Perna canaliculus) is an industry valued at NZ$ 336 M per annum and is ~ 80% reliant on the natural supply of wild mussel spat harvested at a single location—Te Oneroa-a-Tōhē—Ninety Mile Beach (NMB)—in northern New Zealand. Despite the economic and ecological importance of this spat supply, little is known about the population connectivity of green-lipped mussels in this region or the location of the source population(s). In this study, we used a biophysical model to simulate the two-stage dispersal process of P. canaliculus. A combination of backward and forward tracking experiments was used to identify primary settlement areas and putative source populations. The model was then used to estimate the local connectivity, revealing two geographic regions of connectivity in northern New Zealand, with limited larval exchange between them. Although secondary dispersal can double the dispersal distance, our simulations show that spat collected at NMB originate from neighbouring mussel beds, with large contributions from beds located at Ahipara (southern end of NMB). These results provide information that may be used to help monitor and protect these important source populations to ensure the ongoing success of the New Zealand mussel aquaculture industry.Aquaculture of New Zealand’s endemic green-lipped mussel ( This reliance means that the success of the aquaculture industry is tied to the resilience of natural populations and to the consistent (predictable) recruitment of juveniles following larval dispersal4. Understanding larval dispersal and the connections amongst populations is, therefore, key to the sustainable management of many aquacultures and fisheries resources7.Aquaculture of many marine species relies on seed supply from wild populations, either by harvesting juveniles directly from natural stocks or collecting settling larvae following natural spawning eventsPerna canaliculus (Gmelin 1791) is endemic to New Zealand and is the country’s most valuable aquaculture species, with an export value of NZ$ 336 M in 2019–20208. Currently, the industry is largely reliant on the harvest of wild spat  that are collected and transported to aquaculture centres around the country where it is used to seed the farms9. The majority of the spat supply, about 80%, is harvested from beach-cast at one location: Te Oneroa-a-Tōhe—Ninety Mile Beach (henceforth NMB), in northern New Zealand10. The remainder is caught on spat catching ropes deployed in areas of natural high settlement or produced by small scale commercial hatcheries11. Despite their importance for the aquaculture industry, the source populations providing the spat at NMB, collectively known as Kaitaia spat, remain unknown12. Identifying the source populations will allow assessment and monitoring of their status, which may lead to their protection, and will help ensure the continuing success of the New Zealand mussel aquaculture industry10.The green-lipped mussel 15. P. canaliculus have a pelagic larval stage with a complex settlement behaviour which sees the settled spat detach and move repeatedly in search of a suitable habitat in which to establish16. Mussel larvae settle preferentially onto fragments of branching macroalgae, sediment, plant material and hydroids , forming a complex mix of organic material including the mussel spat18. These macroalgae/spat mixtures, which are negatively buoyant due to the weight of the spat, tend to form aggregations  that drift across the seafloor until they are either washed onto a beach, like NMB, or deposited on more suitable habitat for the mussels . Mussel spat can then detach from the mixture to re-settle , a step which can happen multiple times and last up to a month before recruitment into adult mussel beds16. At NMB, this two-phase settlement process involving the macroalgae/spat is most pronounced and must be taken into account to identify the source populations of the spat supply.Green-lipped mussels are most commonly found in the northern and central parts of New Zealand, living in dense beds in the shallow sub-tidal environment and in aggregations in the intertidal environment20. Alternatively, when the source locations are unknown, particles may be backtracked from the settlement areas by following the current fields back in time. This approach has been used to study the effect of oceanographic structure on dispersal22, to reveal source populations24, or to reconstruct the pathways followed by larvae25. In the present study, we developed a realistic Lagrangian dispersal model26 to backtrack the mussel spat collected at NMB to their source populations, a method used successfully to identify source areas for green-lipped mussel spat in a different part of New Zealand27. Our model runs on a recently developed regional oceanographic model for New Zealand28 and accounts for the factors known to influence the transport of larvae and spat, such as the coastal circulation at NMB, local meteorological forcings, and the complex dispersal and settlement dynamics of green-lipped mussels31.The dispersal of mussel larvae and spat can be studied indirectly using Lagrangian based numerical models that aim to reproduce their trajectories from a source population to a settlement area32. In this case, all currents of the oceanographic model are reversed and the positions of the particles are advected backward over a time-step. However, this simple approach does not simulate turbulence unresolved by the oceanographic model which can increase the uncertainties of the estimates33, but including randomness to backtracking simulation requires specific considerations32. Therefore, to decrease the uncertainties we supplemented the backtracking estimates with a forward-tracking model integrating turbulence. This model was then used to investigate the regional population connectivity of P. canaliculus , while barriers to dispersal will lead to genetic divergence over time. Previous work attempted to identify the source populations of Kaitaia spat using genetic markers36, elemental signatures38, or a combination of both12. However, these studies either did not find any genetic structure amongst the different populations of the North Island36 or reported limited regional differences12. The present paper focusses on comparing regional connectivity modelled estimates with the results of genetic analyses to provide new insights into the mechanisms influencing the genetic structure of mussel in the NMB region and to help identify the source populations of the NMB spat supply. Altogether, our findings may be used to help safeguard the future of New Zealand’s green-lipped mussel aquaculture industry.Several studies have shown a correlation between the dispersal estimates of biophysical models and genetic analyses because connectivity amongst populations is determined by the exchanges of individuals and therefore of genesP. canaliculus. Experiments were conducted to (1) identify the primary settlement areas of mussels harvested at NMB, (2) identify the source sites supplying spat to NMB, (3) estimate the regional connectivity amongst wild populations  of ucted to  identifyMaps of the yearly probability density functions of the trajectories of the macroalgae/spat, aggregated across the 5 months of release, for the three years of runs  showed that trajectories aggregated in the southern region of NMB . Hokianga, Tiriparepa/Scott Point, Ahipara, and Awaroa were the main source sites with a combined averaged contribution of 69.9% across all years. The largest inter-annual variability was found for Hokianga  = 2.71, p = 0.3447).Because the spawning biomass of mussels at each site is unknown, the site-specific contributions to the NMB spat supply were also normalised by site geographical area in the model . There were no connections going from the northern block to the southern block of connectivity.The 10-year averaged secondary connectivity Fig. B was res6 did not identify pronounced genetic structure amongst the six populations, with all pairwise estimates of p-value of 0.4963 for the DOR generation 1 and correlation of p-value of 0.6305 for the DOR generation 10). There was no evidence of a relationship between modelled and genetic connectivity.Genotypic analyses011 Fig. B. In con011 Fig. A and DOR011 Fig. C showed 011 Fig. D. The Ma40 and backtracking simulations have been proven useful to identify the origin of biotic and abiotic material44. However, these models must realistically represent the larvae biological and behavioural characteristics45. The biophysical model developed for this study is unique in that it represents the complex dispersal processes of both the larvae and macroalgae/spat mixture for P. canaliculus, and considers separately primary and secondary settlement. Simulating this two-step dispersal allowed us to map the regional connectivity amongst local populations and identify potential areas of primary settlement.This study is the first to attempt to identify the source populations of the Kaitaia spat with a biophysical model of dispersal. This numerical approach is increasingly being used to study the connectivity of marine populations30. The rocky reefs located in Te Kōhanga/Shipwreck Bay and around Tiriparepa/Scott Point have been documented as receiving both mussel larvae and spat in large quantities during the spawning season47. Additional primary settlement areas were identified in the middle of Ahipara Bay and offshore of NMB, at the southern end of the beach. These areas do not correspond to known rocky reefs and could therefore indicate areas where mussel larvae and bottom-drifting macroalgae are accumulated by mesoscale eddies, a mechanism known to favour larval settlement48. This result is supported by observations of great concentrations of mussel larvae and loose lying macroalgae within Ahipara Bay46. This suggests that larvae could also settle offshore on drifting biological fragments coming from distant reefs.The backtracking simulations of the macroalgae/spat collected at NMB identified a few areas where the trajectories aggregated and where larvae could have settled. Some of the primary settlement areas identified in our study are located close to the coast, which supports the idea that mussel larvae recruit to attached macro-algae that then fragment due to the additional weight of the seed and drift toward NMB30. Strong winds and storms increase the production of drifting macroalgae whilst wind-driven currents favour the transport of macroalgae/spat along the shore and across the surfzone49. Understanding the dynamics behind the aggregation and transport mechanisms of macroalgae/spat will lead to better forecasting of the variability of spat supply at NMB.The rocky reef areas identified in Te Kōhanga/Shipwreck and Ahipara Bay are common to all the years simulated and, therefore, are more likely to represent the actual primary settlement areas than the areas at Tiriparepa/Scott Point or offshore NMB, which were only observed in 2016 and 2017, respectively. However, even within this restricted area, the exact location of the primary settlement area is likely to vary from year to year depending on the patterns of winds, known to drive the variability of the NMB spatfall51. However, the mussel bed at Tiriparepa/Scott Point goes through pronounced density fluctuations, with mass mortality events attributed to the accumulation of macroalgae/spat rafts that out-compete the adult mussels47. Therefore, this population might not be a stable and reliable source of larvae for the NMB spatfall each and every year. Similarly, we estimate that Hokianga is an important contributor in certain years  but due to its large inter-annual variability, it might also not be a long term stable source of larvae. The spat supply contribution of Ahipara, when scaled up to reflect the size of the local mussel population , may be significant, and overall more stable in the long term. Furthermore, both backtracking and forward tracking simulations highlighted the contribution of Ahipara to the overall connectivity as an important source of larvae and macroalgae/spat. In conclusion, we suggest that the mussel population at Ahipara is likely to be the main contributor to the NMB spatfall events.All of the mussel populations connected to NMB through the two-step dispersal process are located in the northern part of our domain and in the neighbourhood of the beach itself. The populations most likely to contribute to the NMB spatfall are located at Hokianga, Tiriparepa/Scott Point and Ahipara. There are large mussel beds in these three locations53. Uncertainties in backtracking simulations could be reduced using complex approaches