Signal Transducer and Activator of Transcription (STAT) 3 and 5 are important effectors of cellular transformation, and aberrant STAT3 and STAT5 signaling have been demonstrated in hematopoietic cancers. STAT3 and STAT5 are common targets for different tyrosine kinase oncogenes (TKOs). In addition, STAT3 and STAT5 proteins were shown to contain activating mutations in some rare but aggressive leukemias/lymphomas. Both proteins also contribute to drug resistance in hematopoietic malignancies and are now well recognized as major targets in cancer treatment. The development of inhibitors targeting STAT3 and STAT5 has been the subject of intense investigations during the last decade. This review summarizes the current knowledge of oncogenic STAT3 and STAT5 functions in hematopoietic cancers as well as advances in preclinical and clinical development of pharmacological inhibitors. V617F, SRC, TEL-ABL, BCR-ABL, TEL-SYK, NPM-ALK, TEL-PDGFR, and mutated forms of FLT3 and KIT receptors . T. TN642H  evasion . In summ+ cells from MM patients [SHP-1 and SOCS1 genes were found to be silenced by hypermethylation in MM patients [Multiple myeloma (MM) is a B cell malignancy characterized by the proliferation of clonal plasma cells in the bone marrow accompanied by secretion of monoclonal immunoglobulin . Constitpatients ,118. Patpatients . STAT3 apatients ,121. Myepatients . Accordipatients . In addipatients . Moreovepatients . Howeverpatients .It was presumed that most, if not all, of the oncogenic activities of STAT3/STAT5 are due to canonical functions. However, STAT3/5 proteins are also active through alternate non-canonical pathways impacting cell transformation. Post-translational modifications affecting canonical and non-canonical roles of STAT3 and/or STAT5 have been extensively reviewed in the literature. To be in line with this review, we will focus on canonical and non-canonical activities of STAT3/5 that have been described in cell transformation .705-STAT3 (herein referred to in this review as P-Y-STAT3), P-Y694-STAT5A (P-Y-STAT5A), and P-Y699-STAT5B (P-Y-STAT5B) proteins dimerize through reciprocal interactions between the SH2 domain of one monomer and the phospho-tyrosine residue of the other. P-Y-STAT3, P-Y-STAT5A, and P-Y-STAT5B form homodimers but P-Y-STAT5A can also dimerize with P-Y-STAT5B. The formation of STAT5A/5B heterodimers is not only dependent on the relative abundance of each protein but can also be differentially affected by upstream signaling events. For instance, BCR-ABL is less efficient in inducing STAT5A/5B heterodimerization than IL-3 [D816V in AML cells was demonstrated to induce the nuclear translocation of STAT5 via the Rac1/PAK1 pathway [+ cells and that Src kinases might be responsible for the cytoplasmic retention of P-Y-STAT5A [In the canonical model, STAT3, STAT5A, and STAT5B are primarily activated by phosphorylation on tyrosine residues Y705, Y694, and Y699, respectively . In a phhan IL-3 . Heterodhan IL-3 . The nuchan IL-3 . Moleculhan IL-3 . The Mgchan IL-3 ,132. Inthan IL-3 . In a si pathway . In sharY-STAT5A ,136. TheF)-induced leukemia in mice [Tyrosine phosphorylated STAT3/STAT5 dimers bind to specific DNA elements found in promoters, enhancers, and the first intron of target genes. These binding sites are characterized by clusters of conserved motifs with an interferon gamma-activated site (GAS)-like core sequence (TTCT/CNA/GGAA). STAT5 was shown to bind as a tetramer on adjacent GAS sequences with high or weak affinity in target gene promoters ,138. The in mice . The NH2 in mice . This do in mice .TKO-mediated activation of STAT3 and/or STAT5 regulates expression of common as well as specific genes involved in hematopoietic cell survival, proliferation, metabolism, hypoxia, autophagy, migration and tumor immune evasion. BCL2-family members, cell cycle-regulated genes, proto-oncogenes such as PIM1, c-MYC, BCL6, and genes involved in cytokine receptor signaling or immune response are often targets of STAT3/STAT5-mediated transcription in hematopoietic cancers . Transcr685-STAT3 also recruits DNMT1 to induce the epigenetic silencing of tumor suppressor genes [STAT3 and STAT5 also undergo acetylation on multiple lysine residues by the CBP/p300 histone acetyltransferase . Acetylaor genes . Acetylaor genes . The conor genes .Methylation of nuclear STAT3 on K140 or K49/K180 by the histone-modifying enzymes SET domain-containing lysine methyltransferase 9 (SET9) or EZH2, respectively, have been reported to mediate opposing effects on STAT3-dependent transcriptional activity ,155,156.V617F activates both STAT3 and STAT5, but only STAT3 negatively regulates JAK2V617F-dependent MPN development [The concomitant activation of STAT3/5 by TKOs is probably the most intriguing event in leukemogenesis as both proteins in cancer cells have compensatory and/or opposing effects on gene expression and cell fate . For inselopment ,67.V617F, or KITD816V. Here, it was shown to interact with the scaffolding adapter Gab2 to favor the activation of the phosphatidylinositol-3-kinase (PI3K)/AKT pathway and leukemic cell survival [V617F-induced ROS generation is mediated by P-Y-STAT5, and binding of P-Y-STAT5 to the NOX2 complex in BCR-ABL+ cells also requires active Rac1 (unpublished data) [+ release in the ER by interacting with inositol 1,4,5-triphosphate receptors (IP3R3), facilitating its proteasomal degradation [Subcellular localization of P-Y-STAT3/5 is not restricted to the nucleus and previously published data highlighted important functions of P-Y-STAT3/5 in the cytoplasm and/or other subcellular organelles of cancer cells. Cytoplasmic localization of P-Y-STAT5 was abundantly found in leukemic cells expressing BCR-ABL, JAK2survival ,135,166.survival . Interacsurvival . BCR-ABLed data) ,59,168. ed data) . Constitradation . Accordiradation ,172,173.727-STAT3 probably require acetylation of K685 because (1) persistent acetylation of K685 on STAT3 has been observed in CLL cells, (2) acetylation of K685 promotes dimer formation, DNA binding and activation of transcription, and (3) K685 acetylation provides CLL cells with survival advantages [727-STAT3 was also found in mitochondria where it contributes to the viability of CLL cells and protects against oxidative stress [V12-transformed cells where it promotes anchorage-independent cell growth and tumor induction in mice [727-STAT3 also supports K-RasG12D-driven hematologic neoplasms [stat3 gene demonstrated the critical role of mitochondrial STAT3 in HSC/HPC function. stat3−/− mice have a blood phenotype with similarities to human diseases of myelodysplastic syndrome (MDS) and MPN, supporting, as mentioned above, a negative regulatory role of STAT3 in MPN development [Constitutive phosphorylation of STAT3 on residue S727, but not on Y705, plays a role in the pathogenesis of CLL by regulating STAT3-dependent expression of genes associated with cell growth and survival . DNA binvantages . In addie stress . The onc in mice . Phospho in mice . Mitochoeoplasms . Retinoieoplasms . Howevereoplasms . Phosphoeoplasms . In addieoplasms . Hematopelopment . These delopment . Inhibitelopment   and DCC-2618 have entered clinical trials with promising results for the treatment of SM [Most agents that are described as indirect STAT3/5 inhibitors actually target upstream kinases such as JAK, Src, BCR-ABL, FLT3, or KIT receptors. Activation of STAT3 and/or STAT5 is dependent on the leukemic cell type in which the kinase is active. The development of IM and related BCR-ABL kinase inhibitors such as nilotinib, dasatinib, bosutinib, and ponatinib has made a major breakthrough in targeted cancer therapy, CML and Phreatment ,203,204.reatment ,90. Howereatment . Althougreatment ,56,62. Treatment . Among treatment ,218,219.reatment . Secondareatment ,220. In reatment . Midostareatment  [222,223V617F mutant are yet to be developed. Consequently, JAK2 inhibitors have been disappointing in their ability to induce molecular remissions in MPN patients, indicating that JAK2 inhibitors do not preferentially target MPN cells over normal cells [V617F-specific inhibitors in the future. Selective JAK and pan-JAK inhibitors that variably affect P-Y-STAT3/5 levels in hematologic neoplasms are presented in V617F-mutated MPN treatment, showed an increased potency for the JAK2V617F mutant [A breakthrough in understanding myeloproliferative diseases occurred after the discovery of GOF mutations in JAK2, leading to the development of small-molecule inhibitors of JAK2 for the treatment of MPNs . In clinal cells . In addiF mutant . AnotherF mutant . FedratiF mutant . Many JAF mutant ,267,268.F mutant ,251,261.Curcuma longa), has been extensively investigated for its anti-tumor effects [Historically, natural compounds have been successfully used in the management of various human diseases. Natural products may also serve as a basis for the synthesis of derivatives aiming to increase their efficacy. There are several compounds that are known to exert anti-tumor effects through their indirect or direct action on STAT3 and/or STAT5 signaling. These natural molecules have a low toxicity profile and can act synergistically with other pharmacological agents to reverse chemoresistance. A number of plant-derived molecules such as avicin D, curcubitacin I, butein, honokiol, capsaicin, celestrol, and piperlongumine have been reported to inhibit growth or survival of leukemia, lymphoma, or myeloma cells in preclinical studies  269,270,276,277. effects . Curcumi effects ,282,283. effects . The syn effects . EC804,  effects ,287. Thi effects . Natural effects ,290. Nap effects . Researc effects . Moreove effects . We alsoCell-based assays for high-throughput screening were employed to identify compounds that specifically block the transcriptional activity of STAT3/5 ,197. ThiT315I mutant cells [STAT5 genes [+ ALL cells [Pimozide was identified by high-throughput drug screening as a potent inhibitor of STAT5. Pimozide was shown to inhibit P-Y-STAT5 and survival of CML and MPN cells without affecting the kinase activity of BCR-ABL, JAK2 or Src ,299. Pimnt cells . The effnt cells . The mecnt cells . Antidiant cells . Both syT5 genes . QuiesceT5 genes . ActivatLL cells . FinallyLL cells .The mechanisms of direct STAT3/5 inhibition include disruption of tyrosine phosphorylation, dimerization, nuclear translocation and/or DNA binding. Targeting the SH2 domain was, therefore, the main focus for the design and identification of selective inhibitors.705) inhibits STAT3 dimerization and tumor cell growth [Dimerization is an essential step in the canonical functions of STAT3 and STAT5. Blocking this process seems to be an optimal solution to directly inhibit aberrant STAT3/5 signaling in hematopoietic cancers. The SH2 domain is not only required for dimer formation but also in the recruitment of STAT3/5 to tyrosine-phosphorylated receptor complexes. Therefore, initial strategies aiming to identify phosphopeptide (P-Y-peptide) inhibitors able to block SH2/P-Y interactions were pursued by several teams. This approach was first attempted by Turkson et al. who demonstrated that the peptide