Philippa Kaur

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Tularemia affects animal welfare, human health, and the environment and is thus better approached from a one-health perspective27. Several studies in the Northern hemisphere28, and more recently in Australia15,16, have provided a vital research track in the epidemiology of this disease. In contrast, studies in Africa are too limited and scarce. The aim of this study was to investigate the presence of tularemia in wild leporids collected in Northern Algeria. These animals are highly susceptible to F. tularensis infection and considered sentinel hosts for surveillance of tularemia. The strategy we used to detect F. tularensis in leporids mainly used molecular, histological and immunohistochemical analyzes of tissues taken from animals found dead or hunted. To the best of our knowledge, detection of F. tularensis by PCR or culture has not been previously reported in wild leporidae in Algeria or other African countries.Animal tissue samples were tested using three qPCR assays of variable sensitivity and specificity. The Type B-qPCR test targets a specific junction between ISFtu2 and a flanking 3′ region, which is considered specific for F. tularensis subsp. holarctica26, the only tularemia agent found in Europe and Asia. The Tul4-qPCR assay targets a simple copy gene encoding a surface protein, which can be found in the genome of all F. tularensis subspecies causing tularemia and that of the aquatic bacterium F. novicida. Because F. novicida has never been isolated from lagomorphs or other animal species, and very rarely from human29, a positive Tul4 qPCR for the studied tissue samples likely indicated the presence of F. tularensis DNA. The ISFtu2 qPCR is considered highly sensitive because multiple copies of this insertion sequence are found in the F. tularensis genome. However, it lacks specificity because ISFtu2 is also found in many other Francisella species25.Two animals were considered “probable” tularemia cases because some of their samples were positive for the three qPCR tests. Ten animals were considered “possible” tularemia cases because their samples were positive for the ISFtu2 and Tul4 qPCRs but not the Type B qPCR. Finally 19 leporids were “uncertain” cases because only samples positive for the ISFtu2 qPCR were found. For the remaining 43 animals, all the tested samples were negative for the three qPCRs. Overall, we detected F. tularensis DNA-positive samples in 12/74 (16.21%) leporids, which strongly suggest that tularemia is present in the lagomorph population of the study area. The positive Type B qPCR tests in two animals suggested that F. tularensis subsp. holarctica could be the involved subspecies. We did not confirm these data by isolating F. tularensis from the studied leporids. However, the isolation of this pathogen from human or animal samples is tedious and has a low sensitivity13. Moreover, most of our samples were not appropriate for F. tularensis culture because of their long-term preservation in ethanol 70° or 10% formalin. Further study using fresh (non-fixed) tissue samples from dead leporids collected in the same study area is needed to definitively confirm the presence of tularemia in these animals and characterize the F. tularensis subspecies and genotypes involved.Although PCR is usually more sensitive than culture for detecting F. tularensis, it also has some limitations. Firstly, the DNA extraction from organs preserved in ethanol for several months was difficult although easier for spleen than for liver samples. Some tissue samples could be lysed only after overnight incubation with proteinase K. Secondly, tissue samples contained PCR inhibitors as demonstrated by better DNA amplification from some samples after their dilution in PCR grade water. To reduce the effect of PCR inhibitors, organ samples with negative qPCR were retested using Bovine Serum Albumin (BSA) and the Real-time PCR system TaqMan (Applied Biosystems, Munich, Germany)30. Finally, DNA regions to be amplified were optimized to obtain high sensitivity and specificity of qPCR tests.IHC detection of F. tularensis in formalin-fixed tissue can be helpful for tularemia diagnosis31,32. For one possible tularemia case, F. tularensis could be detected on immunohistochemical (IHC) examination of a liver sample using a specific anti-F. tularensis antibody. The intensity and localization of positive staining were comparable to those previously recorded for other animals32,33. IHC did not provide interpretable findings for four other tested specimens. Such negative results might be explained by an inhomogeneous distribution of infectious foci in the involved organs as well as a low bacterial inoculum in infected tissues. This has been previously demonstrated in tularemia granulomatous lesions in cell types like epithelial cells of the kidney, testis, and epididymis, hepatocytes, and bronchiolar epithelial cells31. Besides, IHC is a delicate technology whose results are highly dependent on the quality and fixation time of the organ tissues34. IHC analysis of dead