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Abstract Plant interactions in extreme environments are often inferred from spatial associations and quantified by means of paired sampling. Yet, this method might be confounded by habitat‐sharing effects. Here, we address whether paired and random sampling methods provide similar results at varying levels of environmental heterogeneity. We quantified spatial associations with the two methods at three sites that encompass different micro‐environmental heterogeneity and stress levels: Mediterranean environments in Canary Islands, Spain, and Sardinia, Italy, and a cold alpine environment in Hokkaido, Japan. Then, we simulated plant communities with different levels of species micro‐habitat preferences, environmental heterogeneity, and stress levels. We found that differences in species associations between paired and random sampling were indistinguishable from zero in a homogeneous space. When simulating codispersion over a decreasing abundance gradient, both sampling methods correctly identified facilitation and distinguished it from codispersion. Yet, the pairwise method provided higher facilitation estimates than the random one. At each site, there were strong differences between beneficiary species in their spatial association with nurse species, and associations became more positive with increasing stress in Spain. Most importantly, there were no differences in results yielded by the two methods at any of the different stress levels at the Spanish and Japanese sites. At the Italian site, although micro‐environmental heterogeneity was low, we found weakly significant differences between methods that were unlikely due to habitat‐sharing effects. Thus, the paired sampling method can provide significant insights into net and long‐term effects of plant interactions in spatially conspicuous environments. 

Biological diversity depends on multiple, cooccurring ecological interactions. However, most studies focus on one interaction type at a time, leaving community ecologists unsure of how positive and negative associations among species combine to influence biodiversity patterns. Using surveys of plant populations in alpine communities worldwide, we explore patterns of positive and negative associations among triads of species (modules) and their relationship to local biodiversity. Three modules, each incorporating both positive and negative associations, were overrepresented, thus acting as "network motifs." Furthermore, the overrepresentation of these network motifs is positively linked to species diversity globally. A theoretical model illustrates that these network motifs, based on competition between facilitated species or facilitation between inferior competitors, increase local persistence. Our findings suggest that the interplay of competition and facilitation is crucial for maintaining biodiversity. 

ABSTRACT MotivationHere, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables IncludedThe database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and GrainSampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and GrainThe earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample‐level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of MeasurementThe database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Formatcsv and. SQL.