Moving vectors to specified coordinates in QGIS?

Moving vectors to specified coordinates in QGIS?

I have a layer of vectors located around, say, (100, 100), and I want to move them to new coordinate like (1000, 1000).

How should I do it using QGIS?

Yep sure can. Like alexgleith said you can use the qgsaffine plugin (from the plugin installer)

The top of the first line is at 0,0 and the top of the second line is at 5,5. At the start the points are along 0 on the X.

Using the Affine plugin we can add 5 to all the X coordinates:

Then after they are all on the second line at X 5, Y 5:

If you want to move them manually, simply start editing, select the features you want to move and then select the move tool and shift them.

To move them by a particular x,y offset, you might be able to use the python plugin "qgsaffine".

I am not up to date with using the python console, but there is probably a solution there too.

The plugin in the accepted answer is not available anymore. The Affine Transformations is available and is very useful.

With this plugin you can create a formula. In the screenshot I shifted all cells with 17.396 (meters) to the north.

In QGIS 3.x there is no need for plugin to move vector shapes any more as there is routine called "Translate" in Toolbox -> Vector Geometry. You simply put offset distance for x and y axis and run it or, - as said above, if you prefer manual positioning, start edit layer, select all entities and move them with "Move feature" tool.

I find the "Numerical vertex edit" plugin very useful for specifying new coordinates for a point.

On an editable layer, use the tool to select a point, then you will be prompted to enter the new coordinates.

There is a plugin calledmoveon the code repository.

It moves shapes from point A and B and does that without complications.

It Move all selected objects from point to point with snap.

Multilevel and geo-statistical modeling of malaria risk in children of Burkina Faso

Previous research on determinants of malaria in Burkina Faso has largely focused on individual risk factors. Malaria risk, however, is also shaped by community, health system, and climatic/environmental characteristics. The aims of this study were: i) to identify such individual, household, community, and climatic/environmental risk factors for malaria in children under five years of age, and ii) to produce a parasitaemia risk map of Burkina Faso.


The 2010 Demographic and Health Survey (DHS) was the first in Burkina Faso that tested children for malaria parasitaemia. Multilevel and geo-statistical models were used to explore determinants of malaria using this nationally representative database.


Parasitaemia was collected from 6,102 children, of which 66.0% (95% confidence interval (CI): 64.0-68.0%) were positive for Plasmodium spp. Older children (>23 months) were more likely to be parasitaemic than younger ones, while children from wealthier households and whose mother had higher education were at a lower risk. At the community level, living in a district with a rate of attendance to health facilities lower than 2 visits per year was significantly associated with greater odds of being infected. Malaria prevalence was also associated with higher normalized difference vegetation index, lower average monthly rainfall, and lower population densities. Predicted malaria parasitaemia was spatially variable with locations falling within an 11%-92% prevalence range. The number of parasitaemic children was nonetheless concentrated in areas of high population density, albeit malaria risk was notably higher in the sparsely populated rural areas.


Malaria prevalence in Burkina Faso is considerably higher than in neighbouring countries. Our spatially-explicit population-based estimates of malaria risk and infected number of children could be used by local decision-makers to identify priority areas where control efforts should be enhanced.


The Culex pipiens complex includes two of the most widespread mosquito species in the world, Cx. pipiens L. (the northern house mosquito) in temperate climates and Cx. quinquefasciatus Say (the southern house mosquito) in tropical and subtropical climates. Although still under debate, two Palaearctic forms or biotypes are usually recognized within Cx. pipiens, the pipiens and the molestus forms [1, 2]. The two forms are morphologically very similar, nonetheless the two forms exhibit important behavioral and physiological differences, such as host choice and breeding sites [3, 4].

The mosquitoes of the Cx. pipiens complex are considered to be the primary vectors of the West Nile Virus (WNV) in Europe and North America [5, 6]. The cycle of infection involves birds and mosquitoes. In particular, migratory birds are instrumental in the introduction of the virus to temperate areas of Eurasia during spring migrations [7]. The mosquitoes belonging to the Cx. pipiens complex feed on both avian and mammalian hosts. This behavior assigns to mosquitoes of the complex the role of bridge-vector for the transmission of the WNV to birds and mammals, including humans [5, 8]. In Italy, after the first report in 1998, WNV became endemic in the North-East after 2008 [9, 10] and since then, in this area, it has been constantly detected in humans, in animals and in the vector mosquitoes [11]. In particular, the areas considered the most affected by WNV outbreak are the wet areas surrounding the Po River, in the Veneto and Emilia Romagna regions [12, 13]. Moreover, the recent finding of simultaneous circulation of two WNV lineages in this area confirms North-Eastern Italy as a high-risk area for WNV emergence [14].