that better integrate stochastic movements which may lead to better estimates of the contributions of mussel populations to the NMB spat supply54. Finally, these estimates would benefit from a better knowledge of the exact location and extent of the mussel beds. Indeed, the model could over- or under-estimate the connectivity by considering mussel reefs in areas where they are absent or by not including large subtidal mussel beds if they were to exist in the region. Conducting targeted benthic surveys would help decrease this uncertainty.Our ability to correctly identify the source populations hinges on the accuracy of the oceanographic model, the uncertainties associated with particle tracking methods, and the appropriate representation of biophysical interaction in the model. While the oceanographic model’s accuracy has been tested for open waters and at meso-to-large scale, near-shore processes that are not well represented in the model may impact the results. A higher resolution model would better represent along and cross-shore transport of particles in the nearshore region, processes that are critical to spat transport, and could impact the retention of particles within Ahipara Bay55, and are also supported by analysis of the population genetic structure of P. canaliculus in New Zealand which reveals high rates of self-recruitment within populations, despite limited genetic structure56. Interestingly, while secondary dispersal can double the dispersal distances, it did not create new connections in the region.We identified two well-defined blocks of connectivity with limited exchange between them. The northern block extends from Tiriparepa/Scott Point (the northernmost population in our study) to Aranga and includes all of NMB. All the populations in this block are interconnected and we identified an area of strong local larval retention around Ahipara Bay. The southern block extends from Muriwai/Collins Bay to Ōakura. The exchanges within this block of connectivity are not as homogeneous as in the northern block, with potentially two sub-groups of connectivity: a northern, well-interconnected group of populations centered around Te Ākau, and a group around Ōakura characterised by a directionality of exchanges with stronger southward than northward connectivity. Dispersal kernels revealed a strong local retention with most settlement happening within 20 km of the release location. These results are in agreement with other studies that suggest that most mussel larvae likely disperse over short distances (10 km or less)58. Ripiro Beach, the longest sandy shore in New Zealand at 107 km in length, is located between Muriwai and Aranga, and there is no intertidal or subtidal rocky habitat that could support green-lipped mussels in this region. In addition, an alongshore north-flowing current prevents larvae from the northern populations travelling to the southern areas. It does, however, allow some larvae to travel from Muriwai/Collins Bay to the northern populations.The patterns of connectivity observed here can be explained by the influence of the currents on larval dispersal , with higher resolution ability64 than mitochondrial DNA SSCPs36 or microsatellite loci6, are expected to be better suited to test the model of two regional groups because they are more adapted to study the structure of populations of highly connected species with large effective population sizes65.The model suggests the presence of two major subpopulations of green-lipped mussels on the west coast of the northern North Island, with limited exchange between them. However, this result is in contrast to findings from studies of the population genetics of 66 and, although the magnitude of the reproductive output is lower than during the primary spawning season, they contribute to population connectivity. Seasonal primary and secondary settlement patterns could be substantially different and could account for the mismatch between model estimates and genetic structure. However, modelling the trickle spawning of P. canaliculus showed very little variability in seasonal dispersal patterns at the national scale20. Overall, linking genetic structure to modelled estimates is complicated because dispersal and settlement of larvae may be decoupled68. Ecological factors not considered in the model, such as food availability, predation rates, and post-settlement survival, may impact genetic connectivity independently of oceanographic factors, creating a mismatch between larval dispersal and realised connectivity69. Overall, further research needs to be conducted to better understand whether patterns of spatial genetic structuring are caused by oceanographic processes70.Additionally, our study only considered the primary spawning season producing the spat harvested at NMB between July and November. However, green-lipped mussels can spawn and settle throughout the year71. If applied to other regions of New Zealand, the method employed in our study can be used to inform local spat-catching operations and decrease the reliance of the industry on the NMB spatfall events27. It can help in finding areas where spat are most likely to accumulate and therefore may be collected on settlement ropes, and it can help in identifying local source populations that may need protection and/or restoration. Our study shows, however, that high spatial resolution models are necessary to fully understand the dispersal and connectivity of populations at a local scale. Oceanographic models with still greater resolution may be required to explain the observed intra-annual and inter-annual variability of spat supply, a result that may lead to better predictions of spatfall events at NMB72. A better understanding of the mechanisms involved in spat settlement, retention, and arrival at NMB may also explain and help predict the infrequent periods when spat have historically not arrived at the beach, which would be invaluable to the New Zealand green-lipped mussel industry by allowing better planning of spat collection activities73.A multi-disciplinary approach combining genetic analysis and biophysical modelling is key to addressing some of the challenges faced by sustainable exploitation of marine resources in the world75. The continental shelf is narrower at the northernmost point (Cape Reinga/Te Rerenga Wairua) with a width of 10 km, and wider at the southern end in the Taranaki region with a width of 100 km75.Ninety Mile Beach (NMB) is located on the western coast of the north of Te-Ika-a-Maui, New Zealand’s North Island Fig. . The wav76. The West Auckland Current (WAC) is weaker and more variable than its eastern counterpart75. The WAC flows north to south from Tiriparepa/Scott Point to Raglan where it meets the north-flowing Westland Current77. A full description of the currents is available in the supplementary material  flow north, as evidenced by the movement of mussel spat that appear first in Ahipara and drift north following NMB46. The mean wind stress at NMB is cross-shore, which could create this alongshore flow and a region of upwelling74.The major currents in the northern region are influenced by the Tasman Front, a series of eddies and meanders originating from the East Australian Current (EAC). At Cape Reinga the EAC bifurcates and flows southward to form the East and West Auckland Currents either side of the Northland peninsula6. There is no published database of the location of wild mussel beds in New Zealand. However, we know that P. canaliculus is abundant on North Island coasts13, so we considered that every rocky shore is a potential habitat for green-lipped mussels. The rocky shore along the coast was visually identified on satellite images and divided into distinct geographic areas of continuous stretches of rocky shore. We identified 18 potential mussel populations  version 3.978. The Moana Hindcast is a large regional model that covers New Zealand coastal and shelf circulation at a grid size of 28. The current study uses data between June and November  for a 10-year period (2008–2017). The hydrodynamic model takes into account atmospheric forcing , provided by the Climate Forecast System Reanalysis (CFSR), National Center for Atmospheric Research (NCAR). Other forcings include fluxes from 42 rivers around New Zealand  and tides .The oceanic conditions were provided by the Moana Hindcast Modelhttp://github.com/opendrift), an open-source Python-based framework for Lagrangian particle modelling26, to simulate the dispersal trajectories of mussel larvae and spat around NMB. We used an OpenDrift sub-module developed specifically for bivalve larvae20 and configured it to represent the dispersal stages of P. canaliculus.We used OpenDrift  over the austral winter and spring (June to November) to match the spat harvesting season at NMB86).OpenDrift was run offline, and the motion of the particles was integrated using a 4th order Runge–Kutta method with a time-step of 900 s. We performed a set of four experiments: two backtracking experiments (to identify the origin of the spat collected on NMB) and two forward tracking experiments . In both cases, the motion of the particles was integrated in three dimensions. For the backtracking experiments, we did not integrate diffusion  to the model. For the forward tracking experiments, unresolved turbulence was modelled using a horizontal diffusion coefficient of 0.1176 20. Release sites -mussel reefs, primary settlement areas, and NMB itself- were selected to represent mussel occurrence  are smaller and younger than mussels collected in northern part of the beach46. Using the size of mussel spat sampled from different locations along NMB46 and the corresponding spat growth rate87, we estimated that the dispersal time (spat age) difference from one end of the beach to the other is ~ 5.5 days. Therefore, we applied a dispersal lag to the spat depending on their release latitude (from 0 to 5.5 days from south to north) and backtracked the spat for a maximum of 25.5 days. Primary settlement areas were located using the density of trajectories between 10 and 25.5 days of dispersal .The trajectories of the spat were backtracked from NMB to potential areas where mussel larvae undergo their primary settlement. We released 200,000 particles per month from 200 points randomly distributed along NMB . Particles were released randomly from a depth of 0 to 30 m , and backtracked for a maximum of 35 days. Dispersal trajectories were stopped when larvae that were aged from 21 to 35 days moved on top of one of the 18 potential mussel populations  and were tracked for a maximum of 30 days. Spat that did not settle after 30 days were removed from the simulation. The particles were given a terminal sinking velocity of 0.001 88, to which we add the transport of spat—was analysed using three metrics: the probability density functions of trajectories, connectivity matrices and dispersal kernels.Dispersal—the sum of larval hatching, transport, survival, and settlementThe probability density functions of trajectories were estimated by computing the probability of presence of the particles within the cells of a 299 89 represents the probability of connection between mussel populations using a matrix j and a settlement-site i, with local retention indicated on the diagonal . Population genetic structure, based on microsatellite variation at 10 polymorphic loci, was assessed for 288 mussels sampled at six sites , and used to compute pairwise A genotypic analysis was conducted as part of a previous study aiming to identify the source of the NMB spat (detailed methods and results are presented in91. The modelled estimates of connectivity were transformed into a metric of distance that could be compared to the population differentiation (6). The averaged connectivity matrix was down-scaled to the population level—with 19 populations p– and standardised across columns to generate a migration matrix. This matrix quantifies the relative contributions of all sources to the total settlement at all receiving populations, and its diagonal represents self-recruitment. The migration matrix was then projected forward in time92 to simulate exchanges amongst mussel populations over 10 generations.We tested for a correlation between genetic empirical data and connectivity estimates following94 such that x and y are migration probabilities between pairs of populations. The resulting distance matrices, named Derived Oceanographic Resistance (DOR), are square diagonal matrices with pairwise distances normalised between 0 and 1 . The correlation of genetic distances (20.Both migration matrices were then used to compute Nei’s Supplementary Information.


