Relationships with place provide critical context for characterizing biocultural diversity. Yet, genetic and genomic studies are rarely informed by Indigenous or local knowledge, processes, and practices, including the movement of culturally significant species. Here, we show how place‐based knowledge can better reveal the biocultural complexities of genetic or genomic data derived from culturally significant species. As a case study, we focus on culturally significant southern freshwater kōura (crayfish) in Aotearoa me Te Waipounamu . Our results, based on genotyping‐by‐sequencing markers, reveal strong population genetic structure along with signatures of population admixture in 19 genetically depauperate populations across the east coast of Te Waipounamu. Environment association and differentiation analyses for local adaptation also indicate a role for hydroclimatic variables—including temperature, precipitation, and water flow regimes—in shaping local adaptation in kōura. Through trusted partnerships between community and researchers, weaving genomic markers with place‐based knowledge has both provided invaluable context for the interpretation of data and created opportunities to reconnect people and place. We envisage such trusted partnerships guiding future genomic research for culturally significant species in Aotearoa NZ and beyond. We, the authorship team, give thanks to the past, present, and future generations of Kāi Tahu, Kāti Mamoe, and Waitaha whānui  and local communities that have guided the conception and writing of this manuscript. Our authorship includes Kāi Tahu researchers and tākata tiaki (guardians) Steph Blair , Brendan Flack , Riki Parata , Makarini Rupene , Paulette Tamati‐Elliffe , Te Atiawa, Ngāti Mutunga), and Dr Matthew Wylie . Roger Moraga, of Spain, is a research bioinformatician. The remaining authors are Pākehā  researchers and practitioners: Dr Aisling Rayne and John Hollows are New Zealand Pākehā; Matthew Dale is Australian Pākehā from Gunaikurnai Country; Dr Priscilla Wehi is New Zealand Pākehā with familial affiliations to Waikato‐Tainui and Ngāpuhi; and Dr Tammy Steeves is Canadian Pākehā. Our collective expertise includes aquaculture, bioinformatics, customary and contemporary mahika kai , conservation genomics, fish biology, ecology, and te reo Māori (Māori language).k in place of the northern ng (underlined in text) unless deemed inappropriate . This reflects local pronunciation and does not necessarily change the meaning of the word . For non‐English words or phrases, definitions are provided in brackets at their first mention. A brief glossary for frequently used Māori terms is also provided in Appendix Herein, we use the Kāi Tahu dialect 1translocation) of plants and animals  systems  is informing how populations are prioritized for conservation , we still consider it useful to explore how characterizing adaptive variation could support conservation decisions, especially for fragmented species facing rapid environmental change  and tī kōuka  were planted from selected stock and were actively managed to optimize their production. Examples of these managed plantings can still be seen in the contemporary landscape.Shellfish beds were seeded with superior strains taken from other areas, and established beds were both enhanced and depleted by biological methods. Stands of trees such as karaka  through common garden experiments involving local weavers, teachers, and students. Beyond their extensive ecological and cultural knowledge of these species, participants “provided land for the plantings, assisted in maintenance and measurement, and fostered extension and educational use of the plantings”  Reciprocally enhanced understanding of complex biocultural histories through genomics and/or place‐based knowledge, including the co‐production of Amid growing initiatives to realize Indigenous rights and interests in genomic resources including data derived from culturally significant species  sought to dispossess Kāi Tahu and other tribal groups of their land, language, and practices. In turn, Pākehā and other non‐Māori brought their own aspirations and ways of managing the environment that continue to shape Aotearoa NZ’s land and waterscapes today.In Te Waipounamu, the South Island of Aotearoa NZ, the whakapapa of many treasured species and places is interwoven with the processes and practices of mahiParanephrops zealandicus , papatipu rūnaka , practitioners, and researchers through place‐based approaches. Namely, there is growing interest in better understanding the whakapapa of populations—especially in relation to ancestral pathways—to enhance environmental well‐being and to reconnect people and place, including through the revitalization of sustainable harvest.There are presently few published resources for kōura beyond the KEEWAI farming manual Hollows, , a handfExamine genome‐wide diversity in kōura informed by place‐based knowledge;Assess the benefits and challenges of characterizing local adaptation in kōura; andKāi Tahu region to meet mahika kai aspirations, including enhanced biocultural resilience.Consider how these data can inform the management of kōura in the Below, we present genotyping‐by‐sequencing data for 19 kōura populations across Te Waipounamu and the ways in which we are weaving these data with place‐based knowledge. In this study, we focus on contemporary translocations and population genetic structure to provide a foundation for future research that will incorporate more comprehensive genomic sampling and place‐based knowledge. We also use redundancy analysis (RDA) to assess whether reduced representation genomic markers are useful for characterizing local adaptation in kōura. Specifically, we aimed to:2We preface this study by acknowledging that requirements and processes for ethical research engagement vary across communities, institutions, and contexts. At the time of this study, there was no formal requirement nor procedure for researchers working with culturally significant species to engage with Māori beyond the responsibilities articulated in Te Tiriti o Waitangi (1840)—which guarantees Māori self‐determination, including sovereignty over their treasured possessions, culture, and species—and the WAI 262 Waitangi Tribunal claim (1991) and subsequent Ko Aotearoa Tēnei report , and KEEWAI. In general, these partnerships began with a kanohi‐ki‐te‐kanohi (‘face‐to‐face”) conversation. For example, the inclusion of a Murihiku site originated through a serendipitous meeting between co‐authors Aisling Rayne and Matthew Dale at a Wairewa cultural monitoring wānaka (learning forum) for tuna (eel Anguilla spp). This meeting led to subsequent conversations, including eventual introductions between Rayne and Murihiku whānau. A year later, co‐authors Dale and Steph Blair identified an opportunity for Rayne to attend a Rangatahi Tumeke camp  come together to reconnect with mahika kai.Our research narrative begins with a dialogue between researchers at the University of Canterbury and p Figure , where cBefore, during, and after sampling, the benefits and risks of examining genetic diversity in kōura were discussed among researchers, practitioners, and tribal representatives. We discussed how this work, together with oral histories of translocation, could help whānau and local communities navigate conservation policy and regulations . It was also critical to ensure that opportunities and risks related to generating, storing, and accessing culturally significant genomic data were understood by all partners, including options for data storage that extend beyond open access.2.2Kāi Tahu across three main regions: Waitaha, Ōtākou, and Murihiku. GPS coordinates are not presented here given the cultural significance of sampling sites. Most sites were identified by tākata tiaki (guardians) or local whānau and, where possible, we involved rakatahi (Māori youth) in sampling. Kōura were only retrieved from four sites in Waitaha due to their increasingly sparse distribution and from six sites in Murihiku where relationships were developed relatively late in the study.Through a combination of place‐based knowledge, grey literature, and publicly available distribution records, we identified and sampled 19 sites in the territory of ka kai (customary food gathering expert) for Ngāi Tūāhuriri. Here, baited whakaweku were set and left for several weeks. When the maramataka  was immediately euthanized by placement in a −80°C bio‐freeze container (Bio‐Bottle™) as per Animal Ethics Committee approval (REF: 2018/04R). High‐molecular‐weight genomic DNA was extracted from tail muscle tissue at the University of Canterbury using the MagJET Magnetic Bead‐Based Nucleic Acid Purification kit (Thermo Fisher Scientific) as per protocol.To assemble a Three libraries of 2x 150 bp reads were prepared at the Institute of Clinical Molecular Biology (Kiel University) and sequenced with the Illumina HiSeq 4000. FastQC v. 0.11.8 Andrews,  was usedProcambarus fallax f. virginalis, which was under 2 Kb.Due to its large size, low heterozygosity, and large number of repeats, MaSuRCA v. 3.2.9 was selected as the assembler for constructing the reference genome. Default parameters for non‐bacterial Illumina assemblies were used except for a k‐mer count threshold of 2.0 (due to high coverage), insert size for pair‐end reads of 350 and standard deviation of 80, and an expected ploidy of 1.0 due to extremely low heterozygosity. Raw data were supplied without trimming, as MaSuRCA includes its own error correction module for which no trimming is generally recommended. Reads with median Phred score 20 or lower were, however, filtered. The resulting assembly had a total of 1,353,458 contigs, with an N50 of 2.8 Kb, and a total assembly length of 2113 Mb. Despite the high level of fragmentation, this compared favorably with the N50 of the best decapod genome available at the time, 2.4Numerous methods were trialed for extracting genomic DNA from kōura pleopods, which are small, tough, and rich in pigments  which used Trim Galore v0.6.6  and Cutadapt v.3.1 . The population program was run with parameters ‐p 1 and ‐r 0.1 . Loci produced from the same restriction enzyme cut site were also merged (‐‐merge‐sites).Stacks v2.54 was used to discover and genotype SNPs using the 2r set to 0.6 and a window size of 1000 sites. Finally, individuals with greater than 10% missing data were removed to minimize bias when assessing genetic diversity and population genetic structure  along traditional pathways, often seeding new populations or augmenting existing ones as part of tribal economies and to provide sustenance on the way  travelled widely, following seasonal food sources around Te Waipounamu, hunting and gathering animals, plants and marine life. (Tamati‐Elliffe)Place‐based knowledge of kōura enabled (Hollows)A forester told me about a night where he released buckets of Central Otago kōura into the North Otago coastal Herbert Forest ponds … I also spoke to an angler [fisherman] with a bucket of kōura who was intending to release them in every creek between Cromwell and Queenstown—there is a population in Arrowtown that is in a really odd location! Past glacial action would have removed species from that region Ferrar, , and kōuPost‐European arrival, miners and contemporary hunters from local communities also translocated kōura, including into isolated catchment heads or fire ponds to be retrieved for food on the way home.Kāi Tahu knowledge of, and relationships with, mahika kai persist, colonization has eroded many details around pre‐European translocation of southern kōura. However, some recent translocations are more readily recounted. Murihiku populations M1 and M6 were translocated from surrounding forest streams in 2012 and 2008, respectively, and selectively harvested since then to enhance growth rates . Kōura in lake W1—which lies adjacent to traditional travel routes and is a popular site for catching trout today—were translocated from elsewhere .While 2.6HIERFSTAT  were implemented in diveRsity v1.9.89 , we used a range of genetic diversity and population genetic differentiation measures to provide greater confidence, especially for future management decisions  were adapted from Brauer