Identifying adaptive loci can provide insight into the mechanisms underlying local adaptation. Genotype–environment association (GEA) methods, which identify these loci based on correlations between ...genetic and environmental data, are particularly promising. Univariate methods have dominated GEA, despite the high dimensional nature of genotype and environment. Multivariate methods, which analyse many loci simultaneously, may be better suited to these data as they consider how sets of markers covary in response to environment. These methods may also be more effective at detecting adaptive processes that result in weak, multilocus signatures. Here, we evaluate four multivariate methods and five univariate and differentiation‐based approaches, using published simulations of multilocus selection. We found that Random Forest performed poorly for GEA. Univariate GEAs performed better, but had low detection rates for loci under weak selection. Constrained ordinations, particularly redundancy analysis (RDA), showed a superior combination of low false‐positive and high true‐positive rates across all levels of selection. These results were robust across the demographic histories, sampling designs, sample sizes and weak population structure tested here. The value of combining detections from different methods was variable and depended on the study goals and knowledge of the drivers of selection. Re‐analysis of genomic data from grey wolves highlighted the unique, covarying sets of adaptive loci that could be identified using RDA. Although additional testing is needed, this study indicates that RDA is an effective means of detecting adaptation, including signatures of weak, multilocus selection, providing a powerful tool for investigating the genetic basis of local adaptation.
Landscape Genetics Holderegger, Rolf; Wagner, Helene H
Bioscience,
03/2008, Letnik:
58, Številka:
3
Journal Article
Recenzirano
Odprti dostop
Landscape genetics is a rapidly evolving interdisciplinary field that integrates approaches from population genetics and landscape ecology. In the context of habitat fragmentation, the current focus ...of landscape genetics is on assessing the degree to which landscapes facilitate the movement of organisms (landscape connectivity) by relating gene-flow patterns to landscape structure. Neutral genetic variation among individuals or direct estimates of current gene flow are statistically related to landscape characteristics such as the presence of hypothesized barriers or the least-cost distance for an organism to move from one habitat patch to another, given the nature of the intervening matrix or habitat types. In the context of global change, a major challenge for landscape genetics is to address the spread of adaptive variation across landscapes. Genome scans combined with genetic sample collection along environmental gradients or in different habitat types attempt to identify molecular markers that are statistically related to specific environmental conditions, indicating adaptive genetic variation. The landscape genetics of adaptive variation may also help answer fundamental questions about the collective evolution of populations.
Summary
Spatial autocorrelation jeopardizes the validity of statistical inference, for example correlation and regression analysis. Restricted randomization methods can account for the effect of ...spatial autocorrelation in the observed data by building it into an empirical null model for hypothesis testing. This can be achieved, for example, based on conditional simulation, which fits a highly parameterized geostatistical model to the observed spatial structure, or, for data observed on a regular transect or grid, with Fourier spectral randomization methods that can flexibly model spatial structure at any scale. This study uses Moran eigenvector maps to extend spectral randomization to irregularly spaced samples.
We present different algorithms to perform restricted randomization to suit different types of research questions: individual randomization of each variable, joint randomization of a group of variables while keeping within‐group correlations fixed, and randomization with a fixed correlation between original data and randomized replicates (e.g. as input for simulation studies). The performance of the proposed Moran spectral randomization methods for regularly and irregularly spaced samples is assessed with correlation analysis of simulated data.
Moran spectral randomization closely matched the spatial structure of original simulated data sets, with identical or nearly identical Moran's I values and power spectra, depending on the algorithm. In correlation analysis of two spatially autocorrelated variables, Moran spectral randomization produced correct type I error rates for stationary spatial data, even for very small and highly irregular samples, but was sensitive to linear trend. When one or both variables lacked spatial structure, Moran spectral randomization tests were more conservative than correlation t‐tests.
The proposed Moran spectral randomization method requires a minimum of parameterization and is able to address multivariate data with spatial structure at multiple scales, with the option of controlling levels of correlation with the original data. It can provide technically unlimited numbers of randomizations even for small samples while closely maintaining the spatial characteristics of uni‐ or multivariate data at all spatial scales. The method is applicable for correlation analysis of stationary, autocorrelated spatial or temporal series. Further research should assess whether the method can be extended to multiple regression analysis.
