Theory suggests evolutionary change can significantly influence and act in tandem with ecological forces via ecological-evolutionary feedbacks. This theory assumes that significant evolutionary change occurs over ecologically relevant timescales and that phenotypes have differential effects on the environment. Here we test the hypothesis that local adaptation causes ecosystem structure and function to diverge. We demonstrate that populations of Trinidadian guppies (Poecilia reticulata), characterized by differences in phenotypic and population-level traits, differ in their impact on ecosystem properties. We report results from a replicated, common garden mesocosm experiment and show that differences between guppy phenotypes result in the divergence of ecosystem structure (algal, invertebrate, and detrital standing stocks) and function (gross primary productivity, leaf decomposition rates, and nutrient flux). These phenotypic effects are further modified by effects of guppy density. We evaluated the generality of these effects by replicating the experiment using guppies derived from two independent origins of the phenotype. Finally, we tested the ability of multiple guppy traits to explain observed differences in the mesocosms. Our findings demonstrate that evolution can significantly affect both ecosystem structure and function. The ecosystem differences reported here are consistent with patterns observed across natural streams and argue that guppies play a significant role in shaping these ecosystems.ecological-evolutionary feedbacks | intraspecific variation | ecosystem function E cosystem ecologists commonly view populations as homogeneous biomass pools in which individuals operate in identical ways to influence nutrient and energy flows (1). Individual organisms can influence ecosystem processes by altering their body size (material storage), changing their consumption and excretion characteristics (material flux) (2), modifying their internal stoichiometry (3), or physically altering their habitat (4, 5). Differences among individuals can, via natural selection, become converted into differences among populations and, hence, in the impact of a locally adapted population on the structure of its ecosystem. Furthermore, empirical evidence suggests the evolution of organismal traits that can affect habitat utilization happens on timescales similar to ecological processes (6). One possible consequence of rapid evolutionary change is that it can change ecological dynamics and set up feedbacks between ecological and evolutionary processes (7-9). Central to this hypothesis is the assumption that phenotypic variation translates into variation in how individuals and populations impact their environment (10).Prior research has already established the links between ecology and evolution. Laboratory studies focused on a model predator-prey interaction demonstrated that evolution of the prey population significantly altered the nature of predator-prey cycles (9). Evidence from natural or seminatural settings have shown t...

Summary 1. Ecological stoichiometry deals with the mass balance of multiple key elements [e.g. carbon (C), nitrogen (N), phosphorus (P)] in ecological systems. This conceptual framework, largely developed in the pelagic zone of lakes, has been successfully applied to topics ranging from population dynamics to biogeochemical cycling. More recently, an explicit stoichiometric approach has also been used in many other environments, including freshwater benthic ecosystems. 2. Description of elemental patterns among benthic resources and consumers provides a useful starting point for understanding causes of variation and stoichiometric imbalance in feeding interactions. Although there is considerable overlap among categories, terrestrially‐derived resources, such as wood, leaf litter and green leaves have substantially higher C : nutrient ratios than other resources of both terrestrial and aquatic origin, such as periphyton and fine particulate organic matter. The elemental composition of these resources for benthic consumers is modulated by a range of factors and processes, including nutrient availability and ratios, particle size and microbial colonisation. 3. Among consumers in benthic systems, bacteria are the most nutrient‐rich, followed (in descending order) by fishes, invertebrate predators, invertebrate primary consumers, and fungi. Differences in consumer C : nutrient ratios appear to be related to broad‐scale phylogenetic differences which determine body size, growth rate and resource allocation to structural body constituents (e.g. P‐rich bone). 4. Benthic consumers can influence the stoichiometry of dissolved nutrients and basal resources in multiple ways. Direct consumption alters the stoichiometry of food resources by increasing nutrient availability (e.g. reduced boundary layer thickness on substrata) or through removal of nutrient‐rich patches (e.g. selective feeding on fungal patches within leaf litter). In addition, consumers alter the stoichiometry of resources and dissolved nutrient pools through the return of egested or excreted nutrients. In some cases, consumer excretion supplies a large proportion of the nutrients required by algae and heterotrophic microbes and alters elemental ratios of dissolved nutrient pools. 5. Organic matter decomposition in benthic systems is accompanied by significant changes in the elemental composition of organic matter. Microbial colonisation of leaf litter influences C : nutrient ratios, and patterns of microbial succession (e.g. fungi followed by bacteria) may be under some degree of stoichiometric control. Large elemental imbalances exist between particulate organic matter and detritivores, which is likely to constrain growth rates and invertebrate secondary production. Such imbalances may therefore select for behavioural and other strategies for dealing with them. Comminution of large particles by benthic consumers alters detrital C : nutrient ratios and can influence the stoichiometry of elemental export from whole catchments. 6. A stoichiometric framework is l...