et al. (p > 0.001) environmental predictors using the packfor R package  to correct for multiple testing . Within each population, expected heterozygosity per population (SH) was significantly lower than observed , but heterozygote excess within populations (mean ISF = –0.063 ± 0.025). Population genetic diversity generally decreased from Murihiku northward to Ōtākou and then Waitaha (Table α = 1.067) and highest expected heterozygosity (OH = 0.067), while M4 contained the highest number of private alleles. Sites around Waitaha represented the least genetically diverse populations, the lowest of these being W4 (SH = 0.018), followed closely by W1 and W2.The final dataset included 3188 SNPs for 159 individuals sampled across 19 sites Table . The mea3.2STF indicated strong and significant genetic structure across the entire distribution . Pairwise STF, which ranged from 0.07 to 0.76, also indicated strong and significant population genetic structure for all pairwise comparisons  estimate of s Figure . STRUCTUs Figure , corresp estimates Figure . Given ts Figure . PCA alsn = 25 nloci = 531; maximal change in mean LnP(K) at K = 2; Figure n = 76, nloci = 1255; maximal change in mean LnP(K) at K = 2; Figure n = 46, nloci = 1942; maximal change in LnP(K) at K = 2; Figure ST within regions  and population genetic structure (Section 3.2) identified three populations  and three further individuals  known or suspected to be translocated or descended from translocated individuals. In line with Hollows (pers. comm.), genetic clustering and admixture inferred from STRUCTURE and PCA suggest that there were multiple translocations of kōura into W3 from sites in northern Ōtākou and Taiari catchments, several hundred kilometers away. The genomic markers also showed admixture within M1, and later discussion with co‐author John Hollows revealed that kōura from M6 had been introduced during the population's recent history. The history of admixed population M3, located at a popular camping and walking site, is not yet known , as are the details around genetic introgression from Ōtākou in two individuals from W2 and M5, respectively.p < 0.001) and indicated that the retained environmental predictors collectively explained 8.71% (adjusted R2 = 6.82%) of the total genetic variation after accounting for population structure, which explained 47.3% of the total variation in the model. All five constrained RDA axes were significant (p < 0.001), although most of the constrained variations  were accounted for in the first (33.0%) and second (25.5%) axes. The first axis was primarily driven by precipitation and temperature seasonality, whereas the second was strongly associated with the mean annual flow and flow seasonality  with these first two axes  loci, we used PCA to reassess population structure using the 353 SNPs identified by RDA. These putatively adaptive SNPs identified similar groupings to genome‐wide loci but notably drew out W4, O5, and O3 as distinct groups and further separated O1 and O2 from the other Ōtākou populations Figure . M2 and 4In this study, trusted partnerships between researchers and IPLC guided the framing of research, sampling co‐design, and interpretation of genomic markers. Place‐based knowledge was critical to enabling respectful sample collection, including through the identification of sites—particularly those not recorded in public databases—and selection of appropriate sampling methods (see Section 2.2). Discussions around past translocations (Section 2.5) also provided important biocultural context for interpreting population genetic structure, which allowed us to confidently adjust our analyses of local adaptation. Most importantly, our partnerships provided opportunities to reconnect with the environment and grow expertise among researchers, practitioners, and whānau, particularly youth. Our experiences also highlight the importance of prioritizing relationships over conventional academic timelines, especially when co‐developing genomic research for widely distributed species. It takes time to bring people together, to build trust, and for communities to decide whether and how to co‐produce and/or share knowledge. Moreover, many practitioners and whānau freely gave their time, knowledge, and energy to this study. Funding has since been secured for tribal‐led programs that include resourcing for relationship building and IPLC expertise (see Section 4.2).4.1Kāi Tahu. Global heterozygosity was significantly lower than expected under Hardy–Weinberg equilibrium, as anticipated given the widespread decline and fragmented distribution of kōura. Within populations, higher than expected heterozygosity and absence of inbreeding by relatedness  reflect patterns of global genetic erosion. In this scenario, the absence of large, outbred populations can distort allele frequencies and lead to erroneous signatures consistent with outbreeding  within highly related populations . Strong population genetic structure indicated by PCA, STRUCTURE, and ubiquitously significant global and pairwise STF lend further support toward low dispersal capacity and fragmentation in kōura. In Waitaha, particularly sparsely distributed and genetically depauperate populations (excluding site W3) may also reflect contemporary land‐use changes  genome, we anticipate future opportunities to explore signatures of local adaptation that incorporate transcriptomic, phenotypic, and experimental data. In the interim, the “holistic” nature of ordination analyses provide a useful starting point for understanding local adaptation in kōura  that better reflects waterscape heterogeneity  or in rapidly changing environments . Our long‐term vision is to build intergenerational capacity to restore the health of the environment, grow future generations as leaders and researchers, and revitalize sustainable harvest of kōura and other mahika kai species.To date, the research team has started conversations to co‐develop more comprehensive sampling and translocation strategies that support diverse aspirations for people and the environment  and the FISHES project  in northern Canada highlight opportunities to grow Indigenous leadership and address socio‐economic challenges. We look forward to seeing diverse Indigenous and local worldviews, expertise, and aspirations centered in future genomic research for culturally significant species.By providing a meeting place for place‐based knowledge outside “black‐box” academia, co‐created research can further draw attention to researcher positionality. Few Western‐trained researchers in ecology and evolution disclose the people and places they are connected to, or the “h Smith, . We are h Smith, . FinallyThe authors declare no conflict of interest.Supplementary MaterialClick here for additional data file.


Sitobion avenae (Fabricius), Rhopalosiphum padi (Linnaeus), Schizaphis graminum (Rondani), and Metopolophium dirhodum —and for a given region, these four aphid species differ in dominance with changes in cultivation practices and climate. Therefore, it is necessary to evaluate the effects of imidacloprid seed treatments on the four different aphid species. In experiments in the laboratory, imidacloprid seed treatments significantly reduced the survival rates of S. avenae, R. padi, and S. graminum to 57.33 ± 2.86%, 12.67 ± 1.92%, and 20.66 ± 2.33%, respectively, but for M. dirhodum, there was no significant difference between the control (96.33 ± 1.08%) and the treatment (97.00 ± 0.98%). The fecundities of the four aphid species were much reduced, especially for R. padi when feeding on treated wheat plants. For the field survey, only three aphid species were considered because the density of S. graminum was too low to be analyzed. The effects of imidacloprid seed treatment on the three aphid species in the field were consistent with the laboratory results. Imidacloprid seed treatment reduced the population sizes of S. avenae and R. padi at rates of 70.30 ± 3.15% and 87.62 ± 2.28%, respectively, for the whole wheat season in the field. For M. dirhodum, imidacloprid seed treatments were less effective, and the densities of M. dirhodum increased on four sample days. From this study, we confirmed that the effect of imidacloprid seed treatment varied with the composition of aphid species, being especially less effective for M. dirhodum.Imidacloprid seed treatments are effective at reducing the cohorts of many insect pests on crops such as cotton, corn, and cereals. The effects of imidacloprid seed treatments depend on the aphid species. In China, there are four wheat aphid species— Triticum aestivum L.) is one of the most important cereal crops and covers about 24 million ha in China, with a total output of about 135 million tons. Throughout its development, wheat is subject to various forms of damage by insect pests, including wheat aphids, wheat midges, and soil pests, which threaten wheat yield and quality. In China, aphids were not the main insect pests of wheat before the 1970s [Sitobion avenae (Fabricius), Rhopalosiphum padi (Linnaeus), Schizaphis graminum (Rondani), and Metopolophium dirhodum . In different regions, the dominant species differ. S. avenae is widespread in China and becomes dominant during the booting stage. R. padi is vigorous in humid regions. S. graminum has been recorded in most provinces of China but does heavy damage only in northern and northwestern China, where drought is common. M. dirhodum is relatively endemic and has been recorded in the middle and north of China, with heavy infestations in some provinces [Wheat  [Wheat aphid control has been primarily dependent on foliar insecticides such as dimethoate, chlorpyrifos, pirimicarb, imidacloprid, and cyhalothrin since the 1990s ,15. The frit L.) ,27. Imidfrit L.) ,19,28. Ifrit L.) . Becausefrit L.) . To datefrit L.) ,21, and M. dirhodum (M. dirhodum on the treated seedlings (97.00 ± 0.98%) was slightly higher than that on the untreated seedlings (96.33 ± 1.08%), but the difference was not significant by paired bootstrap test. Survival rates on the other treated seedlings were otherwise significantly different from those on untreated seedlings, and R. padi had the lowest survival (12.67 ± 1.92%).Wheat aphids were placed on wheat blades on the thirtieth day after germination, and 5 days later, the survival rates of all aphid species feeding on untreated wheat tillers were significantly higher than those on treated wheat tillers, except for dirhodum . The surR. padi had the highest fecundity, and S. graminum had the second-highest fecundity: one adult of each species could lay 7.97 ± 0.187 offspring and 7.27 ± 0.11 offspring, respectively, in five days. S. avenae and M. dirhodum had similar lower fecundity on control plots and produced 6.86 ± 0.09 offspring and 6.86 ± 0.09 offspring, respectively. Imidacloprid seed treatment was able to reduce the fecundity of the four aphid species significantly compared with control plots (R. padi produced no offspring in five days in the test. M. dirhodum had the highest fecundity (4.17 ± 0.10)—significantly more than that of S. avenae (0.15 ± 0.00) and S. graminum (0.05 ± 0.01).Fecundity varied with the aphid species on control plots and was significantly different across the species . R. padiol plots . R. padiS. avenae, R. padi, S. graminum, and M. dirhodum—but only three aphid species were considered because the density of S. graminum was too low to be analyzed. The population sizes of wheat aphids reached peaks around the middle of May every year during the wheat grouting stage  and M. dirhodum  were much larger than those in 2015 (112.16 ± 18.18 and 50.17 ± 15.77). During the peak periods, imidacloprid seed treatments could also significantly reduce the total aphid population by mainly reducing S. avenae and R. padi, at rates of 55.13~77.67% and 82.86~97.16%, respectively. For M. dirhodum, the effect of imidacloprid seed treatments was not significant on most survey days, except on the 23rd of April; unexpectedly, imidacloprid seed treatments increased M. dirhodum’s density on the 22nd of May and the 3rd of June, when the population sizes in the treated plots were higher than those in the control plots —especially in the heavy occurrence year of 2017. In 2016, the density