Distribution and Habitat Suitability of the Malabar Slender Loris (Loris lydekkerianus malabaricus) in the Aralam Wildlife Sanctuary, India
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThis paper models the current distribution of Malabar slender loris in a wildlife Sanctuary in southern India and the habitat suitability for the species under different climate change scenarios. The article is interesting, as there are few papers in the literature studying the effects of climate change on the distribution and habitat of lorises. Overall, it reads well and is well-structured. My main comments are twofold:
- The use of some terms and concepts is somewhat confusing, making it difficult at some points to understand what the authors are doing. Authors seem to use ‘species distribution models’ and ‘habitat suitability models’ interchangeably’. A similar problem arises with ‘occupancy’, ‘occurrence’, and ‘abundance’. This creates confusion about the objectives, methods, and results. Please clarify it and use the terms consistently throughout the manuscript.
- Data collection needs to be better explained. The authors need to provide more information regarding the number and length of transects, the distance between transects, distribution in the study area (random, stratified by habitat or regular transects), etc. In summary, basic information is missing to be able to understand if the data are representative and therefore the results are reliable. The dates and duration of fieldwork should also appear in the manuscript (it is not the same to record 400 lorises in one season than over several years). Were counts on transects completed only once or several times/year?
On the other hand, the authors use two different methods for loris presence data. Are the data obtained by the two systems compatible? If occasional nocturnal encounters occur while moving through the areas covered by the transects, how can they be sure that they are not overestimating presence? The authors should explain this part better and demonstrate that their data are not pseudoreplications. In my opinion, they should provide convincing evidence that the use of different surveying methods is combinable and thus the results are reliable.
General comments:
- References to figures in the text are not always correct. Please, check and correct.
- The authors could include more information on the study species, is it a solitary or social species? Social structure may influence detectability and sampling.
- Section 2.3 Apart from the justification for using Maxent, this section does not provide information on methods.
The discussion section should be strengthened by being more specific and including more implications that directly arise from the results. It currently reads quite vaguely.
- References: check the list of references and the numbering in the text. For example, reference 30 is before reference 29, and reference 35 is before reference 34. Also, not all references in the bibliography are in the same format.
Detailed comments:
L 53-54. It is redundant with lines 48-49.
L 98-99. Please clarify what is the objective of the transects, to study occupancy? Abundance? How do you define occurrence? I suggest trying to stick to the objective of the study or define what you mean by each term.
L101. How many were detected by each of the methods used?
L118. Something seems to be missing
L120. Have you considered the option of including NDVI as an important variable in the distribution of Loris? It seems reasonable that being an arboreal species this could be an important predictor variable in its distribution.
L147. What value do you use after rescaling? It might be interesting to evaluate the models with the mean (which is the one I assume you have used) and with the standard deviation, as it can provide information about the “variability” of the predictor variables.
L 147. Would it be possible to use a resolution of 0.5km, the same as that used for loris observations? Another relevant point is how many pseudo-absences or background points the authors used. This needs to be explained in the text regarding the modelling approach implemented.
L155. Please, check if ‘habitat distribution model’ is what they mean.
L160. By layers, do you mean variables? I recommend being consistent with the terminology, which will help the reader follow and understand the text better.
L 161. Explain or indicate in Table 1 which variables remain as the 14 final predictors. On the other hand, the number of presences to build the models is low, and the usage of such a low number of occurrences with a relatively high number of variables can result in overfitted models. How have the authors dealt with this?
L178. Did they use the absolute values of AICc? Or Δ AICc >2? Have you tried model averaging?
L214-217. What threshold have you used? Also, they need to explain what the L1 model is. And in which direction those variables are important.
L221. Table 1 shows all the initial variables. The variables used to build the models must be indicated.
L228-230. I do not understand the reference to Figure 3.
L232-234. Could you explain how these variables influence the spatial distribution, please?
Table 2 (legend): What does ‘model L1 4’ mean? Also, why does elevation appear in the table?
L238-239. Please, rewrite and clarify
Fig 3 (legend): the map shows habitat suitability, not the species distribution. Please, correct it.
Fig 3. It is not cited correctly in the text.
L243. Fig 4 is not correctly cited in the text.
