Pollinator foraging tactics have divergent consequences for the mating system of a tropical plant.

Resolving the consequences of pollinator foraging behaviour for plant mating systems is a fundamental challenge in evolutionary ecology. Pollinators may adopt particular foraging tactics: complete trapline foraging (repeated movements along a fixed route), sample-and-shift trapline foraging (a variable route that incorporates information from previous experiences) and territorial foraging (stochastic movements within a restricted area). Studies that integrate these pollinator foraging tactics with plant mating systems are generally lacking. We investigate the consequences of particular pollinator foraging tactics for Heliconia tortuosa. We combine parentage and sibship inference analysis with simulation modelling to: estimate mating system parameters; infer the foraging tactic adopted by the pollinators; and quantify the impact of pollinator foraging tactics on mating system parameters. We found high outcrossing rates, ubiquitous multiple paternity and a pronounced departure from near-neighbour mating. We also found that plants repeatedly receive pollen from a series of particular donors. We infer that the pollinators primarily adopt complete trapline foraging and occasionally engage in sample-and-shift trapline foraging. This enhances multiple paternity without a substantial increase in near-neighbour mating. The particular pollinator foraging tactics have divergent consequences for multiple paternity and near-neighbour mating. Thus, pollinator foraging behaviour is an important driver of the ecology and evolution of plant mating systems.

Comparing methods for detecting multilocus adaptation with multivariate genotype-environment associations.

Identifying adaptive loci can provide insight into the mechanisms underlying local adaptation. Genotype-environment association (GEA) methods, which identify these loci based on correlations between genetic and environmental data, are particularly promising. Univariate methods have dominated GEA, despite the high dimensional nature of genotype and environment. Multivariate methods, which analyse many loci simultaneously, may be better suited to these data as they consider how sets of markers covary in response to environment. These methods may also be more effective at detecting adaptive processes that result in weak, multilocus signatures. Here, we evaluate four multivariate methods and five univariate and differentiation-based approaches, using published simulations of multilocus selection. We found that Random Forest performed poorly for GEA. Univariate GEAs performed better, but had low detection rates for loci under weak selection. Constrained ordinations, particularly redundancy analysis (RDA), showed a superior combination of low false-positive and high true-positive rates across all levels of selection. These results were robust across the demographic histories, sampling designs, sample sizes and weak population structure tested here. The value of combining detections from different methods was variable and depended on the study goals and knowledge of the drivers of selection. Re-analysis of genomic data from grey wolves highlighted the unique, covarying sets of adaptive loci that could be identified using RDA. Although additional testing is needed, this study indicates that RDA is an effective means of detecting adaptation, including signatures of weak, multilocus selection, providing a powerful tool for investigating the genetic basis of local adaptation.

Directed dispersal by rotational shepherding supports landscape genetic connectivity in a calcareous grassland plant.

Directed dispersal by animal vectors has been found to have large effects on the structure and dynamics of plant populations adapted to frugivory. Yet, empirical data are lacking on the potential of directed dispersal by rotational grazing of domestic animals to mediate gene flow across the landscape. Here, we investigated the potential effect of large-flock shepherding on landscape-scale genetic structure in the calcareous grassland plant Dianthus carthusianorum, whose seeds lack morphological adaptations to dispersal to animals or wind. We found a significant pattern of genetic structure differentiating population within grazed patches of three nonoverlapping shepherding systems and populations of ungrazed patches. Among ungrazed patches, we found a strong and significant effect of isolation by distance (r = 0.56). In contrast, genetic distance between grazed patches within the same herding system was unrelated to geographical distance but significantly related to distance along shepherding routes (r = 0.44). This latter effect of connectivity along shepherding routes suggests that gene flow is spatially restricted occurring mostly between adjacent populations. While this study used nuclear markers that integrate gene flow by pollen and seed, the significant difference in the genetic structure between ungrazed patches and patches connected by large-flock shepherding indicates the potential of directed seed dispersal by sheep across the landscape.

