Pagrus major) in Kagoshima Bay, Japan. Our analyses found that the stock enhancement program reduced genetic diversity of the population, but the genetic effect diminished with increased size of the wild population. Increases to the seaweed communities and reduced fishing efforts were the primary factors associated with the wild population recovery; effects of aquaculture were much smaller. Our results represent crucial evidence that hatcheries for enhancement and conservation of populations cannot be successful over the long term unless sufficient efforts are also made to reduce harvest rates and rehabilitate natural habitats.Release of captively-bred individuals into the wild is one of the most popular tools in fisheries, forestry, and wildlife management, and introgression of hatchery-reared animals into wild populations is of global concern. However, research and monitoring of impacts on wild populations are generally lacking, and the benefit of hatcheries for long-term fisheries and conservation goals is unclear. Using spatio-temporal genetic monitoring and a four-dacade time series of catch data, we quantified the effects on the size and genetic diversity of wild populations of one of the world’s largest marine stock enhancement programs–the red sea bream ( Huge numbers (>2.6 × 1010) of juveniles of 180 marine species are released into the wild every year in more than 20 countries, but in most cases monitoring efforts are insufficient to properly assess program effectiveness3. Research and monitoring of possible impacts on wild populations are generally lacking1. Although the ability of artificial propagation to maintain populations in the short term is well documented4, the benefit of hatcheries for long-term fisheries and conservation objectives remains unclear5. The most serious concern is genetic impacts of hatchery-reared animals on wild populations, which has long been discussed for salmon and marine species10. Modern parentage assignment studies provide evidence that the relative reproductive success (RRS) of hatchery-reared steelhead trout11, Chinook and coho salmon12 and farmed Atlantic salmon14 can be only half or less that of wild fish. Marine aquaculture is rapidly growing in Asia, the Mediterranean, and other areas in the world3, and genetic effects of huge escapements from fish farms have become a real concern15. Introgression of farmed Atlantic salmon into 62 wild Norwegian populations of Atlantic salmon was reported at ~6% on average17, with varying effects on size and age at maturity18 and other phenotypic and demographic characteristics20. More recently, genetic impacts of farm escapees have been reported for marine fishes24. A systematic review on marine stock enhancement and sea ranching highlighted evidence for substantial gene flow from hatcheries, reduction in genetic diversity in stocked populations, and changes in population structure and life history traits. However, empirical evidence for fitness reduction in stocked and/or farm-escaped populations is lacking, which might be attributed to intrinsic difficulty in collecting the appropriate data3. A rigorous evaluation would need simultaneous monitoring data for time series of population abundance, incorporated with stocking effects and genetic diversity. However, such data are largely not available due to the difficulty of long-term monitoring.Hatchery release is one of the most popular tools in fisheries, forestry, and wildlife managementPagrus major) in Kagoshima Bay, Japan. Red sea bream is a commercially important, demersal fish widely distributed along the Japanese coasts. It is an iconic species for the Japan marine stock enhancement program, with ~9 million juveniles released every year3. Red sea bream mature at 3-4 years25 and spawn during March-May in western Japan. Pelagic larvae are transported to coastal areas and/or bays by tidal currents26. They grow into pelagic juveniles of 10 mm total length about 30 days after hatching and settle on coastal areas or inner bays when juveniles become demersal26. In this demersal stage, the juveniles mainly feed on copepods, which are abundant near the bottom and in seaweed communities26. Density gradients of copepods are the driver for early juveniles to move into their nursery grounds in shallow waters, where gammaridean amphipods, the most important foods for demersal juveniles, are abundant26. When juveniles grow to ~7 cm, young red sea bream begin to leave nursery grounds and move to sallow sandy bottoms. The most critical habitat could be seaweed beds for red sea bream throughout the life cycle, because its habitat complexity reduces vulnerability of juvenile and may determines the year-class strength27. We also quantify the effect of the KB hatchery releases and farm escapees in the top production area on genetic diversity of wild population, based on our spatio-temporal monitoring data for red sea bream. Finally, we infer the effect of captive breeding on fitness of stocked populations by analyzing published genotype data of the red sea bream in Japan and revisiting the results of previous studies.Using a spatio-temporal genetic monitoring and four-dacade time series of catch data, we quantified the effects on the size and genetic diversity of wild populations of one of the world’s largest marine stock enhancement programs–the red sea bream  have been released since 1974 to achieve the goal of recovering the depleted commercial catch. The annual number of juveniles released ranged from 55 to 1,297 thousand and the average ± standard deviation (s.d.) was 636 ± 354 thousand. Red sea bream are caught by pole and line, gochi-nets (surrounding seine), gill nets, and long lines in KB. More than 80% of red sea bream caught in KB are landed at the Kagoshima City Fish Market (KCFM). During 1989–2015, surveys of Kagoshima Prefectural Fisheries Technology and Development Center (KPFTDC) examined ~1.6 million red sea bream to identify hatchery fish caught in KB and landed at fish markets (see Supplementary Information). We organized various datasets for monitoring stocking effects, including environmental data to quantify the effect of the KB red seabream stock enhancement program. Age composition and mean body weights of hatchery and wild fish by age were estimated based on the age–weight key, where ages were determined by reading otolith annuli. Age classes were one to eight, with the last being a “plus” class that included fish older than eight. The total numbers of landings were estimated for hatchery and wild fish by age classes based on commercial landings.KB is a semi-closed bay of 1,129 kmuth Fig. . The tott = 7.135, P = 2.88 × 10−7; t = 9.023, P = 1.90 × 10−7), but there was no significant difference between them, with the average of 13.9 ± 1.2% . Since 1989, a total of 1,598,560 red sea bream landed on fish markets have been checked for the deformity of the internostril epidermis   in the landings in the inner KB (IKB), central KB, and outside the bay were highest in 1990 at 83.3%, 33.5%, and 7.4%; however, Ph decreased consistently after 1991, and hatchery fish currently contribute only ~1.0% to KB landings.Catch of hatchery fish increased after the start of the program and reached 126 tonnes in 1991, but then consistently decreased and by 2016 had dropped to 3 tonnes Fig. . Cumulaton) Fig. . The estSargassum fusiforme) in KB during the increasing period of 1989–2015 ]. The AIC value for the selected model was 160.92 and that of the full model was 164.77, a result that is consistent with the recent increase in the community size of Hijiki in KB (see Supplementary Information).In contrast to declining catch of hatchery fish, the catch of wild fish began to increase after 1991 and in 2016 reached the maximum post- release program of 168 tonnes Fig. . This in015 Fig. . In contish Fig. . A negatish Fig. . The varK = 2), which are mostly explained by genetic differentiation between farmed and the other samples  was 0.01122 ± 0.01140 (s.d.), and the UPGMA dendrogram described the population structure consistent with the Bayesian clustering analysis  and 238 different haplotypes were obtained. We also genotyped 642 individuals collected during 2002 and 2011  at nine microsatellite loci, but we only used the genotypes of five loci successfully genotyped Fig. . Hatcherles Fig. . Hatchersis Fig. . The allsis Fig. . The ressis Fig. . The tessis Fig.  and S4. sis Fig. . In contEvidence exists for reduced genetic diversity, survival rates, and reproductive success in hatchery and farmed fish, and for reduced genetic diversity, changes in life history traits and genetic structure in stocked populations. However, this is the first study to demonstrate that the genetic effect of marine stock enhancement diminished with increased size of the wild population. Increases to the seaweed community and reduced fishing efforts were the dominant factors associated with the wild population recovery. Genetic effects of aquaculture were much smaller.27. In fact, a series of field surveys28 found that communities of Hijiki and Zostera marina were abundant . Under the assumption of a generation time of 4.8 years29 (see Supplementary Data), the survival rate of hatchery-reared red sea bream was reduced by 49 ± 6% (s.e.m.) per generation [1 − exp((−0.1391 ± 0.0123) × 4.8) = 0.487 ± 0.058] after 1991 based on the average recapture rate of 1-year and 8-year-old fish. This agreed with the meta-analysis of steelhead, brown trout and Atlantic salmon, which showed captive breeding reduced RRS by ~40% per generation in captivity11. However, it is noted that the catch of hatchery-reared red sea bream increased until 1991 before declining . The long seed production period of steelhead might lead to substantial adaptation in captivity even in a single generation rearing in hatcheries31. The exponential decay rate of fitness was higher in the red sea bream than salmonids, suggesting that marine fish can be vulnerable to genetic effects of captive breeding after fitness effects emerged. Exponential decay rates did not significantly differ in 1-year-old and 8-year-old recapture rates. Recaptures of the red sea bream after Age 1 (recruitment to fishery) exponentially decreased until Age 7 and cumulative recapture rates were release-year (cohort) specific and decreased along the years  captively reared in the 100-ming Fig. , showingPh values in IKB 32. The Bayesian clustering analysis suggested very weak population differentiation in the Japanese distribution range . The EBFST population structure was consistent with our result for the genetic effect of aquaculture, which was much smaller than that of hatchery releases, as the sample from Wakayama  was closer to wild populations than to the KB sample . The pairwise EBFST values were very small , showing that effects of hatcheries and aquaculture can be spread farther by very high gene flow. So, more areas would be affected, but none very strongly in a meta-population. High gene flow from wild populations could also facilitate backcrossing to wild populations, which might work to diminish the genetic effects of captive breeding over generations if they were additive33. In fact, the genetic effects of the early generation hatchery-reared steelhead on gene expression were additive34, and RRS of the wild-born steelhead F2 descendants (0.87)35 recovered from that of the first-generation hatchery-born fish (0.55)11. Salmonids are semelparous with pair-spawning and fecundity is ~3,000 eggs per female. In contrast, red sea bream is iteroparous with multiple episodes of group-spawning during a spawning period, and fecundity is ~1–2 million eggs. The magnitude of diminishing genetic effects of captive propagation could be larger for marine fishes such as red sea bream than salmonids.The high mixing proportion of hatchery fish in IKB indicated substantial gene flow from hatcheries Fig. . InteresIKB Fig. , implyinple Fig. . The den36 could reduce the effective population size, and hence reduce genetic diversity, in a combined captive–wild system. Typically, not all parents contribute to production of fertilized eggs in spawning tanks and farm net-cages. In such cases, the R-L effect can be more substantial for even modest stocking rates37. Our results suggest that the R-L effect occurs even for hatchery fish with reduced fitness. The genetic effect can be more persistent for cases with equal and/or similar fitness of artificially-produced animals compared to wild ones. Theoretical work of Matsuishi and colleagues38 predicted the rate of replacement of the wild gene by the hatchery gene at a locus in a closed population at equilibrium. Their model assumed that wild and hatchery fish are semelparous with equal sex ratio and have the genotypes WW and HH, respectively, at a locus. We summarized the results of Matsuishi et al. for cases of the additive effect  and stocking rates (R). Even if f = 0, portions of the wild gene will be replaced by the hatchery gene by the hybrids. Complete replacement (=the wild gene <1%) can occurs for cases of f ≥ 0.6 and R ≥ 0.4. Assuming R = 0.1 and equal fitness for wild and hatchery fish (f = 1), more than 99% (50%) of wild genes are expected to be replaced by hatchery genes in 49 (8) generations38  effectect Fig. , which sthan 99% 0% of wilThe biological function of hatcheries is to compensate for natural reproduction by artificial propagation techniques. Our results indicate that hatcheries for enhancement and conservation of populations cannot be successful over the long term unless sufficient effort to reduce fishing efforts and to rehabilitate the degraded nursery and spawning habitat is also made.Ph (proportion of hatchery fish in the landings), and accumulated reclaimed land. We estimated fishing efforts on red sea bream (1989–2015) using a simple regression based on total fishing efforts in KB by five years . Wheareas the catch in a given year was created by the fishing effort of that year, fishing effort could affect reproduction in the previous several years, and this effect was included in the catch. Rational determination of time-lags for the reproduction effect was difficult because of overlapping generations in the catch. Therefore, we used fishing effort data without considering time-lags. The regression analyses used R function lm, and step(lm) for stepwise selection of the explanatory variables by AIC.We analyzed the causes of increased catches of wild red seabream in KB for the recovering period (1989–2015), where the objective variable was wild red seabream catch, and the explanatory variables were harvests of Hijiki , SST (sea surface temperature), fishing effort , 39 The 2002 and 2004 samples were obtained from several fishing ports in the East China Sea (ECS0203) where few releases had been made, and the IKB (IKB0204), including wild fish without DIE. Hatchery-released individuals recaptured in the bay (HA0204) were identified DIE, and wild fish without DIE. In addition to the previously published samples, we collected new wild fish samples from the same IKB (IKB11) and East China Sea (ECS10) sites to compare the genetic effects in KB in 2009–2011 to those in 2002–2004. Hatchery fish were not collected in 2011 because catch was very low, but hatchery fish were produced from the same brood stock during this period (see Supplementary Information). Therefore, IKB04 and IKB11 were affected by the repeatedly-used hatchery broodstock, which can be represented by HA0204. We also collected wild fish samples from Yorishima (YR10), Okayama Prefecture, Uwakai Sea (UK09), Ehime Prefecture, and a sample from a fish farm in Uwakai Sea (FA09) to evaluate the genetic effects of escapees and spawning of farmed fish. Selective breeding of red sea bream for fast growth has been carried out since the 1960s, leading to successful domestication of a major strain (see Supplementary Information), which is widely used in western Japan, including Uwakai Sea where UK09 was collected. All fish samples bought from fishers and a fish farm were sent with ice to our laboratory at Tokyo University of Marine Science and Technology. Muscle tissue from each specimen was stored in 99.5% ethanol for DNA extraction.Eight samples totalling 642 red sea bream from six geographical regions were collected between 2002 and 2011 , of which 167 fish collected during 2002 and 2004 were previously published samples39. The 465-bp mtDNA control regions (mtDNA-CR) were sequenced in 313 fish and aligned with our previously published sequences of 180 fish40. The sequences were aligned using ClustalX41. Haplotypes were defined based on the sequence data using DnaSP ver. 5.1042.Genomic DNA was extracted using the QuickGene Mini-80 , according to the manufacturer’s instructions. DNA fragments corresponding to the CR [~500 base pairs (bp)] were amplified by polymerase chain reaction (PCR) according to our previous studyPagrus major43  and Pagrus auratus44 , which is closely related to P. major. An excess number of alleles per locus and a wide range in base pair length were detected at the Pma4 locus; thus, identifying some alleles, particularly long alleles, was rather difficult43. We could not amplify the DNA fragments or detect the primer dimer in the GT2, GT4, and GA2B loci. Thus, four loci  were excluded. All loci were scored for all individuals, including previously published samples by capillary electrophoresis using an ABI PRISM 3130xl Genetic Analyzer with fluorescent dye-labeled primers. The thermal cycles for PCR amplification were conducted in 10 µL aliquots of a mixture containing 1.0 µL genomic DNA as template (~0.1 µg), 0.1 µL KOD FX, 0.1 µL of 10 µM primers, 5 µL 10 × Ex PCR buffer (8 mM MgCl2), 2.0 µL dNTP mixture, and 1.7 µL sterile water using a thermal cycle profile published previously44. Genotype data quality was evaluated using Microchecker45 to detect scoring errors and null alleles. Linkage disequilibrium and conformation to Hardy–Weinberg equilibrium (HWE) were tested using GENEPOP 4.246 . A total of 157 alleles were found at the five microsatellite loci. Scoring errors caused by stuttering, large allele dropout, or null alleles were not detected. Linkage equilibrium was observed for all pairs of loci in all wild samples and HA0204, but linkage disequilibrium was found in UK09 (between Pma1 and Pma2) and in FA (for seven of 10 locus pairs). HWE was observed for all loci in IKB11. Departures from HWE were observed in three loci in the FA09 and HA0204 samples, and in one locus in all wild fish samples except IKB11, but HWE was confirmed in all loci and samples after Bonferroni correction , as implemented in Genepop. We estimated genetic differentiation between populations in terms of Wright’s FST52. We estimated the posterior means of pairwise FST values (EBFST)53 for high gene flow species54 between all population pairs based on the haplotype and allele frequencies using EBFST function in R package FinePop1.4.1. Based on the EBFST values, we drew UPGMA trees using MEGA755. We also ran STRUCTURE56 under a burn-in of 100,000 iterations, followed by 500,000 Monte Carlo–Monte Carlo (MCMC) repetitions for the number of putative original populations K = 1–5. We used the conditional-likelihood method for genetic stock identification57 using BASEMIX59 and ONCOR60. Standard errors of the mixing proportion estimates were calculated considering the variances in the gene frequencies of the baseline populations using BASEMIX. The 95% confidence intervals for the mixing proportion estimates were calculated with 1,000 bootstraps in ONCOR. Both programs gave similar estimates for the moderate mixing proportion values (Supplementary Table The number of haplotypes, haplotype diversitySupplementary InformationSupplementary Data


Castanopsis tribuloides, C. calathiformis, and Lithocarpus polystachyus all of which were proven previously to be effective native tree species in the restoration of upland evergreen forests in northern Thailand when using the seed sample collection method. We tested our hypothesis as to whether the genetic diversity of a plant population that had been planted from the seeds of 4–6 adult trees would be lower and whether incidences of fixation index (Fis) would be higher among the second generation seedlings of these three Fagaceae species in isolated forest restoration trial plots. Microsatellite primers were selected from the entire genome sequence of C. tribuloides and the genetic sequences of C. tribuloides, L. polystachyus, and C. calathiformis were analyzed. Our results indicated a high degree of genetic diversity (He) in C. tribuloides (0.736) and C. calathiformis (0.481); however, a low level of genetic diversity was observed in L. polystachyus (0.281) within the restored forest. The fixation index for the second generation of L. polystachyus and C. calathiformis in the restored forest showed evidence of inbreeding. These results imply the efficiency of the seed sample collection method and verify that it does not reduce the level of genetic diversity in C. tribuloides and C. calathiformis. However, it may result in incidences of an inbreeding phenomena, suggesting the need to increase the number of adult trees used at the seed collection stage.Restoring isolated patches of forest ecosystems in degraded landscapes could potentially lead to genetic loss and inbreeding. Therefore, this study determined the occurrence of genetic diversity among the tree species  Deforestation in northern Thailand has resulted in