Roadkill-Data-Based Identification and Ranking of Mammal Habitats
Round 1
Reviewer 1 Report
I think authors provide a complete (and useful for conservation gestors) information around wildlife road kills. In addition, they analyse it throughout several aspects like habitat attractiveness, driver’s severity, hot spots, etc. The text is well written, and the information in methods is enough. I think this ms has enough quality for publication in Land and I only suggest several minor changes. They are mostly focused on some assumptions or bias that should be mentioned in the text.
Table 1. I find a problem with road-kill data that although it has nit solution it should be better remarked in methods and discussion. As the Police Traffic Supervision Service and the and Nature Research Centre have not the mandate to count all road kills, the survey is not systematic. For this reason, we found the paradox of 10741 road killed Roe-deers and only one Rattus rattus. It is obvious that these two institutions report only the most attractive wildlife that they found in the road. In method authors mention that the numbers are less than the reality, but they should also mention the bias regarding the taxonomic groups.
Lines 156-172. This paragraph is important for the interpretation. I think in the text authors should only say the methodological aspects, like variables considered and how they have been calculated. For example, the colour and kind of line of each element should be mentioned only in the figure caption to gain fluency in the main text. This comment is for all the methods section.
Lines 249-272. This is due to the lack of data on small mammals resulting from the kind of data mentioned above.
290-293. See the previous comment. This should be mentioned.
- You also assume that the collectors of data (Police) of each area stops with the same frequency depending on taxonomic groups. This is obviously not true and it should be mentioned as assumption.
Author Response
Dear Reviewer
Thank you very much for your very helpful and detailed suggestions, which helped us to clarify and improve our manuscript. We carefully discussed all your comments and did required changes to address them. Please find below our answers to your comments. We hope that we managed to address all you concerns and could give a better flow to the manuscript text.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
This is an interesting paper, which applies a novel approach to identify regions with significant mammal road mortality and characterizes habitat patches that are the sources of the animals killed at the hotspots. Two be honest, I don’t fully understand how TOPSIS and SAW are used - these decision support techniques are new to me.
The figures are very helpful for understanding what was done – I would have been lost without them. The data appendices are also helpful.
Major comments:
1. All mammal species were included. Inclusion of the small mammals is surprising to me – only a tiny fraction of road-kill of these mammals are ever recorded, and a number of these are human-associated. I would have though restricting the analysis to the large ungulates (deer, moose, bison, boar) would have provided a less noisy or biased indicator of risky hotspots.
- Conventionally, analysts characterize land cover / use around a hotspot at appropriate buffer distances, and compare the habitat composition to that around random points, using GLM or MaxEnt. The authors should discuss in greater detail why they thing their approach is better than the conventional approach.
3) The authors should discuss how they would test their predictions (Validate the model). I am still not sure how this model could be used for mitigation, and judging from the sections 4.2 and 4.3, the authors aren’t either.
4) How was the ranking of ‘severity for drivers’ done? Density of accident records? There are some problems with this:
a) Risk from the drivers perspective are a function of per driver risk. The data used in this study are absolutely numbers of accidents, ignoring traffic volume. So a road with 10,000 vehicles per day and 100 accidents per day is classified as higher risk than a road with 1,000 vehicles per day and 90 accidents per day – even though the risk to a driver is far higher in the latter.
b) MVCs with the small animals (rodents, rabbit, stoat) are no problem for a driver – probably barely noticeable. The only significant risk for drivers are MVCs with the large mammals, especially the ungulates.
5) This paper is VERY hard to read. The authors need to have someone who is fluent in English to carefully edit this paper. At the moment, it is very hard to read.
Minor comments:
Page 1, line 34: Trampolines? I don’t think that is what is meant, but I have no idea what the authors are referring to.
Page 1, line 46: ecological niche factor analysis = ecological niche modeling?
Page 3, line 109: ‘artificial areas’ = developed areas
Author Response
Dear Reviewer
Thank you very much for your very helpful and detailed suggestions, which helped us to clarify and improve our manuscript. We carefully discussed all your comments and did required changes to address them. Please find below our answers to your comments. We hope that we managed to address all you concerns and could give a better flow to the manuscript text.
Author Response File: Author Response.pdf