Effect of rotational shepherding on demographic and genetic connectivity of calcareous grassland plants.

Response to habitat fragmentation may not be generalized among species, in particular for plant communities with a variety of dispersal traits. Calcareous grasslands are one of the most species-rich habitats in Central Europe, but abandonment of traditional management has caused a dramatic decline of calcareous grassland species. In the Southern Franconian Alb in Germany, reintroduction of rotational shepherding in previously abandoned grasslands has restored species diversity, and it has been suggested that sheep support seed dispersal among grasslands. We tested the effect of rotational shepherding on demographic and genetic connectivity of calcareous grassland specialist plants and whether the response of plant populations to shepherding was limited to species dispersed by animals (zoochory). Specifically, we tested competing dispersal models and source and focal patch properties to explain landscape connectivity with patch-occupancy data of 31 species. We fitted the same connectivity models to patch occupancy and nuclear microsatellite data for the herb Dianthus carthusianorum (Carthusian pink). For 27 species, patch connectivity was explained by dispersal by rotational shepherding regardless of adaptations to zoochory, whereas population size (16% species) and patch area (0% species) of source patches were not important predictors of patch occupancy in most species. [Correction made after online publication, February 25, 2014: Population size and patch area percentages were mistakenly inverted, and have now been fixed.] Microsite diversity of focal patches significantly increased the model variance explained by patch occupancy in 90% of the species. For D. carthusianorum, patch connectivity through rotational shepherding explained both patch occupancy and population genetic diversity. Our results suggest shepherding provides dispersal for multiple plant species regardless of their dispersal adaptations and thus offers a useful approach to restore plant diversity in fragmented calcareous grasslands.

Rethinking the linear regression model for spatial ecological data.

The linear regression model, with its numerous extensions including multivariate ordination, is fundamental to quantitative research in many disciplines. However, spatial or temporal structure in the data may invalidate the regression assumption of independent residuals. Spatial structure at any spatial scale can be modeled flexibly based on a set of uncorrelated component patterns (e.g., Moran's eigenvector maps, MEM) that is derived from the spatial relationships between sampling locations as defined in a spatial weight matrix. Spatial filtering thus addresses spatial autocorrelation in the residuals by adding such component patterns (spatial eigenvectors) as predictors to the regression model. However, space is not an ecologically meaningful predictor, and commonly used tests for selecting significant component patterns do not take into account the specific nature of these variables. This paper proposes "spatial component regression" (SCR) as a new way of integrating the linear regression model with Moran's eigenvector maps. In its unconditioned form, SCR decomposes the relationship between response and predictors by component patterns, whereas conditioned SCR provides an alternative method of spatial filtering, taking into account the statistical properties of component patterns in the design of statistical hypothesis tests. Application to the well-known multivariate mite data set illustrates how SCR may be used to condition for significant residual spatial structure and to identify additional predictors associated with residual spatial structure. Finally, I argue that all variance is spatially structured, hence spatial independence is best characterized by a lack of excess variance at any spatial scale, i.e., spatial white noise.

Urbanization and a green corridor do not impact genetic divergence in common milkweed (Asclepias syriaca L.).

Urbanization is altering landscapes globally at an unprecedented rate. While ecological differences between urban and rural environments often promote phenotypic divergence among populations, it is unclear to what degree these trait differences arise from genetic divergence as opposed to phenotypic plasticity. Furthermore, little is known about how specific landscape elements, such as green corridors, impact genetic divergence in urban environments. We tested the hypotheses that: (1) urbanization, and (2) proximity to an urban green corridor influence genetic divergence in common milkweed (Asclepias syriaca) populations for phenotypic traits. Using seeds from 52 populations along three urban-to-rural subtransects in the Greater Toronto Area, Canada, one of which followed a green corridor, we grew ~ 1000 plants in a common garden setup and measured > 20 ecologically-important traits associated with plant defense/damage, reproduction, and growth over four years. We found significant heritable variation for nine traits within common milkweed populations and weak phenotypic divergence among populations. However, neither urbanization nor an urban green corridor influenced genetic divergence in individual traits or multivariate phenotype. These findings contrast with the expanding literature demonstrating that urbanization promotes rapid evolutionary change and offer preliminary insights into the eco-evolutionary role of green corridors in urban environments.