numerous small, isolated forest fragments, within which plant inbreeding may occur, leading to levels of reduced genetic diversity and increasing the risk of species extirpations.Ficus spp.), Fabaceae , and Fagaceae , as well as those of other indigenous families, such as Euphorbuaceae , Meliaceae , and Rosaceae   has adapted and modified the framework species method  to restoasoides) .Forest restoration success can be measured both in terms of the number of plant species that re-establish  and the C. acuminatissima (Bl.) A. DC., Quercus semiserrata and Prunus cerasoides D. Don within Doi Suthep-Pui National Park  from nearby small-scale, community tree nurseries. Full details of the study site and plot system were published previously  Rehder & EH Wilsin, Castanopsis tribuloides (Sm.) A. DC., and Lithocarpus polystachyus  Rehder, respectively. The specimens were identified by Dr. Wattana Tanming, from the Queen Sirikit Botanic Garden in Thailand. Castanopsis spp. and Lithocarpus spp. are classified as monoecious trees. These are widely distributed throughout temperate and tropical forest ecosystems . Regarding C. tribuloides and L. polystachyus trees in the restoration plating plots, these were saplings collected from 4 to 6 adult trees from FORRU’s Phenology trail at Doi Suthep-Pui National Park.The genetic basis for the L. polystachyus (31 trees and 35 seedling samples), C. tribuloides (28 trees and 29 seedling samples), and C. calathiformis (17 adult tree samples and 118 seedling samples) for testing purposes  were collected from restoration plots planted in the years 1998, 2000, 2006, and from the nearest natural forests, along the Dong-Seng ridge line (DSR), Dong-Seng upper line (DSU), and Dong-Seng middle line (DSM)  Fig. 1)Fig. 1). ®). The quality and quantity (® and 1% agarose gel electrophoresis.Genomic DNA was extracted from leaves using a genomic DNA extraction kit (RBCquantity  of the gC. tribuloides were sequenced via Next Generation Sequencing (NGS)   (Waikhamng (NGS) . These pstachyus . Thus, tstachyus . Then, t®, a genotyping software, version 2.6.4 (Soft Genetics LLC). The microsatellite allele sizes were sized by comparing the peak of each sample with references of allele sizes, which were then converted into alleles size and entered into a spreadsheet  and observed (Ho) heterozygosity using GenAlEx version 6.5 . The fixK value ranged from 1 to 10 and a burn-in and Chain Monte Carlo (MCMC) were presented as 100,000 replicates (K) in the samples, Structure Harvester was used  of C. calathiformis. Consequently, those primers were selected to study the genetic diversity and population genetic structure of the three Fagaceae species.Nineteen primers selected from hiformis . ThirteeC. calathiformis, and 13 primers for L. polystachyus, with 11 primers being perfect microsatellite types and two being compound microsatellite types. Thus, the transferability ratio (amplified/tested markers) from C. tribuloides to C. calathiformis was 63.16 and from C. tribuloides to L. polystachyus was 68.42.Cross-species tests showed that 12 primers could successfully amplify L. polystachyus, C. tribuloides, and C. calathiformis as well as the potential changes in genetic diversity from the adult trees to their offspring.To investigate whether the genetic diversity of the framework tree species was maintained in restored plots we analyzed the population genetic diversity of L. polystachyus, we also considered the samples collected from the restored forest as a single population and recorded its genetic diversity indexes, including observed heterozygosity (Ho) = 0.171, expected heterozygosity (He) = 0.281, and fixation index (Fis) = 0.432. We analyzed two subpopulations of adult trees and seedlings, resulting in the genetic indexes of 0.200, and 0.142 (Ho), and 0.302 and 0.261 (He), and 0.337 and 0.457 (Fis), respectively. In contrast, we also considered the samples collected from the nearest natural forest as a single population and the genetic diversity indexes were recorded as 0.377 (Ho), 0.550 (He) and 0.314 (Fis). While considering the two subpopulations of adult trees and seedlings in the natural forest, we found that the genetic diversity indexes of the subpopulations were 0.356 and 0.398 (Ho), and 0.564 and 0.537 (He), and 0.369 and 0.258 (Fis), respectively. These results suggest that the genetic diversity of the adult tree subpopulations versus seedlings might not differ in the restored areas. However, the subpopulation genetic diversity indexes of both the adult trees and seedlings collected from the restored areas were lower than those collected from the natural forest , 0.731 (He), and 0.056 (Fis). The genetic diversity indexes of the subpopulations for adult trees and seedlings were 0.658 and 0.724 (Ho), 0.722 and 0.739 (He), and 0.088 and 0.025 (Fis), respectively. This result showed that the genetic diversity of the trees that were planted in restored areas did not differ from that of the natural forest  simulation with comparison of 31 candidate parents and 35 progenies. The success rate was reflected by a strict confidence level at 4%, relaxed confidence level at 12%, and delta scores at 3.12. The result did not show any assigned progeny to be related to the candidate parent. For C. tribuloides, with 10 candidate parents and 29 progeny trees, the success rate at strict confidence was 26%, relaxed confidence was 49%, and delta score was 9.76. The result showed that three candidate parents were assigned to 15 progenies  were given priority during forest restoration in a previous project as they are known to be native and dominant species in late successional forests, and/or some species are pioneer species in south Asian forests  and L. polystachyus (68.42) were higher than that of C. cuspidata to C. chinensis (30.8)  . Castanoomosomes , resulti vestita .L. polystachyus, C. tribuloides, and C. calathiformis in restoration planting plots were used as representative data for the distribution heterozygosity in genetic populations within the restoration areas at Ban Mae Sa Mai, Chiang Mai, Thailand.The high levels of genetic diversity within the three Fagaceae species populations may be related to their biological characteristics, including being monoecious and outcrossing plants with high crossing rates as well as utilizing wind pollination while having a long life span . SpeciesL. polystachyus (0.432) and C. calathiformis (0.332) in restored forests showed evidence of inbreeding. Such inbreeding within this study may have been caused by an excess of homozygosity  were higher than the observed heterozygosity (Ho), leading to a positive fixation index (Fis)  results of zygosity . The expex (Fis) . We assuex (Fis) . The seen trends . Thus, sn trends . FurtherThe paternity analysis of the three Fagaceae species showed that some candidate parents were assigned to progenies by comparing SSR genotype data. These species are monoecious; thus, male and female trees could not be distinguished. Pollination was revealed to involve both self- and cross-pollination. The model used to test paternity was with an unknown sex model; therefore, some candidate parents were assigned to progenies, and some were not assigned. Moreover, the similarity of genotype backgrounds in parents and a low number of microsatellite loci could be decreased as a result of assignment between candidate parents and progenies . FurtherCastanopsis species might be caused by sexual outcrossing, allowing transfer of allele(s) among populations  of the second generation to complete the genetic diversity analysis for these species.The analyses of the population genetic structure confirmed further that there is no difference between the adult tree and seedling subpopulations in both restored and natural forests. Frequent genetic exchange among the populations has prevented genetic drift within plant populations . Moreoveulations . In thisDeforestation and habitat fragmentation can lower genetic diversity. These situations cause reduction in genetic diversity, plant population size, allelic richness, polymorphism, and heterozygosity, which can increase incidences of inbreeding and ‘bottleneck’ events in populations. Moreover, fragmentation reduces gene flow and outcross breeding, resulting in low genetic diversity .C. tribuloides and L. polystachyus adult trees show flowering and fruiting, respectively, and were able to be collected. Such a low number of genetic sources could lead to low genetic diversity in later generations, and thus, this was taken into consideration when interpreting data. For C. calathiformis, the seedlings that were planted in the 1998 plots collected from Chiang Rai showed high value(s) in the restoration plots among both adult tree and seedling populations. This result concerning the genetic diversity of C. calathiformis (restored area), and C. tribuloides  showed high values, but low levels for L. polystachyus (restored forest). Notably, C. calathiformis and L. polystachyus in the restored forest displayed a positive value of fixation index (Fis). It is probable that a decrease in genetic information is relative to the narrow range of the genetic basis of the planting stock  to ensure that they have adequate genetic diversity to adapt to changing conditions . Howeverng stock . Thus, tosystems .L. polystachyus, C. tribuloides, and C. calathiformis, utilizing the framework tree species used for restoration of upland evergreen forests in degraded sites within northern Thailand. Novel SSR markers developed from NGS data for C. tribuloides were successfully used to derive genetic information and determine genetic variability among these three closely related species. High genetic variability and heterozygosity within C. tribuloides  showed that the seed collection method used by FORRU-CMU effectively conserved genetic diversity during the forest restoration process. However, regarding L. polystachyus and C. calathiformis in the restored forest, an increasing Fis value was detected. Consequently, seeds for planting stock production should be collected from a larger number of adult trees to increase heterozygosity and reduce incidences of inbreeding. The genetic information from this study can be used for better planning of forest restoration and improved forest ecosystem conservation in the future.This study assessed the genetic diversity and population structure of 10.7717/peerj.6958/supp-1Table S1Click here for additional data file.10.7717/peerj.6958/supp-2Table S2An average number of alleles (Na), Observed heterozygosity (Ho), Expected heterozygosity (He).(-) no amplification(*) amplified success in all taxaClick here for additional data file.10.7717/peerj.6958/supp-3Table S3Click here for additional data file.


