Reply on RC2

Thank you for the invitation to review this manuscript. I have found the paper interesting and enjoyed learning about the study system. The paper is ambitious and presents management recommendations that would be of relevance to policymakers and land users. I have made comments and suggestion, which are listed below, aiming to support the authors in their ambition to offer evidence-based management solutions to coastal wetlands.

L97-100. The main objective of the manuscript was to assess the effects of land-use change, including variations within seasons. Additionally, the effects of tidal inundation were assessed to confirm that our measurements in mangroves and saltmarsh (the ones most affected by tides) were not strongly affected by the time of sampling (low vs high tide).
Four or three sampling events? This is a bit unclear to me. Is it correct that you measured during different tides only once? You need to consider if that is enough in the context of seasonally. The tidal impacts are a bit unclear to me; from the final sentence in the introduction, it sounds to me that all of the wetlands are impacted by tides? Please clarify this.
Author's Response: As explained above, the measurements of low vs high tide was just a one-time additional experiment to verify that tide was not strongly affecting our sampling design. Mangroves and saltmarshes are the sites that were directly affected by tides; hence, they were the focus of our tidal effects experiment. The freshwater tidal forests are indirectly affected, as high tides can push groundwater into the forest. We have clarified this in the method section and deleted this statement from the main hypothesis in the Introduction.
I suggest you swap the order of section 2.2 and 2.3 as you refer to the gas chromatography set up in the current section 2.2 Author's Response: Thanks for the suggestion, but we think that the current order goes well with the flow of information. Section 2.2 refers to a gas isotope ratio mass spectrometer (L-121), not a gas chromatograph (L145). Section 2.3.
You need to include some detail on the spatial distribution of your samples. What is the size of the sampled area, and how did you determine if it is representative of other systems with similar land use? I have the feeling that there is a risk of pseudoreplication, but I cannot assess that without some more detail. Suppose you have subsamples within the same area rather than independent replicate samples from each land-use class that need to be reflected in your conclusions. If you do not have independent replicates, you do not have the statistical basis for making statements relating to land use, and you can only state that the sites are different, so you need to be much more cautious in your recommendations in the discussion.
What method was used for the randomisation?

Author's Response:
We added details on the spatial distribution of the sampling area in revised Figure 1 (P6-L113).
We acknowledge the limitation of this study in terms of land use replication. For this study, we wanted to focus on addressing the small-scale variability of each land use and temporal variations. Furthermore, land-use level replication of our studies was limited due to inaccessibility of these sites due to permission for access into farms, adverse weather during most of the year (e.g. during very hot conditions or during flooding), safety risk due to crocodiles and the high cost of sample analysis (>$AUD 8,000 per experiment). We described this in the discussion section: L320-323. The GHG fluxes in our study represented the difference between the sites due to the limitation of our studies in time and space because of the inaccessibility of these sites during most of the year; however, we tried to increase the robustness of our experiment by focussing on small scale variation (five chambers per site) and importantly, time variation (seasonal-3 seasons for two years).
How did you deal with areas with vegetation?

Author's Response:
We did not place incubation chambers on vegetation where possible because our objective was to measure GHG emissions from the soil. This was elaborated in the methods section as follows: L130-132. On each sampling date, five chambers were installed at random locations 5 cm deep in the soil a day before taking samples (Rashti et al., 2015). The chambers were placed on areas without vegetation because our objective was to measure GHG emissions from the soil. However, below-ground roots that obstructed with collar installation were cut.
What number of gas samples were collected from each chamber after the initial tests? During with season, did you test for linearity?

Author's Response:
Four samples were collected from each chamber at 0, 20, 40 and 60 minutes to measure the linearity of the GHG fluxes over time. However, for GHG flux calculations, we collected two samples from all five chambers at 0 and 60 minutes. Linearity was tested for all chambers during dry-hot seasons and one chamber per site for all other seasons. Our previous experience with this method has taught us that this is the most cost-effective way to measure GHG from wetlands (Kavehei et al., 2021) and agricultural lands (Rashti et al., 2015). Linearity results were provided in supplementary files (S2). We clarified this in the manuscript as follows: L156-160. For the sampling period during the hot and dry season (21 ˗ 29 October 2018), gas samples were collected at 0, 20, 40 and 60 minutes from all chambers to perform a linearity test for measuring increase or decrease in the concentration of the gas with time. For subsequent experiments, a linearity test was performed on subset chambers for each site (Rashti et al., 2016), and an R 2 value of > 0.7 was recorded for all tested samples with a linear trend for CO 2 , CH 4 and N 2 O over the experimental period (S2).
I think you may well have impacts of ebullition of CH4; there are signs of that in Figure  2. If you could not test for linearity for CH 4 fluxes, especially during the flooded period, your fluxes may not be correct.

