Wendel W. Raymond | USGS Science

USGS2026

Severity and certainty metric for kelp stressors: A tool for research, management, and conservation

Foundation species face multiple threats, and many are in decline worldwide. Kelps are no exception, with declines documented throughout their range. Stressors such as warming, eutrophication, and reduced water quality—often linked to human activities—are well recognized, yet their relative severity and certainty have not been systematically evaluated. Such a framework may aid scientific, regulatory, and conservation organizations in prioritizing actions and decision making. Here we build on a conceptual model of stressors to kelp in the Salish Sea by rating stressor severity and scientific certainty. We convened a panel of regional kelp experts to perform the rating exercise for two canopy taxa, Nereocystis luetkeana and Macrocystis tenuifolia , as well as a grouping for understory species, including both the sporophyte (adult) and gametophyte (juvenile) life stages for these algae. Our process produced a rich dataset which can be separated by stressor, taxa, and life stage allowing for broad applications. The panel identified temperature, low nutrients, and substrate change/benthic sedimentation as the most severe stressors to kelp, with relatively high certainty for most taxa. Certainty scores were relatively high for Nereocystis and Macrocystis sporophytes but were considerably lower for understory species and the gametophyte life stage, for most stressors, highlighting research gaps. Our data set could be applied for management prioritization, setting ecological baselines, and spatial modeling exercises. We encourage scientific, regulatory, and conservation organizations to consider both our data and our process when evaluating their needs. As foundation species continue to face threats, exercises such as this can provide scientists and managers with actionable indices that support strategies to stem degradation.

Ecological Indicators↗

USGS2019

Trends and carrying capacity of sea otters in Southeast Alaska

Sea otter populations in Southeast Alaska (SEAK) have increased dramatically from fewer than 500 translocated animals in the late 1960s. The recovery of sea otters to ecosystems from which they had been absent has affected coastal food webs, including commercially important fisheries, and thus information on expected growth and equilibrium abundances can help inform resource management. We compile available survey data for SEAK and fit a Bayesian state-space model to estimate past trends and current abundance. Our model improves upon previous analyses by partitioning and quantifying sources of estimation error, accounting for over-dispersion of aerial count data, and providing realistic measurements of uncertainty around point estimates of abundance at multiple spatial scales. We also provide the first estimates of carrying capacity (K) for SEAK, at both regional and sub-regional scales, and analyze growth rates, current population status and expected future trends. At the regional scale, the population increased from 13,221 otters in 2003 (95% credible interval 9,990 – 16,828) to 25,584 otters in 2011 (CI95 18,739 – 33,163). The average annual growth rate in southern SEAK (7.8%) was higher than northern SEAK (2.7%); however, growth varied at the sub-regional scale and there was a negative relationship between growth rates and the number of years sea otters were present in an area. Local populations vary in terms of current densities and expected future growth: the mean estimated density at K was 4.2 (1.58) sea otters per km2 of habitat (defined as the sub-tidal benthos between 0-40m depth) and current densities correspond on average to 50% of projected equilibrium values (range = 1% to 97%) with the earliest-colonized sub-regions tending to be closer to K. Assuming a similar range of equilibrium densities for currently un-occupied habitats in SEAK, the projected value of K for all of SEAK is 74,650 sea otters (CI95 =36,778–136,506). Future analyses can improve upon the precision of K estimates by employing more frequent surveys at index sites and incorporating environmental covariates into the process model to generate habitat-specific estimates of equilibrium density.

Alaska↗