Ammopiptanthus nanus (M. Pop.) Cheng f. is a very important resource plant that integrates soil and water conservation, afforestation of barren mountains, and ornamental, medicinal, and scientific research functions and is also a critically endangered plant in China, remaining in only six small fragmented populations in the wild. These populations have been suffering from severe anthropomorphic disturbances, causing further losses in genetic diversity. However, its genetic diversity level and genetic differentiation degree among the fragmented populations are still unclear. Inthis study, DNA was extracted from fresh leaves from the remnant populations of A. nanus, and the inter-simple-sequence repeat (ISSR) molecular marker system was used to assess its level of genetic diversity and differentiation. The result was that its genetic diversity is low at both species and population levels, with only 51.70% and 26.84% polymorphic loci, respectively. The Akeqi population had the highest genetic diversity, whereas the Ohsalur and Xiaoerbulak populations had the lowest. There was significant genetic differentiation among the populations, and the value of the genetic differentiation coefficient (Gst) was as high as 0.73, while the gene flow value was as low as 0.19 owing to spatial fragmentation and a serious genetic exchange barrier among the populations. It is suggested that a nature reserve and germplasm banks should be established as soon as possible for elimination of the anthropomorphic disturbances, and mutual introductions between the populations and introduced patches of the species, such as with habitat corridors or stepping stones, should be performed simultaneously to improve the genetic diversity of the isolated populations for the conservation of this plant. Genetic diversity is the amount of genetic variability present among individuals of a variety or population within a species. It is the product of the recombination of genetic material (DNA) during the inheritance process, mutation, gene flow, and genetic drift , and it Genetic diversity within a species is a determinant of its successful response to natural or anthropogenic disturbance events, and the level of genetic diversity also determines evolutionary trends in a species. High genetic diversity improves species’ adaptability ; thus, dHabitat fragmentation can transform a large and continuous population into several small, isolated parts surrounded by heterogeneous landscapes ,11. The A. nanus is the Leguminaceae family and is a solo broad-leaved evergreen plant in the desert region of northwest China [A very valuable relict plant, st China . It is ost China  and in tst China ,17, growst China  and coldst China ,20,21 asst China , which wHowever, climate change and long-term anthropogenic and natural disturbance events have resulted in a thin population of the species, with only six small, isolated surviving populations ,24,25, aAlthough the level of genetic diversity and the population differentiation of the plant has been examined, there are great differences between the results of different studies. The results of Ge et al. (2005) showed that there was a low level of genetic differentiation, with only 8.5% of genetic differences occurring among populations ; howeverThere are many molecular markers available these days to analyze genetic diversity and population genetic structure in plants. The most frequently used molecular markers are inter-simple-sequence repeats (ISSR) . The maiA. nanus were collected in this study, and inter-simple-sequence repeat (ISSR)-based genetic structure analysis was conducted for examining the genetic diversity level and genetic differentiation extent of the species and proposing effective conservation strategies.Therefore, fresh leaves from all surviving populations of A. nanus  surviving in Wuqia county in China . The extracted DNA was checked for purity and quality by agarose gel electrophoresis and was stored at −80 °C.Taq PCR Mix, and 8.5 μL of ddH2O. Initial denaturation was performed at 94 °C for 5 min, followed by 45 cycles of 30 s at 94 °C, 60 s at 50–54 °C (depending on the primer), and 120 s at 72 °C; the final extension was at 72 °C for 7 min.Polymerase chain reaction (PCR) amplification was performed using a Mastercycler Nexus X2 (Germany). The total volume of the amplification system was 25 μL, containing 2 μL of template DNA, 2 μL of primers, 12.5 μL of 2 × Nuclear DNA was amplifified by PCR using ISSR primers from the University of British Columbia primer set 9 . Following an initial screening from 100 primers, 10 primers that obtained maximum numbers of reliable and reproducible polymorphisms were then selected for analysis of the populations .A), the effective number of alleles per locus (NE), Shannon’s information index (I), Nei’s gene diversity index (H), the genetic differentiation coefficient , gene flow level (Nm), genetic distance, and genetic similarity were calculated using the POPGENE 32 software. SPSS 20.0 software was used for statistical analysis of the genetic diversity of A. nanus at species and population levels, and Duncan multiple comparisons were used for significant difference tests of NA, NE, H, and I among the populations (p < 0.05). The data are presented in the form of mean ± standard error. UPGMA clustering maps were constructed using NTSYS 2.1 software.Only bands that were clearly recorded across all populations were used. The ISSR profiles for each sample were scored on the presence or absence of the amplified products. The percentage of polymorphic loci (P), the average number of observed alleles per locus  at the species and population levels, respectively. However, the diversity parameters differed significantly among the six populations , with a mean value of 0.66. The genetic similarity between the Ohsalur and Akeqi populations was the highest, but that between Ohsalur and Tuopa populations was the lowest .An UPGMA clustering map of the populations was constructed using NTSYS 2.1 software , which sStudy of genetic diversity and the population differentiation of an endangered species is a basis for exploring the adaptation of the species to its environment and is core to conservation biology. The genetic diversity of plant populations is determined by many factors, such as habitat type, reproductive mode, genetic mutation, genetic drift, and gene flow . The perA. mongolicus [A. nanus were also extremely low at both species and population levels in the present study.The genetic diversity revealed in our study is lower than that detected by Zhao et al.  and is mngolicus . The H aA = 1.43, NE = 1.26, H = 1.53, and I = 0.23).Zhao et al. (2016) found that the Kangsu population had the highest genetic diversity, the Xiaoerbulak population had the lowest, and the Ohsalur population was moderately diverse . HoweverA. nanus communities, and camels and goats are the main herbivores and they prefer the buds, shoot tips, young branches, and leaves. Although A. nanus is a shrub, its branches are fragile; individuals along gullies are susceptible to damage from rocks rolling down along the mountain slope owing to flooding, and approximately 80% of individuals along gullies were broken by rocks  waltoni Cockerell, Megachile  sp., and Halictus sp. are its main pollinators [A complex breeding system was found in ectively . It has linators ; their eA. nanus. The present populations likely evolved from a few individuals that survived a previous bottleneck event; thus, genetic similarity among individuals within the present population is very high, owing to a founder effect [Wuqia County is the historical refuge of the Tertiary glacial period relic species r effect .An UPGMA clustering map showed that the genetic distance between Akeqi and Ohsalur populations was the shortest, although the spatial distance between the two sites was the greatest. Previous studies showed that the genetic distance between populations was proportional to the spatial distance between them ,36,39; hA. nanus face serious threats from low genetic diversity, isolation, and lack of gene flow, resulting in strong inter-population genetic differentiation. These populations have been suffering from severe anthropomorphic disturbances, causing further losses in genetic diversity. Survival of the endangered plant has been negatively affected by some anthropomorphic interference events, reducing the fitness of the species in its native habitat. The population size has been decreasing and is rarely replenished by seedlings. Thus, genetic diversity of the species will continue to decrease if no positive steps are taken.Populations of Therefore, a nature reserve for this species and germplasm banks should be established as soon as possible, and anthropomorphic disturbances, such as felling, road-building, or grazing activities, should be strictly prohibited. Second, manual interventions through seed sowing and mutual transplantation among populations should be carried out to increase populations’ genetic diversity. In particular, since the Akeqi and Heiziwei populations are highly genetically diverse, seeds can be collected from these populations and widely sown in other populations to improve their genetic diversity.Identifying species distribution and habitat change is an important part of effective conservation management. Nevertheless, economic development and rapid population growth worldwide in the past few decades have led to dramatic changes in land cover, habitat fragmentation and loss of species, especially in China ,45. HabiLandscape connectivity has potential effects on the survival, fitness, gene flow, diversity, and colonization of distinct small populations and has changed habitat suitability in the area. Habitat corridors save limited land resources and are the main countermeasure for species to reduce the negative impacts of habitat fragmentation and loss, which can promote the connectivity of habitat patches, increase habitat connectivity, and maintain material, energy, and gene flows and interactions among populations in remaining habitat patches . The ecoA. nanus’s habitats are facing the threat of fragmentation and loss, posing a serious challenge to its survival and diversity. Introduced patches of A. nanus should be planted in suitable ranges between the six surviving populations as habitat corridors or stepping stones to reduce their degree of isolation and increase gene flow among them; thereby, the adaptability potential of this species may be improved.Due to the long-term impact of human activities, A. nanus, there are few plant species available for insects to feed on, and the pest damage faced by the endangered plant is becoming increasingly severe. During our continuous years of investigation, we found that the insect infestation rate of seeds in natural populations is as high as 90%; rampant pest infestation is one of the main factors causing low seed yield and population renewal barriers for this plant. Etiella zinckenell is the pest that poses the most serious threat to the seeds of A. nanus, so it is necessary and urgent to conduct studies on prevention and control of this pest in the future, making sure its population density can be controlled at a low level to avoid a major outbreak of this pest.The reasons for the endangerment of this plant are multifaceted, among which pests in nature are one of the important reasons. Due to the harsh environment of


