Temporal heterogeneity increases with spatial heterogeneity in ecological communities

Collins, Scott L.; Avolio, Meghan L.; Gries, Corinna; Hallett, Lauren M.; Koerner, Sally E.; Pierre, Kimberly J. La; Rypel, Andrew L.; Sokol, Eric R.; Fey, Samuel B.; Flynn, Dan F. B.; Jones, Sydney K.; Ladwig, Laura M.; Ripplinger, Julie; Jones, Matt B.

doi:10.1002/ecy.2154

Cited by 62 publications

(43 citation statements)

References 65 publications

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“…Thus, we created nine simulated communities covering a wide range of species richness and evenness combinations (see Appendix S5 for methods). For each of these nine richness-evenness combinations, we also simulated two well-studied aspects of community variability (Collins et al 2018a) in all possible combinations: (1) spatial variability, a measure of community heterogeneity or beta diversity; and (2) temporal variability, a measure of compositional change over time or turnover. We simulated these aspects of variability using a parameter that controls temporal autocorrelation and one that controls correlation between replicates at each time point (see Appendix S5: Table S1 and Fig.…”

Section: How Are Rac and Multivariate Measures Affected By Species Rimentioning

confidence: 99%

A comprehensive approach to analyzing community dynamics using rank abundance curves

et al. 2019

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Univariate and multivariate methods are commonly used to explore the spatial and temporal dynamics of ecological communities, but each has limitations, including oversimplification or abstraction of communities. Rank abundance curves (RACs) potentially integrate these existing methodologies by detailing species‐level community changes. Here, we had three goals: first, to simplify analysis of community dynamics by developing a coordinated set of R functions, and second, to demystify the relationships among univariate, multivariate, and RACs measures, and examine how each is influenced by the community parameters as well as data collection methods. We developed new functions for studying temporal changes and spatial differences in RACs in an update to the R package library(“codyn”), alongside other new functions to calculate univariate and multivariate measures of community dynamics. We also developed a new approach to studying changes in the shape of RAC curves. The R package update presented here increases the accessibility of univariate and multivariate measures of community change over time and difference over space. Next, we use simulated and real data to assess the RAC and multivariate measures that are output from our new functions, studying (1) if they are influenced by species richness and evenness, temporal turnover, and spatial variability and (2) how the measures are related to each other. Lastly, we explore the use of the measures with an example from a long‐term nutrient addition experiment. We find that the RAC and multivariate measures are not sensitive to species richness and evenness and that all the measures detail unique aspects of temporal change or spatial differences. We also find that species reordering is the strongest correlate of a multivariate measure of compositional change and explains most community change observed in long‐term nutrient addition experiment. Overall, we show that species reordering is potentially an understudied determinant of community changes over time or differences between treatments. The functions developed here should enhance the use of RACs to further explore the dynamics of ecological communities.

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Section: How Are Rac and Multivariate Measures Affected By Species Rimentioning

confidence: 99%

A comprehensive approach to analyzing community dynamics using rank abundance curves

et al. 2019

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“…Thus, differences in the magnitude of the biodiversity response between studies, systems, or organism groups might not only reflect differing impacts of drivers, but also varying abilities to respond due to the spatial species distribution of the surroundings (Collins et al . ). This makes direct comparison of compositional responses to environmental change difficult.…”

Section: Introductionmentioning

confidence: 97%

“…Compositional responses to changing environmental conditions might be limited if low beta diversity reduces rates of immigration and consequently constrains temporal turnover. Thus, differences in the magnitude of the biodiversity response between studies, systems, or organism groups might not only reflect differing impacts of drivers, but also varying abilities to respond due to the spatial species distribution of the surroundings (Collins et al 2018). This makes direct comparison of compositional responses to environmental change difficult.…”

Section: Introductionmentioning

confidence: 99%

Spatial heterogeneity in species composition constrains plant community responses to herbivory and fertilisation

et al. 2018

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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.

