Landscape genetics of the copal tree, Bursera cuneata (Burseraceae): The key role of the Tropical Dry Forest in shaping connectivity at the regional scale

Landscape genetics of the copal tree, Bursera cuneata (Burseraceae): The key role of the Tropical Dry Forest in shaping connectivity at the regional scale | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Landscape genetics of the copal tree, Bursera cuneata (Burseraceae): The key role of the Tropical Dry Forest in shaping connectivity at the regional scale Yessica Rico, Marisol Zurita-Solis, Bode Olukolu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7724981/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Land-use changes in tropical dry forests (TDF) have rapidly reduced native vegetation, disrupting gene flow dynamics of tree species. Bursera cuneata is a co-dominant TDF tree in central Mexico, is threatened by habitat loss and overexploitation. We investigated landscape drivers of functional connectivity of B. cuneata across scales to inform species conservation efforts. We genotyped 227 B. cuneata individuals from 33 populations across five hydrological basins at 10,499 single-nucleotide polymorphism (SNP) loci. We examined spatial patterns of genetic structure among hydrological basins and the landscape drivers of gene flow. We applied gravity models that incorporated at-site (slope and east aspect) and between-site (terrain roughness, habitat suitability, and habitat cover) factors influencing B. cuneata gene flow. Clustering analyses showed genetic structure among basins, with the highest differentiation for Balsas (BAL) and Mexico (MEX). Gravity models revealed that functional connectivity is a scale-dependent process. Specifically, terrain roughness was the primary factor of connectivity at finer scales (1,000–3,000 m), while the TDF became the main driver at regional scales (>4,000 m). We recommend protecting and prioritizing crucial TDF remnants to maintain large-scale gene flow by integrating urban natural parks as important links to prevent genetic isolation between urban and rural populations. Biological sciences/Genetics/Population genetics Biological sciences/Ecology/Molecular ecology Burseraceae functional connectivity gene flow SNPs tropical dry forest Mexico Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The Tropical Dry Forest (TDF) is a critically important ecosystem, representing 42% of the world’s tropical forests, hosting a diverse group of endemic plants and animals (Murphy and Lugo 1986; Rzedowski 1991; Blackie et al. 2014; Mesa-Sierra et al. 2022b). Its ecological significance is driven by its pronounced seasonal variability, diverse topography, and soil properties (Powers et al. 2018; Méndez-Toribio et al., 2017; Quisehuatl-Medina et al., 2023). These factors not only sustain high species richness but also play a vital role in global carbon sequestration (Mesa-Sierra et al. 2022a). In Mexico, despite being one of the most extensive ecosystems, it is highly threatened, having lost approximately 75% of its original area (Trejo and Dirzo 2000; Rzedowski 2006). The remaining TDFs are mostly transformed into secondary forests and scattered within anthropized landscapes of agriculture, cattle pastures, and urban developments (Bezaury-Creel 2010). Habitat loss and increasing isolation threaten plant communities by affecting essential ecological processes, such as limiting propagule dispersal and gene flow among populations (Auffret et al ., 2017). Among the flora of the Mexican TDF are trees of the genus Bursera (Rzedowski and Kruse 1979; Rzedowski and Gevara-Féfer 1992; Espinosa et al. 2006; Steinmann 2021), whose global center of species diversity and endemicity is the western Balsas Basin (Becerra 2005; Becerra and Venable 2008). The Bursera genus consists of aromatic trees and shrub species (~ 100) with resinous leaves and trunks (Rzedowski and Medina-Lemos, 2023). Bursera has held cultural importance since pre-Colombian times, providing aromatic resins for religious and medicinal purposes, which continues to date (Montúfar-López, 2016). Habitat loss poses the most critical threat to the survival of Bursera species. Recent assessments reveal that 67% of Bursera taxa are listed as threatened on the IUCN Red List, primarily due to ongoing TDF loss and degradation (Fuentes et al ., 2019). Landscape genetics, which explicitly integrates spatial variables with population genetic data (Holderegger et al ., 2010; Cruzan and Hendrickson, 2020), has become a key tool for quantifying functional connectivity (i.e, gene flow). This approach directly informs conservation planning in anthropized landscapes (Balzotti et al ., 2020), such as the TDF, where only a small portion is protected under natural parks (Mesa-Sierra et al ., 2022b). For plants, functional connectivity refers to the effective dispersal of seeds and pollen across the landscape matrix mediated by dispersal vectors and local factors influencing establishment (Auffret et al ., 2017). Thus, connectivity is shaped by drivers operating at multiple spatial scales. At a fine scale, topographic features, such as slope aspect and steepness, influence microclimate conditions (e.g., humidity and temperature), which affect propagule establishment (Méndez-Toribio et al., 2014, 2016). These heterogeneous microhabitats found in steep rocky areas can be crucial for specialized species (Eisenlohr et al., 2013). At a broader, regional scale, functional connectivity is driven by land cover composition, remnant forest patches, and transitional habitats that facilitate vector movement (Dileo et al., 2014; Cristóbal-Pérez et al., 2021). Therefore, to effectively evaluate the conservation status of vulnerable tree species in the TDF, it is essential to assess how these multi-scale landscape drivers influence functional connectivity (Cruzan and Hendrickson, 2020; Hoban et al., 2021). Large areas of the TDF along the Trans-Mexican Volcanic Belt (TMVB), particularly in the states of Michoacán, México State, Guanajuato, Querétaro, and Morelos, have been dramatically lost to agricultural expansion, livestock grazing, and mining (Hernández-Oria, 2007). This region has also undergone intensive urban and industrial development due to commercial growth. Among the affected species is Bursera cuneata , a dominant TDF tree (Cué-Bär et al ., 2006), whose wood is used for handicrafts in Michoacán, and which has experienced significant local population declines (Rzedowski and Guevara-Féfer, 1992; Luft-Dávalos and Álvarez Icaza, 2009). Bursera cuneata shows a patchy distribution across five environmentally diverse hydrological basins within central Mexico's Trans-Mexican Volcanic Belt (Rzedowski and Guevara-Féfer, 1992; Flores-Estrella et al ., 2007; Caballero et al ., 2010). These basins, Lerma-Chapala (CHA; 500- 2,000 m.a.s.l.), Pátzcuaro (PTZ; 2,000-2,050 m. a.s.l.), Cuitzeo (CTZ; 1,800- 3,400 m.a.s.l.), México (MEX; 2,240 m.a.s.l.), and Balsas (BAL; 0- 2,000 m.a.s.l.), display significant environmental and topographic heterogeneity (INEGI, 2021). The species primarily inhabits TDF and transitional zones with Temperate Forest (Israde-Alcántara et al ., 2005), with the most severe TDF degradation occurring in PTZ, CTZ, and MEX basins (Mesa-Sierra et al ., 2022b). In some areas, such as Mexico City, the species occurs in natural areas and parks within the urban metropolis, where it maintains small and isolated remnant populations. Despite the ecological importance of Bursera species, conservation genetic studies remain scarce, and most studies available are on their phylogenetic relationships (e.g. Becerra and Venable, 1999; Becerra, 2003; Weeks and Simpson, 2007; Rosell et al ., 2010), with a single study on gene flow (Dunphy and Hamrick, 2007). Here, using single-nucleotide polymorphisms (SNPs), we investigated spatial patterns of genetic structure in B. cuneata across five hydrological basins, where we expect to find genetic differentiation due to the marked topographic and environmental heterogeneity among basins. Furthermore, we assessed the relative influence of topographic, environmental, and the proportion of habitat composition on functional connectivity across spatial scales. We expected that, at local scales, east aspect and steepness likely influence Bursera establishment (Méndez-Toribio et al., 2014), while habitat suitability and the proportion of TDFs drive gene flow at broader scales. Conversely, the proportion of urbanization and agricultural areas may restrict pollinator movement and seed dispersal. Materials and methods Study species and sampling Bursera cuneata is a dioecious tree, up to 10 m tall, with gray or gray-reddish non-exfoliating bark. The inflorescences are open panicles, blooming from April to July. The main seed vectors are birds of the genera Melanerpes, Icterus, and Myiarchus (Cultid-Medina and Rico 2020). Although coyotes and mice may also disperse the seed. The pollinators in the genus Bursera are insects such as Diptera of the genus Strigoderma and Diogmites , Coleoptera of the genus Bleparida and Chrysoprasis , and Hymenoptera of the genus Apis and Hypanthidium , and some lizards Microlophus (Rivas-Arancibia et al. 2015; Maya-Elizarrarás et al. 2024). We randomly collected two to three leaves from juvenile and adult trees along remnant TDF fragments across the five basins between the years 2017 to 2019. We registered the GPS coordinates for each sampled tree. Leaves were preserved in sealable plastic bags with silica gel until their DNA extraction. Our sampling included 33 populations from the five hydrological basins, of which CHA, PTZ, and CTZ were the basins with the highest occurrence of B. cuneata (Fig. 1). DNA extraction and molecular analysis Approximately 20-30 mg of genomic DNA from each of 350 samples was extracted from dried tissue using the Cetyl Trimethyl Ammonium Bromide (CTAB) protocol with pre-wash steps to eliminate excess polyphenols (Healey et al., 2014). DNA quantification and purity evaluation were obtained using Quant-iT TM PicoGreen TM dsDNA Assay and NanoDropTM 2000 (Thermo Fisher Scientific), respectively. A total of 25 ng of high molecular weight DNA was sequentially digested with NsiI-HF and NlaIII restriction enzymes. Barcoded adapters were incorporated into genomic fragments following the OmeSeq-qRRS (quantitative reduced representation sequencing) method (Yencho and Olukolu 2022). Following library preparation, the libraries were diluted to 10 nmol/l and sequenced on a single lane of the NovaSeq S4 flow cell system (150-bp paired-end reads). SNP filtering The OmeSeq-qRRS raw reads were demultiplexed and quality filtered using the automated pipeline ngsComposer (Kuster et al. 2021; https://github.com/XXXX/). Variant calling and filtering were performed using the GBSapp automated pipeline (https://github.com/XXXX/). Using this pipeline, all individual reads were mapped against the whole-genome assembly of B. cuneata (Rico et al. 2022). Only reads with at least a mapping quality of 20 and that area was uniquely mapped (i.e. excludes reads derived from paralogous sequences) were used for variant calling. These variants were subjected to additional filtering to retain only biallelic markers using the R package vcfR (Knaus and Grünwald 2017). To assess the neutral genetic structure and genetic diversity, we used variants with a Phred quality level > 30 (indicating high confidence in variant calling) with the R package hierfstat (Goudet 2005), and the R package adegenet (Jombart and Ahmed 2011). Loci potentially under selection (outlier loci) were removed to consider loci only under neutral processes. For this, we considered population structure evaluated with the snmf function of the R package LEA (Frichot and François 2015) and controlling the false discovery rates by adjusting the P values with the genomic inflation value (λ), setting the rates at q = 0.05 with the Benjamini-Hochberg algorithm (François et al. 2016). Moreover, we used the R package pcadapt v.4.3 (Privé et al. 2020) by selecting the first three components to identify outlier loci (MAF > 0.05). The matching outlier loci detected by LEA and pcadapt were removed from the total SNP dataset for subsequent analyses. Genetic diversity, structure, and migration rates We calculated genetic diversity parameters per basin, such as the number of private alleles ( A p ), total number of alleles ( A ), nucleotide diversity (π), observed ( H o ) and unbiased expected heterozygosity ( uH e ), and fixation index ( F IS ) using the R package dartR (Gruber et al. 2018). We performed an Analysis of Molecular Variance (AMOVA) to evaluate the amount of genetic variance explained among populations, and among the five basins using the R package adegenet , and poppr (Kamvar et al. 2014). F ST pairwisecomparisons among basins were estimated using dartR and hierfstat . To assess genetic structure, we used the R package adegenet to perform a Discriminant Analysis of Principal Components (DAPC), which is a robust genetic clustering method free of Linkage disequilibrium (LD), and Hardy-Weinberg (HW) assumptions (Jombart and Ahmed 2011). We designated the five basins as a priori grouping (CHA, PTZ, CTZ, BAL, and MEX) to visualize genetic and spatial structure. We graphically visualized the first two discriminant functions using scatter plots in R. Also, we implemented a sparse non-negative matrix factorization ( snmf ) from LEA R package, setting K = 1 to 7 with 100,000 iterations. To choose the optimal K, the cross-entropy criterion was used. For this, LEA considers the ancestry coefficients, based on the number of ancestral populations ( snmf ), which are related to the number of principal components of the genomic data, and the latent factor mixed models ( lfmm ) (Frichot and François 2015). The individual ancestry coefficients were grouped and averaged per population and plotted in the geographic space using pie charts. Moreover, spatial patterns of genetic structure were evaluated by performing Moran´s Eigenvector Maps (MEM) in the R package MEMgene (Galpern et al. 2014) using individual GPS coordinates and genetic distances. We plotted significant positive or negative Moran Eigenvector maps that contained the largest genetic variation. To evaluate the effects of isolation by distance (IBD) on genetic differentiation, we performed a Mantel test using the population F ST distances against Euclidean distances. Significance was estimated by permuting observations 1,000 times using the R library vegan v.2.5.7 (Oksanen et al. 2020). Lastly, we estimated the