PY*LKTK  assays, BP-1-108 was found to be the more potent STAT5 inhibitor . BP-1-10ML cells . IST5-00ll lines . Patientf Ph+ALL . A chromf Ph+ALL . The samf Ph+ALL . SAR stuf Ph+ALL .Y640F mutant [V617F, as well as in BL and MM cell lines [Withania somnifera, was found to interact with the SH2 domain of STAT3. Consequently, Withaferin A prevented IL-6–mediated or persistently activated P-Y-STAT3 and induced apoptosis in MM cells [Structure-based high-throughput virtual screening of four different chemical libraries followed by cell-based reporter assays identified STA-21 (or deoxytetrangomycin) as a new SH2 domain-targeting inhibitor of STAT3 . STA-21 F mutant . Among tF mutant . OPB-311ll lines . Treatmell lines . In the ll lines . Althougll lines . Lastly,MM cells . In a siMM cells .STAT3/5 bind to specific DNA response elements within promoters to mediate transcriptional activation of target genes. Concerted efforts were therefore made to identify specific inhibitors targeting the DBD of STAT proteins. Most of the DBD inhibitors presented in this section were historically used against STAT3 and proved to be effective in some hematopoietic cancers.Platinum (IV) compounds such as CPA-1, CPA-7, and platinum (IV) tetrachloride, were shown to inhibit STAT3 DNA binding activity . IS3 295+ immune and malignant cells to block the oncogenic activity of STAT3 and to promote an antitumor immune response. Both TLR9 stimulation and concurrent STAT3 inhibition were critical for immune-mediated therapeutic effects. GQ-ODN are G-rich oligodeoxynucleotides that form intra- and inter-molecular four-stranded structures [Another class of STAT DBD inhibitors being used are duplex ODN (decoy oligonucleotides or dODN) and GQ-ODN. dODNs targeting STAT proteins are double-stranded DNA molecules mimicking the consensus STAT DNA binding sequence. These duplex ODNs act by competitively inhibiting the DNA binding of STAT proteins to their endogenous promoter elements thereby preventing their nuclear function . For insructures . For exaructures . Computeructures . However+ T cells in a mouse model of AML [Oligonucleotide-based inhibition of STAT3 and STAT5 has also been achieved by using antisense oligonucleotides (ASO) or mRNA knockdown (siRNA) . Althougl of AML . Intrave727-STAT3 has been evidenced [Despite intensive efforts made during the last 15 years, clinically applicable STAT3/5 inhibitors still remain elusive. Toxicity, poor tumor targeting, lack of cell penetrance and rapid degradation are some examples of the problems to be considered and addressed. There are also several other reasons that could hinder the development of such inhibitors. First, STAT3 and STAT5 display multifaceted activity due to their canonical and non-canonical functions in cancer cells. The definition of constitutively active STAT3/5 only based on P-Y-STAT3 and/or P-Y-STAT5 levels as described in chapter 3 may not be broadly representative. For example, the use of decoy ODNs or SH2 domain-targeting inhibitors would not be effective in CLL cells, in which a predominant mitochondrial localization and function of P-Svidenced . Furthervidenced . Moreovevidenced ,32. One videnced . In suchvidenced ,60,62. Ividenced . Activatvidenced . In a sividenced . Combinevidenced . In addividenced ,298. Besvidenced ,114. In The design and development of STAT3/5 inhibitors evolved rapidly during the last 10 years. Computer simulations and other in silico studies such as high-throughput virtual screening, cell-based assays, biophysical and biochemical approaches led to the identification of selective STAT3/5 inhibitors with potent anticancer effects. STAT3/5 inhibitors are likely to become a valuable addition to the expanding arsenal of drugs against hematopoietic cancers and solid tumors. However, there are some limitations to this relative success story that must be taken into account. Optimization of STAT3/STAT5 inhibitors by chemical modifications and drug delivery systems are both required for resolving issues linked to stability, cell permeability, and targeted delivery to tumor cells. The development of antibodies conjugated to STAT3/5 inhibitors or antibody-conjugated nanoparticles harboring STAT3/5 inhibitors might be promising but remains challenging. Identification of new inhibitors targeting the non-canonical functions of STAT3/5 is also desirable in treating some hematologic malignancies. Last but not least, the optimization of combination therapies using STAT3/5 inhibitors with molecules targeting tyrosine kinases or other key players in cancer will be required for finding the right combination that safely unlocks drug resistance in hematologic cancers.


We examined the effect of social distancing on changes in visits to urban hotspot points of interest. In a pandemic situation, urban hotspots could be potential superspreader areas as visits to urban hotspots can increase the risk of contact and transmission of a disease among a population. We mapped census-block-group to point-of-interest (POI) movement networks in 16 cities in the United States. We adopted a modified coarse-grain approach to examine patterns of visits to POIs among hotspots and non-hotspots from January to May 2020. Also, we conducted chi-square tests to identify POIs with significant flux-in changes during the analysis period. The results showed disparate patterns across cities in terms of reduction in hotspot POI visitors. Sixteen cities were divided into two categories using a time series clustering method. In one category, which includes the cities of San Francisco, Seattle and Chicago, we observed a considerable decrease in hotspot POI visitors, while in another category, including the cities of Austin, Houston and San Diego, the visitors to hotspots did not greatly decrease. While all the cities exhibited overall decreased visitors to POIs, one category maintained the proportion of visitors to hotspot POIs. The proportion of visitors to some POIs (e.g. restaurants) remained stable during the social distancing period, while some POIs had an increased proportion of visitors (e.g. grocery stores). We also identified POIs with significant flux-in changes, indicating that related businesses were greatly affected by social distancing. The study was limited to 16 metropolitan cities in the United States. The proposed methodology could be applied to digital trace data in other cities and countries to study the patterns of movements to POIs during the COVID-19 pandemic. Urban mobility and movement patterns are important characteristics of urban dynamics, reflecting the collective human behaviour and social interactions –7. Asses [et al.  develope [et al.  develope [et al.  found th [et al.  developeet al. and Hamedmoghadam et al. used the OD matrix and a coarse-grain approach to study the mobility among hotspots and non-hotspots [et al. [et al. [While the reduction in overall movements and mobility could promote containment, it is equally important to monitor and evaluate movement reduction to urban hotspots. In comparing the effectiveness of social distancing measures between cities, it has been observed that epidemic spread trajectories are different, while the overall mobility reduction is similar across cities. These disparate trajectories could be in part due to differences in movement patterns to urban hotspots. Urban hotspots and sub-centres usually have higher populations and employment densities and more points of interest (POIs) compared with other areas of cities ,14. Urbahotspots ,19. The hotspots ,21. Therhotspots . In addihotspots ,24. Mazz [et al.  extracte [et al.  mapped tIn summary, the extant studies demonstrated that the characterization of urban mobility and movement patterns are important to understand the collective human behaviour and social interactions, which are critical for the development of effective epidemic control measures. Also, urban hotspots are gravity activity centres that usually have a higher density of populations and POIs, which could be potential ‘superspreaders' during the pandemic situation. Therefore, understanding the patterns of visits to POIs in urban hotspots is important for developing and monitoring epidemic control measures. Extant studies, however, rarely studied the patterns of visits to POIs in urban hotspots under pandemic situations. Hence, in this paper, we investigated the patterns of population visits to urban hotspots using origin–destination networks from census-block-groups (CBGs) to POIs in 16 cities of United States based on the digital trace data from SafeGraph. The POI data enable the identification of urban hotspots to evaluate changes in visiting urban hotspots due to social distancing measures during the COVID-19 pandemic. We also identified POIs that had significant flux-in decreases and what POI-associated business categories were greatly affected during COVID-19. These POIs and business categories could expect a significant flux-in increase after the shelter-in-place orders are lifted. The results of this study could help decision-makers better monitor and evaluate epidemic/pandemic control measures, as well as reopening policies and strategies. In this paper, we adopted a modified coarse-grain approach for separating the hotspot and non-hotspot nodes in mapped CBG-POI movement networks [2.We used POI data provided by SafeGraph to map the CBG-POI movement networks. SafeGraph aggregates POI data from diverse sources  and removes private identity information to anonymize the data. The POI data include base information of a POI, such as the location name, address, latitude, longitude, brand and business category. SafeGraph uses the standard North American Industry Classification System (NAICS) to classify POI business categories. The data reveal the visit pattern of POIs including the aggregated number of visits to the POI during the data range, the number of visits to the POI each day over the period and the aggregated number of visitors to the POI from CBGs during the period (e.g. one week and one month).In this paper, we used the POI data: Weekly Pattern Version 2, to study movement patterns in 16 cities in the United States. The Safegraph weekly pattern data provide information related to the visits to POIs and cover the entire United States. The data were aggregated weekly (Monday to Sunday) . Among t2.1.i and j represent CBGs and POIs with mapped geolocation. Links in the CBG-POI movement networks represent visits from CBGs to POIs, and non-negative weights of links, We mapped the CBG-POI movement networks based on the number of visitors to POIs from CBGs. The mapped CBG-POI networks are directed and weighted bipartite networks, where pairs of nodes 2.2.et al. [et al. [In this study, we focused on the patterns of visits to POIs from CBGs to examine movement patterns across hotspot and non-hotspot clusters. Therefore, the coarse-grain approach that clusters hotspot and non-hotspot nodes in CBG-POI movement networks were used ,18,19. I2.3.F to represent the original CBG-POI bi-adjacency matrix, then we could reduce F to the coarse-grained matrix ΛWe used a coarse-grain approach to examine