animal tissues remains challenging, especially in case of tissue necrosis34.In our limited case series we found a F. tularensis infection prevalence in leporids of 2.7% (2/74) for probable tularemia cases and 16.2% (12/74) when considering both probable and posible cases. We cannot make a guess about the prevalence of tularemia because our series is not representative of the general lagomorph population in the study area. In Germany, F. tularensis DNA was detected in 1.1% of European Brown hares and 2.4% of wild rabbits collected between 2009 and 201435. Higher infection rates were reported in the same country, including 11.8% (100/848 animals) in hares collcted in the North Rhine-Westphalia region36 and 30% (55/179) in brown hares collected between 2010 and 2016 in Baden-Wuerttemberg37. In Hungary, the prevalence of tularemia in hares was evaluated at 4.9–5.3%38. In Portugal, prevalences of 4.3% and 6.3% were reported in brown hares and wild rabbits, respectively39. However, the comparison of the reported tularemia prevalences in leporids is irrelevant because studies involved different animal species and geographic areas, and used different methods for F. tularensis detection.Two possibilities could explain the lack of detection of tularemia in Algeria before this study. The first hypothesis is that this disease was not searched for in previous years, while it could have been present in this country for decades. The second hypothesis is that tularemia was recently imported in Algeria. Migratory birds may have been involved in the long-distance spread of F. tularensis40. These hosts can be infested by ectoparasites such as ticks which are the primary vectors of tularemia41,42. They can also spread the bacteria in the hydro-telluric environment through their secretions and feces18,43,44. An alternative possibility is that F. tularensis-infected animals (especially game animals) have been imported in Algeria from endemic countries. Whatever the mode of introduction of tularemia in Algeria, the dissemination of this disease over time might have been facilitated by the ability of F. tularensis to infect multiple hosts and its better survival in a cool environment45, which characterizes Northern Algeria climate. The emergence or re-emergence of tularemia in other countries has been related to climate change, human-mediated movement of infected animals, and wartime resulting in a significant rise of F. tularensis infections in the rodent populations39,46.In our study, infected animals were collected throughout 4 years, although more frequently in autumn. Probable and possible tularemia cases were mainly collected during the hunting season (i.e., September, October, November, and December). Animals could not be collected in February because of heavy rains and in May and June because it corresponds to female leporids’ lactation period. In most endemic countries, tularemia cases are typically more frequent in late spring, the summer months, and early autumn37,47,48,49,50. Occasionally, fatal tularemia cases in hares have been predominantly reported during the cold season11,51. The climatic conditions can affect tularemia outbreaks in animals, depending on the reservoir involved and the predominant modes of infection52.We detected tularemia more frequently in female than in male hares, and the reverse was true for wild rabbits. The prevalence of tularemia in male or female lagomorphs varies between studies. In Sweden, Morener et al.50 reported a tularemia case series only involving male hares. In the same country, Borg et al.50 observed an overrepresentation of females in the epizootic of 1967. They suggested that, compared to males, females had a higher risk of exposure to infected mosquitoes or were more vulnerable to tularemia because they were pregnant or had just given birth to a litter50. Tularemia was found in a few juveline leporids, which might be explained by a shorter exposure time to F. tularensis, a higher death rates due to higher susceptibility to F. tularensis infection or easier predation by their natural enemies, or more frequent hunting of adults compared to the juveniles53.Tularemia is usually more frequently detected in leporids found dead than in hunted animals. As an example, a German study reported a higher prevalence of tularemia in hares found dead (2.9%) than in hunted ones (0.7%)35. In our study, most qPCR-positive animals were hunted. Our study might not be representative of the prevalence of tularemia in either population because most collected animals had been hunted.The incubation period and clinical presentation of tularemia in leporids vary according to the species considered. Tularemia is typically an acute disease in mountain hares (Lepus timidus) in Scandinavia and has a chronic pattern in European brown hares (Lepus europaeus) in Central Europe50. The incubation time and clinical presentation of tularemia can be different in Cape hares (Lepus capensis). Wild rabbits are less sensitive to F. tularensis infection than hares31,39,54. An extended incubation period and chronic evolution of tularemia