Urbanization is altering landscapes globally at an unprecedented rate. While ecological differences between urban and rural environments often promote phenotypic divergence among populations, it is ...unclear to what degree these trait differences arise from genetic divergence as opposed to phenotypic plasticity. Furthermore, little is known about how specific landscape elements, such as green corridors, impact genetic divergence in urban environments. We tested the hypotheses that: (1) urbanization, and (2) proximity to an urban green corridor influence genetic divergence in common milkweed (Asclepias syriaca) populations for phenotypic traits. Using seeds from 52 populations along three urban-to-rural subtransects in the Greater Toronto Area, Canada, one of which followed a green corridor, we grew ~ 1000 plants in a common garden setup and measured > 20 ecologically-important traits associated with plant defense/damage, reproduction, and growth over four years. We found significant heritable variation for nine traits within common milkweed populations and weak phenotypic divergence among populations. However, neither urbanization nor an urban green corridor influenced genetic divergence in individual traits or multivariate phenotype. These findings contrast with the expanding literature demonstrating that urbanization promotes rapid evolutionary change and offer preliminary insights into the eco-evolutionary role of green corridors in urban environments.
1. Understanding the drivers and spatial scale of gene flow is essential for the management of species living in fragmented landscapes. In plants, contemporary pollen flow is typically modelled as a ...single spatial process, with pollen flow declining exponentially within a short distance of mother plants. However, growing evidence suggests that many species do not conform to these patterns, often showing an excess of long-distance dispersal events or sometimes even multimodality in dispersal kernels. This suggests that a single function might be insufficient to capture the true complexity of pollination, which in reality is often achieved by multiple pollinators that vary in their foraging ranges and interactions with the landscape. 2. We reconstructed realized pollen flow and assessed pollen immigration for seven populations of the insect-pollinated herb Pulsatilla vulgaris. We quantified the effects of distance, floral resources and landscape composition over multiple spatial scales and tested the hypotheses that within-population pollen flow is related to resources and landscape context measured locally, and that among-population pollen flow is related to features measured at larger spatial scales. 3. We found that pollen flow within populations was more likely to occur amongst near neighbours, but that among-population pollen flow was random with respect to source populations. We further found that local floral density could explain patterns of within-population pollination distances and population-level selfing rates, whereas pollen immigration rates were best explained by the proportion of forest within a radius of 500 m around focal populations. 4. Synthesis. Together, our results suggest that within- and among-population contemporary pollen flow may be governed by different underlying processes, possibly related to differences in the foraging range and habitat use of bee species that contribute to pollination at different scales. This highlights the critical need for researchers to take a more pollinator-eyed view of contemporary pollen flow in plants by (1) recognizing that within- and among-population gene flow by pollen may depend on different sets of pollinators that respond to features at different spatial scales (2) considering additional factors that may alter attractiveness, detectability and accessibility of plants to pollinators beyond the effects of distance.
1. Dispersal is essential for species to survive the threats of habitat destruction and climate change. Combining descriptions of dispersal ability with those of landscape structure, the concept of ...functional connectivity has been popular for understanding and predicting species' spatial responses to environmental change. 2. Following recent advances, the functional connectivity concept is now able to move beyond landscape structure to consider more explicitly how other external factors such as climate and resources affect species movement. We argue that these factors, in addition to a consideration of the complete dispersal process, are critical for an accurate understanding of functional connectivity for plant species in response to environmental change. 3. We use recent advances in dispersal, landscape and molecular ecology to describe how a range of external factors can influence effective dispersal in plant species, and how the resulting functional connectivity can be assessed. 4. Synthesis. We define plant functional connectivity as the effective dispersal of propagules or pollen among habitat patches in a landscape. Plant functional connectivity is determined by a combination of landscape structure, interactions between plant, environment and dispersal vectors, and the successful establishment of individuals. We hope that this consolidation of recent research will help focus future connectivity research and conservation.
