More than the sum of the parts: annual partitioning within spatial guilds underpins community regulation

Magurran, Anne E.; Henderson, Peter

doi:10.1098/rspb.2018.0659

Cited by 9 publications

(13 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We compared the observed degree of synchrony to null models of independent species or spatial dynamics based on cyclic‐shift permutation (Hallett et al. , Magurran and Henderson ). For each synchrony index, this permutation preserved the observed temporal autocorrelation within lower constituent hierarchical levels, but separated correlation among species at either local (

φ_{normalP false\to normalC, k}

and

φ_{normalP false\to C, L}

) or regional (

φ_{normalP false\to C, R}

) scales and correlation among either local populations of a given species (

φ_{i, normalL false\to normalR}

and

φ_{P, L false\to normalR}

) or local communities (

φ_{C, L false\to normalR}

) (Table ; Appendix : Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Whether through local communities or metapopulations, species asynchrony, spatial asynchrony, or both should in theory stabilize metacommunity biomass (Loreau et al 2003, Wang and Loreau 2014, 2016. However, the extent to which species and spatial asynchrony combine and interact across different hierarchical levels to stabilize metacommunity biomass is rarely measured in natural systems (but see Houlahan et al 2007, Gonzalez and Loreau 2009, Vasseur et al 2014, Wilcox et al 2017, Magurran and Henderson 2018, Thorson et al 2018, Wang et al 2019.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Species insurance trumps spatial insurance in stabilizing biomass of a marine macroalgal metacommunity

Lamy

Wang

Renard

et al. 2019

Ecology

View full text Add to dashboard Cite

Because natural ecosystems are complex, it is difficult to predict how their variability scales across space and levels of organization. The species‐insurance hypothesis predicts that asynchronous dynamics among species should reduce variability when biomass is aggregated either from local species populations to local multispecies communities, or from metapopulations to metacommunities. Similarly, the spatial‐insurance hypothesis predicts that asynchronous spatial dynamics among either local populations or local communities should stabilize metapopulation biomass and metacommunity biomass, respectively. In combination, both species and spatial insurance reduce variation in metacommunity biomass over time, yet these insurances are rarely considered together in natural systems. We partitioned the extent that species insurance and spatial insurance reduced the annual variation in macroalgal biomass in a southern California kelp forest. We quantified variability and synchrony at two levels of organization (population and community) and two spatial scales (local plots and region) and quantified the strength of species and spatial insurance by comparing observed variability and synchrony in aggregate biomass to null models of independent species or spatial dynamics based on cyclic‐shift permutation. Spatial insurance was weak, presumably because large‐scale oceanographic processes in the study region led to high spatial synchrony at both population‐ and community‐level biomass. Species insurance was stronger due to asynchronous dynamics among the metapopulations of a few common species. In particular, a regional decline in the dominant understory kelp species Pterygophora californica was compensated for by the rise of three subdominant species. These compensatory dynamics were associated with positive values of the Pacific Decadal Oscillation, indicating that differential species tolerances to warmer temperature and nutrient‐poor conditions may underlie species insurance in this system. Our results illustrate how species insurance can stabilize aggregate community properties in natural ecosystems where environmental conditions vary over broad spatial scales.

show abstract

φ_{normalP false\to normalC, k}

and

φ_{normalP false\to C, L}

) or regional (

φ_{normalP false\to C, R}

) scales and correlation among either local populations of a given species (

φ_{i, normalL false\to normalR}

and

φ_{P, L false\to normalR}

) or local communities (

φ_{C, L false\to normalR}

) (Table ; Appendix : Fig.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Species insurance trumps spatial insurance in stabilizing biomass of a marine macroalgal metacommunity

Lamy

Wang

Renard

et al. 2019

Ecology

View full text Add to dashboard Cite

show abstract

“…The starting point is typically an assemblage time series, in which species identities (and ideally species abundances) have been recorded using the same methodology. The first F I G U R E 1 Estuarine fish, in the UK's Bristol Channel (Henderson, 2007;Magurran & Henderson, 2018) have been sampled using consistent methodology for 37 years. This figure illustrates the types of structural and compositional change in assemblage time series that measures of temporal β diversity may seek to capture.…”

Section: Measuring Temporal β Diversitymentioning

confidence: 99%

“…Estuarine fish, in the UK’s Bristol Channel (Henderson, ; Magurran & Henderson, ) have been sampled using consistent methodology for 37 years. This figure illustrates the types of structural and compositional change in assemblage time series that measures of temporal β diversity may seek to capture.…”

