Impact of multiple stressors on juvenile fish in estuaries of the northeast Pacific.

A key step in identifying global change impacts on species and ecosystems is to quantify effects of multiple stressors. To date, the science of global change has been dominated by regional field studies, experimental manipulation, meta-analyses, conceptual models, reviews, and studies focusing on a single stressor or species over broad spatial and temporal scales. Here, we provide one of the first studies for coastal systems examining multiple stressor effects across broad scales, focused on the nursery function of 20 estuaries spanning 1,600 km of coastline, 25 years of monitoring, and seven fish and invertebrate species along the northeast Pacific coast. We hypothesized those species most estuarine dependent and negatively impacted by human activities would have lower presence and abundances in estuaries with greater anthropogenic land cover, pollution, and water flow stress. We found significant negative relationships between juveniles of two of seven species (Chinook salmon and English sole) and estuarine stressors. Chinook salmon were less likely to occur and were less abundant in estuaries with greater pollution stress. They were also less abundant in estuaries with greater flow stress, although this relationship was marginally insignificant. English sole were less abundant in estuaries with greater land cover stress. Together, we provide new empirical evidence that effects of stressors on two fish species culminate in detectable trends along the northeast Pacific coast, elevating the need for protection from pollution, land cover, and flow stressors to their habitats. Lack of response among the other five species could be related to differing resistance to specific stressors, type and precision of the stressor metrics, and limitations in catch data across estuaries and habitats. Acquiring improved measurements of impacts to species will guide future management actions, and help predict how estuarine nursery functions can be optimized given anthropogenic stressors and climate change scenarios.

Fishery catch records support machine learning-based prediction of illegal fishing off US West Coast.

Illegal, unreported, and unregulated (IUU) fishing is a major problem worldwide, often made more challenging by a lack of at-sea and shoreside monitoring of commercial fishery catches. Off the US West Coast, as in many places, a primary concern for enforcement and management is whether vessels are illegally fishing in locations where they are not permitted to fish. We explored the use of supervised machine learning analysis in a partially observed fishery to identify potentially illicit behaviors when vessels did not have observers on board. We built classification models (random forest and gradient boosting ensemble tree estimators) using labeled data from nearly 10,000 fishing trips for which we had landing records (i.e., catch data) and observer data. We identified a set of variables related to catch (e.g., catch weights and species) and delivery port that could predict, with 97% accuracy, whether vessels fished in state versus federal waters. Notably, our model performances were robust to inter-annual variability in the fishery environments during recent anomalously warm years. We applied these models to nearly 60,000 unobserved landing records and identified more than 500 instances in which vessels may have illegally fished in federal waters. This project was developed at the request of fisheries enforcement investigators, and now an automated system analyzes all new unobserved landings records to identify those in need of additional investigation for potential violations. Similar approaches informed by the spatial preferences of species landed may support monitoring and enforcement efforts in any number of partially observed, or even totally unobserved, fisheries globally.

UPRLIMET: UPstream Regional LiDAR Model for Extent of Trout in stream networks.

Predicting the edges of species distributions is fundamental for species conservation, ecosystem services, and management decisions. In North America, the location of the upstream limit of fish in forested streams receives special attention, because fish-bearing portions of streams have more protections during forest management activities than fishless portions. We present a novel model development and evaluation framework, wherein we compare 26 models to predict upper distribution limits of trout in streams. The models used machine learning, logistic regression, and a sophisticated nested spatial cross-validation routine to evaluate predictive performance while accounting for spatial autocorrelation. The model resulting in the best predictive performance, termed UPstream Regional LiDAR Model for Extent of Trout (UPRLIMET), is a two-stage model that uses a logistic regression algorithm calibrated to observations of Coastal Cutthroat Trout (Oncorhynchus clarkii clarkii) occurrence and variables representing hydro-topographic characteristics of the landscape. We predict trout presence along reaches throughout a stream network, and include a stopping rule to identify a discrete upper limit point above which all stream reaches are classified as fishless. Although there is no simple explanation for the upper distribution limit identified in UPRLIMET, four factors, including upstream channel length above the point of uppermost fish, drainage area, slope, and elevation, had highest importance. Across our study region of western Oregon, we found that more of the fish-bearing network is on private lands than on state, US Bureau of Land Mangement (BLM), or USDA Forest Service (USFS) lands, highlighting the importance of using spatially consistent maps across a region and working across land ownerships. Our research underscores the value of using occurrence data to develop simple, but powerful, prediction tools to capture complex ecological processes that contribute to distribution limits of species.

Insights into estuary habitat loss in the western United States using a new method for mapping maximum extent of tidal wetlands.

Effective conservation and restoration of estuarine wetlands require accurate maps of their historical and current extent, as well as estimated losses of these valued habitats. Existing coast-wide tidal wetland mapping does not explicitly map historical tidal wetlands that are now disconnected from the tides, which represent restoration opportunities; nor does it use water level models or high-resolution elevation data (e.g. lidar) to accurately identify current tidal wetlands. To better inform estuarine conservation and restoration, we generated new maps of current and historical tidal wetlands for the entire contiguous U.S. West Coast (Washington, Oregon, and California). The new maps are based on an Elevation-Based Estuary Extent Model (EBEEM) that combines lidar digital elevation models (DEMs) and water level models to establish the maximum historical extent of tidal wetlands, representing a major step forward in mapping accuracy for restoration planning and analysis of wetland loss. Building from this new base, we also developed an indirect method for mapping tidal wetland losses, and created maps of these losses for 55 estuaries on the West Coast (representing about 97% of historical West Coast vegetated tidal wetland area). Based on these new maps, we estimated that total historical estuary area for the West Coast is approximately 735,000 hectares (including vegetated and nonvegetated areas), and that about 85% of vegetated tidal wetlands have been lost from West Coast estuaries. Losses were highest for major river deltas. The new maps will help interested groups improve action plans for estuarine wetland habitat restoration and conservation, and will also provide a better baseline for understanding and predicting future changes with projected sea level rise.