contemporary migration rates of B. cuneata across the five basins using BayesAss v. 3.0.5.7 . (Wilson and Rannala 2003), with 10,000,000 Markov Chain Monte Carlo (MCMC) iterations and 1,000,000 of burning. Functional connectivity hypotheses We downloaded the 2017 Land Use/Land Cover images from Sentinel at 10 m resolution (ESRI Living Atlas: https://livingatlas.arcgis.com/landcover/) to obtain one raster using QGIS v. 3.32 for the landscape genetic analysis (see below). That raster was reclassified to include the following covers: 1) Tropical Dry Forest (TDF), 2) Temperate Forest (pine-oak; TF), 3) permanent and temporary agricultural areas including bare soil (Agri), 4) urban cities (Urb), 5) main water bodies (Lake Cuitzeo, Lake Pátzcuaro, Yuriria lagoon). Also, we characterized the topography of the study area by calculating the slope, terrain roughness (the variability of the terrain’s surface, indicating the unevenness of the terrain), and east aspect (most collection populations were found on the east-facing hillsides, and eastness was calculated as the sine of the aspect, values ranging from 1 to -1), using a digital elevation model at 10 m resolution from INEGI (https://www.inegi.org.mx/) using the terrain function in the raster v.3.5 package (Hijmans 2022). To include the environment in the landscape genetic analysis, we built a habitat suitability model using Ecological Niche Models (ENM) for B. cuneata . We used 405 points: 227 occurrences of B. cuneata (samples analyzed in this study, see results), and 117 records downloaded from The Global Biodiversity Information Facility (GBIF; https://www.gbif.org/es/). To clean the data, we used the niche tool box ( NTBOX ) R package (Osorio-Olvera et al. 2020), considering that the specimens with insufficient locality information were discarded. All occurrences were thinned at five kilometers due to a higher density of occurrences than the training data sets. As variable predictors, we used the 19 bioclimatic variables at 30 s from WorldClim (https://www.worldclim.org/data/worldclim21.html). The bioclimatic layers were delimited by five terrestrial ecoregions downloaded from World Wildlife (https://www.worldwildlife.org/publications/terrestrial-ecoregions-of-the-world). The ecoregions were: Bajío dry forests, Central Mexican scrub, Balsas dry forests, Sierra Madre del Sur pine-oak forests, and Trans Mexican Volcanic Belt pine-oak forests. The 19 variables were tested individually and in a multivariate analysis, discarding correlated variables using R package adegenet ( r ≥ 0.8), and using the first two synthetic axes from 250 Principal Components Analysis (PCA) as covariates. Individual occurrences were used to extract values from the 19 bioclimatic layers. To minimize the redundancy variable, Pearson’s correlation was used with a threshold of 0.8 using the R package NTBOX. Once the uncorrelated variables were obtained and to have a better balance between the number of occurrences and the number of bioclimatic variables, the niche model was built with the 0.7 threshold (Dormann et al. 2012), under the maximum entropy algorithm in MAXENT v3.4.1 (Phillips et al. 2017), using in the KUENM v.1.1.1 R package (Cobos et al. 2019). Some levels of the model were evaluated by varying regularization multiplier (RM) from 0.5 to 10 every 0.5, and feature classes linear (L), quadratic (Q), hinge (H), product (P), and threshold (T) in five fixed combinations: L, LQ, LQH, LQHP, and LQHPT, which resulted in 85 candidate niche models. The evaluation was made using 10,000 random background points, five percent of training data omission rate, 20 % for bootstrapping to calculate the partial Receiver Operating Characteristic (pROC) with 10,000 iterations. Also, it was considered for the model evaluation, “evaluate” in R, using 20% of the points that were left out during model training. In addition to this, the dismo and Biodiversity R packages were used to evaluate the Ecoclimatic Index (EI), which represents the climatic suitability of the species under the regional climate (Kriticos et al. 2015; Yoon and Lee 2023). With the same package, the average value of True Skill Statistic (TSS) was calculated, which is the metric used to evaluate the efficiency of machine learning in the species distribution model (Allouche et al. 2006). The best model was selected from the lowest values of the corrected Akaike information criterion (AICc) and the omission rate. Landscape genetic analysis To quantify landscape-genetic relationships in B. cuneata , we implemented a spatial gravity model using the GeNetIt R package (v.0.1-6; Evans and Murphy 2022). This graph-theoretic framework represents populations as nodes, with edges (straight lines) connecting nodes as proxies of gene flow. Edges were weighted by geographic distance and landscape elements derived from the topographic variables, vegetation type covers, and the habitat suitability model. One of the key advantages of gravity models is their ability to incorporate both at-site and between-site landscape characteristics, allowing a simultaneous evaluation of the local and broad-landscape effects on functional connectivity. To test landscape effects on B. cuneata gene flow, we developed several hypotheses where tropical dry forest (TDF), temperate forest (TF), agriculture (Agri), urban areas (Urb), terrain roughness (Rough), and suitable habitat areas (Niche) may facilitate or restrict gene flow at the landscape scale, while slope steepness (Slope) and east aspect (Aspect) may influence local establishment. As a null model, we used Euclidean distance (Geo), in which no landscape effects are predicted. Genetic distances between populations were quantified using the Nei estimator (Nei 1978) using adegenet R package and with 1-Nei as a measure of gene flow in the gravity models. All landscape variables were standardized to 30 m resolution. To assess how functional connectivity changes at different scales and capture greater environmental variation (Murphy et al. 2010), we created five buffers of 1,000 m to 5,000 m for each landscape variable based on the movement range of B. cuneata dispersers (Cultid-Medina and Rico 2020). Using GeNetIt for continuous variables, we calculated each buffer's mean, maximum, and minimum, while for categorical variables, the relative percentage of each landscape cover was estimated. To ensure independence at the nodes when estimating the gravity models, the variables were transformed to natural logarithms to linearize the equation and implement mixed effects models. We tested the correlation between variables ( r ≥ 0.7) to eliminate correlated variables for multivariate gravity models. In addition to univariate models, we built three combined models, which include variable combinations predicted to significantly influence functional connectivity: Model comb1) Geo, TDF, and Niche (functional connectivity depends on environmentally suitable populations and the proportion of TDF between populations); Comb2) Geo, TDF, and TF (functional connectivity depends on the proportion of TDF and TF between populations); and comb3) Geo, TDF, TF, Niche, and Rough (functional connectivity depends on environmentally suitable populations, proportion of TDF and TF forests, and the roughness of the terrain). We tested univariate between-site and at-site models, and combined models using maximum-likelihood (ML), including the null model (Geo), and for each of the five buffer scales. Models with a P value > 0.05, ∆AIC < 4 as the cut-off (Akaike 1973; Romero-Báez et al. 2024), and the lowest AIC were selected. Moreover, we calculated the percentage of explained variance (PVE) using the function fit.gnet in GeNetIt package to compare between models. For the final model, the restricted maximum likelihood (REML) statistics were used, and confidence intervals measured by Cohen’s D were estimated to evaluate each variable's relative importance. These also give a positive or negative value for the size effect, indicating the directionality of the model components (Grizzard and Shaw 2017). Results Of the 350 individuals sequenced using the OmeSeq-qRRS method, only 227 passed various quality filtering thresholds. Following quality filtering, the data set included 11,731 SNPs with < 30 % of total missing SNPs and 0.05) while 3,110 loci were suggested by pcadapt (MAF > 0.05). A total of 1,232 loci were shared between the two methods, which were then removed, resulting in a neutral data set of 10,499 neutral biallelic loci used in subsequent analyses. Genetic diversity, structure, and migration rates The genetic diversity values obtained were similar among basins, but CTZ basin has the highest value (π = 0.3117), followed by BAL (π = 0.1964), and PTZ (π = 0.1919). In contrast, CHA (π = 0.1787), and MEX (π = 0.1580) showed the lowest values of diversity (Table 1). CHA and BAL basins showed the highest F IS values; BAL showed the highest number of private alleles. The CHA and BAL basins exhibited the highest F IS values, with BAL displaying the highest number of private alleles(Table 1). The AMOVA among populations showed no significant genetic variance explained (1.1 %, F ST = 0.09, P > 0.05). When analyzing the five basins, we observed a significant genetic variance explained among basins of 8.5% ( F CT = 0.0095, P < 0.01), although the greatest genetic variation was found within populations (52%, F ST = 0.00086, P < 0.001) (Table 2). The F ST values obtained per basin showed that MEX basin has high F ST values and was the most differentiated basin (Appendix S1). The LEA analysis identified the optimal number of genetic clusters as K = 5 (Appendix S2). The PTZ basin exhibited high admixture with neighboring basins, such as CHA and CTZ, while lower admixture with MEX and BAL. In contrast, BAL and MEX showed higher genetic differentiation from the rest (Fig. 2A). The DAPC showed that BAL was the most separated from the rest, followed by MEX along DA axis 1, while CTZ was separated along the DA axis 2 (Fig. 2B), and the DAPC also showed that CHA was located on DA axis 3 (Fig. 2C). Moreover, CTZ, PTZ, and CHA, showed higher genetic similarity, which is consistent with the results revealed by LEA . The Moran´s Eigenvector Maps (MEM) analysis revealed two significant and positive MEM vectors. The MEM1 ( r = 0.0686, P = 0.039) represents the first eigenvector in this analysis, capturing the broadest spatial pattern. In our case, MEM1 showed a west-east separation, into two main clusters. The first group included CHA, PTZ, and most of CTZ basins, while the second group included BAL and MEX, and the most eastern populations of CTZ (Fig. 3A). The MEM2 ( r = 0.0451, P = 0.017) represents the second eigenvector, capturing a fine-scale spatial structure. Our results, MEM2 identified the most geographically distant basins as the most differentiated (Fig. 3B). Contemporary migration rate analysis showed that the CHA contributed most strongly to gene flow to adjacent basins. CHA provides 15% of migrants to CTZ, and 11% of migrants to PTZ. The MEX basin is the second largest contributor to the CTZ, with 14% of migrants, and to PTZ with 9% of migrants, while other basins had lower migration rates (Fig. 4; Appendix S3). The Mantel test was not significant ( r = 0.07; P > 0.05 ), indicating no relationship between genetic and geographic distances. Habitat suitability model The ENM result showed that the final uncorrelated variables were minimum temperature of the coldest month (BIO6: 41.9%), precipitation seasonality (BIO15:19.2%), temperature annual range (BIO7: 12.5%), precipitation of the wettest quarter (BIO16: 11.7%), precipitation of the warmest quarter (BIO18: 8.4%), mean temperature of the driest quarter (BIO9: 4.2%), and precipitation of the coldest quarter (BIO19: 6.3%). The species distribution model identified the center-south region of the country (including eastern Michoacán, Mexico City, and northern Morelos) as having the most extensive suitable habitat, covering an area of 79,103 km² (Appendix S4). The selected TPR+TRN model ( P = 0.144751), which included product and threshold features, highlighting interactions among climatic variables and suggested abrupt ecological limits in the species’ distribution. The model showed excellent predictive performance (AUC = 0.9465111). The metric used to evaluate the efficiency of machine learning in the species distribution model was TSS = 0.6119, indicating that the model is good for predicting presences and absences. The obtained value of EI = 36 indicated optimal habitat suitability for B. cuneata (Coetzee et al. 2009). Landscape genetics Gravity model analyses across spatial scales (1,000-5,000 m) revealed scale-dependent predictors of functional connectivity for the between-site factors, while the at-site factors (slope steepness and east aspect) had no effect on any spatial scale. The best model selected for 1,000 and 2,000 m was Rough, which showed the lowest AICs and highest PVE values (46.7% and 45.1%, respectively) (Table 3). For the 3,000 and 4,000 m scales, the Comb3 model (TDF, TF, Niche, and Rough) was selected as the best-performing model with the highest PVE values (52.4% and 49.5 %, respectively) and the lowest AICs. Particularly, at 3000 m, Rough had the highest significant and positive relative importance (Cohen´s D = 0.44), followed by TDF (Cohen´s D = 0.38). In contrast, at 4,000m, TDF showed greater relative importance (Cohen´s D = 0.40) than Rough (Cohen´s D = 0.37). Lastly, at 5,000 m, Comb2 (TDF and TF) was selected as the best-performing model with the lowest AIC, although it did not show the highest PVE (12.4%). TDF showed positive and greater relative importance (Cohen´s D = 0.54) than TF (Cohen´s D = - 0.33), which had a negative effect (Table 3, Appendix S5). Discussion Our landscape genetics study of B. cuneata across central Mexico showed weak to moderate spatial patterns of genetic structure among the five hydrological basins, and with asymmetrical contemporary migration rates. Our gravity model analysis of functional connectivity identified terrain roughness and tropical dry forest (TDF) cover as scale-dependent predictors of gene flow in B. cuneata, with largest importance of TDF at a regional scale. The following sections provide a detailed discussion of the main findings. Genetic diversity and patterns of spatial genetic structure Our results revealed high genetic diversity across all five basins, with nucleotide diversity (π) values exceeding those reported for other tree species, such as Pinus pinaster , P. radiata (π = 0.00186), P. taeda (π = 0.00853), Pseudotsuga menziesii (π = 0.00655), and Eucalyptus pellita ) (π = 0.00066, Wang et al ., 2023). Among the basins, CTZ exhibited the highest genetic diversity, whereas MEX showed the lowest. This pattern agrees with field observations: in Michoacán, B. cuneata populations, particularly in the CTZ basin, maintain high densities, and the Bursera genus is common in remnant tropical dry forest fragments (Rzedowski and Medina-Lemos, 2023). In contrast, abundance has declined markedly in MEX because of rapid urbanization pressures (Carreón and Soler, 2007). Notably, we found substantially higher inbreeding coefficients ( F IS ) in B. cuneata compared to relatives such as B. simaruba ( F IS = 0.024; Dunphy and Hamrick, 2007). The highest values occurred in peripheral basins, particularly BAL ( F IS = 0.44) and CHA ( F IS = 0.45). Specifically, populations of the CHA basin, such as La Alberca Protected Natural Area, persist within intensified agricultural landscapes with extensive livestock grazing (Ramírez-Ramos, 2023), which exacerbates genetic isolation. Genetic structure analyses, including Bayesian and Multivariate clustering approaches along with Moran's Eigenvector Maps, revealed two hierarchical patterns of differentiation: (1) five genetic clusters with higher differentiation of BAL, and (2) a broader west-east genetic differentiation across the species' range. Geographically proximate basins (CHA, PTZ, and CTZ) showed higher admixture, while BAL and MEX showed higher genetic differentiation. BAL is a recognized center of endemism for the Bursera genus (Espinosa et al., 2006), and its high differentiation may be attributed to its large size, high topographic and environmental complexity, and steep climatic gradients ranging from warm, dry conditions in eastern Michoacán to cooler, stable environments in Morelos (Toledo-Manzur, 1984; Espinosa et al., 2006; Gámez et al., 2014; Steinmann, 2021). Contemporary migration rates support this pattern, and although asymmetrical gene flow occurs among distant basins (e.g., MEX, CHA, and PTZ), connectivity with BAL is reduced. At a regional scale, Moran's Eigenvector Maps highlighted significant west-east genetic differentiation. Although the species is spatially aggregated within TDF fragments, where gene flow is more likely among nearby populations than distant ones, we did not detect isolation by distance (IBD). This suggests that geographic distance alone does not explain patterns of genetic structure. Instead, the west-east divergence may reflect a shared biogeographic history. A large portion of the distribution of B. cuneata is found within the Trans-Mexican Volcanic Belt (TVB), a transition zone between the Neotropical and Nearctic regions (Gámez et al., 2012). Similar genetic patterns in TVB taxa have been reported in Asteraceae (e.g., Acourtia lepidopoda , Stevia hintonii , and Microspermum flaccidum ; Villaseñor et al. 2021), oaks ( Quercus deserticola , Rodríguez-Gómez et al. 2018), and rodents ( Peromyscus maniculatus , Léon-Tapia et al. 2021), which are likely the result of the TVB's gradual geological development, where volcanic activity began in the west during the Miocene, while eastern regions emerged later in the Pliocene-Pleistocene (Mastretta-Yanes et al. 2015). Together, these results reflect that the B. cuneata spatial genetic patterns reflect both contemporary and historical processes. Future studies should include phylogeographical approaches to evaluate the role of biogeographic barriers, particularly TVB’s Miocene-Pliocene fragmentation, and genotype-environment associations (GEA) of candidate selective loci underlying local adaptation to unique microsite conditions across the species distribution. Drivers of functional connectivity Gravity model results demonstrate that B. cuneata gene flow responds differentially to landscape features across spatial scales. At smaller scales, 1,000 to 3,000 m, terrain roughness emerged as the most significant factor driving functional connectivity, while at broader scales (4,000 to 5,000 m), thepresence of the native habitat (TDF) became more important. The positive association with rugged topography aligns with B. cuneata preference for topographically complex volcanic landscapes (locally called "malpaíses") (Rzedowski and Guevara-Féfer 1992). These habitats harbor specialized flora with traits adapted to rocky, nutrient-poor soils and tolerance to drought (Trejo and Dirzo, 2002; Méndez-Toribio et al. 2017). Notably, the proportion of TDF cover at larger scales (> 4,000 m) was the main driver of functional connectivity for B. cuneata , consistent with the strong ecological specialization of Bursera on this ecosystem (Rzedowski and Guevara-Féfer 1992; De Nova et al., 2011). This was further supported by the ecological niche model (ENM), which showed strong spatial congruence between areas of high habitat suitability for B. cuneata and the distribution of TDF in the region. The ENM result underscores TDF’s role as a critical factor for maintaining functional connectivity at regional scales, where terrain roughness declines in importance beyond 3,000 m, likely reflecting species-specific dispersal dynamics. Although seed dispersal and pollination studies in B. cuneata are lacking, studies for other Bursera species suggest that birds are the primary seed vector, and with secondary dispersal by small reptiles and mammals (Almazán-Núñez et al. 2021), while pollination is carried out by flies, bees, and beetles (Rivas-Arancibia et al. 2015). These dispersal mechanisms may explain why terrain roughness does not restrict the movement of gene flow by vectors, as they can navigate rugged topography, highlighting the availability of TDF habitat at larger scales as the key factor enabling gene flow across fragmented landscapes. At broader scales (>4,000 m), the temperate forest acted as a dispersal barrier for B. cuneata , even though the species occasionally occurs in transitional zones between temperate forest and tropical dry forest. This barrier effect is likely due to the cooler, humid, and frost-prone conditions in temperate forests, which are unsuitable for the establishment of Bursera species (Alfaro Reyna et al., 2019). These climatic constraints reinforce TDF as the primary habitat of B. cuneata . In contrast, other landscape factors, such as agricultural and urban land covers, had no significant effects on functional connectivity, despite their increasing presence in the region. This absence of signal may reflect time-lagged genetic responses to recent anthropogenic changes, as long-lived plant species often exhibit delayed genetic effects following habitat fragmentation (Aguilar et al., 2008; Aavik et al ., 2019). Conservation implications Land-use changes are the main threats to the persistence of TDF in Mexico (Koleff et al. 2009), risking long-term genetic erosion through reduced gene flow, increased inbreeding, and genetic drift of TDF forest tree species. Our results suggest that conserving TDF remnant forests is critical for maintaining functional connectivity in Bursera cuneata , particularly among the CHA-PTZ-CTZ basins in Michoacán, which are important genetic reservoirs. Even in urbanized regions like Mexico City, remnant trees in protected natural parks (La Reserva del Pedregal de San Ángel) may function as stepping-stones for pollinator and seed vector movements, providing key connectivity between urban and rural populations. Other important areas to focus on are the habitat connection between MEX and BAL basins through the Chichinautzin Biological Corridor, which is an important zone harboring biodiversity (Flores-Estrella et al. 2007), including endemic species like Bursera cuneata . We propose protecting and prioritizing crucial TDF remnants in Michoacan and the Chichinautzin Corridor to maintain large-scale gene flow by integrating urban natural parks as important links to prevent genetic isolation between urban and rural B. cuneata populations. Conclusions This study provided a comprehensive landscape genomic assessment of B. cuneata , which combined extensive population sampling across the species' range, using SNP markers. Our main findings showed high levels of genetic diversity with patterns of genetic structure among basins and a historical west-east genetic divergence. Our analysis underscores that functional connectivity is a scale-dependent process. Specifically, terrain roughness was the primary factor of connectivity at finer scales (1,000–3,000 m), while tropical dry forest cover became the main driver at broader scales (>4,000 m). These findings highlight the need for targeted conservation measures that preserve functional connectivity for B. cuneata by promoting TDF connectivity across regional scales, especially in landscapes undergoing rapid fragmentation. Declarations Funding This study was funded by the Secretaria de Ciencias, Humanidades, Tecnología e Innovación (SECIHTI) (CB2016-283237). Acknowledgements We thank for their assistance in collecting leaf material to Benjamín Castillo Ponce, Eduardo Quintero Melecio, Bruno A. Gutiérrez Becerril, Victor Reyes Pino, Stephanie Aguilera López, Tania Andrade Ortiz, Sergio Nicasio Arzeta, Sergio Zamudio, and several field assistants from local communities. Also, thanks to Mayra Castro Morales for her assistance in DNA extractions, and Ingrid Lara and Antonio González Rodríguez for the DNA quantification using Qubit. Contributions YR and MSZ conceptualized the study. 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DOI: 10.1016/S0006-3207(99)00188-3 Trejo I, Dirzo R (2002) Floristic Diversity of Mexican seasonally dry tropical forest. Biodiv Conserv 11:2063-2084. DOI: 10.1023/A:1020876316013 Villaseñor JL, Ortíz E, Juárez (2021) Transition zones and biogeographic characterization of endemism in three biogeographic provinces of Central Mexico. Bot Sci 99(4): 938-954. DOI: 10.17129/botsci.2768 Wang C, Lan J, Wang J, He W, Lu W, Lin Y et al. (2023) Population structure and genetic diversity in Eucalyptus pellita based on SNP markers. Front Plant Sci 14:1278427. DOI: 10.3389/fpls.2023.1278427 Weeks A, Simpson BB (2007) Molecular phylogenetic analysis of Commiphora (Burseraceae) yields insight on the evolution and historical biogeography of an "impossible" genus. Mol Phylogenet Evol 42(1):62-79. DOI: 10.1016/j.ympev.2006.06.015 Wilson GA, Rannala B (2003) Bayesian Inference of recent migration rates using multilocus geno-types. Genetics 163(3):1177-1191. Yencho GC, Olukolu BA (2022) Compositions and Methods Related to Quantitative Reduced Representation Sequencing. Available online: https://patents.google.com/patent/US20220243267A1/ visit on 20 May 2025. Yoon S, Lee WH (2023) Application of true skill statistics as a practical method for quantitatively assessing CLIMEX performance. Ecol Indic 146:109830. DOI: 10.1016/j.ecolind.2022.109830 Tables Table 1 . Genetic diversity estimates for the five hydrological basins: Lerma-Chapala basin (CHA), Patzcuaro Lake (PTZ), Cuitzeo Lake (CTZ), Balsas River basin (BAL) and Mexico´s basin (MEX). We calculated genetic diversity using the 10,499 SNPs. The total number of individuals ( N ), we calculated the private alleles per basin ( Ap ), the number total alleles ( A ), the nucleotide diversity ( π ). We calculated the observed heterozygosity (Ho), unbiased expected heterozygosity ( u He ), and the inbreeding coefficient for each basin ( F IS ). Locality ID N Ap A π Ho uHe F IS Lerma-Chapala CHA 14 6 16230 0.1787 0.0988 0.1682 0.4487 Patzcuaro PTZ 87 314 20404 0.1919 0.1267 0.1906 0.3396 Cuitzeo CTZ 88 555 20650 0.3117 0.1337 0.1932 0.3863 Balsas River BAL 28 597 18620 0.1964 0.1133 0.1916 0.4436 Mexico MEX 10 2 14876 0.158 0.0894 0.1441 0.4232 Table 2. AMOVA results in the 33 populations of B. cuneata and between five hydrological basins: Lerma-Chapala basin (CHA), Patzcuaro Lake (PTZ), Cuitzeo Lake (CTZ), Balsas River basin (BAL) and Mexico´s basin (MEX). Analyzed groups: 1) No predefined groups. 2) populations grouped in the five basins. * P < 0.01 ** P < 0.001 df Estimated variance % Statistics Among populations 1 0.11 1.1 F ST = 0.09 Within populations 225 0.15 98.9 F ST = 0.01 Total 226 0.26 100 Between basins 4 1.51 8.51 F CT = 0.009* Between localities within basins 32 2.85 39.46 F ST = 0.008* Within populations 190 2.99 52.03 F ST = 0.0008** Total 226 7.35 100 Table 3. Gravity models based on landscape variables that limit or promote the gene flow in Bursera cuneata, considering between-site and at-site factors at 1,000 to 5,000 m scales. We calculated AIC, ∆AIC values and the percentage of variation explained (PVE). Abbreviations: geographic distance (Geo), tropical dry forest (TDF), temperate forest (TF), agriculture (Agri), ecological niche model (Niche), urban areas (Urban), terrain roughness (Rough), East Aspect (Aspect), and slope (Slope). Combined models: comb1) Geo + TDF + Niche; comb2) Geo + TDF + TF; and comb3) Geo + TDF + TF + Niche + Rough. K number of model parameters. Bold letters denote the best-performing model for each spatial scale. Scale 1000 2000 3000 4000 5000 Models AIC ∆AIC PVE AIC ∆AIC PVE AIC ∆AIC PVE AIC ∆AIC PVE AIC ∆AIC PVE K Between-site: Geo -3446.08 60.164 26.92 -3446.08 59.503 28.64 -3446.08 79.058 26.94 -3446.08 72.118 25.69 -3446.08 88.308 21.12 2 TDF -3457.57 48.668 26.36 -3457.15 48.434 26.74 -3482.82 42.319 39.86 -3487.04 31.15 40.75 -3505.82 28.567 49.11 3 TF -3460.34 45.905 15.57 -3462.23 43.349 16.05 -3465.92 59.217 14.62 -3465.16 53.032 20.7 -3462.72 71.664 23.43 3 Agri -3435.05 71.192 1.76 -3436.26 69.322 1.8 -3435.57 89.562 1.91 -3435.51 82.689 5.7 -3435.87 98.51 5.97 3 Niche -3437.43 68.815 9.13 -3440.18 65.4 16.64 -3442.65 82.487 10.61 -3437.58 80.61 2.9 -3438.57 95.815 4.28 3 Urban -3438.65 67.589 3.35 -3437.87 67.707 3.73 -3436.21 88.927 3.84 -3436.37 81.823 3.1 -3436.7 97.685 3.21 3 Rough -3506.24 0 46.72 -3505.58 0 45.08 -3505.72 19.411 43.64 -3499.67 18.521 36.99 -3488.32 46.062 26.92 3 At-site: Aspect -3436.89 69.346 0.43 -3436.89 68.685 1.7 -3436.89 88.24 0.9 -3436.89 81.3 2.9 -3436.89 97.49 0.53 3 Slope -3437.02 69.224 0.68 -3437.02 68.563 1.9 -3437.02 88.118 0.19 -3437.02 81.178 0.59 -3437.02 97.368 1.81 3 Combined Models: Comb1 -3446.83 59.408 24.28 -3448.4 57.184 18.01 -3475.91 49.222 19.31 -3475.66 42.53 11.98 -3495.18 39.206 14.71 4 Comb2 -3464.88 41.36 35.1 -3467.68 37.901 24.76 -3494.57 30.566 18.21 -3502.28 15.918 11.59 -3534.38 0 12.46 4 Comb3 -3501.28 4.957 55.54 -3500.83 4.754 37.38 -3525.13 0 52.44 -3518.19 0 49.54 -3524.75 9.635 57.93 6 Additional Declarations