Studies have demonstrated that STAT3 is essential in maintaining self-renewal of embryonic stem cells (ESCs) and modulates ESC differentiation. However, there is still lack of direct evidence on STAT3 functions in ESCs and embryogenesis because constitutive STAT3 knockout (KO) mouse is embryonic lethal at E6.5–E7.5, prior to potential functional role in early development can be assessed. Therefore, in this study, two inducible STAT3 ESC lines were established, including the STAT3 knockout (InSTAT3 KO) and pSTAT3 overexpressed (InSTAT3 CA) using Tet-on inducible system in which STAT3 expression can be strictly controlled by doxycycline (Dox) stimulation. Through genotyping, deletion of STAT3 alleles was detected in InSTAT3 KO ESCs following 24 hours Dox stimulation. Western blot also showed that pSTAT3 and STAT3 protein levels were significantly reduced in InSTAT3 KO ESCs while dominantly elevated in InSTAT3 CA ECSs upon Dox stimulation. Likewise, it was found that STAT3-null ESCs would affect the differentiation of ESCs into mesoderm and cardiac lineage. Taken together, the findings of this study indicated that InSTAT3 KO and InSTAT3 CA ESCs could provide a new tool to clarify the direct targets of STAT3 and its role in ESC maintenance, which will facilitate the elaboration of the mechanisms whereby STAT3 maintains ESC pluripotency and regulates ESC differentiation during mammalian embryogenesis. Signal transducer and activator of transcription 3 (STAT3) belongs to the STAT family and is the only member essential for early embryo development since STAT3 null mice were found to be embryonic lethal at E6.5 to E7.5 . SubsequSTAT3 is an essential mediator downstream of leukemia inhibitory factor (LIF) in maintaining ESC pluripotency. Studies have proved that STAT3 activation is essential to maintain ESC self-renewal –15. In tIn addition to maintenance of ESC self-renewal and pluripotency, STAT3 also implemented a role in embryogenesis and cell-fate determination. STAT3 is highly expressed in mouse oocytes and becomes phosphorylated and translocates into the nucleus in the four-cell and later stage embryos, indicating that activated STAT3 is present in preimplantation embryos . During STAT3 conventional all-tissue knockout mice have previously been found early embryonic lethal at E6.5 to E7.5, as a result of embryos rapidly degenerated between E6.5 and E7.5 with no obvious mesoderm formation . In factin vitro through self-renewing division [ESCs originate from the inner cell mass of blastocyst, which is an early stage of preimplantation embryo , 29. ESCdivision –32. Therdivision –37.in vitro inducible STAT3 KO and STAT3 CA system of mouse ESCs was constructed where STAT3 expression can be strictly controlled by Dox adding or withdrawing. The present study attempts to utilize InSTAT3 KO ESCs to demonstrate the role of STAT3 in cardiac lineage differentiation and in the initiation of cardiac differentiation from ESCs. The findings will provide a new tool to elaborate the functions of STAT3 in ESC pluripotency and differentiation into certain lineages.Hence, in this study, an 2. Medium was changed every two days and the cells were subcultured when they reached 70–80% confluency. Cells were made to undergo two passages after thawing before commencement of experiments.ESCs were maintained in undifferentiated state with Glasgow Minimum Essential Medium , supplemented with 15% ES-qualified fetal bovine serum , 1 mM sodium pyruvate , 0.1 mM nonessential amino acids , 0.1 mM 2-mercaptoethanol , 50 U/ml penicillin , 50 ug/ml streptomycin , and 1000 U/ml murine LIF  on gelatin-coated culture dish at 37°C in humidified air with 5% COThe 3rd generation Tet-on System was purchased from Clontech Laboratories (Cat. number 631167) . This Tet-On 3G System is an inducible gene expression system for mammalian cells. The system consists of the Tet-On 3G transactivator, TetR , and conFull length of mouse wild-type STAT3 cDNA was cloned from ESCs cDNA pool by PCR, constitutive activated STAT3 (CA-STAT3) or overexpression of pSTAT3 was obtained from pXJ40-CA (kindly provided by Professor Cao Xinmin) , and Nlsμg of plasmid DNA was diluted in Opti-MEM medium  for a final volume of 50 μl. 2 μl of Lipofectamine 2000 was diluted in 48 μl Opti-MEM medium followed by a 5 min incubation at room temperature. Diluted DNA was added to Lipofectamine 2000 diluted at 1 : 1 ratio and incubated for 20 min at room temperature. After incubation, the DNA/Lipofectamine mixture was added to each well and gently swirled to mix. The transfected cells were incubated at 37°C with 5% CO2. After about 4 to 6 hours, media was removed and replaced with fresh media. Then, cells were moved back to 37°C incubator with 5% CO2 until cells reached full confluency for harvest, and the transfection efficiency was checked by viewing in a fluorescent microscopy.Transfection was done when cells were grown to 70–80% of confluency. Cells were transfected by Lipofectamine 2000 , and the manufacturer's protocol was followed. 5 D) PCR amplification protocol consisted of one initial denaturation step for 5 minutes at 95°C, followed by 35 cycles: denaturation (95°C for 30 s), annealing for 30 s at 65°C, and extension at 72°C for two minutes. The PCR amplified samples were subjected to subsequent gel electrophoresis. 2% agarose gel was used to visualize the STAT3D band. The gel electrophoresis was run at the voltage 100 volt for about 40 minutes. The gel was then viewed under UV. The size of gene segment was determined by 100 base pair DNA ladder . List of primers used in genotyping are included in The STAT3 deleted  according to the manufacturer's instructions, purified by the RNeasy mini kit , and then reverse transcribed using the M-MLV Reverse Transcriptase system  into cDNAs. The cDNA products were used for semiquantitative RT-PCR with KAPA SYBR® FAST Universal 2X qPCR Master Mix (KK4600)  using a 7300 Real-Time PCR machine (Applied Biosystems). All gene-expression analyses were normalized against the housekeeping gene GAPDH. The sequences of primers used are included in β-actin level.Cells were washed twice with ice-cold PBS and lysed in whole cell extract buffer supplemented with protease inhibitor . The supernatant was collected by centrifugation, and protein concentrations were measured with BCA assay . The absorbance of the proteins in the lysates was then measured using GeneQuant 1300 machine with wavelength set at 595 nm. Appropriate amount of lysate samples was mixed with sample buffer, separated by SDS-polyacrylamide gel electrophoresis of a gel percentage dependent on the size of the target protein, and transferred onto a polyvinylidene difluoride (PVDF) membrane  in cold room. After blocking with 5% nonfat milk in washing buffer, the membranes were incubated with the indicated primary antibodies  at 4°C oSlides were washed with PBS solution, fixed with 4% paraformaldehyde  for 10 min at room temperature, and permeabilized with 0.2% Triton X-100  in PBS for 10 min. Then slides were blocked with 3% BSA  in PBS with 0.1% Triton X-100 for 10 min, followed by incubation with indicated primary antibodies  at 4°C o2 cell culture dish, and the dish was filled with PBS to prevent desiccation of EBs. Hanging drops were incubated at 37°C in humidified air with 5% CO2 for 3 days. EBs were then seeded on gelatin-coated plate on the 4th day of differentiation. Dox was added to differentiation medium to a final concentration of 1 ug/ml at the corresponding day indicated in the diagram  and suspended in Glasgow Minimum Essential Medium supplemented with 15% Tet system approved fetal bovine serum , 1 mM sodium pyruvate, 0.1 mM nonessential amino acids, 0.1 mM 2-mercaptoethanol, 50 U/ml penicillin, 50 ug/ml streptomycin, and 50 ug/ml ascorbic acid  (EB medium) . ESCs we diagram . EB mediImages of inducible ESCs were captured with a Zeiss AxioVision 4.7 or Nikon A1R-A1 confocal microscopy system. Images were taken after 24 hours Dox induction.flox/flox (F/F) [To determine the biological functions of STAT3 in ESCs, we generated the transgenic mouse ESCs harboring a Dox-inducible Cre mCherry transgene and STAT3 CA GFP transgene using an inducible Tet-on system, designed as InSTAT3 KO and InSTAT3 CA ESCs. The regulatory vector pEF1a-Tet was transfected into the mouse STAT3ox (F/F)  ESCs or D) alleles were analysed by PCR according to the previous study [D was detected as a 480 base pair fragment. Inducible Cre (iCre) ESCs following 24 hours Dox induction and STAT3flox/flox (F/F) ESCs were genotyped. Mouse heart tissue samples which included the hearts from STAT3-deficient mice (C/F/D), heterozygous (C/F/+), and STAT3flox/flox mice (F/F) were used as positive control. The STAT3D allele was detected as a 480 bp fragment in STAT3-deficient heart (C/F/D) and STAT3 KO ESCs (flox/flox (F/F) heart and F/F ESCs  domain which is essential for STAT3 function . Some of the regulated genes have been validated by qRT-PCR, such as suppression of cytokine signaling 3 (SOCS3), inhibitor of DNA-binding 1 (Id1), and GATA-binding protein 6 (GATA6). SOCS3 expression was upregulated upon overexpression of pSTAT3 while downregulated when STAT3 was KO . Id1 is In vertebrates, distinct sets of cardiac transcription factors to control gene expression in the developing heart are represented by the GATA family, the homeobox Nkx2.5 transcription factors, and the myocyte enhancer factor 2 (Mef2) transcription factors. Nkx2.5 and Mef2c individual mutants display early embryonic lethality and defeats in cardiac looping morphogenesis –59. MoreSeveral studies have demonstrated that mesoderm posterior basic helix-loop-helix transcription factor 1 (Mesp1) significantly promotes cardiovascular differentiation during embryonic development and pluripotent stem cell differentiation. Importantly, it has been proved that Mesp1 resides at the top of the cellular and transcriptional hierarchy that orchestrates multipotent cardiovascular progenitor (MCP) specification , 66. ThuImportantly, the inducible ESCs' capability of cardiac differentiation has been briefly examined. The results showed that InSTAT3 KO mESCs could be differentiated into beating cardiomyocytes at 9th day of EB culture. While STAT3 was deleted at day 0 (Dox was added at day 0), no beating cardiomyocytes was observed. According to these observations, it can thus be concluded that STAT3 is involved in initial stages of cardiac differentiation of ESCs. In conclusion, inducible STAT3 CA and STAT3 KO ESC system established in the present study could provide an excellent tool to demonstrate the functions or targets of STAT3 during modulation of ESC differentiation into certain tissue progenitor cells. Moreover, it has been reported that inhibition of STAT3 activity leads to downregulation of key specific cardiac genes . LikewisFurthermore, through partial gain- or loss-of-function approaches, the transcription profiles of STAT3 expression in ESCs were reported. Mouse STAT3 overexpressing ESCs which composed of the entire coding regions of mouse STAT3 to increase overall STAT3 expression was profiled , 81. Thein vitro inducible STAT3 knockout and pSTAT3 overexpressed ESC system where STAT3 expression level can be strictly controlled by Dox stimulation. This allows the study of STAT3 functions in ESC pluripotency maintenance. Moreover, it has provided another unique tool to demonstrate STAT3 functions involved in ESC differentiation into various lineages of cells. Taken together, the results of this study provide the opportunity and basis for further elaboration of the mechanisms, whereby STAT3 maintains ESC pluripotency and regulates ESC differentiation during mammalian organogenesis.In summary, the present study has provided an