the mobility pattern ,18,19. TΛ, each sub-matrix could be calculated as follows [M represents the set of hotspot CBGs and p represents the set of hotspot POIs determined in step 2. Equations (2.3)–(2.6) illustrate how we calculated the proportion of each type of movements. We normalized each mobility type by the total mobility flow. Therefore, the proportion of each type of movement, HH, NH, HN and NN In matrix  follows ,18,19:2.2.4.We conducted a clustering analysis after we obtained four types of movements in cities. We used the sum of proportions of two movements, HH + NH, as an indicator to cluster movement patterns within cities. These two movements would contribute to the spreading of the epidemic because the extent of visits to the hotspot POIs could increase the transmission rate of COVID-19. We scaled the time series data related to movement patterns so that each time series had zero mean and unit standard deviation. This step enabled us to focus on comparing the shapes and trends of time series data. We compared three algorithms , cross-correlation) for time series clustering –29 and u2.5.p-value for each node. Because we conducted each test separately for each node, the degree of freedom is 1, and we adjusted p-value for multiple tests using the Benjamini–Hochberg false discovery rate (FDR) correction [p-value < 0.01), which could reflect significant flux-in changes. Furthermore, because each POI has its NAICS code indicating its business activity, we can identify the extent to which social distancing measures affected business activities.We compared CBG-POI matrices from two milestone dates (e.g. 1 March and 29 March). We summed columns of the matrices to obtain the weighted node degree centrality of POIs. Then we calculated differences in the weighted degree centrality of each pair of POI nodes in the two matrices: rrection . We test3.3.1.We can observe from We also investigated the proportion of each type of movements in 16 cities. We can observe from For category 2 cities, the proportion of HH and NH movements showed a clear downward trend, while the proportion of HN and NN movements had a clear upward trend. The barycentre of two city categories illustrated in In addition, we can observe from 3.2.The next set of results indicates one dominant POI in hotspots across all 16 cities: restaurants and other eating places (NAICS code: 7225). Museums, historical sites and similar institutions (NAICS code: 7121) was the second dominant POI in many of the studied cities. Based on the description of NAICS, museums, historical sites and similar institutions encompass several sub-categories, including museums, historical sites, zoos and botanical gardens, nature parks and other similar institutions. Surprisingly, the proportion of visitors to these two POIs remained fairly stable during the unfolding of the COVID-19 pandemic and the enforcement of shelter-in-place orders. As illustrated in Because we determined POI hotspots based on the total number of visitors to POIs, the evolution of the proportion of visitors to POIs in hotspots could provide insights about movement patterns of people across different cities. The results showed that although the absolute number of visitors decreased for all the POIs during COVID-19, the proportion of visitors to restaurants and museums remained dominant in most cities. Also, the results showed that the proportion of visitors to grocery stores and healthcare facilities increased, while the proportion of visitors to amusement and recreation industries decreased. Furthermore, the patterns of visits to POIs did not show a relationship with city categories based on movements to hotspots. Instead, the visits to POIs highly depended on the attributes of cities. For example, gasoline station was the second highest visited POI hotspot in Houston and was third in Dallas and Detroit, while representing only a small proportion of hotspot POI visitors in New York. Museums, historical sites and similar institutions was the second highest proportion of hotspot POI visitors in most studied cities, such as Dallas, Detroit, Philadelphia, Los Angeles and San Jose, while it formed a small proportion of hotspot POI visitors in Jacksonville, Fort Worth and Houston.3.3.Based on the number of nodes with significant flux-in changes, we identified several businesses highly affected by the COVID-19 pandemic, including restaurants and other eating places, museums, historical sites and similar institutions, lessors of real estate, elementary and secondary schools (NAICS code: 6111), support activities for air transportation (NAICS code: 4881) and religious organizations (NAICS code: 8131). Also, some of the affected POIs varied across the 16 cities. The results indicate that some POIs are universally affected across all cities during the January to May period examined in this study. The effects of the pandemic on other POIs varied across cities and months. For example, the effect on support activities for air transportation visits was related to travel restrictions which had the greatest impact on New York and San Francisco. We can observe from 4.et al. [et al. [et al. [In this paper, we focused on the patterns of visits to POIs from CBGs during the COVID-19 pandemic. The results of this study provide a deeper insight into the effect of social distancing on changes in population visits to hotspot POIs during the COVID-19 pandemic. The results showed that the absolute number of visitors to POIs showed a downward trend in the 16 studied cities. One category of cities sustained the proportion of movements to hotspot POIs, while another category of cities reduced the proportion of movements to hotspot POIs and increased the proportion of movements to non-hotspots POIs. Another COVID-19 study in Italy demonstrated that human mobility in Italy was strongly related to the spread and control of COVID-19 . Movemenet al.  consider [et al.  showed t [et al.  identifiAlso, we conducted chi-square tests to pinpoint POIs with significant flux-in changes. The process could be a good complement to the coarse-grain approach that was adopted to analyse the CBG-POI movement network. The coarse-grain approach clustered nodes to hotspots and non-hotspots and grouped individual CBG-POI flows into four types of movements. While the approach could provide a useful picture of human movements among hotspots and non-hotspots, it cannot provide information about single POIs. The understanding of the flux-in changes for single POIs is important for the examination of pandemics. Because our study focused on the effects of social distancing measures and shelter-in-place orders, the POIs with significant flux-in changes showed decreased visitors during the studied period. This set of results could provide additional insights regarding community response to COVID-19 and help monitor the control measure effectiveness. On the other hand, these POIs could expect significant flux-in increases after the shelter-in-place orders are lifted. Specifying these POIs could provide valuable information to develop reopening policies and strategies (e.g. multi-steps to reopen POIs with significant flux-in changes).Other research directions could be explored based on the findings of this study. For example, based on the results of the proportions of visitors to POIs during the studied period across cities, we could refine the understanding of essential and non-essential services for humans in urban disruptions, such as natural hazards and epidemic outbreaks –35, and The research also has some limitations. The results of the study were based on social distancing and shelter-in-place orders in the United States. In other countries with different policies and cultures, the results may be different. Also, the data cannot consider the interactions among POIs. Decreased visits to one POI may affect visits to another POI. Furthermore, we tried to study movement patterns in some less-populated cities in the United States that were highly affected by COVID-19, such as Randolph, Terrell and Early in Georgia, as well as Union, Bergen and Hudson in New Jersey. The movement data in these cities, however, were very sparse and difficult to build the CBG-POI movement network. The results in this paper, therefore, focused on cities with large populations.


Many studies have revealed the characteristics and spatial-temporal dynamics of the thermal environment in specific cities or urban agglomerations (UA), as well as the associated determining factors. However, few studies focus on the changing relationships  among inner cities’ heat islands in a UA, which represent not only the detailed dynamics of regional thermal environment (RTE), but also the changing competition and cooperation among cities in a developing UA. In this study, we used Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products to map and analyze the detailed dynamics of the Beijing-Tianjin-Hebei (BTH) UA thermal environment. From 2001 to 2015, the mean surface urban heat island intensity (SUHII) of the BTH increased significantly, and the surface urban heat islands (SUHIs) in the southern BTH have rapidly increased, expanded and connected, eventually forming a large heat islands agglomeration. According to correlation analysis, urban sprawl probably led to the expansion and enhance of SUHIs in the south plain, while the forest has significantly alleviated urban heat island effect in northern mountains. The results expose the detailed evolution process of BTH thermal environment, and the changing relationships among the inner cities. In a developing UA, mitigation solutions  are in demand to stop the formation of a great heat region. The “urban agglomeration (UA)”, represents groups of cities with compact spatial organization and close economic connections. An UA includes at least one core city (the biggest and most important one) and some satellite cities, and they tend to have ordered and clear hierarchy and division of functions  . UAs areHigher temperatures in urban areas (namely UHI) will accelerate the accumulation of harmful gases and smoke, and greatly increase the occurrence of digestive, nervous or respiratory system diseases, such as bronchitis, emphysema, asthma, sinusitis, pharyngitis, etc. ,8,9. CouRemote sensing has been widely used for investigating urban thermal environment and the associated drivers during the urbanization process, as it can quickly and frequently monitor large area surface change with lower cost, compared to filed survey ,14,15. MReference  found thHowever, few studies focus on the changing relationships  among inner cities’ UHIs, which reflects the detailed RTE dynamics, and probably the changing competition and cooperation among inner cities along with the UA’s development. This study aims to: (1) quantify the annual dynamics of an UA’s thermal environment and the influences of the associated determining factors; and (2) reveal the changing difference, correlation, and interaction among the inner cities’ thermal environment within the UA. Through this work, it is expected to explore the detailed evolution process and driving