Author's Response:
Yes. Methane ebullition effects were reflected in the wet pasture ecosystem through high emissions, and these sites were flooded during most of the year. We measured the linearity of one chamber in each site for three days in the flooded period to present precise fluxes, and the R 2 value was ranged between 0.6-0.9. This was described in the manuscript as following: L158-160. For subsequent experiments, a linearity test was performed on subset chambers for each site (Rashti et al., 2016), and an R 2 value of > 0.7 was recorded for all tested samples with a linear trend for CO 2 , CH 4 and N 2 O over the experimental period.
Some of your areas look as if they have standing water; how did you sample gas fluxes on these? Did you use floating chambers? Please add more detail about the sampling.
Author's Response: Our sites in mangroves, saltmarsh and ponded pasture ecosystem had always standing water; however, the water was never deep enough to require floating chambers. Therefore, we used the static chambers but with the lateral holes opened to allow water movement and with vertical extension to avoid full submersion. We have added the details in the manuscript as follows: L100-102. We carried out GHG measurements with static chambers, which had lateral holes that could be left covered with rubber bungs at low water levels and left open at high water levels to allow water movement. During high tide measurements, vertical extensions of the PVC chambers were used to avoid submersion.
Your statistics are not clear to me. Please add some more detail to make it clear how you analysed for variation and interactions between the two main factors in your study site and season.
L327-330. The GHG fluxes in our study represented the difference between the sites due to the limitation of our studies in time and space because of the inaccessibility of these sites during most of the year; however, we tried to increase the robustness of our experiment by focussing on small scale variation (five chambers per site) and importantly, time variation (seasonal-3 seasons for two years).
L338-343. Within the sampled sites, land use seemed the highest predictor of the GHG fluxes found in this study. This result suggests that restoration of wet ponded pastures and sugarcane to coastal tidal wetlands, even freshwater tidal forests, could mitigate total GHG emissions (CH 4 + N 2 O) derived from agricultural activities. Of especial interests are ponded pastures, which, when wet, can have GHG emissions with values 200-fold than any other land use. If these high emissions are persistent in other sites, ponded pastures could provide an opportunity to reduce emissions through land use management practices. These incentives could financially benefit farmers and provide additional cobenefits derived from coastal wetland restoration.
In the discussion, I think it is important to consider if your space for time model is valid, i.e. is it plausible that the current agricultural system would revert to function as the natural system you measured fluxes from? This needs careful discussion as ecosystem restoration does often not take you back to the starting point, or at least it can take a long time for the restored system to regain its original functions.

Author's Response:
The potential for GHG mitigation for changing agricultural lands to wetlands is promising; however, there is still uncertainty of whether degraded land can be successfully reverted to wetlands. It is likely that, instead, a new type of ecosystem could be created (Hobbs et al. 2009), and that legacy of land use could last for years (Ardon et al. 2017). However, this study suggests that this potential should be further explored in similar land uses in tropical regions. Additionally, future monitoring of newly created wetlands would provide information on whether and when the full GHG mitigation can be achieved through wetland creation or restoration.
L304-309 You have not measured these parameters, so you can only speculate that they cause low emissions. The way this statement is phrased suggests your study has demonstrated this, which is not the case. Please rephrase.
Author's Response: The paragraph was rephrased as follows: L269-278. The relatively low CH 4 emissions from all the natural wetlands could be attributed to the presence of terminal electron acceptors like iron, sulphate, manganese and nitrate, which result in low rates of methanogenesis (Fumoto et al., 2008;Kögel-Knabner et al., 2010;Sahrawat, 2004). Although not measured in this study, it is likely that sulphate reducing bacteria outcompete methane-producing bacteria (methanogens) in the presence of high sulphate concentrations in tidal wetlands, resulting in low CH 4 production. Additionally, competition between methanogens and methanotrophs (CH 4 consuming bacteria) could result in a net balance of low CH 4 production despite freshwater conditions (Maietta et al. 2020).
Describe how you calculate your CO 2 eq in the methods section and present this in the results before discussing these data.

Author's Response:
We described the CO 2 eq calculation method in the methods section as following: L160-163. For comparing GHG effects of CH 4 and N 2 O fluxes, CO 2-equivalent (CO 2-eq ) were calculated by multiplying CH 4 and N 2 O fluxes to global warming potentials of 25 and 298, respectively (IPCC 2007). It must be noted that GHG fluxes represented radiative balance in our study, as recently suggested by Neubauer S.C. (2021).
Plant mediated emissions of CH 4 and N 2 O are likely to be important in your system. As this would impact your overall conclusion regarding the global warming potential of the different sites, I think you need to discuss this.