DOI: 10.25829/bexis.31738-9
title: Species interactions and predictions of co-occurrence methods for plant and butterfly species in 2008, used in "Co-occurrence patterns do not predict mutualistic interactions between plant and butterfly species"

description: Biotic interactions are crucial for determining the structure and dynamics of communities, but direct measurement of these interactions can be time- and resource-intensive, especially when numerous species are involved. Inferring species interactions from species co-occurrence patterns is increasingly being used; however, recent studies have highlighted some limitations. This is an assembled dataset that contains species-level interaction data for pairs of vascular plants and day-active (adult) Lepidoptera species and the prediction results of three different co-occurrence methods. Additionally, flower availability and flower visitation data was included. It considers, 51 and 21 lepidopteran and 135 and 51 plant species, for the regions Schwäbische Alb (ALB) and Schorfheide-Chorin (SCH) respectively, that simultaneously occurred in the field observations (abundance data) and showed species interaction in the literature. Co-occurrences were calculated by including 89 sites (EPs) in total, 46 in ALB and 43 in SCH, for which we found overlapping field and literature data. We also include other measures like the number of sites that a plant or butterfly species occurred at. The data collection/analysis was part of the project TrophCost within the framework of the DFG-funded joint project Biodiversity Exploratories (https://www.biodiversity-exploratories.de).
description: Important notes: The paper is based on this dataset. To reproduce this dataset use the following BExIS datasets: 31733, 31734, 31735, 31736, 31737, 12526, 23586, 4981, 4964 and 10160; and the R scripts in the repository (10.5281/zenodo.10931678).|Detailed description of study design: To explore how well co-occurrence methods can predict mutualistic species interactions in permanent grasslands we compiled two long-term datasets of interactions between day-active Lepidoptera and herbaceous plant species from the literature, Ebert (2005) for ALB and Richert and Brauner (2018) for SCH (access the data through datasets IDs 31733, 31734, 31735, 31736, and 31737). We chose these books because they represent the most comprehensive work on butterfly-plant visitations/interactions at each respective region. We then compared these pairwise interaction data with observational abundance and presence/absence data collected in 2008 in the same regions (ID 12526 v.2 for butterflies; ID 23586 v.2 for plants). To do that, we generated pairwise species associations using three different co-occurrence methods. In a posterior analysis we matched those to the empirically measured, literature-based mutualistic associations using sensitivity and specificity analyses and compared the strength of the associations. We also added flower "abundance" data (i.e., flower availability; IDs 4981 and 4964) into the dataset to check whether using flower abundance instead of just plant abundance data, increases the accuracy of the co-occurrence models and added empirical flower visitation data (ID 10160) to validated our results.|Sampling design: 1) Gather occurrence data for all butterflies and plants in 2008.<br>2) Harmonize species names. Check ID 31733 for details.<br>3) Gather all species interactions from the literature search for all species present in 1. Check ID 31734, 31735, 31736, 31737 for details.<br>4) Calculate pairwise co-occurrences for presence/absence (P/A) and abundance data of plant species<br>5) Repeat 4 with abundance data of flowers instead of plant species.<br>6) Use flower visitation data to validate results.|Data preparation: For the co-occurrence analyses, we used three species association techniques in a pairwise approach, testing against the empirical interaction data. Of these, two were based on P/A data and one on abundance data. We first filtered the interaction data to include only the species present in the occurrence data. For the P/A data, we transformed the abundance data into Boolean (0/1) and then calculated associations using two different techniques: the Relative Interaction Intensity index (RII; Armas, Ordiales, and Pugnaire 2004) and the probabilistic method proposed by Veech (2013, 2014) using the cooccur package in R (Griffith, Veech, and Marsh 2016). To derive RII, we first calculated the observed co-occurrences (i.e., the number of sites with both species) and the expected co-occurrences through probability theory following Bowers and Brown (1982) and Veech (2006), and then calculated the species associations (RII) using the formula described by Armas et al. (2004). To select the significant pairs, we developed a pairwise implementation of null models using the “curveball algorithm” or sim9 in the R package EcoSimR (Gotelli, Hart, and Ellison 2015; Strona et al. 2014). We used 1000 randomizations and 5N for the number of swaps, following the rule of thumb of &gt; 4N (more than four times the total number of links in the network; Maslov and Sneppen 2002; Neal et al. 2023). P-values were obtained by calculating the upper and lower tail probabilities by taking the average number of times that RII of the observed matrix was higher or lower than the RII in the 1000 random matrices. For the probabilistic method, we used effect sizes standardized by the number of sites as a measure of co-occurrence intensity. The probability of co-occurrence at a frequency greater than the observed frequency was used as a measure of significance and calculated analytically using the hypergeometric distribution.