the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Nov 2024
Measuring and modeling waterlogging tolerance to predict the future for threatened lowland ash forests
Abstract. Emerald ash borer is an invasive pest causing widespread mortality of ash trees (Fraxinus spp.) across the U.S. Broad-scale models can help identify management strategies to maintain lowland ash ecosystems. Simulating lowland forest dynamics in landscape models has been problematic because lowland hydrology is extremely complex, making most hydrology algorithms intractable at landscape scale. A succession extension (PnET-Succession) of the LANDIS-II forest landscape model was recently updated to include simple algorithms to approximate lowland hydrology, but estimating parameters of tree species’ waterlogging tolerance is difficult. We describe empirical experiments conducted to generate such estimates and illustrate their behavior in single-cell and landscape simulations. Simulated water stress mimicked two critical characteristics of the empirical experiment: 1) there was little difference in simulated stress variables between the well-drained and intermediate flooding treatments and 2) simulated water stress of species aligned with empirical waterlogging tolerance. We used the landscape model to scale the empirical experiment to landscape scales of space and time. When the simulation experiment was extended to 90 years, species productivity plateaued or peaked at a level that could be supported by the precipitation inputs and rooting zone depth. In a virtual experiment testing the competition outcomes between two species, the more waterlogging tolerant species did much better under the flooding treatment, but also tended to do better under the drained treatment because it never produced droughty conditions. When the updated waterlogging parameters were applied at landscape scale under future climate change and assisted migration (AM) scenarios, the mean biomass density of native species declined, and the introduced AM species increased as climate gradually changed and introduced cohorts thrived. Species that are waterlogging tolerant were able to persist under all Assisted Migration-Climate Change scenarios, and to a limited extent were able to colonize (and ephemerally dominate) upland sites. Well-parameterized landscape models provide a powerful tool to conduct simulation experiments involving novel situations such as climate change, invasive (or intentionally migrated) tree species, invasive insects or diseases, and proposed management strategies.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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RC1: 'Comment on egusphere-2024-3332', Anonymous Referee #1, 21 Dec 2024
Review of “Measuring and modeling waterlogging tolerance to predict the future for threatened lowland ash forests” Gustafson et al. submitted to EGUsphere
General comments:
This manuscript describes two related studies: an empirical experiment to test trees for tolerance to water logging, and a modeling study that uses the water logging metrics to model long-term dynamics of tree species in different climate change scenarios. The manuscript contributes both to scientific progress and practical forest management as the questions addressed are important for both understanding forest dynamics and selection of trees to plant in wet forest ecosystems.
Specific comments:
There were some confusing inconsistencies in the suites of AM species in different parts of the paper and a few possible errors:
From page 7 lines 198-203: “We simulated two AM [assisted migration] scenarios… under two climate futures… for 300 years… The less aggressive AM strategy (Medium Range) planted species with ranges just south of the range of endemic species on the study landscape, and the more aggressive strategy (LongRange) planted species with ranges extending into the deep south of the U.S. (Table 1).”
The species listed in table 1 for Medium range include silver maple, American sycamore, and American elm. The species listed for long range are river birch and swamp white oak. Bald cypress is listed for both.
Figure 9 graphs have Medim range species listed as silver maple, river birch, Atlantic white cedar, American sycamore, Swamp white oak, bald cypress, and American elm. The species on the long range graph are river birch, Atlantic white cedar, swamp white oak, bald cypress, and American elm.
In table 3, all species including the native species red maple, black ash, eastern larch (tamarack), and black spruce, are included in the medium range scenario. For the long range scenario, American sycamore and silver maple are listed as NOT planted.
Eastern white cedar appears in both table 1 and table 3, but Atlantic white cedar is noted in figure 9. Should that “Atlantic” white cedar actually be Eastern white cedar?
The actual range of American sycamore and silver maple go very far south, and it seems that they should be considered “long range” trees based on the text on lines 198-203. The range of one species listed as long range, swamp white oak, is more northerly and should probably be considered “medium range.” Is it possible that the species suites in the long range and medium range scenarios were accidentally reversed?
I suggest that the authors please correct any errors and provide additional explanation of what species were included in the different modeled scenarios. Iverson et al. 2015 Black ash replacements paper had a nice system to categorize potential assisted migration species that might be helpful to look at. Once corrections have been made, change the discussion on lines 296-299 if necessary.
Other than that, there were only a few minor suggestions or questions:
Line 57: “..drivers of lowland hydrology have complex spatial (horizontal and vertical) components.” Please add a sentence to provide examples of these components and perhaps identify any that PnET is able to successfully model.
Line 93. Please add a sentence to describe whether the water was stagnant or flowing/aerated and what kinds of ecosystems this might represent. Stagnant vs. flowing water can make a difference to oxygen availability to roots. Flooding effects on trees may be very different for river floodplains vs. wet forests with stagnant water.
Table 1 and line 134-140. It might be worthwhile to describe in plain language what soil water potential means. Defining H1 and H2 as soil water potential above which photosynthesis slows or stops is a bit confusing, because you are using “above which” to mean a larger negative number, which some people who understand math but not soil water potential would think of as “below” not “above.” For example, the legend of figure 4 provides the helpful information, “higher values represent less moisture” and because absolute value is presented in these graphs it is easier to understand.
Figure 8. It seems very strange that the photosynthesis and growth of a tree species appear to be exactly the same no matter what other tree species it is growing with. Why would the effect of competition not differ among species? That may be an important limitation of the model to include in the discussion.
Citation: https://doi.org/10.5194/egusphere-2024-3332-RC1 -
AC3: 'Reply on RC1', Eric Gustafson, 07 Jan 2025
Thank you for these thoughtful and helpful comments. Please see following response/comment for point-by-point response to your review. Note: Our response was duplicated by the system. I do not know how to delete one of them.
Citation: https://doi.org/10.5194/egusphere-2024-3332-AC3
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AC3: 'Reply on RC1', Eric Gustafson, 07 Jan 2025
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AC1: 'Comment on egusphere-2024-3332', Eric Gustafson, 07 Jan 2025
Response to reviewer RC1
RC1-Review of “Measuring and modeling waterlogging tolerance to predict the future for threatened lowland ash forests” Gustafson et al. submitted to EGUsphere.
General comments:
This manuscript describes two related studies: an empirical experiment to test trees for tolerance to water logging, and a modeling study that uses the water logging metrics to model long-term dynamics of tree species in different climate change scenarios. The manuscript contributes both to scientific progress and practical forest management as the questions addressed are important for both understanding forest dynamics and selection of trees to plant in wet forest ecosystems.
Specific comments:
There were some confusing inconsistencies in the suites of AM species in different parts of the paper and a few possible errors:
From page 7 lines 198-203: “We simulated two AM [assisted migration] scenarios… under two climate futures… for 300 years… The less aggressive AM strategy (Medium Range) planted species with ranges just south of the range of endemic species on the study landscape, and the more aggressive strategy (LongRange) planted species with ranges extending into the deep south of the U.S. (Table 1).”
The species listed in table 1 for Medium range include silver maple, American sycamore, and American elm. The species listed for long range are river birch and swamp white oak. Bald cypress is listed for both.
Figure 9 graphs have Medim range species listed as silver maple, river birch, Atlantic white cedar, American sycamore, Swamp white oak, bald cypress, and American elm. The species on the long range graph are river birch, Atlantic white cedar, swamp white oak, bald cypress, and American elm.
RESPONSE: Table 1 refers to empirical experiment species only and Figure 9 shows landscape case study results, which includes many species not included in the empirical experiment. We modified Table 1 to indicate that river birch appeared in Both planting scenarios. Species were included in both planting scenarios when there were few species planting options (AM) for certain forest type harvest prescriptions. Note: Table 1 shows ONLY species included in the empirical experiment, so Atlantic white cedar is not listed. We added this sentence to the caption of Table 1: “Species not included in the empirical experiment are not shown.”
RC1- In table 3, all species including the native species red maple, black ash, eastern larch (tamarack), and black spruce, are included in the medium range scenario. For the long range scenario, American sycamore and silver maple are listed as NOT planted.