mechanism of the UA’s thermal environment.2. The BTH area has a monsoon-influenced humid continental climate, characterized by hot, humid summers due to the East Asian monsoon, and cold, windy, dry winters influenced by the vast Siberian anticyclone. The yearly rainfall ranges between 300 mm and 800 mm, the yearly mean temperatures fluctuates from 4 °C to 17 °C. The northern part of the BTH is mountainous (near the Taihang Mountains and the Yanshan Mountains), with an altitude ranging from 200 m to 1,000 m. The southern part of the BTH area is the North China Plain close to the Bohai Sea.This study was conducted in the BTH region, one of the most developed and populated UA in China , as showIn recent decades, the BTH region has experienced rapid economy development, urban expansion and population immigration, which result in serious air pollutions and urban heat islands ,34. Studhttp://www.gscloud.cn/), rather than the NASA website. Images partially covering this region must be merged to cover the whole area of the BTH. Besides, the original sinusoidal projection has to be adjusted to match that of other data. Both the image merging and reprojecting were conducted in the MODIS Reprojection Tool. Then, noise caused by cloud or fog were filtered with the band math tool of ENVI 5.4. The left blank pixels were then repaired with the kriging interpolation. Finally, yearly LST images were produced by averaging the monthly LST products, and used for analyzing the yearly dynamics of the BTH thermal environment later.MODIS products have been successfully used to conduct thermal environment study in BTH area ,32,33. Hhttps://lpdaac.usgsgov/data_access/data_pool), as they are not available in the Geospatial Data Cloud. The products were then rescaled from 500 m to 1000 m, to match the resolution of the other data. MODIS composite land-cover products  were selected to delimit the urban area. They were downloaded from the official website of NASA’s Land Processes Distributed Active Archive Center (http://www.gscloud.cn/). They were used to analyze the influence of vegetations on RTE in this study.The monthly MODIS NDVI products (MOD13A3) at a 1-km resolution were also downloaded from the Chinese Geospatial Data Cloud (http://www.dsac.cn/), and then rescaled to 1000 m. As an important indicator of human activity, The night-time light (NTL) data has been successfully implemented in urbanization and thermal environment studies ,38,39,40At first, the spatial-temporal dynamics of BTH thermal environment from 2001 to 2015 were mapped, in terms of yearly surface urban heat island (SUHI) distribution. Then, the general tendency was further analyzed with the Mann-Kendall (M-K) test.SUHI represents the phenomenon that the urban surface temperature is higher than that of surrounding suburbs. As an important feature of urban thermal environment, its spatial-temporal dynamics can suggest the development process of an UA’s thermal environment . Here, t suburbs . 0 indicates no evident trend, while the optional Ha suggests an evident trend (upward or downward). Below statistic is used for the series data X1, …, nX , where 1 indicates a strongest positive correlation, −1 means a strongest negative correlation, while 0 indicates there is no correlation between X and Y.Spearman’s correlation coefficient can be used to investigate the relationship of two variables ( follows :(11)rxy=At first, the overall spatial-temporal dynamics of BTH thermal environment from 2001 to 2015 were produced and shown in In 2001, large strong heat islands were apparently located in Beijing and Tianjin, the core cities of this region; while other cities were covered by small strong heat islands (points). Since then, strong heat islands of all cities expanded rapidly, along with the expansion of the cities. By 2015, strong heat islands had connected with each other, forming a great regional strong heat island, which occupied the central and southern parts of the BTH area. From 2001 to 2015, the strong heat islands grew from initially covering several isolated city centers, to eventually covering more than half of the BTH agglomeration. According to further statistics, the mean SUHII of the BTH increased significantly from 2001 to 2015, at a rate of 0.1 °C/year . According to the M-K test results a, the SUThen, changing relationships between inner cities’ UHIs from 2001 to 2015were investigated with landscape metrics .According to As shown in Landscape analyses were then conducted on SUHIs of typical cities  to discover the difference among BTH inner cities, and shown in According to The PLADJ and COHESION of Shijiazhuang increased remarkably from 2001 to 2015, by about 28% and 20%, respectively, suggesting that the level of connectedness and degree of aggregation of the strong heat islands had went up apparently. The PLADJ and COHESION of Zhangjiakou increased much slowly with several fluctuations, by about 10% over the study period, indicating the level of connectedness and degree of aggregation of the strong heat islands did not rise by much. With regard to Beijing, the PLADJ and COHESION fluctuated gently during the study period, and did not change much eventually. It can be observed that the differences of all indicators between Beijing and Shijiazhuang, which were great in 2001, had almost disappeared by 2015. In order to investigate the possible driving mechanism, the Spearman’s correlation coefficients between SUHII and influence factors  were calculated and shown in Results reveal that SUHII is significantly negatively correlated with vegetation coverage, significantly positively correlated with impervious surfaces, and less significantly positively correlated with NTL. This means vegetation could reduce the SUHII to some extent, while human activities (urban expansion and heating) will increase the SUHII. For instance, the area of UHIs in mountainous cities  is always small, as their economic development and urban expansion are seriously limited by steep terrain and inconvenient transports. Besides, surrounding forests can affect the transmission of solar radiation by absorbing and transferring it into biological energy, thus reducing the local SUHII . OppositIn recent decades, UA has become the dominant trend of global urbanization ,2,3. BecAs municipalities directly under the Chinese Government, Beijing and Tianjin have experienced rapid economic development and urban sprawl in recent years ,26. FiguIt is worth noting that the regional mean SUHI had a sharp increase at 2008, which was probably caused by the preparations for the Beijing Olympic Games ,56. A laMonitoring the changing relationships  among inner cities’ UHIs could investigate the complex and variable competition and cooperation among cities. This study can help understand the development history of BTH and related environmental effects, especially in thermal pollution. The serious UHI not only affects people’s life and work, but also restricts the further improvement of a city or an UA. Hence, it is very important to reduce the UHI intensity and alleviate the impact of the urban high temperature, by carefully investigating the dynamics, driving mechanism and environment influence of UHI. In this study, we also assessed the relationship between the dynamics and land use changes, socio-economic development in the BTH. The results indicate that land use changes have significant influence on UHI dynamics. Hence, in the future for urban planning and design, designers should strengthen the construction of ecological corridors to facilitate mass and energy exchange between urban areas and their surroundings. Through scientific urban layout, UHI mitigation could be achieved. Our study also reveals that UHI intensity significantly positively correlates with economic activities. To mitigate the UHI effect in the BTH, it is necessary to control the increase in the amount of energy consumption by developing new towns to disperse urban overspill, improving the combustion efficiency of fuel and advocating for public transportation. In this research, the detailed development process of BTH thermal environment was mapped and analyzed using the MODIS LST product. Overall, the mean SUHII of BTH increased gradually from 2001 to 2015, with a dramatic jump in 2008, which was probably caused by the preparations for the Olympic Games. According to detailed maps and landscape analyses, the SUHI effect in the southern BTH area has significantly increased, expanded, and connected, eventually forming a great agglomeration. Benefiting from the national policies for regional economic development, the differences among the inner cities have dramatically decreased. According to the Spearman’s correlation coefficient, urban sprawl led to the rapid expansion of the SUHI effect in the south plain, while vegetation played an important role in alleviating high temperatures in the north mountain area. The results reveal the detailed dynamics of BTH thermal environment and the changing relationships among inner cities’ heat islands, as well as possible driving mechanisms. All results may server as a scientific reference for developing better strategies for BTH sustainable development and environmental protection. In future study, more indicators or methods will be necessary to completely discover the complex relationships among inner cities’ heat islands.


Avicennia germinans) population genetics with 12 nuclear microsatellite loci along three replicate coastlines in the United States (only two of three conform to underlying “abundant‐centre” assumptions). We then test an implicit prediction of CMH (reduced genetic diversity may constrain adaptation at range margins) by measuring functional traits of leaves associated with cold tolerance, the climatic factor that controls these mangrove distributional limits. CMH predictions were confirmed only along the coastlines that conform to “abundant‐centre” assumptions and, in contrast to theory, range margin A. germinans exhibited functional traits consistent with greater cold tolerance compared to range cores. These findings support previous accounts that CMH may not be a general rule across species and that reduced neutral genetic diversity at range margins may not be a constraint to shifts in functional trait variation along climatic gradients.The central‐marginal hypothesis (CMH) posits that range margins exhibit less genetic diversity and greater inter‐population genetic differentiation compared to range cores. CMH predictions are based on long‐held “abundant‐centre” assumptions of a decline in ecological conditions and abundances towards range margins. Although much empirical research has confirmed CMH, exceptions remain almost as common. We contend that mangroves provide a model system to test CMH that alleviates common confounding factors and may help clarify this lack of consensus. Here, we document changes in black mangrove ( West and East Florida ot Table . Insteadot Table .An implicit prediction of CMH is that limited adaptive genetic diversity at range margins can reduce evolutionary potential and constrain adaptation to environmental conditions