Geographic Variation in Migratory Grasshopper Recruitment under Projected Climate Change

Round 1

Reviewer 1 Report

This work developed a multi-level, joint modeling framework to assess the response of nymph recruitment through the year 2040 at the Wyoming, US.

Methods

1. From the text, it is not clear to me which grid points were used for the sample locations. It can have a big impact on the results and on the interpretation of the results, if just one grid point was used or if the average over sample locations was used.
2. From the text in line 97 say that "Nineteen bioclimatic variables were derived at 2.5 minutes spatial resolution from each GCM projection….". It would be better if the authors could show or list those variables.
3. It might be better if the authors could explain why they selected three GCMS, including the IPSL-97 CM6A-LR, CanESM5, and the MIROC6.
4. From the text, I am not sure if this work used bias correction of the GCMs data for the analysis or not. Because GCMs have been the key source of information for developing climate scenarios, and they serve as the foundation for assessing climate change implications at all scales, from local to global. Impact studies, on the other hand, rarely use GCM outputs directly since climate models reveal systematic error (biases) due to inadequate geographical resolution, simplified physics, and thermodynamic processes, numerical methods, or incomplete knowledge of climate system dynamics. GCM simulations have huge errors when compared to historical observations. As a result, bias-correcting the raw climate model outputs is critical in order to develop climate projections that are more suitable for application modeling.
5. The results are in Figure 9, which shows the projected recruitment rate for each climate change scenario. It would be nice if the authors could add the ensemble results. Because an ensemble can aid in the representation of new resources for studying ranges of plausible climate change responses in relation to a given forcing.

Comments for author File: Comments.pdf

Author Response

AUTHOR RESPONSE

 

Manuscript ID: geographies-1561565

Title: Geographic variation in migratory grasshopper recruitment under projected climate change.

Authors: John M. Humphreys, Robert B. Srygley, David H. Branson

 

REVIEWER COMMENTS ARE SHOWN FOLLOWING THE WORD “COMMENT” AND AUTHOR RESPONSES ARE PROVIDED AFTER THE WORD “RESPONSE.”  PLEASE NOTE THAT ALL BELOW DESCRIBED REVISIONS HAVE BEEN HIGHLIGHTED IN THE UPDATED MANUSCRIPT.

 

REVIEWER 1:

COMMENT: This work developed a multi-level, joint modeling framework to assess the response of nymph recruitment through the year 2040 at the Wyoming, US.

RESPONSE: Yes, this summary is correct and we thank the reviewer for their time and comments.

 

 

COMMENT: 1. From the text, it is not clear to me which grid points were used for the sample locations. It can have a big impact on the results and on the interpretation of the results, if just one grid point was used or if the average over sample locations was used.

 

RESPONSE: Thank you for highlighting the need for clarification regarding our modeling approach, the manuscript has been revised to address this concern.  LINES 155-158 of the manuscript have been updated to include the sentence, “In our application, the LGCP were modeled as non-homogeneous, point processes using individual point locations and associated, point-specific attributes (“marks”) without any aggregation to grid cells or other areal units.” 

 

Rather than using a dense grid or raster format, our LGCP models were designed using point process models.  This means that point attributes (commonly called “marks”) were not summed or averaged to grid cells (areas), rather all points (dimensionless) were assessed directly and individually as components of a continuously varying surface.  The Pennino et al, 2019 article cited in the manuscript provides an accessible overview of LGCP point process models.

 

Reference:

Pennino, M.G.; Paradinas, I.; Illian, J.B.; Muñoz, F.; Bellido, J.M.; López-Quílez, A.; Conesa, D. Accounting for preferential sampling in species distribution models. Ecology and Evolution 2019, 9, 653–663. doi:10.1002/ece3.4789

 

 

COMMENT: 2. From the text in line 97 say that "Nineteen bioclimatic variables were derived at 2.5 minutes spatial resolution from each GCM projection….". It would be better if the authors could show or list those variables.