RESPONSE: This table shows selected species found on the simulated landscapes (both native and AM species) under different scenarios of AM and climate. The columns indicate the AM planting scenario (climate and AM strategy applied across the landscape for 300 years), and the rows provide the landscape average outcomes from the scenarios (both climate and planted species) for selected species of interest, some of which were not planted in either scenario (see footnotes). Many references to footnotes were omitted in the previous manuscript version, but the table has been updated to accurately show which species were planted under each scenario, and native species (including those also planted in one AM scenario) are clearly identified. The caption was revised to read: “Table 3. Mean (active landscape cells, two replicates) biomass density at year 300 of the landscape case study of cohorts of selected species under selected AM and climate scenarios, comparing landscape outcomes when prior or revised waterlogging parameters were used.” Additionally, Table 1 was revised for greater accuracy and consistency with Table 3.
RC1- Eastern white cedar appears in both table 1 and table 3, but Atlantic white cedar is noted in figure 9. Should that “Atlantic” white cedar actually be Eastern white cedar?
RESPONSE: Table 3 includes a subset of all species included in the landscape experiment (some of which had revised parameters from those used in previous studies). Caption was revised to read: “Table 3. Mean (active landscape cells, two replicates) biomass density at year 300 of the landscape case study of cohorts of selected species under selected AM and climate scenarios, comparing landscape outcomes when prior or revised waterlogging parameters were used.” Figure 9 shows selected species from the landscape experiment (including Atlantic white cedar), as noted in the caption.
RC1- The actual range of American sycamore and silver maple go very far south, and it seems that they should be considered “long range” trees based on the text on lines 198-203. The range of one species listed as long range, swamp white oak, is more northerly and should probably be considered “medium range.” Is it possible that the species suites in the long range and medium range scenarios were accidentally reversed?
RESPONSE: We generally considered the approximate latitudinal center (and/or northern range boundary) of a species’ range when choosing species for assisted migration. Note that the range of endemic red maple extends into southern Florida, so the southern edge of a range is less indicative of a species’ ability to thrive if moved to the north. Finding “replacement species” for specific forest types was not always easy, so some “replacement species” were used in both AM strategies, simply because other options were not expected to be successful replacements. We revised the text at line 200 to read: “The less aggressive AM strategy (MediumRange) planted species with ranges centered to the south of the range of endemic species on the study landscape, and the more aggressive strategy (LongRange) planted species having ranges centered even further south.” This revision also deleted reference to Table 1, which lists only species of the empirical experiment.
RC1- I suggest that the authors please correct any errors and provide additional explanation of what species were included in the different modeled scenarios. Iverson et al. 2015 Black ash replacements paper had a nice system to categorize potential assisted migration species that might be helpful to look at. Once corrections have been made, change the discussion on lines 296-299 if necessary.
RESPONSE: the text at lines 296-299 did not require revision given the revisions described above. These revisions have hopefully removed the considerable ambiguity that was present in the prior draft.
RC1- Other than that, there were only a few minor suggestions or questions:
Line 57: “..drivers of lowland hydrology have complex spatial (horizontal and vertical) components.” Please add a sentence to provide examples of these components and perhaps identify any that PnET is able to successfully model.
RESPONSE: The point of this sentence (and paper) is that these drivers are too complex for a forest landscape model to simulate. Getting into the details of the complexity that is ignored by our approach seems considerably beyond the scope of this paper. To superficially answer your curiosity, horizontal components include horizontal surface flows (into and off of grid-cells) driven by macro- and micro-topography of a watershed, horizontal subsurface flows driven by inputs and topography and bedrock. Vertical flows involve inputs, crusts, soil texture, water holding capacity, permeability of soil horizons and bedrock (and in some ecosystems, permafrost), and water table height. These are things that we non-hydrologists are aware of, and they likely just scratch the surface. We believe that enumeration of these details would actually distract from the understanding of our study.
RC1- Line 93. Please add a sentence to describe whether the water was stagnant or flowing/aerated and what kinds of ecosystems this might represent. Stagnant vs. flowing water can make a difference to oxygen availability to roots. Flooding effects on trees may be very different for river floodplains vs. wet forests with stagnant water.
RESPONSE: again, the model does not model flowing v. non-flowing water. The experiment was designed to be relevant to the model, and as described, was conducted using stock tanks with water level height in planting pots controlled experimentally. “Flooding” is perhaps a misleading word to use here, so this line was revised to read: “soil saturation (water level maintained at 0, 14, 27 cm below the soil surface), referred to as flooded…”
RC1- Table 1 and line 134-140. It might be worthwhile to describe in plain language what soil water potential means. Defining H1 and H2 as soil water potential above which photosynthesis slows or stops is a bit confusing, because you are using “above which” to mean a larger negative number, which some people who understand math but not soil water potential would think of as “below” not “above.” For example, the legend of figure 4 provides the helpful information, “higher values represent less moisture” and because absolute value is presented in these graphs it is easier to understand.
RESPONSE: in plain language, we are talking about soil water potential above field capacity. We revised this to read: “We used the means of these calculated fWater equivalent values to estimate the PnET-Succession waterlogging tolerance parameters (H1 and H2) for each species. H1 is the soil water potential (water potential, units=ABS(pressurehead)) beyond which photosynthesis completely shuts down (due to waterlogging) and H2 is the soil water potential at which photosynthesis begins to decline.”
RC1- Figure 8. It seems very strange that the photosynthesis and growth of a tree species appear to be exactly the same no matter what other tree species it is growing with. Why would the effect of competition not differ among species? That may be an important limitation of the model to include in the discussion.
RESPONSE: We note that photosynthesis (Figure 8A) may be the same, but biomass accumulation may not (Figure 8B). This figure presents hypothetical competition simulations mimicking the experimental setup, so water levels (depth from soil surface to water) were kept constant (as in the experiment). Thus, species water stress (fWater) was constant through time and independent of competitors because the level of water in the soil was held constant, which we accept is not the case in nature. Recall that fWater is a reduction factor applied to Amax (photosynthetic rate under optimal conditions). In this experimental case, a species competitive ability is determined by it waterlogging tolerance – the species that is more tolerant of that constant water level (higher value of fWater) will be (constantly) less stressed than the species that is less tolerant of that water level (lower fWater value). However, biomass accumulation depends somewhat on the productivity capacity of each species. A waterlogging-tolerant species with low productivity (low Amax) might accumulate more biomass than a highly productive (high Amax) species that is less waterlogging-tolerant on a chronically inundated site. However, the reviewer can rest assured that in simulating a realistic situation, cohorts transpire water from the soil, monthly mean soil water potential responds to all water inputs and outputs, and each species responds to that water potential according to their waterlogging and drought tolerance parameter settings. In that case, a simulated two-species competition would behave as the reviewer expects (winners and losers as a function of waterlogging tolerance, productivity and shade tolerance).
Citation: https://doi.org/10.5194/egusphere-2024-3332-AC1 -
AC2: 'Comment on egusphere-2024-3332', Eric Gustafson, 07 Jan 2025
Response to reviewer RC1
RC1-Review of “Measuring and modeling waterlogging tolerance to predict the future for threatened lowland ash forests” Gustafson et al. submitted to EGUsphere.
General comments:
This manuscript describes two related studies: an empirical experiment to test trees for tolerance to water logging, and a modeling study that uses the water logging metrics to model long-term dynamics of tree species in different climate change scenarios. The manuscript contributes both to scientific progress and practical forest management as the questions addressed are important for both understanding forest dynamics and selection of trees to plant in wet forest ecosystems.
Specific comments:
There were some confusing inconsistencies in the suites of AM species in different parts of the paper and a few possible errors:
From page 7 lines 198-203: “We simulated two AM [assisted migration] scenarios… under two climate futures… for 300 years… The less aggressive AM strategy (Medium Range) planted species with ranges just south of the range of endemic species on the study landscape, and the more aggressive strategy (LongRange) planted species with ranges extending into the deep south of the U.S. (Table 1).”