All 19 loci produced heterologous amplification. The numbers of alleles per locus ranged from one to 21; the levels of observed and expected heterozygosity per locus ranged from 0.000 to 1.000 and from 0.000 to 0.847, respectively. Transferability of the loci was tested in the related species Eomecon chionantha.Based on the genome skimming data of nSSR markers revealed polymorphism in the four studied populations and may contribute to investigations of the genetic diversity of S. canadensis and E. chionantha.The developed  Sanguinaria canadensis L.  is the only species of Sanguinaria L., a genus endemic to eastern North America. Bloodroot is a polymorphic species in regard to the morphology of its petals and stems, which vary greatly among individuals. Despite this polymorphism, most botanists consider the different phenotypes to represent variation within a single species; the variation is clearly continuous, exhibiting intermediate forms, and having no obvious correlation with either geography or habitat  markers for S. canadensis using genome skimming and applied these markers to characterize the genetic diversity and population structure of four natural populations. Furthermore, we tested their cross‐amplification in the related species Eomecon chionantha Hance. Our results suggest that these nSSRs markers will be valuable for future studies on population genetics and phylogeography of S. canadensis across its entire geographic range.Simple sequence repeats (SSRs) have been shown to be highly useful genetic markers for assessing genetic diversity and characterizing population genetic structure  and the mitochondria of Nelumbo nucifera Gaertn. (NC_030753) were removed, so that only nuclear contigs remained. BLAST 2.2.26  of Allele identification was performed using GeneMarker version 2.2.0  with default parameters. The presence of null alleles and their bias on genetic diversity were evaluated based on the expectation maximization method implemented in FreeNA  of 68 Table , suggestS. canadensis. These markers can be applied to investigate population genetics and phylogeography of S. canadensis and its close relatives.In this study, 19 highly polymorphic nSSR markers were successfully developed for APPENDIX S1. The remaining 221 primers developed through the pipeline for Sanguinaria canadensis.Click here for additional data file.


Anolis lizards, Anolis carolinensis and Anolis porcatus, have been allopatric, yet this period of independent evolution has not led to substantial species‐specific morphological differentiation, and therefore, they might not be reproductively isolated. In this study, we determined the genetic consequences of localized, secondary contact between the native green anole, A. carolinensis, and the introduced Cuban green anole, A. porcatus, in South Miami. Using 18 microsatellite markers, we found that the South Miami population formed a genetic cluster distinct from both parental species. Mitochondrial DNA revealed maternal A. porcatus ancestry for 35% of the individuals sampled from this population, indicating a high degree of cytonuclear discordance. Thus, hybridization with A. porcatus, not just population structure within A. carolinensis, may be responsible for the genetic distinctiveness of this population. Using tree‐based maximum‐likelihood analysis, we found support for a more recent, secondary introduction of A. porcatus to Florida. Evidence that ~33% of the nuclear DNA resulted from a secondary introduction supports the hybrid origin of the green anole population in South Miami. We used multiple lines of evidence and multiple genetic markers to reconstruct otherwise cryptic patterns of species introduction and hybridization. Genetic evidence for a lack of reproductive isolation, as well as morphological similarities between the two species, supports revising the taxonomy of A. carolinensis to include A. porcatus from western Cuba. Future studies should target the current geographic extent of introgression originating from the past injection of genetic material from Cuban green anoles and determine the consequences for the evolutionary trajectory of green anole populations in southern Florida.In allopatric species, reproductive isolation evolves through the accumulation of genetic incompatibilities. The degree of divergence required for complete reproductive isolation is highly variable across taxa, which makes the outcome of secondary contact between allopatric species unpredictable. Since before the Pliocene, two species of  We use multilocus nuclear genotypes and mitochondrial haplotypes to distinguish between contemporary and past gene flow, allowing us to test whether secondary contact has eroded putative species boundaries or whether the two sister species are reproductively isolated and coexist as genetically distinct taxonomic units.Empirical studies that document genetically cryptic hybridization patterns are rare , and 4 µl of 50 ng/µl genomic DNA. Cycles started with initial denaturation at 94°C for 2 min, followed by 35 cycles of 94°C for 45 s, Tm for 45 s, 72°C for 1 min, and a final elongation step at 72°C for 10 min. PCR products were purified and sequenced at the Rhode Island Genomics and Sequencing Center.We amplified a region of 343–571 bp of the mtDNA NADH dehydrogenase subunit two using primers from Campbell‐Staton et al.  and two Taq polymerase (New England Biolabs® Inc.), and 2 µl of 20 ng/µl genomic DNA. Cycles started with initial denaturation at 94°C for 2 min, followed by 35 cycles of 94°C for 45 s, Tm for 45 s, 72°C for 1 min, and a final elongation step at 72°C for 10 min. Samples were genotyped at the DNA Analysis Facility on Science Hill at Yale University. Markers for all samples were analyzed with the software GeneMapper® v4.1 and visually checked to ensure accurate peak calling.We amplified 18 microsatellite markers using PCR with fluorescently labeled primers. We used seven newly designed primers , and A. carolinensis from one sampling location 135 km north of Miami , five sampling locations of Rousset, . Allelic2.5K = 1–6. We conducted 10 independent runs for each scenario with a burn‐in of 500,000 and 1,000,000 MCMC iterations. We used delta K to determine the most likely number of clusters following the Evanno method  from 10 independent runs with CLUMPP  was sequentially increased starting with one cluster. The model fit for K clusters was determined with the Bayesian information criterion (BIC).First, we performed a Bayesian cluster analysis with STRUCTURE v2.3.4  were not monophyletic. Six haplotypes representing thirty samples (35%) were nested within a well‐supported clade of A. porcatus from western Cuba, whereas twenty haplotypes representing 56 samples (65%) clustered with A. carolinensis from southern Florida  from HWE. Excluding those loci from the analysis did not affect the results and we thus included them in subsequent analyses. Allelic richness was similar across populations . FST values showed similar degrees of differentiation between populations  deviated significantly .The Bayesian cluster analysis using STRUCTURE recovered three distinct genetic clusters Figure . IndividK = 2 in the STRUCTURE analysis, the MIA population remained a distinct cluster while SFL and WCU formed a single genetic cluster . Similar distances between cluster centroids indicated equal degrees of genetic differentiation among the populations Figure . The MIA3.4p < 0.001, Supporting Information Table Tree‐based maximum‐likelihood analysis of microsatellite markers supported one migration event between WCU and MIA Figure a. The weTA = 887, mode TA = 554, 95% CI 245–2,670; Table RA = 0.24 (95% CI 0.14–0.35), which is similar to the maximum‐likelihood coefficient of admixture λ = 0.31 obtained from the summary statistics . Thus, reproductive barriers between A. porcatus and A. carolinensis appear weak or absent despite divergence in allopatry since before the Pliocene. Thus, secondary contact after species introduction has led to hybridization and formed a genetically distinct green anole population of hybrid origin.In an effort to characterize the genetic consequences of secondary contact between the native A. porcatus and A. carolinensis. Potential factors facilitating differentiation include reduced gene flow with populations of the parental species  population, which formed a distinct genetic cluster, would have been indistinguishable from within‐species population structure. However, addition of a non‐recombining mtDNA marker revealed maternal ancestry from both A. carolinensis from southern Florida and A. porcatus from western Cuba are not reproductively isolated and interbreed successfully after secondary contact, leading to a fusion of the previously distinct lineages. The species status of A. porcatus and A. carolinensis has changed repeatedly over the last decades based on morphological traits (Gray, Anolis porcatus was considered a subspecies of A. carolinensis (Gray, A. carolinensis species complex revealed paraphyly of A. porcatus, dividing this species into eastern and western clades in Cuba, with the western clade being sister to A. carolinensis (Glor et al., A. carolinensis and A. porcatus. According to the biological species concept, populations of distinct species are incapable of effectively interbreeding with one another (Mayr, A. porcatus from western Cuba, which should be subsumed into the earlier named A. carolinensis.Our study provides genetic evidence that the formerly independent lineages ts Gray, . Anolis is Gray,  until deis Gray, . Howeveris Gray, . Geneticer Mayr, , which ier Mayr, , supportAnolis species have been introduced to Florida and some from multiple native‐range source populations in Cuba (Kolbe et al., A. porcatus haplotypes from Miami (Kolbe et al., A. porcatus involves a single or multiple independent introductions.Several 5A. porcatus and A. carolinensis and to test whether weak or absent reproductive barriers have led to hybridization and erosion of putative species boundaries between green anoles in South Miami. Mismatch between cytoplasmic and nuclear DNA as well as genetic evidence for past gene flow supports that A. porcatus and A. carolinensis are not reproductively isolated and that secondary contact has led to hybridization and fusion of formerly independent lineages. Therefore, the western Cuban lineage of A. porcatus should be subsumed taxonomically into A. carolinensis given its priority (Gray, The aim of this study was to characterize the genetic consequences of secondary contact between ty Gray, . A majorNone declared.Johanna Wegener designed the project, collected specimens in the field, contributed substantially to data collection in the laboratory and supervision of undergraduate work, conducted the data analysis, data interpretation, manuscript writing, and revisions. Jessica N. Pita Aquino contributed substantially to data collection in the lab, interpretation of the data, and manuscript revisions. Jessica Atutubo and Adam Moreno contributed substantially by collecting data in the laboratory, interpreting data, and revising the manuscript. Dr. Jason J. Kolbe made substantial contributions by designing the project, by interpreting data analyses and results, and by playing a critical role throughout the manuscript revision process. Click here for additional data file.


