Data from: Shifting thresholds: rapid evolution of migratory life histories in steelhead/rainbow trout, Oncorhynchus mykiss

Expression of phenotypic plasticity depends on reaction norms adapted to historic selective regimes; anthropogenic changes in these selection regimes necessitate contemporary evolution or declines in productivity and possibly extinction. Adaptation of conditional strategies following a change in the selection regime requires evolution of either the environmentally influenced cue (e.g., size-at-age) or the state (e.g., size threshold) at which an individual switches between alternative tactics. Using a population of steelhead (Oncorhynchus mykiss) introduced above a barrier waterfall in 1910, we evaluate how the conditional strategy to migrate evolves in response to selection against migration. We created 9 families and 917 offspring from 14 parents collected from the above- and below-barrier populations. After 1 year of common garden-rearing above-barrier offspring were 11% smaller and 32% lighter than below-barrier offspring. Using a novel analytical approach, we estimate that the mean size at which above-barrier fish switch between the resident and migrant tactic is 43% larger than below-barrier fish. As a result, above-barrier fish were 26% less likely to express the migratory tactic. Our results demonstrate how rapid and opposing changes in size-at-age and threshold size contribute to the contemporary evolution of a conditional strategy and indicate that migratory barriers may elicit rapid evolution toward the resident life history on timescales relevant for conservation and management of conditionally migratory species

egg-data

Egg size and number data is in ‘egg-data.csv’ as follows: Column 2: MA: Individual ID of mother Column 3: ORIGIN: A or B (i.e. Above- or Below-barrier) Column 4: EGG_NO: total number of eggs in clutch Column 5: EGG_SIZE: in m

smolt-data

Offspring data is in ‘smolt-data.csv’ as follows: Column 1: PIT: Passive Integrated Transponder number used for individual ID Column 2: FL: Fork length in mm Column 3: mass: in grams Column 4: Sex: Male, Female, or NA (i.e. failed to assign) Column 5: cross: AA or BB (i.e. pure above-barrier cross or pure below-barrier cross) Column 6: Ma: Individual ID of mother Column 7: MOM: A or B (origin of Ma) Column 8: Pa: Individual ID of father Column 9: DAD: A or B (origin of Pa) Column 10: score: smolt condition: 1, 2, 3, 4, or NA (see explanation below) Column 11: smolt: 0 or 1 (non-smolt or smolt

movement-data

movement-data.csv Column 1: PIT: Passive Integrated Transponder number used for individual ID Column 2: cross: AxA, BxB, Hybrid, AxF1, BxF1, F1 (The latter four crosses are from a separate study not discussed in the corresponding manuscript. They were raised in the same manner as the AxA and BxB crosses discussed in the corresponding manuscript) Column 3: FL: Fork length in mm Column 4: mass: in grams Column 5: score: 1, 2, 3, 4 or NA (see explanation below) Column 6: smolt: 0 or 1 (i.e. non-smolt or smolt) Column 7: detect: 0 or 1 (i.e. not detected or detected

README

README file for the four data files

saltwater-challenge-data

Data from the seawater challenge experiment are in saltwater-challenge-data.csv as follows: Column 1: PIT: Passive Integrated Transponder number used for individual ID Column 2: cross: AxA, BxB, Hybrid, AxF1, BxF1, F1 (The latter four crosses are from a separate study not discussed in the corresponding manuscript. They were raised in the same manner as the AxA and BxB crosses discussed in the corresponding manuscript) Column 3: FL: Fork length in mm Column 4: mass: in grams Column 5: score: 1, 2, 3, 4 or NA (see explanation below) Column 6: smolt: 0 or 1 (non-smolt or smolt) Column 7: sex: Male, Female, or NA (i.e. failed to assign) Column 8: fate: 0 or 1: (i.e. dead or alive at the end of the experiment) Column 9: Date: date individual was declared dead Column 10: Time: time individual was declared dea

Anthropogenic land-use signals propagate through stream food webs in a California, USA, watershed