There is no duality of interest Supplementary Files SupplementalinformationLandscapegenetics.docx Suplemental material Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: revise 22 Jan, 2026 Review # 2 received at journal 19 Dec, 2025 Review # 3 received at journal 08 Dec, 2025 Review # 1 received at journal 19 Nov, 2025 Reviewer # 4 agreed at journal 14 Nov, 2025 Reviewer # 3 agreed at journal 12 Nov, 2025 Reviewer # 2 agreed at journal 08 Nov, 2025 Reviewer # 1 agreed at journal 06 Nov, 2025 Reviewers invited by journal 27 Oct, 2025 Editor assigned by journal 26 Sep, 2025 First submitted to journal 26 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7724981","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":535790478,"identity":"bbea3f67-0387-43df-ab53-3714f63acca9","order_by":0,"name":"Yessica Rico","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYLCCBBDBDMQfDMCsBuK1MM4Aa2EkQgsMMPMwEKHF4Hj74w8PauwYzNuZHz62Kbgjx9/e2MB0sw2PljNnzCQSjiUzyBxmMzbOMXhmLHHmYANzzhk8Wm7ksDEkNjAzSDDzsEnnGBxO3CAB5OZU4NFy//njD4kN9RAtFgaH6yFaDPDZwmAAVHMYooXB4HCCASFbJM/kgPxynEeCmc3YsMfgsOEMoF8O4/ML3/Hjjz/+qKmWk+A//PDBjz+H5fnbmw8+zsUTYgoHIDQPiugB3BoYGOQb8MmOglEwCkbBKAABAGZ5TK98z7cYAAAAAElFTkSuQmCC","orcid":"","institution":"Instituto de Ecologia","correspondingAuthor":true,"prefix":"","firstName":"Yessica","middleName":"","lastName":"Rico","suffix":""},{"id":535790479,"identity":"4c07d332-7b60-48c4-b7c3-4aa7e7ce15dc","order_by":1,"name":"Marisol Zurita-Solis","email":"","orcid":"https://orcid.org/0000-0002-9026-6552","institution":"Instituto de Ecologia","correspondingAuthor":false,"prefix":"","firstName":"Marisol","middleName":"","lastName":"Zurita-Solis","suffix":""},{"id":535790480,"identity":"1013835b-bda0-452c-af6b-9ec4c8d1da61","order_by":2,"name":"Bode Olukolu","email":"","orcid":"https://orcid.org/0000-0003-4143-8909","institution":"University of Tennessee","correspondingAuthor":false,"prefix":"","firstName":"Bode","middleName":"","lastName":"Olukolu","suffix":""}],"badges":[],"createdAt":"2025-09-26 23:43:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7724981/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7724981/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95292835,"identity":"da9f7f43-cd97-4192-ba23-15a136140e37","added_by":"auto","created_at":"2025-11-06 11:25:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":689555,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical location of the 33 populations collected for \u003cem\u003eB. cuneata\u003c/em\u003e covering the states of Michoacán, Mexico City and Morelos. The populations were divided according to the hydrological basins: Lerma-Chapala basin (CHA), Patzcuaro Lake (PTZ), Cuitzeo Lake (CTZ), Balsas River basin (BAL) and Mexico´s basin (MEX).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7724981/v1/7aaea1008c30b75a90df7409.png"},{"id":95314788,"identity":"7b023e72-543b-4b65-94eb-211cff5e48d0","added_by":"auto","created_at":"2025-11-06 15:53:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":479055,"visible":true,"origin":"","legend":"\u003cp\u003eResults from clustering analysis: A. The genetic structure obtained in \u003cem\u003eLEA\u003c/em\u003e with the best K = 5 is shown using a barplot and pie charts on a map for each sampled population in \u0026nbsp;\u003cem\u003eBursera cuneata\u003c/em\u003e, as well as its distribution in the Lerma-Chapala basin (CHA), Patzcuaro Lake (PTZ), Cuitzeo Lake (CTZ), Balsas River basin (BAL) and Mexico´s basin (MEX). B. The DAPC shows differentiation among five hydrological basins (CHA, PTZ, CTZ, BAL, and MEX basins), defined a priori. C. DAPC made from principal components one and three.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7724981/v1/5277db0f9de79185d1af4d4c.png"},{"id":95292838,"identity":"95208144-d59e-47bd-a94f-57953c9dc6d5","added_by":"auto","created_at":"2025-11-06 11:25:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":264791,"visible":true,"origin":"","legend":"\u003cp\u003eResults from Moran’s Eigenvector Maps (MEM). Larger circles represent a greater contribution to the spatial pattern of each eigenvector. A. MEM1 and B. MEM2\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7724981/v1/c1eabf4cf580d9e2336a472c.png"},{"id":95292836,"identity":"d4277111-08ae-43dc-924b-ef78367d4da2","added_by":"auto","created_at":"2025-11-06 11:25:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":399442,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eBayesAss \u003c/em\u003eanalysis results from contemporary migration rates among the Lerma-Chapala basin (CHA), Patzcuaro Lake (PTZ), Cuitzeo Lake (CTZ), Balsas River basin (BAL) and Mexico´s basin (MEX), denoting the main asymmetrical gene flow rates among basins in percentage of migrants.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7724981/v1/3c7d35b660ab795dc9dde556.png"},{"id":95316167,"identity":"fd1175de-6845-4ff2-a6cb-2d5346466f43","added_by":"auto","created_at":"2025-11-06 15:57:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2588005,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7724981/v1/f72f3e24-d639-4dcb-b227-940fe516fbbf.pdf"},{"id":95292839,"identity":"8e277420-f89f-4ccd-95cd-906c3624fc66","added_by":"auto","created_at":"2025-11-06 11:25:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":711141,"visible":true,"origin":"","legend":"Suplemental material","description":"","filename":"SupplementalinformationLandscapegenetics.docx","url":"https://assets-eu.researchsquare.com/files/rs-7724981/v1/cdc3b8af297951c2e25853b4.docx"}],"financialInterests":"There is no duality of interest","formattedTitle":"Landscape genetics of the copal tree, Bursera cuneata (Burseraceae): The key role of the Tropical Dry Forest in shaping connectivity at the regional scale","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Tropical Dry Forest (TDF) is a critically important ecosystem, representing 42% of the world’s tropical forests, hosting a diverse group of endemic plants and animals (Murphy and Lugo 1986; Rzedowski 1991; Blackie et al. 2014; Mesa-Sierra et al. 2022b). Its ecological significance is driven by its pronounced seasonal variability, diverse topography, and soil properties (Powers et al. 2018; Méndez-Toribio et al., 2017; Quisehuatl-Medina et al., 2023). These factors not only sustain high species richness but also play a vital role in global carbon sequestration (Mesa-Sierra et al. 2022a). In Mexico, despite being one of the most extensive ecosystems, it is highly threatened, having lost approximately 75% of its original area (Trejo and Dirzo 2000; Rzedowski 2006). The remaining TDFs are mostly transformed into secondary forests and scattered within anthropized landscapes of agriculture, cattle pastures, and urban developments (Bezaury-Creel 2010). Habitat loss and increasing isolation threaten plant communities by affecting essential ecological processes, such as limiting propagule dispersal and gene flow among populations (Auffret \u003cem\u003eet al\u003c/em\u003e., 2017). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong the flora of the Mexican TDF are trees of the genus \u003cem\u003eBursera\u003c/em\u003e (Rzedowski and Kruse 1979; Rzedowski and Gevara-Féfer 1992; Espinosa et al. 2006; Steinmann 2021), whose global center of species diversity and endemicity is the western Balsas Basin (Becerra 2005; Becerra and Venable 2008). The \u003cem\u003eBursera\u0026nbsp;\u003c/em\u003egenus consists of aromatic trees and shrub species (~ 100) with resinous leaves and trunks (Rzedowski and Medina-Lemos, 2023). \u003cem\u003eBursera\u003c/em\u003e has held cultural importance since pre-Colombian times, providing aromatic resins for religious and medicinal purposes, which continues to date (Montúfar-López, 2016). Habitat loss poses the most critical threat to the survival of \u003cem\u003eBursera\u003c/em\u003e species. Recent assessments reveal that 67% of \u003cem\u003eBursera\u003c/em\u003e taxa are listed as threatened on the IUCN Red List, primarily due to ongoing TDF loss and degradation (Fuentes \u003cem\u003eet al\u003c/em\u003e., 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLandscape genetics, which explicitly integrates spatial variables with population genetic data (Holderegger \u003cem\u003eet al\u003c/em\u003e., 2010; Cruzan and Hendrickson, 2020), has become a key tool for quantifying functional connectivity (i.e, gene flow). This approach directly informs conservation planning in anthropized landscapes (Balzotti \u003cem\u003eet al\u003c/em\u003e., 2020), such as the TDF, where only a small portion is protected under natural parks (Mesa-Sierra \u003cem\u003eet al\u003c/em\u003e., 2022b). For plants, functional connectivity refers to the effective dispersal of seeds and pollen across the landscape matrix mediated by dispersal vectors and local factors influencing establishment (Auffret \u003cem\u003eet al\u003c/em\u003e., 2017). Thus, connectivity is shaped by drivers operating at multiple spatial scales. At a fine scale, topographic features, such as slope aspect and steepness, influence microclimate conditions (e.g., humidity and temperature), which affect propagule establishment (Méndez-Toribio et al., 2014, 2016). These heterogeneous microhabitats found in steep rocky areas can be crucial for specialized species (Eisenlohr et al., 2013).\u0026nbsp;At a broader, regional scale, functional connectivity is driven by land cover composition, remnant forest patches, and transitional habitats that facilitate vector movement (Dileo et al., 2014; Cristóbal-Pérez et al., 2021). Therefore, to effectively evaluate the conservation status of vulnerable tree species in the TDF, it is essential to assess how these multi-scale landscape drivers influence functional connectivity (Cruzan and Hendrickson, 2020; Hoban et al., 2021).\u003c/p\u003e\n\u003cp\u003eLarge areas of the TDF along the Trans-Mexican Volcanic Belt (TMVB), particularly in the states of Michoacán, México State, Guanajuato, Querétaro, and Morelos, have been dramatically lost to agricultural expansion, livestock grazing, and mining (Hernández-Oria, 2007). This region has also undergone intensive urban and industrial development due to commercial growth. Among the affected species is \u003cem\u003eBursera cuneata\u003c/em\u003e, a dominant TDF tree (Cué-Bär \u003cem\u003eet al\u003c/em\u003e., 2006), whose wood is used for handicrafts in Michoacán, and which has experienced significant local population declines (Rzedowski and Guevara-Féfer, 1992; Luft-Dávalos and Álvarez Icaza, 2009). \u003cem\u003eBursera cuneata\u0026nbsp;\u003c/em\u003eshows a patchy distribution across five environmentally diverse hydrological basins within central Mexico's Trans-Mexican Volcanic Belt (Rzedowski and Guevara-Féfer, 1992; Flores-Estrella \u003cem\u003eet al\u003c/em\u003e., 2007; Caballero \u003cem\u003eet al\u003c/em\u003e., 2010). These basins, Lerma-Chapala (CHA; 500- 2,000 m.a.s.l.), Pátzcuaro (PTZ; 2,000-2,050 m. a.s.l.), Cuitzeo (CTZ; 1,800- 3,400 m.a.s.l.), México (MEX; 2,240 m.a.s.l.), and Balsas (BAL; 0- 2,000 m.a.s.l.), display significant environmental and topographic heterogeneity (INEGI, 2021). The species primarily inhabits TDF and transitional zones with Temperate Forest (Israde-Alcántara \u003cem\u003eet al\u003c/em\u003e., 2005), with the most severe TDF degradation occurring in PTZ, CTZ, and MEX basins (Mesa-Sierra \u003cem\u003eet al\u003c/em\u003e., 2022b). In some areas, such as Mexico City, the species occurs in natural areas and parks within the urban metropolis, where it maintains small and isolated remnant populations.\u003c/p\u003e\n\u003cp\u003eDespite the ecological importance of\u0026nbsp;\u003cem\u003eBursera\u003c/em\u003e species, conservation genetic studies remain scarce, and most studies available are on their phylogenetic relationships (e.g. Becerra and Venable, 1999; Becerra, 2003; Weeks and Simpson, 2007; Rosell\u0026nbsp;\u003cem\u003eet al\u003c/em\u003e., 2010), with a single study on gene flow (Dunphy and Hamrick, 2007). Here, using single-nucleotide polymorphisms (SNPs), we investigated spatial patterns of genetic structure in \u003cem\u003eB. cuneata\u003c/em\u003e across five hydrological basins, where we expect to find genetic differentiation due to the marked topographic and environmental heterogeneity among basins. Furthermore, we assessed the relative influence of topographic, environmental, and the proportion of habitat composition on functional connectivity across spatial scales. We expected that, at local scales, east aspect and steepness likely influence \u003cem\u003eBursera\u003c/em\u003e establishment (Méndez-Toribio et al., 2014), while habitat suitability and the proportion of TDFs drive gene flow at broader scales. Conversely, the proportion of urbanization and agricultural areas may restrict pollinator movement and seed dispersal.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eStudy species and sampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBursera cuneata\u003c/em\u003e is a dioecious tree, up to 10 m tall, with gray or gray-reddish non-exfoliating bark. The inflorescences are open panicles, blooming from April to July. The main seed vectors are birds of the genera \u003cem\u003eMelanerpes, Icterus,\u003c/em\u003e and \u003cem\u003eMyiarchus \u003c/em\u003e(Cultid-Medina and Rico 2020). Although coyotes and mice may also disperse the seed. The pollinators in the genus \u003cem\u003eBursera \u003c/em\u003eare insects such as Diptera of the genus \u003cem\u003eStrigoderma\u003c/em\u003e and \u003cem\u003eDiogmites\u003c/em\u003e, Coleoptera of the genus \u003cem\u003eBleparida\u003c/em\u003e and \u003cem\u003eChrysoprasis\u003c/em\u003e, and Hymenoptera of the genus \u003cem\u003eApis\u003c/em\u003e and \u003cem\u003eHypanthidium\u003c/em\u003e, and some lizards \u003cem\u003eMicrolophus\u003c/em\u003e (Rivas-Arancibia et al. 2015; Maya-Elizarrarás et al. 2024). \u003c/p\u003e\n\u003cp\u003eWe randomly collected two to three leaves from juvenile and adult trees along remnant TDF fragments across the five basins between the years 2017 to 2019. We registered the GPS coordinates for each sampled tree. Leaves were preserved in sealable plastic bags with silica gel until their DNA extraction. Our sampling included 33 populations from the five hydrological basins, of which CHA, PTZ, and CTZ were the basins with the highest occurrence of \u003cem\u003eB. cuneata\u003c/em\u003e (Fig. 1). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA extraction and molecular analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproximately 20-30 mg of genomic DNA from each of 350 samples was extracted from dried tissue using the Cetyl Trimethyl Ammonium Bromide (CTAB) protocol with pre-wash steps to eliminate excess polyphenols (Healey et al., 2014). DNA quantification and purity evaluation were obtained using Quant-iT\u003csup\u003eTM\u003c/sup\u003e PicoGreen\u003csup\u003e TM\u003c/sup\u003e dsDNA Assay and NanoDropTM 2000 (Thermo Fisher Scientific), respectively. A total of 25 ng of high molecular weight DNA was sequentially digested with NsiI-HF and NlaIII restriction enzymes. Barcoded adapters were incorporated into genomic fragments following the OmeSeq-qRRS (quantitative reduced representation sequencing) method (Yencho and Olukolu 2022). Following library preparation, the libraries were diluted to 10 nmol/l and sequenced on a single lane of the NovaSeq S4 flow cell system (150-bp paired-end reads). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNP filtering\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe OmeSeq-qRRS raw reads were demultiplexed and quality filtered using the automated pipeline ngsComposer (Kuster et al. 2021; https://github.com/XXXX/). Variant calling and filtering were performed using the GBSapp automated pipeline (https://github.com/XXXX/). Using this pipeline, all individual reads were mapped against the whole-genome assembly of \u003cem\u003eB. cuneata\u003c/em\u003e (Rico et al. 2022). Only reads with at least a mapping quality of 20 and that area was uniquely mapped (i.e. excludes reads derived from paralogous sequences) were used for variant calling. These variants were subjected to additional filtering to retain only biallelic markers using the R package\u003cem\u003e vcfR\u003c/em\u003e (Knaus and Grünwald 2017). To assess the neutral genetic structure and genetic diversity, we used variants with a Phred quality level \u0026gt; 30 (indicating high confidence in variant calling) with the R package \u003cem\u003ehierfstat \u003c/em\u003e(Goudet 2005), and the R package \u003cem\u003eadegenet\u003c/em\u003e (Jombart and Ahmed 2011).\u003c/p\u003e\n\u003cp\u003eLoci potentially under selection (outlier loci) were removed to consider loci only under neutral processes. For this, we considered population structure evaluated with the \u003cem\u003esnmf\u003c/em\u003e function of the R package \u003cem\u003eLEA\u003c/em\u003e (Frichot and François 2015) and controlling the false discovery rates by adjusting the \u003cem\u003eP values\u003c/em\u003e with the genomic inflation value (λ), setting the rates at q = 0.05 with the Benjamini-Hochberg algorithm (François et al. 2016). Moreover, we used the R package \u003cem\u003epcadapt \u003c/em\u003ev.4.3 (Privé et al. 2020) by selecting the first three components to identify outlier loci (MAF \u0026gt; 0.05). The matching outlier loci detected by LEA and pcadapt were removed from the total SNP dataset for subsequent analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic diversity, structure, and migration rates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe calculated genetic diversity parameters per basin, such as the number of private alleles (\u003cem\u003eA\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e), total number of alleles (\u003cem\u003eA\u003c/em\u003e), nucleotide diversity (π), observed (\u003cem\u003eH\u003csub\u003eo\u003c/sub\u003e\u003c/em\u003e) and unbiased expected heterozygosity (\u003cem\u003euH\u003csub\u003ee\u003c/sub\u003e\u003c/em\u003e), and fixation index (\u003cem\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/em\u003e) using the R package \u003cem\u003edartR \u003c/em\u003e(Gruber et al. 2018). We performed an Analysis of Molecular Variance (AMOVA) to evaluate the amount of genetic variance explained among populations, and among the five basins using the R package \u003cem\u003eadegenet\u003c/em\u003e, and \u003cem\u003epoppr \u003c/em\u003e(Kamvar et al. 2014). \u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e pairwisecomparisons among basins were estimated using \u003cem\u003edartR\u003c/em\u003e and \u003cem\u003ehierfstat\u003c/em\u003e. \u003c/p\u003e\n\u003cp\u003eTo assess genetic structure, we used the R package\u003cem\u003e adegenet\u003c/em\u003e to perform a Discriminant Analysis of Principal Components (DAPC), which is a robust genetic clustering method free of Linkage disequilibrium (LD), and Hardy-Weinberg (HW) assumptions (Jombart and Ahmed 2011). We designated the five basins as a priori grouping (CHA, PTZ, CTZ, BAL, and MEX) to visualize genetic and spatial structure. We graphically visualized the first two discriminant functions using scatter plots in R. Also, we implemented a sparse non-negative matrix factorization (\u003cem\u003esnmf\u003c/em\u003e) from \u003cem\u003eLEA\u003c/em\u003e R package, setting \u003cem\u003eK\u003c/em\u003e = 1 to 7 with 100,000 iterations. To choose the optimal\u003cem\u003e K,\u003c/em\u003e the cross-entropy criterion was used. For this, \u003cem\u003eLEA \u003c/em\u003econsiders the ancestry coefficients, based on the number of ancestral populations (\u003cem\u003esnmf\u003c/em\u003e), which are related to the number of principal components of the genomic data, and the latent factor mixed models (\u003cem\u003elfmm\u003c/em\u003e) (Frichot and François 2015). The individual ancestry coefficients were grouped and averaged per population and plotted in the geographic space using pie charts. \u003c/p\u003e\n\u003cp\u003eMoreover, spatial patterns of genetic structure were evaluated by performing Moran´s Eigenvector Maps (MEM) in the R package \u003cem\u003eMEMgene\u003c/em\u003e (Galpern et al. 2014) using individual GPS coordinates and genetic distances. We plotted significant positive or negative Moran Eigenvector maps that contained the largest genetic variation. To evaluate the effects of isolation by distance (IBD) on genetic differentiation, we performed a Mantel test using the population\u003cem\u003e F\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003edistances against Euclidean distances. Significance was estimated by permuting observations 1,000 times using the R library \u003cem\u003evegan v.2.5.7\u003c/em\u003e (Oksanen et al. 2020). Lastly, we estimated the contemporary migration rates of \u003cem\u003eB. cuneata\u003c/em\u003e across the five basins using \u003cem\u003eBayesAss v. 3.0.5.7\u003c/em\u003e. (Wilson and Rannala 2003), with 10,000,000 Markov Chain Monte Carlo (MCMC) iterations and 1,000,000 of burning. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional connectivity hypotheses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe downloaded the 2017 Land Use/Land Cover images from Sentinel at 10 m resolution (ESRI Living Atlas: https://livingatlas.arcgis.com/landcover/) to obtain one raster using QGIS v. 3.32 for the landscape genetic analysis (see below). That raster was reclassified to include the following covers: 1) Tropical Dry Forest (TDF), 2) Temperate Forest (pine-oak; TF), 3) permanent and temporary agricultural areas including bare soil (Agri), 4) urban cities (Urb), 5) main water bodies (Lake Cuitzeo, Lake Pátzcuaro, Yuriria lagoon). Also, we characterized the topography of the study area by calculating the slope, terrain roughness (the variability of the terrain’s surface, indicating the unevenness of the terrain), and east aspect (most collection populations were found on the east-facing hillsides, and eastness was calculated as the sine of the aspect, values ranging from 1 to -1), using a digital elevation model at 10 m resolution from INEGI (https://www.inegi.org.mx/) using the terrain function in the \u003cem\u003eraster \u003c/em\u003ev.3.5 package (Hijmans 2022). \u003c/p\u003e\n\u003cp\u003eTo include the environment in the landscape genetic analysis, we built a habitat suitability model using Ecological Niche Models (ENM) for \u003cem\u003eB. cuneata\u003c/em\u003e. We used 405 points: 227 occurrences of \u003cem\u003eB. cuneata\u003c/em\u003e (samples analyzed in this study, see results), and 117 records downloaded from The Global Biodiversity Information Facility (GBIF; https://www.gbif.org/es/). To clean the data, we used the niche tool box (\u003cem\u003eNTBOX\u003c/em\u003e) R package (Osorio-Olvera et al. 2020), considering that the specimens with insufficient locality information were discarded. All occurrences were thinned at five kilometers due to a higher density of occurrences than the training data sets. As variable predictors, we used the 19 bioclimatic variables at 30 s from WorldClim (https://www.worldclim.org/data/worldclim21.html). The bioclimatic layers were delimited by five terrestrial ecoregions downloaded from World Wildlife (https://www.worldwildlife.org/publications/terrestrial-ecoregions-of-the-world). The ecoregions were: Bajío dry forests, Central Mexican scrub, Balsas dry forests, Sierra Madre del Sur pine-oak forests, and Trans Mexican Volcanic Belt pine-oak forests. \u003c/p\u003e\n\u003cp\u003eThe 19 variables were tested individually and in a multivariate analysis, discarding correlated variables using R package \u003cem\u003eadegenet\u003c/em\u003e (\u003cem\u003er\u003c/em\u003e ≥ 0.8), and using the first two synthetic axes from 250 Principal Components Analysis (PCA) as covariates. Individual occurrences were used to extract values from the 19 bioclimatic layers. To minimize the redundancy variable, Pearson’s correlation was used with a threshold of 0.8 using the R package \u003cem\u003eNTBOX.\u003c/em\u003e Once the uncorrelated variables were obtained and to have a better balance between the number of occurrences and the number of bioclimatic variables, the niche model was built with the 0.7 threshold (Dormann et al. 2012), under the maximum entropy algorithm in \u003cem\u003eMAXENT\u003c/em\u003e v3.4.1 (Phillips et al. 2017), using in the \u003cem\u003eKUENM\u003c/em\u003e v.1.1.1 R package (Cobos et al. 2019). Some levels of the model were evaluated by varying regularization multiplier (RM) from 0.5 to 10 every 0.5, and feature classes linear (L), quadratic (Q), hinge (H), product (P), and threshold (T) in five fixed combinations: L, LQ, LQH, LQHP, and LQHPT, which resulted in 85 candidate niche models. The evaluation was made using 10,000 random background points, five percent of training data omission rate, 20 % for bootstrapping to calculate the partial Receiver Operating Characteristic (pROC) with 10,000 iterations. Also, it was considered for the model evaluation, “evaluate” in R, using 20% of the points that were left out during model training. In addition to this, the \u003cem\u003edismo\u003c/em\u003e and \u003cem\u003eBiodiversity \u003c/em\u003eR packages were used to evaluate the Ecoclimatic Index (EI), which represents the climatic suitability of the species under the regional climate (Kriticos et al. 2015; Yoon and Lee 2023). With the same package, the average value of True Skill Statistic (TSS) was calculated, which is the metric used to evaluate the efficiency of machine learning in the species distribution model (Allouche et al. 2006). The best model was selected from the lowest values of the corrected Akaike information criterion (AICc) and the omission rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLandscape genetic analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo quantify landscape-genetic relationships in \u003cem\u003eB. cuneata\u003c/em\u003e, we implemented a spatial gravity model using the \u003cem\u003eGeNetIt\u003c/em\u003e R package (v.0.1-6; Evans and Murphy 2022). This graph-theoretic framework represents populations as nodes, with edges (straight lines) connecting nodes as proxies of gene flow. Edges were weighted by geographic distance and landscape elements derived from the topographic variables, vegetation type covers, and the habitat suitability model. One of the key advantages of gravity models is their ability to incorporate both at-site and between-site landscape characteristics, allowing a simultaneous evaluation of the local and broad-landscape effects on functional connectivity. To test landscape effects on \u003cem\u003eB. cuneata\u003c/em\u003e gene flow, we developed several hypotheses where tropical dry forest (TDF), temperate forest (TF), agriculture (Agri), urban areas (Urb), terrain roughness (Rough), and suitable habitat areas (Niche) may facilitate or restrict gene flow at the landscape scale, while slope steepness (Slope) and east aspect (Aspect) may influence local establishment. As a null model, we used Euclidean distance (Geo), in which no landscape effects are predicted. Genetic distances between populations were quantified using the Nei estimator (Nei 1978) using \u003cem\u003eadegenet\u003c/em\u003e R package and with 1-Nei as a measure of gene flow in the gravity models. All landscape variables were standardized to 30 m resolution.