No abstract

Most cetacean species are wide-ranging and highly mobile, creating significant challenges for researchers by limiting the scope of data that can be collected and leaving large areas un-surveyed. Aerial surveys have proven an effective way to locate and study cetacean movements but are costly and limited in spatial extent. Here we present a semi-automated pipeline for whale detection from very high-resolution (sub-meter) satellite imagery that makes use of a convolutional neural network (CNN). We trained ResNet, and DenseNet CNNs using down-scaled aerial imagery and tested each model on 31 cm-resolution imagery obtained from the WorldView-3 sensor. Satellite imagery was tiled and the trained algorithms were used to classify whether or not a tile was likely to contain a whale. Our best model correctly classified 100% of tiles with whales, and 94% of tiles containing only water. All model architectures performed well, with learning rate controlling performance more than architecture. While the resolution of commercially-available satellite imagery continues to make whale identification a challenging problem, our approach provides the means to efficiently eliminate areas without whales and, in doing so, greatly accelerates ocean surveys for large cetaceans.

Using satellite imagery, drone imagery, and ground counts, we have assembled the first comprehensive global population assessment of Chinstrap penguins (Pygoscelis antarctica) at 3.42 (95th-percentile CI: [2.98, 4.00]) million breeding pairs across 375 extant colonies. Twenty-three previously known Chinstrap penguin colonies are found to be absent or extirpated. We identify five new colonies, and 21 additional colonies previously unreported and likely missed by previous surveys. Limited or imprecise historical data prohibit our assessment of population change at 35% of all Chinstrap penguin colonies. Of colonies for which a comparison can be made to historical counts in the 1980s, 45% have probably or certainly declined and 18% have probably or certainly increased. Several large colonies in the South Sandwich Islands, where conditions apparently remain favorable for Chinstrap penguins, cannot be assessed against a historical benchmark. Our population assessment provides a detailed baseline for quantifying future changes in Chinstrap penguin abundance, sheds new light on the environmental drivers of Chinstrap penguin population dynamics in Antarctica, and contributes to ongoing monitoring and conservation efforts at a time of climate change and concerns over declining krill abundance in the Southern Ocean.

The emergence of very high-resolution (VHR) satellite imagery (less than 1 m spatial resolution) is creating new opportunities within the fields of ecology and conservation biology. The advancement of sub-meter resolution imagery has provided greater confidence in the detection and identification of features on the ground, broadening the realm of possible research questions. To date, VHR imagery studies have largely focused on terrestrial environments; however, there has been incremental progress in the last two decades for using this technology to detect cetaceans. With advances in computational power and sensor resolution, the feasibility of broad-scale VHR ocean surveys using VHR satellite imagery with automated detection and classification processes has increased. Initial attempts at automated surveys are showing promising results, but further development is necessary to ensure reliability. Here we discuss the future directions in which VHR satellite imagery might be used to address urgent questions in whale conservation. We highlight the current challenges to automated detection and to extending the use of this technology to all oceans and various whale species. To achieve basin-scale marine surveys, currently not feasible with any traditional surveying methods (including boat-based and aerial surveys), future research requires a collaborative effort between biology, computation science, and engineering to overcome the present challenges to this platform’s use.

Though climate change is widely known to negatively affect the distribution and abundance of many species, few studies have focused on species that may benefit. Gentoo Penguin (Pygoscelis papua) populations have grown along the Western Antarctic Peninsula (WAP), a region accounting for ~ 30% of their global population. These trends of population growth in Gentoo Penguins are in stark contrast to those of Adélie and Chinstrap Penguins, which have experienced considerable population declines along the WAP attributed to environmental changes. The recent discovery of previously unknown Gentoo Penguin colonies along the WAP and evidence for southern range expansion since the last global assessment in 2013 motivates this review of the abundance and distribution of this species. We compiled and collated all available recent data for every known Gentoo Penguin colony in the world and report on previously unknown Gentoo Penguin colonies along the Northwestern section of the WAP. We estimate the global population of Gentoo Penguins to be 432,144 (95th CI 338,059-534,114) breeding pairs, with approximately 364,359 (95th CI 324,052-405,132) breeding pairs (85% of the population) living in the Atlantic sector. Our estimates suggest that the global population has increased by approximately 11% since 2013, with even greater increases (23%) along the WAP. The Falkland Islands population, which comprises 30% of the global population, has remained stable, though only a subset of colonies have been surveyed since the last comprehensive survey in 2010. Our assessment identifies South Georgia and sub-Antarctic islands in the Indian Ocean as being the most critical data gaps for this species.