Assessing seasonality of tick abundance in different climatic regions
Seasonality of host-seeking Ixodes ricinus nymph abundance in relation to climate
Recommendation: posted 01 October 2023, validated 01 October 2023
Yoccoz, N. (2023) Assessing seasonality of tick abundance in different climatic regions. Peer Community in Ecology, 100507. 10.24072/pci.ecology.100507
Tick-borne pathogens are considered as one of the major threats to public health – Lyme borreliosis being a well-known example of such disease. Global change – from climate change to changes in land use or invasive species – is playing a role in the increased risk associated with these pathogens. An important aspect of our knowledge of ticks and their associated pathogens is seasonality – one component being the phenology of within-year tick occurrences. This is important both in terms of health risk – e.g., when is the risk of encountering ticks high – and ecological understanding, as tick dynamics may depend on the weather as well as different hosts with their own dynamics and habitat use.
Hoch et al. (2023) provide a detailed data set on the phenology of one species of tick, Ixodes ricinus, in 6 different locations of France. Whereas relatively cool sites showed a clear peak in spring-summer, warmer sites showed in addition relatively high occurrences in fall-winter, with a minimum in late summer-early fall. Such results add robust data to the existing evidence of the importance of local climatic patterns for explaining tick phenology.
Recent analyses have shown that the phenology of Lyme borreliosis has been changing in northern Europe in the last 25 years, with seasonal peaks in cases occurring now 6 weeks earlier (Goren et al. 2023). The study by Hoch et al. (2023) is of too short duration to establish temporal changes in phenology (“only” 8 years, 2014-2021, see also Wongnak et al 2021 for some additional analyses; given the high year-to-year variability in weather, establishing phenological changes often require longer time series), and further work is needed to get better estimates of these changes and relate them to climate, land-use, and host density changes. Moreover, the phenology of ticks may also be related to species-specific tick phenology, and different tick species do not respond to current changes in identical ways (see for example differences between the two Ixodes species in Finland; Uusitalo et al. 2022). An efficient surveillance system requires therefore an adaptive monitoring design with regard to the tick species as well as the evolving causes of changes.
Goren, A., Viljugrein, H., Rivrud, I. M., Jore, S., Bakka, H., Vindenes, Y., & Mysterud, A. (2023). The emergence and shift in seasonality of Lyme borreliosis in Northern Europe. Proceedings of the Royal Society B: Biological Sciences, 290(1993), 20222420. https://doi.org/10.1098/rspb.2022.2420
Hoch, T., Madouasse, A., Jacquot, M., Wongnak, P., Beugnet, F., Bournez, L., . . . Agoulon, A. (2023). Seasonality of host-seeking Ixodes ricinus nymph abundance in relation to climate. bioRxiv, ver.4 peer-reviewed and recommended by Peer Community In Ecology. https://doi.org/10.1101/2022.07.25.501416
Uusitalo, R., Siljander, M., Lindén, A., Sormunen, J. J., Aalto, J., Hendrickx, G., . . . Vapalahti, O. (2022). Predicting habitat suitability for Ixodes ricinus and Ixodes persulcatus ticks in Finland. Parasites & Vectors, 15(1), 310. https://doi.org/10.1186/s13071-022-05410-8
Wongnak, P., Bord, S., Jacquot, M., Agoulon, A., Beugnet, F., Bournez, L., . . . Chalvet-Monfray, K. (2022). Meteorological and climatic variables predict the phenology of Ixodes ricinus nymph activity in France, accounting for habitat heterogeneity. Scientific Reports, 12(1), 7833. https://doi.org/10.1038/s41598-022-11479-z
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.
Tick observatories were supported by the CC-EID and Climatick projects: “Adaptation of Agriculture and Forests to Climate Change” metaprogramme of the French National Research Institute for Agriculture, Food and Environment (INRAE)
Evaluation round #2
DOI or URL of the preprint: https://doi.org/10.1101/2022.07.25.501416
Version of the preprint: 2
Author's Reply, 19 Sep 2023
Decision by Nigel Yoccoz, posted 18 Jul 2023, validated 18 Jul 2023
Dear Contributors to PCI Ecology,
You have accounted for most comments by the reviewers and myself, but there are some minor issues that need to be addressed.