Human ancient DNA studies have revealed high mobility in Europe’s past, and have helped to decode the human history on the Eurasian continent. Northeastern Europe, especially north of the Baltic Sea, however, remains less well understood largely due to the lack of preserved human remains. Finland, with a divergent population history from most of Europe, offers a unique perspective to hunter-gatherer way of life, but thus far genetic information on prehistoric human groups in Finland is nearly absent. Here we report 103 complete ancient mitochondrial genomes from human remains dated to AD 300–1800, and explore mtDNA diversity associated with hunter-gatherers and Neolithic farmers. The results indicate largely unadmixed mtDNA pools of differing ancestries from Iron-Age on, suggesting a rather late genetic shift from hunter-gatherers towards farmers in North-East Europe. Furthermore, the data suggest eastern introduction of farmer-related haplogroups into Finland, contradicting contemporary genetic patterns in Finns. These include for instance the initial colonization of Europe by modern humans c. 40 kya3. In the advent of the Neolithic revolution, these U subgroups were largely supplanted by farmer-associated haplogroups H, HV, J, K, N1a, T2 and W5. The subsequent spread of Yamnaya-related people and Corded Ware Culture in the late Neolithic and Bronze Age were accompanied with the increase of haplogroups I, U2 and T1 in Europe (See8 and references therein).In terms of mitochondrial DNA (mtDNA), these cultural turnovers and population migrations in Europe involved also changes in the haplogroup composition. In hunter-gatherer populations the dominating mitochondrial lineage has been U, especially its subgroups U4, U5a and U5b9), Hummervikholmen in Norway  and Yuznuy Olennij Ostrov on Lake Onega, Russia . In addition, Mesolithic to Bronze-Age DNA data from several sites in the Baltic countries have been published14.Whereas the ancient DNA (aDNA) composition and its changes in mainland Europe are increasingly well understood, northeastern Europe has been far less studied. The oldest human DNA analyzed in this region derive from the Mesolithic burial sites in Huseby Klev, western Sweden . Unlike most of the Europe, the Middle Ages starts as late as 1200 AD in western parts of country and 1300 AD in east and shifts into Early modern period in the beginning of 16th century .In terms of both genetics and culture, modern Finns show a unique combination of eastern and western European elements, which most likely reflects the settlement history. The first archaeological evidence of human presence in Finland dates relatively late to c. 11000–9000 years ago, soon after the continental ice sheet retreated. According to the archaeological record, the region has since supported a continuous human occupation until today23. This genetic uniqueness derives from both reduced genetic diversity25 and an Asian influence to the gene pool24. Within Finland, an unusually strong genetic border bisects the population along a northwest to southeast axis27, and is interpreted to reflect an ancient boundary between hunter-gatherer and farmer populations28. The expanse of agriculture north-east of this border was probably limited by environmental factors, especially the length of the growing season. Later, this border has most likely acted in demarcating the spread of western and eastern political and cultural impacts influencing the placement of first political border between Sweden and Novgorod through the middle of Finland (Treaty of Noteborg 1323 AD).Linguistically, contemporary Finns and Saami differ from most other Europeans in speaking a Uralic language, unrelated to the majority of European languages, which belong to the Indo-European language family. Finns are also genetically distinct from their neighboring populations and form outliers in the genetic variation within EuropeIn order to gain better insight into the genetic history of Finns, we here describe 103 complete mitochondrial genomes reconstructed from bone samples from ten burial sites in southern Finland and the Republic of Karelia, Russia  or high contamination levels  from the MCMC are reported in Supplementary Table www.geneious.com). For each of them, the majority call supported the previously assigned haplogroup.The contamination rates of the 103 samples were further evaluated by Schmutzi14C dates for 42 individuals  were obtained from remains of a c. 5-year-old child  and an older woman  from Hollola. Identical haplotypes (haplogroup H85) were also observed in a middle-aged adult  and a c. 18-month-old child  from Hollola. At the Hiitola site, identical haplotypes (haplogroup W6) were shared between two individuals from distinct graves . At the Tuukkala site, two individuals showed identical haplotypes . At Turku, two adults shared the same haplotype belonging to the basal haplogroup H (samples TU582 and TU588). Haplotypes for 103 individuals are presented in Supplementary Table As the subsequent statistical methods assume that samples derive from unrelated individuals, five samples - one of each identical haplotype pairs within sites  - were removed from the subsequent analyses due to their possible maternal relatedness.The mean number of pairwise differences, calculated from complete mitochondrial genomes, was highest within Porvoo (MNPD = 33.7 ± 16.8) and lowest within Renko (MNPD = 21.8 ± 10.8)  had average U and H frequencies of 58.3% and 27.8%, respectively, whereas the corresponding values in the eastern cluster  were 20.0% and 53.3%. In IAM east the highest frequency for an individual subhaplogroup was 30.0% obtained for H1. Strikingly, this U/H ratio is the opposite compared to contemporary eastern and western Finns.18. However, the Levänluhta individuals also carried mtDNA haplogroups that are absent or rare among the Saami population today, U5a, H1 (0.0–4.0%34) and haplogroups K and T. The Levänluhta site clearly showed a unique composition, which resulted in significant genetic distances to all other ancient sites at sequence level, with ΦST values of >10% (see below).Among the 12 Levänluhta samples, five individuals carried haplogroup (hg) U5b, four of which belonged to the sub-hg U5b1b1a. Additionally, the Levänluhta site included three individuals with hg U5a, resulting in a total frequency of 66.7% for hg U5. In contrast, with only two haplotypes of sub-hg H1, the frequency of hg H was well below values observed in modern European populations. The high U5b1b1a frequency resembles that observed today in Saami populations of northern Europe. This actually corresponds well to a related recent study that is showing the close genetic affinity between Levänluhta individuals and modern Saami14C dated to 955–1390 calAD . Haplogroup U5b1 was entirely absent, and the U haplotypes observed belong to subhaplogroup U4, U5b2 and U2. Lineage U2 is prevalent in some Uralic speaking groups today36. The overall haplogroup distribution in Luistari was more similar to the modern European populations dominated by agriculture-associated Neolithic haplogroups H and occurrences of T2 and W1 (see Introduction), than in Levänluhta and Hollola sites.A rather different picture emerged from the Luistari samples, showing a substantial genetic distance to Levänluhta  and U8 , which are atypical for contemporary Finns.The two easternmost sites, Hiitola and Tuukkala, proved genetically distant from the western Levänluhta and Hollola sites, despite being approximately contemporaneous with the Hollola individuals. The Neolithic signal in the mtDNA gene pool of ancient Finns in general was much stronger in the east. Both Hiitola and Tuukkala samples showed high frequencies of hg H , together with other Neolithic haplogroups J, K, W and X. Notably, these eastern sites shared three haplogroups: H1a7, H1a8a and H10g. According to GenBank searches these three haplogroups are rare in modern populations: for H1a7 four modern sequences were found, two in Finnish (KY620272 and MF686118), one in Swedish (KJ487971) and one in British (GU797829) populations. For haplogroup H1a8a only two matches were found, one among Finnish (JX153203) and one of an unknown origin (JQ701944), whereas three modern sequences were found for haplogroup H10g: two Finnish (KR732275 and MF497508) and one from Russia (GU122976). Notably, H1* are known to be common in modern Karelia. Contrasting IAM sites and contemporary Finns, EMM sites harbors high prevalence of haplogroup T; frequency in EMM is as high as 21.9%, while in other Finnish populations the frequency is less than 8% When contrasted with haplogroup frequencies observed in contemporary Finns, our simulations  and Tuukkala  . The distance between Levänluhta and contemporary Finns was smaller but still significant, with a distance to contemporary north-east (NE) ΦST = 0.04077 and to contemporary south-west (SW) slightly higher ΦST = 0.06473. While Luistari differed only from Levänluhta, the Hollola site differed both from Hiitola and the EMM , but not from Levänluhta  . When considering the genetic distances between individual sites, it has to be noted that the relatively low sample sizes might affect the ΦST values and the results should be interpreted with caution. However, for pooled IAM and EMM sites  Fig. . The lar05) Fig. . Hiitolasee Fig. , for whiΦST = 0.01670, p = 0.00488) and EMM  but not from contemporary NE . In addition, EMM and contemporary SW differed from each other . Conversely, IAM east differed from the contemporary NE (ΦST = 0.00849) more than from contemporary SW (ΦST = 0.00514).Clustering the IAM sites further roughly according to their geographical location to IAM south-west  and IAM east  further demonstrated the pattern opposite to modern mtDNA diversity distribution Fig. . IAM souHaplotype level median-joining network  among the Saami, the archetypal nomadic population lacking many farmer-associated haplogroups38. Distance from the eastern reference point was the only predictor included in the model (with significance of 0.031 for farmer associated haplogroups and 0.082 for other haplogroups). Assuming that haplogroups U and H can be associated to hunter-gatherers and farmers, respectively, the results suggest a spread of the more central European like, farmer-related haplogroups spreading from the east. However, as mentioned above, association of hg V is unclear. Omitting V from the hunter-gatherer group does not change results noteworthily   based on haplogroup frequencies. We plotted the first two components of the PCA plot for ancient Finns, 31 other ancient populations, contemporary Finns and Saami, which account for 55% of the total variance Figs , S4. IntHere we report 103 human mitochondrial DNA genomes from approx. 