Conceptual framework and uncertainty analysis for large-scale, species-agnostic modelling of landscape connectivity across Alberta, Canada.

Sustainable land-use planning should consider large-scale landscape connectivity. Commonly-used species-specific connectivity models are difficult to generalize for a wide range of taxa. In the context of multi-functional land-use planning, there is growing interest in species-agnostic approaches, modelling connectivity as a function of human landscape modification. We propose a conceptual framework, apply it to model connectivity as current density across Alberta, Canada, and assess map sensitivity to modelling decisions. We directly compared the uncertainty related to (1) the definition of the degree of human modification, (2) the decision whether water bodies are considered barriers to movement, and (3) the scaling function used to translate degree of human modification into resistance values. Connectivity maps were most sensitive to the consideration of water as barrier to movement, followed by the choice of scaling function, whereas maps were more robust to different conceptualizations of the degree of human modification. We observed higher concordance among cells with high (standardized) current density values than among cells with low values, which supports the identification of cells contributing to larger-scale connectivity based on a cut-off value. We conclude that every parameter in species-agnostic connectivity modelling requires attention, not only the definition of often-criticized expert-based degrees of human modification.

Alternative Quantifications of Landscape Complementation to Model Gene Flow in Banded Longhorn Beetles [Typocerus v. velutinus (Olivier)].

Rapid progression of human socio-economic activities has altered the structure and function of natural landscapes. Species that rely on multiple, complementary habitat types (i.e., landscape complementation) to complete their life cycle may be especially at risk. However, such landscape complementation has received little attention in the context of landscape connectivity modeling. A previous study on flower longhorn beetles (Cerambycidae: Lepturinae) integrated landscape complementation into a continuous habitat suitability 'surface', which was then used to quantify landscape connectivity between pairs of sampling sites using gradient-surface metrics. This connectivity model was validated with molecular genetic data collected for the banded longhorn beetle (Typocerus v. velutinus) in Indiana, United States. However, this approach has not been compared to alternative models in a landscape genetics context. Here, we used a discrete land use/land cover map to calculate landscape metrics related to landscape complementation based on a patch mosaic model (PMM) as an alternative to the previously published, continuous habitat suitability model (HSM). We evaluated the HSM surface with gradient surface metrics (GSM) and with two resistance-based models (RBM) based on least cost path (LCP) and commute distance (CD), in addition to an isolation-by-distance (IBD) model based on Euclidean distance. We compared the ability of these competing models of connectivity to explain pairwise genetic distances (R ST) previously calculated from ten microsatellite genotypes of 454 beetles collected from 17 sites across Indiana, United States. Model selection with maximum likelihood population effects (MLPE) models found that GSM were most effective at explaining pairwise genetic distances as a proxy for gene flow across the landscape, followed by the landscape metrics calculated from the PMM, whereas the LCP model performed worse than both the CD and the isolation by distance model. We argue that the analysis of a continuous HSM with GSM might perform better because of their combined ability to effectively represent and quantify the continuous degree of landscape complementation (i.e., availability of complementary habitats in vicinity) found at and in-between sites, on which these beetles depend. Our findings may inform future studies that seek to model habitat connectivity in complex heterogeneous landscapes as natural habitats continue to become more fragmented in the Anthropocene.

The Landscape Genetic Signature of Pollination by Trapliners: Evidence From the Tropical Herb, Heliconia tortuosa.