Figs 3 and 4. Please, unify the format in the identification of the graphics (a, b, and c).
Fig 4. I find it difficult to interpret since the scale is different in each one, so the colors are not very intuitive. I think using the same scale in all the figures would help to visually understand the result. On the other hand, it seems that both the increase and decrease in precipitation affect the suitable habitat for the loris since the south-central zone of the habitat is no longer suitable for the species in all the models. Currently, it seems to me that the interpretation is that the change from the current situation is what affects the model result, but probably when all the images have the same scale it will be easier to interpret.
L248-249. Should be explained in the methods.
L250. See the main comment on habitat suitability and species distribution.
L 268. Please, include ‘local’ before species'viability.
L 278. What variations? Please, be more precise.
L 293-295. I don't understand how this cite relates to the paper. Please, explain the idea better.
L302-334. Throughout these paragraphs, seemingly contradictory arguments are presented, and some ideas are repeated. Please, rewrite to clarify and simplify the repeated ideas.
L360-362. It sounds a bit odd to me that ‘lorises reflect ecosystem health’, when they prefer semi-degraded habitats near human populations (L277-280). More so, according to the authors, the reduction in habitat in the RCP 2.6 scenario is due to a possible increase in evergreen forests (L.338-339) which is a natural, non-degraded forest type.
L364. Please replace ‘anthropogenic pressure’ with distance from roads or another more appropriate term, because it is the only variable related to anthropogenic pressure that is significant in the models.
L368-372. In my opinion, these are not conclusions derived from the results of the study, where precipitation is identified as the most influential variable in the habitat suitability models.
Author Response
- The use of some terms and concepts is somewhat confusing, making it difficult at some points to understand what the authors are doing. Authors seem to use ‘species distribution models’ and ‘habitat suitability models’ interchangeably’. A similar problem arises with ‘occupancy’, ‘occurrence’, and ‘abundance’. This creates confusion about the objectives, methods, and results. Please clarify it and use the terms consistently throughout the manuscript
We agree with the reviewer that there was some confusion in the methods. We have now checked it. This is mainly due to the fact that we refer to two linked papers, but we have now clarified. Maxent is a species distribution model using presence only data. It does not matter how the data are collected, that is the flexibility of Maxent. Also it models habitat suitability (it is possible to integrate habitat variables in the model). So you can have both species distribution and habitat suitability with Maxent.
- Data collection needs to be better explained. The authors need to provide more information regarding the number and length of transects, the distance between transects, distribution in the study area (random, stratified by habitat or regular transects), etc. In summary, basic information is missing to be able to understand if the data are representative and therefore the results are reliable. The dates and duration of fieldwork should also appear in the manuscript (it is not the same to record 400 lorises in one season than over several years). Were counts on transects completed only once or several times/year?
we have now added more explaination in the methods.
On the other hand, the authors use two different methods for loris presence data. Are the data obtained by the two systems compatible? If occasional nocturnal encounters occur while moving through the areas covered by the transects, how can they be sure that they are not overestimating presence? The authors should explain this part better and demonstrate that their data are not pseudoreplications. In my opinion, they should provide convincing evidence that the use of different surveying methods is combinable and thus the results are reliable.
We have now clarified that the data were collected via line transects, apart from 10 extra observations. But Maxent models, since they need presence only data and they automatically remove pseudoreplications, are well known to be flexible in the type of data used (e.g. it is common to use presence data from citizen science combined with camera traps and line transects).
Detailed comments:
L 53-54. It is redundant with lines 48-49.
Changes made
L 98-99. Please clarify what is the objective of the transects, to study occupancy? Abundance? How do you define occurrence? I suggest trying to stick to the objective of the study or define what you mean by each term.
Changes made
L101. How many were detected by each of the methods used?
Changes made
L118. Something seems to be missing
Did not understand
L120. Have you considered the option of including NDVI as an important variable in the distribution of Loris? It seems reasonable that being an arboreal species this could be an important predictor variable in its distribution.
Thank you for your suggestion. Indeed, NDVI is an important variable which could predict the distribution of an arboreal species. We had included NDVI while performing our models and missed to add it in the tables and now we have included it in the text.