The Masai giraffe has experienced a population decline from 70,000 to 35,000 in the past three decades and was declared an endangered subspecies by the IUCN in 2019. The remaining number of Masai giraffe are geographically separated by the steep cliffs of the Gregory Rift escarpments (GRE) in Tanzania and Kenya dividing them into two populations, one west and one east of the GRE. The cliffs of the GRE are formidable barriers to east–west dispersal and gene flow and the few remaining natural corridors through the GRE are occupied by human settlements. To assess the impact of the GRE on Masai giraffe gene flow, we examined whole genome sequences of nuclear and mitochondrial DNA (mtDNA) variation in populations located east (Tarangire ecosystem) and west (Serengeti ecosystem) of the GRE in northern Tanzania. Evidence from mtDNA variation, which measures female‐mediated gene flow, suggests that females have not migrated across the GRE between populations in the Serengeti and Tarangire ecosystems in the past ~289,000 years. The analysis of nuclear DNA variation compared to mtDNA DNA variation suggests that male‐mediated gene flow across the GRE has occurred more recently but stopped a few thousand years ago. Our findings show that Masai giraffes are split into two populations and fulfill the criteria for designation as distinct evolutionary significant units (ESUs), which we denote as western Masai giraffe and eastern Masai giraffe. While establishing giraffe dispersal corridors across the GRE is impractical, conservation efforts should be focused on maintaining connectivity among populations within each of these two populations. The importance of these efforts is heightened by our finding that the inbreeding coefficients are high in some of these Masai giraffe populations, which could result in inbreeding depression in the small and fragmented populations. The steep escarpments of the GRV are formidable barriers to migration of Masai giraffe. To assess the impact of the GRV on Masai giraffe gene flow, we examined nuclear and mitochondrial DNA variation in subpopulations east and west of the GRV in Tanzania. Strikingly, evidence from mtDNA variation suggests that females have not migrated across the GRV between major subpopulations in the Serengeti and Tarangire ecosystems since the approximate time that Masai giraffes emerged as a (sub)species 250 kya. However, male‐mediated gene flow across the GVR has occurred over most of the history of Masai giraffes but may have ceased in the recent past as natural migratory routes across the escarpment have been blocked by human activities. These findings suggest that the Masai giraffe is now split into two separate metapopulations and in the absence of gene flow will eventually become separate species with populations of <20,000. Both ecosystems conserve biodiversity and large landscapes, and support two of Africa's few remaining long‐distance migrations of large mammals including the white‐bearded wildebeests (Connochaetes taurinus) and the plains zebras (Equus quagga) and along with their major predators including lions (Panthera leo) and leopards (Panthera pardus)  bisect the Masai giraffe populations in northern Tanzania into two distinct regions: west of the GRE including the Serengeti Ecosystem and east of the GRE including the Tarangire Ecosystem Figure . In tota2 for females and 157 km2 for males  , shot from a Model 196 Pneu‐Dart cartridge‐fired projector, to obtain skin biopsies. The type U Pneu‐darts are designed to collect a 3cc tissue plug and then immediately drop off the animal. The veterinarian aimed the rifle at flat surfaces on thighs or shoulders from a distance between 10 and 30 m. We removed the tissue samples from the needles, placed them in a 2 mL microcentrifuge tube, and secured the tube in a cool box with ice packs and froze them to −20°C within 6 h of collection. The samples were collected from different giraffe groups within a short time to minimize sampling closely related individuals and by choosing animals with dissimilar coat spot patterns and/or separated by a 1‐km distance. Animals with similar coat patterns are more likely to be related  and isolated tissue DNA with the Monarch Nucleic Acid Purification Kits using the manufacturer's protocol, but we increased incubation time to 12 h. to ensure the whole tissue was completely lysed. We extracted DNA from the samples at the Nelson Mandela African Institution of Science and Technology.d) and nucleotide diversity (π) using DnaSP whereas pairwise genetic fixation (FST) was calculated using the Arlequin version 3.5   of the 100 Masai giraffe dart biopsy samples using short read Illumina sequencing. We sequenced 25 samples at a medium coverage level  and 75 samples at low coverage level . Low coverage genomic sequence data are sufficient to accurately estimate most population genetic parameters utilizing genotype likelihood estimations as described below. To assess mitochondrial genetic variation, we extracted and assembled the whole mitochondrial genome  for the 100 dart biopsy samples. Uniquely indexed libraries for each of the samples were prepared at the Pennsylvania State University Huck Institute's genomic core and sequenced at the Pennsylvania State University Hershey genomics core on an Illumina NovaSeq. The samples were prepared with unique index sequence attached to the template. All samples were sequenced at the Sequencing facility in Hershey Medical Center, as 150 bp paired‐end reads on a multiplexed sequencing NovaSeq Illumina instrument. Bcl2fastq software was used to demultiplex the mixed sequences. During the demultiplexing, reads were checked against the adapter sequence (‐‐mask‐short‐adapter‐reads = 10) and the bases run into the adapter were turned to Ns. The 75 samples sequenced at low coverage yielded ~1.66× average peak depth of mapped reads and the 25 samples sequenced at medium coverage yielded ~24.1× average peak depth of mapped reads. In order to avoid the magnitude difference in coverage in the population analyses, we down‐sampled the 25 medium coverage reads by 85% using the random sample process (seed = 11) in seqkit v0.11.0  that was previously reported  and a full implementation of EM ML algorithm. The former produces an estimate of all parameters such as the individual inbreeding and site frequency, which is required as the initial values to start the iterations in the latter. To avoid the algorithm converging to local maxima, the approximated EM was run 20 times with a convergent criteria (‐‐min_epsilon) of 1e−5 and random generated initial values  in each run. We chose the parameters file of the run that exhibited the largest global log likelihood as the initial values in the full EM ML run (without ‐‐approx_EM flag) where a convergent criterion of 1e−9 was used.We estimated individual inbreeding coefficients 2.10K from 1 to 5 for five subpopulations where K is the number of ancestry states. The log likelihood of each K was extracted from the output log file and plotted against the K value. From the plot, the value of K was determined as the point at which the slope of line segments shifts downward. The result from NGSadmix (.qopt file) contains the inferred proportions for each individual and was plotted using the R package “xadmix”   haplotypes as they are to each other  as the calibration time and 289 kya using Giraffa .To assess mtDNA variation we sequenced a 1140 bp fragment for 320 individuals Table  and the G. c. thornicrofti), which mtDNA whole genome sequence data are available, exhibited the WMG1 haplotype  also revealed a large degree of differentiation on the PCoA1 axis between populations lying east and west of the GRE  haplotype, which may have resulted from an introgression event  sites. The Tarangire NP (TNP) and Manyara Ranch (MRC) giraffes exhibited the smallest FST (0.0140) whereas the highest FST values (0.0773–0.0832) were between the four western and eastern Masai giraffe pairwise populations that lie across the Manyara‐Natron escarpment from each other . We evaluated three potential dispersal routes  on geographic distance , while significant correlations for nuDNA FST were found for the Engaresero‐Salei WEP (R2 = 0.880**) and the Manyara‐Eyasi WEP (R2 = 0.874**) using the Rousset method , we estimated the correlation between pairwise population es Table  of mtDNAs Figure  as well Rousset, , and theRousset, . A highld Figure . No sign4The Masai giraffe population has plummeted in the past 30 years as the result of human activities including illegal hunting and land use changes creating fragmented populations with reduced opportunities for dispersal among them  the founders of the western and eastern sides of the GRE were independent, arising from ancestral Masai giraffe populations that already possessed different mtDNA haplotypes and (2) the western side of the GRE was founded first and subsequently