Animal-mediated pollination is essential for the maintenance of plant reproduction, especially in tropical ecosystems, where pollination networks have been thought to have highly generalized structures. However, accumulating evidence suggests that not all floral visitors provide equally effective pollination services, potentially reducing the number of realized pollinators and increasing the cryptic specialization of pollination networks. Thus, there is a need to understand how different functional groups of pollinators influence pollination success. Here, we examined whether patterns of contemporary pollen-mediated gene flow in Heliconia tortuosa are consistent with the foraging strategy of its territorial or traplining hummingbird pollinators. Territorial hummingbirds defend clumps of flowers and are expected to transfer pollen locally. In contrast, traplining hummingbirds forage across longer distances, thereby increasing pollen flow among forest fragments, and are thought to repeatedly visit particular plants. If trapliners indeed visit the same plants repeatedly along their regular routes, this could lead to a situation where neighboring plants sample genetically distinct pollen pools. To test this hypothesis, we genotyped 720 seeds and 71 mother plants from 18 forest fragments at 11 microsatellite loci. We performed TwoGener analysis to test pollen pool differentiation within sites (among neighboring plants within the same forest fragment: Φ SC ) and between sites (among forest fragments: Φ CT ). We found strong, statistically significant pollen pool differentiation among neighboring mother plants (Φ SC = 0.0506), and weaker, statistically significant differentiation among sites (Φ CT = 0.0285). We interpret this pattern of hierarchical pollen pool differentiation as the landscape genetic signature of the foraging strategy of traplining hummingbirds, where repeatable, long-distance, and high-fidelity routes transfer pollen among particular plants. Although H. tortuosa is also visited by territorial hummingbirds, our results suggest that these pollinators do not contribute substantially to successful pollination, highlighting differences in realized pollination efficiency. This cryptic reduction in the number of realized pollinators potentially increases the vulnerability of pollination success to the decline of populations of traplining hummingbirds, which have been shown to be sensitive to forest fragmentation. We conclude that maintaining habitat connectivity to sustain the foraging routes of trapliners may be essential for the maintenance of pollen-mediated gene flow in human-modified landscapes.

Spatial detection of outlier loci with Moran eigenvector maps.

The spatial signature of microevolutionary processes structuring genetic variation may play an important role in the detection of loci under selection. However, the spatial location of samples has not yet been used to quantify this. Here, we present a new two-step method of spatial outlier detection at the individual and deme levels using the power spectrum of Moran eigenvector maps (MEM). The MEM power spectrum quantifies how the variation in a variable, such as the frequency of an allele at a SNP locus, is distributed across a range of spatial scales defined by MEM spatial eigenvectors. The first step (Moran spectral outlier detection: MSOD) uses genetic and spatial information to identify outlier loci by their unusual power spectrum. The second step uses Moran spectral randomization (MSR) to test the association between outlier loci and environmental predictors, accounting for spatial autocorrelation. Using simulated data from two published papers, we tested this two-step method in different scenarios of landscape configuration, selection strength, dispersal capacity and sampling design. Under scenarios that included spatial structure, MSOD alone was sufficient to detect outlier loci at the individual and deme levels without the need for incorporating environmental predictors. Follow-up with MSR generally reduced (already low) false-positive rates, though in some cases led to a reduction in power. The results were surprisingly robust to differences in sample size and sampling design. Our method represents a new tool for detecting potential loci under selection with individual-based and population-based sampling by leveraging spatial information that has hitherto been neglected.

Model selection with multiple regression on distance matrices leads to incorrect inferences.

In landscape genetics, model selection procedures based on Information Theoretic and Bayesian principles have been used with multiple regression on distance matrices (MRM) to test the relationship between multiple vectors of pairwise genetic, geographic, and environmental distance. Using Monte Carlo simulations, we examined the ability of model selection criteria based on Akaike's information criterion (AIC), its small-sample correction (AICc), and the Bayesian information criterion (BIC) to reliably rank candidate models when applied with MRM while varying the sample size. The results showed a serious problem: all three criteria exhibit a systematic bias toward selecting unnecessarily complex models containing spurious random variables and erroneously suggest a high level of support for the incorrectly ranked best model. These problems effectively increased with increasing sample size. The failure of AIC, AICc, and BIC was likely driven by the inflated sample size and different sum-of-squares partitioned by MRM, and the resulting effect on delta values. Based on these findings, we strongly discourage the continued application of AIC, AICc, and BIC for model selection with MRM.