at the distributional limit, which can impede further expansion . Fewer samples (n = 9–11) were collected opportunistically at additional sites between 2015 and 2016, and were used in region‐wide analyses  trees that were all approximately the same height. Each of the 10 leaves per tree was from the most fully‐expanded, undamaged leaf pair on an individual branch and located in direct sunlight.We measured functional traits for all sites with 2.32O, and 2 μl (~20 ng) of genomic DNA  following the standard protocol, with an extended incubation step of 45 min. Initial tests included 17 previously‐published nuclear microsatellite loci , inbreeding coefficients, allelic richness (AR) standardized to minimum sample size (n = 9), and genetic differentiation (measured with FST) with corresponding p‐values determined with 104 permutations and adjusted for multiple comparisons in FSTAT 2.9.3.2. We also calculated G″ST  in FSTAT 2.9.3.2 for statistical analyses.For each collection site, we calculated the number of polymorphic loci and private alleles in GenAlEx 6.5  and intersite genetic differentiation within each distribution range . FST sample sizes varied depending on the total number of collection sites with n ≥ 23 genetic samples for each distribution range. We used Kruskal‐Wallis tests and Dunn's tests for post hoc multiple comparisons with p‐values adjusted for the false discovery rate with the Benjamini‐Hochberg procedure. Unless otherwise noted, we performed statistical analyses in R 3.4.2  and geon = 1,083 individuals) with STRUCTURE 2.3  and assigns each sampled individual to these clusters based on multi‐locus genotypes. We used the admixture model with correlated allele frequencies, and did not consider geographic location. The analysis consisted of 10 replicate runs of 500,000 recorded steps after a burnin of 100,000 steps at each K value from 1 to 30. We used StrAuto ] or where values reach a relative plateau  in STRUCTURE HARVESTER  and Florida , and then WFL  and EFL  separately. Run conditions were identical to the initial analysis, but we tested different ranges of K because of variation in collection site numbers . We used the LOCPRIOR model  with Nei's genetic distances in GenAlEx 6.5 as an additional line of evidence for population structure along the entire USA range. We then performed PCoA for each of the three distribution ranges separately. We plotted the first two axes with the R‐package ggplot2 Wickham, .S; allelic richness, AR) and latitude, and between genetic differentiation (FST) and latitude. For each distribution range, latitude was highly correlated with distance to range core , a recommended predictor variable for these analyses . Use of either predictor did not qualitatively change correlation results.We tested explicit predictions of CMH Table  along eaSD) in TX‐LA, 0.91 ± 0.07 in WFL, and 0.93 ± 0.08 in EFL, and included at least 75% of the total allele pool per locus. We discarded these common alleles and filtered the remaining alleles based on the following criteria: (a) present in range margin site(s), (b) present in ≥ 2 range core sites, and (c) at least three copies (5% of collection site) present at range margin site(s). We included the last two criteria to avoid extremely rare or private alleles from skewing results. The resulting data set consisted of nine alleles in TX‐LA , eight in WFL , and eight in EFL , with frequencies across range core sites of 0.06 ± 0.03 (SD), 0.09 ± 0.04, and 0.07 ± 0.04, respectively. We calculated the ratio of each allele's mean range margin frequency to its mean range core frequency, and transformed with the natural logarithm. We used a one‐sided t test to determine whether the ratio of margin to core allele frequency for each of the three range margins was greater than zero.Rare alleles can spread and become more frequent at expanding range margins because of strong genetic drift, a process called allele surfing  was measured with an area meter . Length (cm) was measured from the leaf tip to the start of the petiole and width (cm) was measured at the widest point of the leaf. Ratio length:width was also calculated as this trait proved informative to differentiate populations of A. marina, another member of the same genus  was measured as leaf area divided by dry weight. We measured these traits for 10 leaves per tree and used the mean value for each tree for analyses. For each of the three distribution ranges separately, we used principal components analysis (PCA) to reduce these functional traits into a limited number of uncorrelated variables. We log‐transformed trait data, centred and scaled values , and performed PCA with the R‐function prcomp. We retained principal components (PC) with eigenvalues >1.We measured five leaf functional traits: area, length, width, ratio length:width, and specific leaf area. Area  . Resolution remained high for subsets from West Florida (WFL) and East Florida (EFL) , but with a marked decrease for Texas‐Louisiana (TX‐LA) (detected with 71.1% probability).Across the 12 nuclear microsatellite loci, potential null alleles were identified at 15% (75 of 492) of collection site – microsatellite locus pairs, but at low frequency (0.05 ± 0.07 [) Tables  and S4. 3.2n = 10 per site) and had increased monomorphism towards range margins (Table SD]) and were found only within range‐core sites in Florida, but within both core and margin sites in TX‐LA. Neutral genetic diversity was highest at lower latitudes in Florida, with maximum values in the southeast, and lowest at Florida range margins and across TX‐LA (Table FIS) was detected within multiple sites, but at higher frequency across range margins (seven of 10 sites) compared to range cores (12 of 31 sites). We found significant genetic differentiation across all collection sites  with a range of intersite values from –0.02 to 0.77  = 61.0, p < .0001; post hoc tests, p < .0001, p < .0001)  = 35.3, p < .0001; post hoc tests, p < .0001, p < .0001), but values were not significantly different between WFL and EFL   was significantly lower in TX‐LA compared to WFL and EFL  and significantly lower in EFL compared to WFL   compared to EFL  and TX‐LA .Among the three distribution ranges, both measures of neutral genetic diversity were significantly higher in WFL and EFL compared to TX‐LA  Figure c. We fouK identified K = 2 across all STRUCTURE analyses (except for the TX‐LA subset); whereas, ln Pr(X|K) identified additional levels of genetic structure that coincided with geographic location. We interpreted these differences between methods as the highest level of genetic structure (∆K) and finer‐scale genetic structure [ln Pr(X|K)] for each analysis . Across the USA range, ∆K identified a clear separation between TX‐LA and Florida  identified K = 11, with additional delineations between both WFL and EFL range margins and their respective cores, and admixture along multiple sections of the Florida range core  identified K = 5, with intersite admixture within the range core and sharp delineations at the most northern margin sites  identified K = 7), except for an anomalous example of within‐range‐core delineation (site code: PI)  identified K = 4, with separation into southern, central, and northern clusters, plus a seemingly noninformative fourth cluster across all sites   Figure .S; allelic richness, AR) was negatively correlated with latitude  and genetic differentiation (FST) was positively correlated with latitude . In WFL, HS was not correlated with latitude , whereas AR was negatively correlated with latitude  and FST was positively correlated with latitude . In TX‐LA, neither HS or AR were correlated with latitude  and FST was not correlated with latitude .We found that EFL and WFL distribution ranges conformed to CMH predictions, but TX‐LA did not Table . In EFL,t test, t[7] = 3.40, p = .006), WFL , and TX‐LA . WFL exhibited the highest mean increase in rare allele frequencies (4.85 ± 3.12 [SD] times greater than range core) compared to EFL (2.66 ± 1.48) and TX‐LA (2.73 ± 2.28).We found evidence of genetic drift at each of the three range margins Table . Rare al3.3We found the largest leaves and highest specific leaf area (SLA) in Southwest and Southeast Florida; whereas, we found lower values towards Florida range margins and across all TX‐LA sites Table . PCA of Leaf size  and life history strategy (SLA) traits had strong, positive loadings on PC1 Figure . Negativrs = –.69, p = .002) and WFL , indicative of functional trait variation better suited to tolerate cold stress at these range margins, but PC1 was not correlated with latitude along TX‐LA   or WFL , but was positively correlated  with latitude along TX‐LA   Figure . PC2 was4We still lack a clear understanding of what controls distributional limits of species  suggests another potential caveat to CMH. This collection site exhibited strong within‐range‐core differentiation and lowest neutral genetic diversity along this distribution range. Land modifications associated with this site's conversion into a conservation area, plus limited hydrological exchange because this portion of the EFL lagoon system experiences highest water residence times Smith, , probablA. germinans in Florida and those in Texas‐Louisiana (TX‐LA), consistent with independent post‐Pleistocene recolonization routes , with values analogous to our lower‐latitude Florida collection sites, and lowest allelic richness in northern Mexico   that control these distributional limits. Both Florida distribution ranges exhibited a change in leaf traits towards those better suited to tolerate cold, consistent with gradients in minimum winter temperatures. However, while WFL exhibited a more continuous change in leaf traits towards the range margin, EFL seemed to exhibit a more abrupt change, in particular at the most northern collection site. Trait variation along environmental gradients can vary depending on rates of gene flow and the strength of genetic drift (Polechová, Functional trait variation of A. germinans found greater chill tolerance in offspring from Texas compared to more‐equatorial regions (Markley, McMillan, & Thompson, Observations of trait variation towards range margins seldom address the relative contributions of genetic differences and environmentally‐induced trait plasticity in explaining these patterns (Chuang & Peterson, A. germinans appear well‐equipped to thrive in their marginal environment, unless directly impacted by anthropogenic changes. This continued proliferation will result in wide‐reaching community‐level effects (Diskin & Smee, Considering ongoing, rapid expansion at all USA range margins (Rogers & Krauss, In conclusion, model systems that meet underlying assumptions and alleviate the influence of common confounding factors can provide important insights into many large‐scale ecological theories (Sagarin et al., J.P.K., and I.C.F. designed the research. J.P.K. performed the research and analyzed the data. R.F.P., J.K.R., and I.C.F. supervised the research. J.P.K. wrote the manuscript with input from all coauthors. Click here for additional data file. Click here for additional data file.