would facilitate the detection of F. tularensis in infected animals. In our study, a similar tularemia prevalence was found in the Cape hares and wild rabbits, which might reflect exposure to a same biotope area and environmental reservoirs of F. tularensis.The pathological lesions of tularemiia in leporids can vary according to the F. tularensis strain involved, the mode and route of infection, and the susceptibility and immune status of the host32,50. In the European brown hares, granulomas with central necrosis have been reported in the lungs and kidneys and occasionally in the liver, spleen, bone marrow, and lymph nodes50. In contrast, only acute necrosis in the liver, spleen, bone marrow, and lymph nodes have been found in Lepus timudus hares in Sweden50. The lesions in the Japanese hare (Lepus brachyurus angustidens) are comparable to those of Lepus timidus, except for cutaneous, lung, brain, and adrenal gland lesions32. In the European rabbit, Oryctolagus cuniculus, tularemia is not associated with identifiable macroscopic tissue lesions39,55. To our knowledge, no reports describing post-mortem lesions in Cape hares with tularemia are available. In this study, similar lesions were found in hares and wild rabbits except necrotic foci only observed in some wild rabbit organs (such as liver, lungs, kidney, ovary). Most animals had pathological lesions of pneumonia, gastritis and enteritis. Kidney lesions and adrenal glands enlargment were oberved. Necrotic lesions were occasionally found in the lungs, liver, spleen and ovary and hemorrhages in the lungs, liver, and intestines.Tularemia is an arthropod-born disease in most endemic areas14,22,28. In our study, 50% of positive leporids were infested by known tularemia vectors such as ticks (Ixodes ricinus56,57, Rhipicephalus sanguineus39), fleas (Spillopsylus cuniculi58), and lice of lagomorphs (Haemodipsus lepori and Haemodipsus setoni59,60). Ticks are the most significant arthropod vectors of tularemia61. Ticks are frequently involved in the transmission of tularemia in North America, including Dermacentor andersoni, D. variabilis, and Amblyomma americanum57,62,63. In Europe, tick-borne tularemia represents 13% to 26% of human cases57,64. The involved species include D. marginatus, D. reticulatus, I. ricinus, R. sanguineus, and Haemaphysalis concinna65,66. Further research on wild leporid sucking arthropods is needed to confirm the presence and clarify the ecology of F. tularensis in Algeria.Our study reports for the first time the detection of F. tularensis DNA in leporids from Northern Algeria. The markers most in favor of tularemia in the animals studied are the positivity of qPCR tests, in particular, the “type B” qPCR test which amplifies a specific DNA sequence of F. tularensis subsp. holarctica, and a positive immunohistological examination in one animal. Further investigation is needed to confirm our results by the isolation of this pathogen from animal samples and determine the F. tularensis subspecies and genotypes involved. This would allow the characterization of the F. tularensis subspecies and genotypes present in Algeria. Furthermore, our findings push us in future studies to seek tularemia in the Algerian human population. To achieve this, interdisciplinary or trans-disciplinary collaborative efforts underpinned by the One Health concept will be necessary. More

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All statistical analyses were performed through R version 4.1.050. Besides the explicitly mentioned packages, the R packages cowplot51, data.table52, dplyr53, ggplot254, itsadug55, purrr56, raster57, sf58, sfheaders59, tibble60 and tidyr61 were key for data handling, data analysis, and plotting. Posterior distributions were summarised through means and credible intervals (CIs). CIs are the highest density intervals, calculated through the package bayestestR62. To summarise multiple posterior distributions, 5000 Monte Carlo simulations were used.Study regionThe study included data from the whole of Switzerland. As an observation unit for records, we chose 1 × 1 km squares (henceforth squares). Switzerland covers 41,285 km2, spanning a large gradient in elevation, climate and land use. It can be divided into five coarse biogeographic regions based on floristic and faunistic distributions and on institutional borders of municipalities63 (Fig. 1b). The Jura is a mountainous but agricultural landscape in the northwest (~4200 km2, 300–1600 m asl; annual mean temperature: ~9.4 °C, annual precipitation: ~1100 mm (gridded climate data here and in the following from MeteoSwiss (https://www.meteoswiss.admin.ch), average 1980–2020, at sites ~500 m asl.)). The Jura is separated from the Alps by the Plateau, which is the lowland region spanning from the southwest to the northeast (~11,300 km2, 250–1400 m asl, mostly below 1000 m asl; ~9.5 °C, ~1100 mm). It is the most densely populated region with most intensive agricultural use. For the Alps, three regions can be distinguished. The Northern Alps (~10,700 km2, 350–4000 m asl; ~9.2 °C, ~1400 mm), which