This scenario has drawn attention to mosquitoes of the Cx. pipiens complex inhabiting the last part of the Po River. In this area, urban zones are mostly alternated with large cultivated areas, rice paddies and natural wetlands. Here, during summer, Cx. pipiens achieves high population densities, creating much annoyance. Despite its epidemiological relevance, population genetic data for the Cx. pipiens complex in this WNV outbreak area are still lacking. We consider it particularly important to understand the population structure and the distribution patterns of Cx. pipiens in a WNV epidemic area. Genetic variability in local Cx. pipiens populations could underlie biological differences in biting and flight behavior, and in spatial and temporal distribution, with effects on the epidemiological patterns of the WNV infection.

Up to date, some surveys about genetic variability for Cx. pipiens complex have been carried out in several European countries using allozymes and mitochondrial markers [15–17]. These studies showed a low genetic variability in most of the populations analyzed compared to the closely related species Cx. torrentium Martini, morphologically very similar to Cx. pipiens and occurring in sympatry with this species throughout Europe and some parts of Asia [1]. Other genetic studies focused on the sympatric occurrence of the molestus and pipiens forms, underlining the presence of asymmetric introgression between the two forms and its implications for the WNV transmission [18, 19].

In this study we inferred the genetic structure of the Cx. pipiens complex along the last part of the Po River using both mitochondrial and nuclear markers. In detail, we studied the genetic diversity of cytochrome c oxidase subunit 1 (COI) and the cytochrome c oxidase subunit 2 (COII) in the populations considered. The cytochrome c oxidase fragment has been frequently used to study mosquito species complexes as well as to compare phylogeographic patterns within closely related taxa e.g. [20–22] due to the high number of copies per cell of the mitochondrial genome, its maternal inheritance and its rare recombination rate. Moreover, in order to investigate the diversity of Culex mosquitoes and to identify possible cryptic taxa, we analyzed the acetylcholinesterase-2 gene (ace-2), a protein-coding gene often used for discrimination within the Culex complex species [21, 23, 24]. Finally, we assessed the possible effect of landscape composition on the genetic diversity. Landscape composition is known to influence mosquito density and diversity [25, 26] but, to our knowledge, studies associating genetic variability and landscape composition are still lacking for Cx. pipiens. This information could help to better identify landscape features that may affect the population structure of mosquitoes, mainly in a high-risk area for arbovirus outbreaks.


Most public health surveillance systems and laboratories rely on serological and molecular assays that were developed to detect specific pathogens. However, conventional laboratory assays are often ineffective at detecting all causative agents of disease. Studies have shown that 40% of gastroenteritis cases (Finkbeiner et al., 2008) and as many as 60% of encephalitis cases (Ambrose et al., 2011) went undetected by conventional laboratory testing. Pathogens can go undetected if they are novel or are not known to previously occur in an area. There are many examples of the emergence of novel pathogens or reemergence of known organisms in new places where the available surveillance systems were inadequate, such as occurred with outbreaks of H7N9 influenza (Gao et al., 2013), Middle East respiratory syndrome coronavirus (MERS-CoV) (van Boheemen et al., 2012 Kindler et al., 2013), and the severe acute respiratory syndrome (SARS) outbreak in 2003 (Wang and Jolly, 2004).