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“…As the samples were repeatedly collected from each site over two years, we also included temporal scale in our analyses to evaluate community shifts related to seasonality and succession that might potentially interfere with spatial distribution or species sorting by local conditions. Temporal effects are usually neglected in metacommunity studies focused on lentic ecosystems, although they were found to substantially affect metacommunity structure across various types of localities and studies (Collins et al., ), as those of lotic habitats (Erős, Sály, Takács, Specziár, & Bíró, ; Sarremejane et al., ), floodplains (Fernandes, Henriques‐Silva, Penha, Zuanon, & Peres‐Neto, ), dynamic experimental mesocosms (Azeria & Kolasa, ), as well as in studies in terrestrial environments, e.g., on plants (Alexander et al., ) and mammals (Delciellos et al., ).…”

Section: Introductionmentioning

confidence: 99%

Spatial context strongly affects community composition of both passively and actively dispersing pool invertebrates in a highly heterogeneous landscape

et al. 2019

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The spatial distribution of suitable habitats and dispersal abilities of the constituent taxa jointly affect the structure of metacommunities in standing freshwaters. Most studies exploring spatial effects on aquatic metacommunities, however, focus on at most a few taxonomic groups. Within two consecutive seasons, we studied spatial patterns in the species richness and composition of three passively dispersing and three actively flying freshwater invertebrate groups (rotifers, microcrustaceans and molluscs vs. hemipterans, aquatic beetles and odonates) in a metacommunity system consisting of 42 newly or recently created fishless pools in a highly heterogeneous Central European sandstone landscape consisting of deep valleys and steep ridges. We hypothesized that the extent to which these dispersal barriers affect invertebrate groups depends on their dispersal mode, and that the ability of each group to colonize new habitats is affected by the landscape morphology. Moreover, we predicted that the history and age of the pools would play a major role in structuring of invertebrate communities. Following the classical island biogeography pattern, habitat size (measured as pool surface area or depth) was the key characteristic influencing species richness for each of the six studied groups (range of explained variation: 10%–58.7%). The number of nearby aquatic habitats (i.e., potential colonization sources) was also an important determinant of species richness for molluscs (18.8%), crustaceans (36.4%) and aquatic beetles (27.2%). After pool size, the most important factor influencing species richness was the presence and functional composition of aquatic macrophytes in the pools, which affected the species richness of odonates (25.2%), aquatic beetles (12.2%), rotifers (11.1%), and crustaceans (8.3%). Valley distances between localities, defined as the shortest distance that avoids crossing steep ridges, explained consistently slightly more variation in species composition (2.6%–12.6%) than did Euclidean distances (1.0%–10.1%) for all six groups. Spatial variables (the valley distance matrix, position of pools within clusters in the landscape, and the number of nearby aquatic habitats) explained more variation in species composition (3.4%–25.4%) than local pool characteristics (2.8%–9.4%) or temporal variation (0%–7.6%) in all taxa except hemipterans, whose species composition was almost equally affected by local (3.3%) and spatial factors (3.4%). We conclude that landscape‐level spatial structure in our study area affects the dispersal and metacommunity assembly of both actively and passively dispersing invertebrates more than studied pool characteristics or temporal variation. The observed congruence between groups with different dispersal modes is likely because flying insects follow similar dispersal routes as the key animal vectors of passive dispersers. Our study highlights the importance of including relevant topography features in studies of aquatic metacommunities in complex and heterogeneous landsc...

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Temporal heterogeneity increases with spatial heterogeneity in ecological communities

Cited by 62 publications

References 65 publications

A comprehensive approach to analyzing community dynamics using rank abundance curves

A comprehensive approach to analyzing community dynamics using rank abundance curves

Spatial heterogeneity in species composition constrains plant community responses to herbivory and fertilisation

Spatial context strongly affects community composition of both passively and actively dispersing pool invertebrates in a highly heterogeneous landscape

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