Temporal heterogeneity increases with spatial heterogeneity in ecological communities

Abstract

Heterogeneity is increasingly recognized as a foundational characteristic of ecological systems. Under global change, understanding temporal community heterogeneity is necessary for predicting the stability of ecosystem functions and services. Indeed, spatial heterogeneity is commonly used in alternative stable state theory as a predictor of temporal heterogeneity and therefore an early indicator of regime shifts. To evaluate whether spatial heterogeneity in species composition is predictive of temporal heterogeneity in ecological communities, we analyzed 68 community data sets spanning freshwater and terrestrial systems where measures of species abundance were replicated over space and time. Of the 68 data sets, 55 (81%) had a weak to strongly positive relationship between spatial and temporal heterogeneity, while in the remaining communities the relationship was weak to strongly negative (19%). Based on a mixed model analysis, we found a significant but weak overall positive relationship between spatial and temporal heterogeneity across all data sets combined, and within aquatic and terrestrial data sets separately. In addition, lifespan and successional stage were negatively and positively related to temporal heterogeneity, respectively. We conclude that spatial heterogeneity may be a predictor of temporal heterogeneity in ecological communities, and that this relationship may be a general property of many terrestrial and aquatic communities.

A comprehensive approach to analyzing community dynamics using rank abundance curves

“…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.…”

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

“…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.…”

“…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.…”

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

“…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., ).…”