Assessing bat-vehicle collision risks using acoustic 3D tracking
Influence of local landscape and time of year on bat-road collision risks
Recommendation: posted 21 December 2020, validated 21 December 2020
The loss of biodiversity is an issue of great concern, especially if the extinction of species or the loss of a large number of individuals within populations results in a loss of critical ecosystem services. We know that the most important threat to most species is habitat loss and degradation (Keil et al., 2015; Pimm et al., 2014); the latter can be caused by multiple anthropogenic activities, including pollution, introduction of invasive species and fragmentation (Brook et al., 2008; Scanes, 2018). Roads are a major cause of habitat fragmentation, isolating previously connected populations and being a direct source of mortality for animals that attempt to cross them (Spellberg, 1998).
While most studies have focused on the effect of roads on larger mammals (Bartonička et al., 2018; Litvaitis and Tash, 2008), in recent years many researchers have grown increasingly concerned about the risk of collision between bats and vehicles (Fensome and Mathews, 2016). For example, a recent publication by Medinas et al. (2021) found 509 bat casualties along a 51-km-long transect during a period of 3 years. Their study provides extremely valuable information to asses which factors primarily drive bat mortality on roads, yet it required a substantial investment of time coupled with the difficulty of detecting bat carcasses. Other studies have used acoustic monitoring as a proxy to gauge risk of collision based on estimates of bat density along roads (reviewed in Fensome and Mathews 2016); while the results of such studies are valuable, the number of passes recorded does not necessarily equal collision risk, as many species may simply avoid crossing the roads. Understanding the risk of collisions is of vital importance for adequate planning of road construction, particularly for key sites that harbor threatened bat species or unusually large populations, especially if these are already greatly impacted by other anthropogenic activities (e.g. wind turbines; Kunz et al. 2007) or unusually deadly pathogens (e.g. white-nose syndrome; Blehert et al. 2009).
The study by Roemer et al. (2020) titled “Influence of local landscape and time of year on bat-road collision risks”, is a welcome addition to our understanding of bat collision risk as it employs a more accurate assessment of bat collision risk based on acoustic monitoring and tracking of flight paths. The goal of the study of Roemer and collaborators, which was conducted at 66 study sites in the Mediterranean region, is to provide an assessment of collision risk based on bat activity near roads. They collected a substantial amount of information for several species: more than 30,000 estimated flight trajectories for 21+ species, including Barbastella barbastellus, Myotis spp., Plecotus sp., Rhinolophus ferrumequinum, Miniopterus schreibersii, Pipistrellus spp., Nyctalus leisleri, and others. They assess risk based on estimates of 1) species abundance from acoustic monitoring, 2) direction of flight paths along roads, and 3) bat-vehicle co-occurrence.
Their findings suggest that risk is habitat, species, guild, and season-specific. Roads within forested habitats posed the largest threats for most species, particularly since most flights within these habitats occurred at the zone of collision risk. They also found that bats typically fly parallel to the road axis regardless of habitat type, which they argue supports the idea that bats may use roads as corridors. The results of their study, as expected, also show that the majority of bat passes were detected during summer or autumn, depending on species, yet they provide novel findings of an increase in risky behaviors during autumn, when the number of passes at the zone of collision risk increased significantly. Their results also suggest that mid-range echolocators, a classification that is based on call design and parameters (Frey-Ehrenbold et al., 2013), had a larger portion of flights in the zone at risk, thus potentially making them more susceptible than short and long-range echolocators to collisions with vehicles.
The methods employed by Roemer et al. (2020) could further help us determine how roads pose species and site-specific threats in a diversity of places without the need to invest a significant amount of time locating bat carcasses. Their findings are also important as they could provide valuable information for deciding where new roads should be constructed, particularly if the most vulnerable species are abundant, perhaps due to the presence of important roost sites. They also show how habitats near larger roads could increase threats, providing an important first step for recommendations regarding road construction and maintenance. As pointed out by one reviewer, one possible limitation of the study is that the results are not supported by the identification of carcasses. For example, does an increase in the number of identified flights at the zone of risk really translate into an increase in the number of collisions? Regardless of the latter, the paper’s methods and results are very valuable and provide an important step towards developing additional tools to assess bat-vehicle collision risks.