 

RESPONSE: Thank you for identifying this oversight.  Variable names and additional information for all bioclimatic, soil, and vegetation data have been added as a supplementary Excel file.  The manuscript has been revised to reference the supplemental information at LINES 106, 120, and 134.  

 

 

COMMENT: 3. It might be better if the authors could explain why they selected three GCMS, including the IPSL-97 CM6A-LR, CanESM5, and the MIROC6.

 

RESPONSE: We concur with the reviewer regarding the need for additional detail.  The manuscript has been revised to include additional explanation in the climate data paragraph beginning at LINE 94.  The new text describes use of model comparison criteria developed by Sanderson et al, 2015, 2015B to compare and select climate models.  In brief, the added text clarifies that a consensus modeling approach was applied to account for uncertainty and that models were chosen due to having been developed by different laboratories and showing above average inter-model distance based on Sanderson et al criteria, which is suggestive of model independence.

 

References:

Sanderson, B.M.; Knutti, R.; Caldwell, P. A Representative Democracy to Reduce Inter-dependency in a Multimodel Ensemble. Journal of Climate 2015, 28, 5171 – 5194.

 

Sanderson, B.M.; Knutti, R.; Caldwell, P. Addressing Interdependency in a Multimodel Ensemble by Interpolation of Model Properties. Journal of Climate 2015B, 28, 5150 – 5170.

 

 

COMMENT: 4. From the text, I am not sure if this work used bias correction of the GCMs data for the analysis or not. Because GCMs have been the key source of information for developing climate scenarios, and they serve as the foundation for assessing climate change implications at all scales, from local to global. Impact studies, on the other hand, rarely use GCM outputs directly since climate models reveal systematic error (biases) due to inadequate geographical resolution, simplified physics, and thermodynamic processes, numerical methods, or incomplete knowledge of climate system dynamics. GCM simulations have huge errors when compared to historical observations. As a result, bias-correcting the raw climate model outputs is critical in order to develop climate projections that are more suitable for application modeling.

 

RESPONSE: Yes, we agree with the reviewer regarding the importance of this issue.  As described at LINE 105 of the original manuscript and LINE 111 of the revised manuscript, “[c]limate forecasts were down-scaled and bias corrected using WorldClim (v2.1) as a baseline, which corresponds to 30-year average climate conditions.” 

 

COMMENT: 5. The results are in Figure 9, which shows the projected recruitment rate for each climate change scenario. It would be nice if the authors could add the ensemble results. Because an ensemble can aid in the representation of new resources for studying ranges of plausible climate change responses in relation to a given forcing.

 

RESPONSE:  Although we recognize that comparing estimates from individual climate models to those displayed for consensus in Fig 9 may be informative in identifying different climate forcings, we feel that this is beyond on the scope of the current manuscript.  This decision was made after considering that (1) the manuscript currently includes more than 10 figures and tables, (2) net change maps would be needed to highlight pairwise differences between all model combinations (as provided in lower panel of current Fig 9), (3) abundance estimates for both nymph and adult stages as produced by all climate models would also be needed to partition demographic influences from climate effects, and (4) because of the variable decomposition performed in this study, any mapped differences between different climate models would not be interpretable in terms of pure temperature or precipitation influences (i.e., differences would instead consist of linear combinations of climate factors as described in Methods Subsection 2.3).     

 

Author Response File: Author Response.docx

Reviewer 2 Report

This manuscript entitled “Geographic variation in migratory grasshopper recruitment under projected climate change” was written by John M. Humphreys et al. This study employed a process-based method and leveraged ten-years of Melanoplus sanguinipes (Msang) field survey data to assess the response of nymph recruitment under projected climate conditions (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) through the year 2040 in the Wyoming (WY), a state located in the Western United States. In my opinion, this work is very interesting and play an important significant value and practical meaning in this field. I recommend to accept this manuscript in present form. In addition, I hope that the authors can further do this research and improve it according to the description of limitation for this study depicted in the part of discussion.

