Abstract The loose‐equilibrium concept (LEC) predicts that ecological assemblages change transiently but return towards an earlier or average structure. The LEC framework can help determine whether assemblages vary within expected ranges or are permanently altered following environmental change.Long‐lived, slow‐growing animals typically respond slowly to environmental change, and their assemblage dynamics may respond over decades, which transcends most ecological studies. Unionid mussels are valuable for studying dynamics of long‐lived animals because they can live >50 years and occur in dense, species‐rich assemblages (mussel beds). Mussel beds can persist for decades, but disturbance can affect species differently, resulting in variable trajectories according to differences in species composition within and among rivers.We used long‐term data sets (10–40 years) from seven rivers in the eastern United States to evaluate the magnitude, pace and directionality of mussel assemblage change within the context of the LEC.Site trajectories varied within and among streams and showed patterns consistent with either the LEC or directional change. In streams that conformed to the LEC, rank abundance of dominant species remained stable over time, but directional change in other streams was driven by changes in the rank abundance and composition of dominant species.Characteristics of mussel assemblage change varied widely, ranging from those conforming to the LEC to those showing strong directional change. Conservation approaches that attempt to maintain or create a desired assemblage condition should acknowledge this wide range of possible assemblage trajectories and that the environmental factors that influence those changes remain poorly understood.
more »
« less
Long‐term changes in temperate marine fish assemblages are driven by a small subset of species
Abstract The species composition of plant and animal assemblages across the globe has changed substantially over the past century. How do the dynamics of individual species cause this change? We classified species into seven unique categories of temporal dynamics based on the ordered sequence of presences and absences that each species contributes to an assemblage time series. We applied this framework to 14,434 species trajectories comprising 280 assemblages of temperate marine fishes surveyed annually for 20 or more years. Although 90% of the assemblages diverged in species composition from the baseline year, this compositional change was largely driven by only 8% of the species' trajectories. Quantifying the reorganization of assemblages based on species shared temporal dynamics should facilitate the task of monitoring and restoring biodiversity. We suggest ways in which our framework could provide informative measures of compositional change, as well as leverage future research on pattern and process in ecological systems.
more »
« less
- Award ID(s):
- 2019470
- PAR ID:
- 10363483
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Global Change Biology
- Volume:
- 28
- Issue:
- 1
- ISSN:
- 1354-1013
- Page Range / eLocation ID:
- p. 46-53
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract There is considerable interest in understanding patterns of β‐diversity that measure the amount of change in species composition through space or time. Most hypotheses for β‐diversity evoke nonrandom processes that generate spatial and temporal within‐species aggregation; however, β‐diversity can also be driven by random sampling processes. Here, we describe a framework based on rarefaction curves that quantifies the nonrandom contribution of species compositional differences across samples to β‐diversity. We isolate the effect of within‐species spatial or temporal aggregation on beta‐diversity using a coverage standardized metric of β‐diversity (βC). We demonstrate the utility of our framework using simulations and an empirical case study examining variation in avian species composition through space and time in engineered versus natural riparian areas. The primary strengths of our approach are that it provides an intuitive visual null model for expected patterns of biodiversity under random sampling that allows integrating analyses across α‐, γ‐, and β‐scales. Importantly, the method can accommodate comparisons between communities with different species pool sizes, and it can be used to examine species turnover both within and between meta‐communities.more » « less
-
Abstract Environmental change can result in substantial shifts in community composition. The associated immigration and extinction events are likely constrained by the spatial distribution of species. Still, studies on environmental change typically quantify biotic responses at single spatial (time series within a single plot) or temporal (spatial beta diversity at single time points) scales, ignoring their potential interdependence. Here, we use data from a global network of grassland experiments to determine how turnover responses to two major forms of environmental change – fertilisation and herbivore loss – are affected by species pool size and spatial compositional heterogeneity. Fertilisation led to higher rates of local extinction, whereas turnover in herbivore exclusion plots was driven by species replacement. Overall, sites with more spatially heterogeneous composition showed significantly higher rates of annual turnover, independent of species pool size and treatment. Taking into account spatial biodiversity aspects will therefore improve our understanding of consequences of global and anthropogenic change on community dynamics.more » « less
-
Zhu, Shanfeng (Ed.)Understanding microbial interactions is fundamental for exploring population dynamics, particularly in microbial communities where interactions affect stability and host health. Generalized Lotka-Volterra (gLV) models have been widely used to investigate system dynamics but depend on absolute abundance data, which are often unavailable in microbiome studies. To address this limitation, we introduce an iterative Lotka-Volterra (iLV) model, a novel framework tailored for compositional data that leverages relative abundances and iterative refinements for parameter estimation. The iLV model features two key innovations: an adaptation of the gLV framework to compositional constraints and an iterative optimization strategy combining linear approximations with nonlinear refinements to enhance parameter estimation accuracy. Using simulations and real-world datasets, we demonstrate that iLV surpasses existing methodologies, such as the compositional LV (cLV) and the generalized LV (gLV) model, in recovering interaction coefficients and predicting species trajectories under varying noise levels and temporal resolutions. Applications to the lynx-hare predator-prey,Stylonychia pustula-P. caudatummixed culture, and cheese microbial systems revealed consistency between predicted and observed relative abundances showcasing its accuracy and robustness. In summary, the iLV model bridges theoretical gLV models and practical compositional data analysis, offering a robust framework to infer microbial interactions and predict community dynamics using relative abundance data, with significant potential for advancing microbial research.more » « less
-
Abstract AimEfforts to predict the responses of soil fungal communities to climate change are hindered by limited information on how fungal niches are distributed across environmental hyperspace. We predict the climate sensitivity of North American soil fungal assemblage composition by modelling the ecological niches of several thousand fungal species. LocationOne hundred and thirteen sites in the United States and Canada spanning all biomes except tropical rain forest. Major Taxa StudiedFungi. Time Period2011–2018. MethodsWe combine internal transcribed spacer (ITS) sequences from two continental‐scale sampling networks in North America and cluster them into operational taxonomic units (OTUs) at 97% similarity. Using climate and soil data, we fit ecological niche models (ENMs) based on logistic ridge regression for all OTUs present in at least 10 sites (n = 8597). To describe the compositional turnover of soil fungal assemblages over climatic gradients, we introduce a novel niche‐based metric of climate sensitivity, the Sørensen climate sensitivity index. Finally, we map climate sensitivity across North America. ResultsENMs have a mean out‐of‐sample predictive accuracy of 73.8%, with temperature variables being strong predictors of fungal distributions. Soil fungal climate niches clump together across environmental space, which suggests common physiological limits and predicts abrupt changes in composition with respect to changes in climate. Soil fungi in North American climates are more likely to be limited by cold and dry conditions than by warm and wet conditions, and ectomycorrhizal fungi generally tolerate colder temperatures than saprotrophic fungi. Sørensen climate sensitivity exhibits a multimodal distribution across environmental space, with a peak in climates corresponding to boreal forests. Main ConclusionsThe boreal forest occupies an especially precarious region of environmental space for the composition of soil fungal assemblages in North America, as even small degrees of warming could trigger large compositional changes characterized mainly by an influx of warm‐adapted species.more » « less
An official website of the United States government