Cercocarpus montanus). We sequenced DNA from 1,440 plants in 48 populations across a broad geographic range. We found that genetic heterogeneity among populations reflected the complex climate and topography across which the species is distributed. We identified temperature and precipitation variables that were useful predictors of genetic differentiation and can be used to generate seed transfer recommendations. These results will be valuable for defining management and restoration practices for mountain mahogany.Ecological restoration often requires translocating plant material from distant sites. Importing suitable plant material is important for successful establishment and persistence. Yet, published guidelines for seed transfer are available for very few species. Accurately predicting how transferred plants will perform requires multiyear and multi‐environment field trials and comprehensive follow‐up work, and is therefore infeasible given the number of species used in restoration programs. Alternative methods to predict the outcomes of seed transfer are valuable for species without published guidelines. In this study, we analyzed the genetic structure of an important shrub used in ecological restoration in the Southern Rocky Mountains called alder‐leaf mountain mahogany ( We analyzed the genetic structure of a common montane shrub that occurs in the Southern Rocky Mountain and is commonly used in restoration projects. We found that environment and topography influenced genetic variation and that genetic diversity was reduced at the environmental margins of its distribution. The results are interpreted to guide seed transfer during ecological restoration. This step resulted in construction of an artificial, partial reference genome containing 111,967 contigs. We used bwa  and sequenced at the University of Texas Genomic Sequencing and Analysis Facility  on the Illumina HiSeq 2500 platform using single‐end 100 bp reads. After filtering reads for oligonucleotides used in library synthesis and the PhiX genome, with subsequent demultiplexing and assignment of reads to individuals, we had 24,000,000 sequence reads for further analysis. We completed a de novo genome assembly with a randomly chosen subset of 2.4×102.3prcomp function in R to summarize genetic variation.Low coverage genotype‐by‐sequencing (GBS) data contain sequencing error. Therefore, we estimated genotypes as the mean of the genotype likelihood distribution and constructed a genetic covariance matrix for all individuals to include the uncertainty inherent in GBS data. We ran a principal components analysis (PCA) of the genetic covariance matrix using the K = 1 to K = 10 from the snmf function. This criterion uses a masked genotype testing set to determine the prediction accuracy of the model at each K value.We used GBS data to estimate ancestral population membership. Genetic cluster algorithms can help explain genetic variation among individuals, and visualize population membership over broad scales  . Climatic variables included thirty‐year normal temperature and precipitation estimates from thin plate spline surfaces (http://forest.moscowfsl.wsu.edu/climate). The precipitation and temperature variable with the highest correlation with Nei's DA were selected and combined to serve as the environmental distance predictor in these models. All predictor variables  of the site frequency spectrum (SFS) using the EM algorithm. Estimates of nucleotide polymorphisms were calculated as πθ .The previously described models used genetic differentiation statistics to identify drivers of genetic structure. However, genetic variation within populations is also important to consider for restoration. Specifically, we asked whether variation in population genetic diversity could be explained by geographic or environmental centrality. We estimated genetic diversity for each population using the program ANGSD  and geographic range centrality.2.6D metrics  accounted for 89.7% of the genetic variation among individuals of mountain mahogany and reflected latitude of origin and the effect of the Continental Divide as a barrier Figure . PC2 accK = 4 , with a range of 0.009–0.108. Pairwise FST had an overall mean of 0.161 and a SD of 0.0856 , while the mean FST among populations on the same side of the divide was 0.135 (SD = 0.07). Pairwise FST was positively correlated with spatial distance, and population pairs from opposite sides of the Continental Divide had elevated FST resulting from the effect of the topographic barrier . We used growing season precipitation and degree days less than 0°C separately as environmental variables, as well as a binary design matrix representing the Continental Divide to quantify the effect of topography on genetic distance. A difference of one degree days less than 0°C was comparable to approximately 8 km, and a 1 cm change in growing season precipitation had the same effect on genetic differentiation as approximately 70 km geographic distance. The Continental Divide had the largest effect on genetic differentiation relative to spatial distance. Crossing the Continental Divide had the same effect on genetic differentiation in mountain mahogany as moving 1.7 × 107 km, a larger distance than our collection area.The BEDASSLE analysis calculated the ratio of environmental and spatial distance effect sizes on genetic differentiation , and we therefore arbitrarily chose πθ for further modeling . We checked the univariate correlation between genetic diversity and all climate and elevation data  and the number of degree days less than zero (DD0) were associated with genetic structure in this species. The association between the environment and genetic differentiation supports seed sourcing guidelines that select collection areas that match the environment of the restoration site.Cercocarpus ledifolius, that co‐occurs in this region ; data curation ; formal analysis ; funding acquisition ; investigation ; methodology ; writing—original draft . C. Alex Buerkle: Conceptualization ; data curation ; formal analysis ; methodology ; validation ; visualization ; writing—review and editing . Daniel E. Runcie: Formal analysis ; validation ; writing—review and editing . Kristina M. Hufford: Conceptualization ; data curation ; formal analysis ; funding acquisition (lead); investigation ; methodology ; project administration (lead); writing—original draft (supporting); writing—review and editing .Supplementary MaterialsClick here for additional data file.


Salmo salar m. sebago) has been critically endangered for the past half-century. As a result of anthropogenic disturbance, especially construction of hydroelectric power plants, the Lake Saimaa salmon has become completely dependent on hatchery broodstock. Recently, habitat restoration has been done in one of the former spawning rivers with the aim of creating a new natural spawning ground for the critically endangered population. Hatchery fish releases have also been revised so that in addition to juveniles, adult fish from the hatchery and from the wild have been released into the restored river. We assessed here if a restored river stretch can be used as a natural spawning ground and juvenile production area with the aim of improving genetic diversity of the critically endangered Lake Saimaa salmon. By constructing a pedigree of the released adults, and juveniles sampled from the restored river, we found that the majority of the released adults had produced offspring in the river. We also found that wild-caught spawners that were released into the restored river had much higher reproductive success than hatchery-reared parents that were released into the restored river at the same time. We found no significant differences in genetic diversity between the parent and offspring generations. Meanwhile, relatedness among different groups of adults and juveniles varied a lot. For example, while the hatchery-reared females were on average half-siblings, wild-caught females showed no significant relatedness. This highlights the importance of using pedigree information in planning the conservation and management of endangered populations, especially when artificial propagation is involved.Supplementing endangered fish populations with captive bred individuals is a common practice in conservation management. The aim of supplementary releases from hatchery broodstocks is to maintain the viability of populations by maintaining their genetic diversity. Landlocked Lake Saimaa salmon ( The maintenance of genetic variation is essential to the survival of natural populations. Genetic variation enables populations to adapt to changing environmental conditions, and to retain their viability by reducing the negative impacts of inbreeding on individual survival and reproduction. To prevent the loss of genetic variation in endangered populations, a common practice has been to supplement them with captive bred individuals . This stThe ultimate goal of most conservation efforts is a genetically diverse, self-sustaining population in a natural environment , devoid Salmo salar m. sebago) became landlocked after the last glaciation ca. 10,000 years ago, and has since been isolated from anadromous salmon populations . This trend was also reflected in the higher values of heterozygosity in the more recent samples  were found to have offspring. The individual reproductive success was similar to earlier studies on anadromous Atlantic salmon  populations in natural and semi-natural environments, that have used a similar approach of measuring reproductive success as the number of young produced )breeding . In the produced –43. Our produced , 42. Howproduced –45. The FIS, was negative, indicating a higher than expected number of heterozygotes for a population in Hardy-Weinberg equilibrium. The high relatedness combined with a negative FIS is likely due to the hatchery population being established from a combination of different groups of fish originating from different areas of the Lake Saimaa, and from different year classes–the management strategy used to avoid inbreeding in controlled matings in hatcheries. The wild-caught parents did not have higher than expected relatedness or a FIS different from zero. Based on these differences, careful management of the genetic background of adults in future stockings should be implemented. For instance, the stocked individuals should be selected so that the rates of loss of genetic variation and levels of inbreeding in future generations can be kept to a minimum. This is possible when the pedigree of the fish is known. Genetic management based on a known pedigree should also ultimately help to resolve the possible trade-offs in conserving genetic variation versus fitness [There is a growing number of encouraging examples, where anadromous Atlantic salmon have started to return to the restored rivers, where they have been previously extirpated e.g. , 46–49).–49.46–49 fitness .FIS was significantly negative, possibly reflecting the mixed origin of the parents. While the mean relatedness estimates give an indication of possible problems to maintenance of genetic variation, from a management perspective it is more relevant to look the distribution of relatedness estimates. Although the mean relatedness is mostly relatively low, there is a lot of variance among pairwise relatedness estimates . Again, despite the high relatedness, the inbreeding coefficient, stimates . This unIn conclusion, our results show that the restored river habitat can function as a natural spawning and reproduction habitat for the critically endangered Lake Saimaa salmon, even when the spawners are transported from elsewhere. Transfer of adults into the restored river has resulted in wild-born offspring, providing a possible solution for the maintenance of genetic diversity and natural reproductive traits in the critically endangered Lake Saimaa salmon. The adults transferred from a hatchery had a clearly lower spawning success than the adults that were caught in the wild. This together with the large variance in the between-individual relatedness estimates highlights the importance of using pedigree information when planning the breeding of broodstock for an endangered population. Although it is premature to evaluate the future success of Ala-Koitajoki spawning population, the results indicate that a combination of genetic management of the hatchery broodstock and natural reproduction in the restored habitat could provide a sustainable solution for the preservation of the critically endangered Lake Saimaa salmon.