Table 1.
Expected effects of (a) spatial, (b) temporal and (c) organisational scale on BEF relationships derived from the theoretical and empirical studies we reviewed. The effects discussed in the main text are in boldface
| (a) | |||
|---|---|---|---|
| Process change as we increase spatial scale | Magnitude of function | Stability of function | Spatial scale at which process applies |
| Statistical properties of aggregation | BEF slope increases as spatial grain becomes coarser due to nonlinear averaging of different finer‐grain BEF relationships, as well as spatial variation in fine‐grain diversity (Thompson et al. 2018), coupled with evidence for spatial variation in fine‐grain BEF slope (Liang et al 2016; Sullivan et al. 2017) | Observation error associated with sampling biodiversity distributions can be a component of biodiversity‐stability relationships and will increase with spatial scale (due to increased environmental heterogeneity and patterns of rarity) and decrease with sample size due to averaging over multiple observations (Mazancourt et al. 2013). | Patch to continental |
| Spatial turnover in species composition increases, due to drift, dispersal limitation or species sorting |
As spatial grain increases, BEF relationship steepens when new species are encountered across space, and erodes when all species have already been encountered (Thompson et al. 2018) Landscapes require more species to maintain functioning than sites because functionally important species differ between sites (Winfree et al. 2018; Lefcheck et al. 2019) |
Habitat selection by mobile organisms can alter effects of diversity on stability of functions (France & Duffy 2006) | Patch to landscapes to regions |
| Heterogeneity and range of environmental conditions increases |
BEF strengthens because of increased expression of niche complementarity (Dimitrakopoulos & Schmid 2004; Tylianakis et al. 2008). Direct effects of environment on ecosystem functions reduce relative importance of biodiversity (e.g. Srivastava & Vellend 2005) |
Insurance hypothesis, both local and spatial, can be explained via temporal and spatial niche complementarity. Both predict stabilising effects of increasing diversity, and these benefits become more apparent with increasing spatial and temporal scales |
Micro‐habitat to patch to landscape to region |
| Dispersal influences local dynamics | In combination with a spatially and temporally heterogeneous environment, moderate dispersal can permit species to efficiently track spatial change in their optimal environment, increasing function (Loreau et al. 2003; Thompson et al. 2017) | Movement from patch‐to‐patch by mobile consumers can either stabilise (Loreau et al. 2003) or destabilise ecosystem function (Marleau et al. 2014) | Patch to region |
| Potential for spatial asynchrony in local population dynamics | Increasing asynchrony predicts an increase in average EF | In combination with a spatially and temporally heterogeneous environment, asynchrony in population dynamics at the local level results in stabilisation at larger spatial scales (Loreau et al. 2003; Wang & Loreau 2016) | Patch to region |
| Spatial coupling of functions between habitats | As spatial scale increases, ecosystem functions include energy and matter flow between habitats. Thus, BEF effects in one habitat may support ecosystem functions in a connected habitat (Alsterberg et al. 2017). | Single habitat/ecosystems to multiple habitats/ecosystems | |
| Allow feedbacks from EF ‐> B | Ecosystem functions enabled by one group of species provide opportunities for other species through niche construction, processing chains and autocatalytic cycles, food webs, facilitation networks and cross‐ecosystem fluxes (Worm & Duffy 2003) | Temporal variance in ecosystem functions affects the persistence of species; intermediate levels of variability often promote diversity (e.g. creating temporal niches, disrupting competitive hierarchies) (Worm & Duffy 2003) | Patch to landscape |
| (b) | |||
|---|---|---|---|
| Processes change as we increase temporal scale | Magnitude of function | Stability of function | Temporal scale |
| Statistical properties of aggregation | BEF slope changes with successional stage (Reich et al. 2012; Lasky et al. 2014; Guerrero‐Ramírez et al. 2017), and aggregation of variable BEF slope is subject to nonlinear averaging (a temporal analogue to Thompson et al. 2018) | One to many generations | |
| Increased cycling of limiting nutrients | Diverse communities are able to accumulate limiting nutrients within ecosystem components, fuelling further growth (Reich et al. 2012) | One to many generations | |
| Increases in time since species addition/deletion allows for species interactions to be realised |
In assembling communities, BEF relationships strengthen due to increasing strength of complementarity (Cardinale et al. 2007). Other species compensate for lost species through vegetative growth or colonisation, reducing influence of biodiversity on function (Kardol et al. 2018) |
Ability to detect temporal complementarity in population dynamics, a main mechanism underlying diversity‐functional stability relationships, increases with observation time (Loreau & Mazancourt 2013) |
One to many generations |
| Increased range of environmental conditions as time span increases (‘reddened environmental noise’) | Dee et al. (2016) found evidence for a performanceeffect of functional diversity, by buffering fisheries yields against within‐year temperature variability. | Diversity can stabilise functions in the face of the destabilising effects of reddened environmental noise (Gonzalez & De Feo 2007) | Years to decades |
| (c) | |||||
|---|---|---|---|---|---|
| Processes change as we increase organisational scale | Magnitude of function | Stability of function | Organisational scale | ||
| Include multiple genotypes of a species | Increased rates of ecosystem functions, due to niche complementarity between genotypes (Schweitzer et al. 2005; Hughes et al. 2008) | Increased functional resistance to disturbance (Hughes & Stachowicz 2004) | From individuals to population | ||
| Include multiple populations of a species | Variability in function is reduced when populations have independent or negatively covarying dynamics (portfolio effect; Schindler et al. 2010) | Single population to metapopulation | |||
| Include multiple trophic levels of a food web | Higher trophic levels can change BEF effects at lower levels, e.g. by altering the relative abundances and interaction strengths of lower‐level species or directly influencing function (Worm & Duffy 2003) | Higher trophic levels can alter the relationship between diversity and ecosystem stability, depending on the strength of the trophic interactions (Thébault & Loreau 2005; Jiang et al. 2009) | Single to multiple trophic levels | ||
| Include multiple communities of a metacommunity | See all ‘patch to landscape’ or ‘patch to region’ entries in spatial scale table | See all ‘patch to landscape’ or ‘patch to region’ entries in spatial scale table | Single community to metacommunities | ||
| Include multiple habitat or ecosystem types of a metaecosystem | Habitat diversity can have strong impacts on ecosystem functioning when habitats complement each other in the types of energy and elemental processing (Alsterberg et al 2017) | Single habitat/ecosystems to multiple habitats/ecosystems | |||