Ecological integrity and conservation challenges in a rapidly changing Arctic: A call for new approaches in large intact landscapes.

Intactness is a commonly used measure of ecological integrity, especially when evaluating conservation status at the landscape scale. We argue that in the large and relatively unfragmented landscapes of the Arctic and sub-Arctic, intactness provides only partial insight for managers charged with maintaining ecological integrity. A recent landscape assessment suggests that 95% of Alaska shows no measured direct or indirect impacts of human development on the landscape. However, the current exceptionally high levels of intactness in Alaska, and throughout the Arctic and sub-Arctic, do not adequately reflect impacts to the region's ecological integrity caused by indirect stressors, such as a rapidly changing climate and the subsequent loss of the cryosphere. Thus, it can be difficult to measure, and manage, some of the conservation challenges presented by the ecological context of these systems. The dominant drivers of change, and their associated ecological and socioeconomic impacts, vary as systems decline in ecological integrity from very high to high, and to intermediate levels, but this is not well understood in the literature. Arctic and sub-Arctic systems, as well as other large intact areas, provide unique opportunities for conservation planning, but require tools and approaches appropriate to unfragmented landscapes undergoing rapid climate-driven ecological transformation. We conclude with possible directions for developing more appropriate metrics for measuring ecological integrity in these systems.

Improving inference for aerial surveys of bears: The importance of assumptions and the cost of unnecessary complexity.

Obtaining useful estimates of wildlife abundance or density requires thoughtful attention to potential sources of bias and precision, and it is widely understood that addressing incomplete detection is critical to appropriate inference. When the underlying assumptions of sampling approaches are violated, both increased bias and reduced precision of the population estimator may result. Bear (Ursus spp.) populations can be difficult to sample and are often monitored using mark-recapture distance sampling (MRDS) methods, although obtaining adequate sample sizes can be cost prohibitive. With the goal of improving inference, we examined the underlying methodological assumptions and estimator efficiency of three datasets collected under an MRDS protocol designed specifically for bears. We analyzed these data using MRDS, conventional distance sampling (CDS), and open-distance sampling approaches to evaluate the apparent bias-precision tradeoff relative to the assumptions inherent under each approach. We also evaluated the incorporation of informative priors on detection parameters within a Bayesian context. We found that the CDS estimator had low apparent bias and was more efficient than the more complex MRDS estimator. When combined with informative priors on the detection process, precision was increased by >50% compared to the MRDS approach with little apparent bias. In addition, open-distance sampling models revealed a serious violation of the assumption that all bears were available to be sampled. Inference is directly related to the underlying assumptions of the survey design and the analytical tools employed. We show that for aerial surveys of bears, avoidance of unnecessary model complexity, use of prior information, and the application of open population models can be used to greatly improve estimator performance and simplify field protocols. Although we focused on distance sampling-based aerial surveys for bears, the general concepts we addressed apply to a variety of wildlife survey contexts.

A road map for designing and implementing a biological monitoring program.

Designing and implementing natural resource monitoring is a challenging endeavor undertaken by many agencies, NGOs, and citizen groups worldwide. Yet many monitoring programs fail to deliver useful information for a variety of administrative (staffing, documentation, and funding) or technical (sampling design and data analysis) reasons. Programs risk failure if they lack a clear motivating problem or question, explicit objectives linked to this problem or question, and a comprehensive conceptual model of the system under study. Designers must consider what "success" looks like from a resource management perspective, how desired outcomes translate to appropriate attributes to monitor, and how they will be measured. All such efforts should be filtered through the question "Why is this important?" Failing to address these considerations will produce a program that fails to deliver the desired information. We addressed these issues through creation of a "road map" for designing and implementing a monitoring program, synthesizing multiple aspects of a monitoring program into a single, overarching framework. The road map emphasizes linkages among core decisions to ensure alignment of all components, from problem framing through technical details of data collection and analysis, to program administration. Following this framework will help avoid common pitfalls, keep projects on track and budgets realistic, and aid in program evaluations. The road map has proved useful for monitoring by individuals and teams, those planning new monitoring, and those reviewing existing monitoring and for staff with a wide range of technical and scientific skills.

Evaluating the power of surface attendance counts to detect long-term trends in populations of crevice-nesting auklets.

Power analyses are essential when developing a long-term monitoring program for a target species whose observation is logistically challenging and expensive. These analyses can be complicated when the observations have a complex variance structure reflecting many factors. Crevice-nesting seabirds such as least and crested auklets Aethia pusilla and Aethia cristatella illustrate both this need and these challenges. They are ecosystem indicators for the Bering Sea, a system expected to undergo large changes. Unfortunately, they are difficult to monitor as colonies occur on remote, hard to access islands in the Aleutians and Bering Sea, and nests occur in crevices underground, preventing direct observation. Current monitoring consists of breeding-season counts of auklets standing on the surface of sample plots in the colony; logically, a substantial decline in nesting population guarantees an eventual substantial decline in surface attendants. Yet, it remains debatable whether these highly variable counts can be used to statistically detect biologically relevant declines in the attending population let alone the nesting population. Subsequently, existing monitoring programs vary widely in survey design, effort levels, and daily summary statistics. The power of different survey designs was assessed by simulating observations from a state model developed from 11 years of observations using mixed-effects models and zero-inflated Poisson-lognormal regression. The analyses illustrate the process required for any monitoring program whose observations are described inadequately by standard statistical models. State model development revealed survey design refinements that reduce sampling variation. For least auklets, current sampling efforts provided 90% power to detect annual declines of 11% ("Critically Endangered" using IUCN Red List criteria), 4.5% ("Endangered"), or 2.4% ("Vulnerable") in two, four, or six generations, respectively; crested auklets took a few years longer. Power was more sensitive to number of days than number of plots. Results appear robust across a range of bird densities, providing guidance for monitoring other colonies or crevice-nesting species with similar life history strategies. Research should now focus on illuminating the relationship between the attending and nesting populations. Given the frequency of complicated variance structures and zero counts in ecological data, the general statistical models used here should prove widely applicable.

Using tolerance intervals to assess recovery of mussel beds impacted by the Exxon Valdez oil spill.

Polycyclic aromatic hydrocarbons (PAH) have been measured in mussel tissues in early spring and summer since 1993 throughout Prince William Sound (PWS) and the Gulf of Alaska (GOA). Season-specific thresholds were established at reference sites to identify 'above background' total PAH levels. Thresholds were estimated using one-sided 99% tolerance limits. Thresholds were similar across reference sites but differed by an order of magnitude across seasons. Trends in total PAH since 1998 were assessed for sites impacted by the 1989 Exxon Valdez oil spill or the Alyeska Marine Terminal. Summer samples exhibited no trends; early spring samples declined. In early spring, all sites were judged 'recovered' by 2004; in summer, one site in western Prince William Sound and two in the western GOA exceeded thresholds by 11ng/g dry weight or less. Robust estimation methods prevented bias from observations affected by unknown releases or laboratory errors.