Complex Life Histories Alter Patterns of Mercury Exposure and Accumulation in a Pond-Breeding Amphibian.

Quantifying how contaminants change across life cycles of species that undergo metamorphosis is critical to assessing organismal risk, particularly for consumers. Pond-breeding amphibians can dominate aquatic animal biomass as larvae and are terrestrial prey as juveniles and adults. Thus, amphibians can be vectors of mercury exposure in both aquatic and terrestrial food webs. However, it is still unclear how mercury concentrations are affected by exogenous (e.g., habitat or diet) vs endogenous factors (e.g., catabolism during hibernation) as amphibians undergo large diet shifts and periods of fasting during ontogeny. We measured total mercury (THg), methylmercury (MeHg), and isotopic compositions (δ 13C, δ15N) in boreal chorus frogs (Pseudacris maculata) across five life stages in two Colorado (USA) metapopulations. We found large differences in concentrations and percent MeHg (of THg) among life stages. Frog MeHg concentrations peaked during metamorphosis and hibernation coinciding with the most energetically demanding life cycle stages. Indeed, life history transitions involving periods of fasting coupled with high metabolic demands led to large increases in mercury concentrations. The endogenous processes of metamorphosis and hibernation resulted in MeHg bioamplification, thus decoupling it from the light isotopic proxies of diet and trophic position. These step changes are not often considered in conventional expectations of how MeHg concentrations within organisms are assessed.

A unifying framework for analyzing temporal changes in functional and taxonomic diversity along disturbance gradients.

Frameworks exclusively considering functional diversity are gaining popularity, as they complement and extend the information provided by taxonomic diversity metrics, particularly in response to disturbance. Taxonomic diversity should be included in functional diversity frameworks to uncover the functional mechanisms causing species loss following disturbance events. We present and test a predictive framework that considers temporal functional and taxonomic diversity responses along disturbance gradients. Our proposed framework allows us to test different multidimensional metrics of taxonomic diversity that can be directly compared to calculated multidimensional functional diversity metrics. It builds on existing functional diversity-disturbance frameworks both by using a gradient approach and by jointly considering taxonomic and functional diversity. We used previously unpublished stream insect community data collected prior to, and for the two years following, an extreme flood event that occurred in 2013. Using 14 northern Colorado mountain streams, we tested our framework and determined that taxonomic diversity metrics calculated using multidimensional methods resulted in concordance between taxonomic and functional diversity responses. By considering functional and taxonomic diversity together and using a gradient approach, we were able to identify some of the mechanisms driving species losses following this extreme disturbance event.

Implementing an Operational Framework to Develop a Streamflow Duration Assessment Method: A Case Study from the Arid West United States.

Streamflow duration information underpins many management decisions. However, hydrologic data are rarely available where needed. Rapid streamflow duration assessment methods (SDAMs) classify reaches based on indicators that are measured in a single brief visit. We evaluated a proposed framework for developing SDAMs to develop an SDAM for the Arid West United States that can classify reaches as perennial, intermittent, or ephemeral. We identified 41 candidate biological, geomorphological, and hydrological indicators of streamflow duration in a literature review, evaluated them for a number of desirable criteria (e.g., defensibility and consistency), and measured 21 of them at 89 reaches with known flow durations. We selected metrics for the SDAM based on their ability to discriminate among flow duration classes in analyses of variance, as well as their importance in a random forest model to predict streamflow duration. This approach resulted in a "beta" SDAM that uses five biological indicators. It could discriminate between ephemeral and non-ephemeral reaches with 81% accuracy, but only 56% accuracy when distinguishing 3 classes. A final method will be developed following expanded data collection. This Arid West study demonstrates the effectiveness of our approach and paves the way for more efficient development of scientifically informed SDAMs.

Classifying Streamflow Duration: The Scientific Basis and an Operational Framework for Method Development.

Streamflow duration is used to differentiate reaches into discrete classes (e.g., perennial, intermittent, and ephemeral) for water resource management. Because the depiction of the extent and flow duration of streams via existing maps, remote sensing, and gauging is constrained, field-based tools are needed for use by practitioners and to validate hydrography and modeling advances. Streamflow Duration Assessment Methods (SDAMs) are rapid, reach-scale indices or models that use physical and biological indicators to predict flow duration class. We review the scientific basis for indicators and present conceptual and operational frameworks for SDAM development. Indicators can be responses to or controls of flow duration. Aquatic and terrestrial responses can be integrated into SDAMs, reflecting concurrent increases and decreases along the flow duration gradient. The conceptual framework for data-driven SDAM development shows interrelationships among the key components: study reaches, hydrologic data, and indicators. We present a generalized operational framework for SDAM development that integrates the data-driven components through five process steps: preparation, data collection, data analysis, evaluation, and implementation. We highlight priorities for the advancement of SDAMs, including expansion of gauging of nonperennial reaches, use of citizen science data, adjusting for stressor gradients, and statistical and monitoring advances to improve indicator effectiveness.