The manuscript describes an extensive sampling of 15N and 13C in wood across the Amazon. A major goal of the project is to investigate whether isotopes can be used to provenance lumber, with relevance to monitoring illegal logging. Towards this goal, random forests models are fitted to the isotope datasets using a large suite of assembled spatial, ecological, pedological, and climatological covariates. RF models are subsequently used to produce isoscapes: spatially resolved isotope predictions across the Amazon. Patterns of isoscape variation are discussed in the context of various ecological factors.

Major comments

1. Given the collection of two independent isotope signals, I wonder at the missed opportunity here to generate a combined isoscape of 13C and 15N variation. It seems the combined variation in both isotopes would be more spatially variable given distinct controls. Mapping the product of the two, or some other decomposition or principal component, could actually generate a more useful metric.
2. The study effort, particularly around sample collection, is ambitious and admirable. I agree that the current results are not particularly useful for provenance verification. But perhaps combined with other isotope maps (strontium?) could be in the future.
3. The machine-learning methods appear robust, but I would be curious to see added some additional discussion around systematic bias of the RF models, which appear to underpredict high values and overpredict low values systematically.

Line 123. Perhaps worth explaining how a sample was collected 215 km from a road, and how frequent such samples are in the dataset. I am imagining this was transported by boat? Then could redefine as distance from road or river?

Fig 1. This figure could be made more useful by somehow depicting sample size at each site, either as point size / color or with a number. This would allow visualization of the distribution of sampling intensity across the region.

L349. You might refer to this as bias. 13C isoscape also exhibits the same bias.

L440. Many sites have 13c variation of around 3-4 per mil. So while I suppose this is true, inter-site variability is not much greater than within site.

General comments

As a stable-isotope researcher with >15 years of experience, I read the work by Souza-Silva et al. on “Machine-learning models of δ¹³C and δ¹⁵N isoscapes in Amazonian wood” with great interest. The authors deliver an exceptionally valuable resource to the community: isotopic measurements for >550 trees distributed across 47 Amazonian sites, coupled with a robust Random-Forest framework that links wood δ¹³C and δ¹⁵N to 74 environmental predictors. The rationale is sound and the methodology state-of-the-art. I therefore strongly recommend its publication. Nevertheless, two issues merit attention before the manuscript reaches its full potential.

Specific comments

1. Basin-scale timber provenancing: although the δ¹³C and δ¹⁵N isoscapes represent a commendable advance, their ability to discriminate Amazonian from non-Amazonian timber (or other regions within the Amazonian) remains equivocal. The observed intra- and inter-site variability is sufficiently large that isotopic signatures from other tropical forests may fall within the Amazonian envelope. I therefore urge the authors to (i) incorporate a comparative dataset of timber collected outside the basin and (ii) provide quantitative classification metrics (sensitivity, specificity) to rigorously demonstrate—and transparently bound—the utility of the isoscapes for large-scale provenance assignment.

2. Conciseness and focus: the manuscript, in its present form, is unduly verbose. Lengthy expositions in the Introduction, Results, and Discussion dilute the key messages and obscure the novel contributions. I strongly recommend a comprehensive tightening of the text—prioritizing only the most essential findings and their implications—to enhance clarity, readability, and impact for the broad readership of this journal.