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ABSTRACT The brown bear population on the Kenai Peninsula, Alaska, has not been empirically estimated previously because conventional aerial methods over this heavily forested landscape were infeasible. We applied a rapid field protocol to a DNA-based, mark-recapture approach on a large and tightly bounded sample frame to estimate brown bear abundance. We used lure to attract bears to barbed wire stations deployed in 145 9-km × 9-km cells systematically distributed across 10,200 km2 of available habitat on the Kenai National Wildlife Refuge and Chugach National Forest during 31 consecutive days in early summer 2010. Using 2 helicopters and 4 2-person field crews, we deployed the stations during a 6-day period and subsequently revisited these stations on 5 consecutive 5-day trap sessions. We extracted DNA to identify individual bears and developed capture histories as input to mark-recapture models. Combined with data from radio-telemetered bears, ≥243 brown bears were alive on the Kenai Peninsula in 2010, but we used only 99 females and 104 males in modeling. We used Akaike's Information Criterion selection and model averaging to estimate 428 (95% lognormal CI = 353-539) brown bears (including all age classes) on the study area. Despite low recaptures rates, we achieved reasonable precision by ensuring geographic and demographic population closure through a spatially comprehensive sample frame and very short sampling window. We reduced bias by including information from rub trees and telemetered females (i.e., occasion 0). Extrapolating the density estimate of 42 bears/1,000 km2 of available habitat on the study area to the Kenai Peninsula suggests a peninsula-wide population of 582 brown bears (95% lognormal CI = 469−719). Despite a density estimate that is low compared to other coastal brown bear populations in Alaska and genetic evidence that suggests this peninsular population is insular, harvest management has been liberalized since 2012. We recommend this population estimate serve as the benchmark for future management. Published 2015. This article is a U.S. Government work and is in the public domain in the USA. [ABSTRACT FROM AUTHOR]

Rates of glacier mass loss in the northern Pacific coastal temperate rainforest (PCTR) are among the highest on Earth, and changes in glacier volume and extent will affect the flow regime and chemistry of coastal rivers, as well as the nearshore marine ecosystem of the Gulf of Alaska. Here we synthesize physical, chemical and biological linkages that characterize the northern PCTR ecosystem, with particular emphasis on the potential impacts of glacier change in the coastal mountain ranges on the surface--water hydrology, biogeochemistry, coastal oceanography and aquatic ecology. We also evaluate the relative importance and interplay between interannual variability and long-term trends in key physical drivers and ecological responses. To advance our knowledge of the northern PCTR, we advocate for cross-disciplinary research bridging the icefield-to-ocean ecosystem that can be paired with long-term scientific records and designed to inform decisionmakers. [ABSTRACT FROM AUTHOR]

The article discusses a study on ecological aspects of the Exxon Valdez oil spill of 1989. It states that the oil spill affected marine-oriented birds in Prince William Sound, Alaska, and mentions that canonical correspondence analysis (CCA) in the study, and mentions the relationship between individual sites exposed by the spill and environmental variables. It notes that birds that were found in surveys between 1989 and 1990 were absent prior to 1989.

Climatically driven changes in snow characteristics (snowfall, snowpack, and snowmelt) will affect hydrologic and ecological systems in Alaska over the coming century, yet there exist no projections of downscaled future snow pack metrics for the state of Alaska. We updated historical and projected snow day fraction (PSF, the fraction of days with precipitation falling as snow) from McAfee et al. We developed modeled snowfall equivalent (SFE) derived from the product of snow-day fraction (PSF) and existing gridded precipitation for Alaska from Scenarios Network for Alaska and Arctic Planning (SNAP). We validated the assumption that modeled SFE approximates historical decadally averaged snow water equivalent (SWE) observations from snowcourse and Snow Telemetry (SNOTEL) sites. We present analyses of future downscaled PSF and two new products, October–March SFE and ratio of snow fall equivalent to precipitation (SFE:P) based on bias-corrected statistically downscaled projections of Coupled Model Intercomparison Project 5 (CMIP5) Global Climate Model (GCM) temperature and precipitation for the state of Alaska. We analyzed mid-century (2040–2069) and late-century (2070–2099) changes in PSF, SFE, and SFE:P relative to historical (1970–1999) mean temperature and present results for Alaska climate divisions and 12-digit Hydrologic Unit Code (HUC12) watersheds. Overall, estimated historical the SFE is reasonably well related to the observed SWE, with correlations over 0.75 in all decades, and correlations exceeding 0.9 in the 1960s and 1970s. In absolute terms, SFE is generally biased low compared to the observed SWE. PSF and SFE:P decrease universally across Alaska under both Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 emissions scenarios, with the smallest changes for RCP 4.5 in 2040–2069 and the largest for RCP 8.5 in 2070–2099. The timing and magnitude of maximum decreases in PSF vary considerably with regional average temperature, with the largest changes in months at the beginning and end of the snow season. Mean SFE changes vary widely among climate divisions, ranging from decreases between −17 and −58% for late twenty-first century in southeast, southcentral, west coast and southwest Alaska to increases up to 21% on the North Slope. SFE increases most at highest elevations and latitudes and decreases most in coastal southern Alaska. SFE:P ratios indicate a broad switch from snow-dominated to transitional annual hydrology across most of southern Alaska by mid-century, and from transitional to rain-dominated watersheds in low elevation parts of southeast Alaska by the late twenty-first century. [ABSTRACT FROM AUTHOR]

Scientific evidence should inform environmental policy, but rapid environmental change brings high ecological uncertainty and associated barriers to the science-management dialogue. Biological invasions of aquatic plants are a worldwide problem with uncertain ecological and economic consequences. We demonstrate that the discrete choice method (DCM) can serve as a structured expert elicitation alternative to quantify expert opinion across a range of possible but uncertain environmental outcomes. DCM is widely applied in the social sciences to better understand and predict human preferences and trade-offs. Here we apply it to Alaska's first submersed invasive aquatic freshwater plant, Elodea spp. (elodea), and its unknown effects on salmonids. While little is known about interactions between elodea and salmonids, ecological research suggests that aquatic plant invasions can have positive and negative, as well as direct and indirect, effects on fish. We use DCM to design hypothetical salmonid habitat scenarios describing elodea's possible effect on critical environmental conditions for salmonids: prey abundance, dissolved oxygen, and vegetation cover. We then observe how experts choose between scenarios that they believe could support persistent salmonid populations in elodea-invaded salmonid habitat. We quantify the relative importance of habitat characteristics that influence expert choice and investigate how experts trade off between habitat characteristics. We take advantage of Bayesian techniques to estimate discrete choice models for individual experts and to simulate expert opinion for specific environmental management situations. We discuss possible applications and advantages of the DCM approach for expert elicitation in the ecological context. We end with methodological questions for future research. • Identified critical salmon habitat characteristics for invaded habitat. • Demonstrated use of a discrete choice model for ecological expert elicitation. • Applied Bayesian approach to estimate individual expert opinions. • Analyzed expert coherence by combining model results with risk rating. • Discussed applicability for informing resource management decisions. [ABSTRACT FROM AUTHOR]