Author Response

AUTHOR RESPONSE

 

Manuscript ID: geographies-1561565

Title: Geographic variation in migratory grasshopper recruitment under projected climate change.

Authors: John M. Humphreys, Robert B. Srygley, David H. Branson

 

REVIEWER COMMENTS ARE SHOWN FOLLOWING THE WORD “COMMENT” AND AUTHOR RESPONSES ARE PROVIDED AFTER THE WORD “RESPONSE.” 

 

REVIEWER 2:

COMMENT: This manuscript entitled “Geographic variation in migratory grasshopper recruitment under projected climate change” was written by John M. Humphreys et al. This study employed a process-based method and leveraged ten-years of Melanoplus sanguinipes (Msang) field survey data to assess the response of nymph recruitment under projected climate conditions (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) through the year 2040 in the Wyoming (WY), a state located in the Western United States. In my opinion, this work is very interesting and play an important significant value and practical meaning in this field. I recommend to accept this manuscript in present form. In addition, I hope that the authors can further do this research and improve it according to the description of limitation for this study depicted in the part of discussion.

 

RESPONSE: We thank the reviewer for their time and supportive comments.

Author Response File: Author Response.docx

Reviewer 3 Report

This study leveraged ten-years of Melanoplus sanguinipes field surveys to assess the response of nymph recruitment to projected climate conditions through the year 2040. This study may provide implications for future Msang population dynamics. However, there are some concerns that the authors should address before it can be considered for publication.

(1) In the introduction, I suggest the authors add more introductions about the effect of climate change on grasshoppers.

(2) The joint model (Model3) designed to concurrently estimate adult and nymph abundance exhibited improved parsimony over models constructed to individually assess the adult and nymph life stages. Is this joint model (Model3) universal and applicable to other species?

(3) I suggest the authors add more mechanisms to explain how climate change affects grasshoppers.

(4) In order to further highlight the innovation of this article, it is better to compare the results of this study with other studies.

(5) A paragraph of limitation discussion should be added to clarify the limitation or uncertainty of current study. For example, the uncertainty of remote sensing data will have some impact on the results of this study (e.g. Shen et al., 2020; Yi et al., 2021; Shen et al., 2021).

References:

Shen et al. Marshland loss warms local land surface temperature in China. Geophysical Research Letters, 2020, 47: e2020GL087648.

Yi et al. Probabilistic faster R-CNN with stochastic region proposing: Towards object detection and recognition in remote sensing imagery. Neurocomputing, 2021, 459: 290-301.

Shen et al. Aboveground biomass and its spatial distribution pattern of herbaceous marsh vegetation in China. Science China Earth Sciences. 2021, 64: 1115-1125.

Author Response

AUTHOR RESPONSE

 Manuscript ID: geographies-1561565

Title: Geographic variation in migratory grasshopper recruitment under projected climate change.

Authors: John M. Humphreys, Robert B. Srygley, David H. Branson

 

REVIEWER COMMENTS ARE SHOWN FOLLOWING THE WORD “COMMENT” AND AUTHOR RESPONSES ARE PROVIDED AFTER THE WORD “RESPONSE.”  PLEASE NOTE THAT ALL BELOW DESCRIBED REVISIONS HAVE BEEN HIGHLIGHTED IN THE UPDATED MANUSCRIPT.

 

REVIEWER 3:

COMMENT: This study leveraged ten-years of Melanoplus sanguinipes field surveys to assess the response of nymph recruitment to projected climate conditions through the year 2040. This study may provide implications for future Msang population dynamics. However, there are some concerns that the authors should address before it can be considered for publication.

 

RESPONSE: We thank the reviewer for their time and believe their comments have improved the manuscript.

 

COMMENT:  (1) In the introduction, I suggest the authors add more introductions about the effect of climate change on grasshoppers.

 

RESPONSE: Thank you, the manuscript introduction has been revisited to better emphasize that climate change may contribute to increased grasshopper outbreak intensity, impact future food security (consume crops), and financially harm the agriculture industry (LINES 38-42).  Additional emphasis has also been given to propose that increased temperature may modify hatch timing, grasshopper access to food (plants), and grasshopper movement (LINES 60-65).    