The species listed in table 1 for Medium range include silver maple, American sycamore, and American elm. The species listed for long range are river birch and swamp white oak. Bald cypress is listed for both.
Figure 9 graphs have Medim range species listed as silver maple, river birch, Atlantic white cedar, American sycamore, Swamp white oak, bald cypress, and American elm. The species on the long range graph are river birch, Atlantic white cedar, swamp white oak, bald cypress, and American elm.
RESPONSE: Table 1 refers to empirical experiment species only and Figure 9 shows landscape case study results, which includes many species not included in the empirical experiment. We modified Table 1 to indicate that river birch appeared in Both planting scenarios. Species were included in both planting scenarios when there were few species planting options (AM) for certain forest type harvest prescriptions. Note: Table 1 shows ONLY species included in the empirical experiment, so Atlantic white cedar is not listed. We added this sentence to the caption of Table 1: “Species not included in the empirical experiment are not shown.”
RC1- In table 3, all species including the native species red maple, black ash, eastern larch (tamarack), and black spruce, are included in the medium range scenario. For the long range scenario, American sycamore and silver maple are listed as NOT planted.
RESPONSE: This table shows selected species found on the simulated landscapes (both native and AM species) under different scenarios of AM and climate. The columns indicate the AM planting scenario (climate and AM strategy applied across the landscape for 300 years), and the rows provide the landscape average outcomes from the scenarios (both climate and planted species) for selected species of interest, some of which were not planted in either scenario (see footnotes). Many references to footnotes were omitted in the previous manuscript version, but the table has been updated to accurately show which species were planted under each scenario, and native species (including those also planted in one AM scenario) are clearly identified. The caption was revised to read: “Table 3. Mean (active landscape cells, two replicates) biomass density at year 300 of the landscape case study of cohorts of selected species under selected AM and climate scenarios, comparing landscape outcomes when prior or revised waterlogging parameters were used.” Additionally, Table 1 was revised for greater accuracy and consistency with Table 3.
RC1- Eastern white cedar appears in both table 1 and table 3, but Atlantic white cedar is noted in figure 9. Should that “Atlantic” white cedar actually be Eastern white cedar?
RESPONSE: Table 3 includes a subset of all species included in the landscape experiment (some of which had revised parameters from those used in previous studies). Caption was revised to read: “Table 3. Mean (active landscape cells, two replicates) biomass density at year 300 of the landscape case study of cohorts of selected species under selected AM and climate scenarios, comparing landscape outcomes when prior or revised waterlogging parameters were used.” Figure 9 shows selected species from the landscape experiment (including Atlantic white cedar), as noted in the caption.
RC1- The actual range of American sycamore and silver maple go very far south, and it seems that they should be considered “long range” trees based on the text on lines 198-203. The range of one species listed as long range, swamp white oak, is more northerly and should probably be considered “medium range.” Is it possible that the species suites in the long range and medium range scenarios were accidentally reversed?
RESPONSE: We generally considered the approximate latitudinal center (and/or northern range boundary) of a species’ range when choosing species for assisted migration. Note that the range of endemic red maple extends into southern Florida, so the southern edge of a range is less indicative of a species’ ability to thrive if moved to the north. Finding “replacement species” for specific forest types was not always easy, so some “replacement species” were used in both AM strategies, simply because other options were not expected to be successful replacements. We revised the text at line 200 to read: “The less aggressive AM strategy (MediumRange) planted species with ranges centered to the south of the range of endemic species on the study landscape, and the more aggressive strategy (LongRange) planted species having ranges centered even further south.” This revision also deleted reference to Table 1, which lists only species of the empirical experiment.
RC1- I suggest that the authors please correct any errors and provide additional explanation of what species were included in the different modeled scenarios. Iverson et al. 2015 Black ash replacements paper had a nice system to categorize potential assisted migration species that might be helpful to look at. Once corrections have been made, change the discussion on lines 296-299 if necessary.
RESPONSE: the text at lines 296-299 did not require revision given the revisions described above. These revisions have hopefully removed the considerable ambiguity that was present in the prior draft.
RC1- Other than that, there were only a few minor suggestions or questions:
Line 57: “..drivers of lowland hydrology have complex spatial (horizontal and vertical) components.” Please add a sentence to provide examples of these components and perhaps identify any that PnET is able to successfully model.
RESPONSE: The point of this sentence (and paper) is that these drivers are too complex for a forest landscape model to simulate. Getting into the details of the complexity that is ignored by our approach seems considerably beyond the scope of this paper. To superficially answer your curiosity, horizontal components include horizontal surface flows (into and off of grid-cells) driven by macro- and micro-topography of a watershed, horizontal subsurface flows driven by inputs and topography and bedrock. Vertical flows involve inputs, crusts, soil texture, water holding capacity, permeability of soil horizons and bedrock (and in some ecosystems, permafrost), and water table height. These are things that we non-hydrologists are aware of, and they likely just scratch the surface. We believe that enumeration of these details would actually distract from the understanding of our study.
RC1- Line 93. Please add a sentence to describe whether the water was stagnant or flowing/aerated and what kinds of ecosystems this might represent. Stagnant vs. flowing water can make a difference to oxygen availability to roots. Flooding effects on trees may be very different for river floodplains vs. wet forests with stagnant water.
RESPONSE: again, the model does not model flowing v. non-flowing water. The experiment was designed to be relevant to the model, and as described, was conducted using stock tanks with water level height in planting pots controlled experimentally. “Flooding” is perhaps a misleading word to use here, so this line was revised to read: “soil saturation (water level maintained at 0, 14, 27 cm below the soil surface), referred to as flooded…”
RC1- Table 1 and line 134-140. It might be worthwhile to describe in plain language what soil water potential means. Defining H1 and H2 as soil water potential above which photosynthesis slows or stops is a bit confusing, because you are using “above which” to mean a larger negative number, which some people who understand math but not soil water potential would think of as “below” not “above.” For example, the legend of figure 4 provides the helpful information, “higher values represent less moisture” and because absolute value is presented in these graphs it is easier to understand.
RESPONSE: in plain language, we are talking about soil water potential above field capacity. We revised this to read: “We used the means of these calculated fWater equivalent values to estimate the PnET-Succession waterlogging tolerance parameters (H1 and H2) for each species. H1 is the soil water potential (water potential, units=ABS(pressurehead)) beyond which photosynthesis completely shuts down (due to waterlogging) and H2 is the soil water potential at which photosynthesis begins to decline.”
RC1- Figure 8. It seems very strange that the photosynthesis and growth of a tree species appear to be exactly the same no matter what other tree species it is growing with. Why would the effect of competition not differ among species? That may be an important limitation of the model to include in the discussion.
RESPONSE: We note that photosynthesis (Figure 8A) may be the same, but biomass accumulation may not (Figure 8B). This figure presents hypothetical competition simulations mimicking the experimental setup, so water levels (depth from soil surface to water) were kept constant (as in the experiment). Thus, species water stress (fWater) was constant through time and independent of competitors because the level of water in the soil was held constant, which we accept is not the case in nature. Recall that fWater is a reduction factor applied to Amax (photosynthetic rate under optimal conditions). In this experimental case, a species competitive ability is determined by it waterlogging tolerance – the species that is more tolerant of that constant water level (higher value of fWater) will be (constantly) less stressed than the species that is less tolerant of that water level (lower fWater value). However, biomass accumulation depends somewhat on the productivity capacity of each species. A waterlogging-tolerant species with low productivity (low Amax) might accumulate more biomass than a highly productive (high Amax) species that is less waterlogging-tolerant on a chronically inundated site. However, the reviewer can rest assured that in simulating a realistic situation, cohorts transpire water from the soil, monthly mean soil water potential responds to all water inputs and outputs, and each species responds to that water potential according to their waterlogging and drought tolerance parameter settings. In that case, a simulated two-species competition would behave as the reviewer expects (winners and losers as a function of waterlogging tolerance, productivity and shade tolerance).