the eastern side was founded by dispersal from the west. The independent origin hypothesis would require circumnavigating the steep GRE either from the north between the gap separating the western and eastern branches of the Great Rift in Uganda and Kenya or possibly from the south through gaps in the Tanzanian Craton in southern Tanzania and Zambia  is positive for all eastern giraffes and negative for all western giraffes. While the  Wright,  and furt Wright, . The ManFST values between eastern and western Masai giraffe populations range between 0.0773 and 0.0832, which are comparable to the difference between plains zebra populations in Etosha NP and Luangwa Valley NP separated by 1800 km  populations in British Columbia and Alaska separated by 2000 km of mountainous terrain and between wolf populations in Russia separated by 2100 km  for Masai giraffe in our study were high, comparable to the F of cheetah (Acinonyx jubatus jubatus)  utilizing the same method for estimating F , common elands (Taurotragus oryx), and giraffes  with no more than 20,000 in each. The proposed designation of western Masai giraffe and eastern Masai giraffe as ESUs is based on meeting the specific criteria that the two populations in question are reproductively isolated Waples,  and that2 Serengeti Ecosystem, with no natural barriers that should impede dispersal and gene flow between them, but dispersal and movement rates among WMG populations are unknown. WMG populations are subject to predation pressure and illegal hunting . LMNP is comprised of a narrow strip of land sandwiched between the Manyara‐Natron Escarpment to the west and Lake Manyara to the east. Wildlife dispersal routes around the southwest and northeast ends of Lake Manyara linking to other areas of the Tarangire Ecosystem  ; data curation (lead); formal analysis ; investigation ; methodology ; project administration (supporting); resources ; supervision (supporting); visualization ; writing – original draft ; writing – review and editing (supporting). Derek E. Lee: Investigation (supporting); writing – original draft (supporting); writing – review and editing (supporting). Lan Wu‐Cavener: Formal analysis (supporting); resources (supporting); writing – review and editing (supporting). David L. Pearce: Formal analysis (supporting); methodology (supporting); software (supporting); writing – review and editing (supporting). Monica L. Bond: Writing – original draft (supporting); writing – review and editing (supporting). Xiaoyi Hou: Resources (supporting); writing – review and editing (supporting). Douglas R. Cavener: Conceptualization (lead); data curation ; formal analysis ; funding acquisition (lead); investigation ; methodology ; project administration (lead); resources ; supervision (lead); validation (lead); visualization ; writing – original draft ; writing – review and editing .Benefits Generated: A research collaboration was developed with scientists from the Tanzania providing genetic samples, all collaborators are included as co‐authors and/or acknowledged for their contributions, and the research addresses a priority concern, in this case the conservation of Masai giraffe in Tanzania and more broadly giraffe East Africa. Our group is committed to international scientific partnerships, as well as institutional capacity building.Permission to conduct this research was provided by the Commission of Science and Technology #2020‐185‐NA‐1990‐172, Tanzania Wildlife Research Institute, Tanzania National Park Authority, Ngorongoro Conservation Authority, Manyara Ranch Conservancy, and IACUC# PROTO201901219 by Penn State University.Appendix S1.Click here for additional data file.


Amphistegina lobifera. In its newly conquered habitat, this prolific calcifier and ecosystem engineer is exposed to environmental conditions that exceed the range of its native habitat. To disentangle which processes facilitated the invasion success of A. lobifera into the Mediterranean Sea we analyzed a ~ 1400 bp sequence fragment covering the SSU and ITS gene markers to compare the populations from its native regions and along the invasion gradient. The genetic variability was studied at four levels: intra-genomic, population, regional and geographical. We observed that the invasion is not associated with genetic differentiation, but the invasive populations show a distinct suppression of intra-genomic variability among the multiple copies of the rRNA gene. A reduced genetic diversity compared to the Indopacific is observed already in the Red Sea populations and their high dispersal potential into the Mediterranean appears consistent with a bridgehead effect resulting from the postglacial expansion from the Indian Ocean into the Red Sea. We conclude that the genetic structure of the invasive populations reflects two processes: high dispersal ability of the Red Sea source population pre-adapted to Mediterranean conditions and a likely suppression of sexual reproduction in the invader. This discovery provides a new perspective on the cost of invasion in marine protists: The success of the invasive A. lobifera in the Mediterranean Sea comes at the cost of abandonment of sexual reproduction.Among the most successful Lessepsian invaders is the symbiont-bearing benthic foraminifera  Unlike normal range extensions, where species are largely tracking their climatic envelope, invasive species conquer entirely new spaces, with a higher probability of facing climatic , physical (light), chemical  or biotic (microbiome and interactome) conditions that exceed the range they experienced within their native habitat. In this context, it is important to understand how a given species can become a successful invader. The challenge of exposure to foreign conditions in the newly conquered space could be counteracted by adaptations. In this scenario, successful invaders can be expected to display a high adaptive potential4. Alternatively, the native population could already possess the key adaptations, for example as a result of its evolutionary history6, past migration events7, or ecological filtering8.Biological invasions driven by climate change are currently profoundly modifying ecological landscapes10, with more new invaders appearing as the ongoing warming makes the Levantine Basin more tropics-like11. Among the particularly successful invaders are symbiont-bearing larger benthic foraminifera (LBF). LBFs inhabit shallow coastal waters, where they commonly live attached to algae or hard substrate12. Foraminifera have limited capacity for active movement during life, but adult specimens can be passively suspended and transported by currents13 and the passive mobility of the minute juveniles, propagules, or flagellated gametes produced by sexual reproduction is likely even larger15. In addition, foraminiferal dispersal mediated by travel in the digestive system of fish was documented in the Mediterranean14. This combination of multiple mechanisms of dispersal results in broad species ranges and lack of regional population differentiation17.A remarkable biological invasion phenomenon known as the Lessepsian invasion has taken place in the Mediterranean Sea since 1869. The opening of the Suez Canal in that year ignited a dramatic and largely uni-directional migration of Indo-Pacific marine species into the Mediterranean. So far, over 600 invasive marine species have been reported in the eastern MediterraneanAmphistegina lobifera Larsen 1976, whose prolifically calcifying invasive populations modify the sediment substrate and displace native species19. After establishing itself in the Levantine Basin, its invasion towards the west accelerated in the last few decades26 and the species now expanded its range to Sicily27. The invasion appears to be sourced entirely from within the western Indian Ocean genotype Ia of the species17, but it remains unknown how the genetic diversity of the invading populations is structured along the invasion gradient.The most successful Lessepsian invader LBF is the diatom-bearing 30) or because the source population is highly structured and only one subpopulation possessed adaptations allowing it to invade33. The combination of a founder effect and exposure to the new environment could also lead to rapid emergence of new variation in the invasive population . In both cases, the reduced variability and increased divergence should show a gradient along the invasion progression36, with most severe effects visible at the invasion front. Alternatively, the source population could have already possessed the necessary adaptations to the new environment and in the presence of a large dispersal potential17, the opening of the Suez Canal could have merely removed a physical barrier after which the invader rapidly fills the free space without genetic differentiation.Theoretically, an invading population could show a reduced genetic diversity compared to the source population because only a fraction of the source population participated in the invasion  region. In foraminifera, the SSU rRNA gene contains fast-evolving variable regions, which provide a resolution within species40 and the ITS rRNA gene region provides an even higher resolution given its higher mutation rate41. Therefore, this marker should allow us to detect genetic differentiation (or a lack thereof) among the invasive populations of A. lobifera and thus constrain to what degree the invasion success of the species is due to novel adaptive change or preexisting adaptations.Here we investigated the population structure of the invasive Amphistegina lobifera along the invasion gradient, we sampled populations of the species in Sicily, where the most recent invasion front has been identified by Guastella et al.27, and in Israel where populations representative of the source population (Red Sea) and pioneer invaders (Eastern Mediterranean Sea) could be collected  and two additional locations North of Brucoli (Cannizzaro and Recanati), assuming that the invasion front may have moved further North since 2017. At each location, pebbles and macroalgae were collected from the depth of 0.5–5 m by snorkeling. The collected substrates were brushed, and the