 Bartonička T, Andrášik R, Duľa M, Sedoník J, Bíl M (2018) Identification of local factors causing clustering of animal-vehicle collisions. The Journal of Wildlife Management, 82, 940–947. https://doi.org/10.1002/jwmg.21467
 Blehert DS, Hicks AC, Behr M, Meteyer CU, Berlowski-Zier BM, Buckles EL, Coleman JTH, Darling SR, Gargas A, Niver R, Okoniewski JC, Rudd RJ, Stone WB (2009) Bat White-Nose Syndrome: An Emerging Fungal Pathogen? Science, 323, 227–227. https://doi.org/10.1126/science.1163874
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 Fensome AG, Mathews F (2016) Roads and bats: a meta-analysis and review of the evidence on vehicle collisions and barrier effects. Mammal Review, 46, 311–323. https://doi.org/10.1111/mam.12072
 Frey‐Ehrenbold A, Bontadina F, Arlettaz R, Obrist MK (2013) Landscape connectivity, habitat structure and activity of bat guilds in farmland-dominated matrices. Journal of Applied Ecology, 50, 252–261. https://doi.org/10.1111/1365-2664.12034
 Keil P, Storch D, Jetz W (2015) On the decline of biodiversity due to area loss. Nature Communications, 6, 8837. https://doi.org/10.1038/ncomms9837
 Kunz TH, Arnett EB, Erickson WP, Hoar AR, Johnson GD, Larkin RP, Strickland MD, Thresher RW, Tuttle MD (2007) Ecological impacts of wind energy development on bats: questions, research needs, and hypotheses. Frontiers in Ecology and the Environment, 5, 315–324. https://doi.org/10.1890/1540-9295(2007)5[315:EIOWED]2.0.CO;2
 Litvaitis JA, Tash JP (2008) An Approach Toward Understanding Wildlife-Vehicle Collisions. Environmental Management, 42, 688–697. https://doi.org/10.1007/s00267-008-9108-4
 Medinas D, Marques JT, Costa P, Santos S, Rebelo H, Barbosa AM, Mira A (2021) Spatiotemporal persistence of bat roadkill hotspots in response to dynamics of habitat suitability and activity patterns. Journal of Environmental Management, 277, 111412. https://doi.org/10.1016/j.jenvman.2020.111412
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 Roemer C, Coulon A, Disca T, Bas Y (2020) Influence of local landscape and time of year on bat-road collision risks. bioRxiv, 2020.07.15.204115, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2020.07.15.204115
 Scanes CG (2018) Chapter 19 - Human Activity and Habitat Loss: Destruction, Fragmentation, and Degradation. In: Animals and Human Society (eds Scanes CG, Toukhsati SR), pp. 451–482. Academic Press. https://doi.org/10.1016/B978-0-12-805247-1.00026-5
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Gloriana Chaverri (2020) Assessing bat-vehicle collision risks using acoustic 3D tracking . Peer Community in Ecology, 100068. 10.24072/pci.ecology.100068
The recommender in charge of the evaluation of the article and the reviewers declared that they have no conflict of interest (as defined in the code of conduct of PCI) with the authors or with the content of the article. The authors declared that they comply with the PCI rule of having no financial conflicts of interest in relation to the content of the article.
Evaluation round #2
DOI or URL of the preprint: 10.1101/2020.07.15.204115
Version of the preprint: 2
Author's Reply, 30 Nov 2020
Decision by Gloriana Chaverri, posted 30 Oct 2020
Dear Charlotte and co-authors,
I thank you for making the changes suggested by the reviewers on your preprint titled “Influence of local landscape and time of year on bat-road collision risks”. I believe you have adequately addressed all of our suggestions; however, new ones have arisen from your changes and from a more thorough revision of the entire text. For this new version I decided not to send it out for review again, but would like you to carefully consider my following suggestions before I give the preprint my recommendation.