300 AD to 1800 AD, a transect both in time and space, which represents thus far the largest collection of individuals with ancient human DNA analyzed from Finland. Analysis of the prehistoric samples from Iron-Age and medieval sites from western and eastern Finland revealed a high overall prevalence of haplogroup U in southwestern sites, in stark contrast with a high frequency of haplogroup H in the east, which is opposite to what is observed in modern day Finland. Moreover, there is relatively high differentiation between the ancient sites.35. Indeed, recent studies considering Levänluhta, in which nuclear genomes have been retrieved, confirm the genetic continuation with the modern Saami population19. The strong drift experienced by the Saami groups of present day, shown by their high levels of LD throughout genome and low diversity in uniparental markers (See38 and references therein) could explain why some mtDNA lineages, such as U5a, would have vanished from present day Saami.Unexpectedly high variation in maternal lineages could be observed between the southwestern Levänluhta, Luistari and Hollola Iron-Age sites. Especially the distribution of U subhaplogroups differed clearly between sites: The oldest site, Levänluhta, represented a high frequency of U5a and the modern Saami-related haplogroup U5b1b1a, which is present in contemporary Finns only in moderate frequency of around 3.0%36 and with lower frequencies around the Baltic Sea, such as in Latvians and Tver Karelians (both around 8%)37. Taking into account that U4 have been prevalent in neighboring areas among Scandinavian43 and Baltic hunter-gatherers44, Baltic Comb Ceramics Culture14 and in Siberia during the Early metal period11, we might be observing ancestries belonging to an earlier layer of ancient inhabitants of the region.Both Luistari and Hollola lacked the U5b1b1a, but instead Hollola displayed a wider variety of other U5 subhaplogroups, such as U5a1, U5a2 and U5b2. In addition, Luistari and Hollola sites showed relatively high frequencies of different subhaplogroups of U4 , which are rare in contemporary Finns and absent from modern Saami. Instead, in contemporary populations, U4 exists in high frequencies in Volga-Ural region (up to 24% in Komi-Zyryans)Taking these different distributions of mtDNA haplogroups from the Iron-Age and medieval sites into consideration, our results suggest three different streams of mitochondrial ancestry: Saami-like haplogroups , non-Saami-like hunter-gatherer related haplogroups  and haplogroups associated with Neolithic farmers . In this context we use ‘Saami-like’ as a term that shows genetic continuity with modern-day Saami groups. Different proportions of these ancestries could be observed both in later EMM sites and also modern-day southwestern and northeastern Finns. This suggests a fluctuation of each of these mitochondrial ancestry proportions over space and time.45. Furthermore, these studies have demonstrated the existence of notable genetic differentiation between southwestern and northeastern parts of Finland27. This differentiation is especially pronounced in Y-chromosomes, showing opposite frequency trends of haplogroups N1c  and I 26.The Finnish population has been a subject of multitude medical genetic studies for many decades. Assessments of genetic diversity have revealed a number of idiosyncrasies in the modern Finnish gene pool. These include, for instance, the enrichment of c. 40 rare genetic diseases and the absence of some major ones in the rest of European metapopulation, as a clear distinction from the largely clinal differences observed in most of Europe35. The genetic substructure within Finland is minimal when at the level of mtDNA haplotypes are considered, but pronounced in the frequencies of haplogroups assumed Paleolithic (here U and V) or Neolithic  in Europe: the palaeolithic haplogroups are more common in the north-east (“Contemporary NE” subpopulation), and Neolithic haplogroups in the south-west (“Contemporary SW”). This, together with the Y-chromosomal subdivision, has been interpreted to reflect an ancient border between populations relying on farming (south-west Finland) and foraging (north-east). The observed genetic border running diagonally from north-west to south-east coincides with differences in a number of linguistic and cultural differences all the way to folk traditions. It also coincides with the first medieval political border, the Treaty of Nöteborg, between the Swedish and Novgorodian spheres of influence agreed in 1323 AD (see28).The modern mitochondrial DNA diversity in Finland resembles that observed in the Central Europe, but holds a relatively high overall frequency of haplogroup U, and also a notable proportion of subhaplogroups which have frequency peaks in or are exclusive to FinlandΦST values: the western cluster was closer to the modern NE subpopulation than to the modern SW subpopulation whereas the eastern cluster showed closer affinity with the modern mtDNA variation in southwestern Finland.The ancient mitochondrial genomes analyzed here show a notable pattern opposite to the modern variation: mtDNA types usually associated with the hunter-gatherer communities were significantly more common in the ancient western cluster  than in the east , with the haplogroup U frequency as high as 58.3%. In contrast, the farming-related lineages were observed in particular in the ancient eastern cluster. This pattern of division between the ancient sites, and the contradictions with their respective local modern population frequencies emerged also in formal testing of pairwise c. 5300–4000 BC)47, the start of agriculture in Finland has been traditionally associated with the Corded-Ware Culture (CWC) arriving across the Baltic Sea approximately 4700 years ago. Indeed, there are scattered findings of animal husbandry from southwestern parts of country starting from c. 2500 calBC48, but in general archaeological evidence supporting transition to agriculture as a consequence of introduction of Corded-Ware culture, are sparse (for discussion see49). Some independent observations of animal domestication50 and cultivation (see22 and references there in) are identifiable during the Bronze Age, but documentation remains still very limited. This suggests that cultivation has probably been relatively uncommon and local for centuries, as little direct evidence for cereal cultivation in Finland prior Iron Age exists49. Pollen records show notable increase of cereals starting only at 100 AD and reaching maximum as late as 1300 AD22 overlapping the time span of Iron Age and Medieval sites presented in this study.Assuming that the haplogroup composition has correlated with the mode of subsistence, the observed pattern of east to west transect suggests a bidirectional spreading of agricultural human groups into Finland. Although there is evidence of sporadic small-scale cultivation in southeastern Finland already during the Neolithic Stone Age . Iron-Age has evidenced high mobility around the Baltic Sea, as evidenced by the genetic and isotope analyses of human remains from 10th to 12th century in Sigtuna, eastern Sweden52.As a support for the late introduction of farming populations in to Finland we do not see strong affinities of western IAM to for example the CWC maternal gene pools from Estonia and LithuaniaThe multinomial logistic regression analysis lent support to the eastern introduction of agriculturally related maternal ancestry. The likely migration routes for the observed ancestral elements were investigated through different combinations of factor dependencies as the multinomial logistic regression. The test revealed the distance from Lahdenpohja on the eastern border of Finland as the only statistically significant variable explaining the differences in the haplogroup composition. Neither time scale, nor distance from the southwestern locations (i.e. Hanko and Uusikaupunki) were supported by the best-fit model. It thus seems likely that the major spread of haplogroup H can be explained by presuming its introduction via the eastern landroute. In accordance with the inference here, population genetic studies of many organisms in Finland as well as in all Fennoscandia have suggested bidirectional colonization of the current habitats. The reasons behind this are largely geographical: the Baltic Sea acts, for most species, as a migration barrier.The Neolithic farmer-related signal in the mtDNA diversity in the Iron-Age samples is mainly found in the southeast, whereas in contemporary population it predominates in the southwest. The reasons for this discrepancy are likely diverse, and could be affected by such recent events as the evacuation of nearly 0.5 M inhabitants of Karelia during the World War II and their resettling into the area of current Finland. However, these evacuees were resettled rather evenly across southern Finland and should not create the observed pattern. It rather suggests that the division between SW and NE Finland had still been more substantial in the early 1900s. Another, more fundamental explanation for the genetic subdivision comes from the environmental demands of sedentary farming. In southwestern Finland the soil is more amenable to field-farming and due to the warming Atlantic effect that gradually shades into more arid continental climate, the growing season in Finland is the longest in the southwestern coast. These environmental differences follow the NW-SW border similarly to the genetic distances. As the country has been sparsely inhabited until modern times, it is plausible that farming oriented populations, in search of more favorable conditions, have over the centuries concentrated into the SW parts of the country.The mitochondrial DNA genomes from Iron-Age Finland show variation that can be linked to either hunter-gatherer or agricultural human groups. These elements are still present in the mitochondrial gene pool of contemporary Finns but relatively evenly distributed throughout the country. In contrast, the Iron-Age mtDNA variation show significant differences between sampling sites, with hunter-gatherer and farmer-associated elements dominating in different regions than today. Rather surprisingly, the agricultural population signal has been stronger in eastern Finland in the past, which might reflect a bidirectional arrival of farming-associated populations into Finland.The human skeletal remains used in this study were collected from five archaeological sites and five historical cemeteries  data from 832 modern Finns53. A dentist’s drill together with cooled-down drill heads for minimal heat exposure were used at the University of Tübingen facility and a “field kit” with Dremel or dentist’s drill were used at the Helsinki University Department of Forensic Medicine.All samples were processed in dedicated aDNA facilities with regularly UV:d and bleach-treated laminar hoods, inside a clean room space. For Levänluhta, Porvoo, Renko, Pälkäne and Hamina sites, sampling was conducted at the University of Tübingen, Germany from the start. For the Luistari, Hiitola, Hollola and Tuukkala sites, the bone powder was produced in a clean-room space for small-scale ancient DNA work at the Helsinki University Department of Forensic Medicine, and then transferred to Tübingen University facilities, stored in plastic tubes. All subsequent laboratory work with the bone powder was conducted at the facilities in Tübingen. A protective overall, facemask, hair net and two layers of disposable gloves were used at all times when handling the samples. Decontamination was carried out by ultraviolet light exposure of the plastic ware, reagents and samples, and by removal of the immediate surface at the point of sampling before drilling into the bone. Teeth were sawed in two, with the exception of the Levänluhta samples, where dentine was already exposed due to heavy fragmentation. Dental pulp and the surrounding dentine were used for DNA sampling, drilling into the crown or the root. For the petrous part of the temporal bone, a wedge was cut off to reveal the inner ear channels and sample was taken from the inside of the channel, as previously described29. For each