The linear regression model, with its numerous extensions including multivariate ordination, is fundamental to quantitative research in many disciplines. However, spatial or temporal structure in the ...data may invalidate the regression assumption of independent residuals. Spatial structure at any spatial scale can be modeled flexibly based on a set of uncorrelated component patterns (e.g., Moran's eigenvector maps, MEM) that is derived from the spatial relationships between sampling locations as defined in a spatial weight matrix. Spatial filtering thus addresses spatial autocorrelation in the residuals by adding such component patterns (spatial eigenvectors) as predictors to the regression model. However, space is not an ecologically meaningful predictor, and commonly used tests for selecting significant component patterns do not take into account the specific nature of these variables. This paper proposes "spatial component regression" (SCR) as a new way of integrating the linear regression model with Moran's eigenvector maps. In its unconditioned form, SCR decomposes the relationship between response and predictors by component patterns, whereas conditioned SCR provides an alternative method of spatial filtering, taking into account the statistical properties of component patterns in the design of statistical hypothesis tests. Application to the well-known multivariate mite data set illustrates how SCR may be used to condition for significant residual spatial structure and to identify additional predictors associated with residual spatial structure. Finally, I argue that all variance is spatially structured, hence spatial independence is best characterized by a lack of excess variance at any spatial scale, i.e., spatial white noise.
Functional trait diversity is a popular tool in modern ecology, mainly used to infer assembly processes and ecosystem functioning. Patterns of functional trait diversity are shaped by ecological ...processes such as environmental filtering, species interactions and dispersal that are inherently spatial, and different processes may operate at different spatial scales. Adding a spatial dimension to the analysis of functional trait diversity may thus increase our ability to infer community assembly processes and to predict change in assembly processes following disturbance or land‐use change. Richness, evenness and divergence of functional traits are commonly used indices of functional trait diversity that are known to respond differently to large‐scale filters related to environmental heterogeneity and dispersal and fine‐scale filters related to species interactions (competition). Recent developments in spatial statistics make it possible to separately quantify large‐scale patterns (variation in local means) and fine‐scale patterns (variation around local means) by decomposing overall spatial autocorrelation quantified by Moran's coefficient into its positive and negative components using Moran eigenvector maps (MEM). We thus propose to identify the spatial signature of multiple ecological processes that are potentially acting at different spatial scales by contrasting positive and negative components of spatial autocorrelation for each of the three indices of functional trait diversity. We illustrate this approach with a case study from riparian plant communities, where we test the effects of disturbance on spatial patterns of functional trait diversity. The fine‐scale pattern of all three indices was increased in the disturbed versus control habitat, suggesting an increase in local scale competition and an overall increase in unexplained variance in the post‐disturbance versus control community. Further research using simulation modeling should focus on establishing the proposed link between community assembly rules and spatial patterns of functional trait diversity to maximize our ability to infer multiple processes from spatial community structure.
The metacommunity concept provides a spatial perspective on community dynamics, and the landscape provides the physical template for a metacommunity. Several aspects of landscape heterogeneity, such ...as landscape diversity and composition, and characteristics of the matrix between habitat patches such as habitat connectivity, and geometry of habitat patches, may moderate metacommunity processes. These aspects of landscape heterogeneity are rarely considered explicitly in the metacommunity discussion, however. We propose landscape contrast (i.e., the average dissimilarity in habitat quality between neighboring patches) as a key dimension of landscape heterogeneity. The concept of landscape contrast unifies discrete and continuous landscape representations (homogeneous, gradient, mosaic and binary) and offers a means to integrate landscape heterogeneity in the metacommunity concept. Landscape contrast as perceived by the organisms affects several fundamental metacommunity processes and may thus constrain which metacommunity models may be observed. In a review of empirical metacommunity studies (
n
= 123), only 22 % of studies were explicit about their underlying landscape model assumptions, with striking differences among taxonomic groups. The assumed landscape model constrained, but did not determine, metacommunity models. Integration and explicit investigation of landscape contrast effects in metacommunity studies are likely to advance ecological theory and facilitate its application to real-world conservation problems.
PDF