\u003c/p\u003e\n\u003cp\u003eTo assess how functional connectivity changes at different scales and capture greater environmental variation (Murphy et al. 2010), we created five buffers of 1,000 m to 5,000 m for each landscape variable based on the movement range of \u003cem\u003eB. cuneata\u003c/em\u003e dispersers (Cultid-Medina and Rico 2020). Using \u003cem\u003eGeNetIt\u003c/em\u003e for continuous variables, we calculated each buffer's mean, maximum, and minimum, while for categorical variables, the relative percentage of each landscape cover was estimated. To ensure independence at the nodes when estimating the gravity models, the variables were transformed to natural logarithms to linearize the equation and implement mixed effects models. We tested the correlation between variables (\u003cem\u003er \u003c/em\u003e≥ 0.7) to eliminate correlated variables for multivariate gravity models. In addition to univariate models, we built three combined models, which include variable combinations predicted to significantly influence functional connectivity: Model comb1) Geo, TDF, and Niche (functional connectivity depends on environmentally suitable populations and the proportion of TDF between populations); Comb2) Geo, TDF, and TF (functional connectivity depends on the proportion of TDF and TF between populations); and comb3) Geo, TDF, TF, Niche, and Rough (functional connectivity depends on environmentally suitable populations, proportion of TDF and TF forests, and the roughness of the terrain). We tested univariate between-site and at-site models, and combined models using maximum-likelihood (ML), including the null model (Geo), and for each of the five buffer scales. Models with a \u003cem\u003eP value\u003c/em\u003e \u0026gt; 0.05, ∆AIC \u0026lt; 4 as the cut-off (Akaike 1973; Romero-Báez et al. 2024), and the lowest AIC were selected. Moreover, we calculated the percentage of explained variance (PVE) using the function \u003cem\u003efit.gnet\u003c/em\u003e in \u003cem\u003eGeNetIt\u003c/em\u003e package to compare between models. For the final model, the restricted maximum likelihood (REML) statistics were used, and confidence intervals measured by Cohen’s D were estimated to evaluate each variable's relative importance. These also give a positive or negative value for the size effect, indicating the directionality of the model components (Grizzard and Shaw 2017). \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOf the 350 individuals sequenced using the OmeSeq-qRRS method, only 227 passed various quality filtering thresholds. Following quality filtering, the data set included 11,731 SNPs with \u0026lt; 30 % of total missing SNPs and \u0026lt; 20 % of missing samples. The analysis of outlier loci in \u003cem\u003eLEA\u0026nbsp;\u003c/em\u003esuggested that 4,342 loci were potentially under selection (MAF \u0026gt; 0.05) while 3,110 loci were suggested by \u003cem\u003epcadapt\u003c/em\u003e (MAF \u0026gt; 0.05). A total of 1,232 loci were shared between the two methods, which were then removed, resulting in a neutral data set of 10,499 neutral biallelic loci used in subsequent analyses. \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic diversity, structure, and migration rates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe genetic diversity values obtained were similar among basins, but CTZ basin has the highest value (π = 0.3117), followed by BAL (π\u0026nbsp;= 0.1964), and PTZ (π\u0026nbsp;= 0.1919). In contrast, CHA (π\u0026nbsp;= 0.1787), and MEX (π = 0.1580) showed the lowest values of diversity (Table 1). CHA and BAL basins showed the highest \u003cem\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/em\u003e values; BAL showed the highest number of private alleles. The CHA and BAL basins exhibited the highest \u003cem\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/em\u003e values, with BAL displaying the highest number of private alleles(Table 1). The AMOVA among populations showed no significant genetic variance explained (1.1 %, \u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e = 0.09, \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). When analyzing the five basins, we observed a significant genetic variance explained among basins of 8.5% (\u003cem\u003eF\u003csub\u003eCT\u003c/sub\u003e\u003c/em\u003e = 0.0095, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01), although the greatest genetic variation was found within populations\u0026nbsp;(52%, \u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e = 0.00086, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) (Table 2).\u0026nbsp;The \u003cem\u003eF\u003csub\u003eST\u0026nbsp;\u003c/sub\u003e\u003c/em\u003evalues obtained per basin showed that MEX basin has high \u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003evalues and was the most differentiated basin (Appendix S1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eLEA\u003c/em\u003e analysis identified the optimal number of genetic clusters as \u003cem\u003eK\u003c/em\u003e = 5 (Appendix S2). The PTZ basin exhibited high admixture with neighboring basins, such as CHA and CTZ, while lower admixture with MEX and BAL. In contrast, BAL and MEX showed higher genetic differentiation from the rest (Fig. 2A). The DAPC showed that BAL was the most separated from the rest, followed by MEX along DA axis 1, while CTZ was separated along the DA axis 2 (Fig. 2B), and the DAPC also showed that CHA was located on DA axis 3 (Fig. 2C). Moreover, CTZ, PTZ, and CHA, showed higher genetic similarity, which is consistent with the results revealed by \u003cem\u003eLEA\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Moran´s Eigenvector Maps (MEM) analysis revealed two significant and positive MEM vectors. The MEM1 (\u003cem\u003er\u003c/em\u003e = 0.0686, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.039) represents the first eigenvector in this analysis, capturing the broadest spatial pattern. In our case, MEM1 showed a west-east separation, into two main clusters. The first group included CHA, PTZ, and most of CTZ basins, while the second group included BAL and MEX, and the most eastern populations of CTZ (Fig. 3A). The MEM2 (\u003cem\u003er\u003c/em\u003e = 0.0451, \u003cem\u003eP\u003c/em\u003e = 0.017) represents the second eigenvector, capturing a fine-scale spatial structure. Our results, MEM2 identified the most geographically distant basins as the most differentiated (Fig. 3B). Contemporary migration rate analysis showed that the CHA contributed most strongly to gene flow to adjacent basins. CHA provides 15% of migrants to CTZ, and 11% of migrants to PTZ. The MEX basin is the second largest contributor to the CTZ, with 14% of migrants, and to PTZ with 9% of migrants, while other basins had lower migration rates (Fig. 4; Appendix S3). The Mantel test was not significant (\u003cem\u003er\u003c/em\u003e = 0.07; \u003cem\u003eP \u0026gt; 0.05\u003c/em\u003e), indicating no relationship between genetic and geographic distances.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHabitat suitability model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ENM result showed that the final uncorrelated variables were minimum temperature of the coldest month (BIO6: 41.9%), precipitation seasonality (BIO15:19.2%), temperature annual range (BIO7: 12.5%), precipitation of the wettest quarter (BIO16: 11.7%), precipitation of the warmest quarter (BIO18: 8.4%), mean temperature of the driest quarter (BIO9: 4.2%), and precipitation of the coldest quarter (BIO19: 6.3%). The species distribution model identified the center-south region of the country (including eastern Michoacán, Mexico City, and northern Morelos) as having the most extensive suitable habitat, covering an area of 79,103 km² (Appendix S4). The selected TPR+TRN model (\u003cem\u003eP\u003c/em\u003e = 0.144751), which included product and threshold features, highlighting interactions among climatic variables and suggested abrupt ecological limits in the species’ distribution. The model showed excellent predictive performance (AUC = 0.9465111). The metric used to evaluate the efficiency of machine learning in the species distribution model was TSS = 0.6119, indicating that the model is good for predicting presences and absences. The obtained value of EI = 36 indicated optimal habitat suitability for \u003cem\u003eB. cuneata\u003c/em\u003e (Coetzee et al. 2009).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLandscape genetics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGravity model analyses across spatial scales (1,000-5,000 m) revealed scale-dependent predictors of functional connectivity for the between-site factors, while the at-site factors (slope steepness and east aspect) had no effect on any spatial scale. The best model selected for 1,000 and 2,000 m was Rough, which showed the lowest AICs and highest PVE values (46.7% and 45.1%, respectively) (Table 3). For the 3,000 and 4,000 m scales, the Comb3 model (TDF, TF, Niche, and Rough) was selected as the best-performing model with the highest PVE values (52.4% and 49.5 %, respectively) and the lowest AICs. Particularly, at 3000 m, Rough had the highest significant and positive relative importance (Cohen´s D = 0.44), followed by TDF (Cohen´s D = 0.38). In contrast, at 4,000m, TDF showed greater relative importance (Cohen´s D = 0.40) than Rough (Cohen´s D = 0.37). Lastly, at 5,000 m, Comb2 (TDF and TF) was selected as the best-performing model with the lowest AIC, although it did not show the highest PVE (12.4%). TDF showed positive and greater relative importance (Cohen´s D = 0.54) than TF (Cohen´s D = - 0.33), which had a negative effect (Table 3, Appendix S5).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion ","content":"\u003cp\u003eOur landscape genetics study of \u003cem\u003eB. cuneata\u003c/em\u003e across central Mexico showed weak to moderate spatial patterns of genetic structure among the five hydrological basins, and with asymmetrical contemporary migration rates. Our gravity model analysis of functional connectivity identified terrain roughness and tropical dry forest (TDF) cover as scale-dependent predictors of gene flow in \u003cem\u003eB. cuneata,\u0026nbsp;\u003c/em\u003ewith largest importance of TDF at a regional scale. The following sections provide a detailed discussion of the main findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic diversity and patterns of spatial genetic structure\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur results revealed high genetic diversity across all five basins, with nucleotide diversity (\u0026pi;) values exceeding those reported for other tree species, such as \u003cem\u003ePinus pinaster\u003c/em\u003e, \u003cem\u003eP. radiata\u003c/em\u003e (\u0026pi; = 0.00186), \u003cem\u003eP. taeda\u003c/em\u003e (\u0026pi; = 0.00853), \u003cem\u003ePseudotsuga menziesii\u003c/em\u003e (\u0026pi; = 0.00655), and \u003cem\u003eEucalyptus pellita\u003c/em\u003e) (\u0026pi; = 0.00066, Wang \u003cem\u003eet al\u003c/em\u003e., 2023). Among the basins, CTZ exhibited the highest genetic diversity, whereas MEX showed the lowest. This pattern agrees with field observations: in Michoac\u0026aacute;n, \u003cem\u003eB. cuneata\u003c/em\u003e populations, particularly in the CTZ basin, maintain high densities, and the \u003cem\u003eBursera\u0026nbsp;\u003c/em\u003egenus is common in remnant tropical dry forest fragments (Rzedowski and Medina-Lemos, 2023). In contrast, abundance has declined markedly in MEX because of rapid urbanization pressures (Carre\u0026oacute;n and Soler, 2007). Notably, we found substantially higher inbreeding coefficients (\u003cem\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/em\u003e) in \u003cem\u003eB. cuneata\u003c/em\u003e compared to relatives such as \u003cem\u003eB. simaruba\u003c/em\u003e (\u003cem\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/em\u003e = 0.024; Dunphy and Hamrick, 2007). The highest values occurred in peripheral basins, particularly BAL (\u003cem\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/em\u003e = 0.44) and CHA (\u003cem\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/em\u003e = 0.45). \u0026nbsp;Specifically, populations of the CHA basin, such as La Alberca Protected Natural Area, persist within intensified agricultural landscapes with extensive livestock grazing (Ram\u0026iacute;rez-Ramos, 2023), which exacerbates genetic isolation.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Genetic structure analyses, including Bayesian and Multivariate clustering approaches along with Moran\u0026apos;s Eigenvector Maps, revealed two hierarchical patterns of differentiation: (1) five genetic clusters with higher differentiation of BAL, and (2) a broader west-east genetic differentiation across the species\u0026apos; range. Geographically proximate basins (CHA, PTZ, and CTZ) showed higher admixture, while BAL and MEX showed higher genetic differentiation. BAL is a recognized center of endemism for the \u003cem\u003eBursera\u003c/em\u003e genus (Espinosa et al., 2006), and its high differentiation may be attributed to its large size, high topographic and environmental complexity, and steep climatic gradients ranging from warm, dry conditions in eastern Michoac\u0026aacute;n to cooler, stable environments in Morelos (Toledo-Manzur, 1984; Espinosa et al., 2006; G\u0026aacute;mez et al., 2014; Steinmann, 2021). Contemporary migration rates support this pattern, and although asymmetrical gene flow occurs among distant basins (e.g., MEX, CHA, and PTZ), connectivity with BAL is reduced.\u003c/p\u003e\n\u003cp\u003eAt a regional scale, Moran\u0026apos;s Eigenvector Maps highlighted significant west-east genetic differentiation. Although the species is spatially aggregated within TDF fragments, where gene flow is more likely among nearby populations than distant ones, we did not detect isolation by distance (IBD). This suggests that geographic distance alone does not explain patterns of genetic structure. Instead, the west-east divergence may reflect a shared biogeographic history. A large portion of the distribution of \u003cem\u003eB. cuneata\u003c/em\u003e is found within the Trans-Mexican Volcanic Belt (TVB), a transition zone between the Neotropical and Nearctic regions (G\u0026aacute;mez et al., 2012). Similar genetic patterns in TVB taxa have been reported in Asteraceae (e.g., \u003cem\u003eAcourtia lepidopoda\u003c/em\u003e, \u003cem\u003eStevia hintonii\u003c/em\u003e, and\u003cem\u003e\u0026nbsp;Microspermum flaccidum\u003c/em\u003e; Villase\u0026ntilde;or et al. 2021), oaks (\u003cem\u003eQuercus deserticola\u003c/em\u003e, Rodr\u0026iacute;guez-G\u0026oacute;mez et al. 2018), and rodents (\u003cem\u003ePeromyscus maniculatus\u003c/em\u003e, L\u0026eacute;on-Tapia et al. 2021), which are likely the result of the TVB\u0026apos;s gradual geological development, where volcanic activity began in the west during the Miocene, while eastern regions emerged later in the Pliocene-Pleistocene (Mastretta-Yanes et al. 2015). Together, these results reflect that the \u003cem\u003eB. cuneata\u003c/em\u003e spatial genetic patterns reflect both contemporary and historical processes. Future studies should include phylogeographical approaches to evaluate the role of biogeographic barriers, particularly TVB\u0026rsquo;s Miocene-Pliocene fragmentation, and genotype-environment associations (GEA) of candidate selective loci underlying local adaptation to unique microsite conditions across the species distribution.