For a scientific discipline to be interdisciplinary it must satisfy two conditions; it must consist of contributions from at least two existing disciplines and it must be able to provide insights, through this interaction, that neither progenitor discipline could address. In this paper, I examine the complete body of peer-reviewed literature self-identified as landscape genetics using the statistical approaches of text mining and natural language processing. The goal here is to quantify the kinds of questions being addressed in landscape genetic studies, the ways in which questions are evaluated mechanistically, and how they are differentiated from the progenitor disciplines of landscape ecology and population genetics. I then circumscribe the main factions within published landscape genetic papers examining the extent to which emergent questions are being addressed and highlighting a deep bifurcation between existing individual- and population-based approaches. I close by providing some suggestions on where theoretical and analytical work is needed if landscape genetics is to serve as a real bridge connecting evolution and ecology sensu lato.
Whether they are used to describe fitness, genome architecture or the spatial distribution of environmental variables, the concept of a landscape has figured prominently in our collective reasoning. The tradition of landscapes in evolutionary biology is one of fitness mapped onto axes defined by phenotypes or molecular sequence states. The characteristics of these landscapes depend on natural selection, which is structured across both genomic and environmental landscapes, and thus, the bridge among differing uses of the landscape concept (i.e. metaphorically or literally) is that of an adaptive phenotype and its distribution across geographical landscapes in relation to selective pressures. One of the ultimate goals of evolutionary biology should thus be to construct fitness landscapes in geographical space. Natural plant populations are ideal systems with which to explore the feasibility of attaining this goal, because much is known about the quantitative genetic architecture of complex traits for many different plant species. What is less known are the molecular components of this architecture. In this issue of Molecular Ecology, Parchman et al. (2012) pioneer one of the first truly genome-wide association studies in a tree that moves us closer to this form of mechanistic understanding for an adaptive phenotype in natural populations of lodgepole pine (Pinus contorta Dougl. ex Loud.).
A recent commentary in Molecular Ecology by Petit (2008) paints a rather grim picture of the utility of nested clade phylogeographical analysis (NCPA) for inferring population history. Drawing on simulation studies based on single locus data sets, including the recent work by Panchal & Beaumont (2007), the potential fallibility of NCPA was characterized as being so dire that the method should be abandoned until further evidence in support of its legitimacy emerges. Here, we reconsider the arguments presented by Petit in light of practical approaches for validating or strengthening inferences drawn from NCPA. As with any method that attempts to distinguish processes and events that shaped spatial-genetic structuring throughout complex evolutionary histories of natural populations, we propose that treatment of NCPA inferences should be set in the context of corroborating evidence (or lack thereof) that support those inferences. Indeed, results from computer simulation, studies lend no support to the idea that NCPA should not be employed for generating plausible hypotheses (i.e. consistent with species biology and landscape history) that can be further tested using other methods. Moreover, cross-validation of NCPA inferences via assessment of multiple independent loci, complementary analyses, and/or prior expectations, should at least partly — perhaps considerably — counter high false-positive rates reported for some inferences. NCPA uniquely offers the ability to explore patterns relating to complex, historical scenarios: an over-reaction to Panchal & Beaumont (2007) should not precipitate throwing out an approach currently with no computationally feasible substitute.
In this issue of Molecular Ecology, authors Robledo-Arnuncio & Garcia present a compelling approach for quantifying seed dispersal in plant populations. Building upon methods previously used for quantification of pollen dispersal, the authors not only examine the behavior of the model with respect to sample sizes, dispersal distance, and the kurtosis of the dispersal function but also provide an empirical example using Prunus mahaleb.