sample, ~50 mg of bone powder was used for the extraction by eluting it in 100 μl of TET . The extracts were used to prepare DNA libraries of 20 μl, without additional treatments. To enable multiplex sequencing, the double-stranded library preparation and the subsequent indexing procedure were performed according to standard recommended protocols for ancient DNA55. The original molecular copy number in the DNA library, as well as the subsequent indexing efficiency, were measured by qPCR, using AccuPrime Pfx polymerase. The molecular copy numbers in pre-indexed libraries varied, ranging from ~1–100 × 108 copies/μl, and the indexed libraries from ~1–100 × 1011 copies/μl, indicating a successful library composition and admissible indexing efficiency. The indexed libraries were amplified using PCR, with heating cycles chosen individually per library according to the copy number after indexing. Amplified libraries were purified using MinElute spin columns with the standard protocol provided by the manufacturer (Qiagen). A qPCR together with Agilent Bioanalyzer 2100 device, and a DNA1000LabChip were subsequently used on the amplified libraries to measure the concentration of DNA, as well as fragment size distribution. A positive control extracted from a cave bear bone, to confirm the success of extraction and library preparation, as well as two negative laboratory controls to measure the levels of contamination were carried along for every batch of 10–16 samples.The extraction was performed according to a modified version of the original protocolet al.56. Complete human mitochondrial DNA sequence was used to produce in-house made baits, which were ligated to adapters. The bait DNA was then purified and denatured to the single stranded form, and attached to streptavidin-coated magnetic beads. Pools of 4–6 samples, combined in equal mass ratios for altogether 2 μg of DNA, were captured with the above mentioned beads, and sequenced on the Illumina platforms: for samples from sites Turku, Hiitola, Tuukkala and Hollola, along with four samples from the Luistari site , single-end sequencing data was produced on HiSeq4000 run for 75 + 8 + 8 cycles, whereas samples from all the other sites, including the rest of the samples from Luistari, paired-end data was produced with NextSeq500 for 2 × 150 + 8 + 8 cycles, at the Max Planck Institute for the Science of Human History, Jena.Mitochondrial genomes were achieved using a mitochondrial in-solution capture as described in Maricic 57. Reads were demultiplexed using both indices, and the adapters were clipped off with the AdapterRemoval program integrated in EAGER, with minimum overlap set to 1 bp. Short reads reaching a minimum of 10 bp overlap were merged, validated by the paired-end fragment compatibility. For the single-end-sequenced samples this step was omitted. Minimum read length of 30 bp and base quality of 20 was required for all final reads. The reads were aligned to the complete human reference genome Hg19 with the BWA mapping algorithm for the shotgun sequence data, whereas CircularMapper, a custom-made tool for circular genomes included in EAGER, was used to map the enriched mitochondrial reads to the human mitochondrial reference genome .Raw-read sequencing data were processed using the EAGER-pipeline for aDNA sequencing dataand deamination patterns at the 5′ and 3′ ends of DNA fragments were inspected for each sample with the program MapDamage59 integrated in EAGER. Further contamination estimation was carried out with Schmutzi32 and ContamMix33 programs.In order to evaluate the authenticity of the reconstructed mitochondrial genome sequences, contamination rates, read-length distributions 60 with respect to PhyloTree version 1761.The mitochondrial consensus-calling in Schmutzi was used to produce the consensus sequences of both the endogenous source and the most likely single contaminating source. Complete consensus sequences were called against the rCRS with a filter value of q20. For the Schmutzi-based consensus sequences a manual correction was performed for position 3107 to correspond to the “N” embedded in rCRS. The mtDNA haplogroups were determined using HaploGrep263. The untrimmed mitochondrial reads from the Eager pipeline were then extracted from BAM files into fastQ files and mapped back to the assembly. ContamMix then evaluates whether the reads assign more probably to their respective consensus or one of the worldwide mitochondrial genomes, i.e possible contaminant source. The ContamMix was run with trimming of seven bases of each side of the read to remove the accumulated damage typical for ancient DNA.For contamination estimation in ContamMix, consensus sequences created by Schmutzi were combined with a reference dataset of 311 mitochondrial genomes from worldwide populations (provided by ContamMix) and aligned with mafft version 7.305www.geneious.com). In this inspection, the majority call support for relevant diagnostic mutations against the rCRS reference genome was compared to the PhyloTree version 17. We applied the automated variant caller in Geneious to the alignments with minimum support of 3x coverage and variant frequency of 66.6% for diagnostic SNPs to confirm the authenticity of the haplogroup assignments.The mitochondrial genome sequences with highest ContamMix estimates were further visually inspected in Geneious 11.0.3 . The results are provided as conventional radiocarbon dates without potential reservoir effect corrections. The radiocarbon dates were calibrated using the OxCal program version 4.366, IntCal 13 as the calibration curve67. For sites with four or more individuals with 14C dates also the boundaries for phase’s start and end were determined with OxCal 4.3. Example of the OxCal code is given in Supplementary Material Radiocarbon dates were produced by the Laboratory of Chronology, Finnish Museum of Natural History Luomus in Helsinki, Finland (Hela) and Klaus-Tschira C14-laboratory in Mannheim, Germany (MAMS). Bone collagen was extracted with the modified Longin methodA summary of statistical analyses performed in this study for different sets of populations and dataset  is presented in Table 68. To explore the genetic diversity within ancient populations, basic diversity indices such as haplotype diversity, mean pairwise distance and nucleotide diversity were calculated for the complete mitochondrial genomes with Arlequin 3.5.2.269. To determine genetic distances between ancient and modern Finns, pairwise ΦST values based on the sequence data were calculated with Arlequin 3.5.2.2. Genetic distances, were calculated by utilizing HVR1 + HVR2, as the contemporary reference data with detailed geographical origin were restricted to HVR regions only28. To estimate the significance for the ΦST values, permutation tests with 10000 permutations were used. The visualization of the ΦST values was done by R heatmap.2 function with hierarchical clustering based on the Euclidean distance. The best-fit models for different datasets were estimated with jModelTest70. The substitution model used was Tamura & Nei71 with gamma correction (shape parameter α = 0.67) and Tamura & Nei with gamma correction (α = 0.44) for the complete sequence data and HVR1 + HVR2 data, respectively. Poly-C region (positions 309–315), AC indels (positions 515–522) and mutational hotspot at position 16519 were masked for the population level analysis. Further to evaluate the relation of ancient individuals to contemporary Finns on a haplotype level, median-joining network analysis72 was performed with PopArt73. Positions bearing more than 5% of missing data were masked for the network analysis.For the statistical analysis, sequences were aligned with Muscle v3.8.3121. For multinomial logistic regression a stepwise forward method was used with entry probability 0.05, and probability was tested with likelihood ratios. To evaluate the impact of grouping the haplogroups into hunter-gatherer and farmer related hgs’ also categories ‘U + V’, ‘H + J + K + T’ and ‘Others’ were tested. In addition, categories ‘U’, ‘H + J + K + T’ and ‘Others’ were tested, due to controversial definition of haplogroup V  Median age of sample (or the mean age of the site if radiocarbon dates were not available) (2) site’s distance (in km) from southern reference point Hanko, (3) site’s distance (in km) from western reference point Uusikaupunki and (4) site’s distance (in km) from eastern reference point Lahdenpohja  using principal component analysis (PCA). PCA was computed using MATLAB and Statistical Toolbox Release 2015b . Populations and haplogroup frequencies used are presented in Supplementary Table Supplementary Materials and FiguresSupplementary Tables


Varecia variegata), as a test case. Here, we examine the relative influence of natural and anthropogenic landscape features that we predict will impose barriers to dispersal and promote genetic structuring across the species range. Using circuit theory, we model functional connectivity among 18 sampling localities using population-based genetic distance (FST). We optimized resistance surfaces using genetic algorithms and assessed their performance using maximum-likelihood population-effects mixed models. The best supported resistance model was a composite surface that included two anthropogenic features, habitat cover and distance to villages, suggesting that rapid land cover modification by humans has driven change in the genetic structure of wild lemurs. Primary conservation priority should be placed on mitigating further forest loss and connecting regions identified as having low dispersal potential to prevent further loss of genetic diversity and promote the survival of other moist forest specialists.In recent decades Madagascar has experienced significant habitat loss and modification, with minimal understanding of how human land use practices have impacted the evolution of its flora and fauna. In light of ongoing and intensifying anthropogenic pressures, we seek new insight into mechanisms driving genetic variability on this island, using a Critically Endangered lemur species, the black-and-white ruffed lemur ( Habitat loss and fragmentation act to rapidly reduce suitable habitat area, quality, and structural connectivity  with potentially dramatic consequences for population viability6. The extent to which fragmentation impacts functional connectivity, however — that is, whether structurally intact corridors actually facilitate movement between habitat patches (see ref.7 for review) — depends largely on a species’ biology and behavior  (reviewed in refs10). While some organisms may thrive in anthropogenically altered habitats and/or can easily traverse a matrix with low structural connectivity , others may be more sensitive to natural and/or anthropogenic change and quickly become both geographically and genetically isolated . Fragmented populations may then face rapid declines in size, demographic isolation, and reduced genetic variation19. In the long-term, they may also face inbreeding depression, reduced evolutionary potential, and increased extinction risk22. Local extinctions of small isolated populations are relatively common24 and thus a species’ persistence depends largely on its dispersal ability, thereby maintaining functional connectivity among its remaining populations26.Anthropogenically driven habitat modification is recognized as the single greatest threat to global biodiversitysensu ref.27) on the spatial patterning