<br>Plant and lepidopteran abundance data were analyzed using Spearman’s rank correlation, a similarity-based technique that allows the calculation of pairwise scores between both taxa without assuming normally distributed data. To reduce the False Discovery Rate (FDR), p-values were adjusted for multiple testing using the Benjamini-Hochberg (BH) method (Benjamini and Hochberg 1995). Flower "abundance" data (flower units) were used to test if the accuracy of the models increased compared to that of using only plant abundance. We used Spearman’s rank correlation coefficients, with the respective P-values corrected for FDR using the BH method.<br>Flower visitations were used for data validation.<br>All non-integer, numeric values were rounded to 5 decimal places.|Literature references: Armas, Cristina, Ramón Ordiales, and Francisco I. Pugnaire. 2004. ‘Measuring Plant Interactions: A New Comparative Index’. Ecology 85 (10): 2682–86. https://doi.org/10.1890/03-0650.<br>Benjamini, Yoav, and Yosef Hochberg. 1995. ‘Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing’. Journal of the Royal Statistical Society: Series B (Methodological) 57 (1): 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x.<br>Bowers, Michael A., and James H. Brown. 1982. ‘Body Size and Coexistnce in Desert Rodents: Chance or Community Structure?’ Ecology 63 (2): 391–400. https://doi.org/10.2307/1938957.<br>Gotelli, Nicholas J., Edmund M. Hart, and Aaron M. Ellison. 2015. EcoSimR: Null Model Analysis for Ecological Data. https://doi.org/10.5281/zenodo.16522.<br>Griffith, Daniel M., Joseph A. Veech, and Charles J. Marsh. 2016. ‘Cooccur : Probabilistic Species Co-Occurrence Analysis in R’. Journal of Statistical Software 69 (Code Snippet 2). https://doi.org/10.18637/jss.v069.c02.<br>Maslov, Sergei, and Kim Sneppen. 2002. ‘Specificity and Stability in Topology of Protein Networks’. Science 296 (5569): 910–13. https://doi.org/10.1126/science.1065103.<br>Neal, Zachary, Annabel Cadieux, Diego Garlaschelli, Nicholas J. Gotelli, Fabio Saracco, Tiziano Squartini, Shade T. Shutters, Werner Ulrich, Guanyang Wang, and Giovanni Strona. 2023. ‘Pattern Detection in Bipartite Networks: A Review of Terminology, Applications and Methods’. arXiv. https://doi.org/10.48550/arXiv.2310.01284.<br>Strona, Giovanni, Domenico Nappo, Francesco Boccacci, Simone Fattorini, and Jesus San-Miguel-Ayanz. 2014. ‘A Fast and Unbiased Procedure to Randomize Ecological Binary Matrices with Fixed Row and Column Totals’. Nature Communications 5 (1): 4114. https://doi.org/10.1038/ncomms5114.<br>Veech, Joseph A. 2006. ‘A Probability-Based Analysis of Temporal and Spatial Co-Occurrence in Grassland Birds’. Journal of Biogeography 33 (12): 2145–53. https://doi.org/10.1111/j.1365-2699.2006.01571.x.<br>Veech, Joseph A. 2013. ‘A Probabilistic Model for Analysing Species Co-Occurrence’. Global Ecology and Biogeography 22 (2): 252–60. https://doi.org/10.1111/j.1466-8238.2012.00789.x.<br>Veech, Joseph A. 2014. ‘The Pairwise Approach to Analysing Species Co-Occurrence’. Edited by Miguel Araújo. Journal of Biogeography 41 (6): 1029–35. https://doi.org/10.1111/jbi.12318.|Data references: Börschig, Carmen, and Jochen Krauss. 2016. ‘Diversity and Abundance of Day Active Butterflies and Day-Active Moths along a Land Use Intensity Gradient (2008)’. Biodiversity Exploratories Information System. https://www.bexis.uni-jena.deddm/data/Showdata/12526?version=2.<br>Ebert, Günter, ed. 2005. Die Schmetterlinge Baden-Württembergs. 10: Ergänzungsband. Grundlagenwerke. Stuttgart: Ulmer.<br>Richert, Arnold, and Oliver Brauner. 2018. ‘Nektarpflanzen und andere Nahrungs- quellen sowie Raupennahrungspflanzen der Tagfalter von Brandenburg und Berlin (Lepidoptera: Rhopalocera et Hesperiidae)’, 87.<br>Schäfer, Deborah, Daniel Prati, Markus Fischer, and Norbert Hölzel. 2018. ‘Vegetation Records for 150 Grassland EPs, 2008 - 2017’. Biodiversity Exploratories Information System. https://www.bexis.uni-jena.deddm/data/Showdata/23586?version=2.<br>Weiner, Christiane, Karl Eduard Linsenmair, and Nico Blüthgen. 2019a. ‘Flower Availability 2008 Alb-Korrigiert’. Biodiversity Exploratories Information System. https://www.bexis.uni-jena.deddm/data/Showdata/4981?version=2.<br>Weiner, Christiane, Karl Eduard Linsenmair, and Nico Blüthgen. 2019b. ‘Flower Availability 2008 Schorfheide’. Biodiversity Exploratories Information System. https://www.bexis.uni-jena.deddm/data/Showdata/4964?version=2.<br>Weiner, Christiane, Michael Werner, Karl Eduard Linsenmair, and Nico Blüthgen. 2016. ‘Flower Visitor Interactions 2008’. Biodiversity Exploratories Information System. https://www.bexis.uni-jena.deddm/data/Showdata/10160?version=3.