Understanding amphidromy in Hawai'i: 'O'opu nakea (Awaous stamineus).

Hawai'i is home to 'o'opu nakea (Awaous stamineus), a culturally significant, endemic, goby that exhibits an amphidromous life cycle characterized by a marine larval stage followed by post-larval recruitment to streams, where they live to become reproductive adults. However, it was recently suggested that their migration to the ocean might not be obligatory, as originally thought. Despite their importance in Hawaiian traditions and the ecology of Hawaiian freshwater ecosystems, we still lack a full understanding of their migratory patterns and life history due to the difficulties in determining the environmental migratory cues that set the timing and location of their migratory paths. This study examined environmental factors, such as mean annual rainfall, streamflow, and water chemistry, to determine if they play a role in whether A. stamineus spend their larval period in the ocean or their entire life cycle in freshwater streams. We sampled A. stamineus (n = 90) from three streams (Kahana, Kahalu'u, and Waimanalo) on the island of O'ahu, Hawai'i that represented the range of hydroclimatic gradient in wet-habitat conditions on the windward side of the island and characterized their migratory pattern using elemental analysis of sagittae, the largest pair of otoliths (calcareous ear structures). Based on otolith strontium:calcium and barium:calcium ratios, we determined if individuals spent their larval period in the ocean or the stream. We found that 100% of individuals displayed clear evidence of marine residence during their larval phase, regardless of the environmental factors the fish experienced. This study highlights the necessity of stream-ocean connectivity for the survival of A. stamineus and emphasizes the importance of stream-mouth conservation and management as it is a critical transition zone in stream-ocean-stream migratory pathways.

The Axes of Life: A Roadmap for Understanding Dynamic Multiscale Systems.

The biological challenges facing humanity are complex, multi-factorial, and are intimately tied to the future of our health, welfare, and stewardship of the Earth. Tackling problems in diverse areas, such as agriculture, ecology, and health care require linking vast datasets that encompass numerous components and spatio-temporal scales. Here, we provide a new framework and a road map for using experiments and computation to understand dynamic biological systems that span multiple scales. We discuss theories that can help understand complex biological systems and highlight the limitations of existing methodologies and recommend data generation practices. The advent of new technologies such as big data analytics and artificial intelligence can help bridge different scales and data types. We recommend ways to make such models transparent, compatible with existing theories of biological function, and to make biological data sets readable by advanced machine learning algorithms. Overall, the barriers for tackling pressing biological challenges are not only technological, but also sociological. Hence, we also provide recommendations for promoting interdisciplinary interactions between scientists.

Conserving stream fishes with changing climate: Assessing fish responses to changes in habitat over a large region.

Changes in climate are known to alter air temperature and precipitation and their associated thermal and hydrological regimes of freshwater systems, and such alterations in habitat are anticipated to modify fish composition in fluvial systems. Despite these expected changes, assessing climate change effects on habitat and fish over large regions has proven challenging. The goal of this study is to describe an approach to assess and identify stream reaches within a large region that are susceptible to climate changes based on responses of multiple fish species to changes in thermal and hydrological habitats occurring with changes in climate. We present a six-step approach to connect climate, habitat, and fish responses, demonstrated through an example to assess effects of climate change on fishes for all stream reaches in a large U.S. ecoregion (955,029 km2). Step 1 identified measures of air temperature and precipitation expected to change substantially in the future. Step 2 identified the climatic measures strongly associated with stream thermal and hydrologic metrics calculated from measured data from a subset of streams. Step 3 linked thermal and hydrologic metrics identified in Step 2 with abundances of fish species from the same stream reaches, and these fishes were combined into groups based on similar associations with specific thermal or hydrologic metrics. Step 4 used the linkages between fish groups and climatic measures and their associated thermal and hydrologic metrics to classify stream reaches. Step 5 assigned all stream reaches into classes based on the established classification under current climate measures and then re-assigned all stream reaches using projected climatic measures for three future time windows. Step 6 assessed changes in classes of stream reaches between current and future climate conditions. Stream reaches projected to change in stream classes were considered "vulnerable" to future climate change, as they would no longer support the same fish composition. The projected vulnerable streams for the years 2040, 2060, and 2090 were mapped and summarized to identify temporal patterns and identify their spatial distribution, along with underlying mechanisms leading to changes. Our results showed that 45.7% of the 320,000 reaches and 49.3% of the overall 650,000 km stream length in the study region were expected to change stream class by the year 2090, with spatially-explicit changes including streams' responding to changing air temperature or precipitation. This study provides critical guidance for integrating climate projections, landscape factors, stream habitat data, and fish data into a meaningful approach for understanding linkage. Outcomes greatly improve our ability to describe habitat changes at a stream reach scale throughout large regions, and they can aid in prioritizing management strategies to adapt to climate change at local and regional scales.

An approach for aggregating upstream catchment information to support research and management of fluvial systems across large landscapes.

The growing quality and availability of spatial map layers (e.g., climate, geology, and land use) allow stream studies, which historically have occurred over small areas like a single watershed or stream reach, to increasingly explore questions from a landscape perspective. This large-scale perspective for fluvial studies depends on the ability to characterize influences on streams resulting from throughout entire upstream networks or catchments. While acquiring upstream information for a single reach is relatively straight-forward, this process becomes demanding when attempting to obtain summaries for all streams throughout a stream network and across large basins. Additionally, the complex nature of stream networks, including braided streams, adds to the challenge of accurately generating upstream summaries. This paper outlines an approach to solve these challenges by building a database and applying an algorithm to gather upstream landscape information for digitized stream networks. This approach avoids the need to directly use spatial data files in computation, and efficiently and accurately acquires various types of upstream summaries of landscape information across large regions using tabular processing. In particular, this approach is not limited to the use of any specific database software or programming language, and its flexibility allows it to be adapted to any digitized stream network as long as it meets a few minimum requirements. This efficient approach facilitates the growing demand of acquiring upstream summaries at large geographic scales and helps to support the use of landscape information in assisting management and decision-making across large regions.