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrivers of functional connectivity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGravity model results demonstrate that \u003cem\u003eB. cuneata\u003c/em\u003e gene flow responds differentially to landscape features across spatial scales. At smaller scales, 1,000 to 3,000 m, terrain roughness emerged as the most significant factor driving functional connectivity, while at broader scales (4,000 to 5,000 m), thepresence of the native habitat (TDF) became more important. The positive association with rugged topography aligns with \u003cem\u003eB. cuneata\u0026nbsp;\u003c/em\u003epreference for topographically complex volcanic landscapes (locally called \u0026quot;malpa\u0026iacute;ses\u0026quot;) (Rzedowski and Guevara-F\u0026eacute;fer 1992). These habitats harbor specialized flora with traits adapted to rocky, nutrient-poor soils and tolerance to drought (Trejo and Dirzo, 2002; M\u0026eacute;ndez-Toribio et al. 2017). Notably, the proportion of TDF cover at larger scales (\u0026gt; 4,000 m) was the main driver of functional connectivity for \u003cem\u003eB. cuneata\u003c/em\u003e, consistent with the strong ecological specialization of \u003cem\u003eBursera\u003c/em\u003e on this ecosystem (Rzedowski and Guevara-F\u0026eacute;fer 1992; De Nova et al., 2011). This was further supported by the ecological niche model (ENM), which showed strong spatial congruence between areas of high habitat suitability for \u003cem\u003eB. cuneata\u003c/em\u003e and the distribution of TDF in the region. The ENM result underscores TDF\u0026rsquo;s role as a critical factor for maintaining functional connectivity at regional scales, where terrain roughness declines in importance beyond 3,000 m, likely reflecting species-specific dispersal dynamics. Although seed dispersal and pollination studies in \u003cem\u003eB. cuneata\u003c/em\u003e are lacking, studies for other \u003cem\u003eBursera\u003c/em\u003e species suggest that birds are the primary seed vector, and with secondary dispersal by small reptiles and mammals (Almaz\u0026aacute;n-N\u0026uacute;\u0026ntilde;ez et al. 2021), while pollination is carried out by flies, bees, and beetles (Rivas-Arancibia et al. 2015). These dispersal mechanisms may explain why terrain roughness does not restrict the movement of gene flow by vectors, as they can navigate rugged topography, highlighting the availability of TDF habitat at larger scales as the key factor enabling gene flow across fragmented landscapes.\u003c/p\u003e\n\u003cp\u003eAt broader scales (\u0026gt;4,000 m), the temperate forest acted as a dispersal barrier for \u003cem\u003eB. cuneata\u003c/em\u003e, even though the species occasionally occurs in transitional zones between temperate forest and tropical dry forest. This barrier effect is likely due to the cooler, humid, and frost-prone conditions in temperate forests, which are unsuitable for the establishment of \u003cem\u003eBursera\u003c/em\u003e species (Alfaro Reyna et al., 2019). These climatic constraints reinforce TDF as the primary habitat of \u003cem\u003eB. cuneata\u003c/em\u003e. In contrast, other landscape factors, such as agricultural and urban land covers, had no significant effects on functional connectivity, despite their increasing presence in the region. This absence of signal may reflect time-lagged genetic responses to recent anthropogenic changes, as long-lived plant species often exhibit delayed genetic effects following habitat fragmentation (Aguilar et al., 2008; Aavik \u003cem\u003eet al\u003c/em\u003e., 2019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConservation implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLand-use changes are the main threats to the persistence of TDF in Mexico (Koleff et al. 2009), risking long-term genetic erosion through reduced gene flow, increased inbreeding, and genetic drift of TDF forest tree species. Our results suggest that conserving TDF remnant forests is critical for maintaining functional connectivity in \u003cem\u003eBursera cuneata\u003c/em\u003e, particularly among the CHA-PTZ-CTZ basins in Michoac\u0026aacute;n, which are important genetic reservoirs. Even in urbanized regions like Mexico City, remnant trees in protected natural parks (La Reserva del Pedregal de San \u0026Aacute;ngel) may function as stepping-stones for pollinator and seed vector movements, providing key connectivity between urban and rural populations. Other important areas to focus on are the habitat connection between\u0026nbsp;MEX and BAL basins through the Chichinautzin Biological Corridor, which is an important zone harboring biodiversity (Flores-Estrella et al. 2007), including\u0026nbsp;endemic species like \u003cem\u003eBursera cuneata\u003c/em\u003e. We propose protecting and prioritizing crucial TDF remnants in Michoacan and the Chichinautzin Corridor to maintain large-scale gene flow by integrating urban natural parks as important links to prevent genetic isolation between urban and rural \u003cem\u003eB. cuneata\u003c/em\u003e populations.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provided a comprehensive landscape genomic assessment of \u003cem\u003eB. cuneata\u003c/em\u003e, which combined extensive population sampling across the species' range, using SNP markers. Our main findings showed high levels of genetic diversity with patterns of genetic structure among basins and a historical west-east genetic divergence. Our analysis underscores that functional connectivity is a scale-dependent process. Specifically, terrain roughness was the primary factor of connectivity at finer scales (1,000–3,000 m), while tropical dry forest cover became the main driver at broader scales (\u0026gt;4,000 m). These findings highlight the need for targeted conservation measures that preserve functional connectivity for \u003cem\u003eB. cuneata\u003c/em\u003e by promoting TDF connectivity across regional scales, especially in landscapes undergoing rapid fragmentation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Secretaria de Ciencias, Humanidades, Tecnología e Innovación (SECIHTI) (CB2016-283237). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank for their assistance in collecting leaf material to Benjamín Castillo Ponce, Eduardo Quintero Melecio, Bruno A. Gutiérrez Becerril, Victor Reyes Pino, Stephanie Aguilera López, Tania Andrade Ortiz, Sergio Nicasio Arzeta, Sergio Zamudio, and several field assistants from local communities. Also, thanks to Mayra Castro Morales for her assistance in DNA extractions, and Ingrid Lara and Antonio González Rodríguez for the DNA quantification using Qubit.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYR and MSZ conceptualized the study. YR conducted field work and provided funding for the project; MSZ, BO, and YR conducted data analysis; MSZ wrote the manuscript; all authors contributed to editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SNP data for 227 individuals at 10,499 loci will be deposited on Dryad upon acceptance.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAavik T, Thetloff M, Tr\u0026auml;ger S, Hern\u0026aacute;ndez-Agramonte IM, Reinula I, P\u0026auml;rtel M (2019) Delayed and immediate effects of habitat loss of the genetic diversity of the grassland plant \u003cem\u003eTrifolium montanum\u003c/em\u003e. Biodivers Conserv 28(12): 3299-3319. 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Flora del Baj\u0026iacute;o y de regiones adyacentes 3:1-46.\u003c/li\u003e\n \u003cli\u003eRzedowski J. Kruse H (1979) Algunas tendencias evolutivas en \u003cem\u003eBursera\u0026nbsp;\u003c/em\u003e(Burseraceae). Taxon 28(1):103-119.\u003c/li\u003e\n \u003cli\u003eRzedowski J. Medina-Lemos R (2023) \u003cem\u003eBursera\u003c/em\u003e. In: Rzedowski J. Medina-Lemos R, \u003cem\u003eeds\u003c/em\u003e. Las especies de Bursera Jacq. ex L. en el Occidente de M\u0026eacute;xico. Mexico: UNAM IB, pp169.\u003c/li\u003e\n \u003cli\u003eSteinmann VW (2021) Flora and vegetation of the Zicuir\u0026aacute;n-Infiernillo Biosphere Reserve, Michoacan, Mexico. Bot Sci 99(3):661-707. DOI: \u003cu\u003e10.17129/botsci.2706\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eToledo-Manzur CA (1984) Contribuciones a la flora de Guerrero: tres especies nuevas del g\u0026eacute;nero \u003cem\u003eBursera\u003c/em\u003e (Burseraceae). Biotica 9(4): 441-449.\u003c/li\u003e\n \u003cli\u003eTrejo I, Dirzo R (2000) Deforestation of seasonally dry tropical forest: A national and local analysis in Mexico. Biol Conserv 94(2):133-142. DOI: \u003cu\u003e10.1016/S0006-3207(99)00188-3\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eTrejo I, Dirzo R (2002) Floristic Diversity of Mexican seasonally dry tropical forest. Biodiv Conserv 11:2063-2084. DOI: \u003cu\u003e10.1023/A:1020876316013\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eVillase\u0026ntilde;or JL, Ort\u0026iacute;z E, Ju\u0026aacute;rez (2021) Transition zones and biogeographic characterization of endemism in three biogeographic provinces of Central Mexico. Bot Sci\u003cem\u003e\u0026nbsp;\u003c/em\u003e99(4): 938-954. DOI: \u003cu\u003e10.17129/botsci.2768\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eWang C, Lan J, Wang J, He W, Lu W, Lin Y et al. (2023) Population structure and genetic diversity in \u003cem\u003eEucalyptus pellita\u0026nbsp;\u003c/em\u003ebased on SNP markers. Front Plant Sci 14:1278427. DOI: \u003cu\u003e10.3389/fpls.2023.1278427\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eWeeks A, Simpson BB (2007) Molecular phylogenetic analysis of \u003cem\u003eCommiphora\u003c/em\u003e (Burseraceae) yields insight on the evolution and historical biogeography of an \u0026quot;impossible\u0026quot; genus. Mol Phylogenet Evol 42(1):62-79. DOI: \u003cu\u003e10.1016/j.ympev.2006.06.015\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eWilson GA, Rannala B (2003) Bayesian Inference of recent migration rates using multilocus geno-types. Genetics 163(3):1177-1191.\u003c/li\u003e\n \u003cli\u003eYencho GC, Olukolu BA (2022) Compositions and Methods Related to Quantitative Reduced Representation Sequencing. Available online: https://patents.google.com/patent/US20220243267A1/ visit on 20 May 2025.\u003c/li\u003e\n \u003cli\u003eYoon S, Lee WH (2023) Application of true skill statistics as a practical method for quantitatively assessing CLIMEX performance. Ecol Indic 146:109830. DOI: \u003cu\u003e10.1016/j.ecolind.2022.109830\u003c/u\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Genetic diversity estimates for the five hydrological basins: Lerma-Chapala basin (CHA), Patzcuaro Lake (PTZ), Cuitzeo Lake (CTZ), Balsas River basin (BAL) and Mexico\u0026acute;s basin (MEX). We calculated genetic diversity using the 10,499 SNPs. The total number of individuals (\u003cem\u003eN\u003c/em\u003e), we calculated the private alleles per basin (\u003cem\u003eAp\u003c/em\u003e), the number total alleles (\u003cem\u003eA\u003c/em\u003e), the nucleotide diversity\u0026nbsp;(\u003cem\u003e\u0026pi;\u003c/em\u003e). We calculated the observed heterozygosity\u0026nbsp;(Ho), unbiased expected heterozygosity\u0026nbsp;(\u003cem\u003eu\u003c/em\u003e\u003cem\u003eHe\u003c/em\u003e), and the inbreeding coefficient for each basin (\u003cem\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/em\u003e).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"761\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003eLocality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003eAp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026pi;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003eHo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003euHe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"31\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eLerma-Chapala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eCHA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e16230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.1787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.4487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"38\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003ePatzcuaro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003ePTZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e20404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.1919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.1267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.3396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"38\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eCuitzeo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eCTZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e20650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.3117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.1337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.3863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"38\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eBalsas River\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eBAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e18620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.1964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.1133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.4436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"38\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMexico\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp; MEX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e14876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.4232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"39\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e AMOVA results in the 33 populations of \u003cem\u003eB. cuneata\u003c/em\u003e and between five hydrological basins: Lerma-Chapala basin (CHA), Patzcuaro Lake (PTZ), Cuitzeo Lake (CTZ), Balsas River basin (BAL) and Mexico\u0026acute;s basin (MEX). Analyzed groups: 1) No predefined groups. 2) populations grouped in the five basins. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01 ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"634\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003eEstimated variance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003eStatistics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eAmong populations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e = 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eWithin populations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e98.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e = 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eBetween basins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e8.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003csub\u003eCT\u003c/sub\u003e\u003c/em\u003e = 0.009*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eBetween localities within basins\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e39.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e= 0.008*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eWithin populations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e52.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e= 0.0008**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e7.