Citation: https://doi.org/10.5194/egusphere-2024-3332-AC2 -
RC2: 'Comment on egusphere-2024-3332', Anonymous Referee #2, 14 Feb 2025
REVISION: Gustafson et al. “Measuring and modeling waterlogging tolerance to predict the future for threatened lowland ash forests” (EGUsphere)
General comments:
This study describes an empirical experiment used to parameterize a forest landscape model, specifically those parameters related to waterlogging tolerance. Additionally, the study shows the effects of this parameterization through simple simulation experiments and a real landscape case study.
Specific comments:
Context:
Lines 10-12: For those not familiar with this ecosystem and species it may not be clear why hydrology is so relevant when it comes to Emerald ash borer.
Climate scenarios RCP6.0 and RCP8.5: Could you show or mention somewhere the difference among the climate scenarios? It is highly relevant for the study but not clear to me whether RCP6 is drier or wetter than RCP8.5 for this landscape
Methodology:
I would suggest including a "Model description" section at the beggining of the Methodology and include there the details about LANDIS-II and PnET-Succession functioning that now are dispersed over the text (Lines 63-74, 141-156, 171-173).
The study shows different empirical and simulating experiments, but uses different terminology to refer to them thoughout the text. This is confusing at times and I believe the text could be improve in this sense. Specific suggestions in this sense are:
- In lines 75-80 each experiment could be given a name that would be consistently used through the text. Terms such as "empirical experiment", "single-species simulation", "two-species simulation" and "case study experiment" for each of the experiments would make easier for the reader to understand you experimental design.
- Line 112 "field experiment" is never more referred as such, it maybe better to stick to "empirical experiment".
- Separate the modelling experiments descriptions in two sections (line 194 onwards) could help understand the different experiments you performed.
- Line 267 "Virtual experiments": the previous section already show results of virtual experiments, maybe a different naming would be more appropriate.Limitations:
Lines 352-353: I think it is important to remark that by extrapolating the field experiment to 90 years you are assuming that adult trees will behave as its seedling, which I believe it is highly unlikely. Knowing that age-specific traits of species are not considered within LANDIS-II this constitutes an important limitation of the model that should be mentioned and discussed in the study.
Other than that, there were only a few minor suggestions or questions:
Lines 114-116: Could you explain why you measured soil moisture and soil water potential in bare soil instead of in the seedling pots? Could the presence of the seedlings modify the soil moisture and soil water pontential?
Lines 137-139: why did you want to ensure that fWater never reach 0?
Lines 166, 168, 170 ("seed dispersal and establishment were turned off", "(MapCode) as specified in the initial conditions input file", "ecoregion soil was a novel soil texture"): I think these are very model-related details that those not familiar with LANDIS-II could find confusing. If needed, it could be nice to rephrase some of the sentences to explain what it means to those readers, and otherwise just omitting it would suffice. Related to this, "ecoregion parameters" (line 169) could be replaced by "soil parameters".
Figure 5: why do you show the net photosynthesis for July if August is your reference month for the empirical experiment?
Figure 5 (and others): You could use the "jitter" function to show overlapping point series
Line 260: how did you do this scaling up?
Citation: https://doi.org/10.5194/egusphere-2024-3332-RC2 -
AC4: 'Reply on RC2', Eric Gustafson, 25 Feb 2025
Thank you for your helpful feedback. Comments and responses appear below.
REVISION: Gustafson et al. “Measuring and modeling waterlogging tolerance to predict the future for threatened lowland ash forests” (EGUsphere)
General comments:
This study describes an empirical experiment used to parameterize a forest landscape model, specifically those parameters related to waterlogging tolerance. Additionally, the study shows the effects of this parameterization through simple simulation experiments and a real landscape case study.
Specific comments:
Context:
Lines 10-12: For those not familiar with this ecosystem and species it may not be clear why hydrology is so relevant when it comes to Emerald ash borer.
RESPONSE: we added this sentence: Black ash is a major component of lowland hardwood forests within their range, and their extirpation has the potential to induce conversion of such forests to a non-forest state.
Climate scenarios RCP6.0 and RCP8.5: Could you show or mention somewhere the difference among the climate scenarios? It is highly relevant for the study but not clear to me whether RCP6 is drier or wetter than RCP8.5 for this landscape.
RESPONSE: we revised the text in the Methods to read: To this end, we simulated two AM scenarios (involving many tree species) for 300 years under two climate futures (RCP6.0 (mean monthly precipitation = 79.14 mm/mo) and RCP8.5 (91.67)) …
Methodology:
I would suggest including a "Model description" section at the beginning of the Methodology and include there the details about LANDIS-II and PnET-Succession functioning that now are dispersed over the text (Lines 63-74, 141-156, 171-173).
RESPONSE: revised as suggested.
The study shows different empirical and simulating experiments, but uses different terminology to refer to them throughout the text. This is confusing at times and I believe the text could be improve in this sense. Specific suggestions in this sense are:
- In lines 75-80 each experiment could be given a name that would be consistently used through the text. Terms such as "empirical experiment", "single-species simulation", "two-species simulation" and "case study experiment" for each of the experiments would make easier for the reader to understand you experimental design.
RESPONSE: we revised the text to consistently use three terms: empirical experiment, temporally extending the empirical experiment, and landscape case study.- Line 112 "field experiment" is never more referred as such, it may be better to stick to "empirical experiment".
RESPONSE: Done.- Separate the modelling experiments descriptions in two sections (line 194 onwards) could help understand the different experiments you performed.
RESPONSE: revised as suggested.
- Line 267 "Virtual experiments": the previous section already show results of virtual experiments, maybe a different naming would be more appropriate.
RESPONSE: we now use the terms “modeling the empirical experiment” and “temporally extending the empirical experiment.”
Limitations:
Lines 352-353: I think it is important to remark that by extrapolating the field experiment to 90 years you are assuming that adult trees will behave as its seedling, which I believe it is highly unlikely. Knowing that age-specific traits of species are not considered within LANDIS-II this constitutes an important limitation of the model that should be mentioned and discussed in the study.
RESPONSE: the empirical experiment was used solely to estimate species-specific waterlogging tolerance parameters in the model, which describe how well the species are able to photosynthesize under varying waterlogging conditions. This species-specific trait of waterlogging tolerance is likely to remain constant over a cohort’s lifetime. Other pre-existing LANDIS-II parameters control other aspects of competitiveness through time, and these have been described and tested elsewhere in the literature. No change was made.
Other than that, there were only a few minor suggestions or questions:
Lines 114-116: Could you explain why you measured soil moisture and soil water potential in bare soil instead of in the seedling pots? Could the presence of the seedlings modify the soil moisture and soil water potential?
RESPONSE: throughout the duration of the empirical experiment, it was percent soil moisture that was measured. However, the PnET-Succession extension of the LANDIS-II model uses soil water potential, not soil moisture and therefore a cross-walk was needed. Using the same pots and same type of soil we were able to replicate the experimental water levels, measuring both soil moisture and soil water potential. This allowed us to convert the previously measured soil moisture values into the soil water potential values required by PnET-Succession.
Lines 137-139: why did you want to ensure that fWater never reach 0?
RESPONSE: in this study we focused on species that had at least some waterlogging tolerance, so H1 (the soil water potential where photosynthesis shuts down) was set to a negative number to prevent such a shutdown (even under the wettest conditions). The mathematical mechanics of this were considered confusing minutiae, so we revised the sentence to read: We arbitrarily set H1 of most species to -1.0 (to ensure that supersaturated soil water conditions never shut down photosynthesis for these waterlogging tolerant species) and then …
Lines 166, 168, 170 ("seed dispersal and establishment were turned off", "(MapCode) as specified in the initial conditions input file", "ecoregion soil was a novel soil texture"): I think these are very model-related details that those not familiar with LANDIS-II could find confusing. If needed, it could be nice to rephrase some of the sentences to explain what it means to those readers, and otherwise just omitting it would suffice. Related to this, "ecoregion parameters" (line 169) could be replaced by "soil parameters".
RESPONSE: revised as suggested.
Figure 5: why do you show the net photosynthesis for July if August is your reference month for the empirical experiment?