recovered material was sieved at 63–500 µm and transferred in 0.5 L jars that were filled with ambient seawater. We then examined the samples under a stereomicroscope and assessed qualitatively the presence of A. lobifera in the samples.To characterize the genetic variability of ted Fig. . During A. lobifera shells in the total assemblage (living + dead) and estimate its population density . The results are provided in Supplementary Table We observed that the species was abundant at Capo Passero, Arenella and Plemmirio but absent or rare in the samples from Brucoli, Cannizzaro and Recanati Fig. e. The coA. lobifera were isolated and individually transferred to micropaleontological slides, where they were air dried and stored at -20 °C . The isolated foraminifera were shipped on dry ice to our laboratory in Bremen, where they were stored at − 80 °C. The sampling in Israel was conducted in October 2019. We sampled at Shikmona, Haifa (Mediterranean Sea) and at Eilat, Gulf of Aqaba (Red Sea) where the presence of A. lobifera has already been documented45. The living specimens for genetic analyses were collected and isolated and transported as described above.Following the exploratory survey, samples for genetic analyses were collected at Capo Passero and Plemmirio and at Avola located midway. The samples were collected as described above, but now living specimens of A. lobifera collected at Okinawa, Japan  in September 2015, that were present in our collection to serve as an outgroup. Each specimen was isolated in 50 µl of DOC buffer and the thick calcite shell was cracked with a sterilized crusher. Following the DOC protocol46, each specimen was incubated at 60 °C for 1 h followed by centrifugation at 10.000 rpm for 5 min and stored at 4 °C until further use.Between 10 and 24 specimens per site were selected for genetic analyses. We also analyzed nine specimens of 41 that is located between the ribosomal Small Sub-Unit (SSU) and the Large Sub-Unit (LSU). The genetic diversity of A. lobifera has been previously documented in the Indo-Pacific and the Mediterranean Sea based on a 600 bp fragment located at the end of the SSU . To make our results compatible with the existing data, we designed a protocol to amplify the same SSU fragment together with the entire ITS. We developed a semi-nested PCR protocol using the primers pairs S14F3 (5′-ACGCAMGTGTGAAACTTG-3′)—L5F (5′—TCGCCGTTACTAAGGRAATC—3′). and S14F1 (5′-AAGGGCACCACAAGAACGC-3′)—L5F46. The amplification was carried out using the GoTaq polymerase (Promega) with a PCR mix containing MilliQ water, 5× green buffer , each primer , MgCl2 , dNTP mix  and GoTaq polymerase , and added DNA extract diluted 1:10 to reduce inhibition, within a final volume of 15 µl. The second PCR was carried out with the same mix but using 1 µl of the 1st PCR as the DNA template. The thermal cycling was as follows for both successive PCRs: initial denaturation at 95 °C for 2 min followed by 35 cycles of 30 s of denaturation at 95 °C, annealing for 30 s at 55 °C and extension at 72 °C for 45 s, followed by a final extension at 72 °C for 2 min.Because we aimed at capturing a population dynamic process, we designed a protocol to access the most variable genomic region known for foraminifera, the Internal Transcribed Spacer (ITS)40, the purified PCR product was cloned using the TOPO® TA Cloning Kit . Amplicons were ligated to a pCR 2.1TOPO® vector, transformed into One Shot™ TOP10 chemically competent Escherichia coli cells, and grown overnight on LB-agar plates containing ampicillin (100 mg/ml). Eight to 16 clones per specimen were selected and placed in 1.5 ml tubes containing 30 µl of MilliQ water and a final PCR was performed and sent for Sanger sequencing with an external provider . Due to the long fragment targeted (~ 1400 bp), each PCR product was sequenced from both ends using the primers S14F1 and L5F. The chromatograms were carefully checked and assembled, and the resulting sequences were deposited on NCBI under the accession numbers OP610171-OP610543. In addition to the new sequences, we also recovered all available A. lobifera sequences and associated metadata available on NCBI that were mostly produced by Prazeres et al.17 and Schmidt et al.25. We limited our query to sequences covering the entire ~ 600 bp fragment of the SSU, resulting in a total of 256 sequences. All newly generated sequences and publicly available sequences are provided with associated metadata in Supplementary Table The PCR product obtained was migrated on a 1.5% agarose gel and visually checked under UV light. The samples showing single bands were selected and purified using the QIAquick PCR purification kit (QIAGEN) following the manufacturer's instructions. Because of the presence of large intra-genomic variability among the multiple copies of the rRNA gene in foraminiferaA. lobifera differ from the source Indo-Pacific populations, we constructed phylogenetic trees and compared the distribution of patristic distances among sequences in the different genetic types of the species as identified by Prazeres et al.17. The patristic distances were analyzed for intra-genomic variability (genetic distances among sequences within the same specimen), for population-level variability (genetic distances among sequences from different specimens occurring at the same population), for regional variability (genetic distances among sequences from specimens occurring at different populations within the same oceanic basin) and for geographical variability (genetic distances among sequences from specimens occurring in different oceanic basins). To this end, we constructed two phylogenetic inferences, one including all the sequences of the dataset but covering only the SSU fragment, and one including all the sequences generated in this study and covering the SSU and ITS fragment. For each inference, the sequences were automatically aligned with MAFFT47 and a phylogenetic tree was inferred using RAxML-NG48 with 100 non-parametric bootstraps and using the substitution model TVM + I + G for the SSU alignment and the TIM2ef + I + G4 for the SSU + ITS alignment that were selected with Modeltest-NG50. Both trees are provided in Supplementary Fig. To assess to what degree the invasive populations of ape51 and vegan52, respectively, from the software R 4.1.153.To identify the factors affecting intra-genomic distances within the invasive genotype, we used beta dispersion analysis based on Principle Coordinate analysis of square-root transformed patristic distances. We calculated the distance-to-centroid for each specimen for further statistical analyses (ANOVA and Wilcoxon test) to quantify the importance of the factors “oceanic basin” and “status of invasion” in structuring genetic diversity. The patristic distances and the statistical analyses were calculated using the packages 54, following an algorithm analogous to that proposed by Excoffier and Smouse55 that first constructs Minimum Spanning Trees (MSTs) from a matrix of pairwise distances (absolute number of differences) among haplotypes and includes all possible MSTs using the parsimony criterion to infer and add missing node haplotypes to the MJN graph. We defined ε = 0 for a more stringent distance criterion to select the most parsimonious pathway56. To allow the comparison of the population structure within the different genotypes, an analysis of molecular variance (AMOVA) between and within oceanic basins and populations was conducted for each genotype.To investigate the phylogeographic relationships among the different populations, haplotype networks were constructed for both the SSU alignment and the newly assembled SSU + ITS alignment. We constructed Median-Joining Networks (MJN)57. Negative values of Tajima's D indicate an excess of low-frequency polymorphisms, consistent with positive directional selection or recent population expansion, whereas positive values indicate an excess of intermediate frequency polymorphism potentially due to balancing selection or population contraction58. Nucleotide diversity, number of haplotypes, and number of segregating sites were also calculated to investigate the degree of polymorphism within the genotypes. The networks and AMOVA were performed in the software PopART 1.756, and the genetic diversity indexes and Tajima’s D were calculated in the packages haplotypes59, ape51, and pegas60, from the software R 4.1.153.In addition to the AMOVA, we also calculated the phi-statistics (Fst) that refers to relative contributions of between-population variations to the overall genetic variation in the whole dataset. The groups were tested for adherence to neutrality (random evolution) assumptions, with Tajima's DA. lobifera along the eastern coast of Sicily reached at least Plemmirio, but not beyond Brucoli. At the same time, we observed that the abundance of Amphistegina in Sicily increased dramatically compared to the observations by Guastella et al.27. For instance, in Capo Passero the species represented 35% of the foraminifera in 2017 and in our sampling in 2019 the relative abundance has risen to 75%. In the other two sites where we found A. lobifera (Arenella and Plemmirio), the species represented 17 and 49% of the foraminifera  sites were observed across the 632 sequences in the SSU alignment  compared to variation among oceanic basins or among populations, including within the invasive genotype Ia. The genotype Ia showed the lowest nucleotide diversity, indicating a reduced degree of polymorphism and mutation (Table p = 0.003). The negative Tajima’s D value indicates fewer haplotypes than segregating sites, which is a sign of an excess of rare alleles and can be considered an indicator of population expansion after a recent bottleneck or recent selective sweep62.The AMOVA of the SSU alignment revealed that within all genotypes of on Table  and it wTo assess whether this pattern is due to genetic processes in the invasive populations of the genotype Ia, we carried out the analysis separately for sequences from the Mediterranean and from the Red Sea, and the Indian Ocean Table . This re41, the SSU + ITS haplotype network showed much higher polymorphism, with 