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The determinants of food chain length (FCL), a crucial aspect of biodiversity due to its effects on ecosystem functioning, have long been debated. Previous studies proposed resource availability, disturbance, and ecosystem size as environmental drivers. However, studies using stable isotope approaches have shown inconsistent results, indicating missing links between environmental drivers and FCL. Here, we hypothesized that species richness and motifs (i.e. three‐species subgraphs) modulated environmental effects on FCL. Combining empirical food webs with ourN‐species food web model, we found that FCL disproportionately changed at low species richness, with saturation at high species richness. This functional response was essential to the interdependent effects of disturbance and ecosystem size in our model. Disturbance more strongly regulated FCL in smaller ecosystems, where species richness was low. Similarly, increasing ecosystem size enhanced FCL under strong, but not weak, disturbance regimes. Our study suggests that internal food web structure should deepen our understanding of how FCL changes over environments. 

Abstract Scale invariance, which refers to the preservation of geometric properties regardless of observation scale, is a prevalent phenomenon in ecological systems. This concept is closely associated with fractals, and river networks serve as prime examples of fractal systems. Quantifying river network complexity is crucial for unveiling the role of river fractals in riverine ecological dynamics, and researchers have used a metric of “branching probability” to do so. Previous studies showed that this metric reflects the fractal nature of river networks. However, a recent article by Carraro and Altermatt (2022) contradicted this classical observation and concluded that branching probability is “scale dependent.” I dispute this claim and argue that their major conclusion is derived merely from their misconception of scale invariance. Their analysis in the original article (fig. 3a) provided evidence that branching probability is scale‐invariant (i.e., branching probability exhibits a power‐law scaling), although the authors erroneously interpreted this result as a sign of scale dependence. In this article, I re‐introduce the definition of scale invariance and show that branching probability meets this definition. This provided an opportunity to address the divergent use of “scale invariance” and “scaling” between fractal theory and ecology. 

Abstract Understanding the drivers of food chain length in natural communities has intrigued ecologists since Elton publicized “food cycles” in the early 20th century. Proposed drivers of food chain length have included productivity, disturbance regime, ecosystem size, and trophic omnivory. However, current theories have largely assumed simple, two‐dimensional habitat architectures and may not be adequate to predict food chain length in ecosystems with a complex, branching structure. Here, we develop a spatially explicit theoretical model that provides an integrated framework for understanding variation in food chain length in branching networks. We show independent, positive influences of ecosystem size and complexity (as indicated by branching properties) on food chain length. However, the effects of ecosystem size and complexity were contingent upon other factors, appearing more clearly in high‐disturbance and high‐productivity regimes. Our results suggest that ecosystem complexity is an important yet overlooked driver of food chain length that may increase the resilience to anthropogenic environmental changes. 

Abstract Ecological communities are assembled through a series of multiple processes, including dispersal, abiotic and biotic filtering, and ecological drift. Although these assembly processes act in concert to structure local communities, their relative importance is considerably variable among study systems. While such contingency of community assembly has been widely appreciated, the empirical and theoretical evidence is scattered around in the literature, and few efforts have been made to synthesize it. In this mini‐review, we summarize the accumulated evidence of the context‐dependency of community assembly rules, to reach a rough generalization of the contingency. Specifically, we argue that spatial and temporal dimensions can serve as general axes that regulate the relative importance of assembly processes. To this end, we synthesize the current understanding of how the relative importance of multiple assembly processes changes with spatial scales and complexity, and with time in the long and short terms. This review concludes that spatial and temporal dimensions can be common currencies of community assembly rules that are shared across various systems. 

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.