of aphids in treated plots was distinct from the control measurement, with the highest density of 372.83 ± 65.34 aphids on the 11th of May, which was below the control level of wheat aphids in China. Unfortunately, imidacloprid seed treatments failed to reduce the density of M. dirhodum in any of the tested years. During the peak periods of all examined years, the population densities of M. dirhodum were increased on four sample days.Imidacloprid seed treatments had significant control effects on Imidacloprid seed treatment can protect young plants from attack by insect pests and is also better for environmental safety than seed treatments with other pesticides ,20. BecaS. graminum, S. avenae, and R. padi  abundance up to 79 days after treatment [S. avenae and R. padi from 2015 to 2017, and this effectiveness lasted for the whole wheat season. However, for M. dirhodum, the densities in treated plots were somehow higher than those in control plots. In previous reports, some increases were noted for Ostrinia nubilalis on treated plots of maize due to the moth preferring to lay eggs on the well-growing plants in the treated field [M. dirhodum has shown higher tolerance to most insecticides compared with the other aphid species [S. avenae especially prefer the wheat ear, flag blades, sticks, and aggregates in the middle and upper parts of plants [M. dirhodum prefers the lower parts of wheat plants. That would result in two impacts: a lower location would protect these aphids from being caught by natural enemies, and the control of S. avenae and R. padi would lead to weak interspecific competition and would broaden the ecological niche of M. dirhodum. This could result in an increase or lesser reduction of M. dirhodum in treated plots.Previous studies showed that in field tests, seed treatment at an imidacloprid rate of 2.5 g/kg effectively reduced greenbug , Hebei province, 2010, by removing natural enemies over many generations. Thereafter, colonies of aphids were raised in the laboratory at the Institute of Plant Protection  for bioassays. All lab aphid clones were maintained on wheat tillers in an environmental chamber at 20 ± 1 °C, at 60 ± 10% RH, and with a photoperiod of 16:8 h (light:dark).Laboratory colonies of wheat aphids comprising Wheat seeds (Lunxuan 987) were purchased from the Fengda Seeds Co., Ltd. .−1) diluted in 10 mL of water was used to treat each kg of seed. This low rate is the widely used recommend dose for wheat planting in China [Seed dressing was carried out by the Beijing branch of the Bayer company for laboratory and field experiments each year, and 2 mL of the formulated product  were nipped on the leaves [Ten dressed seeds or untreated seeds were planted in each plastic flowerpot . When wheat tillers had 3~5 blades, transparent polyethylene ecological boxes , Hebei province . Here, wheat cultivation has a long history, and a previous study confirmed large populations of wheat aphids, including dirhodum . DressedR. padi, S. avenae, and M. dirhodum, were counted by visually searching 100 tillers of each sampling in six plots when the aphid density was less than 200 aphids per 100 tillers and searching 20 tillers of each sampling in six plots when the aphid density was more than 200 aphids per 100 tillers [From the reviving period, surveys in the treated and control plots were conducted approximately weekly beginning on 15 April 2015, 17 April 2016, and 4 April 2017 and continuing until the middle maturity stage. Ten samplings were conducted uniformly in each plot. On each sampling day, the numbers of winged and wingless individuals of different species, including  tillers . When ithttp://140.120.197.173/Ecology/prod02.htm, accessed 14 November 2022) in this study with 100,000 resamplings to estimate the standard errors of the survival rate and fecundity of each treatment and the control, following references [A bootstrap technique was conducted using TWOSEX-MSChart (downloaded from the website ferences ,45,46. TS. avenae and R. padi but not that of M. dirhodum in laboratory tests and field surveys. There is a major risk of pest damage in areas where M. dirhodum occur, because our results showed a lesser control effect of imidacloprid seed treatment on M. dirhodum.Seed treatment with imidacloprid is popular in large wheat production regions in China. This study reconfirmed that imidacloprid seed treatments are very effective, but the effectiveness varies with the composition of aphid species. Imidacloprid seed treatment reduced the populations of


Sousa chinensis) is a vulnerable marine mammal species that inhabits shallow, coastal waters from Southeast China, southward throughout Southeast Asia, and westward around the Bay of Bengal to eastern India. Polymorphic microsatellites are useful for elucidating ecological and population genetics‐related questions. Here, 18 new polymorphic microsatellites were developed from S. chinensis genomic DNA by Illumina MiSeq sequencing. Population genetic analyses were conducted on 42 S. chinensis individuals from three geographic locations, including the Xiamen Bay of China, the Western Gulf of Thailand, and Andaman Sea. Our microsatellite data revealed a strong and significant population structure among the three sampling regions . Pairwise mutual information index also demonstrated high levels of genetic differentiation between different region pairs . Moreover, Structure analysis inferred three genetic clusters, with the high assignment probabilities of 95.92%, 99.47%, and 99.68%, respectively. Principal coordinate analysis plots of individuals divided entire genotypes into three clusters, indicating high level of genetic differentiation. Our results indicated the strong genetic structure in S. chinensis populations is a result of geographic distances. Other factors such as environmental variables, anthropogenic interference, and social behavior may also have contributed to population differentiation.The Indo‐Pacific humpback dolphin ( Sousa chinensis) is a vulnerable marine mammal species that inhabits shallow, coastal waters. Here, 18 polymorphic loci were used for genetic analysis of S. chinensis individuals from three geographic locations, including the Xiamen Bay of China, the Western Gulf of Thailand, and Andaman Sea. Our microsatellite data reveal a strong and significant population structure among the three sampling regions . Structure analysis and principal coordinate analysis plots of individuals divided entire genotypes into three clusters, indicating high level of genetic differentiation.The Indo‐Pacific humpback dolphin ( The relevant CITES Permits (No. 2018CN/IC000475/CH) and HS Code (4103909010) were obtained for import of samples. All samples were collected from dead, stranded individuals.2.2S. chinensis were collected from 2010 to 2018, and included 15 individuals obtained along the coast of Xiamen in China (XM), 19 individuals collected from the Western Gulf of Thailand (WG), and 21 individuals collected along the Andaman Sea coast (AS)  Figure . Genomic2.3Purified genomic DNA was quantified by TBS‐380 fluorometer (Turner BioSystems Inc.). High‐quality DNA  of a single individual collected at XM was used to generate an enriched library and sequenced on the Illumina MiSeq platform by Majorbio Bio‐Pharm Technology Co., Ltd. The detailed procedures were as follows. First, the DNA sample was sheared into 400–500‐bp fragments using a Covaris M220 Focused Acoustic Shearer following the manufacturer’s protocol. Then, an Illumina sequencing library was prepared from the sheared fragments using the NEXTflex™ Rapid DNA‐Seq Kit. Briefly, 5′ ends were first end‐repaired and phosphorylated. Next, the 3′ ends were A‐tailed and ligated to the sequencing adapters. The third step was to enrich the adapter‐ligated products using PCR. Finally, the prepared library was used for paired‐end Illumina sequencing (2 × 150 bp) on an Illumina HiSeq X Ten machine. After filtering low quality and duplicated sequences and removing adapter‐related reads, a total of 2,174,959 clean reads were assembled using SOAPdenovo version 2.04 software . The total PCR volume 20 μl) consisted of approximately 50 ng of genomic DNA, 1×Multiplex PCR Kit (Takara), 0.2 μM of each primer (forward and reverse), and ddH0 μl cons2.5p‐value‐based multiple testing of Bonferroni sequential correction was performed with Myriads version 1.1  based on genotyping data generated from the six multiplex PCR panels. The length of the burn‐in period was set to 105 iterations, followed by 106 in the number of Markov Chain Monte Carlo iterations. The LOCPRIOR model was chosen to infer possible weak population structure with the assistance of sample group information. The number of inferred K was set between 1 and 10, and 20 independent replicates were run for each K value. Subsequently, the Structure Harvester version 0.6.94  was performed in GenAIEx based on the standardized covariance of the individual‐by‐individual genetic distance matrix. Mantel analysis  were discarded because of poor amplification success. For the remaining 42 individuals, no contamination was detected during multiple PCRs, and no genotyping errors were observed when randomly retesting the six individuals. Finally, genotypes of 18 microsatellites for 42 individuals were used in this genetic study, including 10 individuals from XM, 16 from WG, and 16 from AS. There was no evidence of allelic dropouts or scoring errors due to stuttering for any of the 18 loci in all the three sampling locations. Allele frequency distributions of different sampling regions based on 18 polymorphic loci are graphically represented in Figure 3.2F2, 51 = 25.372, p < .001). There were also significant differences among eNa , I  and Ho  values. Additionally, significant differences were found among He  and uHe  from any of the three geographic regions. The results revealed that the levels of genetic diversity detected for S. chinensis in XM were the lowest.Genetic diversity estimates of Na, eNa, I, Ho, He, and uHe values for each locus and each region are summarized in Table 3.3p > .05) in XM. Six loci  were monomorphic. All the 18 loci showed polymorphism in WG and AS. One locus (Sch5373) in AS and four loci  in WG showed significant heterozygosity deficits after sequential Bonferroni correction  among the three geographic regions. The pairwise sHua ranged from 0.272 to 0.339, which also demonstrated high levels of genetic differentiation between different region pairs (p < .001). Estimates of pairwise FST and sHua values are presented in Table S. chinensis individuals are summarized in Table Genetic differentiation among different sampled locations was estimated by comparing pairwise K at K = 3  was apparent, with the high assignment probabilities of 95.92%, 99.47%, and 99.68%, respectively. No possible partitions within cluster were detected. PCoA plots of individuals based on standardized covariance of the genetic distance matrix also divided all genotypes into three clusters, indicating a high level of genetic differentiation  between the individual‐by‐individual genetic distances and the geographic distances, indicating a pattern of IBD among the three geographic regions  across seasons from 2010 to 2015  have been reported for S. chinensis between populations in China and Thailand based on mtDNA sequence data  and reside only in coastal  and estuarine waters  and humpback dolphins ; formal analysis (lead); methodology (lead); software (lead); writing – original draft (lead); writing – review and editing . Watchara Sakornwimon: Formal analysis ; investigation ; methodology (lead); software (lead); writing – original draft (lead). Rachawadee Chantra: Investigation (lead); methodology ; resources . Liyuan Zhao: Formal analysis ; software ; writing – review and editing . Fuxing Wu: Formal analysis ; software ; writing – review and editing . Reyilamu Aierken: Formal analysis ; software ; writing – review and editing . Kongkiat Kittiwattanawong: Conceptualization (lead); project administration (lead); supervision (lead); validation . Xianyan Wang: Conceptualization (lead); funding acquisition (lead); project administration (lead); supervision (lead); validation .Supplementary MaterialClick here for additional data file.