Coccidioides immitis and C. posadasii. These fungi are found throughout the arid regions of North and South America; however, our understanding of genetic diversity and disease in South America is limited. In this report, we analyze 10 new genomes of Coccidioides posadasii from regions bordering the Caribbean Sea. We show that these populations are distinct and that isolates from Venezuela are likely a result of a recent bottleneck. These data point to patterns that might be observed when investigating recently established populations.Valley Fever is a fungal disease caused by two species of fungi:  Coccidioides posadasii is a pathogenic fungus that causes coccidioidomycosis in many arid regions of the Americas. One of these regions is bordered by the Caribbean Sea, and the surrounding landscape may play an important role in the dispersion of C. posadasii across South America through southeastern Mexico, Honduras, Guatemala, and Venezuela. Comparative phylogenomic analyses of C. posadasii reveal that clinical strains from Venezuela are genetically distinct from the North American populations found in (i) Arizona and (ii) Texas, Mexico, and the rest of South America (TX/MX/SA). We find evidence for admixture between the Venezuela and the North American populations of C. posadasii in Central America. Additionally, the proportion of Venezuelan alleles in the admixed population decreases as latitude (and distance from Venezuela) increases. Our results indicate that the population in Venezuela may have been subjected to a recent bottleneck and shows a strong population structure. This analysis provides insight into potential for Coccidioides spp. to invade new regions. In spite of their human health impact, fungal pathogens are currently understudied , 2. To d8–An important aspect of variation is not only the total magnitude within a species but also how that variation is apportioned across populations , 12. Dis14–Coccidioides immitis and Coccidioides posadasii  and the rest of the range  Arizona and (ii) Texas, Mexico, and South America (TX/MX/SA)  , 27.C. posadasii is critical to understanding the spread of the pathogen across the Americas. Based on microsatellite analyses, the introduction of C. posadasii into South America has been dated to between 9,000 and 140,000 years ago (ya), and was proposed to follow the Amerindian intercontinental migration  analysis, revealed that C. posadasii lineages that gave rise to TX/MX/SA and Arizona emerged 700,000 ya (700 kya), and C. posadasii Guatemala emerged about 200 kya . When we included the C. immitis 202 strain as the outgroup, the alignment had 464,281 SNPs. The difference in the numbers of polymorphic sites between these two alignments reflects the genetic differentiation between C. immitis and C. posadasii.Reads from the 51 C. posadasii strain Nuevo Leon-1 alignment. The transversion model (TVM) coupled with ascertainment bias correction (+ASC) and four categories of discrete gamma model (+G4) was the best fit for the alignment for the C. posadasii and C. immitis 202 alignment. We then used these alignments and models of molecular evolution to generate a maximum likelihood phylogenetic tree. We rooted the tree with C. immitis, the closest relative of C. posadasii.First, we found that the general time reversible (GTR) model coupled with empirical base frequencies (+F), ascertainment bias correction (+ASC), and nine categories of FreeRate model (+R9) was the best fit for the C. posadasii encompasses two major clades, one formed exclusively by Arizona isolates (C. posadasii clade I) and the other more heterogeneous clade that includes all the isolates from South America, Central America, and Texas, as well as a few isolates from Arizona  showed that the conservative age root to the whole Coccidioides tree (spanning both 5.1 and 12.8-million years ago [mya] root age hypotheses) is within 4 to 15 mya. Most population splits happened within ∼150,000 to 250,000 ya. The times to most recent common ancestors (TMRCAs) of Venezuelan samples were inferred to be within ∼1,400 to 5,500 ya (birth-death tree prior), or 1,500 to 5,700 ya (Bayesian skyline tree prior); The TMRCA of Guatemala samples was within ∼22,000 to 87,000 ya (birth-death), or 22,500 to 87,600 ya (Bayesian skyline) . The brine) see .C. posadasii clade I contains two main groups and harbors the majority of the Arizona strains; one composed of isolates from Tucson/Pima County, and one mostly (but not exclusively) composed of isolates from Phoenix/Maricopa County . The second group of the non-Arizonan isolates of C. posadasii, encompassing isolates from South America, appear nested within isolates from Texas and Mexico . The ex(Arizona ). This sC. posadasii using population genetics approaches. First, we assessed how genetic diversity was apportioned among isolates with a principle-component analysis (PCA). We performed the analysis in two different ways. First, we included C. immitis and C. posadasii in the sample  separates the TX/MX/SA population from the rest of the C. posadasii Arizona populations. We find isolates that show genetic variation patterns that are intermediate between populations. The isolates from Guatemala and the isolates GT-120 , Tucson 7, and Tucson 2, appear as intermediaries between the Arizona and Venezuela clusters  from Mexico and Texas appear as intermediates between Arizona and TX/MX/SA , but there is also a nontrivial proportion of ancestry that is associated with the Arizona group (P ≈ 0.15). The results are also largely consistent with but not identical to those from previous attempts to determine the partition of genetic variation within C. posadasii . This value is in line with the levels of intraspecific variation described for other fungal species . A more extreme example is that of Venezuela, where diversity is ∼50 times lower than that of other C. posadasii populations (πVenezuela = 0.09%). We next compared these values to determine whether interpopulation pairs show a higher genetic distance than intrapopulation pairs. We find that this is the case in most comparisons , because no incongruent signals in the SNP matrix were found in the neighbor-net network and pairwise homoplasy index (PHI) test analyses. On the other hand, the Arizona population showed a strong signal of recombination , which supports the balanced MAT1-1 and MAT1-2 distribution observed being consistent with outcrossing and sexual reproduction , TX/MX/SA (C), AZ clade 1 (D), Venezuela (E), and Guatemala (F). Connected edges represents incongruent signal in the genomic data. Results for PHI tests are displayed above each individual phylogenetic network: P = 0.0 indicates recombination, while P = 1 indicates clonality. Download FIG S1, PDF file, 2.6 MB.Phylogenetic network and recombination analysis within different Copyright © 2019 Teixeira et al.2019Teixeira et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the C. posadasii populations. We studied the geographical partition of shared ancestry in the Caribbean group within C. posadasii. We used ADMIXTURE to infer the contribution from the minor population  as a proxy for the proportion of admixture in each of the Caribbean group isolates. The results from ADMIXTURE show a similar clustering to the one revealed from fastSTRUCTURE and also suggest the presence of three populations within C. posadasii . These C. posadasii using whole-genome sequence comparisons. The magnitude and partitioning of genetic variation in human fungal pathogens are a key, yet underappreciated, aspect of pathogen biology. Coccidioides represents a clear example of this paucity. Even though coccidioidomycosis exists in Central and South America, very little is known about the biology of the fungus in these regions. Preliminary population assessments indicated that isolates from Central America belong to C. posadasii, but these are genetically differentiated from both the C. posadasii TX/MX/SA clade and the Arizona clade  were positive for Coccidioides, and sequencing of one of the ribosomal internal transcribed spacers (ITS2) suggested that all the environmental samples from the region are, indeed, C. posadasii  to qualitatively change our conclusions. Second, it is possible the Venezuela lineage originated somewhere else  and went extinct in its location of origin. If this lineage was only able to establish itself in Venezuela, then the hypothesis of human-driven migration of C. posadasii to South America would be compatible with our results. Third, other animal migrations and host-pathogen associations have not been explored. The “Great American Biotic Interchange” resulted in many species moving from North America to South America and vice versa, and any of these could be preferred or novel host species. Finally, the vast majority of isolates analyzed to date are derived from human patients, and we cannot be fully confident of their site of origin or that human clinical isolates represent overall genetic diversity of Coccidioides. The patterns of dispersion of fungal pathogens, including C. posadasii, remain an underexplored topic.One proposed hypothesis to explain  America . Early s America . Most po America . Phoenix America . The conHistoplasma exemplifies this utility. Recently, four species of Histoplasma were formally adopted following genomic differentiation. These lineages differ in their genome sizes, in their abilities to cause disease, and their geographical distributions  within the Venezuelan population and according to the typical bipolar mating system of Eurotiomycetes, both opposite mating type cells (MAT1-1 and MAT1-2) are need for sexual recombination. Thus, we conclude that this is likely an extreme bottleneck event and not a signature of speciation.The magnitude of genetic differentiation seems to be insufficient to describe the different lineages of ulations . The reshallenge , which cC. posadasii are differentiated and exchange alleles. Understanding geographic population subdivision is a prerequisite to address if genetic variation associated with virulence might be originating across the geographic range of a species. Intraspecific population structure and speciation are part of a continuum that is modulated by the strength of reproductive isolation and the extent of genetic divergence between lineages  across different populations. This retention of ancestral alleles might be due to balancing selection, or simply by chance  DNA extraction. To culture isolates and extract DNA, we grew Coccidioides  Library preparation and sequencing. We prepared sequencing libraries for the 10 de novo sequenced isolates using the Kapa Biosystems kit  and ∼1 μg of DNA of each isolate according to the manufacturer’s protocols. We then multiplexed individual libraries using 8-bp indexes and quantified using quantitative PCR (qPCR) in a 7900HT system  using the Kapa library quantification kit . We pooled libraries that were quantified using qPCR as described above and sequenced them on an Illumina HiSeq 2500 instrument  at the Translational Genomics Research Institute (TGen), aiming for a coverage of 100× per isolate. Resulting paired-end reads had a length of 125 bp.C. posadasii (51 reads) and C. immitis (21 reads) isolates from the Sequence Read Archive (SRA). All accession numbers are listed We obtained 72 publicly available sequence reads for de novo references, we used the Unmanned Genome Assembly Pipeline (UGAP [https://github.com/jasonsahl/UGAP]), which uses the genome assembly algorithm SPAdes v3.10.1 (Coccidioides raw reads from the Sequence Read Archive (SRA) deposited under accession no. SRR3468064 (C. posadasii Nuevo Leon-1) and SRR1292225 (C. immitis strain 202). We used Burrows-Wheeler Aligner (BWA) v0.7.7  v0.7.7  to aligno Leon-1  or C. imrain 202 . We remorain 202 , 77. To rain 202  as beingC. posadasii isolates, we built a maximum likelihood (ML) phylogenetic tree using genome-wide SNP data. We used concatenated genome-wide SNPs as Coccidioides as we expect low levels of genealogical discordance (http://tree.bio.ed.ac.uk/software/figtree/).To study the genealogical relationships among cordance , 80. We cordance ; we usedcordance , 84. TreWe used BEAST2 (v2.6.1 ) to infeC. posadasii using two different methods. First, we used principal-component analysis (PCA), which provides a graphical representation of the partition of genetic variance in a population sample. To do so, we restricted our data set to biallelic sites and used the R package adegenet (C. posadasii