of neutral and adaptive genetic variation among individuals and within populations30. The landscape genetics paradigm is a relatively recent improvement over more traditional population genetics methods in that it incorporates spatially explicit data to investigate the influence of landscape heterogeneity on gene flow and genetic variation within and among populations31. In so doing, landscape genetic analyses enable researchers to identify suitable areas in the matrix  that promote gene flow and maintain landscape connectivity33.Landscape genetic methods allow researchers to quantify the dispersal ability of a species by examining the relative effects of landscape composition, configuration, and matrix quality  or spatial predictions of ecological processes36. Although acceptable approaches, they suffer in that they test a limited resistance space , and resistances are not objectively assessed prior to model building. Expert opinion is often insufficient to accurately predict movement . In fact, even when ecological processes are known and modelled, there is no guarantee that these processes will be meaningful to animal movement and gene flow36. Recently, however, Peterman39 developed a more objective method to assign resistance values to both continuous and categorical landscape layers using genetic algorithms. This technique enables the parameterization of landscape surfaces with no a priori information about the direction or magnitude of the effect a landscape feature has on a species’ movement. The use of genetic algorithms for parameterizing resistance surfaces allows for a wider search of the parameter space for possible resistance values. New analytical tools (e.g. ResistanceGA39) allow for novel implementation of this optimization and assignment method, along with a selection criterion to discern between single and composite surfaces using linear mixed effects models fit with the maximum-likelihood population effects (MPLE) parameterization40. Here, we incorporate these new methods to explore the impacts of landscape heterogeneity on gene flow and genetic structure in Madagascar, using Critically Endangered black-and-white ruffed lemurs (Varecia variegata), which range along the eastern rainforest corridor, as an illustrative test case.Until recently, landscape genetic studies have typically used expert opinion to parameterize resistance values for the landscape features in question41. As such, it is often identified as a global conservation priority43. Since the 1950s, approximately half of Madagascar’s remaining moist forest has been lost to anthropogenic habitat modification; its remaining forest habitats continue to face rapid conversion and associated fragmentation which threaten the country’s unique biota45. Ruffed lemurs are relatively large-bodied46 (3.5–4.5 kg), arboreal, obligate frugivores that are exclusive to Madagascar’s eastern rainforest corridor47, though they are thought to have historically ranged as far south and west as Toliara, roughly 415 km southwest of their southernmost distribution today48. Although known to persist in degraded habitats49 and forest fragments52, the species exhibits a preference for low- to mid-altitude primary rainforest47. Ruffed lemurs live in large social groups53, maintain large home ranges55 , and rely on a spatiotemporally patchy diet56. The species dominates the upper canopy and rarely comes to the ground57; thus, dispersing individuals are unlikely to cross an open landscape matrix. Moreover, ruffed lemurs are highly selective feeders, and can be considered ecological specialists58, making them especially vulnerable to habitat modification59. In fact, they are among the first taxa to disappear in the face of habitat disturbance60. Consequently, remaining populations of this species are fragmented into several geographically distinct localities with seemingly limited potential for reproductive contact61.Madagascar is among the most diverse and the most threatened biodiversity hotspots in the world61. These patterns differ geographically, in that northern sites are characterized by greater genetic diversity and gene flow than southern localities61. It is possible that these differences are related to landscape connectivity; however, the extent to which different landscape features impact dispersal and gene flow in this species remains unexplored.Recent studies have found low levels of genetic diversity among the remaining ruffed lemur populations and high levels of genetic differentiation62; mountain lions63; bonobos64; western gorillas16; radiated tortoises65) and have been argued to play a particularly important role in the biogeography of Malagasy strepsirrhines69. We therefore expect rivers to explain a large proportion of the genetic differentiation found among sites. Additionally, because ruffed lemurs are arboreal specialists, are ecologically constrained, and prefer primary rainforest habitats47, we predict that anthropogenic pressure in the form of roads, reduced habitat cover, and proximity to human habitation will impose significant barriers to gene flow. Finally, although ruffed lemurs prefer undisturbed, low- to mid-altitude forest, the species has been shown to persist in degraded habitats52 and at higher elevations70 . Thus, although we expect forest degradation and elevation to have some influence on population genetic structure, these are predicted to show weaker relationships with genetic distance than other landscape features identified above. To our knowledge, ours is only the second study to use both Peterman’s39 recent method of resistance surface optimization with genetic algorithms and maximum-likelihood population-effects (MLPE) mixed models40 for model selection of a wild, endemic vertebrate across its entire known geographic extent, and the first to do so in Madagascar. Using newly developed landscape genetics methods that more objectively identify resistance surfaces and resistance surface complexity should yield more robust results compared to prior approaches.Here we investigate the relationships between genetic structure and landscape features to identify factors that impede or facilitate ruffed lemur dispersal  on a range-wide scale. We test the null hypothesis of isolation-by-distance and then consider the effects of five potentially related landscape features that we predict will impose barriers to dispersal and promote genetic structuring among sampling localities: (1) rivers, (2) topography, (3) roads, (4) habitat cover, and (5) proximity to human activity. Natural barriers such as rivers have been linked to the genetic structuring of several vertebrate taxa   sampled from 18 localities spanning the species’ known range  using the program FSTAT2.9.3.271. Given that continuous sampling was not possible at such a broad range, population-based metrics were chosen over individual-based statistics72.We used the original dataset from Baden sensu McRae73 to model functional connectivity amongst sites. The isolation-by-resistance (IBR) model predicts a positive relationship between genetic distance and the resistance distance, a graph theoretic distance metric based in circuit theory73. Circuit theory is a theoretical framework that treats landscapes as conductive surfaces, using electrons flowing across an analogous circuit to model individuals or genes moving across the landscape74. Circuit theory simultaneously considers all possible pathways between two sampling localities when assessing landscape influence on gene flow, thus allowing for a more thorough investigation of these processes than least cost path (LCP) analysis75 (but see ref.76). We calculated circuit-based connectivity using Circuitscape 4.077. To do so, we developed five resistance surfaces (see details below) and assessed these in relation to the population-based pairwise genetic distance, FST.We used isolation-by-resistance http://www.vegmad.org/) to three primary habitat types such that primary forest represented a combination of humid and littoral forests, degraded habitat was derived from the degraded forest classification, and matrix was derived from all other categories. River and road data were downloaded from DIVA-GIS (http://www.diva-gis.org/), clipped to include only major features , and then rasterized. Topographic position index, a measure that compares the elevation of each cell in the raster to the adjacent landscape and calculates a quantitative value that is indicative of the cell’s relative position , was generated from a 30 arcsecond resolution digital elevation model  using the Topography Tools toolbox in ArcGIS v10.3.178. Village locations were obtained by selecting point locations with a designation of “Populated Places” from a gazetteer of foreign geographic feature names . Village proximity was derived by calculating the distance of each cell in a 1 km resolution raster to the nearest village center. All surfaces were converted to a uniform geographic coordinate system , resampled at a resolution of 1 km, and clipped to the study extent for analysis. Previous work has shown that changes in spatial resolution do not significantly alter the results of landscape genetic analyses79. Therefore, this resolution was chosen as a trade-off between retaining detail across the landscape and minimizing processing time for analyses.We selected five landscape features hypothesized to influence ruffed lemur movement and gene flow: (1) rivers, 2) topography, (3) roads, (4) habitat type, and (5) proximity to human habitation  distances to identify the proportion of our data influenced by geographic distance alone.Distance is implicitly incorporated into the resistance distances calculated by Circuitscape, and thus straightline Euclidean distance between sites  was not included as an additional factor in our models. We did, however, conduct a linear regression between pairwise Euclidean and genetic . Inspired by principles of biological evolution, genetic algorithms create a population of individuals with traits (parameters to be optimized) encoded on “chromosomes”. The fittest individuals from each generation  survive to reproduce80. Parameter space is explored via ‘mutations,’ whereby new parameter values are generated, as well as via ‘crossover’, whereby genetic information is exchanged. The population continues to evolve – that is, parameterization of the resistance surfaces run – until a sufficient number of generations have passed without an improvement in fitness81 (see ref.39 for details). This approach makes no a priori assumptions about the direction or magnitude of the resistance between landscape and genetic distances, allowing for a more thorough investigation of the relationship between landscape features and gene flow than more widely used methods .Resistances between sampling localities were calculated in Circuitscape 4.0et al.36. Genetic algorithm settings can be found in Supplementary Materials.Continuous surfaces were optimized using Monomolecular and Ricker transformations, while categorical surfaces were optimized by holding one feature constant at a value of 1 and then adjusting resistance values for all other features between the values of 0 and 3500 following Peterman 83), which was determined from linear mixed effects models with MLPE parameterization84 and evaluated by maximum likelihood in lme485. To account for our uneven sampling design, we conducted bootstrap resampling using 75% of the data (13 sampling locations) to control