RESPONSE: In the manuscript we state that August was used to parameterize the model because it not only represents peak growing season photosynthesis, but also provided enough time for the water level treatments effects to be fully realized. The data in figure 5 are model simulations and while we could have selected the month of August, users of such data are often interested in mid-summer (July) results. Because a large portion of the manuscript focuses on a comparison of old model parameter values to new values, rather than on a specific month, we believe that presenting the data as used is preferable to an artificial construct.
Figure 5 (and others): You could use the "jitter" function to show overlapping point series.
RESPONSE: we revised Figures 3, 5, 6 using the position_dodge option in the geom function.
Line 260: how did you do this scaling up?
RESPONSE: we revised this sentence to read: Note that the units reported for biomass density are scaled up from planting pots to a full m2 of area.
Citation: https://doi.org/10.5194/egusphere-2024-3332-AC4
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AC4: 'Reply on RC2', Eric Gustafson, 25 Feb 2025
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-3332', Anonymous Referee #1, 21 Dec 2024
Review of “Measuring and modeling waterlogging tolerance to predict the future for threatened lowland ash forests” Gustafson et al. submitted to EGUsphere
General comments:
This manuscript describes two related studies: an empirical experiment to test trees for tolerance to water logging, and a modeling study that uses the water logging metrics to model long-term dynamics of tree species in different climate change scenarios. The manuscript contributes both to scientific progress and practical forest management as the questions addressed are important for both understanding forest dynamics and selection of trees to plant in wet forest ecosystems.
Specific comments:
There were some confusing inconsistencies in the suites of AM species in different parts of the paper and a few possible errors:
From page 7 lines 198-203: “We simulated two AM [assisted migration] scenarios… under two climate futures… for 300 years… The less aggressive AM strategy (Medium Range) planted species with ranges just south of the range of endemic species on the study landscape, and the more aggressive strategy (LongRange) planted species with ranges extending into the deep south of the U.S. (Table 1).”
The species listed in table 1 for Medium range include silver maple, American sycamore, and American elm. The species listed for long range are river birch and swamp white oak. Bald cypress is listed for both.
Figure 9 graphs have Medim range species listed as silver maple, river birch, Atlantic white cedar, American sycamore, Swamp white oak, bald cypress, and American elm. The species on the long range graph are river birch, Atlantic white cedar, swamp white oak, bald cypress, and American elm.
In table 3, all species including the native species red maple, black ash, eastern larch (tamarack), and black spruce, are included in the medium range scenario. For the long range scenario, American sycamore and silver maple are listed as NOT planted.
Eastern white cedar appears in both table 1 and table 3, but Atlantic white cedar is noted in figure 9. Should that “Atlantic” white cedar actually be Eastern white cedar?
The actual range of American sycamore and silver maple go very far south, and it seems that they should be considered “long range” trees based on the text on lines 198-203. The range of one species listed as long range, swamp white oak, is more northerly and should probably be considered “medium range.” Is it possible that the species suites in the long range and medium range scenarios were accidentally reversed?
I suggest that the authors please correct any errors and provide additional explanation of what species were included in the different modeled scenarios. Iverson et al. 2015 Black ash replacements paper had a nice system to categorize potential assisted migration species that might be helpful to look at. Once corrections have been made, change the discussion on lines 296-299 if necessary.
Other than that, there were only a few minor suggestions or questions:
Line 57: “..drivers of lowland hydrology have complex spatial (horizontal and vertical) components.” Please add a sentence to provide examples of these components and perhaps identify any that PnET is able to successfully model.
Line 93. Please add a sentence to describe whether the water was stagnant or flowing/aerated and what kinds of ecosystems this might represent. Stagnant vs. flowing water can make a difference to oxygen availability to roots. Flooding effects on trees may be very different for river floodplains vs. wet forests with stagnant water.
Table 1 and line 134-140. It might be worthwhile to describe in plain language what soil water potential means. Defining H1 and H2 as soil water potential above which photosynthesis slows or stops is a bit confusing, because you are using “above which” to mean a larger negative number, which some people who understand math but not soil water potential would think of as “below” not “above.” For example, the legend of figure 4 provides the helpful information, “higher values represent less moisture” and because absolute value is presented in these graphs it is easier to understand.
Figure 8. It seems very strange that the photosynthesis and growth of a tree species appear to be exactly the same no matter what other tree species it is growing with. Why would the effect of competition not differ among species? That may be an important limitation of the model to include in the discussion.
Citation: https://doi.org/10.5194/egusphere-2024-3332-RC1 -
AC3: 'Reply on RC1', Eric Gustafson, 07 Jan 2025
Thank you for these thoughtful and helpful comments. Please see following response/comment for point-by-point response to your review. Note: Our response was duplicated by the system. I do not know how to delete one of them.
Citation: https://doi.org/10.5194/egusphere-2024-3332-AC3
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AC3: 'Reply on RC1', Eric Gustafson, 07 Jan 2025
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AC1: 'Comment on egusphere-2024-3332', Eric Gustafson, 07 Jan 2025
Response to reviewer RC1
RC1-Review of “Measuring and modeling waterlogging tolerance to predict the future for threatened lowland ash forests” Gustafson et al. submitted to EGUsphere.
General comments:
This manuscript describes two related studies: an empirical experiment to test trees for tolerance to water logging, and a modeling study that uses the water logging metrics to model long-term dynamics of tree species in different climate change scenarios. The manuscript contributes both to scientific progress and practical forest management as the questions addressed are important for both understanding forest dynamics and selection of trees to plant in wet forest ecosystems.
Specific comments:
There were some confusing inconsistencies in the suites of AM species in different parts of the paper and a few possible errors:
From page 7 lines 198-203: “We simulated two AM [assisted migration] scenarios… under two climate futures… for 300 years… The less aggressive AM strategy (Medium Range) planted species with ranges just south of the range of endemic species on the study landscape, and the more aggressive strategy (LongRange) planted species with ranges extending into the deep south of the U.S. (Table 1).”
The species listed in table 1 for Medium range include silver maple, American sycamore, and American elm. The species listed for long range are river birch and swamp white oak. Bald cypress is listed for both.
Figure 9 graphs have Medim range species listed as silver maple, river birch, Atlantic white cedar, American sycamore, Swamp white oak, bald cypress, and American elm. The species on the long range graph are river birch, Atlantic white cedar, swamp white oak, bald cypress, and American elm.
RESPONSE: Table 1 refers to empirical experiment species only and Figure 9 shows landscape case study results, which includes many species not included in the empirical experiment. We modified Table 1 to indicate that river birch appeared in Both planting scenarios. Species were included in both planting scenarios when there were few species planting options (AM) for certain forest type harvest prescriptions. Note: Table 1 shows ONLY species included in the empirical experiment, so Atlantic white cedar is not listed. We added this sentence to the caption of Table 1: “Species not included in the empirical experiment are not shown.”
RC1- In table 3, all species including the native species red maple, black ash, eastern larch (tamarack), and black spruce, are included in the medium range scenario. For the long range scenario, American sycamore and silver maple are listed as NOT planted.
RESPONSE: This table shows selected species found on the simulated landscapes (both native and AM species) under different scenarios of AM and climate. The columns indicate the AM planting scenario (climate and AM strategy applied across the landscape for 300 years), and the rows provide the landscape average outcomes from the scenarios (both climate and planted species) for selected species of interest, some of which were not planted in either scenario (see footnotes). Many references to footnotes were omitted in the previous manuscript version, but the table has been updated to accurately show which species were planted under each scenario, and native species (including those also planted in one AM scenario) are clearly identified. The caption was revised to read: “Table 3. Mean (active landscape cells, two replicates) biomass density at year 300 of the landscape case study of cohorts of selected species under selected AM and climate scenarios, comparing landscape outcomes when prior or revised waterlogging parameters were used.” Additionally, Table 1 was revised for greater accuracy and consistency with Table 3.
RC1- Eastern white cedar appears in both table 1 and table 3, but Atlantic white cedar is noted in figure 9. Should that “Atlantic” white cedar actually be Eastern white cedar?
