Dataset DOI: 10.5061/dryad.tht76hfb5

Description of the data and file structure

This data was published in:

Tremlett, C.J., Chapman, M., Maher, K.H., Keller, A., Blüthgen, N., Peh, K.S.H. and Zamora‐Gutierrez, V., 2024. High resource overlap and a consistently generalised pattern of interactions in a bat–flower network in a seasonally dry landscape. Ecology and Evolution, 14(10), p.e70367.   

https://doi.org/10.1002/ece3.70367

Pollination is an ecosystem process that is crucial to maintain biodiversity and ecosystem function. Bats are important pollinators in the tropics and are an integral part of complex plant–pollinator interaction networks. However, network analysis–based approaches are still scarce at the plant species and bat community levels.

We captured bats returning to the roost from feeding (numbers captured depended on activity) and collected samples of pollen from the head, chest and wings of captured bats. Sampling was conducted at three bat roosts (Atoyac 19.99174, −103.50488; San Cayetano 20.13014, −103.5658; Cueva del Ermitaño 20.0812, −103.5965) in the Sayula Basin, Jalisco, in central Mexico. We visited the three roosts every 2 months from April 2017 to February 2018, making a total of six sampling trips.

We used metabarcoding to identify plant taxa present in pollen from fur and faecal samples collected across 1 year from three nectar-feeding bat roosts in central Mexico. We calculated the frequency of occurrence of plant taxa and assembled a zoocentric network of bat–plant interactions (i.e., bipartite interaction matrices). We constructed a year-long network, encompassing the entire period of sampling, two seasonal networks comprising the wet and dry seasons, and six individual networks from sampling at two-month intervals across the year.

We used these interaction networks to calculate three network-level metrics, focussing on quantitative indices, which have been found to be less sensitive to sampling intensity and network size: H2', linkage density and niche overlap. To assess the role of the bat pollinators within networks, we also calculated the discrimination/selectivity index d’ for each bat species, which measures how selective a flower visitor is relative to the abundance of available resources.

Files and variables

File: Interaction_network_april.csv

Description: 

We calculated the presence/absence of plant taxa in pollen and faecal samples collected from each bat individual captured in April and used these data to create a weighted adjacency matrix showing the summed interactions between bat species and plant taxa.

Variables

• plant_species:
• Leptonycteris yerbabuenae:
• Choeronycteris mexicana:

File: Interaction_network_august.csv

Description: 

We calculated the presence/absence of plant taxa in pollen and faecal samples collected from each bat individual captured in August and used these data to create a weighted adjacency matrix showing the summed interactions between bat species and plant taxa.

Variables

• plant_species:
• Leptonycteris yerbabuenae:

File: Interaction_network_december.csv

Description: 

We calculated the presence/absence of plant taxa in pollen and faecal samples collected from each bat individual captured in December and used these data to create a weighted adjacency matrix showing the summed interactions between bat species and plant taxa.

Variables

• plant_species:
• Leptonycteris nivalis:
• Leptonycteris yerbabuenae:
• Choeronycteris mexicana:

File: Interaction_network_dry.csv

Description: 

We calculated the presence/absence of plant taxa in pollen and faecal samples collected from each bat individual captured in the dry season (December, February, April) and used these data to create a weighted adjacency matrix showing the summed interactions between bat species and plant taxa.

Variables

• plant_species:
• Leptonycteris yerbabuenae:
• Choeronycteris mexicana:
• Leptonycteris nivalis:

File: Interaction_network_february.csv

Description: 

We calculated the presence/absence of plant taxa in pollen and faecal samples collected from each bat individual captured in February and used these data to create a weighted adjacency matrix showing the summed interactions between bat species and plant taxa.

Variables

• plant_species:
• Leptonycteris yerbabuenae:
• Leptonycteris nivalis:
• Choeronycteris mexicana:

File: Interaction_network_june.csv

Description: 

We calculated the presence/absence of plant taxa in pollen and faecal samples collected from each bat individual captured in June and used these data to create a weighted adjacency matrix showing the summed interactions between bat species and plant taxa.

Variables

• plant_species:
• Leptonycteris yerbabuenae:
• Choeronycteris mexicana:
• Anoura geoffroyi:

File: Interaction_network_october.csv

Description: 

We calculated the presence/absence of plant taxa in pollen and faecal samples collected from each bat individual captured in October and used these data to create a weighted adjacency matrix showing the summed interactions between bat species and plant taxa.

Variables

• plant_species:
• Anoura geoffroyi:
• Leptonycteris yerbabuenae:
• Choeronycteris mexicana:

File: Interaction_network_wet.csv

Description: 

We calculated the presence/absence of plant taxa in pollen and faecal samples collected from each bat individual captured in the wet season (June, August, October) and used these data to create a weighted adjacency matrix showing the summed interactions between bat species and plant taxa.

Variables

• plant_species:
• Leptonycteris yerbabuenae:
• Choeronycteris mexicana:
• Anoura geoffroyi:

File: Interaction_network_complete.csv

Description: 

We calculated the presence/absence of plant taxa in pollen and faecal samples collected from each bat individual captured across the whole year and used these data to create a weighted adjacency matrix showing the summed interactions between bat species and plant taxa.

Variables

• plant_species:
• Leptonycteris yerbabuenae:
• Choeronycteris mexicana:
• Anoura geoffroyi:
• Leptonycteris nivalis: