Search

Biotic interactions are crucial for determining the structure and dynamics of communities; however, direct measurement of these interactions can be challenging in terms of time and resources, 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. To our knowledge, no attempt has been made to test the accuracy of the existing methods for detecting mutualistic interactions in terrestrial ecosystems. In this study, we compiled two literature‐based, long‐term datasets of interactions between butterflies and herbaceous plant species in two regions of Germany and compared them with observational abundance and presence/absence data collected within a year in the same regions. We tested how well the species associations generated by three different co‐occurrence analysis methods matched those of empirically measured mutualistic associations using sensitivity and specificity analyses and compared the strength of associations. We also checked whether flower abundance data (instead of plant abundance data) increased the accuracy of the co‐occurrence models and validated our results using empirical flower visitation data. The results revealed that, although all methods exhibited low sensitivity, our implementation of the Relative Interaction Intensity index with pairwise null models performed the best, followed by the probabilistic method and Spearman's rank correlation method. However, empirical data showed a significant number of interactions that were not detected using co‐occurrence methods. Incorporating flower abundance data did not improve sensitivity but enhanced specificity in one region. Further analysis demonstrated incongruence between the predicted co‐occurrence associations and actual interaction strengths, with many pairs exhibiting high interaction strength but low co‐occurrence or vice versa. These findings underscore the complexity of ecological dynamics and highlight the limitations of current co‐occurrence methods for accurately capturing species interactions. [ABSTRACT FROM AUTHOR]

Background Large research consortia can generate tremendous amounts of biological information, including high-resolution soil, vegetation, and climate data. While this knowledge stock holds invaluable potential for answering evolutionary and ecological questions, making these data exploitable for modelling remains a daunting task due to the many processing steps required for synthesis. This might result in many researchers to fall back to a handful of ready-to-use data sets, potentially at the expense of statistical power and scientific rigour. In a push for a more stringent approach, we introduce BEpipeR, an R pipeline that allows for the streamlined synthesis of plot-based Biodiversity Exploratories data. Methods BEpipeR was designed with flexibility and ease of use in mind. For instance, users simply choose between aggregating forest or grassland data, or a combination thereof, effectively allowing them to process any experimental plot data of this research consortium. Additionally, instead of coding, they parse most processing information in a user-friendly way through parameter sheets. Processing includes, among others, the creation of a spatially explicit plot-ID template, data wrangling, quality control, plot-wise aggregations, the calculation of derived metrics, data joining to a large composite data set, and metadata compilation. Results With BEpipeR, we provide a feature-rich pipeline that allows users to process Biodiversity Exploratories data in a flexible and reproducible way. This pipeline might serve as a starting point for aggregating the numerous data sets of this and potentially similar research consortia. In this way, it might be a primer for the construction of consortia-wide composite data sets that take full advantage of the consortia’s rich information stocks, ultimately boosting the visibility and participation of individual research projects. Conclusions The BEpipeR pipeline permits the user-friendly processing and plot-wise aggregation of Biodiversity Exploratories data. With modifications, this framework may be easily adopted by other research consortia.

ABSTRACT Biotic interactions are crucial for determining the structure and dynamics of communities; however, direct measurement of these interactions can be challenging in terms of time and resources, 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. To our knowledge, no attempt has been made to test the accuracy of the existing methods for detecting mutualistic interactions in terrestrial ecosystems. In this study, we compiled two literature‐based, long‐term datasets of interactions between butterflies and herbaceous plant species in two regions of Germany and compared them with observational abundance and presence/absence data collected within a year in the same regions. We tested how well the species associations generated by three different co‐occurrence analysis methods matched those of empirically measured mutualistic associations using sensitivity and specificity analyses and compared the strength of associations. We also checked whether flower abundance data (instead of plant abundance data) increased the accuracy of the co‐occurrence models and validated our results using empirical flower visitation data. The results revealed that, although all methods exhibited low sensitivity, our implementation of the Relative Interaction Intensity index with pairwise null models performed the best, followed by the probabilistic method and Spearman's rank correlation method. However, empirical data showed a significant number of interactions that were not detected using co‐occurrence methods. Incorporating flower abundance data did not improve sensitivity but enhanced specificity in one region. Further analysis demonstrated incongruence between the predicted co‐occurrence associations and actual interaction strengths, with many pairs exhibiting high interaction strength but low co‐occurrence or vice versa. These findings underscore the complexity of ecological dynamics and highlight the limitations of current co‐occurrence methods for accurately capturing species interactions.

Most forests in Central Europe are subject to human management, especially timber harvesting, which alters soil conditions by creating gaps in the forest canopy and reduces the availability of deadwood. However, consequences of these management practices, in particular the combined effect of gap formation and deadwood removal, have been largely neglected. Here, we investigated the abundance, diversity and community composition of oribatid mites (Acari: Oribatida) in forests where deadwood availability and gap formation were manipulated in a full factorial design (Control, Gap, Deadwood, Gap + Deadwood). Forest gaps of about 30 m in diameter were established by felling trees, which were subsequently removed (Gap) or left on site as deadwood (Gap + Deadwood). Nearby forest sites without gaps were enriched with the equal amount of felled trees (Deadwood) or left untreated (Control). Experimental treatments were established at 29 forest sites including deciduous and coniferous stands in three regions of Germany, i.e. the Swabian Alb, Hanich-Dün and Schorfheide-Chorin in the south, center and north, respectively. One year after establishment of the treatments, we sampled 10,777 individuals and 104 species of oribatid mites. Deadwood addition significantly increased the density of oribatid mites in the Hainich-Dün from 24,036 to 46,658 ind. m-². Specifically, species of Oppiidae with relatively short life cycles and high reproductive output benefited from the additional deadwood supply, resulting in shifts in community compositions between treatments with and without deadwood. By contrast, mean oribatid mite density decreased from an average of 64,083 in controls to 36,621 ind. m-² in forest gaps of the Schorfheide-Chorin and the Swabian Alb. Presumably, adverse abiotic conditions, mainly amplified drought, induced higher mortality rates in these regions. Overall, deadwood addition and forest gap formation had strong effects on oribatid mite communities, although the magnitude of each effect was similar between forest types and largely determined by regional context. This emphasizes that the effects of forest management practices on oribatid mite communities cannot be generalized across regional scales, suggesting that management plans should carefully consider regional differences. • Large logs of deadwood increase the density of oribatid mites in forest soil. • Forest gaps reduce the density and species number of oribatid mites. • Effects of deadwood addition and forest gap formation differ between regions. • Deadwood addition to forest gaps does not mitigate detrimental effects of gaps. [ABSTRACT FROM AUTHOR]