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Lepidium subulatum, a dominant Iberian shrubby gypsophyte.Gypsum ecosystems are edaphic islands surrounded by a matrix that is inhospitable to gypsum soil plant specialists. These naturally fragmented landscapes are currently exacerbated due to man-made disturbances, jeopardising their valuable biodiversity. Concomitant action of other fragmentation drivers such as linear infrastructures may increase the already high threat to these specialists. Although some evidence suggest that gypsophytes are not evolutionary dead-ends and can respond to fragmentation by means of phenotypic plasticity, the simultaneous action of barriers to genetic flow can pose a severe hazard to their viability. Here, we evaluated the effect of a highway with heavy traffic on the genetic flow and diversity in the species We tested the possible existence of bottlenecks, and estimated the genetic diversity, gene flow and genetic structure in the remnant populations, exploring in detail the effect of a highway as a possible barrier.FST and FIS, did not seem to be affected. The presence of some level of genetic flow in both sides along the highway could explain the relatively high genetic diversity in the habitat remnants.Results showed variability in genetic diversity, migrants and structure. The highway had a low impact on the species since populations can retain high levels of genetic diversity and genetic parameter, like Natural fragmentation and their exacerbation by agriculture and linear infrastructures seem to be negligible for this species and do not limit its viability. The biological features, demographic dynamics and population structures of gypsum species seem to be a valuable, adaptive pre-requisite to be a soil specialist and to maintain its competitiveness with other species in such adverse stressful conditions. Fragmentation is known as a pervasive anthropogenic process in which habitat loss triggers the division of the available habitat into patches, creating small, isolated and low quality fragments which are subject to the action of other concomitant negative biological processes  Are patches isolated according to the distance between them? If so, is the road enhancing the fragmentation or the connectivity between patches? Finally (iii) are genetic parameters, environmental variables and landscape features related and/or modified by the presence of the highway?Here, we performed a study of landscape genetics in a widely distributed and abundant Iberian gypsophile, ubulatum . This isubulatum , 2018. Tubulatum  and charLepidium subulatum L. (Brassicaceae) in an endemic chamaephyte of the Iberian Peninsula with a few populations in North Africa, specialist of gypsum soils  4,438,624, 489,512; the most south-easterly population (Pop 12) 4,432,335, 495,277). The climate is arid, with an annual average temperature and rainfall of 13.8 °C and 440 mm, respectively and almost no rainfalls during the summer . The area is dissected northwest to south east for more than 15 kms by the A-3 highway, which has operated for more 35 years in its present configuration . We subjected the samples to a sulphuric acid digest and then used an SKALAR San++ Analyser to estimate N and P , we collected two soils samplings per patch with a core ten centimetres deep and six centimetres in diameter. Samples were analysed for total amount of nitrogen (N), phosphorous (P), potassium (K), organic carbon, pH, conductivity and glucosidase and phosphatase’s activity in Nutrilab/URJC  1 U of Taq-polymerase  and 1.2 μl of DNA (DNA had a concentration range of 10–30 ng/ul). The thermocycler’s protocol was: 4 min at 95 °C, followed by 30 cycles of 45 s at 95 °C, 45 s at 51 °C  and 1 min at 72 °C, followed by a final step of 7 min at 72 °C. PCRs were checked using electrophoresis in agarose gel pre-stained with Gel Red . Lastly, the amplified fragments were analysed with an ABI 3730XL sequencer  of the Unidad de Genómica .Material collection was approved by the Consejería de Educación e Investigación de la Comunidad de Madrid . The genetic study was performed with eight specific microsatellites markers: Lsub01, Lsub02, Lsub03, Lsub04, Lsub05, Lsub07, Lsub08, Lsub12 . DNA wasHE and HO), the inbreeding coefficient (F), and the number of migratory individuals (Nm), using GenAlEx 6.5 software  were affected by some soil variables  which are surrogates of habitat quality, connectivity (Ci), other landscape variables  and population density. For this, we utilised General Linear Models (GLMs) using R v.3.3.0 , each one with a burn-in period of 105 iterations followed by 106 MCMC iterations and 104 thinning. Analyses were developed with allelic correlation frequencies and genetic admixture. According to the different population sampled, we used the STRUCTURESelector  with each population of the other highway side to observe if the value was significantly different with the FST average of the first side. We also analysed the effect of highway and geographic distance between pairs of populations using Generalised Least Squares (GLS) models with the nlme and corMLPE packages  and the distance between populations. We selected the best model using AIC, using FST as the dependent variable and geographic distance, highway side and their interactions as the independent variable.To evaluate the effect of the highway as a modifier agent of the genetic landscape, genetic diversity and differentiation was estimated, considering the two sides of the motorway (NE vs. SW population groups) separately. In addition, an AMOVA , performed by GenAlEx 6.5 software , was usepackages . The corn = 10.4 ± 1.8) and population 4, the lowest (n = 4.6 ± 1.2) (n = 3.8 ± 0.9), whereas population 12 was the richest (n = 8.3 ± 0.6) (n = 5) . Population 21 showed the highest expected heterozygosity, while population 4 had the lowest   (F) was 0.1 ± 0.1, with great variation between populations. Population 5 had the lowest inbreeding coefficient, showing a patent heterozygosity excess (F = 0.0 ± 0.2); meanwhile, population 12 had the highest coefficient (F = 0.3 ± 0.3)  . When th6 ± 1.2) . In this3 ± 0.6) . The tot3 ± 0.6) . In cont (n = 5) . Expectectively) . Neverthctively) . Finally3 ± 0.3) . The pre3 ± 0.3) .FST coefficients, showed an average value between population pairs of 0.1, suggesting moderate genetic differentiation across the study area  was 4.02 . Populatudy area  suggestin = 22 and n = 13, respectively)  , while tctively) . Ancientctively) . Only twFST values contained as predictors the Euclidean distance between patches or the highway side . Namely, the Evanno method suggest K = 4 as the most plausible value. MedMedK and MedMeaK support a K = 7 and MaxMedK and MaxMeaK indcated that K = 8 . In contrast, sPCA showed a global structure  but no a fine scale pattern  and expected heterozygosis , were positively related to the population density of the patch . This variation might be associated with selective drivers that constrain the populations, but also to neutral processes that may foster different levels of genetic diversity in the remnant populations, including biological, demographic and ecological features. Although selective and neutral processes are acting, L. subulatum should sustain levels of genetic diversity that are enough to maintain plastic responses to different environmental conditions  of this species. This type of infrastructure could act as a barrier but could also improve the gene flow between the both sides due to the movement of vehicles or animals, with principal or auxiliary roads acting as dispersal pathways . Other pns pairs . TherefoFST values could indicate that there is not a deficit in heterozygotes in the remnants. The exchange of genetic material could be mainly favoured by some pollination features , while 20 neighbours were necessary to connect the network in the sPCA, although the global structure was also the most important. Nonetheless, both cases showed an important admixture of population of both sides of the highway. Consequently, the highway did not exert a significant barrier effect. This high level of admixture in the genetic clustering might be associated with the presence of different factors that foster the dispersal of the species and maintain the high levels of genetic diversity. Efficient seed dispersal among patches could be associated with the active presence of cattle or mainly stationary  sheep herds within the area. Highways could work as a barrier but also improve plant dispersal, since vertebrates used roads to move, creating corridors , observed heterozygosity (HO), inbreeding coefficient (F), genetic differentiation (FST) and migratory individuals’ number (Nm). The highway side informs about the populations’ situation respect to the highway.Presence of null alleles, Hardy-Weinberg equilibrium, average values of allelic richness, private alleles’ number, expected heterozygosity (Click here for additional data file.10.7717/peerj.10533/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.10533/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.10533/supp-4Supplemental Information 4The upper part of the matrix shows the gene flow that a population emits. The lower part od the matrix shows the gene flow that a population receives.Click here for additional data file.10.7717/peerj.10533/supp-5Supplemental Information 5The upper part of the matrix shows the gene flow that a population emits. The lower part od the matrix shows the gene flow that a population receives.Click here for additional data file.


DCDC2 and manner of articulation in 51 populations spanning five continents, and adjusting for geographical proximity, and genetic and linguistic relatedness. RU1-1 alleles, acting through increased expression of DCDC2, appear to increase auditory processing precision that enhances stop-consonant discrimination, favouring retention in some populations and loss by others. These findings enhance classical linguistic theories by adding a genetic dimension, which until recently, has not been considered to be a significant catalyst for language change.Classic linguistic theory ascribes language change and diversity to population migrations, conquests, and geographical isolation, with the assumption that human populations have equivalent language processing abilities. We hypothesize that spectral and temporal characteristics make some consonant manners vulnerable to differences in temporal precision associated with specific population allele frequencies. To test this hypothesis, we modelled association between RU1-1 alleles of  Genetics strongly influences functions essential for oral communication, articulation , hearingentories .DCDC2 is associated with reading and language performance and related disorders of dyslexia, specific language impairment, and speech sound disorders [DCDC2, is also associated with normal variation in phonological processing [DCDC2 promoter [Dcdc2 modifies speech-sound discrimination between consonants [DCDC2 transcription, some READ1 alleles can enhance temporal precision and speech sound discrimination to favour retention or acquisition of selected consonantal contrasts. In the present study, we build on and expand our previous results to investigate the relationship between the population frequency of RU1-1 and the numbers of specific manners of consonants used in a language. Consonant manners differ in their temporal and spectral characteristics. The perception of these different manners may be more or less sensitive to the differences in temporal processing conferred by RU1-1 specific regulation of DCDC2, favouring retention or acquisition of selected consonantal contrasts.isorders . READ1, ocessing . It has promoter , which cpromoter . Functionsonants ,10 and insonants ,12. ThesConsonants are described by three cardinal articulatory features: manner of articulation, place of articulation, and voicing . Manner We hypothesize that the acoustic differences among consonants create perceptual challenges that may make some consonants more vulnerable to loss of temporal precision than others  and thus2.r = 0.314, p = 0.025) in their respective population, replicating our previous results.We modelled association between RU1-1 alleles and manner of articulation in 51 populations, spanning five continents and all major language families while accounting for geographical proximity, and genetic and linguistic relatedness. For the current analysis we added nine populations from the 1000 Genomes Project (1KG)  to the 4r = 0.398 (p = 0.0038), however RU1-1 frequency and numbers of stops clustered by regional location , while linguistic relatedness does not correlate with either genetic relatedness  or geographical proximity . The result of the Mantel tests suggests that all three effects should be taken into account in the modelling of the association between the five manners of articulation and RU1-1, given that linguistic relatedness is relatively independent from geographical proximity and genetic relatedness, and geographical proximity and genetic relatedness are only moderately correlated.To better understand these three effects, we evaluated how genetic relatedness, geographical proximity, and linguistic relatedness correlate with each other using the Mantel test, a statistical test of the correlation between distance matrices. Statistical significance was determined using 1000 permutations. Genetic relatedness and geographical proximity are the most strongly correlated with k-out analyses by population from k = 1 (jackknife) to k = 30 . The directions of the beta-values remained positive for stops and negative for nasals for 95% of the subsets even when almost 50%  were excluded, showing that the associations were not driven by particular subsets.To control for confounding due to effects of genetic relatedness, geographical proximity, and linguistic relatedness, we created a generalized linear regression model and included the major principal components (PCs) for each of the three sets of pairwise distances between populations. Insufficient populations were available to simultaneously examine all five manners, vowels, tones, and the control variables (the PCs) in the regression model . Thereforections . To eval3.Our results revealed that RU1-1 is associated positively with stops and negatively with nasals in 51 populations, adjusting for geographical proximity, and genetic and linguistic relatedness. To contextualize our results, we examined consonant processing in published animal models. Recordings from the auditory cortex of wild-type rats in response to different consonant manners showed that neurograms from stops have the sharpest onset peaks, are the most distinctive manner of articulation, and can be distinguished better than other manners in the first 40 milliseconds (ms) . By contDcdc2 in rodents also supports a prominent role in differential perception of stops and nasals. RNAi knockdown (KD) of Dcdc2 in rats leads to an increase in excitability in neurons located in the auditory cortex, as evidenced by both spontaneous and stimulus-driven action potentials and shorter onset latency in firing [Dcdc2 knockout (KO) mouse also shows an increase in neuronal excitability demonstrated by an elevated resting membrane potential in whole-cell patch clamp studies of brain slices [Grin2B, a subunit of the N-methyl-D-aspartate receptor (NMDAR), and restored by an NMDAR antagonist. Behaviorally, Dcdc2 KD rats have defective consonant detection in speech streams presented at all speeds, and altered neurograms at very high speeds recorded from the auditory cortex. Dcdc2 KO mice have impaired rapid auditory processing [Dcdc2 is important for potentiating baseline excitability and temporal precision in neurons of the rodent auditory cortex, which are critical for the sensitive and accurate perception of specific sound targets, and consonants in particular. Human DCDC2 promoter reporter-gene constructs show that the most frequent RU1-1 allele confers higher expression than the most frequent RU1-2 allele [DCDC2 expression, and as suggested by the rodent experiments, could lead to increased temporal precision in the auditory cortex and enhanced consonant discrimination  in population prevalence of a bias in favour of a linguistic change can disproportionately increase the number of speakers by up to 45% . In adding birds .4.Language is continuously evolving to meet the needs both of the speaker and of the listener . The nee