Variogram analysis of the spatial genetic structure of continuous populations using multilocus microsatellite data.

A geostatistical perspective on spatial genetic structure may explain methodological issues of quantifying spatial genetic structure and suggest new approaches to addressing them. We use a variogram approach to (i) derive a spatial partitioning of molecular variance, gene diversity, and genotypic diversity for microsatellite data under the infinite allele model (IAM) and the stepwise mutation model (SMM), (ii) develop a weighting of sampling units to reflect ploidy levels or multiple sampling of genets, and (iii) show how variograms summarize the spatial genetic structure within a population under isolation-by-distance. The methods are illustrated with data from a population of the epiphytic lichen Lobaria pulmonaria, using six microsatellite markers. Variogram-based analysis not only avoids bias due to the underestimation of population variance in the presence of spatial autocorrelation, but also provides estimates of population genetic diversity and the degree and extent of spatial genetic structure accounting for autocorrelation.

Quantifying dispersal and establishment limitation in a population of an epiphytic lichen.

Dispersal is a process critical for the dynamics and persistence of metapopulations, but it is difficult to quantify. It has been suggested that the old-forest lichen Lobaria pulmonaria is limited by insufficient dispersal ability. We analyzed 240 DNA extracts derived from snow samples by a L. pulmonaria-specific real-time PCR (polymerase chain reaction) assay of the ITS (internal transcribed spacer) region allowing for the discrimination among propagules originating from a single, isolated source tree or propagules originating from other locations. Samples that were detected as positives by real-time PCR were additionally genotyped for five L. pulmonaria microsatellite loci. Both molecular approaches demonstrated substantial dispersal from other than local sources. In a landscape approach, we additionally analyzed 240 snow samples with real-time PCR of ITS and detected propagules not only in forests where L. pulmonaria was present, but also in large unforested pasture areas and in forest patches where L. pulmonaria was not found. Monitoring of soredia of L. pulmonaria transplanted to maple bark after two vegetation periods showed high variance in growth among forest stands, but no significant differences among different transplantation treatments. Hence, it is probably not dispersal limitation that hinders colonization in the old-forest lichen L. pulmonaria, but ecological constraints at the stand level that can result in establishment limitation. Our study exemplifies that care has to be taken to adequately separate the effects of dispersal limitation from a limitation of establishment.

Using simulations to evaluate Mantel-based methods for assessing landscape resistance to gene flow.

Mantel-based tests have been the primary analytical methods for understanding how landscape features influence observed spatial genetic structure. Simulation studies examining Mantel-based approaches have highlighted major challenges associated with the use of such tests and fueled debate on when the Mantel test is appropriate for landscape genetics studies. We aim to provide some clarity in this debate using spatially explicit, individual-based, genetic simulations to examine the effects of the following on the performance of Mantel-based methods: (1) landscape configuration, (2) spatial genetic nonequilibrium, (3) nonlinear relationships between genetic and cost distances, and (4) correlation among cost distances derived from competing resistance models. Under most conditions, Mantel-based methods performed poorly. Causal modeling identified the true model only 22% of the time. Using relative support and simple Mantel r values boosted performance to approximately 50%. Across all methods, performance increased when landscapes were more fragmented, spatial genetic equilibrium was reached, and the relationship between cost distance and genetic distance was linearized. Performance depended on cost distance correlations among resistance models rather than cell-wise resistance correlations. Given these results, we suggest that the use of Mantel tests with linearized relationships is appropriate for discriminating among resistance models that have cost distance correlations <0.85 with each other for causal modeling, or <0.95 for relative support or simple Mantel r. Because most alternative parameterizations of resistance for the same landscape variable will result in highly correlated cost distances, the use of Mantel test-based methods to fine-tune resistance values will often not be effective.