Section: Introductionmentioning

confidence: 99%

Temporal β diversity—A macroecological perspective

Magurran

Dornelas

Moyes

et al. 2019

Global Ecol Biogeogr

Self Cite

108

View full text Add to dashboard Cite

Issue Biodiversity change, that is how the taxonomic identities and abundances of species in ecological systems are changing over time, has two facets: temporal α diversity and temporal β diversity. To date, temporal α diversity has received most attention even though compositional shifts in assemblages exceed expectations based on ecological theory. Growing concern about the state of the world’s biodiversity highlights the need for better understanding of the extent, and consequences, of compositional reorganization in ecological systems. Challenges Most methods of measuring β diversity have been developed in a spatial context. We discuss the additional challenges involved in the assessment of temporal change, summarize existing methodological approaches, highlight the importance of establishing relevant baselines, and identify the need for appropriate null models of temporal β diversity. Given considerable potential for research on the macroecology of temporal β diversity we suggest future directions and challenges. Conclusions Although data availability remains the main impediment to improved quantification of temporal β diversity at macroecological scales, there are substantial opportunities for improved methodology and theory. Taxonomic β diversity has received most attention, but other dimensions of diversity, including functional and phylogenetic, should be part of integrated assessments of biodiversity change. Future approaches need to be ecologically meaningful and interpretable as well as statistically robust.

show abstract

“…For example, studies on the scaling of population fluctuations revealed the central role of temporal environmental variability in shaping ecological communities (Chisholm et al, 2014;Jabot & Lohier, 2016;Kalyuzhny, Schreiber, et al, 2014;Kalyuzhny, Kadmon, & Shnerb, 2015), and studies focusing on long-term changes in abundance and diversity revealed that population-level regulation is often weak (Kalyuzhny, Seri, et al, 2014;Knape & de Valpine, 2012;Ziebarth, Abbott, & Ives, 2010), while total-abundance and species diversity are indeed regulated (Brown, Ernest, Parody, & Haskell, 2001;Goheen, White, Ernest, & Brown, 2005;Gotelli et al, 2017;Magurran & Henderson, 2018). Moreover, in recent years there is an increasing interest in understanding richness trends and compositional turnover, partly motivated by concerns over the effect of anthropogenic activities on ecological communities (Elahi et al, 2015;Magurran et al, 2018;McGill et al, 2015;Vellend et al, 2013). Several studies have shown that while some local communities show richness trends, negative and positive changes may cancel out in multiple communities worldwide (Dornelas et al, 2014;Vellend et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Null models for community dynamics: Beware of the cyclic shift algorithm

Kalyuzhny

2019

Preprint

View full text Add to dashboard Cite

AimTemporal patterns of community dynamics are drawing increasing interest due to their potential to shed light on assembly processes and anthropogenic effects. However, interpreting such patterns considerably benefits from comparing observed dynamics to the reference of a null model. For that aim, the cyclic shift permutations algorithm, which generates randomized null communities based on empirically observed time series, has recently been proposed. The use of this algorithm, which shifts each species time series randomly in time, has been justified by the claim that it preserves the temporal autocorrelation of single species. Hence it has been used to test the significance of various community patterns, in particular excessive compositional changes, biodiversity trends and community stability. InnovationHere we critically study the properties of the cyclic shift algorithm for the first time. We show that, unlike previously suggested, this algorithm does not preserve temporal autocorrelation due to the need to "wrap" the time series and assign the last observations to the first years.Moreover, this algorithm scrambles the initial state of the community, making any dynamics that results from deviations from equilibrium seem excessive. We exemplify that these two issues lead to a highly elevated type I error rate in tests for excessive compositional changes and richness trends. ConclusionsCaution is needed when using the cyclic shift permutation algorithm and interpreting results obtained using it. Interpretation is further complicated because the algorithm removes all correlations between species. We suggest guidelines for using this method and discuss several possible alternative approaches. More research is needed on the best practices for using null models for temporal patterns.

show abstract

More than the sum of the parts: annual partitioning within spatial guilds underpins community regulation

Cited by 9 publications

References 35 publications

Species insurance trumps spatial insurance in stabilizing biomass of a marine macroalgal metacommunity

Species insurance trumps spatial insurance in stabilizing biomass of a marine macroalgal metacommunity

Temporal β diversity—A macroecological perspective

Null models for community dynamics: Beware of the cyclic shift algorithm

Contact Info

Product

Resources

About