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The turnover of plant-frugivore interactions along plant range expansion: consequences for natural colonisation processes

Cite this dataset

Isla, Jorge; Jácome-Flores, Miguel; Arroyo, Juan M.; Jordano, Pedro (2023). The turnover of plant-frugivore interactions along plant range expansion: consequences for natural colonisation processes [Dataset]. Dryad.


Plant-animal mutualisms such as seed dispersal are key interactions for sustaining plant range shifts. Whether the organisation of interactions with seed dispersers is reconfigured along the expansion landscape template, and its effects accelerating or slowing colonisation, remain elusive. Here we analyse plant-frugivore interactions in a scenario of rapid population expansion of a Mediterranean juniper. We combined complex network analyses with intensive field surveys, sampling interactions between individual plants and frugivores by DNA-Barcoding and phototrapping over two seasons. We assess the role of intrinsic and extrinsic intraspecific variability in shaping interactions and we estimate the contribution of individual plants to seed rain. The whole interaction network was highly structured, with a distinct set of modules including individual plants and frugivore species arranged concordantly along the expansion gradient. The modular configuration found was partially shaped by individual neighbourhood context (density and fecundity) and phenotypic traits (cone size). Interaction reconfiguration resulted in a higher and uneven contribution to seed dispersal rain by individuals of the expansion boundaries, providing signals of the colonisation local-history. Our study provides novel insights into the key role of mutualistic interactions in colonisation scenarios by promoting fast plant expansion processes.


Study area

The study was carried out in the Doñana National Par (Spain). We selected three 1-ha stands along the juniper natural regeneration gradient. In the three stands, all individual junipers were identified and geo-referenced. The mature stand, named "Sabinar del Marqués" (MAR), has the highest juniper density and is an old forest with a high dominance of J. phoenicea. Advancing towards the colonisation front, the second stand called "Sabinar del Ojillo" (OJI) represents a stage of intermediate maturity with a lower juniper density. The last stand is named "Sabinar de Colonización" (COL) and represents a colonisation front or expansion area. We established five evenly distributed subplots in each stand. We used these subplots to randomly select 35 focal individual plants in each stand (N = 105). Fieldwork was carried out during two consecutive fruiting seasons (October 2018–May 2019 and September 2019–May 2020).

Plant-animal interactions survey


Eight camera traps were placed and rotated periodically through the 35 focal plants in each area. Camera traps were active for 10 days in each focal plant and then changed to eight other individuals. Cameras were fitted at 1 m above ground and 2-3 m from the focal plant focusing on both ground and the bottom of the plant. The cameras were active day and night, in video mode, with 10 s per video and 2 s between recordings. All camera-trap records containing frugivore visits were recorded. All those detections in which the animal was only walking/flying, or showed a behaviour different from a cone-foraging visit (e.g., passive perching or scent marking) were not recorded as visitation events. Each pairwise interaction (ind-sp) was standardised by sampling effort (time and plant cover area). Because the cameras were unable to record the whole plant, we weighted the interactions by the percentage of the recorded plant surface. In the field, we estimated that the camera recorded 60% of the surface for small plants (<20m2 cover), 40% for medium plants (20-40m2 cover) and 20% for large plants (>40 m2 cover). For time standardisation, we used the percentage of days recorded per plant with respect to the entire study. 


We used DNA-barcoding to identify the bird and mammal species that visit focal plants by collecting scats (or regurgitated seeds) in seed traps under the 105 individual junipers. One seed trap per plant was placed, except for thelargest plants where two trays were installed to maximise the area covered. Additionally, we delimited a rectangular soil surface next to the seed trap where we also collected scats to increase the sampled surface. We followed the sample collection, DNA extraction and PCR protocols described in González-Varo et al. [1, 2]. In the case of mammal samples, visual identification in the field was also cross-checked with DNA-barcoding identification. Frugivore species identification was based on a 272-bp mitochondrial DNA region (COI: cytochrome c oxidase subunit I). All bird samples were amplified by PCR using the COI-fsd-degF and COI-fsdR primers [2]. Mammals samples were amplified following protocols and primers of Alcaide et al. [3]. PCR product was sequenced and verified for its matching with COI sequences from Barcode of Life Data (BOLD) and Nucleotide Basic Local Alignment Search Tool (BLAST from NCBI) databases. For those samples without successful amplification we performed nested PCR where we used the same primers on the amplicon of AWCintF2/AWCintR4 (Avian DNA barcodes, [4]). We considered as a successful identification, all the sequences that showed a >98% percentage of similarity and at least 100-bp.

  1. González-Varo JP, Arroyo JM, Jordano P. 2014 Who dispersed the seeds? The use of DNA barcoding in frugivory and seed dispersal studies. Methods Ecol. Evol. 5, 806–814. (doi:10.1111/2041-210X.12212)
  2. González-Varo JP, Carvalho CS, Arroyo JM, Jordano P. 2017 Unravelling seed dispersal through fragmented landscapes: Frugivore species operate unevenly as mobile links. Mol. Ecol. 26, 4309–4321. (doi:10.1111/mec.14181)
  3. Alcaide M, Rico C, Ruiz S, Soriguer R, Muñoz J, Figuerola J. 2009 Disentangling vector-borne transmission networks: A universal DNA barcoding method to identify vertebrate hosts from arthropod bloodmeals. PLoS One4, 1–6. (doi:10.1371/journal.pone.0007092)
  4. Lijtmaer, D. A., Kerr, K. C. R., Stoeckle, M. Y., & Tubaro PL. 2012 DNA Barcoding Birds: From fieldvollection to sataanalysis. DNA Barcodes: Methods and Protocols. Totowa, NJ: Humana Press. 

Data standarization for network analyses

By standardising both datasets to the same units of time and area, we were able to merge them in the form of an adjacency matrix to get the final interaction matrix [5]. Each cell of this matrix represents the estimated number of individuals-species visits for the two reproductive episodes pooled. We used this matrix to construct the individual-based weighted network between the 105 individual junipers and the frugivores that visit them. 

5. Quintero E, Isla J, Jordano P. 2021 Methodological overview and data-merging approaches in the study of plant–frugivore interactions. Oikos e08379, 1–18. (doi:10.1111/oik.08379)

Plant phenotype and neighbourhood context

Individual phenotype and neighbourhood context were thoroughly sampled for the 105 focal juniper individuals. We classify plant attributes hierarchically. Individual intraspecific neighbourhood context was described by the juniper density and cone productivity in a buffer of 100 m2. We measured in the field plant heights, two maximum cross diameters of the canopy projection, canopy area, and number of cones (cone crop size). We harvested 50 cones from each plant to measure their diameter, length, mass, number of seeds, average seed mass, pulp mass, and the number of seeds per cone. We used the manually depulped seeds (n = 45,560 seeds) to estimate individual seed viability for each plant by a flotation experiment. Finally, for 82 of the 105 plants for which sufficient pulp was available, we estimated the nutritional characteristics of the pulp (ash, protein, fibre, and lipids) using standard laboratory procedure (see Supp. Mat. section for a detailed list and measurement procedures description). 

Usage notes

The only software needed to open the datasets and replicate the analyses is Rstudio. 


Ministerio de Ciencia e Innovación, Award: CGL2017-82847-P

European Commission, Award: SUMHAL, LIFEWATCH-2019-09-CSIC-13, POPE 2014-2020

Ministerio de Ciencia e Innovación, Award: PRE2018- 085916

Sistema Nacional de Investigadores, Award: CONACYT (73215)