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Data from: The landscape genetic signature of pollination by trapliners: evidence from the tropical herb, Heliconia tortuosa


Torres-Vanegas, Felipe et al. (2021), Data from: The landscape genetic signature of pollination by trapliners: evidence from the tropical herb, Heliconia tortuosa , Dryad, Dataset,


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: ΦSC) and between sites (among forest fragments: ΦCT). We found strong, statistically significant pollen pool differentiation among neighboring mother plants (ΦSC = 0.0506), and weaker, statistically significant differentiation among sites (ΦCT = 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.


The study was conducted in the human-modified landscape (~ 31,000 ha) surrounding the Organization for Tropical Studies Las Cruces Biological Station in southern Costa Rica (8º47N, 82º57W). We used a stratified-random sampling design to select 20 focal forest fragments that represented a gradient of forest amount within a 1-km radius and a gradient of fragment size. Within each focal fragment, we selected H. tortuosa plants at a random location (‘site’) sampled within 500 m of an access point. Starting from this sampling location, we marked the first five plants with inflorescences (‘mothers’) and sampled leaf tissue. We randomly selected two bracts on a single inflorescence of each mother and collected all fruits. Samples were amplified at 11 microsatellite loci in three multiplex reactions.

We tested for significant pollen pool differentiation among sites and among mothers nested within sites by performing a hierarchical analysis of molecular variance (AMOVA) based on pollen haplotypes (TwoGener). We fitted several hierarchical AMOVA models where we tested for significant pollen pool differentiation among sites and among mothers nested within sites (i.e., pairwise genetic distance ~ site / mother). Statistics of genetic differentiation (Φ) were estimated for each level by partitioning the total observed genetic variation in allele frequencies into within- and among-level variance components. These Φ-statistics are analogous to F-statistics and provide estimates of the genetic differentiation of pollen pools sampled among sites (ΦCT) and among individual mothers within sites (ΦSC).

To evaluate limitations associated with our sampling design, we simulated pollen pool differentiation among sites and among mothers within sites to assess the type I error rates and the statistical power of the hierarchical AMOVA models we fitted. Simulations were completed with different levels of genetic diversity and pollen pool differentiation.

Usage Notes


All files necessary to complete the TwoGener analysis include the TwoGener_ prefix. Files include:

1. TwoGener_README.txt.

2. TwoGener_Raw_Data.csv

3. TwoGener_AMOVA.rmd


All files for the paternity analysis implemented in COLONY include the Paternity_ prefix. Files include:

1. Paternity_README.txt

2. Paternity_OffspringGenotypes.csv

3. Paternity_MothersGenotypes.csv

4. Paternity_MaternalSibs.csv

5. Paternity_MarkerErroRate.csv

6. Paternity_ExclusionMaternity.csv

7. Paternity_ExclusionMaternalSibs.csv

8. Paternity_Selfed_Seeds.csv

9. Paternity_Project_Information.txt


The R code for the simulations of pollen pool differentiation is provided.

1. Simulations.rmd

Mating System

All files for the estimation of outcrossing rates in MLTR include the MatingSystem_ prefix. Files include:

1. MatingSystem_README.txt

2. MatingSystem_Seeds.txt

3. MatingSystem_Mothers_Seeds.txt

Loci Information

Basic information for the 11 microsatellite loci were estimated in CERVUS and are given in:

1. Loci_Information.txt

2. Loci_Information_Genotypes.txt


Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)

Natural Sciences and Engineering Research Council of Canada

National Science Foundation, Award: NSF-DEB-1050594; NSF-DEB-1457837