Line 20: remove moreover. Line 23: Remove “bats avoiding vehicles or not” from the text, and keep it to what you can measure (bat-car co-occurrence). Line 42: change maximal to greatest. Lines 42-43: this sentence is confusing, as it seems that there might only be these two options. Do you mean that bats followed the road axis regardless of the type of habitat present? Line 57: change enhances to increases (remove increasing at the beginning of this line). Line 58: put comma after parenthesis. Lines 58-59: What do you mean by “results concerning the role of traffic or speed limit on road-kills were CONTRASTED for other vertebrate taxa”? Lines 60-62: Please modify this sentence. My suggestion is: Studies on a variety of animal groups also found that preferred habitats for foraging or movement, described at the home-range scale (e.g. presence or absence of woodland, cropland, wetland …), are more often… Line 64: I think here you are referring to habitat rather than landscape features. Line 76: Do you mean here that gaps in vegetation are an important factor that is known to increase road collisions? Line 93: Change qualified to classified. Line 101: Add road here, for example “decreases bat road crossing”. Lines 107-109: You need a reference to support this claim. Doing this here is very important since you are not providing any evidence in the introduction, or elsewhere, that flight direction is relevant for understanding collision risk. Lines 121-125: This last part does not seem to be adding anything to the main topic of this paragraph, so I’d suggest you remove it. Lines 129-133: I would recommend you summarize this part. My suggestion: “The aim of our study is to assess the effects of the local habitat, coupled with bat density and movement patterns, on road collision risks.” Right now it’s too long and fairly repetitive, since some of the specific methods mentioned in this section are basically tackling the same goals. Also, note that I’ve changed landscape to habitat, since you are looking at small-scale, not large-scale, features. Lines 140-143: I am not sure you have provided sufficient evidence as to why you’d expect bat density or the proportion of animals flying in the zone at collision risk to influence collision risk in different ways in different contexts. I think the ideal approach is to simply mention that it’s expected that larger number of bats flying in the risky zone would mean greater collision risks. If not, then there may be site, habitat and/or species-specific traits that influence collision risks. Lines 143-150: I think it is now great that you have added some predictions, but in my opinion, they still need a lot of work. Let’s take it one by one: (1) a higher bat density at good quality habitats (i.e. tree rows near streams and tall trees) and at roads with a lower traffic rate. These are two separate predictions, right? Also, is it possible to generalize that having tree rows near streams and tall tress is a good habitat for all species considered? One alternative here is to simply mention that previous studies have found greater activity in sites with those type of conditions, similarly to what’s mentioned in lines 92-94. Provide some evidence (perhaps a reference) that can support the second part of this prediction (more cars = fewer bats). Your second prediction, (2) a higher proportion of individuals in the zone at collision risk when vegetation grows closer to the road and in habitats with dense vegetation at each side of the road compared to habitats without trees. Are these two, again, separate predictions since they represent different explanatory variables? For your third prediction, (3) a correlation between the orientation of bat trajectories and the orientation of linear vegetation, you need a reference to support this assertion. If there are no previous studies that provide this evidence, then you need to remove this from your predictions. Overall, I don’t understand the need to separate your predictions by response variable. For example, why expect that traffic affects bat density but not flying in the zone at collision risk, or that habitat configuration affects flying in the zone at collision risk but not bat density? Line 172: Abbreviate genus name upon second mention (e.g. Pinus halepensis). Table 1 (legend): What is the second sentence referring to? Line 198: Change cumulated to accumulated. Line 263: I don’t understand why you mention Figure 2 here. I would suggest to remove this. Figure 3: Why is the estimation of flight trajectory orientation a qualitative component of your model? I am still not entirely convinced that this estimate, flight trajectory, is adding much to our understanding of collision risk, primarily because you have not provided evidence from previous studies that either a parallel or perpendicular trajectory entails greater risks. Line 314: Change “to fly” to flying. Line 315: Is this estimate including flights only at a vehicle height or does this include at vehicle height on the road? Lines 326-328: I am still not convinced that you can safely assume this (still seems speculative), so I recommend that you delete this sentence. Lines 329-332: This is still something that must be addressed carefully, mainly in terms of providing evidence that a parallel, or perpendicular, flight path orientation entails greater collision risks. Lines 362-363: Can you please explain this a bit better? I am not sure first why you did this, or how. Why do you need your explanatory variables to follow a normal distribution? How did you normalize them? Line 364: remove the error message. Line 394: Change problem to problems. Lines 394-397: What was done with the models that did not converge? Legend figure 4: Please arrange the order of the x-axis categories in the same order as those displayed in the graphs (e.g. first F, then FE, DPT, etc.). Line 401: Landscape (habitat?) type refers to which variables in table 2? Perhaps what is needed is a new main column in table 2 called habitat/landscape type and then subdivide it with pertinent categories. Table 2: Please switch columns and rows. Your columns should be the explanatory variables, and rows should be species. The same applies to tables 3 to 5. Line 442: I believe some results should be provided to understand this a bit better, so please remove “results not shown” and provide more evidence for your assertion. At the moment the only way to understand flight trajectories is to look at the estimate of the model in table 5, which is not that straightforward. Lines 457-460: It is normally not widely accepted that paragraphs are composed of a single sentence. In this case, I believe these two ideas should be merged into a single paragraph. Line 500: change to explanatory variables. Lines 507-508: you need a reference here. Lines 518-522: My opinion is that you do not need to explain all of your results, especially if there is no way to explain them without resorting to speculation. For example, you mention that there is higher social activity in N. leisleri, but you have provided no evidence for this. I’d suggest you remove this entire sentence. Line 547: The word “avoided” implies that bats actively reduce their activity in the zone at risk when they perceive traffic, which you are not providing evidence for. I also do not agree with lines 550-551: “These results show that bats spatially avoid the vicinity of vehicles”. I guess you’d need a similar set of data as that obtained by Zurcher et al. (2010), where you can clearly see that bats change their course when cars are detected, to explain a reduction of a species’ activity at the zone at collision risk during higher traffic. Some of the speculation in this paragraph continues in the next one, particularly in line 558 (“more reluctant to approach vehicles”) and line 563 (“their vehicle avoidance behaviour”). Lines 567-575: You are focusing this section on the more speculative ideas rather than those for which you might have greater support from previous studies (and your own data). Why is there an increase in flights at the zone of risk during the summer, for example? You did not explain this in the discussion. This increase may simply be the result of an increase in bat density during the summer (which you clearly show in your data), and not necessarily because there is a larger proportion of those flights occurring in the zone at risk (implying riskier behaviors or greater naïveté). The only two species for which I believe you have sufficient evidence of an increase in the number of bat passes at risk per night as the years progresses (and greater during the autumn compared to the summer) are P. pygmaeus and Plecotus sp. For those two species, the number of bat passes at risk per night may increase due to an overall increase in bat density and/or a larger proportion of flights occurring at the zone of risk. Without additional data, such as whether those flights at risk are predominantly juvenile, or evidence from other species which also have volant juveniles during autumn, the section about juveniles and sexual activity driving risky behaviors is speculative. Lines 627-628: I believe the aims of your study can be attained (at least approximated) with direct counts of carcasses, yet with a significantly greater investment of time. Maybe this would be worth mentioning, adding a reference to studies that have counted carcasses to asses risk (e.g. Medinas et al. 2020: https://doi.org/10.1016/j.jenvman.2020.111412). Lines 633-635: add a reference to this sentence. Line 651: change to “habitat loss for numerous”. Lines 665-666: Remove authors from parenthesis.
I thank you for your continuous effort to improve this wonderful preprint, and hope to see it finalized soon.
Evaluation round #1
DOI or URL of the preprint: 10.1101/2020.07.15.204115v1
Version of the preprint: 1
Author's Reply, 05 Oct 2020
Decision by Gloriana Chaverri, posted 20 Aug 2020
Dear Charlotte and co-authors,
We have already received comments (see below) on your preprint titled “Influence of local landscape and time of year on bat-road collision risks”. Overall, the reviewers seem enthusiastic about your work, as am I, but point to a few issues that hopefully you can address before I decide to recommend the preprint. As you will notice, in addition to the comments by Brock Fenton and Mark Brigham, two of Mark’s students kindly helped in the evaluation process and I am sure you will find their suggestions useful.
I also have a few comments of my own. First, I agree with Mark in that there is some speculation in the discussion that should be avoided. Some examples where I see speculation in the discussion: lines 487-491, 514, 519-520, 524-526, 535-537.
Second, I am also having some difficulty with a few of your response variables and their contribution to your main question: what factors increase collision risks in bats? At the moment the only variable I think really would help you answer your question is the number of bat passes at collision risk per night. At the moment you are including in your model 1) number of bat passes per night, which does not directly measure risk. You also include the 2) proportion of bat flights at vehicle height, yet this will not provide you with a total risk estimate. For example, in site A say you only detect two passes in a given night and both are at vehicle height, but in site B you detect 100 passes and 50 are at vehicle height; with these data you will find that 100% of passes are at risk at site A and only 50% at site B, when clearly more animals are at risk in site B. This will have major implications if and when proposing strategies to reduce vehicle collisions with bats. The other response variable, 3) vehicle avoidance, is very confusing in my opinion and assumes that bats are actively avoiding vehicles, which you are not providing strong evidence for. So to recapitulate, my suggestion is that a much more straightforward and clean model would only need to include bat passes at collision risk per night as your response variable. I see no need to include trajectory orientation since your variable measuring collision risk already takes into account the fact that bats are entering the zone of risk (above the road and at vehicle height).
Finally, I would like to congratulate you on this wonderful work and all the effort undertaken to provide such detailed information on this very important topic.
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