RESPONSE: Table 3 includes a subset of all species included in the landscape experiment (some of which had revised parameters from those used in previous studies). Caption was revised to read: “Table 3. Mean (active landscape cells, two replicates) biomass density at year 300 of the landscape case study of cohorts of selected species under selected AM and climate scenarios, comparing landscape outcomes when prior or revised waterlogging parameters were used.” Figure 9 shows selected species from the landscape experiment (including Atlantic white cedar), as noted in the caption.
RC1- The actual range of American sycamore and silver maple go very far south, and it seems that they should be considered “long range” trees based on the text on lines 198-203. The range of one species listed as long range, swamp white oak, is more northerly and should probably be considered “medium range.” Is it possible that the species suites in the long range and medium range scenarios were accidentally reversed?
RESPONSE: We generally considered the approximate latitudinal center (and/or northern range boundary) of a species’ range when choosing species for assisted migration. Note that the range of endemic red maple extends into southern Florida, so the southern edge of a range is less indicative of a species’ ability to thrive if moved to the north. Finding “replacement species” for specific forest types was not always easy, so some “replacement species” were used in both AM strategies, simply because other options were not expected to be successful replacements. We revised the text at line 200 to read: “The less aggressive AM strategy (MediumRange) planted species with ranges centered to the south of the range of endemic species on the study landscape, and the more aggressive strategy (LongRange) planted species having ranges centered even further south.” This revision also deleted reference to Table 1, which lists only species of the empirical experiment.
RC1- I suggest that the authors please correct any errors and provide additional explanation of what species were included in the different modeled scenarios. Iverson et al. 2015 Black ash replacements paper had a nice system to categorize potential assisted migration species that might be helpful to look at. Once corrections have been made, change the discussion on lines 296-299 if necessary.
RESPONSE: the text at lines 296-299 did not require revision given the revisions described above. These revisions have hopefully removed the considerable ambiguity that was present in the prior draft.
RC1- Other than that, there were only a few minor suggestions or questions:
Line 57: “..drivers of lowland hydrology have complex spatial (horizontal and vertical) components.” Please add a sentence to provide examples of these components and perhaps identify any that PnET is able to successfully model.
RESPONSE: The point of this sentence (and paper) is that these drivers are too complex for a forest landscape model to simulate. Getting into the details of the complexity that is ignored by our approach seems considerably beyond the scope of this paper. To superficially answer your curiosity, horizontal components include horizontal surface flows (into and off of grid-cells) driven by macro- and micro-topography of a watershed, horizontal subsurface flows driven by inputs and topography and bedrock. Vertical flows involve inputs, crusts, soil texture, water holding capacity, permeability of soil horizons and bedrock (and in some ecosystems, permafrost), and water table height. These are things that we non-hydrologists are aware of, and they likely just scratch the surface. We believe that enumeration of these details would actually distract from the understanding of our study.
RC1- Line 93. Please add a sentence to describe whether the water was stagnant or flowing/aerated and what kinds of ecosystems this might represent. Stagnant vs. flowing water can make a difference to oxygen availability to roots. Flooding effects on trees may be very different for river floodplains vs. wet forests with stagnant water.
RESPONSE: again, the model does not model flowing v. non-flowing water. The experiment was designed to be relevant to the model, and as described, was conducted using stock tanks with water level height in planting pots controlled experimentally. “Flooding” is perhaps a misleading word to use here, so this line was revised to read: “soil saturation (water level maintained at 0, 14, 27 cm below the soil surface), referred to as flooded…”
RC1- Table 1 and line 134-140. It might be worthwhile to describe in plain language what soil water potential means. Defining H1 and H2 as soil water potential above which photosynthesis slows or stops is a bit confusing, because you are using “above which” to mean a larger negative number, which some people who understand math but not soil water potential would think of as “below” not “above.” For example, the legend of figure 4 provides the helpful information, “higher values represent less moisture” and because absolute value is presented in these graphs it is easier to understand.
RESPONSE: in plain language, we are talking about soil water potential above field capacity. We revised this to read: “We used the means of these calculated fWater equivalent values to estimate the PnET-Succession waterlogging tolerance parameters (H1 and H2) for each species. H1 is the soil water potential (water potential, units=ABS(pressurehead)) beyond which photosynthesis completely shuts down (due to waterlogging) and H2 is the soil water potential at which photosynthesis begins to decline.”
RC1- Figure 8. It seems very strange that the photosynthesis and growth of a tree species appear to be exactly the same no matter what other tree species it is growing with. Why would the effect of competition not differ among species? That may be an important limitation of the model to include in the discussion.
RESPONSE: We note that photosynthesis (Figure 8A) may be the same, but biomass accumulation may not (Figure 8B). This figure presents hypothetical competition simulations mimicking the experimental setup, so water levels (depth from soil surface to water) were kept constant (as in the experiment). Thus, species water stress (fWater) was constant through time and independent of competitors because the level of water in the soil was held constant, which we accept is not the case in nature. Recall that fWater is a reduction factor applied to Amax (photosynthetic rate under optimal conditions). In this experimental case, a species competitive ability is determined by it waterlogging tolerance – the species that is more tolerant of that constant water level (higher value of fWater) will be (constantly) less stressed than the species that is less tolerant of that water level (lower fWater value). However, biomass accumulation depends somewhat on the productivity capacity of each species. A waterlogging-tolerant species with low productivity (low Amax) might accumulate more biomass than a highly productive (high Amax) species that is less waterlogging-tolerant on a chronically inundated site. However, the reviewer can rest assured that in simulating a realistic situation, cohorts transpire water from the soil, monthly mean soil water potential responds to all water inputs and outputs, and each species responds to that water potential according to their waterlogging and drought tolerance parameter settings. In that case, a simulated two-species competition would behave as the reviewer expects (winners and losers as a function of waterlogging tolerance, productivity and shade tolerance).
Citation: https://doi.org/10.5194/egusphere-2024-3332-AC1 -
AC2: 'Comment on egusphere-2024-3332', Eric Gustafson, 07 Jan 2025
Response to reviewer RC1
RC1-Review of “Measuring and modeling waterlogging tolerance to predict the future for threatened lowland ash forests” Gustafson et al. submitted to EGUsphere.
General comments:
This manuscript describes two related studies: an empirical experiment to test trees for tolerance to water logging, and a modeling study that uses the water logging metrics to model long-term dynamics of tree species in different climate change scenarios. The manuscript contributes both to scientific progress and practical forest management as the questions addressed are important for both understanding forest dynamics and selection of trees to plant in wet forest ecosystems.
Specific comments:
There were some confusing inconsistencies in the suites of AM species in different parts of the paper and a few possible errors:
From page 7 lines 198-203: “We simulated two AM [assisted migration] scenarios… under two climate futures… for 300 years… The less aggressive AM strategy (Medium Range) planted species with ranges just south of the range of endemic species on the study landscape, and the more aggressive strategy (LongRange) planted species with ranges extending into the deep south of the U.S. (Table 1).”
The species listed in table 1 for Medium range include silver maple, American sycamore, and American elm. The species listed for long range are river birch and swamp white oak. Bald cypress is listed for both.
Figure 9 graphs have Medim range species listed as silver maple, river birch, Atlantic white cedar, American sycamore, Swamp white oak, bald cypress, and American elm. The species on the long range graph are river birch, Atlantic white cedar, swamp white oak, bald cypress, and American elm.
RESPONSE: Table 1 refers to empirical experiment species only and Figure 9 shows landscape case study results, which includes many species not included in the empirical experiment. We modified Table 1 to indicate that river birch appeared in Both planting scenarios. Species were included in both planting scenarios when there were few species planting options (AM) for certain forest type harvest prescriptions. Note: Table 1 shows ONLY species included in the empirical experiment, so Atlantic white cedar is not listed. We added this sentence to the caption of Table 1: “Species not included in the empirical experiment are not shown.”
RC1- In table 3, all species including the native species red maple, black ash, eastern larch (tamarack), and black spruce, are included in the medium range scenario. For the long range scenario, American sycamore and silver maple are listed as NOT planted.
