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Record W4408168725 · doi:10.1002/ecm.70003

A mechanistic statistical approach to infer invasion characteristics of human‐dispersed species with complex life cycle

2025· article· en· W4408168725 on OpenAlexaboutno aff
Nikunj Goel, Andrew M. Liebhold, Cléo Bertelsmeier, Mevin B. Hooten, Kirill S. Korolev, Timothy H. Keitt

Bibliographic record

VenueEcological Monographs · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesUniversity of Texas at AustinNational Institutes of HealthNational Science Foundation
KeywordsEcologyBiologyEvolutionary biology

Abstract

fetched live from OpenAlex

Abstract The rising introduction of invasive species through trade networks threatens biodiversity and ecosystem services. Yet, we have a limited understanding of how transportation networks determine spatiotemporal patterns of range expansion. This knowledge gap may stem from two reasons. First, current analytical models fail to integrate the invader's life‐history dynamics with heterogeneity in human‐mediated dispersal patterns. Second, classical statistical methods often fail to provide reliable estimates of model parameters, such as the time and place of species introduction and life‐history characteristics, due to spatial biases in the presence‐only records and lack of informative demographic data. To address these gaps, we first formulate an age‐structured metapopulation model that uses a probability matrix to emulate human‐mediated dispersal patterns. The model reveals that an invader spreads radially along the shortest network path, such that the inter‐patch network distances decrease with increasing traffic volume and reproductive value of hitchhikers. Next, we propose a hierarchical Bayesian statistical method to estimate model parameters using presence‐only data and prior demographic knowledge. To show the utility of the statistical approach, we analyze zebra mussel ( Dreissena polymorpha ) expansion in North America through the inland commercial shipping network. Our analysis suggests that zebra mussels might have been introduced before 1981, indicating a lag of 5 years between the time of introduction and first detection in late 1986. Furthermore, using our statistical model, we estimated a one in three chance that they were introduced near Kingsville (Ontario, Canada), where they were first reported. We also find that survival, fecundity, and dispersal during early life (1–2 years) play a critical role in determining the expansion success of these mollusks. These results underscore the importance of fusing prior scientific knowledge with observation and demographic processes in a Bayesian framework for conceptual and practical understanding of how invasive species spread by human agency.

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 score0.040
Threshold uncertainty score0.999

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)0.0020.000

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 head0.024
GPT teacher head0.236
Teacher spread0.212 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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