L147. What value do you use after rescaling? It might be interesting to evaluate the models with the mean (which is the one I assume you have used) and with the standard deviation, as it can provide information about the “variability” of the predictor variables.
Most of the layers that we used was downloaded at 1 Km spatial resolution (especially bioclimatic variables) and hence since these variables were calculated at 1 km spatial resolution, we did not do such a comparison. Moreover, generally such studies for primates are conducted at 1 km spatial resolution since most of the layers are at that resolution.
L 147. Would it be possible to use a resolution of 0.5km, the same as that used for loris observations? Another relevant point is how many pseudo-absences or background points the authors used. This needs to be explained in the text regarding the modelling approach implemented.
Most of the layers that we used was downloaded at 1 Km spatial resolution (especially bioclimatic variables). Resampling those layers to 0.5 resolution will provide the same values, and hence it is better to model at the resolution of most of the layers to avoid spatial autocorrelation. For resampling to a specific resolution, generally you require layers with higher resolution which is generally not available, especially in the case of current and future bio-climatic layers. We use maximum of 10,000 random background points and we have added this in the manuscript.
L155. Please, check if ‘habitat distribution model’ is what they mean.
Yes, its mentioned on line 153
L160. By layers, do you mean variables? I recommend being consistent with the terminology, which will help the reader follow and understand the text better.
Yes, its mentioned on 154-155, that ‘we treated all the chosen variables as continuous variables”,
L 161. Explain or indicate in Table 1 which variables remain as the 14 final predictors. On the other hand, the number of presences to build the models is low, and the usage of such a low number of occurrences with a relatively high number of variables can result in overfitted models. How have the authors dealt with this?
We have now mentioned the 14 predictor variables in the text as a new table. Model selection is the best way to avoid the problems due to small sample size in Maxent. We performed model selection using AICc in ENM Tools. We performed different models using stepwise selection of predictors, features and regularization parameters and selected the best model from that, which can reduce overfitting and underfitting when we have small sample sizes. We have now mentioned this in the methods.
“Studies suggest that further optimization of models may be required when you have small sample sizes. We performed such optimization in finding the best variables contributing to the model by stepwise selection of the predictors. In the initial model, we ran the model with the 14 predictors, and in the subsequent models we dropped 1 predictor each, until we reached 2 predictors.
Further, we adjusted the present models to the varying regularisation multipliers (1, 2 and 5) values, and the complexity of models was changed by altering MaxEnt features Linear (L), Product (P), Quadratic (Q), Hinge (H) and a combination of these features, viz., LQ, HQ, LQH, LQP, LQT, QHP, QHT, QHPT and AUTO [33]. We used the raw output format for testing the model and ran it for 5000 iterations and 30 replicates, including a subsampling procedure. We evaluated the contribution of each bioclimatic variable by using the Jack-knife protocol [22]. Each of these models were ran for each optimization step protocol discussed in the previous paragraph. The selection of models with best predictors, features and regularization multipliers will reduce overfitting and underfitting of models with small sample sizes.
We used the program ENMTools 1.4.4 [20, 35] to evaluate the models of varying predictors, complexities and regularisation multiplier values. We selected the model with the lowest AICc as the most suitable model depicting the present distribution of slender loris in Aralam Wildlife Sanctuary. AICc values perform better than BIC (Bayesian information criterion) or AUC (area under the curve) values for choosing best models [34, 36, 37].”
L178. Did they use the absolute values of AICc? Or Δ AICc >2? Have you tried model averaging? or AUC (area under the curve) values for choosing best models
- We selected the models with lowest AICc based on:
Warren, D.L.; Seifert, S.N. Ecological niche modeling in Maxent: The importance of model complexity and the performance of model selection criteria. Ecol Appl 2011, 21, 335–342. https://doi.org/10.1890/10-1171.1
Wordley, C.F.R.; Sankaran, M.; Mudappa, D.; Altringham, J.D. Landscape scale habitat suitability modeling of bats in the Western Ghats of India: Bats like something in their tea. Biol Conserv 2015, 191, 529–536. https://doi.org/10.1016/j.biocon.2015.08.005
After finding the best model, we ran 30 replications of the best model, and used average map for our analysis.
