A fearful scourge to the penguin colonies: Southern giant petrel (Macronectes giganteus) predation on living Magellanic penguins (Spheniscus magellanicus) may be more common than assumed.

Southern giant petrels (Macronectes giganteus) are important consumers that range across the oceans throughout the southern hemisphere. In Argentina, previous studies have shown they eat primarily pinnipeds and penguins, which they are assumed to scavenge, although there are occasional anecdotes of them attacking living penguins. Here we describe a predation attempt by a trio of southern giant petrels on a molting adult Magellanic penguin (Spheniscus magellanicus) at the large colony at Punta Tombo, Argentina. We relate giant petrel attendance patterns at the colony to the penguins' phenology, showing how giant petrel numbers rise with the increasing prevalence of vulnerable penguins. We suggest that living penguins-both fledglings and adults-may constitute a more seasonally significant proportion of the giant petrel diet than previously assumed, and their capture may represent a specialized predation technique.

Changing course: Relocating commercial tanker lanes significantly reduces threat of chronic oiling for a top marine predator.

A goal for conservation biologists is to show that policies enacted on behalf of an imperiled species results in direct benefits for it. In Argentina, tens of thousands of Magellanic penguins (Spheniscus magellanicus) were estimated to have died from chronic oil pollution each year through the early 1980s. From 1982 to 1990, surveys at sites along approximately 900 km of Chubut Province coastline found that >60 % of penguin carcasses had evidence of oiling in some years. In response to these findings, as well as pressure from non-governmental organizations and the public, provincial and federal authorities in Chubut moved the commercial tanker lanes 20 nautical miles farther offshore in 1997 and required oil tankers to have double hulls. During a second round of surveys in 2001, using most of the same sites as the first survey period, the number of dead and oiled penguins dropped effectively to zero. A policy change not only led to fewer oiled penguins, but also likely increased the survival of adult Magellanic penguins near some of their most significant breeding colonies in Argentina.

On Identifying and Mitigating Bias in Inferred Measurements for Solar Vector Magnetic-Field Data.

The problem of bias, meaning over- or under-estimation, of the component perpendicular to the line-of-sight [ B ⊥ ] in vector magnetic-field maps is discussed. Previous works on this topic have illustrated that the problem exists; here we perform novel investigations to quantify the bias, fully understand its source(s), and provide mitigation strategies. First, we develop quantitative metrics to measure the B ⊥ bias and quantify the effect in both local (physical) and native image-plane components. Second, we test and evaluate different options available to inversions and different data sources, to systematically characterize the impacts of these choices, including explicitly accounting for the magnetic fill fraction [ f f ]. Third, we deploy a simple model to test how noise and different models of the bias may manifest. From these three investigations we find that while the bias is dominantly present in under-resolved structures, it is also present in strong-field, pixel-filling structures. Noise in the spectropolarimetric data can exacerbate the problem, but it is not the primary cause of the bias. We show that fitting f f explicitly provides significant mitigation, but that other considerations such as the choice of χ 2 -weights and optimization algorithms can impact the results as well. Finally, we demonstrate a straightforward "quick fix" that can be applied post facto but prior to solving the 180 ∘ ambiguity in B ⊥ , and which may be useful when global-scale structures are, e.g., used for model boundary input. The conclusions of this work support the deployment of inversion codes that explicitly fit f f or, as with the new SyntHIA neural-net, that are trained on data that did so.