a b s t r a c t Human development of watersheds can change aquatic ecosystems via multiple pathways. For instance, human rural development may add nutrients to ecosystems. We used naturally occurring stable isotopes in stream food webs to investigate how land use affects stream ecosystems across a gradient of land development in the San Lorenzo watershed, California. Road density was used as a proxy for land development. We found that streams in watersheds with higher road densities had elevated concentrations of phosphate and nitrate. Furthermore, algal ␦ 15 N values increased as a function of nitrate concentration, but saturated at approximately 6‰. This saturating pattern was consistent with a two-source mixing model with anthropogenic and watershed sources, fit using Bayesian model fitting. In sites that had >2.6 km roads km −2 , anthropogenic sources of N were estimated to represent >90% of the N pool. This anthropogenic N signal was propagated to stream consumers: rainbow trout (Oncorhynchus mykiss), signal crayfish (Pacifasticus leniusculus), and benthic invertebrate ␦ 15 N were positively correlated with algal ␦ 15 N. Even relatively low density rural human land use may have substantial impacts on nutrient cycling of stream ecosystems. © 2014 Elsevier GmbH. All rights reserved. Introduction Human land-use impacts freshwater ecosystems via multiple pathways, such as through nutrient loading and habitat alteration Stable isotopes are increasingly used to investigate how anthropogenic land-use alters aquatic ecosystems. For example, nitrogen stable isotope ratios (␦ 15 N) can identify potential sources of nitrogen as well as inform rates of nutrient transformations The ecological effects of human land use can be illuminated through the study of gradients that span urban to rural developments Materials and methods Study system We examined 12 sites within the San Lorenzo River watershed (Santa Cruz County, CA, USA) that spanned a gradient of human land-use intensity. Sites were located on the numerous relatively small (first and second order) streams within the watershed and were part of a larger study (D. B. Herbst, unpublished data). From this larger set of candidate sites, sites were chosen to stratify a gradient in human land-use intensity and minimize differences in gradient and stream size. With two exceptions, sites were located on different streams. Elevations in this coastal watershed range from 979 m to sea level where the San Lorenzo River enters the Monterey Bay. The climate is Mediterranean, with 76-153 cm rain yr −1 . Tributaries drain steep soils of weathering granite, schists, marble, and marine deposits consisting of sandstones, shales and mudstones The San Lorenzo watershed has a history of excess anthropogenic nutrient inputs Field study Each site consisted of a riffle-pool sequence ranging from 40 to 60 m in length. Sites encompassed an anthropogenic gradient of the watershed which ranged from locations with little anthropogenic influence to locations with higher levels of anthropogenic influence such as road crossing and rural development. We used catchment road density (km km −2 ) as an index of human land-use intensity for each site. At each site, we collected primary producers (periphyton), and consumers (benthic invertebrates; rainbow trout, Oncorhynchus mykiss; signal crayfish, Pacifasticus leniusculus) for stable isotope analyses and water samples to obtain nutrient concentrations. All sampling was conducted in June 20-26, 2009 at near base stream flow conditions. Primary producer biomass was characterized by algae (periphyton) scraped from cobbles collected from both a region of slowand fast-water within the sampling site. Previous work has found that water velocity can influence algal stable isotope signatures At each site benthic invertebrates were collected by a Surber sample (0.5 mm mesh; sampling to a depth of 10 cm) in both a region of fast and slow stream flow. Samples were preserved in 70% ethanol. We note that preservation in ethanol can slightly alter isotope signatures (shifting ␦ 13 C approximately 1‰ and ␦ 15 N approximately 0.4 ‰; Venturra and Jeppesen, 2009). We did not adjust for this shift because it is likely relatively consistent within invertebrates that have fairly constrained stoichiometry (as opposed to across taxonomic groups with vastly different stoichiometry). Prior to preparation for stable isotope analysis, invertebrates were sorted and identified to family and functional feeding group (filterer, detritivore, herbivore, or predator) according to Fish and crayfish were collected by three-pass depletion electrofishing. Block nets at the upper and lower extent of each site prevented movement in or out of the site during surveys. Signal crayfish (Pacifastacus leniusculus; n = 35, approximately three per site) and rainbow trout (O. mykiss; n = 65, approximately five per site) were selected as focal species as they were the most abundant top aquatic consumers present across the different sampling sites. Orbital carapace length (crayfish) or total fork length (trout) and wet weight (to the nearest 0.1 g) were measured on-site for each sampled organism. Crayfish muscle tissue and rainbow trout caudal fin clips Algae and benthic invertebrates were oven dried until they reached a constant weight (approximately 48 h at 60 • C), whereas crayfish and trout samples were freeze-dried. To remove 13 Cdepleted lipids, all consumer samples were flushed with three cycles of petroleum ether at 1200 psi in a Dionex ASE 200 Accelerated Solvent Extraction System. Algae and crayfish tissue samples were ground into a homogenous powder with an agate mortar and pestle. Larger benthic invertebrates were also ground into a fine powder, whereas multiple individuals of the same species were aggregated into one sample for smaller invertebrates. Trout fin clips were left intact. Samples were weighed into 5 mm × 9 mm (algae, mean ± standard deviation = 4800 ± 31 g) or 3 mm × 5 mm (benthic invertebrates, 589 ± 53 g; crayfish muscle, 698 ± 30 g; fish fins, 673 ± 78 g) tin capsules (Costech Analytical Technologies). Stable isotope analyses Sample ␦ 13 C and ␦ 15 N were measured on a Carlo Erba 1108 elemental analyzer coupled to a ThermoFinnigan Delta Plus XP isotope ratio mass spectrometer (University of California, Santa Cruz Stable Isotope Laboratory). Sample isotope ratios were corrected relative to working standards with C:N ratios similar to the samples. For internationally calibrated in-house standards, analytical precision of ␦ 13 C and ␦ 15 N was less than 0.2‰. Water nutrients We collected duplicate water samples to measure total nitrate and phosphate concentrations at each site. Unfiltered water samples were collected just under the water surface in acid-rinsed high-density polyethylene (HDPE) bottles and were kept frozen at −20 • C until analysis. Frozen water samples were thawed, filtered through 0.7 m GF/F filters (Whatman), and analyzed for nitrate and phosphate concentrations using a QuikChem 8000 Flow Injection analyzer (Lachat Instruments) at the University of California, Santa Cruz Marine Analytical Labs. Isotopes and mixing models To address our question of how human land-use alters stream N cycling, we used a Bayesian information theoretic approach to estimate models and parameters. Bayesian mixing models allow the use of prior information for parameters and the propagation of uncertainty into estimates of posterior probability distributions of contributions to isotopic mixtures (e.g., where the stable isotope signature of the mixture (␦ 15 N mix ) was a function of proportional contributions of the two sources, anthropogenic sources (f a ) and background watershed sources (f b ), and their respective isotope signatures (␦ 15 N a and ␦ 15 N b ) and where f a + f b = 1. While ␦ 15 N a and ␦ 15 N b were not measured in this study, previous studies have indicated that ␦ 15 N a are generally enriched (prior probability distribution was uniformly distributed between 5‰ and 18‰) and ␦ 15 N b are generally depleted (prior probability distribution uniformly distributed between −2‰ and 2‰) (e.g., where N t was the measured concentration of nitrate at the site and N b was the unknown amount of that nitrate that was from background watershed sources. The prior probability distribution for N b was uniformly distributed between 0 and 10 M NO 3 − ; we did not have direct or published measurements of this prior and thus used a wide and uninformative prior. Through assuming that background watershed nitrate was constant but unknown across sites and estimating this parameter, we estimated the contribution of anthropogenic nitrate as N t − N b . It is important to note that these analyses were focused on the N that was available for algae to uptake and transfer into their stable isotope signatures. Given that algae have relatively fast turnover times, we think that our single measurements of nitrate during low summer baseflow are likely good approximations of N available to algae during this summer baseflow period. Therefore, our period of inference was restricted to this time frame. Furthermore, it is possible that other processes are contributing to observed isotopic patterns which would suggest a potentially different model structure (see section "Discussion"). Bayesian parameter estimation We estimated the posterior probability distributions of parameters and fit models using a Bayesian approach. Specifically, we estimated posterior probability distributions for N b , ␦ 15 N a , and ␦ 15 N b based on the data and our prior specifications. Bayesian modeling is based on the premise that the probability of a given combination of parameter values is defined by the likelihood of the data given the model and the prior belief in the parameter set. Markov chain Monte Carlo (MCMC) sampling was performed in JAGS in R Results The streams sampled in this survey of the San Lorenzo watershed varied in their land-use intensity. Specifically, the road density of the stream basins ranged from 0.89 to 6.13 road km km −2 . Sites with higher human land-use generally had higher nutrient concentrations N isotopes of algae showed a strong positive saturating relationship with nitrate concentratio