L214-217. What threshold have you used? Also, they need to explain what the L1 model is. And in which direction those variables are important.
We have included it both in methods and results. We have now explained what L1 model is in the results section. We have already discussed about the direction of variables in the results.
“The best model based on AICc scores was L1 (Feature- linear; Regularization multiplier-1; Predictors-Bio18, road, Bio14, Elevation) with AUC Training and AUC Testing values of 0.71 and 0.67 respectively. The final modeled outputs show that 23 km2 is suitable for the occupancy of lorises (using the 10-percentile training of presence logistic threshold ).”
“The response curves (Figures S1, S2) show a positive correlation between predicted habitat suitability and Bio14 (Precipitation of the Driest Month) and Bio18 (Precipitation of the Warmest Quarter), while distance from roads negatively influences suitability.”
L221. Table 1 shows all the initial variables. The variables used to build the models must be indicated.
Table 2 added
L228-230. I do not understand the reference to Figure 3.
Changes made
L232-234. Could you explain how these variables influence the spatial distribution, please?
This suggests that more lorises can be found in Bio14 (Precipitation of the Driest Month) and Bio18 (Precipitation of the Warmest Quarter) and less when there are roads.
Table 2 (legend): What does ‘model L1 4’ mean? Also, why does elevation appear in the table?
L1 is the best model based on AICc scores. Explained on line 213.
L238-239. Please, rewrite and clarify
Rewritten
Fig 3 (legend): the map shows habitat suitability, not the species distribution. Please, correct it.
Corrections made
Fig 3. It is not cited correctly in the text.
Changes made
L243. Fig 4 is not correctly cited in the text.
Changes made
Figs 3 and 4. Please, unify the format in the identification of the graphics (a, b, and c).
Fig 4. I find it difficult to interpret since the scale is different in each one, so the colors are not very intuitive. I think using the same scale in all the figures would help to visually understand the result. On the other hand, it seems that both the increase and decrease in precipitation affect the suitable habitat for the loris since the south-central zone of the habitat is no longer suitable for the species in all the models. Currently, it seems to me that the interpretation is that the change from the current situation is what affects the model result, but probably when all the images have the same scale it will be easier to interpret.
We have now made all figures in same scale. We agree with your interpretation. The distribution of species is affected both by extreme increase in precipitation and decrease in precipitation, which might be making habitats unsuitable for the loris. We have made changes in results and discussion. We have added methods in the methods section.
L248-249. Should be explained in the methods.
Changes made
L250. See the main comment on habitat suitability and species distribution.
Changes made
L 268. Please, include ‘local’ before species'viability.
added
L 278. What variations? Please, be more precise.
Changes made
L 293-295. I don't understand how this cite relates to the paper. Please, explain the idea better.
Changes made
L302-334. Throughout these paragraphs, seemingly contradictory arguments are presented, and some ideas are repeated. Please, rewrite to clarify and simplify the repeated ideas.
Changes made
L360-362. It sounds a bit odd to me that ‘lorises reflect ecosystem health’, when they prefer semi-degraded habitats near human populations (L277-280). More so, according to the authors, the reduction in habitat in the RCP 2.6 scenario is due to a possible increase in evergreen forests (L.338-339) which is a natural, non-degraded forest type.
Changes made
L364. Please replace ‘anthropogenic pressure’ with distance from roads or another more appropriate term, because it is the only variable related to anthropogenic pressure that is significant in the models.
changes made
L368-372. In my opinion, these are not conclusions derived from the results of the study, where precipitation is identified as the most influential variable in the habitat suitability models.
Paragraph removed
Reviewer 2 Report
Comments and Suggestions for AuthorsOverall solid research. The main thing missing is detailed description of the field survey methods. See comment Line 99-100 and Line 309-322
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Introduction ~ It will be good to give a bit more information on Malabar Slender loris, especially its conservation status in India
Line 89 ~ it will be helpful to clarify what kind of plantations by mentioning the types of crops. I would assume it was not a tree farm or an orchard
Line 99-100 ~ please clarify if the survey was done visually or auditorily or maybe other means of detection was used. If I understand correctly, lorises have distinct calls and can be found by listening.
Line 104 – package “Wallace”?