Conventional diagnostic tests used by most reference laboratories require culture, microscopy, serology, and polymerase chain reaction (PCR). Such tools are useful for pathogen detection but only if culture conditions, test sensitivity, and primers are compatible and suitable for the microbial target. Other molecular approaches can be used to capture a wider range of pathogenic species such as multiplex PCR that targets highly conserved DNA regions or multiplex assays that target many of the most common pathogens known to cause similar symptoms. However, it is worth noting that even when multiplex assays are used, pathogens not included in the multiplexing may go undetected. The use of 16S rDNA was first proposed by Woese and Fox (1977) and Woese et al. (1990) as a tool for the molecular identification and characterization of microorganisms. The 16S rDNA gene is highly conserved among prokaryotes and some parts of its sequence are hypervariable between species, which makes it an ideal marker for species identification and for understanding evolutionary relationships (Gill et al., 2006 Sogin et al., 2006 Dethlefsen et al., 2008 McInerney et al., 2008 Tringe and Hugenholtz, 2008 Sunagawa et al., 2009). Metagenomics allows for comparisons of genetic material from multiple samples. One of the most common metagenomic approaches is deep amplicon sequencing (DAS), which employs universal primer to amplify parts of the 16S rRNA gene from specimens. A major benefit of metagenomics is the simultaneous detection of all microorganisms in clinical samples without prior knowledge of their identities. In addition, metagenomics has the potential to detect rare and novel pathogens. Current surveillance assays are limited in their ability to detect the emergence of novel pathogens or ones not previously known to be present in a given region. Metagenomic approaches can fulfill such gaps by identifying unknown etiological agents and assisting in the development of a new test for pathogen detection (Miller et al., 2013 Mokili et al., 2013 Wan et al., 2013).

Metagenomic approaches are especially suitable for zoonotic diseases. It is estimated that more than 60% of human pathogens are of animal origin (Taylor et al., 2001). Rodents are major reservoirs that account for a wide range of emerging zoonotic diseases in humans and livestock (Jones et al., 2008 Meerburg et al., 2009). Co-infection of multiple pathogens within individual rodents is frequently observed and the interaction between pathogens can have significant effects (Cox, 2001). Such co-infections can cause rodents to be more or less susceptible to other microparasites (Tadin et al., 2012). Generally, multiple infections in wildlife can increase disease severity in a host (Lello et al., 2005), affecting the survival and reproduction of animal hosts (Davidar and Morton, 2006 Holmstad et al., 2008). Disease surveillance in rodents and other wildlife can provide important information for public health preparedness. Surveillance can also be used to measure biodiversity and disease emergence which are both directly linked to the stability of ecosystems (Keesing et al., 2010 Grogan et al., 2014). Metagenomic approaches combined with NGS can be powerful tools to disentangle complex patterns of pathogen transmission among ectoparasites, animal reservoirs, and humans. For example, NGS has been used to perform blood meal analysis to determine the wide-range of animals that vectors feed on and possible reservoirs (Alcaide et al., 2009). NGS has also been useful in finding unexpected pathogens not normally associated with particular vectors (Vayssier-Taussat et al., 2013) and has been used to show the genetic diversity of bacteria that are specific to certain animal hosts and vectors (Pierlé et al., 2014 Swei et al., 2015). Such information can be used to correlate infections in people with important vectors and reservoir hosts.

In this study, metagenomics and NGS technology were used to characterize human (patients with undifferentiated febrile illness (UFI)), reservoir host (rodents and small mammals), and ectoparasite (chiggers, ticks, fleas, and lice) populations for bacterial pathogens. All samples were collected from Nan province in northern Thailand. Since all samples were from the same sites, bacteria could be compared from different populations to determine potential vectors and reservoirs. Nan province is highly endemic for scrub typhus, caused by the agent Orientia tsutsugamushi, and one of the major goals of this study was to determine the etiology and transmission dynamics of scrub typhus in the area. Another goal was to identify other bacterial pathogens that were under-reported or not previously known from this region. NGS results were verified by conventional methods such as real-time PCR, PCR, and DNA sequencing to confirm the pathogenic potential of detected bacteria and to better characterize those important pathogens to the species level. In addition, serological tests were performed to determine the seroprevalence and the history of human exposure to the pathogens detected by the NGS approach. The in-depth characterization of bacteria performed in this study from humans, animal hosts, and ectoparasites allowed us to determine the transmission dynamics of pathogens and identify several new and previously unreported pathogens from this area.


Barombi Kotto, Cameroon serves as a reference location for assessing intervention strategies against Schistosoma haematobium.

As part of a pilot study, the whole community was treated with praziquantel, inclusive of pre-school-age children (PSAC) and their mothers. One year later, egg-patent infections were reassessed and water contact patterns of 12 pairs of PSAC and their mothers were measured with global positioning system (GPS) data loggers.