for bias following Ruiz-Lopez et al.86; these data were randomly selected without replacement and then optimized surfaces were fit to the selected data. Following 10,000 iterations, average rank and average model weight (87 identify 39 to ensure convergence on  the top model(s). Following optimization, we again conducted a bootstrap model selection using 10,000 iterations and average rank, We evaluated the resistance optimization process for each surface  using AICc  of the observed population genetic structure . These landscape features are thought to operate on much deeper time scales than anthropogenic change in terms of their influence on an organism’s dispersal abilities, population connectivity, and genetic structure89. In an earlier study, we identified genetic clustering on either side of the Mangoro—the largest river in eastern Madagascar—lending support to these findings, and leading us to hypothesize that Madagascar’s rivers structure V. variegata genetic diversity61. We were therefore surprised to find little support for this hypothesis. Rather, our results suggest that anthropogenic factors are the primary drivers of ruffed lemur genetic structure.Recent studies have identified a widespread, latitudinally-structured phylogeographic pattern in several of Madagascar’s fauna, including its humid-forest mammals95), particularly in montane species where river headwaters are still relatively small96. However, rivers have been strongly implicated in the biogeography of Malagasy fauna, particularly primates69, and it is unlikely that Varecia are exempt from these patterns. Thus, we propose two alternative explanations for this finding. On the one hand, our results may be a product of methodological constraints, including our inability to filter our waterways landscape layer to include only the largest rivers (such as the Mangoro), as freely available data for this feature do not contain sufficient metadata to differentiate between major and minor waterways. Alternatively, we argue that natural signatures of migration and drift are being swamped by patterns caused by anthropogenic activities.There is some debate surrounding the extent to which rivers present complete barriers to vertebrate movement  proved to be a more suitable conduit to gene flow was surprising, but this finding is likely driven by the orientation of matrix to our ruffed lemur sampling localities. In our study, very few sampling locations were directly separated by matrix. Rather, with the exception of a few sites , samples were collected from protected, primary rainforest sites that were separated from the surrounding matrix by intervening degraded forest habitat. For this reason, black-and-white ruffed lemurs (or at least those included in this study) rarely encounter matrix, and it therefore did not impose a significant resistance to their movement and dispersal.Animals are more likely to disperse through landcover that closely reflects the habitat in which they evolved52.Still, it was surprising that matrix proved more permeable than degraded habitat. It is possible that this is an artefect of the classification system used, and that degraded habitat in this classification scheme represents what might be perceived as ‘matrix’ to ruffed lemurs , effectively serving as an additional barrier to movement between patches. There is, however, indirect evidence that individuals may cross highly degraded regions at short distances, such as those that segregate rainforest fragments99. The average individual black-and-white ruffed lemur home range is ~15–20 ha55, giving an inferred migration capacity of no more than 200 km. This indicates a large dispersal capacity that would likely reduce any time lag exhibited by historic landscapes. Furthermore, all nearest neighbor sampling localities were located within this maximum dispersal distance, suggesting a high likelihood that ruffed lemurs reach at least one or more sampling locations during dispersal events. Given the orientation of the existing range for this species, however, the distances between non-neighboring sampling localities (up to 861.74 km) was often beyond the assumed functional dispersal distances of the taxon – a sampling strategy that may have decreased the signature of landscape effects on gene flow during population-level analyses100. At present, there are clear limitations in researcher ability to directly observe or test ruffed lemurs’ dispersal capacity. Indeed, our analysis is among the first to quantitatively relate landscape features to a measure of dispersal using genetic data. With rapid improvements in telemetry, it is now feasible to track individuals via GPS collars long-term, allowing for the possibility of capturing dispersal events and gathering spatially-explicit movement data moving forward .The exact dispersal capacity of black-and-white ruffed lemurs is at present unknown; however, maximum mammalian dispersal distances have been shown to scale isometrically with home range sizeAs with habitat degradation, we also found increased resistance to movement with increasing proximity to human habitation. Curiously, gene flow increased again after approximately 8 km of isolation; this is likely because most villages in Madagascar are situated well within 8 km of forest, such that dispersing ruffed lemurs would rarely encounter habitat located at distances greater than 8 km from human habitation.101. Several factors influence the degree to which observed genetic structure reflects contemporary versus historical landscapes, including generation time, dispersal distance, and population size and demographics of the study species, as well as genetic metrics chosen for analysis102. Black-and-white ruffed lemurs have relatively fast generation times for primates of their body size (approximately 7–8 years52), suggesting that an influence from historic landscape features may no longer be detectable in contemporary genetic structure102. Moreover, although forest loss was already well-advanced as early as the 1600s, most significant habitat modification has occurred in the past 70 years45, suggesting that signatures of habitat modification have predominated as drivers of genetic structure in ruffed lemurs in less than ten generations. With vagile species, this generation time has shown to be sufficient for detecting newly introduced barriers to gene flow103.Unfortunately, we were unable to test the impacts of historical forest cover on the genetic structure of ruffed lemurs, as landscape maps such as these are not georeferenced and publicly available. This is an important consideration, given that historical habitat loss may yet impact future primate populations due to time lags in population responses31, with a significant underrepresentation of tropical regions despite the vast majority of worldwide biodiversity being found therein41. Madagascar is considered both a biodiversity hotspot and a conservation priority due to its high levels of endemism in most taxonomic categories for a relatively small geographic area and extremely high loss of primary habitat41. Presently, over 90% of endemic lemur taxa are classified as Vulnerable and anthropogenic activities have long been implicated as a primary cause for taxonomic decline104. Given their sensitivity to habitat degradation, ruffed lemurs are often used as an indicator species and present an opportunity to assess the influence of anthropogenic activities across a large region of Madagascar. Our investigation suggests that rapid land cover modification by humans  has driven change in genetic structure of this indicator species, masking any signatures of natural influences.To date, the vast majority of landscape genetic analyses have been conducted in temperate locations45. Connecting regions of low dispersal, particularly those in the southern and northern-most sampling sites, should therefore be targeted as a primary conservation priority in order to connect currently isolated ruffed lemur populations. Prior studies indicate that increased fragmentation and/or small fragment size are related to increases in genetic differentiation and lowered genetic diversity . Higher levels of genetic variability have been described in the northern ruffed lemur populations; however, forest loss and fragmentation have continued in this region and across the country at rates of up to 2.5% habitat lost per year since the collection of these data105. Additionally, northern populations experience greater intensities of hunting pressures compared to southern populations108, which may drive extreme losses in population size and further reduce genetic variability and increase population differentiation through greater levels of inbreeding or local extinctions. Hunting in southern localities has been recently recognized as a threat to populations as well109. Further investigations into the local drivers of genetic structure in this taxon are currently underway and will clarify the regional impacts of natural features and landcover modification on more immediate dispersal capacity, as well as more explicitly assess the impacts of historical landcover of observed genetic structure in this taxon.At present, Madagascar’s natural forests cover approximately 8.9 Mha  and include 4.4 Mha (50%) of moist forests; however, estimated annual deforestation rates have progressively increased since 2005, and approximately half of Madagascar’s forest (46%) is now located at less than 100 m from the forest edgesensu refs111) whose habitat requirements for persistence are believed to encapsulate those of an array of associated species. As such, it is likely that the trends we see here may be echoed by other moist forest specialists, either currently or in the near future. One somewhat controversial approach might therefore be to promote ruffed lemurs as conservation ‘flagships’ (sensu ref.112) for Madagascar’s eastern rainforest corridor. Although this concept has faced some criticism , there is nevertheless evidence of greater ‘willingness-to-pay’ among private sponsorship programs for conservation focusing on charismatic species116. Ruffed lemurs, the largest living members of the lemurid family, can be easily recognized by their “especially long, luxuriant coat relative to other lemurs”117 and their suspensory feeding acrobatics118; they may therefore be appropriate ‘Cinderella (flagship) species’ sensu Smith et al.119 that can be used to attract public support for conservation efforts. Planning conservation action around this taxon, both logistically and for promotional purposes, may be one strategy toward protecting the diverse eastern rainforest species in Madagascar. These and other novel solutions will become increasingly important, as anthropogenic activities including those demonstrated herein continue to overwhelm the impact of natural processes on ecology with far-reaching, long-term and cascading consequences for the whole Earth system121 (reviewed in ref.122).Our study uses ruffed lemurs as a test case; however, they also represent a dispersal-, resource-, and area-limited ‘umbrella’ species (Supplementary Tables & Figures


