Genetic structure of a foundation species: scaling community phenotypes from the individual to the region

118

Research in community genetics seeks to understand how the dynamic interplay between ecology and evolution shapes simple and complex communities and ecosystems. A community genetics perspective, however, may not be necessary or informative for all studies and systems. To better understand when and how intraspecific genetic variation and microevolution are important in community and ecosystem ecology, we suggest future research should focus on three areas: (i) determining the relative importance of intraspecific genetic variation compared with other ecological factors in mediating community and ecosystem properties; (ii) understanding the importance of microevolution in shaping ecological dynamics in multi-trophic communities; and (iii) deciphering the phenotypic and associated genetic mechanisms that drive community and ecosystem processes. Here, we identify key areas of research that will increase our understanding of the ecology and evolution of complex communities but that are currently missing in community genetics. We then suggest experiments designed to meet these current gaps.

Section: What Is the Relative Importance Of Intraspecific Genetic Varmentioning

confidence: 99%

Community genetics: what have we accomplished and where should we be going?

Hersch‐Green

Turley

Johnson

2011

118

“…The central tenet of autoregression-based approaches is that only patterns in the specific phenotypic components are free of phylogenetic non-independence (box 1). Though now rarely used in cross-species analyses, spatial and genetic autocorrelation approaches have been used more recently in analyses of within-species character divergence [71] and community genetics [13,23,73]. Box 1.…”

Section: Coping With Phylogenetic Non-independence In Cross-species Cmentioning

confidence: 99%

“…Pairwise distances can be derived from a user-defined hierarchy (e.g. of spatial groupings or taxonomy) or from empirical, geographical or phylogenetic distances [13,23,70,71]. Specific trait values for each of n species are estimated by fitting an autoregression model of the form:…”

Section: Coping With Phylogenetic Non-independence In Cross-species Cmentioning

confidence: 99%

“…Many studies treat populations as statistically independent replicates (if using population means) or incorporate population as a fixed effect in analyses using individual/ clone data, with each population contributing equal weight [2,[10][11][12][13]18,[22][23][24][25]. However, two processes operating at the population level (figures 1 and 2)-exchange of migrants (gene flow) and shared common ancestry (phylogenetic non-independence)-mean that sampled population phenotypes (and hence associated community-extended phenotypes) are often not statistically independent.…”

Section: Introductionmentioning

confidence: 99%

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Controlling for non-independence in comparative analysis of patterns across populations within species

Stone¹,

Nee²,

Felsenstein

2011

135

127

How do we quantify patterns (such as responses to local selection) sampled across multiple populations within a single species? Key to this question is the extent to which populations within species represent statistically independent data points in our analysis. Comparative analyses across species and higher taxa have long recognized the need to control for the non-independence of species data that arises through patterns of shared common ancestry among them (phylogenetic non-independence), as have quantitative genetic studies of individuals linked by a pedigree. Analyses across populations lacking pedigree information fall in the middle, and not only have to deal with shared common ancestry, but also the impact of exchange of migrants between populations (gene flow). As a result, phenotypes measured in one population are influenced by processes acting on others, and may not be a good guide to either the strength or direction of local selection. Although many studies examine patterns across populations within species, few consider such non-independence. Here, we discuss the sources of non-independence in comparative analysis, and show why the phylogeny-based approaches widely used in cross-species analyses are unlikely to be useful in analyses across populations within species. We outline the approaches (intraspecific contrasts, generalized least squares, generalized linear mixed models and autoregression) that have been used in this context, and explain their specific assumptions. We highlight the power of 'mixed models' in many contexts where problems of non-independence arise, and show that these allow incorporation of both shared common ancestry and gene flow. We suggest what can be done when ideal solutions are inaccessible, highlight the need for incorporation of a wider range of population models in intraspecific comparative methods and call for simulation studies of the error rates associated with alternative approaches.

“…Owing to unidirectional introgression, hybridization represents a partial genetic continuum between P. fremontii and P. angustifolia, which results in a large amount of genetic variation (Keim et al 1989;Martinsen et al 2001) with associated variation in community and ecosystem phenotypes that affect individuals, community structure, biodiversity and ecosystem processes . The abundance of ecological data on Populus has great potential for synthesizing the effects of introgression across levels of organization and environments (Bangert et al 2008).…”

Section: Introductionmentioning

confidence: 99%

From genes to ecosystems: a synthesis of the effects of plant genetic factors across levels of organization

Bailey

Schweitzer

Úbeda

et al. 2009

236

272

Using two genetic approaches and seven different plant systems, we present findings from a metaanalysis examining the strength of the effects of plant genetic introgression and genotypic diversity across individual, community and ecosystem levels with the goal of synthesizing the patterns to date. We found that (i) the strength of plant genetic effects can be quite high; however, the overall strength of genetic effects on most response variables declined as the levels of organization increased. (ii) Plant genetic effects varied such that introgression had a greater impact on individual phenotypes than extended effects on arthropods or microbes/fungi. By contrast, the greatest effects of genotypic diversity were on arthropods. (iii) Plant genetic effects were greater on above-ground versus below-ground processes, but there was no difference between terrestrial and aquatic environments. (iv) The strength of the effects of intraspecific genotypic diversity tended to be weaker than interspecific genetic introgression. (v) Although genetic effects generally decline across levels of organization, in some cases they do not, suggesting that specific organisms and /or processes may respond more than others to underlying genetic variation. Because patterns in the overall impacts of introgression and genotypic diversity were generally consistent across diverse study systems and consistent with theoretical expectations, these results provide generality for understanding the extended consequences of plant genetic variation across levels of organization, with evolutionary implications.