entail the area from the lower Prealps, which are rather densely populated and largely used agriculturally, up to the northern alpine mountain range. The Central Alps (~11,300 km2, 450–4600 m asl; ~9.5 °C, ~800 mm) comprise of the highest mountain ranges in Switzerland and the inner alpine valleys characterised by more continental climate (i.e., lower precipitation). Intensive agricultural land use is concentrated in the lower elevations and agriculture in higher elevations is mostly restricted to grassland areas used for summer grazing. The Southern Alps (~3800 km2, 200–3800 m asl; ~10.4 °C, 1700 mm) range from the southern alpine mountain range down to the lowest elevations of Switzerland and are clearly distinguished from the other regions climatically, as they are influenced by Mediterranean climate, resulting in, e.g., milder winters. Besides differences between biogeographic regions, climate, land use and changes therein vary greatly between different elevations (Supplementary Fig. S9). To account for these differences, we split the regions in two elevation classes at the level of squares. All squares with a mean elevation of less than 1000 m asl were assigned to the low elevation, whereas squares above 1000 m asl were assigned to the high elevation (no squares in the Plateau fell in the high elevation). This resulted in nine bioclimatic zones (Fig. 1b), for which separate species trends were estimated in the subsequent analyses. The threshold of 1000 m asl enabled a meaningful distinction based on the studied drivers (climate and land-use change) and was also determined by the availability of records data (high coverage in all nine bioclimatic zones).Species detection dataWe extracted records of butterflies (refers here to Papilionoidea as well as Zygaenidae moths), grasshoppers (refers here to all Orthoptera) and dragonflies (refers here to all Odonata) from the database curated by info fauna (The Swiss Faunistic Records Centre; metadata available from the GBIF database at https://doi.org/10.15468/atyl1j, https://doi.org/10.15468/bcthst, https://doi.org/10.15468/fcxtjg). This database unites faunistic records made in Switzerland from various sources including both records by private persons and from projects such as research projects, Red-List inventories or checks of revitalisation measures. Only records with a sufficient precision, both temporally (day of recording) and spatially (place of recording known to the precision of 1 km2 or less), were used for analyses. Besides temporal and spatial information, information on the observer and the project (if any) was obtained for each record. All records made by a person/project on a day in a square were attributed to one visit, which was later used as replication unit to model the observation process (see below).We included records from the focal time range 1980–2020. Additionally, we included records from 1970–1979 for butterflies in occupancy-detection models to increase the robustness of mean occupancy estimates. We excluded the mean occupancy estimates for these additional years from further analyses to cover the same period for all groups. Prior to analyses, following the approach in ref. 26, we excluded observations of non-adult stages and observations from squares that only were visited in 1 year of the studied period, because these would not contain any information on change between years64. This resulted in 18,018 squares (15,248 for butterflies, 9870 for grasshoppers, 5188 for dragonflies) and 1,448,134 records (879,207 butterflies, 272,863 grasshoppers, 296,064 dragonflies) that we included in the analyses (Supplementary Fig. S2). The three datasets for the different groups were treated separately for occupancy-detection modelling, following the same procedures for all three groups. To determine detections and non-detections for each species and visit, which could then be used for occupancy-detection modelling, we only included visits that (a) did not originate from a project, which had a restricted taxonomic focus not including the focal species, (b) were not below the 5% quantile or above the 95% quantile of the day of the year at which the focal species has been recorded26 and (c) were from a bioclimatic zone, from which the focal species was recorded at least once.Occupancy-detection modelsWe used occupancy-detection models65,66 to estimate annual mean occupancy of squares for the whole of Switzerland and for the nine bioclimatic zones for each species (i.e., mean number of squares occupied by a species), mostly following the approach in ref. 26. We fitted a separate model for each species, based on different datasets for the three groups. We included only species that were recorded in any square in at least 25% of all analysed years. Occupancy-detection models are hierarchical models in which two interconnected processes are modelled jointly, one of which describes occurrence probability (ecological process; used to infer mean occupancy), whereas the other describes detection probability (observation process)65. The two processes are modelled through logistic regression models. The occupancy model estimates occurrence probability for all square and year combinations, whereas the observation model estimates the probability that a species has been detected by an observer during a visit. More formally, each square i in the year t has the latent occupancy status zi,t, which may be either 1 (present) or 0 (absent). zi,t depends on the occurrence probability ψi,t as follows$${z}_{i,t}sim {{{mbox{Bern}}}}left({psi }_{i,t}right)$$