using fastSTRUCTURE v1.0 (K) and the probability that each strain belongs to each population. SNPs were assumed to be unlinked under the admixture model based on previous assessments of linkage disequilibrium in Coccidioides . Since there were two values of π for each value of xyD, we compared xyD values to the highest value of π in a pair of populations. We adjusted the significance threshold to account for multiple comparisons to P = 0.00138 (0.05/36).One of the proxies of cryptic speciation is that isolated species show considerably larger genetic differentiation than the magnitude of intrapopulation variation . We studto MEGA7 , and theC. posadasii population from Venezuela have evidence of functional mating type loci based on sequence similarity to known mating type sequence. The Onygenalean mating type locus contains one of two forms of unrelated sequences (known as the idiomorph) in a syntenic but unique segment that gives mating type identity to fungal cells. To identify the mating type of the Venezuelan C. posadasii isolates, we followed the same strategy previously used to identify the MAT1-1 or the MAT1-2 locus in this pathogen  and MAT1-2 (containing the HMG box MAT1-2-1 gene [EF472258.1]) loci as reference sequences to query the Venezuelan C. posadasii read files. All isolates examined to date carry only one of these loci and are classified as either a MAT1-1 or MAT1-2 genotype. Phylogenetic networks were produced using the neighbor-net network (NN) algorithm within each population to understand conflicting signal in genomic data. The overall recombination within each C. posadasii population was then analyzed using the PHI test, which also checks for incompatible nucleotide sites in a given DNA alignment. Both phylogenetic networks and PHI tests were performed on SplitsTree4 software . In the case of the Caribbean population, we regressed the proportion of admixture to the distance from the putative donor population, Venezuela.Previous studies have found that species of  alleles , 34. OurDMIXTURE . We infef4 statistics to assess the proportion of admixture. We restricted this analysis to the Guatemala population. We used admixtools /f4(AOBC), where X corresponds to the potentially admixed population . O, B, A, and C are four populations that are known to branch at four distinct positions along the phylogeny. O refers to the outgroup; in this case, we used C. immitis. A and C are the donors (or close relative to the donors) of the admixed lineage. In this case, we used A = Phoenix and C = Venezuela. B is a population that does not harbor introgression; we used B = Tucson. We found no difference in the estimations by swapping Tucson and Phoenix (data not shown).Next, we used mixtools  implemenmixtools , and theC. posadasii populations. We used the proportion of admixture inferred from ADMIXTURE when K = 3 in C. posadasii (the best-fitting scenario [see Results]). We then calculated the distance between the collection site of a given sample (defined to the level of country or state) and Caracas using a haversine formula (Finally, we tested whether the contribution of Venezuela to the admixed population between Venezuela and Arizona followed the expectation that as distance from Venezuela increased, the contribution of the Venezuela population would decrease. Our hypothesis was that individuals from Central America were admixed and the product of gene exchange between the North American and South American  formula . The disPRJNA438145 with sample accession no. SRR6830879 to SRR683088 (All the raw reads for the 10 isolates we sequenced in this article have been deposited at SRA under BioProject no. RR683088 .


Carcinus maenas) were independently introduced to eastern North America, the first in the early 19th century and the second in the late 20th century. These lineages first came into secondary contact in southeastern Nova Scotia, Canada (NS), where they hybridized, producing latitudinal genetic clines. Previous studies have documented a persistent southward shift in the clines of different marker types, consistent with existing dispersal and recruitment pathways. We evaluated current clinal structure by quantifying the distribution of lineages and fine‐scale hybridization patterns across the eastern North American range  using informative single nucleotide polymorphisms . In addition, temporal changes in the genetic clines were evaluated using mitochondrial DNA and microsatellite loci (n = 9–11) over a 15‐year period (2000–2015). Clinal structure was consistent with prior work demonstrating the existence of both northern and southern lineages with a hybrid zone occurring between southern New Brunswick (NB) and southern NS. Extensive later generation hybrids were detected in this region and in southeastern Newfoundland. Temporal genetic analysis confirmed the southward progression of clines over time; however, the rate of this progression was slower than predicted by forecasting models, and current clines for all marker types deviated significantly from these predictions. Our results suggest that neutral and selective processes contribute to cline dynamics, and ultimately, highlight how selection, hybridization, and dispersal can collectively influence invasion success.Two genetically distinct lineages of European green crabs ( A secon) Roman, . Over th) Roman, . In addi) Roman, .C. maenas in eastern North America has been well characterized in previous studies  between 2011 and 2015 and compare to previous studies. We used single nucleotide polymorphisms (SNPs) selected to discriminate between northern and southern lineages, building directly on previous studies of population structure using SNPs   were previously genotyped using restriction site‐associated DNA sequencing (RAD‐seq) on y et al. , and usigenepopedit  following the manufacturer's protocols, including the STA (Specific Target Amplification) step, using 96.96 genotyping Integrated Fluidic Circuits (IFC) and read on an EP1 platform (Fluidigm) and analysed using SNP Genotyping Analysis software (Fluidigm). Each 96‐well extraction included 10 samples that were repeated on the plate  to ensure there were no processing errors  and to ensure consistent clustering interpretation. The set‐up for each IFC also included gBlocks as positive controls (see above for details). After genotyping analysis, individual samples below the genotype quality threshold (>9 failed loci) were removed from the data set . A subset of samples (12%) was reanalysed, from the original tissue where permitted, to calculate the genotype error rate. The genotype error rate was calculated to be 0.16% based on methods outlined in Pompanon, Bonin, Bellemain, and Taberlet . This is2.3FST) between all adult and juvenile samples collected in 2015. p‐values were corrected to control for false discovery rate (FDR) using the p.adjust function in R software , Cole Harbour, NS (CLH) and North Harbour, NL (NOH). Differences between the genetic clines between sampling year and life stages were also investigated . We chose K = 2 because we expected to identify two genetic clusters (derived from northern and southern lineages) with hybridization occurring in parts of the range based on previous C. maenas studies, their known invasion history and the informative SNPs chosen for our study . We chose to consider only a single recombinant hybrid class because these later generation hybrids are unreliably detected. Next, we validated our hybrid results using GENODIVE  between the two hybrid regions. Simulated data generated from hybriddetective  group (simulated F2 × F2 crosses). All individuals within southeastern NL (four sites) and the hybrid zone  were used for INTROGRESS analyses with simulated pure north and pure south individuals used as the parental populations.Hybridization patterns in the hybrid zone and the introduced hybrid region in southeastern NL were further investigated using the R package 2.5C. maenas to differ in their population frequencies and clinal progression over time  gene and microsatellite markers  to provide further information about the processes shaping the genetic clines and to compare marker types, particularly because different marker types have been shown in kers see .C. maenas in North America, we used the same approach as Darling et al.  determined by STRUCTURE . The four sites in southeastern NL within Placentia Bay  and Fortune Bay (FTB) were excluded from cline analyses, as these sites could skew our analyses because they do not represent an area of natural hybridization between lineages , where models assumed different combinations of (a) scaling of frequency intervals  and (b) fitting of exponential decay of the tails at the ends of the cline . Model selection was performed using corrected Akaike Information Criterion (AICc) scores, and ML cline centre and width were extracted from the best model. Clines were modelled with 95% credible cline region, and ML estimates for cline centre and width included two log‐likelihood low and high support limits. For comparisons between marker types and years, significant differences were determined based on two log‐likelihood support limits for the cline centre and width. For diploid markers, models were weighted by the effective number of alleles following a similar approach as Macholán et al. . In general, FST was lowest among adult and juvenile samples collected from the same location  and more northern sites (YRM to CNB) were 0.04 and 0.05, respectively, whereas the mean pairwise FST value between southern versus northern sites was 0.19.Next, pairwise Results from STRUCTURE revealed the distribution of two genetic clusters corresponding to the northern and southern lineages Figure . Interme3.2C. maenas range, individuals were first assigned to three possible genotype classes , where approximately 93% of the genotyped individuals were assigned with a posterior probability greater than 0.85. Subsequent assignment of individuals into pure type, first‐generation hybrid (F1) or recombinant hybrid  classes revealed no F1 hybrids across the study range thus leaving all hybrids to be classified as recombinant hybrids , show that the clines have not progressed as far south as previously predicted by Pringle et al.  for 2015 , many SNP clines were coincident with the 2015 COI and microsatellite cline    for the centre of the clines, and different marker types revealed different patterns. For microsatellites, the rate of change decreased between 2000 and 2015 Figure b, wherea4C. maenas into eastern North America provides an unprecedented opportunity to explore secondary contact as it occurs and examine the factors that promote temporal stability of genetic clines. Consistent with previous studies, clinal analyses revealed two genetically distinct groups of C. maenas corresponding to southern and northern lineages derived from two independent introductions  overlapped in cline centre and width, and the widths of these clines were also concordant with that of the SNP cline based on 1 hybrids were found across the entire range, even though our panel of 96 SNPs proved capable of distinguishing F1s in simulated data sets. Our results are consistent with those of Jeffery et al.  and an expanding hybrid zone that impedes the interaction of pure types  (Lord & Williams, C. maenas from different regions (Best et al., Our study indicates that the genetic clines of e et al. , perhaps5C. maenas genetic clines over time counter to dispersal‐based predictions. It is likely that both selective and neutral processes shape the current clines, and future work using complex realistic models of dispersal and experimental work on quantifying the competitive ability and physiology of hybrid and genetically pure individuals are necessary to better understand the processes restricting the range limits of each lineage and help predict future dynamics of the cline. In addition, climate change and human‐mediated transport may lead to continued alterations and range shifts in C. maenas distributions. Our study highlights the current challenges and complexities in forecasting invasion dynamics and emphasizes the importance of temporal monitoring using genetic markers to understand the spread of invasive species and inform management decisions.In our study, multiple genetic marker types suggest a slowed southward movement of None declared.https://doi.org/10.5061/dryad.130vb65.Data available from the Dryad Digital Repository:  Click here for additional data file.


Southern African cities face several challenges including management of rapid urbanization, rising populations, expanding informal settlements; adequate water and other service provision, and a host of governance challenges. Climate change and variability add a compounding effect to this complex, multi stressor context. Addressing the complexity requires an understanding of urban ecosystems functioning and interactions amongst the built and natural environment (climate) and human systems. In this paper we argue that learning is essential for cities to be resilient to current and future challenges. We profile the Future Resilience for African CiTies And Lands (FRACTAL) project which contributed towards climate resilient development by providing relevant climate information for decision-making at the city regional scale in southern Africa. Following FRACTAL’s city-to-city learning approach of sharing good practices, knowledge and experiences framed around transdisciplinary research, the study cities of Harare, Lusaka, Windhoek and Durban conducted city learning exchange visits between 2017 and 2018. We used a mixed methods approach to collect and analyze historical climate and hydrological data and current socio-economic and development data among the cities. A qualitative, in-depth, case study comparative analysis was used to identify similarities and differences as well as lessons drawn from the learning process during the city exchanges and these were complimented by desktop studies. Results showed water scarcity, large informal settlements, reliance on external water and energy sources, inadequate protection of ecologically sensitive resources and service provision as some of the common complications in the cities. Several lessons and transferable practices learnt from the cities included effective water conservation and waste management and the use of public-private partnerships in Windhoek, community engagements in Durban and Lusaka while lessons on decisive leadership in dealing with informal settlements emanated from Harare’s limited informal settlements. Lastly, Durban’s Adaptation Charter and integrated climate planning provided lessons for biodiversity protection and mainstreaming climate change at city governance level. While we recognize that cities are context-specific we consider these good practices as being broadly transferable to other southern African cities. We conclude that social, experiential and structured learning can be an innovative way of multi-stakeholder engagement and a useful approach to increase city resilience planning across southern Africa and cities that face similar developmental challenges. Southern African cities, being densely populated, urban environments with expanding populations as compared to the rest of the countryside, are vulnerable to harsh weather, climate variability, extreme climate events and associated risks among other factors ; 2]; ; ; 5]; ; . For insThe multi-stressor context, which is compounded by climate change and variability, means that these cities need to respond swiftly, flexibly and creatively to develop systematic, systemic, sustainable and resilient solutions to the climate crisis. The multi-stressor context requires numerous responses, which may consist of responses in physical planning such as ‘hardening up of infrastructure systems, including storm-drainage systems, water supply and treatment plants with protective physical improvements; protection or relocation of solid waste management facilities, energy generation and distribution systems; and consolidation of hydro-geologically fragile areas’ , and/or In this paper we argue from the perspective that cities are connected, complex as well as vulnerable entities, which can be regarded as urban ecosystems. We aim to contribute to the argument on, and advocacy for, city to city learning. We center our discussion around the question of how useful city exchanges are in facilitating learning from experience and good practices in urban ecosystems and contributing towards building resilience in southern Africa. We draw attention to how such learning can contribute towards developing the capacity of cities in order to build climate resilience. We anticipate that lessons and good practices are relevant for a broader set of urban ecosystems that are dealing with similar environmental, climatic, demographic, institutional and developmental challenges, particularly in sub-Saharan Africa. We frame our argument by: i) outlining an overview of the various urban ecosystem issues for four southern African cities, and ii) examining the learning from each city and how it can potentially be taken on board for resilience building by the cities concerned.Climate research has traditionally been conducted in siloed approaches with little to no linkages between the science, policy and society; a characteristic which has repressed the application of science for addressing societal problems ; 29]; ; . The disThere is a growing awareness that new ways of knowledge production and decision-making must move beyond disciplinary silos, and that approaches such as transdisciplinary research, and knowledge co-production may provide the means to do so ; 35]; ; . DisciplThe concept of learning is based on the premise that change occurs across spatial and temporal scales and we argue that the interconnectedness of socio-ecological systems presents the need to increase our global cognitive, behavioural and moral intelligence through learning . This brwhat if we assumed that learning is as much a part of our human nature as eating and sleeping, that is both life-sustaining and inevitable? The co-development of new knowledge generated through collaborative, transdisciplinary research efforts, fosters diverse ways of learning, including the concept of experiential ‘learning as you go’ which implies iterative, reflective and feedback mechanisms. The use of these learning concepts have been on the rise, given funders’ recent (in the past decade) inclination towards larger-scale, impactful research that solves complex problems; versus the traditional and mere generation of new knowledge and the need for socially robust knowledge in adapting to climate change and building resilience ; ; ; 23]; ; . New appsilience ; 50]; ..what if FRACTAL) research project employed and emphasized the use of city learning and contextualizing of issues, by understanding decision-making processes in cities as social learning concepts and in a bid to help build resilience through informed decision-making in the project’s study cities. This research project, which ran for a four-year period from 2015 to 2019; operated in nine cities in southern Africa, gave the participating cities impetus to learn together using a city-to-city or cross-city learning approach, and thus provided the basis for this paper. The specific goal of FRACTAL was to advance scientific knowledge of the southern African regional climate responses to human drivers of climate change and enhance the integration of scientific climate knowledge into city regional decision-making for building resilience ; ; . This reAlthough the quantity and quality of water supply are central to water security ; 63]; ; , water gFor all the cities, there is tacit recognition that complexity of issues threatening water security in the region cannot be addressed by any one actor or one sector alone and therefore, collective leadership is required. An innovative, multi-stakeholder approach to collaboration at all levels, now enshrined in Sustainable Development Goal (SDG) 17, is emerging whereby partners increased their understanding of water risks, raise awareness of these risks and advocate for change, and harmonize resources and capacities to engage in effective, on-the-ground projects. For the city of Lusaka, the water security initiative is a case in point and in Windhoek the efficacious water reclamation systems that are governed by a PPP resonate the importance of collaboration.status quo should not be maintained, and Lusaka and other cities in the region can learn from CoW where water conservation and wastewater reclamation is being done.The lessons and best practices of water conservation from the CoW are relevant to other FRACTAL cities such as Cape Town, Harare, Johannesburg and Lusaka, which are in the arid belt of the region and therefore susceptible to drought episodes as well as other southern African cities in the same zone . This isGreen business opportunities at the community level can help create an enabling environment for active participation in climate change mitigation and adaptation projects ; 82. A kvis-à-vis building resilience in the cities as low-income households and informal households often located in unsuitable areas are most vulnerable to the compounding effects of both non-climatic and climate risks such as floods in Lusaka. The exchange visit brought out the advantages existing in CoW where temporary unplanned and illegal structures are constructed using corrugated iron sheets. These can easily be removed or moved for upgrading and planning given that most of these settlements are situated in unsuitable land.The multi-faceted and interconnected nature of several issues that cross the ecological-socio-economic divide resonate with situations across most cities in southern Africa. One lesson learnt through the exchange visits was how to address the challenge of informal settlements A lesson for other southern African and African cities on dealing with informal settlements that mushroom as a result of population expansion and urbanization and the subsequent failure of service delivery can be drawn from Harare and Zimbabwe. Formalisation of informal settlements and building initiatives in Harare may provide lessons to other African cities that have problems with informal settlements and associated risks such as the famous Kibera in Nairobi, partially informal Khayelitsha township in Cape Town, and in Dar es Salaam that has an informal settlement areal coverage of 70%  comparab2 and greenhouse gases and ultimately global warming. We found comparable efforts towards use of renewable energy sources for Harare and Windhoek with projects such as the Southern African Solar Training and Demonstration Initiative (SOLTRAIN) and the developments at the SADC Centre for Renewable Energy and Efficiency (SACREE) that both Namibia and Zimbabwe are party to.The City of Windhoek provides an exceptional case as the function of energy provision is designated at city level, whereas in most African countries the energy provider is often a public entity at national level. However, most southern African countries and indeed cities, which are large consumers of energy, rely on hydropower energy except for South Africa, and Botswana which mostly rely on fossil fuels, and coal in particular . There iEach city has taken up a water governance system which appears to be feasible and workable. There are several multiple roles of city authorities in governance which influence resilience. These include the role cities play as regulators, as taxation and licensing authorities; as strategic land-use planners and developers; consumers and providers of goods and services. This makes local authorities exceptionally well positioned to lead and influence climate adaptation interventions and stimulate behaviour change among citizens and businesses. Adaptation measures are needed in this and many cities in the region given projections of population exponential population growth.The differing contexts of water governance demonstrated for the four cities of Harare, Windhoek, Lusaka and Durban are an example of the use of diverse solutions within African cities. Often, one size fits