Aedes albopictus, is one of the most invasive species in the world. Native to the tropical forests of Southeast Asia, over the past 30 years it has rapidly spread throughout tropical and temperate regions of the world. Its dramatic expansion has resulted in public health concerns as a consequence of its vector competence for at least 16 viruses. Previous studies showed that Ae. albopictus spread has been facilitated by human-mediated transportation, but much remains unknown about how this has affected its genetic attributes. Here we examined the factors that contributed to shaping the current genetic constitution of Ae. albopictus in the Iberian Peninsula, where the species was first found in 2004, by combining population genetics and Bayesian modelling. We found that both mitochondrial and nuclear DNA markers showed a lack of genetic structure and the presence of worldwide dominant haplotypes, suggesting regular introductions from abroad. Mitochondrial DNA showed little genetic diversity compared to nuclear DNA, likely explained by infection with maternally transmitted bacteria of the genus Wolbachia. Multilevel models revealed that greater mosquito fluxes (estimated from commuting patterns and tiger mosquito population distribution) and spatial proximity between sampling sites were associated with lower nuclear genetic distance, suggesting that rapid short- and medium-distance dispersal is facilitated by humans through vehicular traffic. This study highlights the significant role of human transportation in shaping the genetic attributes of Ae. albopictus and promoting regional gene flow, and underscores the need for a territorially integrated surveillance across scales of this disease-carrying mosquito.The Asian tiger mosquito,  Aedes (Stegomyia) albopictus (Skuse 1894) is a highly invasive species that originated in the tropical forests of Southeast Asia1. However, in the late 1970s it started a dramatic expansion throughout tropical and temperate regions of the world and it is now present in the five populated continents2. This species is an ecological generalist capable of rapid evolution and, with the aid of man, speedy colonization of new habitats1. While in its native range Ae. albopictus inhabits forested areas, breeding in natural sites such as tree holes, bromeliads and bamboo stumps, it has now adapted to breed also in artificial man-made water containers from urban and suburban human settlements3. This species has an opportunistic feeding behaviour with a strong preference for mammals, especially humans5. Furthermore, in temperate regions, its eggs can survive the cold winters by entering diapause3. This ecological adaptability has important implications for the epidemiology of several mosquito-borne diseases since the tiger mosquito is a competent laboratory vector of at least 16 viruses6. While Ae. aegypti is considered as the principal vector of dengue and Zika, Ae. albopictus is a less efficient epidemic vector , having developed an enhanced transmission for chikungunya facilitated by genetic adaptation (E1-A226V substitution) of the ECSA strain9. Ae. albopictus has a role on sporadic autochthonous disease transmission of arboviruses  in Europe, and therefore its surveillance and control are considered a regional priority10.The Asian tiger mosquito, 11. Ae. albopictus is one of top invasive alien species in the world, being considered, together with Ae. aegypti, the most costly invasive species13. Its expansion in Europe coincides with the wave of introductions of invasive species in the continent, which started almost 40 years ago as a result of globalization14. Considering its limited flight range (less than 200 m/day)15, a crucial element for its success is the longevity of its desiccation-resistant eggs16, which can be passively transported by humans through commercial shipping of used tires and aquatic plants3 and ground vehicles17. In Spain, Ae. albopictus was first detected in Sant Cugat del Vallès, Catalonia, in 200418 as a result of the nuisances associated with its aggressive anthropophilic behaviour19. Almost twenty years later, this species is well established in the Mediterranean coast of Spain and it is now colonizing inner territories of the Iberian Peninsula20.Invasive alien species have historically been spread by humans. However, advances in transportation logistics resulting in higher air traffic and sea-born trading are driving a more rapid dispersal of non-indigenous species in the world, including many vectors of human diseases22. It is, however, during the initial dispersal when proactive management efforts can be more cost-effective for preventing the establishment of invasive species23. For instance, successful surveillance in New Zealand in the 1990s intercepted the entrance of Ae. albopictus24, which has not been reported so far in this country. On the other hand, once this has become established, it is highly difficult to eradicate16. In this respect, studying Ae. albopictus population genetic structure and identifying its dispersal routes, its main drivers and scales, is crucial to understand the tiger mosquito’s spread and design integrated surveillance and preparedness strategies26. Previous population genetic studies pointed to a worldwide chaotic dispersion pattern in Ae. albopictus, with Europe harbouring several distinct genotypes that have been linked to multiple independent introductions (e.g.28). Furthermore, in Europe human transportation networks have been shown to have facilitated the spread of this mosquito and conditioned its genetic and demographic patterns32.A biological invasion is a three-step process, which involves: initial dispersal, establishment of self-sustaining populations and spread to neighbouring habitats34, it is crucial to analyse both mitochondrial (mtDNA) and nuclear (nDNA) DNA data to understand what major factors contributed to shaping the genetic attributes of invasive populations. In this regard, the presence of maternally-transmitted endosymbiotic bacteria may be relevant, as they can distort mtDNA phylogenies and reduce mtDNA diversity, a process which may have little to no effects on nDNA variation35. If a maternally-inherited symbiont confers a selective advantage to its hosts, the mtDNA variants originally associated with the symbiont can rapidly spread through the host population and go to fixation, thus resulting in a great increase in the frequency of a single or few mtDNA haplotypes36. Such symbionts are widespread in many arthropod species, being Wolbachia the most common maternally-inherited symbiont on the planet38. For this reason, incorporation of nDNA data is essential to corroborate results derived from mtDNA.As different marker types can tell different storiesAe. albopictus requires an interdisciplinary approach39. For instance, population genetics and multilevel modelling analyses can be crucial to shed light on Ae. albopictus patterns of dispersal. Besides, it has been shown that genetic diversity in this species has been related to a higher adaptive potential29 and a diversification of its interactions with the pathogens it carries39. Understanding movement patterns in its populations in endemic areas is crucial to disentangle the dynamics of disease transmission between vectors and humans15. In this study we assess geographic patterns of mtDNA and nDNA variation in Ae. albopictus and use this information to investigate the species’ dispersal routes across Spain. We also compare features of mtDNA and nDNA variation and assess whether they are incongruent, and test for Wolbachia presence to ascertain whether mtDNA diversity is affected by this maternally-transmitted parasite.It is now widely accepted that a progress in the study of the ecology of c oxidase gene subunit 1—COI) we obtained 471 sequences of 513 bp (424 from Spain + 40 from France + 7 from Greece)  included 470 sequences of 286 bp (424 sequences from Spain + 30 from France + 16 from Greece), while for the mitochondrial DNA alignment  and 10 (nucleotide diversity) times higher than for COI. Indeed, overall haplotype and nucleotide diversities were 0.7084 ± 0.025 and 0.00566 ± 0.00048 for ITS2, respectively, and 0.2196 ± 0.027 and 0.00054 ± 0.00008 for COI. We found 78 haplotypes defined by 83 polymorphic sites (21 parsimony informative) for ITS2, whereas only 18 haplotypes defined by 20 polymorphic sites (5 parsimony informative) were detected in the case of COI , which displayed higher variation (π range: 0–0.022); the same relationship with haplotype diversity was not significant  analysis revealed no apparent evidence of distinct genetic groups or geographic consistency within the nuclear dataset, although it did show a certain level of drift over time Fig. . Most ofAgain using the individual sampled mosquitoes, a Mantel test of correlation revealed a significant positive correlation between ITS2 pairwise genetic distances and spatial distance , and negative correlations with potential tiger mosquito flux  and spatial proximity . The Mantel test did not find evidence of correlation between ITS2 genetic distance and temporal distance.wsp and 16S markers, 49 (79%) from 25 localities tested positive for infection for both markers. Two additional individuals yielded positive amplifications for 16S only and were thus excluded in reporting Wolbachia prevalence.From a total of 62 samples screened using the μ) of the beta distribution are shown in Fig. π), shown in Fig. μ and positive in all models of π, even when both variables are included together (model 5 -M5- and M6), indicating that greater spatial proximity and mosquito fluxes are associated with lower ITS2 genetic distance between sampled mosquitoes, with a higher probability of zero distances. In contrast, the coefficient on the temporal distance variable is positive in all models of μ and negative in all models of π , indicating that greater passage of time between samples is associated with greater ITS2 genetic distance and with lower probability of zero distances  of zero ITS2 genetic distance  has probably facilitated significantly the tiger mosquito’s movement and invasion process41. Our findings are consistent with these but go a step further by suggesting that the “hitchhiking” of Ae. albopictus in cars observed in Eritja, Palmer, Roiz, Sanpera-Calbet and Bartumeus30 actually helps to explain observed patterns of population genetics. While genetic variability increases with spatial distance, car transport can strongly reduce this effect. The high-resolution of our dataset makes it possible to show the significant role of human transportation in shaping the genetic constitution of Ae. albopictus and promoting regional gene flow. Mosquito