130 haplotypes and 895 segregating sites across 373 sequences . Alternatively, the lower sequence diversity could also be a consequence of a selective sweep62. In this scenario, the reduced diversity in the observed marker could signal strong positive selection against another allele located in the proximity, indicating that the invasion could be associated with the presence of some particularly favorable traits. However, the pattern of low nucleotide diversity and high polymorphism is observed already in the source population from the Red Sea. This would imply that the observed bottleneck or selective sweep already affected the Red Sea population and was not associated with the Lessepsian invasion. Indeed, during each glacial sea level lowstand, the Red Sea becomes hypersaline and inhospitable to most marine organisms, and the basin is repopulated during each deglaciation from the Indian Ocean66, with the last such event dating to about 11,000 years ago . The observed reduced genetic diversity could be the result of this last population expansion and seems consistent with a bridgehead effect68, where the source population for the invasion into the Mediterranean Sea was in fact a result of a previous expansion from the Indian Ocean into the Red Sea. This effect could have preconditioned the Red Sea population towards a greater invasiveness, thus facilitating the subsequent colonization of the Mediterranean, but further reducing the genetic variability of the invasive population.At the same time, we observe that the invasive and Red Sea populations show reduced genetic diversity compared to the Indian Ocean populations of the genotype Ia Table . This paAmphistegina may have invaded the Mediterranean Sea on earlier occasions , or even that A. lobifera is native to the Mediterranean Sea71. The genetic structure of all Mediterranean populations analyzed to date indicate that if Amphistegina colonized the Mediterranean Sea in a more distnat past, it must have failed to establish a permanent population, until the most recent invasion event. If the current A. lobifera populations occurring in the Mediterranean Sea would be descending from ancient native or earlier Pleistocene invasive populations, then we should have observed distinct genetic differences compared to the Red Sea population, which was not the case.This reduced genetic variability observed in the Mediterranean Sea is consistent with a recent invasion. Previous studies suggested that 72. However, the loss is likely not random, because it affected all the different types invading the Mediterranean. If it would occur in only one population, then we should see reduced intra-genomic variability and reduced variability among individuals as well. Therefore, the most plausible alternative that explains both, the reduced intra-genomic variability and a high genetic variation among specimens of the same population, would be a change in the reproductive strategy towards the suppression of meiotic recombination. This is because sexual reproduction with recombination would be expected to promote genetic variation both among and within individuals. Hence, the observed reduction in intra-genomic variability could be a consequence of a shift towards an asexual reproductive mode that favors gene conversion-like processes leading to a loss of heterozygosity .Surprisingly, rather than a signal of genetic differentiation within the invading populations, we observe a strong and significant reduction of gene-copy variability throughout the Mediterranean populations Fig. ; Table 376. However, such process should not be acting within the Mediterranean population only, and it is therefore less likely that this process would be responsible for the particular profile we observed.Such gene conversion processes during asexual reproduction could lead to homogenization within a genome. In the presence of continuous re-seeding of the invasive populations from the Red Sea, the large variability among individuals would be preserved. Therefore, we speculate that the genetic structure of the invasive population reflects two processes: high dispersal potential of a pre-adapted Red Sea source population and a suppression of sexual reproduction. It is important to highlight, however, that the observed homogenization could also be a result of concerted evolution, where multiple copies of a gene within a genome evolve in a coordinated mannerAmphistegina is known to have a trimorphic life cycle involving a sexual generation (agamont) and two asexual generations (gamont and schizont), with no necessity for strict alternation between asexual and sexual generations . In symbiotic organisms, a change in reproduction strategy can also be linked to the process of obtaining and maintaining symbiosis. Like in many other symbiont-bearing organisms, in LBFs, asexual reproduction is associated with a vertical transfer of symbionts to the offspring. Offspring generated by multiple fission has a large size (~ 40–50 µm) and can receive the cytoplasm of the parent together with the symbionts78. On the other hand, the tiny gametes (2–3 µm) cannot carry the symbionts and therefore the zygotes must acquire them from the environment. As a result, in cyclic schizogony , the symbiont culture is maintained without the need to receive new symbionts from the environment78, although such uptake of symbionts from the environment  remains a possibility. This could provide an explanation for the lack of sexual reproduction in the invasive A. lobifera. In this scenario, the ability to reproduce by cyclic schizogony would represent an advantage, or even a prerequisite, for populating regions with environmental conditions that do not allow the acquisition of new symbionts77. However, it is unclear whether enhanced cyclic schizogony in the Mediterranean individuals is an actual adaptation or a plastic response from A. lobifera facing extreme conditions. Therefore, it is equally possible that the invaders do not engage in sexual reproduction (gamete release), or they do, and their attempts are not successful.Like many other foraminifera 25. Like in northern Red Sea corals8, the retention of high tolerance in A. lobifera is likely the result of thermal filtering of Indian Ocean populations entering the Red Sea from the south. Therefore, it is possible that already the northern Red Sea populations of A. lobifera live under stressful environmental conditions that lead to partial suppression of sexual reproduction.Interestingly, the slightly but significantly reduced intra-genomic variability in the Red Sea population indicates that the same process, but to a lesser degree, may act already on the northern Red Sea populations of the species. Compared to the Indian Ocean range of the species, the northern Red Sea already represents thermal conditions close to the cold limit of the species range. However, the Red-Sea populations possess tolerance to high temperatures87). For instance, the reed canary grass Phalaris arundinacea (Poaceae) rapid invasion in wetlands in North America has been attributed to clonal subsidy, morphological plasticity, and nutrient availability88. And the invasiveness of the aquatic plant Myriophyllum aquaticum  is regarded to be facilitated by clonal integration, which has been found to significantly improve the growth and photosynthetic performance of daughter ramets89.There are numerous observations of shifts in reproductive strategy in association with biological invasions and several well-known invasive species are asexual, with most examples known among plants (e.g.,90. In contrast, asexual reproduction can be an effective strategy to rapidly increase population size during the colonization of new areas80 but has the disadvantage of decreasing the ability of the invasive population to react to future change by adaptation. Therefore, if the invasive Amphistegina is unable to reproduce sexually in the Mediterranean, its future proliferation may be affected by the loss of adaptive potential in the face of the projected continued environmental changes in their newly conquered space91. Finally, the hypothesis of the loss of sexual reproduction could be tested on other invasive foraminiferal species in the Mediterranean. The outcomes of this analysis may enhance our understanding of adaptive behavioral patterns within this group under diverse stressful conditions and contribute to predicting the species that are more likely to be successful invaders.That marine invasion may impede sexual reproduction among protists has, however, not been documented before. This is significant because sex has an obvious long-term advantage by creating new combinations of genes that allow adaptation to future changing conditionsA. lobifera in the Mediterranean Sea is facilitated by the combination of preadaptation and a high dispersal ability, with sustained re-seeding of the Mediterranean from the Red Sea. The invasion involves many of the genotypes present in the Red Sea population, rather than a specific subtype, indicating that the preadaptation to invasion was widespread in the source population. At the same time, the invasive populations show reduced intragenomic variability associated with sustained high genetic variation among specimens, which can be explained by a lower average heterozygosity due to increased gene conversion during asexual reproduction. The invasion therefore appears to be associated with a sustained change in reproductive strategy towards the abandonment of sex. Either the sexual reproduction is not triggered or cannot be completed due to adverse environmental conditions in the new habitat or, alternatively, because the zygotes have difficulty in acquiring symbionts from the environment. Either way, this discovery provides a new perspective on the cost of invasion in marine protists. If the invasion is facilitated by or requires a shift towards cyclic schizogony, the short-term gain of invasion into new habitats may be offset by a long-term loss of adaptive potential.Our results revealed that the invasion of the symbiont-bearing foraminifera Supplementary Information.Supplementary Table 1.Supplementary Table 2.