all solutions are advocated for and sometimes even funded for Africa, forgetting the different contextual realities. However, we acknowledge that there is still room for cities to learn from each other on implementation in order to improve on water service delivery and water resource management. Multiple actors, often through partnership, tend to intervene in urban climate change governance ; 82]. T. T82]. TBy engaging in compact partnerships for Durban with neighbouring municipalities, learning, resources and skills are shared and an integrated response to climate change has been developed in this city-region. This increases the scale at which work is done, often encapsulating whole catchments and substantially increasing the number of benefiting city-region residents. Lessons and good practice therefore emanated from Durban in the way in which climate change is integrated in planning processes and climate adaptation is facilitated. The structures and approaches in Durban present learning point as such efforts of integrating climate action into policy can be replicated in other southern African cities. It is worth noting though that similar efforts such as the development of Windhoek’s climate change response strategy; Lusaka’s water security initiative and some engagements on improving planning and mainstreaming national and transformative adaptation plans at the city level in Harare are all initiatives that have been borne partly from the FRACTAL engagements and from the learning experiences of the city exchanges.Cities can be regarded as complex urban ecosystems, which, like other types of ecosystems, are threatened by climate change. Additionally, southern African cities face multiple stressors and complex challenges. As with natural ecosystems, resilience against climate change lies firstly in the healthy functioning of the city and secondly in the ability of the city to adapt to changes. A crucial element in adapting is having the ability to learn and implement best practices, often from other cities facing similar challenges. As indicated in this paper, some cities have come up with innovative ways of tackling challenges such as water insecurity, informal settlements and climate change. These provide learning points for other cities that are grappling with climate change and other, similar developmental issues, also bearing in mind that context matters. In this paper we identified five elements  of a city that need to be considered when grappling with and building resilience to climate change impacts in southern African cities. Among these learning points are the issue of public-private partnerships, which southern African cities can embrace in dealing with issues such as water scarcity and management issues and where great strides in improving water service delivery to the city residents through water reclamation, have been made in the case of the City of Windhoek. This has placed the city on a success trajectory, which promises to lessen water availability issues that are compounded by climatic risks.Another learning point was that community engagement and inclusion to deal more successfully with solid waste allows for local participation and green job creation in cities as evidenced by Durban and Lusaka. Community engagement ensures better understanding of the context as the solutions are made together ‘with’ rather than ‘for’ the people on the ground and who are affected by the day to day challenges. There is, however, evidence of the need for continued capacity building and improvement of the model used to implement such approaches such as in Harare’s clean up campaigns. One other critical learning point is on decisive leadership in dealing with informal settlements as exemplified by Harare. Intolerance of informal settlements and upgrading of these settlements, are strategies that promise to address criminality and disease in the low-income areas of the cities, which other cities can emulate and implement to the extent possible. Lastly, as highlighted in Durban, development and approval of city strategies and action plans on climate change adaptation provide an opportunity for cities to integrate climate change in planning processes and cascade national policies to the city level for the benefit of city residents. Mainstreaming of climate change also directly or indirectly addresses other developmental challenges such as biodiversity loss and water security.We conclude that the evidence of efforts towards environmental protection, improved urban amenities and infrastructure and ultimately service delivery in the southern African cities studied gives an indication that to a large extent, there is scope for these cities to learn from each other to build and develop resilience. Therefore, city to city learning provides a useful platform for the exchange and uptake of ideas, practices and strategies to enhance the resilience of urban ecosystems particularly across regions such as southern Africa where similar socio-environmental issues are encountered. These lessons can certainly be useful for similar cities and regions.S1 File(DOC)Click here for additional data file.S2 File(DOC)Click here for additional data file.S3 File(DOC)Click here for additional data file.S4 File(DOC)Click here for additional data file.S5 File(DOC)Click here for additional data file.S6 File(DOC)Click here for additional data file.S7 File(DOC)Click here for additional data file.S8 File(DOC)Click here for additional data file.S9 File(DOC)Click here for additional data file.S10 File(DOC)Click here for additional data file.


Relevant to the p hypothesis, recent genetic research has found surprisingly high genetic correlations between pairs of psychiatric disorders. Here, for the first time, we compare genetic correlations from different methods and examine their support for a genetic p factor. We tested the hypothesis of a genetic p factor by applying principal component analysis to matrices of genetic correlations between major psychiatric disorders estimated by three methods—family study, genome-wide complex trait analysis, and linkage-disequilibrium score regression—and on a matrix of polygenic score correlations constructed for each individual in a UK-representative sample of 7 026 unrelated individuals. All disorders loaded positively on a first unrotated principal component, which accounted for 57, 43, 35, and 22% of the variance respectively for the four methods. Our results showed that all four methods provided strong support for a genetic p factor that represents the pinnacle of the hierarchical genetic architecture of psychopathology.It has recently been proposed that a single dimension, called the  However, these higher-order dimensions also correlate with each other3, which suggests the possible existence of a general factor of psychopathology4. This general factor has been called the p factor5 as it captures the shared variance across psychiatric symptoms, and predicts a multitude of poor outcomes and general life impairment7.High comorbidity rates among psychiatric disordersp factor in that genetic influences on psychopathology appear to be general across disorders rather than specific to each disorder. For example, psychiatric disorders do not breed true—parental psychopathology predicts offspring psychiatric disorders but with little specificity8. Family research has found substantial genetic correlations between pairs of disorders, such as Major Depression and Generalized Anxiety Disorder9 and Schizophrenia and Bipolar Disorder10. Genetic overlap between internalizing and externalizing higher-order constructs has also been noted11, consistent with the hypothesis of a general p factor. The culmination of this research is a recent study of more than 3 million full and half-siblings using Swedish national register data that found evidence for a general genetic factor that pervades eight major psychiatric disorders as well as convictions for violent crimes12. Although genetic correlations were not presented, the average loading was 0.45 on a general genetic factor.Family studies support the hypothesis of a genetic p factor. The first hint came from genome-wide association (GWA) findings that single- nucleotide polymorphisms (SNPs) found to be associated with Schizophrenia were also associated with bipolar disorder13. In 2013, genetic correlations were first estimated from linear mixed model analyses  of individual genotype data for five psychiatric disorders in the Psychiatric Genomics Consortium (PGC)14. Schizophrenia, Bipolar Disorder and Major Depressive Disorder yielded the highest genetic intercorrelations (average = 0.53); the average genetic correlation among the five disorders, including Autistic Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder, was 0.22.Genomic research also supports the hypothesis of a genetic 15 has made it possible to estimate genetic correlations from GWA summary statistics rather than requiring genotype data for individuals. This method is based on correlations in effect sizes across disorders taking into account linkage disequilibrium and the SNP heritabilities of the disorders. LDSC genetic correlations derived from summary GWA statistics for the same five PGC disorders are remarkably similar to the GCTA genetic correlations described above that used individual genotype data16. A recent LDSC analysis of eight psychiatric disorders again showed considerable correlations between Schizophrenia, Bipolar Disorder and Major Depressive Disorder (average = 0.41), and yielded an average genetic correlation of 0.2117, highlighting the relevance of testing the hypothesis of a genetic p factor.Linkage-Disequilibrium Score Regression (LDSC)p is to correlate genome-wide polygenic scores (GPS), although some GPS correlations between pairs of psychiatric disorders have been reported18. A GPS for a disorder is created for an individual by summing the count of alleles shown in GWA studies to be associated with the disorder, after weighting the alleles by the strength of their association19. The previously described PGC dataset was used to create polygenic scores for each of the five disorders13, and polygenic scores for Schizophrenia, Bipolar Disorder and Major Depressive Disorder predicted liability variance in the other disorders, again suggesting genetic overlap. However, as new GWA studies have been published since for Schizophrenia, Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder with considerably increased sample sizes, replication is needed. GPS correlations between disorders are related to genetic correlations, but differ from the genetic correlations estimated from other methods because they index both the relationship between individual-specific genetic effects for traits in the population and genetic effects derived from an independent analysis. Nonetheless, GPS correlations provide another opportunity to test the hypothesis of a genetic p factor.Another approach that has not yet been systematically applied to test for a genetic p factor, we test whether a general p factor also emerges from genomic data. In the present study, we bring together genetic correlations for major psychiatric disorders derived from four genetic methods . We applied principal component analysis to correlation matrices derived from these four methods and estimate the amount of genetic variance explained by a genetic p factor. For the GPS approach, we constructed GPS for eight psychiatric disorders for each individual in a sample of 7 026 unrelated individuals from the Twins Early Development Study (TEDS)20.Based on the overwhelming evidence that favors a general Our hypothesis was that a general genetic factor would emerge from factor analyses of correlations