizing mechanisms.This test of neutral theory was criticized based on the idea that the synchrony might be produced by dispersal alone.Here, I revisit this test of neutral theory by explicitly calculating the synchrony expected in these forests using a novel simulation method enabling examination of the distribution of a species over large spatial and temporal scales. MethodsA novel neutral simulation algorithm tracking only the focal species was used to calculate the neutral expectation for spatial synchrony properties examined empirically by Clark and MacLachlan [(2003) Stability of forest biodiversity.Nature 423:635-8] using fossil pollen data from eight lake sites.The coefficient of variation (CV) in a species' relative abundance across the eight sites (initiated at about 10% with a small CV) was calculated for 10 runs over a 10 000 year time interval.The CV reflects the level of spatial synchrony in that less synchronous dynamics should lead to more variation across space (a higher equilibrium CV), and in particular, a greater increase in the CV over time from a small initial value.A 'two dimensional t' fat-tailed dispersal kernel was assumed with parameters set to the median derived from seed trap data for deciduous wind-dispersed trees.Robustness of results to assumed dispersal distance, density of trees on the landscape, site sizes, age at maturity and starting spatial distribution were checked. Important FindingsIn contrast to the prediction of Clark and MacLachlan that, in the absence of stabilization, the CV across the sites should increase over time from levels observed at the beginning of the Holocene, under fat-tailed dispersal my neutral model robustly predicted only a brief (50 years) and small increase in the CV.I conclude that purely fitnessequalized species coexistence cannot be rejected based on the observed lack of increase in the CV across the eight sites in southern Ontario over the Holocene.Synchronous variation in environmental factors could alternatively explain the observed synchrony without the need for stabilization.However, neither dispersal nor environmental synchrony seems likely explanations for the quick widespread recovery of Tsuga in the Holocene after its seeming decimation, likely due to a pest outbreak.

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)0.001
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.012
GPT teacher head0.215
Teacher spread0.203 · 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

Citations10
Published2012
Admission routes1
Has abstractyes

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