No abstract

Dispersal is a process of central importance for the ecological and evolutionary dynamics of populations and communities, because of its diverse consequences for gene flow and demography. It is subject to evolutionary change, which begs the question, what is the genetic basis of this potentially complex trait? To address this question, we (i) review the empirical literature on the genetic basis of dispersal, (ii) explore how theoretical investigations of the evolution of dispersal have represented the genetics of dispersal, and (iii) discuss how the genetic basis of dispersal influences theoretical predictions of the evolution of dispersal and potential consequences.Dispersal has a detectable genetic basis in many organisms, from bacteria to plants and animals. Generally, there is evidence for significant genetic variation for dispersal or dispersal‐related phenotypes or evidence for the micro‐evolution of dispersal in natural populations. Dispersal is typically the outcome of several interacting traits, and this complexity is reflected in its genetic architecture: while some genes of moderate to large effect can influence certain aspects of dispersal, dispersal traits are typically polygenic. Correlations among dispersal traits as well as between dispersal traits and other traits under selection are common, and the genetic basis of dispersal can be highly environment‐dependent.By contrast, models have historically considered a highly simplified genetic architecture of dispersal. It is only recently that models have started to consider multiple loci influencing dispersal, as well as non‐additive effects such as dominance and epistasis, showing that the genetic basis of dispersal can influence evolutionary rates and outcomes, especially under non‐equilibrium conditions. For example, the number of loci controlling dispersal can influence projected rates of dispersal evolution during range shifts and corresponding demographic impacts. Incorporating more realism in the genetic architecture of dispersal is thus necessary to enable models to move beyond the purely theoretical towards making more useful predictions of evolutionary and ecological dynamics under current and future environmental conditions. To inform these advances, empirical studies need to answer outstanding questions concerning whether specific genes underlie dispersal variation, the genetic architecture of context‐dependent dispersal phenotypes and behaviours, and correlations among dispersal and other traits.

At present, the disciplines of evolutionary biology and ecosystem science are weakly integrated. As a result, we have a poor understanding of how the ecological and evolutionary processes that create, maintain, and change biological diversity affect the flux of energy and materials in global biogeochemical cycles. The goal of this article was to review several research fields at the interfaces between ecosystem science, community ecology and evolutionary biology, and suggest new ways to integrate evolutionary biology and ecosystem science. In particular, we focus on how phenotypic evolution by natural selection can influence ecosystem functions by affecting processes at the environmental, population and community scale of ecosystem organization. We develop an eco-evolutionary model to illustrate linkages between evolutionary change (e.g. phenotypic evolution of producer), ecological interactions (e.g. consumer grazing) and ecosystem processes (e.g. nutrient cycling). We conclude by proposing experiments to test the ecosystem consequences of evolutionary changes.

Summary1. Precipitation is considered to be a key driver of ecosystem processes in mesic grasslands, and climate models predict changes in the amount and intensity of precipitation under future global change scenarios. Although most experimental rainfall studies decrease precipitation, seasonal rainfall is predicted to increase in the northern Great Plains under climate change. 2. We analysed changes in community composition and structure of upland and lowland native tallgrass prairie in central Kansas, USA, subjected to 19 years of irrigation designed to eliminate moisture stress throughout the growing season. 3. Irrigation had limited effects on species richness in both upland and lowland prairie. Total cover increased significantly and consistently with irrigation in drier uplands and in more mesic lowlands. Abundance of rhizomatous, tall, perennial species as well as C 3 forbs increased with irrigation. 4. The strongest response to irrigation came within the dominant functional type, C 4 perennial grasses. Panicum virgatum became the dominant species in irrigated lowlands, whereas Andropogon gerardii remained the dominant species in irrigated uplands and in control plots. Overall, irrigation had less effect on community composition and structure than other known drivers of grassland structure and function. 5. In comparison with other studies, our results demonstrate that water addition has less of an impact than fire, grazing or nitrogen addition on composition and dynamics in this mesic grassland. The strongest response to long-term irrigation occurred within the dominant functional type: tall, perennial, rhizomatous, C 4 grasses. Thus, functional redundancy will buffer this ecosystem from potential increases in rainfall due to climate change. Finally, our results highlight the limited utility of qualitative functional traits to predict how this mesic grassland will respond to climate change.

We estimated the historical range of variability (HRV) of forest landscape structure under natural disturbance regimes at the scale of a physiographic province (Oregon Coast Range, 2 million ha) and evaluated the similarity to HRV of current and future landscapes under alternative management scenarios. We used a stochastic fire simulation model to simulate presettlement landscapes and quantified the HRV of landscape structure using multivariate analysis of landscape metrics. We examined two alternative policy scenarios simulated by two spatially explicit simulation models: (1) current management policies for 100 years into the future and (2) the wildfire scenario with no active management until it reached the HRV.The simulation results indicated that historical landscapes of the province were dynamic, composed of patches of various sizes and age classes ranging from 0 to Ͼ800 years including numerous, small, unburned forest islands. The current landscape was outside the HRV. The landscape did not return to the HRV in the 100 years under either scenario, largely because of lack of old-growth forests and the abundance of young forests. Under the current policy scenario, development of landscape structure was limited by the spatial arrangement of different ownerships and the highly contrasting management regimes among ownerships. As a result, the vegetation pattern after 100 years reflected the ownership pattern. Surprisingly, the wildfire scenario initially moved the landscape away from the HRV during the first 100 years, after which it moved toward the HRV, but it required many more centuries to reach it. Extensive forest management and human-caused fires in the 20th century have left legacies on the landscape that could take centuries to be obliterated by wildfire.Departure from the HRV can serve as an indicator of landscape conditions, but results depend on scale and quantification of landscape heterogeneity. The direct application of the concept of HRV to forest policy and management in large landscapes is often limited since not all ownerships may have ecological goals and future climate change is anticipated. Natural disturbance-based management at large scales would not show the projected effects on landscape structure within a typical policy time frame in highly managed landscapes.