derived from each of the four genetic methods. We also investigated the extent to which all disorders load on this general factor and the magnitude of their loadings.21. Written informed consent was obtained from parents. Project approval was granted by King’s College London’s ethics committee for the Institute of Psychiatry, Psychology and Neuroscience (05.Q0706/228).This study included 7 026 unrelated , genotyped individuals from TEDS, a longitudinal birth cohort that recruited over 1 5000 twin pairs between 1994–1996 who were born in England or Wales. Despite some attrition, the remaining cohort, as well as the genotyped subsample have been shown to represent the UK populationLDpred22, which modifies the summary statistic coefficients based on information on linkage disequilibrium (LD) and a prior on the effect size of each SNP. The final GPS is obtained as the sum of the trait-increasing alleles , weighted by the posterior effect size estimates. For our analyses, we used a prior that assumes a fraction of causal markers of 1  based on GWA summary statistics only, and relies on the principle that the presence of LD in the study sample is correlated with the upward bias of GWA test statistics15. Cross-trait LDSC16 is an extension of this method and makes it possible to estimate the genetic relationship between two traits. For each SNP, this method establishes the covariance of the test statistics for trait x and trait y, and regresses this value on the LD score of that SNP , whereby the slope represents the genetic covariance. The genetic correlation is obtained by standardizing the covariance by the SNP-h2 for both traits h2 results using LDSC, see Supplementary Table LDSC is a method used to estimate SNP-heritability  for different disorders. For example, if cases of one disorder are genetically more similar to cases of a different disorder than to the respective controls, a positive genetic correlation can be inferred. For this study, we used published cross-sample GCTA genetic correlations14, which included five psychiatric disorders: Schizophrenia, Bipolar Disorder, Major Depressive Disorder, Autism Spectrum Disorder, and Attention-Deficit/Hyperactivity Disorder. , as well as convictions for Violent Crimes. Schizoaffective disorder was redundant with Schizophrenia (genetic correlation = 0.99) and thus omitted here , which aims to maximize variation of the first principal component25. We applied PCA to genetic correlation matrices derived from family analysis (8 × 8 matrix), GCTA (5 × 5 matrix), LDSC (8 × 8 matrix), and GPS (8 × 8 matrix) to estimate the loadings of each psychiatric trait on this component and the variance explained by the first principal component.To test the hypothesis that a general genetic t-statistic of the correlation coefficients, we were able to derive empirical p-values based on the t-statistic distribution with n−2 degrees of freedom26. Significance testing was applied only to family and GPS loadings because we were unable to obtain degrees of freedom for GCTA and LDSC data, which is required for the calculation of t. All tests were two-tailed and a significance level of α = 0.05 was accepted as statistically significant. In addition to testing statistical significance, we calculated the proportion of factor loadings with a magnitude of ≥|0.30|. This value is a commonly used threshold in the factor analysis literature, as it indicates that the factor explains ~10% of the variance in the measure27, therefore substantially contributing to the factor.We also tested the statistical significance of the factor loadings, which represent correlations between the original standardized variables and the factors. By calculating the 28 of eigenvalue λ > 1; (ii) parallel analysis29, and (iii) scree plot inspection30  the Kaiser criterion31, using the hornpa32 package to perform parallel analysis, the psych33 package to conduct PCA , and the GPArotation34 package to apply oblique rotation. Analysis scripts are available from the first author upon request.Analyses were performed in the open-source software Rh2 estimates).Figure PCA provided converging evidence for a general psychopathology factor. Figure p-values ≤ 1.65 × 10−41), even though the GPS data showed some of the lowest loadings. When we applied the conventional threshold of ≥|0.30|, we found that most of the loadings met this threshold: 100% of the disorders in family data, 80% in GCTA data, 88% in LDSC data, and 75% in GPS data. The variation in factor loadings across the four methods can be explained by the inclusion of different disorders, as average loadings for the disorders in common were highly similar .Figure Schizophrenia, Bipolar, and Depression consistently had the highest loadings on the first unrotated principal component across all genetic approaches with the exception of the GPS method, where Bipolar was not amongst the highest loading disorders.To test whether GPS results changed when applying a different prior as part of the GPS calculation, we re-ran PCA using GPS based on the fraction of causal markers of 0.10. Results were almost identical . However, when any of these disorders were included in a study, they consistently contributed to a genetic h2 is generally lower than family-h2 because the SNP-h2 estimate does not include rare variants and nonadditive effects, this downward bias influences both numerator and denominator to equal extents when calculating genetic correlations 35. However, if the correlation between causal SNPs is stronger for common variants than for rare variants, the SNP genetic correlation estimate would be higher than family study estimates, because only common SNPs are included in the analysis16. Nevertheless, for the disorders in common, family data produced higher average genetic correlations (0.49) than GCTA (0.34) and LDSC (0.37). An alternative explanation involves differences in sample ascertainment and psychiatric diagnoses. In most genomic studies, sampling strategies may select ‘pure’ cases and exclude cases with other co-occurring conditions, and such ‘pure’ cases do not represent the disordered population36. In contrast, family data used in this study12 were based on a non-hierarchical approach to classification, thus allowing for greater overlap among the disorders.Although the four genetic methods yielded similar patterns of correlations and patterns of loadings on the first unrotated principal component, they differed in the magnitude of their estimates of correlations and loadings, even when only considering the disorders in common . In principle, genetic correlations calculated through GCTA and LDSC should not differ substantially from family study estimates. Even though univariate SNP-GPS results, which are based on the most conceptually distinct method, yielded the lowest overall correlations. A GPS is the aggregation of all genetic effects found in an independent GWA analysis in respect to an individual’s genotype. Therefore, GPS correlations index the extent to which the total variance of individuals’ GPS for one trait covaries with GPS for other traits. Two possible reasons why GPS correlations may be the lowest are that (i) in addition to true effects, a GPS includes the measurement error for all the SNPs tested across the genome in GWA analysis and (ii) a GPS is generated using genotypes from one cohort and effect sizes from a second, independent cohort.p factor? The positive manifold of the genetic p factor is agnostic about its causes. There are several, equally plausible hypotheses for the mechanisms that cause cross-disorder correlations37. One possible pathway may be biological pleiotropy, where DNA variants are causally involved in the development of several traits related to psychopathology. An alternative explanation is mediated pleiotropy, in which comorbidity occurs because DNA variants increase risk for one disorder, and then this disorder causes other disorders in turn. A third hypothesis is that DNA variants cause some general impairment that forms the core of various disorders, consequently producing genetic correlation between specific diagnoses. That is, the thousands of DNA variants associated with each symptom or disorder might affect all personality and cognitive processes that increase risk, thus providing many pathways to psychopathology.What causes this genetic p, it does not explain all, or even most, of the genetic variance. Assuming a hierarchical model with p at the highest level7, broader psychiatric dimensions at a middle level, and specific psychopathologies at the lowest level, the question is how much genetic variance is accounted for by the three levels. In the realm of cognitive abilities, there continues to be debates about the nature of the middle level38.Although it is remarkable how much genetic variance is explained by p, there is less clarity in our results about the nature of the second level of the hierarchical structure, as represented by the rotated factor solutions. One rotated factor consistently includes Schizophrenia and Bipolar Disorder. However, the other rotated factor is less clear. For example, although Attention-Deficit/Hyperactivity Disorder loads on the second factor, it clusters positively with Depression and Autism Spectrum Disorder in the LDSC and GPS results, positively with Anxiety, substance abuse and Crime in the family results, and negatively with Autistic Spectrum Disorder in the GCTA and GPS results. It may be that the second level of the hierarchical structure will remain unclear until analyses of this type begin to use a transdiagnostic approach, that is, using symptoms to build a hierarchical model from the ground up. As these data become available in the future, we will be able test the genetic p factor model more formally by contrasting it to alternative models.As compared to p factor is even more general than psychiatric disorders. The same approach can be used to investigate the genetic relationship between psychiatric disorders and personality traits, cognitive traits, structural and functional brain traits, medical and neurological disorders, and physiological traits. However, here we chose to focus on the extent to which a genetic p factor emerges from genomic analyses of psychiatric disorders themselves.Another issue for future research is the extent to which the h2 and family study heritability estimates. We used the most recent publicly available GWA summary statistics, some of which are considerably underpowered. This limitation most affects our GPS analysis, which predicts genetic risk at the level of individuals. The modest SNP-h2 and measurement error of the GWA studies from which the GPS were derived are partly responsible for the low correlations between the GPS. More powerful GWA studies are in progress, and we are optimistic that new GPS will have improved predictive accuracy. More generally, GWA studies focused on phenotypic p should be able to capture genetic p to a greater extent than trying to derive genetic p from GWA studies of separate disorders that are sometimes diagnosed as ‘pure’ cases that exclude other diagnoses.As noted, our analyses are limited to the data that currently exist, including the power of current GWA studies and the disorders included in these studies. A fundamental limitation is ‘missing heritability’, the gap between SNP-p factor that represents a continuous, underlying dimension of psychiatric risk using four distinct genetic methods. As GWA studies continue to increase in sample size as well as in the diversity of their target traits, our current results suggest that a genetic p factor could be useful in psychiatric research.In conclusion, we report strong evidence for a genetic Supplementary Online Material