(1)
The occupancy status is linked to the detection/non-detection data yi,t,j at square i in year t at visit j as$${y}_{i,t,, j}{{|}}{z}_{i,t}sim {mathrm {Bern}}({z}_{i,t}{p}_{i,t,j})$$
(2)
where pi,t,j is the detection probability.The regression model for occurrence probability (occupancy model) looked as follows$${{mbox{logit}}}({psi }_{i,t})={mu }_{o}+{beta }_{o1}{{{{{rm{elevatio}}}}}}{{{{{{rm{n}}}}}}}_{i}+{beta }_{o2}{{{{{rm{elevatio}}}}}}{{{{{{rm{n}}}}}}}_{i}^{2}+{alpha }_{o1,i}+{alpha }_{o2,i}+{gamma }_{r(i),t}$$
(3)
with μo being the global intercept, elevationi being the scaled elevation above sea level and αo1,i, αo2,i and γr(i),t being the random effects for fine biogeographic region (12 levels, Supplementary Fig. S10; these were again defined based on floristic and faunistic distributions and followed institutional borders63), square and year. The random effects for fine biogeographic region and square were modelled as follows:$${alpha }_{o1}sim {{{{{rm{Normal}}}}}}left(0,{sigma }_{o1}right)$$
(4)
and$${alpha }_{o2}sim {{{{{rm{Normal}}}}}}left(0,{sigma }_{o2}right)$$
(5)
The random effect of the year was implemented with separate random walks per zone following ref. 67, which allowed the effect to vary between the nine bioclimatic zones, while accounting for dependencies among consecutive years. Conceptually, in random walks, the effect of 1 year is dependent on the previous year’s effect, resulting in trajectories with less sudden changes between consecutive years. This was implemented as follows:$${gamma }_{r,t}sim left{begin{array}{c}{{{{{rm{Normal}}}}}}left(0,{1.5}^{2}right){{{{rm{for}}}}},t=1\ {{{{{rm{Normal}}}}}}left({gamma }_{r,t-1},{sigma }_{gamma r}^{2}right){{{{rm{for}}}}},t , > ,1end{array}right.$$
(6)
with$${sigma }_{gamma r}sim {{mbox{Cauchy}}}left(0,1right)$$
(7)
The regression model for detection probability (observation model) looked as follows$${{{{rm{logit}}}}}({p}_{i,t,j}) =, {mu }_{d}+{beta }_{d1}{{{{{rm{yda}}}}}}{{{{{{rm{y}}}}}}}_{j}+{beta }_{d2}{{{{{rm{yda}}}}}}{{{{{{rm{y}}}}}}}_{j}^{2}+{beta }_{d3}{{{{{rm{shortlis}}}}}}{{{{{{rm{t}}}}}}}_{j}+{beta }_{d4}{{{{{rm{longlis}}}}}}{{{{{{rm{t}}}}}}}_{j} \ quad+ {beta }_{d5}{{{{{rm{exper}}}}}}{{{{{{rm{t}}}}}}}_{j}+{beta }_{d6}{{{{{rm{projec}}}}}}{{{{{{rm{t}}}}}}}_{j}+{beta }_{d7}{{{{{rm{targeted}}}}}}_{{{{{rm{projec}}}}}}{{{{{{rm{t}}}}}}}_{j} \ quad+ {beta }_{d8}{{{{{rm{redlis}}}}}}{{{{{{rm{t}}}}}}}_{j}+{alpha }_{d1,t}$$
(8)
where μd is the global intercept, ydayj is the scaled day of the year of visit j, shortlistj and longlistj are dummies of a three-level factor denoting the number of species recorded during the visit (1; 2–3; >3), and expertj, projectj, targeted_projectj and redlistj are dummies of a five-level factor denoting the source of the data. The source might either be a common naturalist observation (reference level), an observation by an expert naturalist, an observation made during a not further specified project, an observation made in a project targeted at the focal species or an observation made during a Red-List inventory. An expert naturalist was defined as an observer that contributed a significant number of records, which was defined as the upper 2.5% quantile of all observers arranged by their total number of records, and that made at least one visit with an exceptionally long species list, which was defined as a visit in the upper 2.5% quantile of all visits arranged by the number of records. The proportions of records originating from these different sources changed across years, but change was not unidirectional and differed among the investigated groups (Supplementary Fig. S11), such that accounting for data source in the model should suffice to yield reliable estimates of occupancy trends. αd1,t is a random effect for year, which was modelled as$${alpha }_{d1}sim {{{{{rm{Normal}}}}}}left(0,{sigma }_{d1}right)$$
(9)
The occupancy and observation models were fitted jointly in Stan through the interface CmdStanR68. Four Markov chain Monte Carlo chains with 2000 iterations each, including 1000 warm-up iterations, were used. Priors of the model parameters are specified in Supplementary Table S5. For the prior distribution of global intercepts, a standard deviation of 1.5 was chosen to not overweight the extreme values on the probability scale. To ensure that chains mixed well, Rhat statistics for annual mean occupancy estimates were calculated through the package rstan69. For Switzerland-wide annual estimates (n = 18,140), 98.0% of values met the standard threshold of 1.1 (99.9% of values More

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