 

COMMENT:  (2) The joint model (Model3) designed to concurrently estimate adult and nymph abundance exhibited improved parsimony over models constructed to individually assess the adult and nymph life stages. Is this joint model (Model3) universal and applicable to other species?

RESPONSE: Thank you, the manuscript conclusion has been revised at LINE 411 to state that, “[a]s a new method, our hierarchical modeling approach may be readily adapted to other species, particularly when there is a need to account for covariation between different life stages, density-dependence across space and through time, or biotic interactions among different species.”

 

COMMENT:  (3) I suggest the authors add more mechanisms to explain how climate change affects grasshoppers.

 

RESPONSE: We thank the reviewer for the suggestion, however as stated beginning at LINE 136 and owing to the problematic nature of drawing mechanistic interpretation from species distribution models (Austin, 2002; De Marco et al, 2018), we implemented a datamining approach that prioritized model performance (correlation) over variable-specific interpretation of species-environment relationships (causation).  Due to this action, our ability to interpret biological mechanisms or draw causal inference was constrained by the fact that we decomposed variables to create synthetic covariates.  Please note that two discussion paragraphs do speculate on possible mechanisms in relation to climate change (see two paragraphs beginning at LINE 318).

 

References:

Austin, M. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling 2002, 157, 101–118.

 

De Marco, Júnior, P.; Nóbrega, C.C. Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation. PLOS ONE 2018, 13, 1–25.

 

COMMENT:  (4) In order to further highlight the innovation of this article, it is better to compare the results of this study with other studies.

 

RESPONSE:  We agree that comparison to other grasshopper models would be informative, however we are unaware of any other study that spatially interpolates/extrapolates grasshopper abundance or recruitment within the same geographic study area (Wyoming).  Studies employing other methods to spatially quantify abundance and reproduction have been applied in Canada (Olfert et al, 2020) and habitat suitability has been assessed for small portions of Wyoming (Kistner-Thomas et al, 2021),  however these approaches are not directly comparable to results presented in our manuscript.

 

References:

Olfert, O.; Weiss, R.M.; Giffen, D.; Vankosky, M.A. Modeling Ecological Dynamics of a Major Agricultural Pest Insect (Melanoplus sanguinipes; Orthoptera: Acrididae): A Cohort-Based Approach Incorporating the Effects of Weather on Grasshopper Development and Abundance. Journal of Economic Entomology 2020, 114, 122–130.

 

Kistner-Thomas, E.; Kumar, S.; Jech, L.; Woller, D.A. Modeling Rangeland Grasshopper (Orthoptera: Acrididae) Population Density Using a Landscape-Level Predictive Mapping Approach. Journal of Economic Entomology 2021, 114, 1557–1567.

 

COMMENT:  (5) A paragraph of limitation discussion should be added to clarify the limitation or uncertainty of current study. For example, the uncertainty of remote sensing data will have some impact on the results of this study (e.g. Shen et al., 2020; Yi et al., 2021; Shen et al., 2021).

 

References:

Shen et al. Marshland loss warms local land surface temperature in China. Geophysical Research Letters, 2020, 47: e2020GL087648.

 

Yi et al. Probabilistic faster R-CNN with stochastic region proposing: Towards object detection and recognition in remote sensing imagery. Neurocomputing, 2021, 459: 290-301.

 

Shen et al. Aboveground biomass and its spatial distribution pattern of herbaceous marsh vegetation in China. Science China Earth Sciences. 2021, 64: 1115-1125.

 

RESPONSE: Thank you, the manuscript has been revised to address this concern and to incorporate references suggested by the reviewer.  Limitations are discussed in the paragraph beginning at LINE 377 and additional citations related to the uncertainly associated with remote sensing products are at LINE 380.

 

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The manuscript is sufficient for a publication. 

Reviewer 3 Report

The authors have addressed all my concerns. I suggest accept this paper in its present form.