Line 104-105 ~ See the comment about line 99-100. Since you mentioned male vs female, please clarify what kind of transect survey allowed you to gather this information.
Line 111-113 ~ I am not sure you need to this and how true it is. MaxEnt is not necessarily better. It has advantages and disadvantages like any other statistical tools. As long as the manuscript clearly describes how a statistical test was conducted, the choice is up to the authors.
Line 123 ~ can you elaborate on what ecological requirements? Were there papers that you cited to gather information on such requirements?
Line 150 ~ it will be a good idea to use * or something in Table 1 to indicate what 14 final predictor variables were.
Line 156-157 ~ Looking at Table 1, I cannot see which variable should not be continuous. They are all measured in numbers. Unless you intentionally transform some into categorical, there isn’t any categorical variable, and I don’t see a reason to transform a variable. Is there something missing here?
Line 169 ~ there must be a mistake here. I don’t think you wanted to refer to table 1. Maybe some supplementary material table?
Line 181-186 ~ again, it feels unnecessary to mention these, unless a reviewer specifically asked for it. AICc is like a common best practice nowadays.
Line 222 ~ again, Table 1 does not tell what 14 chosen variables were.
Line 224 ~ Table 3, what is 23? I think present in the parentheses should be moved above 23 as a column name.
Line 255-256 ~ did you mean no preference to any forest type? I am not sure why you had to specially mention evergreen rain forests.
Line 276-283 ~ it might be a good idea to clearly mention no forest type preference was found (based on table 2 and your results)
Line 309-322 ~ since distance to road had a negative effect on habitat suitability, meaning lorises were more likely to be found near roads, I am wondering if the survey method could introduce biases as well. If the transect survey was done on roads, of course models would show that road being a positive factor. Refer back to the comment on Line 99-100, it is extremely important to add more information the survey method.
Author Response
Introduction ~ It will be good to give a bit more information on Malabar Slender loris, especially its conservation status in India
Information added
Line 89 ~ it will be helpful to clarify what kind of plantations by mentioning the types of crops. I would assume it was not a tree farm or an orchard
Timber plantations
Line 99-100 ~ please clarify if the survey was done visually or auditorily or maybe other means of detection was used. If I understand correctly, lorises have distinct calls and can be found by listening.
The entire methodology has been added
Line 104 – package “Wallace”?
Added
Line 104-105 ~ See the comment about line 99-100. Since you mentioned male vs female, please clarify what kind of transect survey allowed you to gather this information.
added
Line 111-113 ~ I am not sure you need to this and how true it is. MaxEnt is not necessarily better. It has advantages and disadvantages like any other statistical tools. As long as the manuscript clearly describes how a statistical test was conducted, the choice is up to the authors.
The advantage here is mentioned in light of this research.
Line 123 ~ can you elaborate on what ecological requirements? Were there papers that you cited to gather information on such requirements?
Changes made
Line 150 ~ it will be a good idea to use * or something in Table 1 to indicate what 14 final predictor variables were.
Another table indicating the 14 variables used has been added
Line 156-157 ~ Looking at Table 1, I cannot see which variable should not be continuous. They are all measured in numbers. Unless you intentionally transform some into categorical, there isn’t any categorical variable, and I don’t see a reason to transform a variable. Is there something missing here?
Table 2 will help understand better
Line 169 ~ there must be a mistake here. I don’t think you wanted to refer to table 1. Maybe some supplementary material table?
Table 2 added
Line 181-186 ~ again, it feels unnecessary to mention these, unless a reviewer specifically asked for it. AICc is like a common best practice nowadays.
We deleted the sentence
Line 222 ~ again, Table 1 does not tell what 14 chosen variables were.
Table 2 is added
Line 224 ~ Table 3, what is 23? I think present in the parentheses should be moved above 23 as a column name.
It is the baseline current value. Added column name.
Line 255-256 ~ did you mean no preference to any forest type? I am not sure why you had to specially mention evergreen rain forests.
Evergreen rainforests are not being predicted as suitable habitat for the future
Line 276-283 ~ it might be a good idea to clearly mention no forest type preference was found (based on table 2 and your results)
These climatic and environmental variations align with the species’ preference for moist deciduous, semi-evergreen, and degraded evergreen forests near human habitation, but they do not prefer prestine evergreen forests, hence we thought we should mention the different forests.