A substantial reduction in general infection prevalence, from 44.8% to 12.2%, was observed but certain PSAC and mothers continued to have egg-patent infections. Analysis of GPS data demonstrated similar water contact levels between the child and mother groups, although certain individuals were numerical outliers.

This study shows the potential of GPS data loggers to clarify the at-risk status of PSAC and mothers.

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4 Discussion

4.1 Adaptive genetic responses to climate

Growth in A. tridentata occurs primarily in the spring, while soil moisture levels are high (Germino & Reinhardt, 2014 Schlaepfer, Lauenroth, & Bradford, 2014 ), yet minimum temperatures are often physiologically limiting and even lethal (Brabec et al., 2016 ). Strong differences in avoidance and tolerance were evidence among seedlings of the subspecies:cytotype groups of A. tridentata related to their mortality and suggested the importance of freezing response as an axis for adaptive differentiation (Brabec et al., 2016 ). At the landscape level, selection pressures of different climates have potentially generated differences in phenology and growth strategies for A. tridentata. One hypothesis would be that interior regions, with a continental climate (greater summer–winter temperature extremes), support big sagebrush populations that have a growth strategy that confers resistance, tolerance, or avoidance of freezing temperatures through physiological mechanisms or deferment/delay of growth until after the spring frost period. In more moderate climates to the south and west, we further hypothesize that A. tridentata initiates growth earlier in spring to capture spring soil moisture, resulting in greater risk of exposure to freezing in the gardens we evaluated. Below, we discuss how results from our analyses support this hypothesis.

Results from the generalized linear mixed model indicated that source-population climate is a strong predictor of survival in A. tridentata. The final generalized linear mixed genecological model explained 44% of variation (conditional R 2 = .439). The genetic effects of the final model, climate variables TDIFF and SMRP along with plant subspecies:cytotype group, explained 17% of the variation (marginal R 2 = .171). Survival patterns follow a southwest to northeast gradient where, in general, populations from higher latitudes and lower longitudes support higher survival (Figure 1). These continental regions, such as Montana and Colorado, have colder winters and have higher survival. These populations appear adapted to low minimum winter temperatures and later springs. Lower survival occurred in plants from areas to the southwest with more moderated climates. Growth in these regions likely begins earlier in the spring to maximize water availability from snowmelt and has low probability of freezing due to milder winters. However, early growth in Ephraim, where there was exposure to late winter extreme low minimum temperatures and more prevalent spring frosts (Fig. S3), puts the plant at risk for freezing and cold-related drought (Lambrecht, Shattuck, & Loik, 2007 ).

Our results support previous research that also indicated the adaptive importance of minimum temperatures (Bower et al., 2014 Erickson, Mandel, & Sorensen, 2004 Horning et al., 2010 Johnson, Sorensen, Bradley St Clair, & Cronn, 2004 Richardson et al., 2014 St. Clair et al., 2013 ). Although A. tridentata is generally thought of as being cold tolerant, winterkill has been documented in natural populations (Walser et al., 1990 ) and common gardens of seedlings at relatively warm sites (Brabec et al., 2016 ). Other studies of A. tridentata also found differences in freeze tolerance associated with source location, such as greater freezing acclimation for seedlings from higher-elevation seed sources (Loik & Redar, 2003 ).

While we found differences in survival rate among plant subspecies:cytotype groups, these differences were highly influenced by source-population. Post hoc comparisons of our survival analysis found that only tetraploid vaseyana (V4x) was significantly different from other subspecies:cytotype groups. This may be attributable to source-population origins rather than true taxonomical differences, considering how many tetraploid vaseyana (V4x) were from the southwest United States (e.g., CAV3, CAV4, NVV3). Previous work has suggested that A. tridentata subspecies are adapted to different habitats (Mahalovich & McArthur, 2004 McArthur, 1994 Wang et al., 1997 ), which can often be defined on local spatial scales. However, our findings suggest that within subspecies, populations are principally adapted to clines in continentality (Table 2).