Boechera stricta, an Arabidopsis thaliana relative, at one location in the central Rocky Mountains and grew them in three treatments that, based on records from an adjacent weather station, simulated hourly temperature changes at the native site during three summers with differing mean temperatures. Treatment had a significant effect on all traits, with 2–3‐fold increase in above‐ and belowground biomass and the highest allocation to roots observed in the treatment simulating the warmest summer on record at the site. Treatment affected bivariate associations between traits, with the weakest correlation between above‐ and belowground biomass in the warmest treatment. The magnitude of quantitative genetic variation for all traits differed across treatments: Genetic variance of biomass was 0 in the warmest treatment, while highly significant diversity was found in average conditions, resulting in broad‐sense heritability of 0.31. Significant genotype × environment interactions across all treatments were found only in root‐to‐shoot ratio. Therefore, temperature variation among summers appears unlikely to account for the observed levels of local genetic variation in size in this perennial species, but may influence family rank order in growth allocation. Our results indicate that natural environmental fluctuations can have a large impact on the magnitude of within‐population quantitative genetic variance.Adaptation to large‐scale spatial heterogeneity in the environment accounts for a major proportion of genetic diversity within species. Theory predicts the erosion of adaptive genetic variation on a within‐population level, but considerable genetic diversity is often found locally. Genetic diversity could be expected to be maintained within populations in temporally or spatially variable conditions if genotypic rank orders vary across contrasting microenvironmental settings. Taking advantage of fine‐resolution environmental data, we tested the hypothesis that temperature heterogeneity among years could be one factor maintaining quantitative genetic diversity within a natural and genetically diverse plant population. We sampled maternal families of  Wigg  Greene  in southeastern Wyoming on 26 July 2012. The inbreeding coefficient in populations of 2.2http://www.wcc.nrcs.usda.gov/nwcc/site?sitenum=772), we programmed growth chamber compartments  to track hourly temperature changes at the site during an 8‐week period on June 1–July 25 in 2009, 2010, and 2012. Thus, each treatment consisted of over 1,300 temperature steps. In the following sections of the article, we will refer to these treatments by the corresponding year. We chose to focus on June and July because an earlier study indicated the most vigorous growth in a treatment simulating early growing season conditions  in all chambers before the experiment, was approximately 250 μmol photons mr ≥ .807, p < .0001 in all treatments; the longest leaf length vs. √aboveground biomass: r ≥ .923, p < .0001 in all treatments). Because qualitatively similar results were obtained with all correlated measures, we will use biomass only as a measure of aboveground plant growth. We sampled plants at 8 weeks because the oldest leaves often start to senesce at this point in chamber conditions  and because an 8‐week period is likely to account for a large proportion of the annual growing season at the home site of the population.We germinated and planted seeds from the 21 families, with ten replicates per family in each treatment, following the protocol in Salmela et al. . We did 2.3We included families with at least four replicates in two treatments in the analysis. Prior to formal statistical tests, we examined whether our data met assumptions of analysis of variance. We detected variance heterogeneity of residuals among treatments in all traits, with variances varying 6.6‐fold for aboveground biomass, 9.5‐fold for belowground biomass, and 4.8‐fold for root‐to‐shoot ratio. These ranges of variation resulted from the 2012 treatment that had the most phenotypic variation in all traits; variance differences between 2009 and 2010 were less than twofold. To determine the influence of unequal variances, we ran all statistical analyses with the complete dataset using both original and transformed measurements. While transformations reduced variance heterogeneity, they did not change the outcome of the analyses or interpretation of the results. This outcome probably reflects the robustness of analysis of variance to unequal variances in the case of similar sample sizes in all treatments. Therefore, we used the raw values in the analyses presented here.2). We estimated variance components for family, block, and residual variation using the REML approach. We examined associations between the traits within treatments using replicate values and Pearson's correlation. Scatterplots suggested the slope between above‐ and belowground biomass varies among treatments. To test for treatment differences in slope (ß1), we performed a regression analysis of aboveground biomass on belowground biomass in which we included treatment as a categorical factor and the aboveground biomass × treatment interaction term.We first tested for the significant effects of family and block within each treatment using general linear models. We consider variation among maternal families to represent genetic diversity within the population, and thus, proportion of total variation explained by among‐family diversity in each treatment is an estimate of broad‐sense heritability . We considered treatment a fixed factor. When nonsignificant at the 0.10 level, we removed the interaction term from the analysis. Given that among‐family variation measures genetic diversity within the population, the family × treatment term defines genotype × environment interactions. We estimated variance components for the random factors using the REML approach. Genotype × environment interactions can maintain diversity if they are caused by rank shifts of genotypes rather than by differences in among‐genotype variance among treatments . In 2010, the family effect was highly significant for all traits. Although 2012 was the most phenotypically variable treatment, we found no significant genetic variation for above‐ or belowground biomass in this year; variation was entirely residual. For root‐to‐shoot ratio, however, family differences in 2012 were highly significant. For aboveground biomass, phenotypic and genetic variation in 2009 were 56.3% and 18.0%, respectively, of those in 2010, resulting in higher broad‐sense heritability in 2010. The pattern was similar for belowground biomass: Phenotypic and genetic variation in 2009 were 64.0% and 16.0%, respectively, of those in 2010. These differences were not due to the two families in 2010 for which we lacked data in 2009; when they were excluded, we obtained very similar estimates of within‐treatment diversity. Similar levels of genetic diversity were expressed in root‐to‐shoot ratio in 2009 and 2010, but broad‐sense heritability in 2009 was lower due to 66.5% more phenotypic variation in this treatment. We observed the highest phenotypic and genetic variation in root‐to‐shoot ratio in 2012. However, due to increases of similar magnitude in all variance components, broad‐sense heritability in 2012 was similar to that in 2010.Table F2,453 = 4.13, p < .05), with very similar estimates in 2009 and 2010 . In 2010, aboveground biomass showed a weak positive correlation with root‐to‐shoot ratio . In 2009 and 2012, this correlation was not significantly different from zero.Above‐ and belowground biomass were positively correlated in all treatments Figure . This as−4) than that of family (5.10 × 10−4). Approximately 63% of the interaction variance was due to rank changes of families across the treatments . For root‐to‐shoot ratio, pairwise comparisons of treatments indicated moderate differences in mean between 2009 and 2010, and highly significant differences between 2009 and 2012, and 2010 and 2012. The difference between 2009 and 2010, despite the lack of significant treatment effects in its two components, was likely caused by small but opposite changes in the two biomass measures: Overall aboveground biomass was slightly larger in 2010 than in 2009, while the opposite pattern was found for belowground biomass (Table p = .0941). In the pairwise comparisons of 2009 and 2010 to 2012, we observed more statistical support for family × treatment interactions than for family. Together with variation in the strength of genetic correlations, the results indicate that differing family responses to 2012 in particular account for the significant interaction term in root‐to‐shoot ratio across all treatments.Table 4In this study, we combined environmental data with quantitative genetic approaches to examine whether temporal environmental heterogeneity could act as one mechanism maintaining genetic variation in growth and its allocation within a plant population experiencing variable conditions at its home site. In order to carry out the experiment in as ecologically relevant chamber conditions as possible, we took advantage of fine‐resolution temperature data from a weather station that was located in the vicinity of the sampled population. This enabled us to quantify the potential of natural among‐year heterogeneity in growing season temperatures to induce genotype × environment interactions in first‐year growth traits.A. thaliana and B. stricta may harbor considerable levels of local quantitative and molecular genetic variation  H.Karst (Chapin & Chapin, B. stricta to examine how biomass and its allocation are related to fitness in the spatially and temporally variable environments in the Rocky Mountains, and how environmental heterogeneity affects survival and reproductive fitness in this species. Because simulating very cold, long‐lasting and temporally variable winter conditions in growth chambers is challenging, such studies will require multiyear field experiments so that families will be exposed to their natural home site conditions.Unlike its two components, root‐to‐shoot ratio exhibited significant family × treatment interactions across all treatments, revealing substantial genetic variation in phenotypic plasticity in this trait within the population (Falconer & Mackay, B. stricta population from the Rocky Mountains. However, the magnitude of genetic variation differed across simulated years, with no genetic diversity in size in the treatment simulating the warmest summer on record at the home site of the population; such conditional neutrality of maternal families could slow the loss of genetic variation. In root‐to‐shoot ratio, family ranks within the population varied in response to natural temperature variability. Therefore, our findings support the role of genotype × environment interactions in maintaining fine‐grained quantitative genetic diversity in growth allocation. Because estimates of quantitative genetic variance are used to forecast extant populations’ adaptive responses to novel environmental conditions (Franks et al., In this experiment, our aim was to measure the potential of natural temperature heterogeneity to elicit genotype × environment interactions in a genetically diverse plant population. In conclusion, our results show that variable experimental environments affect traits differently, that the amount of genetic diversity expressed in a population can vary greatly depending on environmental conditions, that among‐trait associations may vary across environments, and that natural populations can contain significant genetic diversity in phenotypic plasticity of quantitative traits. Our study revealed limited potential of natural heterogeneity in summer temperature to change biomass ranks of maternal families in the