Line 309-322 ~ since distance to road had a negative effect on habitat suitability, meaning lorises were more likely to be found near roads, I am wondering if the survey method could introduce biases as well. If the transect survey was done on roads, of course models would show that road being a positive factor. Refer back to the comment on Line 99-100, it is extremely important to add more information the survey method.
Survey method added
Reviewer 3 Report
Comments and Suggestions for AuthorsThis study is very relevant and well to assure the protection and conservation of the species.
The Introduction is well-written and forms a better impression of the current situation of the species
The Materials and Methods section succinctly describes what was actually done.
Some comment: Lines 15, 107-108, 113, 123, 154 etc. - authors use "modeling" (American English) and "modelling" (British English) while there are noticeable differences between these two varieties. Please use one of them.
The results section reports all the data collected, and summarises the outcomes for each part of the data.
The Discussion sector is well written. It introduced the purpose of the study and provided an in-depth description of the methodology. The discussion informs about the larger implications of the study based on the results.
The conclusions summarise the main points of the paper and sufficiently emphasize the significance of the findings and recommendations for future work
Suggestion: minor revision
Comments for author File: 
Comments.pdf
Author Response
This study is very relevant and well to assure the protection and conservation of the species.
The Introduction is well-written and forms a better impression of the current situation of the species
Thank you
The Materials and Methods section succinctly describes what was actually done.
Some comment: Lines 15, 107-108, 113, 123, 154 etc. - authors use "modeling" (American English) and "modelling" (British English) while there are noticeable differences between these two varieties. Please use one of them.
replaced
The results section reports all the data collected, and summarises the outcomes for each part of the data.
The Discussion sector is well written. It introduced the purpose of the study and provided an in-depth description of the methodology. The discussion informs about the larger implications of the study based on the results.
The conclusions summarise the main points of the paper and sufficiently emphasize the significance of the findings and recommendations for future work
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsThe authors have done a good work incorporating the requested changes. The manuscript has now improved in readability and clarity. I congratulate the authors. However, I believe there are still some minor issues, both in form and content, that should be addressed.
Specific Comments:
L104: Please check reference ‘224’.
L230: I recommend that the authors consider, for the final selection, all models with ΔAICc < 2 in relation to the best-ranked model. Even if these models are not used to project habitat distribution under future scenarios, I believe they should at least be presented to the readers (in case any model meets this ΔAICc criterion).
L280: Please revise the sentence: '… from the road, and Precipitation of the driest month (Bio 14), and Elevation'.
L284–288: The sentence beginning with ‘The environmental variable…’ is repeated twice. Please revise to avoid redundancy.
L288: The reference to Figure 3 is incorrect. Please verify and correct it.
L293–294: Consider rephrasing the sentence ‘…Can be found in Bio 14 and Bio 18…’. As currently written, the intended meaning is unclear.
L300: In the section titled ‘3.2. Prediction in changes in habitat suitability’, I suggest making section titles more consistent throughout the manuscript to help readers follow the structure. For example, in section 2.6, the title used is ‘Future Climatic Projections…’
L365: In the sentence referring to ‘…temperature changes…’, do the authors actually mean ‘precipitation changes’? The entire paragraph discusses precipitation rather than temperature. Please check and correct if necessary.
Table 1: Please complete the description of ‘LIGHT’ by indicating that it refers to nocturnal light disturbance.
Table 2: Please include the meaning of the abbreviation ‘SI No’ in the legend.
Figures 3 and 4: Please unify the format used to identify panels (a, b, c) across both figures.
Figure 4: I find this figure difficult to interpret, as each panel uses a different color scale, which makes the comparison between them less intuitive. I suggest using the same scale across all panels to facilitate visual interpretation. The authors mention that this has been addressed, but the figure appears unchanged from the previous version. In any case, this is a suggestion; using a unified scale is not required, but if the figure remains as before, the response should acknowledge that the suggestion was not implemented, or it might be a versioning error.
