Species richness change across spatial scales

Abstract

Humans have elevated global extinction rates and thus lowered global scale species richness. However, there is no a priori reason to expect that losses of global species richness should always, or even often, trickle down to losses of species richness at regional and local scales, even though this relationship is often assumed. Here, we show that scale can modulate our estimates of species richness change through time in the face of anthropogenic pressures, but not in a unidirectional way. Instead, the magnitude of species richness change through time can increase, decrease, reverse, or be unimodal across spatial scales. Using several case studies, we show different forms of scale‐dependent richness change through time in the face of anthropogenic pressures. For example, Central American corals show a homogenization pattern, where small scale richness is largely unchanged through time, while larger scale richness change is highly negative. Alternatively, birds in North America showed a differentiation effect, where species richness was again largely unchanged through time at small scales, but was more positive at larger scales. Finally, we collated data from a heterogeneous set of studies of different taxa measured through time from sites ranging from small plots to entire continents, and found highly variable patterns that nevertheless imply complex scale‐dependence in several taxa. In summary, understanding how biodiversity is changing in the Anthropocene requires an explicit recognition of the influence of spatial scale, and we conclude with some recommendations for how to better incorporate scale into our estimates of change.

Scaling‐up biodiversity‐ecosystem functioning research

“…Tests of the theory we have reviewed here will require scale‐explicit multivariate data amenable to more sophisticated statistical methods that can assess scale dependence in BEF relationships. For that, we need multiscale measures of ecosystem processes (Soranno et al 2019) and biodiversity change (Barnes et al ; Chase et al ). For measuring biodiversity change at different scales, BEF research must harness current methodological developments (Bush et al ), like metagenomics, eDNA (Cristescu & Hebert ), remote sensing (Pau & Dee ; Rocchini et al ) and multi‐site monitoring networks and experiments.…”

“…Even while the distribution of biodiversity reflects gradients of energy and limiting resources, it also contributes to how effectively those gradients are exploited to confer ecosystem functioning, such as variability in the rates of primary and secondary production (Baldocchi ; Niu et al ; Pappas et al ; Jia et al ). Yet, understanding how feedbacks between biodiversity and ecosystem functioning occur, and vary from local to biogeographic scales, is a major challenge (Enquist et al , ; Grace et al ; Gross & Cardinale ; Violle et al ; Guidi et al ; Maestre et al ; Tréguer et al ; Bagousse‐Pinguet et al ), one that is urgent to resolve as biodiversity change occurs at multiple scales in response to climate warming, species introductions and habitat degradation (Reichstein et al ; Snelgrove et al ; Isbell et al ; Chase et al ).…”

No consistent effects of humans on animal genetic diversity worldwide

“…Trends in intraspecific genetic diversity are expected to be scale‐dependent, as are trends in other dimensions of biodiversity like taxonomic, phylogenetic and functional diversity (McGill et al ; Jarzyna & Jetz ; Schlaepfer et al ; Chase et al ). Moreover, human disturbances occurring at different scales may have contrasting effects on genetic diversity.…”

Global fern and lycophyte richness explained: How regional and local factors shape plot richness

“…extent and resolution/grain size) in ecological analyses has been acknowledged since the 1950s (e.g. Chase et al, 2019;Hutchinson, 1953;Levin, 1992;Rahbek, 2005;Ricklefs, 1987;Whittaker, 1977), the full implementation of scale effects in global analyses has been hindered by lack of both data and appropriate methods (Beck et al, 2012). Today, advanced statistical methods (e.g.…”