Repatriation of a historical North Atlantic right whale habitat during an era of rapid climate change.

Climate change is affecting species distributions in space and time. In the Gulf of Maine, one of the fastest-warming marine regions on Earth, rapid warming has caused prey-related changes in the distribution of the critically endangered North Atlantic right whale (Eubalaena glacialis). Concurrently, right whales have returned to historically important areas such as southern New England shelf waters, an area known to have been a whaling ground. We compared aerial survey data from two time periods (2013-2015; 2017-2019) to assess trends in right whale abundance in the region during winter and spring. Using distance sampling techniques, we chose a hazard rate key function to model right whale detections and used seasonal encounter rates to estimate abundance. The mean log of abundance increased by 1.40 annually between 2013 and 2019 (p = 0.004), and the mean number of individuals detected per year increased by 2.23 annually between 2013 and 2019 (R2 = 0.69, p = 0.001). These results demonstrate the current importance of this habitat and suggest that management options must continually evolve as right whales repatriate historical habitats and potentially expand to new habitats as they adapt to climate change.

Influence of image resolution and evaluation algorithm on estimates of the lacunarity of porous media.

In recent years, experience has demonstrated that the classical fractal dimensions are not sufficient to describe uniquely the interstitial geometry of porous media. At least one additional index or dimension is necessary. Lacunarity, a measure of the degree to which a data set is translationally invariant, is a possible candidate. Unfortunately, several approaches exist to evaluate it on the basis of binary images of the object under study, and it is unclear to what extent the lacunarity estimates that these methods produce are dependent on the resolution of the images used. In the present work, the gliding-box algorithm of Allain and Cloitre [Phys. Rev. A 44, 3552 (1991)] and two variants of the sandbox algorithm of Chappard et al. [J. Pathol. 195, 515 (2001)], along with three additional algorithms, are used to evaluate the lacunarity of images of a textbook fractal, the Sierpinski carpet, of scanning electron micrographs of a thin section of a European soil, and of light transmission photographs of a Togolese soil. The results suggest that lacunarity estimates, as well as the ranking of the three tested systems according to their lacunarity, are affected strongly by the algorithm used, by the resolution of the images to which these algorithms are applied, and, at least for three of the algorithms (producing scale-dependent lacunarity estimates), by the scale at which the images are observed. Depending on the conditions under which the estimation of the lacunarity is carried out, lacunarity values range from 1.02 to 2.14 for the three systems tested, and all three of the systems used can be viewed alternatively as the most or the least "lacunar." Some of this indeterminacy and dependence on image resolution is alleviated in the averaged lacunarity estimates yielded by Chappard et al.'s algorithm. Further research will be needed to determine if these lacunarity estimates allow an improved, unique characterization of porous media.