Additional observations:
Punctuation: There are several punctuation issues throughout the manuscript. I recommend a thorough revision. For example, double periods ('..') in lines 54 and 79; missing final periods in lines 87 and 236; and double commas in L311 ('figure4,,').
Formatting: Please use either 'Figure' or 'figure' consistently throughout the manuscript. For example, line 273 uses 'figure 2' (lowercase), while line 306 uses 'Figure 3' (uppercase).
References: Please review and correct the formatting of the references. For example, references 4 to 7, 25, and 28 each follow a different style. Consistency is needed throughout the bibliography.
Author Response
The authors have done a good work incorporating the requested changes. The manuscript has now improved in readability and clarity. I congratulate the authors. However, I believe there are still some minor issues, both in form and content, that should be addressed.
Specific Comments:
L104: Please check reference ‘224’.
Thank you for noticing, removing the error.
L230: I recommend that the authors consider, for the final selection, all models with ΔAICc < 2 in relation to the best-ranked model. Even if these models are not used to project habitat distribution under future scenarios, I believe they should at least be presented to the readers (in case any model meets this ΔAICc criterion).
We have added this to the manuscript now “There were other models with low AICc and among them the 2 other best models with ΔAICc <2 was H2 (Predictors- all 14 predictors; Bio18, road and Bio14 contributed to more than 99% of the model), and Q1 (predictors- road and Bio14).
L280: Please revise the sentence: '… from the road, and Precipitation of the driest month (Bio 14), and Elevation'.
Of the 14 variables used for modeling (Table 2), only four variables significantly impacted the spatial distribution of lorises. These variables were Precipitation of the Warmest Quarter (Bio 18), followed by the distance from the road, Precipitation of the Driest Month (Bio 14), and Elevation (Table 3).
Sentence revised to make it more readable
L284–288: The sentence beginning with ‘The environmental variable…’ is repeated twice. Please revise to avoid redundancy.
Repeated sentence removed
L288: The reference to Figure 3 is incorrect. Please verify and correct it.
Changes made
L293–294: Consider rephrasing the sentence ‘…Can be found in Bio 14 and Bio 18…’. As currently written, the intended meaning is unclear.
Deleted the sentence. Elevation did not play any role in the model.
L300: In the section titled ‘3.2. Prediction in changes in habitat suitability’, I suggest making section titles more consistent throughout the manuscript to help readers follow the structure. For example, in section 2.6, the title used is ‘Future Climatic Projections…’
Projected Shifts in Habitat Suitability Under Future Climate Scenarios, changes made
L365: In the sentence referring to ‘…temperature changes…’, do the authors actually mean ‘precipitation changes’? The entire paragraph discusses precipitation rather than temperature. Please check and correct if necessary.
Sentence added to explain better
Together, temperature and precipitation act as interdependent ecological filters
Table 1: Please complete the description of ‘LIGHT’ by indicating that it refers to nocturnal light disturbance.
Changes made
Table 2: Please include the meaning of the abbreviation ‘SI No’ in the legend.
Added in the table
Figures 3 and 4: Please unify the format used to identify panels (a, b, c) across both figures.
Changes made
Figure 4: I find this figure difficult to interpret, as each panel uses a different color scale, which makes the comparison between them less intuitive. I suggest using the same scale across all panels to facilitate visual interpretation. The authors mention that this has been addressed, but the figure appears unchanged from the previous version. In any case, this is a suggestion; using a unified scale is not required, but if the figure remains as before, the response should acknowledge that the suggestion was not implemented, or it might be a versioning error.
We apologize with this. It was a versioning error and we had made figures at same scale. We attach the new figure in the manuscript.
Additional observations:
Punctuation: There are several punctuation issues throughout the manuscript. I recommend a thorough revision. For example, double periods ('..') in lines 54 and 79; missing final periods in lines 87 and 236; and double commas in L311 ('figure4,,').
Changes made
Formatting: Please use either 'Figure' or 'figure' consistently throughout the manuscript. For example, line 273 uses 'figure 2' (lowercase), while line 306 uses 'Figure 3' (uppercase).
Changes made
References: Please review and correct the formatting of the references. For example, references 4 to 7, 25, and 28 each follow a different style. Consistency is needed throughout the bibliography.
Changes made