movement can be affected by human activities like commuting and human-made structures like roads, which combined act as bridges for dispersal by favouring gene flow and promoting genetic mixing32.Our models indicate that at very small spatial scales  the genetic variation measured by ITS2 is sharply reduced, likely representing seasonal mosquito pools that come from a main source. Beyond these scales, genetic variability steadily increases with spatial distance, as a clear positive correlation between dispersal distance and genetic variation appears. This is reflected in the combined effects of the  spatial proximity variable and the linear spatial distance variable. More interestingly, our models suggest that human transportation has a role in shaping Ae. albopictus is also common, and point to a pattern of regular introductions of the species from abroad, through e.g. transportation of used tires and aquatic plants, which allows for survival and establishment at long distances of whole batches of eggs. Previous genetic studies of Ae. albopictus have also showed little or no genetic structure according to geography, both in the native and introduced range of the species (reviewed in39). Such a genetic feature is likely the consequence of the high level of human-mediated spread from several genetically distinct source populations followed by global dispersal, and it is concordant to what has been found in other wide-ranging invasive insect species, especially those that are closely associated with humans, e.g. the German and American cockroaches 43 and the longhorn crazy ant Paratrechina longicornis44. Taken together, our results highlight the role of human activities in promoting unintentional mid- and long-distance dispersal and thus in shaping the current genetic structure of insect species commonly found in human-modified landscapes.At a much broader spatial scale, we find a general lack of genetic structure and geographic consistency among haplotypes across the large expanse of the Iberian Peninsula, with numerous haplotypes shared among several distant areas . This suggests that human-mediated large-scale dispersal of Aedes46, and this can pose a challenge in DNA sequencing and analysis. In this study, all individuals presenting intragenomic heterogeneity were thus excluded from analysis. As for future directions, although ITS2 has proved to be a useful marker for studies on the spread of Ae. albopictus47, the reassessment of the species’ genetic diversity and population structure through the use of molecular markers with greater variability and/or potential, such as microsatellites and single nucleotide polymorphisms (SNPs), could provide more detailed and deeper insights into the description of fine-scale dispersal patterns, gene flow and introduction routes.Our study has some limitations that should be considered. First, the modelled genetic diversity range, which was obtained from ITS2 pairwise genetic distances, is low. This likely has to do with the relatively low variability of the analysed genetic marker. Indeed, nuclear genes, as well as mitochondrial fragments, are expected to be less variable and bear lower resolving power than highly mutating markers, e.g. microsatellites. Second, although we rely on a relatively large number of sampling sites, additional sampling across larger areas of Spain could provide better balance and a wider range of values for the models. Third, intragenomic heterogeneity  of ITS2 has been reported in several mosquito species including the genus Ae. albopictus levels of nuclear variation within the range of most insects, but extremely low mtDNA variation both within and among populations51. Interestingly, we found a high infection rate (79%) of Wolbachia in the studied Ae. albopictus samples. Wolbachia is a genus of maternally-inherited endosymbiotic bacteria that is known to induce male killing, feminization, parthenogenesis and cytoplasmic incompatibility, which facilitate its spread within the arthropod population52. Wolbachia is capable of inducing selective sweeps in mtDNA, i.e. fixation of a single or few mtDNA haplotypes that may become widespread in the host population through cytoplasmic hitchhiking driven by Wolbachia invasions36. Selective sweeps on mtDNA have been shown to not only reduce haplotype diversity producing a characteristic single star-like network, but also to cause the remaining set of haplotypes to deviate from neutrality35. Within Culicidae, the natural presence of Wolbachia has been documented in more than 30 species (e.g.56), with Ae. albopictus harbouring significantly lower mtDNA diversity than the uninfected species55. In light of this, our results point to Wolbachia as causative agent for the lack of mitochondrial polymorphism here recovered, as suggested for other insect species, e.g. Acraea butterflies57 and the cherry fruit fly Rhagoletis cerasi58. Nevertheless, it has to be noted that Wolbachia can show seasonal fluctuations in infection rates59, which were not addressed in this study. Hence, the infection rate reported here may not capture a representative overview of Wolbachia infection. Moreover, the low variability in mtDNA in the introduced ranges of Ae. albopictus could also be caused by demographic processes, such as genetic drift or population bottleneck during rapid colonization60. However, demographic processes cause changes in variation for both mitochondrial and nuclear markers, even though mtDNA is expected to show a stronger response61. Furthermore, the lack of mtDNA variation was also found in the native range of Ae. albopictus , strengthening our hypothesis of a Wolbachia-induced selective sweep. Alternatively, we cannot rule out that the low COI diversity here detected may be at least partially due to the length of the analysed fragment, as higher diversity has been observed when targeting larger COI fragments (> 1300 bp)64. Further research is needed to test this hypothesis.Global patterns of mtDNA and nuclear variation were highly discordant, with mtDNA showing little genetic diversity and a single star-like haplotype network. This is in agreement with previous studies showing Ae. albopictus populations and arthropods in general65, and disentangle their phylogeographic history66. Nevertheless, there is broad recognition that mtDNA can commonly be under selection, thus challenging the assumption of neutrality postulated by several population, phylogeographic and phylogenetic studies68. Selection can arise due to e.g. mito-nuclear co-evolution, adaptation of mtDNA to different climatic/environmental conditions, and selective sweeps of beneficial mtDNA haplotypes68. Mitochondrial DNA is a single, linked molecule with low or no recombination, meaning that selective processes such as selective sweeps can have a profound impact on the apparent rate of genetic drift33. Therefore, we suggest caution should be used in drawing conclusions from mtDNA alone and, whenever possible, aim for a multilocus approach to achieve a correct understanding of the genetic population structure and history of the study species.Mitochondrial DNA has been extensively used to shed light on the geographic origin of invasive Ae. albopictus samples were collected during the period 2011–2015 at 140 locations encompassing most of the current species distribution in Spain  in a final volume of 250 µL using the HotShot protocol70, and for COI, primers LCOI490 (5′-GGTCAACAAATCATAAAGATATTGG-3′) and HCO2198 (5′-TAAACTTCAGGGTGACCAAAAAATCA-3′)71 or the degenerated primers ZplankF1_M13 (5′-TGTAAAACGACGGCCAGTTCTASWAATCATAARGATATTGG-3′) and ZplankR1_M13 (5′-CAGGAAACAGCTATGACTTCAGGRTGRCCRAARAATCA-3′)72 that had the M13 primers attached following the suggestion of Ivanova, Zemlak, Hanner and Hebert73. Further details on amplification and sequencing are presented in the Ae. albopictus sequences from each gene (retrieved from GenBank) using the ClustalW algorithm in MEGA 774.We amplified two gene regions, including one nuclear ribosomal gene (ITS2) and one mitochondrial fragment (COI). The following primers were used for amplification and sequencing: for ITS2, primers ITS-CP-P1A (5′-GTGGATCCTGTGAACTGCAGGACACATG-3′) and ITS-CP-P1B (5′-GTGTCGACATGCTTAAATTTAGGGGGTA-3′)Wolbachia, we analysed 62 randomly selected samples . Individuals were selected aiming to cover all sampling years and all studied provinces. Two molecular markers were used for detecting Wolbachia infection, namely wsp and 16S rDNA. Primers used for amplification and sequencing were: for the wsp marker, primers 81F (5′-TGGTCCAATAAGTGATGAAGA-3′) and 691R (5′-AAAAATTAAACGCTACTCCA-3′)76, while for 16S, primers 16SF (5′-CGGGGGAAAAATTTATTGCT-3′) and 16SR (5′-AGCTGTAATACAGAAAGTAAA-3′)78. Amplification conditions followed Wiwatanaratanabutr53 in the case of wsp, and Heddi, Grenier, Khatchadourian, Charles and Nardon78 in the case of 16S. PCR products were visualized on a 1% agarose gel. To validate the results, two PCR replicates were run for each sample . A third replicate was run for samples that showed incongruent results based on the two prior replicates. Wolbachia infection was confirmed by two successful amplifications of both molecular markers. In light of the high infection rate detected in the selected samples (see Results), we decided to exclude COI from population structure and multilevel modelling analyses (see below), as mtDNA diversity was likely affected by the presence of Wolbachia.To test whether mtDNA variability could be affected by the presence of endosymbiotic bacteria of the genus 79. To estimate gene genealogies, we used HAPLOVIEWER, which turns trees built from traditional phylogenetic methods into haplotype genealogies80. We estimated the phylogeny using a maximum-likelihood approach as implemented in RAxML 7.7.181, with a GTRCAT model rate of heterogeneity and no invariant sites for COI, and a gamma model rate of heterogeneity and invariant sites (GTR+G+I) for ITS2. The most appropriate model of nucleotide evolution was selected using jModelTest 2.1.382 under the Akaike information criterion (AIC). Input data were COI or ITS2 sequences from each individual, subsequently collapsed into haplotypes. The best tree was selected for network construction in HAPLOVIEWER.Genetic diversity indices including number of haplotypes, number of polymorphic sites, haplotype diversity and nucleotide diversity for mtDNA and nuclear DNA were estimated for the whole dataset and for each analysed province using DNASP 6.11.0183. Pairwise distances between ITS2 sequences were calculated in MEGA by estimation of evolutionary divergence over sequence pairs, using the Kimura 2-parameter substitution model. MDS analysis was then performed with the pairwise genetic