Evaluating the effects of two newly emerging plant pathogens on northern Aotearoa-New Zealand forests using an individual-based model
Bibliographic record
Abstract
• We refined an individual-based model of Aotearoa-NZ forest dynamics. • We used it to explore the effects of two new pathogens in northern NZ forests. • Kauri dieback may significantly alter these forests’ composition and function. • Myrtle rust appears to have less severe effects but may alter early succession. Globally, forest ecosystems face many threats to their resilience. During the last 20 years, two plant pathogens have become of widespread concern in Aotearoa-New Zealand (NZ): kauri dieback (a soil-borne disease caused by Phytophthora agathidicida and affecting Agathis australis , a large and long-lived gymnosperm) and myrtle rust (a wind-borne disease caused by Austropuccinia psidii and affecting members of the Myrtaceae). The long-term consequences of these pathogens are unclear, but they could drive significant changes in forest composition and ecosystem function. Here, we use an individual-based forest model for northern NZ, enabling us to explore some of these stressors. The model has previously been used to explore the dynamics of northern forests in NZ. To examine how myrtle rust and kauri dieback might affect forest dynamics under various scenarios we refined the model by (i) representing additional species, (ii) including an underlying edaphic gradient in addition to competition for light, (iii) simulating the effects of the plant pathogens kauri dieback and myrtle rust and (iv) porting the model from NetLogo 6 to Julia. Our simulation experiments suggest that myrtle rust may hasten the decline of two early successional species that we evaluated but has less effect on carbon storage. On the other hand, kauri dieback may lead to the stand-level loss of all Agathis australis and a median decline in aboveground live carbon storage of up to 55 % compared to undiseased stands after 500 years. The model experiments do not identify any interactive effects between the two pathogens. As with other efforts to model NZ's forest ecosystems, the model struggles to capture the regeneration dynamics of very long-lived species. Regeneration dynamics and evaluating a broader pool of the tree species common in the forests of northern NZ are where we will focus on future model development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".