Highly polymorphic microsatellite markers in Pulsatilla vulgaris (Ranunculaceae) using next-generation sequencing.

PREMISE OF THE STUDY: We developed novel microsatellite markers for the perennial plant Pulsatilla vulgaris (Ranunculaceae) to investigate the effects of fragmentation on gene flow in this imperiled species. METHODS AND RESULTS: We identified microsatellites and developed primers based on 454 shotgun sequences. We identified 14 markers that were polymorphic and produced clean bands. Of these, eight could be analyzed as diploids. Genotyping of 97 individuals across two populations revealed these markers to be highly polymorphic with seven to 17 alleles per locus and observed heterozygosity from 0.41 to 0.83. CONCLUSIONS: The markers are highly informative and will be used to test if the reintroduction of shepherding in southern Germany improves genetic connectivity among fragmented populations of P. vulgaris. The combination of diploid and tetraploid markers presented here will be useful in resolving the polyploidization history of this and related species.

Landscape-level gene flow in Lobaria pulmonaria, an epiphytic lichen.

Epiphytes are strongly affected by the population dynamics of their host trees. Owing to the spatio-temporal dynamics of host tree populations, substantial dispersal rates--corresponding to high levels of gene flow--are needed for populations to persist in a landscape. However, several epiphytic lichens have been suggested to be dispersal-limited, which leads to the expectation of low gene flow at the landscape scale. Here, we study landscape-level genetic structure and gene flow of a putatively dispersal-limited epiphytic lichen, Lobaria pulmonaria. The genetic structure of L. pulmonaria was quantified at three hierarchical levels, based on 923 thalli collected from 41 plots situated within a pasture-woodland landscape and genotyped at six fungal microsatellite loci. We found significant isolation by distance, and significant genetic differentiation both among sampling plots and among trees. Landscape configuration, i.e. the effect of a large open area separating two forested regions, did not leave a traceable pattern in genetic structure, as assessed with partial Mantel tests and analysis of molecular variance. Gene pools were spatially intermingled in the pasture-woodland landscape, as determined by Bayesian analysis of population structure. Evidence for local gene flow was found in a disturbed area that was mainly colonized from nearby sources. Our analyses indicated high rates of gene flow of L. pulmonaria among forest patches, which may reflect the historical connectedness of the landscape through gene movement. These results support the conclusion that dispersal in L. pulmonaria is rather effective, but not spatially unrestricted.

Effect of disturbances on the genetic diversity of an old-forest associated lichen.

Lichens associated with old forest are commonly assumed to be negatively affected by tree logging or natural disturbances. However, in this study performed in a spruce-dominated sylvopastoral landscape in the Swiss Jura Mountains, we found that genetic diversity of the epiphytic old-forest lichen Lobaria pulmonaria depends on the type of disturbance. We collected 923 thalli from 41 sampling plots of 1 ha corresponding to the categories stand-replacing disturbance (burnt), intensive logging (logged) and uneven-aged forestry (uneven-aged), and analysed the thalli at six mycobiont-specific microsatellite loci. We found evidence for multiple independent immigrations into demes located in burnt and logged areas. Using spatial autocorrelation methods, the spatial scale of the genetic structure caused by the clonal and recombinant component of genetic variation was determined. Spatial autocorrelation of genotype diversity was strong at short distances up to 50 m in logged demes, up to 100 m in uneven-aged demes, with the strongest autocorrelation up to 150 m for burnt demes. The spatial autocorrelation was predominantly attributed to clonal dispersal of vegetative propagules. After accounting for the clonal component, we did not find significant spatial autocorrelation in gene diversity. This pattern may indicate low dispersal ranges of clonal propagules, but random dispersal of sexual ascospores. Genetic diversity was highest in logged demes, and lowest in burnt demes. Our results suggest that genetic diversity of epiphytic lichen demes may not necessarily be impacted by stand-level disturbances for extended time periods.