4.2 Microclimate variation influences survival

Survival of A. tridentata differed substantially between common-garden sites (Table 2, Fig. S2). These differences were most striking between Ephraim and Majors Flat, which are separated only by 8 km. Ephraim is located in a basin, whereas Majors Flat is located 415 m higher in the mountains. However, minimum temperatures were consistently lower at Ephraim (Table 1) as result of cold air drainage during the spring and summer and temperature inversions (i.e., “atmospheric decoupling” Daly, Conklin, & Unsworth, 2010 Schuster, Kirchner, Jakobi, & Menzel, 2013 ) during the winter. Daily minimum temperatures during the winter in Ephraim were an average of 2.7°C and 6.8°C less than Majors Flat and Orchard, respectively. These differences were most pronounced during subregional temperature inversion events (settling and trapping of cold air within meters to km above the earth surface) where minimum temperatures were at most 18.6°C and 28.5°C less at Ephraim than Majors Flat and Orchard, respectively (Fig. S3). Another climatic factor that may have impacted survival is low snow depth. A large winter precipitation gradient occurs between basins and ranges, with a larger accumulation of snow in the mountains rather than the basins. We hypothesize that low minimum temperatures and shallow snow depth in Ephraim impacted the low survival in the Ephraim garden, especially in subspecies vaseyana (20% survival). The current study design does not allow us to specifically test for the presence of microclimate adaptations we suggest that future work examine this. Differences in microclimate, namely freezing temperatures, have been known to be an important determinant in other desert species distribution (Franco & Nobel, 1989 Loik & Nobel, 1993 Shreve, 1911 ). Further, research in A. tridentata has found low survival in vaseyana in areas where a typically high snow pack was absent, yet high survival in areas with a snow cover present (Hanson, Johnson, & Wight, 1982 ). Further, Loik and Redar ( 2003 ) suggested that A. tridentata seedlings at higher elevations may have less exposure to freezing due to greater prevalence of snow. Consistent with this, Brabec et al. ( 2016 ) found that subspecies vaseyana had the least and wyomingensis had the greatest physiological avoidance and resistance to freezing among subspecies, and they proposed the seemingly ironic differences were due to greater insulating snow cover at higher elevation during winter and spring. These results suggest that selection for cold tolerance may relate to local-scale microclimate variation, which we propose to be an important topic for further investigation.

Many traits can affect fitness, influencing growth and fecundity, along with survival. This study demonstrates moving warm-adapted plants into cold climates decreases their survival. While there is little evidence for the inverse, low survival among cold-adapted plants in a warm climate, trade-offs in other fitness traits may exist. For example, seed yield data suggest that cold-adapted plants had reduced fecundity in warmer climates compared to warm-adapted plants (B.A. Richardson, unpublished data). Another possibility, which was beyond the scope of this experiment, is that cold-adapted plants may have lower establishment success in warmer climates compared to warm-adapted plants.

4.3 Applications

Future climate scenarios predict an increase in global mean temperatures and an increase in extreme weather patterns. As the climate that A. tridentata is adapted to is displaced, plants will need to migrate to new areas, a slow task for a sessile organism (Shaw & Etterson, 2012 ). The movement of plant species in response to rapid climatic warming will frequently be slower than phenotypic and adaptive genetic changes required to adjust to the novel climate. Due to population differentiation, the effects of climate change are likely to vary throughout a species range (Davis, Shaw, & Etterson, 2005 ). While locations subject to frequent cold air pooling are not likely to escape regionally increasing temperatures, they may act as microrefugia against the amplified temperature trends and variations (Dobrowski, 2011 ). Through genecological modeling of future climate scenarios, it is likely that as the climate changes, A. tridentata will not be genetically suited to the environment in which it currently grows, resulting in extirpation of some populations.

Movement of seed populations to areas outside of their adaptive breadth can have a negative impact on fitness (Hereford, 2009 ). Restoration of A. tridentata after fires or other disturbances has been a management priority over the past decade, yet success of these efforts has been varied (Arkle et al., 2014 Knutson et al., 2014 ). Planting A. tridentata seed outside their adaptive breadth could result in unsuccessful establishment and/or low fitness and provide an opportunity for invasive species encroachment leading to a loss of species diversity and ecosystem degradation (reviewed in Dumroese, Luna, Richardson, Kilkenny, & Runyon, 2015 ). Previous research has shown that sagebrush species/subspecies are adapted to different ecological niches such as elevation and soil type (McArthur, 1994 ). Moving sagebrush populations to different climatic or edaphic conditions is therefore not recommended (Mahalovich & McArthur, 2004 ). Our work is the first step to creating climate-based guidelines for the transfer of seed for restoration purposes in A. tridentata. Being able to map traits relating to climate across the landscape is one practical advantage of genecology, which is particularly useful in highly heterogeneous environments such as western North America. This research will be the first step to delineate seed transfer zones, guidelines to ensure that seed used for restoration is adapted to the site.