distance matrix, using the cmdscale function of the stats package in R 4.1.284.Population structure of nuclear DNA was characterized using classical multidimensional scaling (MDS), also known as principal coordinates analysis85. We constructed spatial proximity as the negative exponential of spatial distance in km, represented as e-d, where e is Euler’s constant and d is spatial distance in km. This approach gives high values for spatial proximity (0.82–1) within the 200 m buffer often taken as the tiger mosquito’s maximum flying distance, with values then quickly dropping and nearing zero by 5 km. The combination of these spatial distance and spatial proximity variables together is intended to capture the effects of inter-point distance at different scales. We constructed the temporal distance variable as the absolute number of years elapsed between the sampling times when each member of the sample pair was captured, rounded up to the nearest year (another way of thinking about this is as the number of mosquito seasons between each capture time). We constructed potential tiger mosquito flux as the potential daily bidirectional gross number of Ae. albopictus moving between each pair of municipalities. We estimated this by combining municipality-level Ae. albopictus risk estimates from the Mosquito Alert citizen science platform86 with commuter flow estimates drawn from the Spanish Labour Force Survey (LFS) in a manner similar to that described in Eritja, Palmer, Roiz, Sanpera-Calbet and Bartumeus30  in meters, calculated using the Vincenty method on the WGS84 ellipsoid as implemented in the geosphere package for R87 between ITS2 pairwise genetic distance and potential tiger mosquito flux, spatial distance, spatial proximity, and temporal distance, using pairs of individual mosquitoes as the unit of analysis. We implemented all tests in the ecodist package for R88, using 1000 permutations with bootstrap confidence limits estimated using 500 iterations.We explored drivers of nuclear genetic structure by carrying out simple Mantel tests of correlationsWe then used Bayesian multiple membership multilevel regressions to model ITS2 pairwise genetic distances between sampled mosquito pairs as a function of potential tiger mosquito flux between sampling sites, spatial distance and spatial proximity between sampling sites, and temporal distance.89, in which the distribution of the stochastic component is a mixture of a beta distribution and a degenerate distribution in 0. Following Figueroa-Zúñiga, Arellano-Valle and Ferrari90 and Branscum, Johnson and Thurmond91, we used the beta distribution for ITS2 genetic distance values in  because it is a highly flexible distribution that is defined on that interval. As these authors have done, we parameterized the beta distribution in terms of a mean (μ) and a parameter (ϕ) that captures precision. The probability density function of a variable (y) in this parameterization is:We relied on zero-inflated beta regressions92, the probability density function of a variable y for this zero-inflated beta (zib) mixture is:π < 1 is the probability of observing an ITS2 distance of 0, and μ and ϕ are defined as above.Since the ITS2 genetic distances in our dataset include zeros, for which the beta distribution is undefined, we used a zero-inflation component in our models. In addition to making it possible to include the zeros in the analysis , this approach also has the advantage of treating them separately, which should minimize any problems associated with the inadvertent sampling of siblings see . As expli as independent random variables y1,…,yn drawn from this zero inflated beta distribution such that π and μ, and a log link to model ϕ. We fit six models (M1–M6) with different combinations of mosquito flux, spatial proximity, temporal distance, and spatial distance as covariates. The models for μ are specified as following:αj represents a random intercept for each of the years in which the mosquitoes in pair i were sampled, and αk represents a random intercept for each of the sampled mosquitoes. This multiple membership approach allows us to account for the effects of sample year and sample itself, which could otherwise confound results given that each pair may be connected to other pairs by the sampling years and sampling that they share93. α0 is the overall model intercept.We treated the observed ITS2 genetic distances between each sample pair ϕ parameter of our beta distributions as a function of the provinces in which each sample in the pair was taken. This allows us to explore how the precision of the beta distribution changes across geography. The choice of provinces as the areal units for this part of the model is based on these units representing patterns of human settlement and activity that should be of relevance to tiger mosquito spreading patterns, while also being large enough to avoid adding too many additional parameters to an already complicated model. The model for ϕ is the same in all six models:We explicitly model the π  as:Finally, we modelled π is almost identical to the systematic part of the model for μ. The only difference is the absence of the sample random intercepts, which are excluded here for computational reasons.The systematic part of the model for Details on model fitting and accuracy estimation are outlined in the Supplementary Information.


Vaccination against Porcine Reproductive and Respiratory Syndrome virus (PRRSv) is widely used to control clinical disease, but the effectiveness appears in some cases to be suboptimal. Field reports have stated the presence of routinely PRRSv-vaccinated but ELISA seronegative sows: the ELISA non-responders. The real extent of this phenomenon  was not yet investigated. In this study, the prevalence of ELISA non-responders was assessed by measuring PRRSv-specific antibodies in 1400 sows, originating from 70 PRRSv-vaccinating sow herds, using IDEXX ELISA (ELISA 1) and CIVTEST E/S ELISA (ELISA 2). Neutralizing antibodies (NAbs) were quantified in a virus neutralization assay. Univariable logistic regression was used to identify herd risk factors for the presence of ELISA non-responders. The global prevalence of non-responders varied from 3.5% (ELISA 1) to 4.1% (ELISA 2), the herd-level prevalence was 40% and the within-herd prevalence ranged from 5% to 20% (ELISA 1) and from 5% to 30% (ELISA 2). The ELISA non-responders had significantly lower NAbs than the ELISA responders. Herds using the combination of one modified live vaccine and one killed vaccine had a significantly reduced risk of having ELISA non-responders. A first assessment of the prevalence and possible consequences of ELISA non-responders has been provided by this study. The clinical importance, origin and underlying immunological mechanisms warrant further research. Nidovirales, family Arteriviridae, subfamily Variarterivirinae, genus Betaarterivirus. Two different species of PRRSv have been described: Betaarterivrus suid 1 (PRRSv-1) and Betaarterivrus suid 2 (PRRSv-2) [Porcine Reproductive and Respiratory Syndrome virus (PRRSv), an enveloped, positive-sense, single-stranded RNA virus with a genome of approximately 15 kb in length, is the causative agent of one of the most devastating diseases in the worldwide swine industry. The virus belongs to the order PRRSv-2) . PRRSv hPRRSv-2) . DiseaseSince PRRSv infection can lead to reproductive failure in gilts and sows and to performance losses in piglets and grow-finishing pigs, it is the major cause of economic losses in the worldwide swine industry. Economic models estimate the annual cost of productivity losses due to PRRS in all national breeding and growing-pig herds in the United States to be USD 664 million .To date, PRRS control is hard to manage, and sow and/or piglet vaccination against PRRSv is widely used for prevention and control of the disease. Unfortunately, the effectiveness of PRRSv vaccination is often unpredictable and suboptimal, with PRRS outbreaks occurring despite routine vaccination being practiced . DespiteTwo large classes of PRRSv vaccines are used: modified live vaccines (MLVs) and inactivated/killed vaccines (KVs). MLVs offer complete and partial protection against homologous and heterologous strains, respectively, but have some safety concerns including the possible reversion to virulence and the risk of recombination between one MLV and a wild PRRSv strain or the recombination between two different MLVs. KVs offer only limited protection  but are beneficial from a safety point of view ,16,17,18In Belgium, several swine veterinarians have reported that pigs (both sows and piglets) often remain ELISA seronegative (ELISA non-responders) despite being vaccinated against PRRSv (data not published). This is also confirmed in several field and experimental studies, in which not all PRRSv-vaccinated animals seem to have detectable ELISA antibodies (Abs) ,20,21,22The main objectives of this cross-sectional field study were to assess the global prevalence (proportion of ELISA non-responding sows), the herd-level prevalence (proportion of herds with ELISA non-responding sows) and the within-herd prevalence (proportion of ELISA non-responding sows per herd) in PRRSv-vaccinating sow herds in Belgium. Secondly, the presence of PRRSv-specific neutralizing antibodies (NAbs) was investigated in a selection of ELISA seronegative and seropositive sows, to obtain a first indication of the possible clinical importance of the ELISA non-responding sows. Finally, possible factors that could be associated with the presence of ELISA non-responders were assessed.Seventy sow herds, with at least 150 breeding sows that practiced routine PRRSv vaccination, were included in the study. Firstly, the proportion of sow herds (sow capacity > 150) within each province in Belgium was assessed using the National Animal Identification and Registration database (Sanitel database). Secondly, a similar proportion of sow herds in each province was selected, ensuring a geographically representative number of sow herds . AccordiTM, Gent, Belgium) was used to score the internal, external and total biosafety practices on each herd [Selected herds were visited once between 21 October 2020 and 7 May 2021 by the principal investigator (PI) and the respective herd veterinarian. A total of 24 different herd veterinarians, belonging to 15 different veterinary practices, collaborated in this study. Within each herd, twenty breeding sows of different parities were selected for blood sampling. The parity distribution of the sampled sows was as follows: 274 (19.57%) first parity, 296 (21.14%) second parity, 267 (19.07%) third parity and 563 (40.21%) 4+ parity sows. The blood samples were