RESPONSE: This table shows selected species found on the simulated landscapes (both native and AM species) under different scenarios of AM and climate. The columns indicate the AM planting scenario (climate and AM strategy applied across the landscape for 300 years), and the rows provide the landscape average outcomes from the scenarios (both climate and planted species) for selected species of interest, some of which were not planted in either scenario (see footnotes). Many references to footnotes were omitted in the previous manuscript version, but the table has been updated to accurately show which species were planted under each scenario, and native species (including those also planted in one AM scenario) are clearly identified. The caption was revised to read: “Table 3. Mean (active landscape cells, two replicates) biomass density at year 300 of the landscape case study of cohorts of selected species under selected AM and climate scenarios, comparing landscape outcomes when prior or revised waterlogging parameters were used.” Additionally, Table 1 was revised for greater accuracy and consistency with Table 3.
RC1- Eastern white cedar appears in both table 1 and table 3, but Atlantic white cedar is noted in figure 9. Should that “Atlantic” white cedar actually be Eastern white cedar?
RESPONSE: Table 3 includes a subset of all species included in the landscape experiment (some of which had revised parameters from those used in previous studies). Caption was revised to read: “Table 3. Mean (active landscape cells, two replicates) biomass density at year 300 of the landscape case study of cohorts of selected species under selected AM and climate scenarios, comparing landscape outcomes when prior or revised waterlogging parameters were used.” Figure 9 shows selected species from the landscape experiment (including Atlantic white cedar), as noted in the caption.
RC1- The actual range of American sycamore and silver maple go very far south, and it seems that they should be considered “long range” trees based on the text on lines 198-203. The range of one species listed as long range, swamp white oak, is more northerly and should probably be considered “medium range.” Is it possible that the species suites in the long range and medium range scenarios were accidentally reversed?
RESPONSE: We generally considered the approximate latitudinal center (and/or northern range boundary) of a species’ range when choosing species for assisted migration. Note that the range of endemic red maple extends into southern Florida, so the southern edge of a range is less indicative of a species’ ability to thrive if moved to the north. Finding “replacement species” for specific forest types was not always easy, so some “replacement species” were used in both AM strategies, simply because other options were not expected to be successful replacements. We revised the text at line 200 to read: “The less aggressive AM strategy (MediumRange) planted species with ranges centered to the south of the range of endemic species on the study landscape, and the more aggressive strategy (LongRange) planted species having ranges centered even further south.” This revision also deleted reference to Table 1, which lists only species of the empirical experiment.
RC1- I suggest that the authors please correct any errors and provide additional explanation of what species were included in the different modeled scenarios. Iverson et al. 2015 Black ash replacements paper had a nice system to categorize potential assisted migration species that might be helpful to look at. Once corrections have been made, change the discussion on lines 296-299 if necessary.
RESPONSE: the text at lines 296-299 did not require revision given the revisions described above. These revisions have hopefully removed the considerable ambiguity that was present in the prior draft.
RC1- Other than that, there were only a few minor suggestions or questions:
Line 57: “..drivers of lowland hydrology have complex spatial (horizontal and vertical) components.” Please add a sentence to provide examples of these components and perhaps identify any that PnET is able to successfully model.
RESPONSE: The point of this sentence (and paper) is that these drivers are too complex for a forest landscape model to simulate. Getting into the details of the complexity that is ignored by our approach seems considerably beyond the scope of this paper. To superficially answer your curiosity, horizontal components include horizontal surface flows (into and off of grid-cells) driven by macro- and micro-topography of a watershed, horizontal subsurface flows driven by inputs and topography and bedrock. Vertical flows involve inputs, crusts, soil texture, water holding capacity, permeability of soil horizons and bedrock (and in some ecosystems, permafrost), and water table height. These are things that we non-hydrologists are aware of, and they likely just scratch the surface. We believe that enumeration of these details would actually distract from the understanding of our study.
RC1- Line 93. Please add a sentence to describe whether the water was stagnant or flowing/aerated and what kinds of ecosystems this might represent. Stagnant vs. flowing water can make a difference to oxygen availability to roots. Flooding effects on trees may be very different for river floodplains vs. wet forests with stagnant water.
RESPONSE: again, the model does not model flowing v. non-flowing water. The experiment was designed to be relevant to the model, and as described, was conducted using stock tanks with water level height in planting pots controlled experimentally. “Flooding” is perhaps a misleading word to use here, so this line was revised to read: “soil saturation (water level maintained at 0, 14, 27 cm below the soil surface), referred to as flooded…”
RC1- Table 1 and line 134-140. It might be worthwhile to describe in plain language what soil water potential means. Defining H1 and H2 as soil water potential above which photosynthesis slows or stops is a bit confusing, because you are using “above which” to mean a larger negative number, which some people who understand math but not soil water potential would think of as “below” not “above.” For example, the legend of figure 4 provides the helpful information, “higher values represent less moisture” and because absolute value is presented in these graphs it is easier to understand.
RESPONSE: in plain language, we are talking about soil water potential above field capacity. We revised this to read: “We used the means of these calculated fWater equivalent values to estimate the PnET-Succession waterlogging tolerance parameters (H1 and H2) for each species. H1 is the soil water potential (water potential, units=ABS(pressurehead)) beyond which photosynthesis completely shuts down (due to waterlogging) and H2 is the soil water potential at which photosynthesis begins to decline.”
RC1- Figure 8. It seems very strange that the photosynthesis and growth of a tree species appear to be exactly the same no matter what other tree species it is growing with. Why would the effect of competition not differ among species? That may be an important limitation of the model to include in the discussion.
RESPONSE: We note that photosynthesis (Figure 8A) may be the same, but biomass accumulation may not (Figure 8B). This figure presents hypothetical competition simulations mimicking the experimental setup, so water levels (depth from soil surface to water) were kept constant (as in the experiment). Thus, species water stress (fWater) was constant through time and independent of competitors because the level of water in the soil was held constant, which we accept is not the case in nature. Recall that fWater is a reduction factor applied to Amax (photosynthetic rate under optimal conditions). In this experimental case, a species competitive ability is determined by it waterlogging tolerance – the species that is more tolerant of that constant water level (higher value of fWater) will be (constantly) less stressed than the species that is less tolerant of that water level (lower fWater value). However, biomass accumulation depends somewhat on the productivity capacity of each species. A waterlogging-tolerant species with low productivity (low Amax) might accumulate more biomass than a highly productive (high Amax) species that is less waterlogging-tolerant on a chronically inundated site. However, the reviewer can rest assured that in simulating a realistic situation, cohorts transpire water from the soil, monthly mean soil water potential responds to all water inputs and outputs, and each species responds to that water potential according to their waterlogging and drought tolerance parameter settings. In that case, a simulated two-species competition would behave as the reviewer expects (winners and losers as a function of waterlogging tolerance, productivity and shade tolerance).
Citation: https://doi.org/10.5194/egusphere-2024-3332-AC2 -
RC2: 'Comment on egusphere-2024-3332', Anonymous Referee #2, 14 Feb 2025
REVISION: Gustafson et al. “Measuring and modeling waterlogging tolerance to predict the future for threatened lowland ash forests” (EGUsphere)
General comments:
This study describes an empirical experiment used to parameterize a forest landscape model, specifically those parameters related to waterlogging tolerance. Additionally, the study shows the effects of this parameterization through simple simulation experiments and a real landscape case study.
Specific comments:
Context:
Lines 10-12: For those not familiar with this ecosystem and species it may not be clear why hydrology is so relevant when it comes to Emerald ash borer.
Climate scenarios RCP6.0 and RCP8.5: Could you show or mention somewhere the difference among the climate scenarios? It is highly relevant for the study but not clear to me whether RCP6 is drier or wetter than RCP8.5 for this landscape
Methodology:
I would suggest including a "Model description" section at the beggining of the Methodology and include there the details about LANDIS-II and PnET-Succession functioning that now are dispersed over the text (Lines 63-74, 141-156, 171-173).