OXT, OXTR) and vasopressin  systems, which are associated with the regulation of social and emotional behavior in distinct populations. The analysis was performed using original WGS (whole genome sequencing) data on Eastern Slavs (SlEast), as well as publicly available data from the 1000 Genomes Project on GBR, FIN, IBR, PUR, BEB, CHB, and ACB populations (the latter were taken as reference). To identify selection signatures, we rated the integrated haplotype scores (iHS), the numbers of segregating sites by length (nSl), and the integrated haplotype homozygosity pooled (iHH12) measures; the fixation index Fst was implemented to assess genetic differentiation between populations. We revealed that the strongest genetic differentiation of populations was found with respect to the AVPR1B gene, with the greatest differentiation observed in GRB (Fst = 0.316) and CHB (Fst = 0.325) in comparison to ACB. Also, high Fst values were found for SNVs of the AVPR1B gene rs28499431, rs33940624, rs28477649, rs3883899, and rs28452187 in most of the populations. Selection signatures have also been identified in the AVP, AVPR1A, OXT, and OXTR genes. Our analysis shows that the OXT, OXTR, AVP, AVPR1A, and AVPR1B genes were subject to positive selection in a population-specific process, which was likely contributing to the diversity of adaptive emotional response types and social function realizations.The neurobiological systems of maintenance and control of behavioral responses result from natural selection. We have analyzed the selection signatures for single nucleotide variants (SNV) of the genes of oxytocin ( Identification of positive selection genomic signals is a traditional population genetics approach based on the analysis of results of whole genome sequencing (WGS) from the perspective of comparison between species and populations. The signals indicate genetic variants corresponding to different adaptation models and, therefore, designate loci of phenotypic variability, thus appearing as objects of particular interest when mapping complex traits ,2,3.Homo sapiens in various landscape and climatic zones . In. In25]. WGS data for Afro-Caribbeans in Barbados (ACB), British from England and Scotland (GBR), Finns in Finland (FIN), Iberian population in Spain (IBR), Puerto Ricans in Puerto Rico (PUR), Bengalis in Bangladesh (BEB), and Han Chinese in Beijing (CHB)  were from the 1000 Genomes Consortium public database. Fifty individuals from each ethnic group were selected in a random order from the general pool of data.Isolation of genomic DNA (gDNA) from whole blood samples was performed manually using a DNA Blood Mini Kit  in accordance with the manufacturer’s protocol. The yield and purity of the isolated gDNA were manually determined using Qubit 4.0 fluorimeter  and NanoDropTM One C Microvolume UV-Vis , respectively. Only gDNA samples with absorbance ratios A260/280 of 1.7–1.9 and A230/260 of 1.8–2.2, in accordance, were selected for further analysis.The amount of 150–500 ng of gDNA was used to prepare Next-Generation Sequencing (NGS) libraries. Libraries were prepared using the Illumina DNA Prep kit  according to the manufacturer’s recommendations using the Tecan Freedom EVO robotic station . The gDNA concentrations in library samples were measured using the Infinite F Nano Plus tablet reader . The size of the resulting libraries was determined with an Agilent D1000 reagent kit using an Agilent 4200 TapeStation . Pooling was performed automatically using a Tecan Freedom EVO robotic station . Each of the 50 samples of the library pool was diluted to the final gDNA concentration of 1.5 nM prior to sequencing. Pool quality control was performed with an Agilent High Sensitivity D1000 Screen Tape reagent kit using an Agilent 4200 TapeStation .WGS was performed on an Illumina NovaSeq 6000  with an S4 reagent kit  upon 300 cycles with 2 × 150 bp paired-end reads with at least 30× average depth of coverage (>350 million reads). The Illumina Dragen Bio-IT platform  was used to align reads to the reference genome (GRCh38).The choice of analytical approaches was based on their specificity to spot various characteristics of the selection signal. In general, selection signals are subdivided into two types: “hard” selection, in which de novo mutations are fixed and the frequency of specific haplotypes is significantly increased in a population, therefore eliminating genetic diversity in the vicinity of the adaptive locus ; and “soplink1.9 program [bcftools software [geno0.05 and mind0.1 tools, respectively. Hard-filtering of rare minor alleles was performed with maf0.05. Within-group data pooling was performed after removing multiallelic SNVs and correcting allele flipping. All data were normalized to the GRCh38 reference. The start and end positions for the OXT, OXTR, AVP, AVPR1A, and AVPR1B gene regions (GRCh 38 assembly) were obtained from the NCBI database (accessed on 15 August 2022) [Quality control, cohort pooling, and removal of duplicate SNVs were performed in accordance . Data fo program . bcftoolsoftware  was usedst 2022) .selscan program [selscan program. Windows with crit = 1 values were considered as candidate regions for selection.Genetic variability was assessed in every population to weight intrapopulation variability. The iHS, nSl, and iHH12 approaches for the detection of selection signals were implemented using the  program . SNVs wiHomo sapiens from Africa and its accustoming to new regions of settling. The genetic differentiation of the ACB and SlEast, ACB and GBR, A and CB and FIN, ACB and IBR, ACB and PUR, ACB and BEB, and ACB and CHB populations for the OXT, AVP, AVPR1A, AVPR1B, OXTR genes was calculated based on the pairwise Fst method proposed by Weir & Cockerham, which was implemented in the VCFTools program [We have compared a number of different populations with the ACB population aiming at the evaluation of changes in genomes, which were accompanying the migration of  program . A diffe program .AVPR1B gene. The average Fst value for all analyzed populations was of 0.277 with the differentiation of GRB to ACB and CHB to ACB of Fst = 0.316 and Fst = 0.325, respectively. Selection signatures have also been identified in the AVP, AVPR1A, OXT, and OXTR genes. The Fst values of population differentiation averaged among all variants of the OXT, OXTR, AVP, AVPR1A, and AVPR1B genes for each individual locus are presented in The greatest genetic population differentiation was observed with respect to the AVPR1B gene. For the GRB to ACB and CHB to ACB populations, the mean Fst for all AVPR1B gene variants was 0.321. At the same time, rs28499431, rs33940624, rs28477649, rs3883899, and rs28452187 showed the highest Fst values in all populations under analysis . Additional SNVs have been identified in all populations except ACB.Based on the results of nSl analysis, the selection was confirmed for all SNVs of the AVPR1B gene were identified only in the GBR population. In other populations, positive selection signals were not detected by the iHH12 method  were identified by the iHS and nSL methods, and four variants  by the nSL method (Variants in the ulations . SelectiL method .AVP gene (AVP gene (variant rs3787482)  .OXTR gene generally did not reveal differentiation; however, some variants showed rather high Fst values. Specifically, the levels of differentiation between ACB and CHB in relation to rs2324728l, rs237884, rs1042778, and rs237895, Fst were above 0.5, and between ACB and BEB in relation to rs2324728l and rs237884, Fst reached 0.5  was found in the PUR population using the iHS method . Three SAnalysis of Fst scores revealed the absence of significant differentiation signals between ACB and other populations in relation to the OXT gene . Of noteThe selection of specific genetic structures in human populations occurs under the pressure of various factors. In general, it aims at the fixation of adaptive changes and, in particular, at the consolidation of complex behavioral programs, which conduce the survival of the individual and the viability of an entire population. Genetic studies make a prodigious contribution not only to understanding the evolution of behavioral traits but also to identifying functional gene variants that influence behavior such as caregiving, tribal care, and cooperation, as well as competition, aggression, and stress response.In our study, we showed that genes of the oxytocin and vasopressin systems are objects of positive selection in the population process. The selection evolves individually in each population and provides a variety of behavioral reactions. The significance of neuropeptides OXT and AVP and their receptors OXTR, AVPR1A, and AVPR1B is determined by the role they play in the regulation of social interaction and stress response, as well as in maintaining water homeostasis and carbohydrate metabolism. Respectively, the selection in the genes of the oxytocin and vasopressin systems can be forced by a combined driving force.We found that variants of the OXT, OXTR, AVP, AVPR1A, and AVPR1B genes showed positive selection signals in populations belonging to Indo-European ethnic groups , in the Han subpopulation (CHB), and in Puerto Ricans of Puerto Rico (PUR), who are of a mixed ethnic origin. Overall, the genetic differentiation of the scrutinized populations was stable, as evidenced by the high Fst fixation scores for the analyzed genes in all populations. It is noteworthy that the selection of SNVs of the AVPR1B gene was the most pronounced. SNVs of other genes showed selection signs that were characteristic only for selected populations.The observed high differentiation of populations in relation to the AVPR1B gene confirms the results of the recent study , which iThe AVPR1B receptor expression has been shown both in peripheral tissues  and in tAVPR1A receptor-mediated signaling is deeply involved in the regulation of physiological functions. It is determined by the effects of vasopressin on glycogenolysis in the liver, insulin secretion, contraction of vascular smooth muscle cells, water reabsorption in the renal tubules (antidiuretic activity), and platelet aggregation ,51. The Our analysis of the selection signatures of the AVP, AVPR1A, and AVPR1B genes shows that population differentiation is accompanied by a selection for variants, which supports variability in stress reactivity among individuals and thus determines the diversity of stress-adaptation strategies. The evolutionary strategy of a positive modulation of the stress response, including positive selection in the genes of the vasopressin system, has certain benefits. The intensive response to stress has advantages from a short-term perspective since it supports a quick and balanced response to changing environmental conditions at any physiological level, including the metabolic and immune systems . The dirOur analysis showed that, in contrast to the vasopressin system, the OXT and OXTR genes are main subjects of a neutral evolution, which occurs due to genetic drift. The relative stability of the OXT and OXTR genes in the population process may be due to the exclusive role of this neuropeptide hormone in childbirth , lactation, and maternal behavior. However, we have shown a high level of selection in the OXTR gene in the PUR population; partially selective signals are also found in other populations but not in CHB. Selected OXTR gene variants have been shown to be associated with changes in sensitivity to exogenous oxytocin and contractility of myometrium, as well as (rs4686302) with preterm birth ,58,59. HThe neuropeptide OXT is also involved in the regulation of behavior. It determines the degree of affiliation in interpersonal interactions by increasing the efficiency and functional significance of neural networks involved in the control of social interactions . We haveAVP, AVPR1A, and AVPR1B genes, as well as, to a lesser degree, in the OXT and OXTR genes. Positive selection in these genes was actualized with the individual scenario in each case of population differentiation. High population differentiation was revealed with respect to genes of the vasopressin system and, most pronouncedly, to the AVPR1B gene, with Fst varied from 0.168 to 0.325. The significance of the OXT and OXTR genes in the control of labor  and the maintenance of maternal behavior was, possibly, the factor determined their greater stability in population formation, which was shown in our study. In general, our results indicate that during the formation of populations, there was a positive selection towards variants that determine the diversity of emotional stress reactions. Despite the elevated probability of accumulation of individuals prone to increased stress-reactivity upon realization of the effects of these SVNs, such selection may be an advantage since it supports immediate behavioral and physiological changes, which result from a balanced reaction of the neuronal, metabolic, and immune systems upon acute environmental stimuli.An analysis of the selection signatures of isolated genomic regions of individuals, which was carried out using general population genetic models, allowed us to show positive selection signals in the