taken by puncture of the vena jugularis externa using the Primavette V serum 7.5 mL blood collection system . Most sows were sampled in the farrowing unit. In a few farms, the herd owners preferred to sample the sows in the gestation unit. All blood samples were stored at 2 °C to 8 °C until transport. The samples were transported to the laboratory of Sciensano (Brussels) within a maximum of 3 days after sampling. Upon arrival in the laboratory, samples were immediately centrifuged (1000g—15 min—4 °C), and serum was collected and subsequently stored at −20 °C until analysis. All herds and samples were coded in an anonymous way using a random, unique identifier. Farm data were collected during the herd visit by means of two questionnaires. First, the BioCheck questionnaire  were analyzed for the presence of PRRSv-specific Abs using commercially available ELISA kits. To avoid the influence of a certain ELISA kit effect, different commercial ELISAs were used to assess the possible non-responsiveness. Abs directed against recombinant PRRSv-1 and PRRSv-2 ORF7 antigens (nucleocapsid protein) were assessed using the IDEXX PRRS X3 Ab test (ELISA 1) , which is widely considered the gold standard for PRRSv Ab testing . CIVTEST1 to 28) in Minimal Essential Medium  with antibiotics , followed by the addition of 50 TCID50 of the PRRSv-1 DV strain (propagated in MARC-145 cells) to each dilution. After incubation for 1 h at 37 °C (5% CO2), 100 µL of a MARC-145 cell suspension (105 cells/mL in MEM-A + 10% FBS) was added to each well, and plates were incubated for at least 72 h at 37 °C (5% CO2). Following incubation, plates were washed 3 times in PBS and heat-fixed by two consecutive heating steps: 30 min at 37 °C and 1 h 20 at 80 °C. After fixation, cells were stained with the monoclonal PRRSv-specific mouse Ab 13 × 102 (1/50 diluted in PBS + 10% goat serum—kindly provided by Prof. Dr. H. Nauwynck) as primary Ab, the HRP-conjugated polyclonal goat anti-mouse Ab  as secondary Ab and a chromogen substrate (19 mL Na-Acetate + 1 mL ACE + 10µL H2O2) for detection. For each sample, the VN titer in one run was determined as the Log2 of the highest dilution in which no staining was observed in one or both replicates. Each sample was tested in two separate runs, and the final VN titer was determined as the mean titer of both runs. Positive (only virus + cells) and negative (only cells + MEM-A) controls were included in each plate. Back titration of the viral stock was performed in each run to ensure homogeneity between different runs. Samples with a VN titer ≥ 2 Log2 were considered to be VN seropositive [The presence of PRRSv-specific NAbs was assessed for a selection of 319 samples, including 81 ELISA seronegative samples (in either or both ELISA 1 and 2), 76 ELISA low seropositive samples (in either or both ELISA 1 and 2) and 162 randomly selected ELISA seropositive samples (in both ELISA 1 and 2), with at least two seropositive samples from each herd. VN was performed as previously described, with slight modifications . In shorpositive .p-values < 0.05 are considered to be significant.GraphPad Prism 9  was used to visualize and analyze results. The hybrid Wilson/Brown method was used to calculate 95% confidence intervals (CI 95%) for proportions. The chi-square test was used to calculate differences in proportions of ELISA seronegative sows between different parities and to calculate differences in proportions of VN seropositive samples between ELISA seronegative and ELISA seropositive sows. Unpaired t-test was used to calculate differences in mean ELISA results between two different parities and to calculate differences in mean VN titers between ELISA low-seropositive and seropositive samples. One-way ANOVA and Tukey’s multiple comparisons test were used to calculate differences in mean ELISA results between all parities and to calculate differences in mean VN titers between ELISA seronegative, low-seropositive and seropositive samples. Unpaired t-test was used to compare differences in mean values of continuous variables  between “seronegative” and “seropositive” herds. For this, herds having at least one ELISA 1 seronegative sow (on 20 sampled) were considered a “seronegative” herd, while herds without any ELISA 1 seronegative sow were considered a “seropositive” herd. Univariable logistic regression was used to investigate the association between categorical herd characteristics , derived from the PRRSv-specific questionnaire, and the presence of at least one ELISA 1 seronegative sow (of 20 sampled) in the herd. Results of the univariable logistic regression are expressed as the odds ratio (OR) for having at least one ELISA 1 seronegative sow (of 20 sampled) for each variable compared to the reference variable. To reduce estimates’ bias, due to small number of events per variable, Firth’s correction was used when perfect separation was observed. The logistic regression was performed using R software (version 4.1.2). Results are shown as mean ± standard deviation (SD). The internal biosecurity score for the study’s herds was 63.4 ± 13.5 , the external biosecurity score was 71.5 ± 8.5 , and the total biosecurity score was 67.7 ± 10.1 . Herds scored the lowest in subcategories “Feed, water and equipment supply”, “Location of the farm” and “Measures between compartments”. Highest scores were achieved in subcategories “Purchase of breeding pigs, piglets and semen”, “Finishing unit” and “Vermin and bird control” .Descriptive data concerning sow, gilt and piglet PRRSv vaccine management  showed ap = 0.58) in the proportion of seronegative sows between parities were observed, with the following proportions: parity 1, 6/274 ; parity 2, 12/296 ; parity 3, 9/267 ; and parity 4+, 22/563 . Individual S/p values ranged from 0.04 (minimum) to 4.51 (maximum) with a mean S/p value for all sampled sows of 1.71 ± 0.83. Mean S/p values per herd ranged from 1.02 (minimum) to 2.94 (maximum). No significant differences (p = 0.46) in mean S/p values between different parities were observed  sows were identified as being PRRSv-seronegative. These 49 seronegative sows originated from 28/70 (40%) of the herds. The within-herd prevalence ranged from 5% to 20% (1 to 4 seronegative sows of 20 sampled). Next to the 49 seronegative sows, 84 sows  were considered low-seropositive. Fifty out of seventy (71.43%) herds had at least one seronegative and/or one low-seropositive sow in ELISA 1. No significant differences (observed .p = 0.47) in the proportion of seronegative sows between different parities were observed, with the following proportions: parity 1, 14/274 ; parity 2, 15/296 ; parity 3, 11/267 ; and parity 4+, 18/563 . Individual IRPC values ranged from 1.59 (minimum) to 243.84 (maximum) with a mean IRPC value for all sampled sows of 102.0 ± 51.69. The mean IRPC value per herd ranged from 33.73 (minimum) to 174.30 (maximum). A significant difference  in mean IRPC value between parity 1 and parity 4+ sows was observed. However, the biological significance of this difference is negligible. No significant differences were observed between the other parities (p < 0.0001) than the mean IRPC value of the 64 PRRSv-1-vaccinating herds (105.1 ± 51.35). Exclusion of the six PRRSv-2-vaccinating herds led to an adjusted proportion of 47/1280  seronegative sows in ELISA 2, which is more similar to the observed proportion of seronegative sows in ELISA 1.In ELISA 2, a slightly higher proportion of sows tested seronegative: 58/1400 . These 58 seronegative sows originated from 28/70 (40%) of the herds and the within-herd prevalence of ELISA 2 non-responding sows ranged from 5% to 30% (1 to 6 seronegative sows of 20 sampled). Next to the 58 seronegative sows, 70 sows  were considered low-seropositive. Forty-four out of seventy (62.86%) herds had at least one seronegative or one low-seropositive sow in ELISA 2. No significant differences (parities . Importap = 0.44) in the proportion of E1−/E2− sows between different parities were observed, with the following proportions: parity 1, 4/274 ; parity 2, 8/296 ; parity 3, 3/267 ; and parity 4+, 8/563 . Of the 26 ELISA 1 seronegative/ELISA 2 seropositive (E1−/E2+) sows, 10/26 (38.46%) were considered low-seropositive in ELISA 2. IRPC values of the remaining 16 E1−/E2+ sows ranged from 30.1 to 86.6, with a mean IRPC value of 45.96 ± 15.48. Of the 35 ELISA 1 seropositive/ELISA 2 seronegative (E1+/E2−) sows, 24/35 (68.57%) were considered low-seropositive in ELISA 1. S/p values of the remaining 11 E1+/E2− sows ranged from 0.64 to 2.13, with a mean S/p value of 1.00 ± 0.46. In summary (p < 0.0001). Exclusion of the six PRRSv-2-vaccinating herds did not alter the overall proportion of E1−/E2− sows: 21/1280  sows were seronegative in both ELISA tests. Exclusion resulted in a slightly better correlation between ELISA 1 and ELISA 2 results, with a Spearman correlation of r = 0.855 .ELISA 1 and 2 results were combined, and 23 out of 1400  sows were identified as seronegative in both ELISA tests. These 23 double-seronegative (E1−/E2−) sows originated from 15/70 (21.4%) herds, with the within-herd prevalence ranging from 5% to 15% (1 to 3 E1−/E2− sows of 20 sampled). No significant differences , 76 ELISA low-seropositive samples  and 162 ELISA seropositive samples , with at least two seropositive samples of each herd. Results of the VN provide additional information concerning the humoral immune status of the routinely vaccinated sows: it is well known that PRRSv-specific ELISA Abs are not necessarily correlated to protection, in contrast to the PRRSv-specific NAbs that have been shown to protect against clinical disease and infection .2). However, there was a quite high proportion of ELISA seronegative samples that tested positive in VN . In contrast, the mean VN titers of both the ELISA low-seropositive samples (2.99 ± 1.67 Log2) and ELISA seropositive samples (3.15 ± 1.87) were positive. Significant differences in VN titer were found between the ELISA seronegative samples and ELISA low-seropositive samples (p = 0.005) and between the ELISA seronegative samples and ELISA seropositive samples (p < 0.0001) but not between the ELISA low-seropositive samples and ELISA seropositive samples (p = 0.50) (p < 0.0001). Exclusion of (low) seropositive samples originating from herds without any ELISA 1 nor ELISA 2 seronegative samples led to a similar conclusion (2) were significantly lower than the VN titers of the 54 remaining, low-seropositive samples  and the VN titers of the 86 remaining seropositive samples . ELISA seronegativity in either ELISA 1, ELISA 2 or both ELISA 1 and ELISA 2 did not influence the presence of NAbs, with no significant differences (p = 0.87) found between the VN titers of