Rates of biogeochemical processes often vary widely in space and time, and characterizing this variation is critical for understanding ecosystem functioning. In streams, spatial hotspots of nutrient transformations are generally attributed to physical and microbial processes. Here we examine the potential for heterogeneous distributions of fish to generate hotspots of nutrient recycling. We measured nitrogen (N) and phosphorus (P) excretion rates of 47 species of fish in an N-limited Neotropical stream, and we combined these data with population densities in each of 49 stream channel units to estimate unit- and reach-scale nutrient recycling. Species varied widely in rates of N and P excretion as well as excreted N:P ratios (6-176 molar). At the reach scale, fish excretion could meet >75% of ecosystem demand for dissolved inorganic N and turn over the ambient NH4 pool in <0.3 km. Areal N excretion estimates varied 47-fold among channel units, suggesting that fish distributions could influence local N availability. P excretion rates varied 14-fold among units but were low relative to ambient concentrations. Spatial variation in aggregate nutrient excretion by fish reflected the effects of habitat characteristics (depth, water velocity) on community structure (body size, density, species composition), and the preference of large-bodied species for deep runs was particularly important. We conclude that the spatial distribution of fish could indeed create hotspots of nutrient recycling during the dry season in this species-rich tropical stream. The prevalence of patchy distributions of stream fish and invertebrates suggests that hotspots of consumer nutrient recycling may often occur in stream ecosystems.

Summary 1. The Lotic Intersite Nitrogen eXperiment (LINX) was a coordinated study of the relationships between North American biomes and factors governing ammonium uptake in streams. Our objective was to relate inter‐biome variability of ammonium uptake to physical, chemical and biological processes. 2. Data were collected from 11 streams ranging from arctic to tropical and from desert to rainforest. Measurements at each site included physical, hydraulic and chemical characteristics, biological parameters, whole‐stream metabolism and ammonium uptake. Ammonium uptake was measured by injection of 15N‐ammonium and downstream measurements of 15N‐ammonium concentration. 3. We found no general, statistically significant relationships that explained the variability in ammonium uptake among sites. However, this approach does not account for the multiple mechanisms of ammonium uptake in streams. When we estimated biological demand for inorganic nitrogen based on our measurements of in‐stream metabolism, we found good correspondence between calculated nitrogen demand and measured assimilative nitrogen uptake. 4. Nitrogen uptake varied little among sites, reflecting metabolic compensation in streams in a variety of distinctly different biomes (autotrophic production is high where allochthonous inputs are relatively low and vice versa). 5. Both autotrophic and heterotrophic metabolism require nitrogen and these biotic processes dominate inorganic nitrogen retention in streams. Factors that affect the relative balance of autotrophic and heterotrophic metabolism indirectly control inorganic nitrogen uptake.

We measured denitrification and total nitrate uptake rates in a small stream (East Fork of Walker Branch in eastern Tennessee) using a new field 15 N tracer addition and modeling approach that quantifies these rates for entire stream reaches. The field experiment consisted of an 8-h addition of 99 atom% K 15 NO 3 and a conservative solute tracer. Two 15 N tracer addition experiments were performed on consecutive days, the first under ambient NO concentra- (Ͼ99%) and comprised about 16% (Ϯ10%) of total NO uptake rate under ambient NO concentrations and about1% (Ϯ1%) of total NO 3 uptake rate with NO addition. Denitrification rate expressed on a mass flux basis was AcknowledgmentsWe thank Jeff Houser, Ramie Wilkerson, and Erica Lewis for their help in the field and laboratory; Suzanne Thomas for analysis of 15 N samples at the Marine Biological Lab; and Melody Bernot and Jennifer Tank for kindly providing the gas sampling vials. We also appreciate advice on sampling and sample analysis from Jennifer Tank and Melody Bernot. We thank David Harris, Stable Isotope Laboratory, University of California, Davis, for performing most of the 15 N analysis. We benefited greatly from discussions with Jim McClelland on transformations of the isotope data and with Wil Wollheim on model development. We also thank two anonymous reviewers for their constructive comments on an earlier version of the manuscript.