Considerations for the Development of a Juvenile Production Estimate for Central Valley Spring-Run Chinook Salmon

Effective species management depends on accurate estimates of population size. There are, however, no estimates of annual juvenile production for Central Valley spring-run Chinook Salmon (“spring run”), a highly imperiled species in California, making it difficult to evaluate population status and effectively manage key issues such as entrainment of this species at water diversions. In recognition of this critical information gap, we initiated an effort to develop a juvenile production estimate (JPE) for spring run, defined here as an annual forecast of the number of juvenile Central Valley spring-run Chinook Salmon that enter the Sacramento–San Joaquin Delta (“Delta”) from the Sacramento Valley. This metric would allow for a more robust scientific assessment of the population, which is needed to effectively manage water to reduce effects on spring run, a key condition of state permit requirements. To help guide this effort, we organized a workshop for stake-holders, managers, and scientists to review some of the key aspects of spring-run biology, examine the management and conservation importance of a JPE, identify knowledge gaps, introduce new tools, and discuss alternative approaches to forecasting the number of spring run emigrating from the Sacramento River drainage and into the Delta. This paper summarizes the spring-run biology, monitoring, and emergent methods for assessment considered at the workshop, as well as the guiding concepts identified by workshop participants necessary to develop a JPE for spring-run Chinook Salmon

Appendix A. More information regarding the application of portfolio theory to river networks, a figure illustrating the vast dendritic network of the Fraser River study system, a figure showing wavelet analyses, and a table that highlights previous research on stability in river processes.

More information regarding the application of portfolio theory to river networks, a figure illustrating the vast dendritic network of the Fraser River study system, a figure showing wavelet analyses, and a table that highlights previous research on stability in river processes