Context dependency and metacommunity structuring in boreal headwater streams

Heino, Jani; Grönroos, Mira; Soininen, Janne; Virtanen, Risto; Muotka, Timo

doi:10.1111/j.1600-0706.2011.19715.x

Cited by 164 publications

(267 citation statements)

References 56 publications

(82 reference statements)

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“…The current study adds to a growing body of literature concerning the mechanisms structuring species distributions (Frisch et al 2012, Heino et al 2012, Peres-Neto et al 2012. Specifically, this study offers a novel way to analyze community data to address the relative impact of variables v www.esajournals.org belonging to different classes.…”

Section: Discussionmentioning

confidence: 99%

Relative importance of environmental, geographic, and spatial variables on zooplankton metacommunities

Dallas

Drake

2014

Ecosphere

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Abstract. Understanding the factors responsible for structuring ecological communities is a central goal in community ecology. Previous work has focused on determining the relative roles of two classes of variables (e.g., spatial and environmental) on community composition. However, this approach may ignore the disproportionate impact of variables within classes, and is often confounded by spatial autocorrelation leading to collinearity among variables of different classes. Here, we combine pattern-based metacommunity and machine learning analyses to characterize metacommunity structure of zooplankton from lakes in the northeast United States and to identify environmental, spatial, and geographic covariates associated with metacommunity structure. Analyses were performed for the entire metacommunity and for three zooplankton subsets (cladocerans, copepods, and rotifers), as the variables associated with community structure in these groups were hypothesized to differ. Species distributions of all subsets adhered to an environmental, spatial, and/or geographic gradient, but differed in metacommunity pattern, as copepod species distributions responded independently of one another, while the entire zooplankton metacommunity, cladocerans, and rotifers replaced one another in discrete groups. While environmental variables were nearly always the most important to metacommunity structure, the relative importance of variables differed among zooplankton subsets, suggesting that zooplankton subsets differ in their environmental tolerances and dispersal-limitation.

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Section: Discussionmentioning

confidence: 99%

Relative importance of environmental, geographic, and spatial variables on zooplankton metacommunities

Dallas

Drake

2014

Ecosphere

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“…However, they also admitted that eigenvector methods have been subjected to rigorous statistical evaluation and have several desirable aspects in decomposing scale-dependent community structuring (Borcard and Legendre 2002;Dray et al 2006;Peres-Neto et al 2006). Unless more accurate models are developed, variation partitioning and spatial modeling techniques used here are effective tools, as the starting point, in exploring regional or local drivers of community patterns (Gilbert and Bennett 2010;Heino et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

“…The third method now is more frequently used in biogeography because of its ability to dissect complex spatial patterns. In contrast, almost all studies on algal patterns still use the first two methods (but see Heino et al 2012). Furthermore, most of these studies only focus on diatoms instead of the entire algal community (Stomp et al 2011), with interest on either richness or community composition, resulting in an incomplete description of algal biogeography.…”

Section: Introductionmentioning

confidence: 99%

Disentangling the roles of spatial and environmental variables in shaping benthic algal assemblages in rivers of central and northern China

Tang

et al. 2013

Aquat Ecol

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Benthic algae were collected from central and northern Chinese rivers to test the hypothesis that geographic location has significant contributions in shaping algal assemblages. We used Moran's eigenvector maps (MEM) to model spatial components and variation partitioning to quantify the influences of spatial and environmental variables on regional patterns of algal richness and community composition, respectively. We found that variation in algal richness was attributed to MEM component 2, 8, and 9 and the quadratic term of N-NO 3 . Regarding abundance data, latitude, longitude, and MEM component 1, 2, and 7 were important spatial variables. Although P-PO 4 , pH, and annual mean temperature were significant environmental variables influencing algal community composition, they were all spatially structured. Among the total explained variance in both algal metrics, spatial proportions were higher than that of environmental variables. We also found that abundant species of Achnanthidium minutissimum, Cocconeis placentula, Cymbella delicatula, Cymbella affinis, Cymbella turgidula, and Synedra ulna displayed clear spatially related patterns. In conclusion, the contributions of spatial and environmental variables to regional variation of algal assemblages are scaledependent. As for our study scale (*1,000 km), spatial control may be more important. Since spatial effects could obscure local environmental impacts on algal communities, appropriate study scale and statistical methods should be taken into account in algal bioassessment. We recommend inclusion of both algal richness and community composition in study of algal Handling Editor: Piet Spaak

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“…We used Principal Coordinates of Neighbor Matrices (PCNM, see Appendix and Supplement) (Borcard et al 2004), which is a special case of distance-based Moran Eigenvector Map (MEM) analysis , to calculate spatial filters from sampling plot coordinates (Borcard and Legendre 2002, Borcard et al 2004, De Bie et al 2012, Legendre et al 2012, Heino et al 2012. PCNM eigenvectors with positive eigenvalues were used as variables to represent different scales of heterogeneity (i.e., spatial filters), with the first eigenvector (PCNM 1) representing the broadest spatial gradient, and each successive eigenvector representing finer scale spatial structure (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…In dispersal-based hypotheses (H2: dispersal-based biogeography) (Hubbell 2001(Hubbell , 2005, emigration and immigration rates in a metacommunity control the composition of local communities and regional diversity patterns; consequently, spatially autocorrelated diversity patterns (spatial structure) arise in a landscape because more propagules move between sites in close spatial proximity than sites that are far apart. Metacommunity theory further predicts that habitat heterogeneity and functional diversity determine the degree to which local (H1, species-sorting by environmental filters) and regional (H2, source-sink dispersal-based dynamics) processes interact to determine local assemblage composition and regional diversity patterns (Martiny et al 2006, Dumbrell et al 2009, Bru et al 2010, Caruso et al 2011, Logue et al 2011, Heino et al 2012.…”

Section: Introductionmentioning

confidence: 99%

Local and regional influences over soil microbial metacommunities in the Transantarctic Mountains

Sokol¹,

Herbold²,

Lee³

et al. 2013

Ecosphere

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Abstract. The metacommunity concept provides a useful framework to assess the influence of local and regional controls over diversity patterns. Culture-independent studies of soil microbial communities in the McMurdo Dry Valleys of East Antarctica (778 S) have shown that bacterial diversity is related to soil geochemical gradients, while studies targeting edaphic cyanobacteria have linked local diversity patterns to dispersal-based processes. In this study, we increased the spatial extent of observed soil microbial communities to cover the Beardmore Glacier region in the central Transantarctic Mountains (848 S). We used community profiling techniques to characterize diversity patterns for bacteria and the cyanobacterial subcomponent of the microbial community. Diversity partitioning was used to calculate beta diversity and estimate among-site dissimilarity in the metacommunity. We then used variation partitioning to assess the relationship between beta diversity and environmental and spatial gradients. We found that dominant groups in the soil bacterial metacommunity were influenced by gradients in pH and soil moisture at the Transantarctic scale (800 km). Conversely, beta diversity for the cyanobacterial component of the edaphic microbial metacommunity was decoupled from these environmental gradients, and was more related to spatial filters, suggesting that wind-driven dispersal dynamics created cyanobacterial biogeography at a local scale (,3 km).

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Context dependency and metacommunity structuring in boreal headwater streams

Cited by 164 publications

References 56 publications

Relative importance of environmental, geographic, and spatial variables on zooplankton metacommunities

Relative importance of environmental, geographic, and spatial variables on zooplankton metacommunities

Disentangling the roles of spatial and environmental variables in shaping benthic algal assemblages in rivers of central and northern China

Local and regional influences over soil microbial metacommunities in the Transantarctic Mountains

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