The study shows different empirical and simulating experiments, but uses different terminology to refer to them thoughout the text. This is confusing at times and I believe the text could be improve in this sense. Specific suggestions in this sense are:
- In lines 75-80 each experiment could be given a name that would be consistently used through the text. Terms such as "empirical experiment", "single-species simulation", "two-species simulation" and "case study experiment" for each of the experiments would make easier for the reader to understand you experimental design.
- Line 112 "field experiment" is never more referred as such, it maybe better to stick to "empirical experiment".
- Separate the modelling experiments descriptions in two sections (line 194 onwards) could help understand the different experiments you performed.
- Line 267 "Virtual experiments": the previous section already show results of virtual experiments, maybe a different naming would be more appropriate.Limitations:
Lines 352-353: I think it is important to remark that by extrapolating the field experiment to 90 years you are assuming that adult trees will behave as its seedling, which I believe it is highly unlikely. Knowing that age-specific traits of species are not considered within LANDIS-II this constitutes an important limitation of the model that should be mentioned and discussed in the study.
Other than that, there were only a few minor suggestions or questions:
Lines 114-116: Could you explain why you measured soil moisture and soil water potential in bare soil instead of in the seedling pots? Could the presence of the seedlings modify the soil moisture and soil water pontential?
Lines 137-139: why did you want to ensure that fWater never reach 0?
Lines 166, 168, 170 ("seed dispersal and establishment were turned off", "(MapCode) as specified in the initial conditions input file", "ecoregion soil was a novel soil texture"): I think these are very model-related details that those not familiar with LANDIS-II could find confusing. If needed, it could be nice to rephrase some of the sentences to explain what it means to those readers, and otherwise just omitting it would suffice. Related to this, "ecoregion parameters" (line 169) could be replaced by "soil parameters".
Figure 5: why do you show the net photosynthesis for July if August is your reference month for the empirical experiment?
Figure 5 (and others): You could use the "jitter" function to show overlapping point series
Line 260: how did you do this scaling up?
Citation: https://doi.org/10.5194/egusphere-2024-3332-RC2 -
AC4: 'Reply on RC2', Eric Gustafson, 25 Feb 2025
Thank you for your helpful feedback. Comments and responses appear below.
REVISION: Gustafson et al. “Measuring and modeling waterlogging tolerance to predict the future for threatened lowland ash forests” (EGUsphere)
General comments:
This study describes an empirical experiment used to parameterize a forest landscape model, specifically those parameters related to waterlogging tolerance. Additionally, the study shows the effects of this parameterization through simple simulation experiments and a real landscape case study.
Specific comments:
Context:
Lines 10-12: For those not familiar with this ecosystem and species it may not be clear why hydrology is so relevant when it comes to Emerald ash borer.
RESPONSE: we added this sentence: Black ash is a major component of lowland hardwood forests within their range, and their extirpation has the potential to induce conversion of such forests to a non-forest state.
Climate scenarios RCP6.0 and RCP8.5: Could you show or mention somewhere the difference among the climate scenarios? It is highly relevant for the study but not clear to me whether RCP6 is drier or wetter than RCP8.5 for this landscape.
RESPONSE: we revised the text in the Methods to read: To this end, we simulated two AM scenarios (involving many tree species) for 300 years under two climate futures (RCP6.0 (mean monthly precipitation = 79.14 mm/mo) and RCP8.5 (91.67)) …
Methodology:
I would suggest including a "Model description" section at the beginning of the Methodology and include there the details about LANDIS-II and PnET-Succession functioning that now are dispersed over the text (Lines 63-74, 141-156, 171-173).
RESPONSE: revised as suggested.
The study shows different empirical and simulating experiments, but uses different terminology to refer to them throughout the text. This is confusing at times and I believe the text could be improve in this sense. Specific suggestions in this sense are:
- In lines 75-80 each experiment could be given a name that would be consistently used through the text. Terms such as "empirical experiment", "single-species simulation", "two-species simulation" and "case study experiment" for each of the experiments would make easier for the reader to understand you experimental design.
RESPONSE: we revised the text to consistently use three terms: empirical experiment, temporally extending the empirical experiment, and landscape case study.- Line 112 "field experiment" is never more referred as such, it may be better to stick to "empirical experiment".
RESPONSE: Done.- Separate the modelling experiments descriptions in two sections (line 194 onwards) could help understand the different experiments you performed.
RESPONSE: revised as suggested.
- Line 267 "Virtual experiments": the previous section already show results of virtual experiments, maybe a different naming would be more appropriate.
RESPONSE: we now use the terms “modeling the empirical experiment” and “temporally extending the empirical experiment.”
Limitations:
Lines 352-353: I think it is important to remark that by extrapolating the field experiment to 90 years you are assuming that adult trees will behave as its seedling, which I believe it is highly unlikely. Knowing that age-specific traits of species are not considered within LANDIS-II this constitutes an important limitation of the model that should be mentioned and discussed in the study.
RESPONSE: the empirical experiment was used solely to estimate species-specific waterlogging tolerance parameters in the model, which describe how well the species are able to photosynthesize under varying waterlogging conditions. This species-specific trait of waterlogging tolerance is likely to remain constant over a cohort’s lifetime. Other pre-existing LANDIS-II parameters control other aspects of competitiveness through time, and these have been described and tested elsewhere in the literature. No change was made.
Other than that, there were only a few minor suggestions or questions:
Lines 114-116: Could you explain why you measured soil moisture and soil water potential in bare soil instead of in the seedling pots? Could the presence of the seedlings modify the soil moisture and soil water potential?
RESPONSE: throughout the duration of the empirical experiment, it was percent soil moisture that was measured. However, the PnET-Succession extension of the LANDIS-II model uses soil water potential, not soil moisture and therefore a cross-walk was needed. Using the same pots and same type of soil we were able to replicate the experimental water levels, measuring both soil moisture and soil water potential. This allowed us to convert the previously measured soil moisture values into the soil water potential values required by PnET-Succession.
Lines 137-139: why did you want to ensure that fWater never reach 0?
RESPONSE: in this study we focused on species that had at least some waterlogging tolerance, so H1 (the soil water potential where photosynthesis shuts down) was set to a negative number to prevent such a shutdown (even under the wettest conditions). The mathematical mechanics of this were considered confusing minutiae, so we revised the sentence to read: We arbitrarily set H1 of most species to -1.0 (to ensure that supersaturated soil water conditions never shut down photosynthesis for these waterlogging tolerant species) and then …
Lines 166, 168, 170 ("seed dispersal and establishment were turned off", "(MapCode) as specified in the initial conditions input file", "ecoregion soil was a novel soil texture"): I think these are very model-related details that those not familiar with LANDIS-II could find confusing. If needed, it could be nice to rephrase some of the sentences to explain what it means to those readers, and otherwise just omitting it would suffice. Related to this, "ecoregion parameters" (line 169) could be replaced by "soil parameters".
RESPONSE: revised as suggested.
Figure 5: why do you show the net photosynthesis for July if August is your reference month for the empirical experiment?
RESPONSE: In the manuscript we state that August was used to parameterize the model because it not only represents peak growing season photosynthesis, but also provided enough time for the water level treatments effects to be fully realized. The data in figure 5 are model simulations and while we could have selected the month of August, users of such data are often interested in mid-summer (July) results. Because a large portion of the manuscript focuses on a comparison of old model parameter values to new values, rather than on a specific month, we believe that presenting the data as used is preferable to an artificial construct.
Figure 5 (and others): You could use the "jitter" function to show overlapping point series.
RESPONSE: we revised Figures 3, 5, 6 using the position_dodge option in the geom function.
Line 260: how did you do this scaling up?
RESPONSE: we revised this sentence to read: Note that the units reported for biomass density are scaled up from planting pots to a full m2 of area.
Citation: https://doi.org/10.5194/egusphere-2024-3332-AC4
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AC4: 'Reply on RC2', Eric Gustafson, 25 Feb 2025
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Eric Gustafson
Dustin Bronson
Marcella Windmuller-Campione
Robert A. Slesak
Deahn Donner
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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