When you write that you pooled years "in order to maximize the power of our estimation", this is not correct. One uses a statistical model that represents the processes generating the data - if there was a large variability between years, it would be wrong to ignore it, that is pooling the data would lead to a model underestimating the uncertainty (and for example leading to too small P-values or equivalently too narrow confidence intervals). You can argue that based on the data the variability between years is small and therefore the model provides a description of the general pattern, but one does not choose a model in order to get a small P-value. Note also that with respect to estimation, it is precision and/or accuracy which are relevant, power is with regards to hypothesis testing.
The equation of the model (l. 176 of the pdf) is still incorrect I believe. You write y_sd in the model equation and on the next line define y_st (same error with epsilon_sd and epsion_st). And the coefficients of the Fourier series are site specific? If not they would result in the same phenological pattern? Same comment with the variance of epsilon, it is site-specific?
I will wait for your response/changes to decide if I will recommend the manuscript - the suggested changes are relatively minor. If you still disagree with my suggestions, you need to provide a more thorough explanation.
Evaluation round #1
DOI or URL of the preprint: https://doi.org/10.1101/2022.07.25.501416
Version of the preprint: 1
Author's Reply, 29 Jun 2023
Decision by Nigel Yoccoz, posted 16 Mar 2023, validated 16 Mar 2023
Both reviewers provided important comments regarding in particular 1) the original contribution of this paper compared to Wongnak et al. (2022), as the same data are analysed but using a different statistical modelling approach, and 2) the lack of recent references and terminology regarding ticks and disease. Clarifying and revising these points will lead to an interesting contribution on the seasonality of tick behaviour and dynamics.
I will focus on the statistical analysis as I think it is quite central to your analysis. As one reviewer commented, you do not explain why you decided to use log-transformed data rather than the negative binomial as in Wongnak et al. (2022). There is some discussion regarding what is the best approach for analysing count data (see e.g. Ives 2015 for one point of view, and O’Hara et al. 2010 cited in Ives for another). One of the main differences is what the model predicts – the negative binomial model predicts the mean abundance (with a log link function), but the model based on the log-transformed data predicts the mean of the log-transformed value (which is different from the log of the mean values). You are most likely aware of the difference, but you would need then to explain the different modelling choices in the two papers. Moreover, as far as I can understand from your model described l. 169, you assume that the shape of the curve is the same every year (ie there is no effect of the year on the coefficients of the Fourier series – you must have include a site effect on the coefficient to allow site-specific shapes, right? You need to specify this in the equation). This may seem a reasonable approximation judging from Figure 2 (and it is probably what you mean when you mention a “typical “ year), but it is most likely to lead to a large underestimation of the uncertainty in the peak date (which you do not assess in this analyses). I understand that this might not be easy to implement (having different coefficients in different years will I believe result in a non-smooth function), but you should at least acknowledge this limitation of the model used.
In addition, I would also like to point out that the term “mountain” is defined internationally with respect to topography and not climate (you have climate belts such as the montane or alpine belts in some mountains, but this is a different issue, related to vegetation). Snethlage et al (2022) have provided the latest implementation of this international definition of mountains (it is the one developed by the Global Mountain Biodiversity Assessment, and used for example by other international organisations when investigating mountain regions; see eg Thornton et al. 2021, 2022) Clearly some areas defined in Joly’s paper as having a “mountainous” climate are not “mountains” according to the international definition. Please use another term that describe the climate characteristics and does not refer to “mountain”.
You need to provide more details regarding the weather stations used.
Ives, A. R. (2015). For testing the significance of regression coefficients, go ahead and log-transform count data. Methods in Ecology and Evolution, 6(7), 828-835. doi:10.1111/2041-210x.12386
Snethlage, M. A., Geschke, J., Ranipeta, A., Jetz, W., Yoccoz, N. G., Körner, C., . . . Urbach, D. (2022). A hierarchical inventory of the world’s mountains for global comparative mountain science. Scientific Data, 9(1), 149. doi:10.1038/s41597-022-01256-y
Thornton, J. M., Palazzi, E., Pepin, N. C., Cristofanelli, P., & Adler, C. (2021). Toward a definition of Essential Mountain Climate Variables. One Earth, 4(6), 805-827. doi:10.1016/j.oneear.2021.05.005
Thornton, J. M., Pepin, N., Shahgedanova, M., & Adler, C. (2022). Coverage of In Situ Climatological Observations in the World's Mountains. Frontiers in Climate, 4. doi:10.3389/fclim.2022.814181