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Gravity models based on landscape variables that limit or promote the gene flow in \u003cem\u003eBursera cuneata,\u0026nbsp;\u003c/em\u003econsidering between-site and at-site factors at 1,000 to 5,000 m scales. We calculated AIC,\u0026nbsp;∆AIC values and the percentage of variation explained (PVE).\u0026nbsp;Abbreviations: geographic distance (Geo), tropical dry forest (TDF), temperate forest (TF), agriculture (Agri), ecological niche model (Niche), urban areas (Urban), terrain roughness (Rough), East Aspect (Aspect), and slope (Slope). Combined models: comb1) Geo + TDF + Niche; comb2) Geo + TDF + TF; and comb3) Geo + TDF + TF + Niche + Rough. K number of model parameters. Bold letters denote the best-performing model for each spatial scale.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"984\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003eScale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 177px;\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 177px;\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 177px;\"\u003e\n \u003cp\u003e3000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 177px;\"\u003e\n \u003cp\u003e4000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 125px;\"\u003e\n \u003cp\u003e5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003eModels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eAIC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e∆AIC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003ePVE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eAIC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e∆AIC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003ePVE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eAIC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e∆AIC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003ePVE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eAIC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e∆AIC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003ePVE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eAIC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e∆AIC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003ePVE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003eK\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\" style=\"width: 984px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBetween-site:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003eGeo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3446.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e60.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e26.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3446.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e59.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e28.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3446.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e79.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e26.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3446.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e72.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e25.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-3446.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e88.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e21.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003eTDF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3457.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e48.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e26.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3457.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e48.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e26.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3482.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e42.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e39.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3487.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e31.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e40.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-3505.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e28.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e49.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003eTF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3460.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e45.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e15.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3462.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e43.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e16.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3465.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e59.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e14.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3465.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e53.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-3462.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e71.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e23.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003eAgri\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3435.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e71.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3436.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e69.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3435.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e89.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3435.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e82.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-3435.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e98.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e5.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003eNiche\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3437.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e68.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e9.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3440.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e65.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e16.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3442.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e82.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e10.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3437.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e80.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-3438.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e95.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3438.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e67.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e3.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3437.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e67.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3436.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e88.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3436.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e81.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-3436.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e97.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003eRough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-3506.24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e46.72\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-3505.58\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e45.08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3505.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e19.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e43.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3499.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e18.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e36.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-3488.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e46.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e26.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\" style=\"width: 984px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAt-site:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003eAspect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3436.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e69.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3436.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e68.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3436.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e88.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3436.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e81.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-3436.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e97.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003eSlope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3437.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e69.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3437.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e68.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3437.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e88.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3437.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e81.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-3437.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e97.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\" style=\"width: 984px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCombined Models:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003eComb1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3446.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e59.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e24.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3448.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e57.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e18.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3475.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e49.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e19.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-3475.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e42.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e11.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-3495.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e39.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n 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\u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e9.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e57.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"heredity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"hdy","sideBox":"Learn more about [Heredity](http://www.nature.com/hdy/)","snPcode":"41437","submissionUrl":"https://mts-hdy.nature.com/cgi-bin/main.plex","title":"Heredity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Burseraceae, functional connectivity, gene flow, SNPs, tropical dry forest, Mexico","lastPublishedDoi":"10.21203/rs.3.rs-7724981/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7724981/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Land-use changes in tropical dry forests (TDF) have rapidly reduced native vegetation, disrupting gene flow dynamics of tree species. Bursera cuneata is a co-dominant TDF tree in central Mexico, is threatened by habitat loss and overexploitation. We investigated landscape drivers of functional connectivity of B. cuneata across scales to inform species conservation efforts. We genotyped 227 B. cuneata individuals from 33 populations across five hydrological basins at 10,499 single-nucleotide polymorphism (SNP) loci. We examined spatial patterns of genetic structure among hydrological basins and the landscape drivers of gene flow. We applied gravity models that incorporated at-site (slope and east aspect) and between-site (terrain roughness, habitat suitability, and habitat cover) factors influencing B. cuneata gene flow. Clustering analyses showed genetic structure among basins, with the highest differentiation for Balsas (BAL) and Mexico (MEX). Gravity models revealed that functional connectivity is a scale-dependent process. Specifically, terrain roughness was the primary factor of connectivity at finer scales (1,000–3,000 m), while the TDF became the main driver at regional scales (\u003e4,000 m). We recommend protecting and prioritizing crucial TDF remnants to maintain large-scale gene flow by integrating urban natural parks as important links to prevent genetic isolation between urban and rural populations.","manuscriptTitle":"Landscape genetics of the copal tree, Bursera cuneata (Burseraceae): The key role of the Tropical Dry Forest in shaping connectivity at the regional scale","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 11:25:47","doi":"10.21203/rs.3.rs-7724981/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2026-01-22T12:31:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-12-19T08:21:12+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-12-08T10:35:13+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-11-19T18:08:03+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-11-14T10:21:46+00:00","index":4,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-11-12T07:47:54+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-11-08T08:50:37+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-11-06T18:04:48+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-10-27T15:21:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-26T22:32:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Heredity","date":"2025-09-26T22:32:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"heredity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"hdy","sideBox":"Learn more about [Heredity](http://www.nature.com/hdy/)","snPcode":"41437","submissionUrl":"https://mts-hdy.nature.com/cgi-bin/main.plex","title":"Heredity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"eaba8acd-3ab9-4fe3-93a9-ef40d10fe0a3","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":56966654,"name":"Biological sciences/Genetics/Population genetics"},{"id":56966655,"name":"Biological sciences/Ecology/Molecular ecology"}],"tags":[],"updatedAt":"2026-05-07T08:30:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-06 11:25:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7724981","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7724981","identity":"rs-7724981","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}