Phlebotomus (Transphlebotomus) mascittii Grassi, 1908 (Diptera: Psychodidae) has been found in several European countries. In Spain, sporadic records were reported in the early ’80s in Catalonia (Northeast Spain), and it was never detected again. Recent entomological surveys carried out between 2004 and 2020 revealed the presence of several specimens of P. mascittii in Spain. The species identification was confirmed by both morphological and molecular analyses. The analyzed specimens belonged to the haplotype (COI_2) defined by one polymorphic site compared to other European specimens. Phlebotomus mascittii was found in low population densities in rural areas associated with livestock farms and in an urban cemetery during the summer season. This study provides the first records of this species in various localities along the Cantabrian cornice (Northern Spain) and represents its westernmost observation in the Palearctic region. The implications of the finding of this uncommon species are discussed at different levels, with emphasis on its suspected role in the transmission of leishmaniosis.

Author summary

Assessing the impact of vector control programmes requires longitudinal measurements of the abundance of insect vectors within intervention areas. Such monitoring can be expensive, especially in the later stages of a successful program where numbers of vectors and cases of disease are low. Efficient monitoring involves a prior selection of monitoring sites that are easy to reach and produce rich information on vector abundance. Here, we used image classification and cost-distance algorithms to produce estimates of accessibility within Koboko district, Uganda, where vector control is contributing to the elimination of sleeping sickness, a neglected tropical disease (NTD). We combine an accessibility surface with pre-existing estimates of tsetse abundance and propose a stratified sampling approach to determine locations which are associated with low cost (lowest travel time) and potential for longitudinal data collection (high pre-intervention abundance). Our method could be adapted for use in the planning and monitoring of tsetse- and other vector-control programmes. By providing methods to ensure that vector control programmes operate at maximum efficiency, we can ensure that the limited funding associated with some of these NTDs has the largest impact.

Citation: Longbottom J, Krause A, Torr SJ, Stanton MC (2020) Quantifying geographic accessibility to improve efficiency of entomological monitoring. PLoS Negl Trop Dis 14(3): e0008096.

Editor: Guilherme L. Werneck, Universidade do Estado do Rio de Janeiro, BRAZIL

Received: June 19, 2019 Accepted: January 28, 2020 Published: March 23, 2020

Copyright: © 2020 Longbottom et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The friction surfaces created are provided as S1 File (3m resolution) and S2 File (0.5m resolution) via FigShare (10.6084/m9.figshare.11837019 and 10.6084/m9.figshare.11837070 respectively). Code used to generate and validate the resistance surfaces can be found as S3 File.

Funding: JL is funded by a Medical Research Council Scholarship (Award no. 1964851). SJT is funded by grants from the Bill & Melinda Gates Foundation (OPP1104516), the Biotechnology and Biological Sciences Research Council, the Department for International Development, The Economic and Social Science Research Council, The Natural Environment Research Council and the Defence, Science and Technology Laboratory, under the Zoonosis and Emerging and Livestock Systems (ZELS) programme (Grant no. BB/L019035/1). The Bill & Melinda Gates Foundation grant OPP1104516 also supports MCS. MCS acknowledges additional funding from the Medical Research Council (MR/M014975/1). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Additional File 1:

Sample metadata including list of discarded contaminants.

Additional File 2:

Methodological Supplement for 18S rRNA gene blocking primer design and validation

Additional File 3:

Results of ordination analyses based on 'basic' and 'decontam' datasets.

Additional File 4:

Sample set phylogenetic background inferred from coxB sequences.

Additional File 5:

Microbiome ontogenetic shift in other Triatoma sp.

Additional File 6:

Significant difference in beta dispersion of the instar range groups (L1-L3 and L4-L6) calculated from the ultraclean dataset.

Additional File 7:

NMDS analyses of T. rubida microbiomes from early instar (L1-L3) individuals found in different N. albigula nests at UADS.

Additional File 8:

Bartonella phylogenetic analysis of gltA sequences retrieved from T. rubida.

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