Eurycea bislineata  populations within and between streams in an urban, suburban, and rural habitat around the New York City (NYC) metropolitan area. We report reduced genetic connectivity between streams within the urban landscape found to correspond with potential barriers to gene flow, that is, areas with more dense urbanization . The suburban populations also exhibited areas of reduced connectivity correlated with areas of greater human land use and greater connectivity within a preserve protected from development. Connectivity was relatively high among neighboring rural streams, but a major roadway corresponded with genetic breaks even though the habitat contained more connected green space overall. Despite greater human disturbance across the landscape, urban and suburban salamander populations maintained comparable levels of genetic diversity to their rural counterparts. Yet small effective population size in the urban habitats yielded a high probability of loss of heterozygosity due to genetic drift in the future. In conclusion, urbanization impacted connectivity among stream salamander populations where its continual influence may eventually hinder population persistence for this native species in urban habitats.Natural landscape heterogeneity and barriers resulting from urbanization can reduce genetic connectivity between populations. The evolutionary, demographic, and ecological effects of reduced connectivity may lead to population isolation and ultimately extinction. Alteration to the terrestrial and aquatic environment caused by urban influence can affect gene flow, specifically for stream salamanders who depend on both landscapes for survival and reproduction. To examine how urbanization affects a relatively common stream salamander species, we compared genetic connectivity of  ReducedLandscape features encountered during dispersal fundamentally affect connectivity between populations. Reduced leaf litter depth, soil moisture . Alternatively, urban habitat degradation in the terrestrial landscape caused by roadways . This species shows extensive dispersal throughout streams  are the most ubiquitous family of salamanders in northeastern North America and comprise a sizeable proportion of vertebrate biomass in temperate forests  How does connectivity affect genetic diversity across habitats? (3) Do geographic distance (IBD) or barriers (IBB) better explain connectivity within each habitat type? We predicted that individuals within the urban habitat would show greater genetic differentiation between neighboring streams compared to the less‐developed suburban and rural habitats. We also predicted that genetic diversity and effective population size would be lowest in the urban habitat due to a loss of connectivity and highest in the rural habitat. Lastly, we predicted that IBD will structure populations in the suburban and rural stream networks, whereas IBB would be more influential in the urban habitat.In this study, we aimed to answer the following questions: (1) Do levels of genetic connectivity differ for 22.1E. bislineata) is a generally abundant stream‐dwelling species throughout its large range, from northern Ontario and Quebec to southern Virginia . The highest salamander diversity in NYC recorded at that time was located on Staten Island (Richmond Co.), which also contains the most well‐documented locations for E. bislineata in NYC.The northern two‐lined salamander (2.2http://www.fws.gov/ventura/species_information/protocolsguidelines/docs/DAFTA), the NY State Department of Environmental Conservation (Permit #1935), and Fordham University's Institutional Animal Care and Use Committee (Protocol No. JMS‐13‐01).Salamanders were located using extensive visual encounter surveys, where a majority of the accessible cover objects were turned over within the stream reach, as well as cover objects located about 2 m from the water along the stream bank. Individuals were then captured by hand or by dipnet and placed in a gallon‐sized plastic bag. We identified salamanders to species level based on morphology , and human population density  within a 12 km2 square extent surrounding each habitat. Calculations were performed in ArcGIS 10.3 . Among these ten streams, half of the study sites were located within forested habitat in the Staten Island Greenbelt State Park, whereas the others were contained within smaller parks or green spaces, and two were located within a golf course  with an RNAse treatment and final elution volume of 75 μl. We generated SNP genotypes for all sampled individuals using a double digest restriction site‐associated DNA sequencing (ddRADseq) protocol  on all three datasets compiled together to retain SNPs shared by all three habitats, to ensure our confidence in performing subsequent comparisons between urban, suburban, and rural habitats across the same SNP loci.We used STACKS software pipeline version 2.3d  and discarded loci that did not occur in at least 2 out of 3 habitats. We chose to maximize the number of polymorphic loci by retaining only those loci shared by 80% of individuals . Then, we used the 10_split_vcf_in_categories.py script to create a separate VCF file retaining only singleton SNPs to use for subsequent analyses . We hereafter refer to this fully filtered SNP dataset as “shared SNP loci,” as these are singleton SNPs shared across habitats and were used for comparative analysis.Then, we conducted additional SNP filtering to reduce the number of duplicate loci in the dataset. Plethodontid salamanders have large genomes  to recreate fully filtered genepop, structure, and plink files for downstream analyses. We also calculated observed heterozygosity (Ho), nucleotide diversity (π), and pairwise FST  to create separate filtered datasets for within‐habitat analyses.Lastly, we reran STACKS 2.6adegenet in R  to reveal the most well‐supported number of clusters present in the dataset. Next, we used optim.a.score to understand the ideal number of PCs to retain. DAPC then uses a multivariate approach to partition within‐ and between‐group variation to maximize discrimination between groups. As a complement to these DAPC analyses, we performed principal components analysis (PCA) in the R statistical environment  using the package K‐clusters corresponding to the lowest cross‐validation error score (cv‐error). We ran this analysis on all 350 individuals for values of K = 3–20 for five iterations at each K value. Then, we ran the program separately for each habitat type  at K = 1–10 for five iterations at each K value. Lawson, Van Dorp, & Falush  package , we performed a standard Mantel test in R using the riverdist  versus overland Euclidean distance analysis (IBD) to assess the relative importance of in‐stream versus overland gene flow.Since this species is known to disperse extensively both up‐ and downstream systems Bruce, , we alsoEEMS; Petkova et al., bed2diffsv‐1 function and used this genetic distance matrix and spatial coordinates for each individual as input to run the EEMS analysis. We used EEMS to estimate whether there is more or less migration between neighboring demes (discrete populations) than predicted by an isolation‐by‐distance model. The total area over which we performed the analysis was a 12 km2 extent surrounding each habitat type. We ran EEMS multiple times for each habitat, starting with the default hyperparameters, then fine‐tuned the proposal variances until the proposals were accepted ~20%–30% of the time , followed closely by differentiation between the urban versus rural habitats (FST = 0.095). The lowest genetic differentiation was between the suburban and rural habitats (FST = 0.038). The population summary statistics revealed similar levels of genetic diversity (OH and π) across the three habitats (HO = 0.265–0.278 and π = 0.272–0.304). The urban habitat contained the greatest number of private alleles, suggesting greater population structure within this habitat  as the most well‐supported number of evolutionary clusters in the dataset. The DAPC showed three distinct genetic clusters for E. bislineata in streams within the urban habitat. The first DF  emphasized genetic differences between stream site U10 (orange) and stream sites in the north  and western (orange) genetic signatures. Additional k‐means clustering options were explored for this analysis (K = 2–5), since their cross‐validation scores did not vary greatly from the most well‐supported K = 3 cv‐value  from streams U1 and U2 (pink), and the K = 5 model separated stream sites U8 and U9  from the remaining sites , showing IBD across the urban habitat . In this urban habitat, both distance over land and distance along the path of a waterway are correlated with genetic differences between individual stream salamanders  separated clusters located on the eastern half  and western half  of the suburban sampling area. The second DF also highlighted genetic differences  across western stream sites, showing genetic differences between individuals sampled within a single stream site, S10, from the rest of the stream sites in the west (purple). The ADMIXTURE cv‐likelihood scores showed the best support for a K = 1 model  from those streams to the south (pink), and K = 4 displayed further substructure between the sites within the west where S10 (blue) separated as its own distinct cluster. As we increased up to K = 5, additional admixture was added to the ancestry of individuals within the eastern streams  from DAPC showed genetic clustering between streams in the southeastern portion of the sampling area  separated from those located more northwest . At higher values of K = 4 and K = 5, there was no more additional structure, only the addition of admixture to the more centrally located stream sites R4–R6  in Japan  in Australia  and Eastern tiger salamanders (Ambystoma tigrinum), which are often sympatric with E. bislineata in northeastern North America. Generally, our results are congruent with other studies that show urbanization affects spatial and population dynamics, as well as genetic structure for a broad range of amphibians  in Connecticut  can maintain genetic diversity in the receiving population  among the urban, suburban, and rural habitats. Similar to our findings, a study on fire salamanders  in Oviedo, Spain, showed genetic differentiation between urban populations was not associated with substantial losses in genetic diversity  may be due to larger population sizes and a lack of significant bottlenecks. In this study area in NYC, immigration from neighboring populations may supplement local population size and genetic variation, making these populations less prone to extinction. Yet our results showed greatly reduced effective population size in the urban compared to the suburban and rural habitats, signifying that these urban stream salamander populations may experience the effects of genetic drift and inbreeding depression in future generations.A reduction in genetic diversity may hinder evolutionary potential in changing environments  to obtain enough DNA samples for robust analysis. Some studies state that genetic diversity is in a transient state, but after a peak the rate of decay is a slow process (Alcala, Streit, Goudet, & Vuilleumier, 4.3E. bislineata in these urban, suburban, and rural habitats in the NYC metropolitan area.Inferences from population genetic studies indicate diverse responses for amphibians living in urban landscapes (Scheffers & Paszkowski, 5E. bislineata. In NYC, despite the close proximity of some green spaces, overland habitats may be too heavily urbanized for dispersal and gene flow to regularly occur. These fragmented areas can promote inbreeding, which can be exacerbated by population declines due to reductions in habitat quality. Overall, this study reaffirms the need to maintain connected greenspace (Jackson & Fahrig, Populations of amphibians are declining worldwide, including even previously common species (Bank et al., None declared.Fig S1‐S3Click here for additional data file.