the 23 E1−/E2− (1.92 ± 1.03 Log2), 26 E1−/E2+ (2.11 ± 1.45 Log2) and 32 E1+/E2− (1.94 ± 1.54 Log2) samples  of these sows were considered seropositive in VN, with VN titers ≥ 2 Log2.The mean VN titer of the ELISA seronegative samples was negative (1.99 ± 1.37 Log = 0.50) a. Furthenclusion b: VN tit samples . The seln = 238), the largest difference  was observed between VN titers of parity 2 (2.54 ± 1.69 Log2) and parity 4+ (3.43 ± 1.60 Log2) sows (n = 81), a large difference  was observed between parity 1 (1.42 ± 1.34 Log2) and parity 4+ (2.43 ± 1.5 Log2) sows. Additionally, a large difference  was found between parity 2 (1.43 ± 0.86 Log2) and parity 4+ sows  seropositive sows (g2) sows c. In the 4+ sows d.p-value = 0.006). The latter is also reflected in the sow PRRSv vaccination scheme used: a significantly reduced risk  was observed in herds vaccinating their sows at 60 and 90 days of gestation, compared to the other vaccination schemes. This scheme was exclusively used in herds vaccinating the sows with an MLV (60 days of gestation) and a KV (90 days of gestation). Herd variables related to gilt management were not significantly related to the presence of ELISA non-responding sows. Finally, the used administration route  was not identified as a possible risk factor for non-responsiveness  presence of multiple, PRRSv-vaccinated but ELISA seronegative (ELISA non-responding) sows in Belgium. Despite the low global prevalence, the found non-responders were widely distributed, with 40% of the sampled herds having at least one non-responding sow on twenty sows sampled. A first indication of the relevance of the ELISA non-responders was given by the results of the VN: the ELISA non-responders had significantly less NAbs compared to the ELISA responders. Finally, the risk of having non-responding sows was significantly reduced when the combination of one MLV (at 60 days of gestation) and one KV (at 90 days of gestation) was used.Seventy Belgian sow herds, originating from six different provinces/regions, were included in this cross-sectional study. All selected herds practiced routine PRRSv vaccination of their sow population. Nevertheless, 36% of the herd owners still reported having had problems related to PRRS in the year preceding the sampling date, confirming that the effectiveness of PRRSv vaccination remains suboptimal in some cases ,10. ResuAll 1400 samples were tested on two commercially available ELISA kits: ELISA 1 and ELISA 2. An overall strong positive correlation was found between the ELISA 1 and ELISA 2 results. However, upon closer inspection of the 84 ELISA seronegative samples (in either or both tests), some discrepancies in classification were observed. Additionally, the 84 ELISA seronegative samples (in either or both tests) were further analyzed on two other ELISA kits: ELISA 3 and 4. Although most of the ELISA 1 and/or ELISA 2 sows were also seronegative in ELISA 3 and 4, some discrepancies in classification were again observed. The discrepancies can be explained by differences in both specificity and sensitivity between different ELISA tests ,30. BierTo obtain a first indication of the relevance of the ELISA non-responders, a VN was performed to determine the presence of PRRSv-specific NAbs. VN was performed against the PRRSv-1 DV strain, the PRRSv strain from which the Porcilis vaccine is derived, the vaccine which was most frequently used in the selected herds. Firstly, VN results showed no meaningful differences between ELISA low seropositive and ELISA seropositive samples, with both groups having high amounts of NAbs and a high proportion of VN positives. This suggests that the observed ELISA low seropositivity is not due to an inadequate response to PRRSv-vaccination but rather the result of pre-existing immunity which might inhibit the humoral boost after vaccination. Secondly, the VN results showed that the ELISA non-responding sows (in either or both ELISA 1 and 2) had significantly lower NAbs compared to the ELISA (low) seropositive sows, with the latter having twofold higher neutralizing titers compared to the ELISA non-responders. Furthermore, a significant difference in the proportion of VN seropositive samples between the ELISA (low) seropositive and ELISA seronegative samples was observed.Based on these results, a first indication of the possible relevance of the ELISA non-responding sows was provided, especially in the proportion of ELISA non-responders that are also negative in VN. However, additional investigation, with a particular focus on the cell-mediated immune responses (CMI), is needed to further explore the immune status of the ELISA non-responders. Finally, the combination of both ELISA and VN might provide a better tool for determining the  immune status of multiple PRRS-vaccinated sows, but this is less feasible in practice. In this study, an adjusted proportion of 46/1400 (3.3%) non-responders was found when both ELISA and VN are taken into account: 46 multiple PRRSv-vaccinated sows tested negative in at least one ELISA kit and in VN. Unfortunately, VN was not carried out against another PRRSv-1 strain, due to a lack of collected serum.A secondary aim of this cross-sectional study was to identify herd risk factors for the presence of ELISA non-responders. Due to the low frequency of ELISA seronegative samples, the large variety in herd variables and the presence of one herd variable with perfect separation, namely the used PRRSv vaccine (combination Porcilis + Progressis), it was not feasible to build a representative multivariable herd model. For this reason, herd risk factors were only analyzed in a univariable way.A strongly reduced risk for the presence of ELISA 1 non-responding sows was found in herds that vaccinate their sows at 60 days of gestation with an MLV and at 90 days of gestation with a KV. Three possible explanations for this reduced risk could be hypothesized. First, ELISA non-responsiveness might arise due to improper vaccination practices, e.g., mistakes in administration route, dosage, etc. By administering two vaccines at two different time points, the risk of improper vaccination is strongly reduced. Second, non-responsiveness might arise from a repeated vaccination effect: routine vaccination with the same MLV might eventually loses the capability of eliciting an adequate immune response ,32. ThisNo other herd risk factors for the presence of ELISA non-responding sows were found. The used MLV strain, which could be hypothesized to be an important determining factor, did not increase nor decrease the risk of having ELISA 1 non-responders. Another possible factor, the administration route (IM vs. ID), did not influence the presence of ELISA 1 non-responders in this study.No significant influence of sow parity on the observed ELISA non-responsiveness was found. However, parity 2 sows had a slightly higher proportion of ELISA 1 and ELISA 2 seronegative samples compared to the other parities. Furthermore, parity 2 sows had less NAbs, with p-values just above the cut-off for significance, compared to parity 4+ sows. These two findings suggest that parity 2 sows might react less to PRRSv vaccination compared to the other parities. Two possible explanations could be proposed for this hypothesis. First, PRRSv immunity might simply increase with increasing parity: the older the sow, the more PRRSv vaccinations (and wild virus interactions) she has received and, consequently, the higher the PRRSv immune status becomes. Parity 1 sows could be considered a distinct group in this hypothesis, since primiparous sows are generally intensively vaccinated in both the origin herd (in case of purchase) and quarantine unit, resulting in a high immune status before entering the sow stable . AlternaThis study, aimed at detecting the presence of PRRSv-vaccinated, ELISA non-responding sows had some clear strengths as well as some limitations. The study was based on a large sampling, covering more than 5% of Belgian sow herds, and was representative for the geographic distribution and herd size of Belgian sow herds. The median number of sows present on the herds was 305, but outliers (in both directions) were included, avoiding a certain herd-size effect: eleven herds had only 200 or less sows on site, while sixteen herds had 500 or more sows on site. In each herd, sows of different parities were sampled, enabling the investigation of parity influence on the observed PRRSv immune responses. All herd visits were carried out by the PI, allowing the PI to verify the answers given on the questionnaires and to ensure that all blood samples were correctly identified . To avoid a certain ELISA kit effect on the observed results, multiple ELISAs were used to determine the immune status of the sampled sows. The cross-sectional design allowed us to have a snapshot of the real field situation at a given time, but this design has some limitations as well. One obvious limitation was the lack of humoral follow-up: ELISA non-responders were not followed-up during this study to investigate whether or not they seroconvert after the next PRRSv vaccination. Furthermore, there was no control over the correct administration of PRRSv vaccination. Finally, selection of herds was not completely at random: the respective herd veterinarians selected herds that were interested to participate in this study. To avoid selection bias as much as possible, different veterinarians participated in this study.The overall proportion of ELISA non-responders was relatively low (3.5–4.1%), but the proportion of herds having ELISA non-responders was quite high (40%), suggesting that this phenomenon is not limited to specific herds. Several hypotheses that might explain the origin of non-responders arose from this study, but additional research is needed to explore these. A first indication of the biological significance of the ELISA non-responders was provided by the fact that ELISA non-responding sows had significantly less NAbs compared to the ELISA responding sows. However, next to the humoral immune responses, cell-mediated immunity (CMI) plays an important role in the protection against PRRSv infection. CMI was not investigated in the current study and could provide a more complete image of the PRRSv immune status of the ELISA non-responders. Finally, it could be hypothesized that piglets born from non-responding sows might lack maternally derived immunity and are consequently less protected against PRRSv infection. Additionally, these piglets might react differently to PRRSv vaccination themselves compared to piglets born from responding sows. The possible consequences of this peculiar sow PRRSv immune status for the progeny warrant further investigation.