In this mini-review the authors argue that light availability created by canopy trees should be included as a factor in species distribution models (SMDs). No doubt that more reliable vegetation models than existing ones are needed to predict global change effects on ecosystems in regions where both forest and non-forest ecosystems can coexist as Alternative Biome States (ABS) forming landscape mosaics. As well, as rightly pointed out in the manuscript, better models of ABS are needed to avoid misguided tree-planting global initiatives. The manuscript is already a resubmission, but I have not reviewed the previous version.

The authors shortly describe solutions for including tree shade in the SDMs, such as the use of high resolution remote sensing. However, since the shading state may be highly dynamic, this approach would be limited to new, high resolution occurrence data collected together with the type of vegetation cover to estimate tree shade. An option could be the use of available vegetation plot databases at global scale, such as sPlot (Bruelheide et al 2019), which could provide both the data on species occurrences (and co-occurrences) and on vegetation traits allowing to infer shading.

Bruelheide, H. et al., 2019. sPlot – A new tool for global vegetation analyses. Journal of Vegetation Science 30, 161-186. https://doi.org/10.1111/jvs.12710

Nevertheless, in the first place, the problem on how to predict tree shade remains. Tree shade is generated by the vegetation development and cannot be taken as an independent predictive factor, as macroclimate can (but microclimate could not). As well, other important factors such as soil conditions change in response to vegetation development. Thus, unless feedbacks are incorporated into spatially-explicit models, it is hard to imagine a true solution for modeling Alternative Biome States (ABS) under the SDM framework. Though in their responses to comments the authors mention that “[m]odelling of forest versus open ecosystems with DGVMs is still in its infancy” there are some examples of DGVMs directly simulating the feedback process that generate ABS. The spatially explicit model developed by Blanco et al. (2014), which expanded Scheiter & Higgins (2009) model, is worth mentioning as one case.

To summarize, the problem on how to predict tree shade is very critical for the main argument of the review, which would require improving the section on “Modelling beyond Gleasonian assumptions”.

Blanco, C.C., Scheiter, S., Sosinski, E., Fidelis, A., Anand, M., Pillar, V.D., 2014. Feedbacks between vegetation and disturbance processes promote long-term persistence of forest–grassland mosaics in south Brazil. Ecological Modelling 291, 224–232. https://doi.org/10.1016/j.ecolmodel.2014.07.024

Scheiter, S., Higgins, S.I., 2009. Impacts of climate change on the vegetation of Africa: an adaptive dynamic vegetation modelling approach. Global Change Biology 15, 2224–2246. https://doi.org/10.1111/j.1365-2486.2008.01838.x

Specific/minor issues:
Several cited references in the main text are missing from References:
L137: Pilon et al. (2021)
L154: Roberts et al. (2016)
L212: Midgley & Bond (2015)
L241-242: Sasaki & Putz (2009)
L243: Valbuena et al. (2020)