Plastic pollution is increasing rapidly throughout the world’s oceans and is considered a major threat to marine wildlife and ecosystems. Although known to cause lethal or sub-lethal effects to vulnerable marine megafauna, population-level impacts of plastic pollution have not been thoroughly investigated. Here, we compiled and evaluated information from peer-reviewed studies that reported deleterious individual-level effects of plastic pollution on air-breathing marine megafauna (i.e. seabirds, marine mammals, and sea turtles) worldwide, highlighting those that assessed potential population-level effects. Lethal and sub-lethal individual-level effects included drowning, starvation, gastrointestinal tract damage, malnutrition, physical injury, reduced mobility, and physiological stress, resulting in reduced energy acquisition and assimilation, compromised health, reproductive impairment, and mortality. We found 47 studies published between 1969 and 2020 that considered population-level effects of plastic entanglement (n = 26), ingestion (n = 19), or both (n = 2). Of these, 7 inferred population-level effects (n = 6, entanglement; n = 1, ingestion), whereas 19 lacked evidence for effects (n = 12, entanglement; n = 6, ingestion; n = 1, both). However, no study in the past 50 yr reported direct evidence of population-level effects. Despite increased interest in and awareness of the presence of plastic pollution throughout the world’s oceans, the extent and magnitude of demographic impacts on marine megafauna remains largely unassessed and therefore unknown, in contrast to well-documented effects on individuals. Addressing this major assessment gap will allow researchers and managers to compare relative effects of multiple threats—including plastic pollution—on marine megafauna populations, thus providing appropriate context for strategic conservation priority-setting.

Many conservation projects relocate sea turtle eggs to hatcheries to protect the sea turtle nests from the anthropogenic and natural threats they face in the early stages of development. The Rescue Center for Endangered Marine Species (CREMA) manages four sea turtle conservation projects on the nesting beaches of the Southern Nicoya Peninsula in Costa Rica, where the predominant nesting activity is from olive ridley turtles (Lepidochelys olivacea). Two of these nesting projects are based in Costa de Oro and San Miguel, which are adjacent beaches divided by an estuary. In this study, we compared the dynamics and rates of human and animal predation of nests prior to being relocated to the hatchery on both nesting beaches from 2012 to 2018. We hypothesized that human take and animal predation were compensatory threats, meaning that lower human take may result in higher animal predation, and vice versa, resulting in a similar number of nests lost to predation overall. We discuss the community-based conservation programs on both beaches, one of which has been monitored since 1998 (San Miguel) and the other of which has been monitored since 2012 (Costa de Oro). We found that Costa de Oro exhibited high rates of human take with up to 51% of nests being extracted per season, which has decreased since the conservation project was established. Human take was significantly higher than animal predation on both beaches and human take was significantly higher in Costa de Oro. While San Miguel exhibited higher animal predation, the difference was not statistically significant. Higher depredation by animals corresponded to higher overall nest abundance on both beaches. We were unable to find evidence that human take or animal predation increased in the absence of the other threat, suggesting a lack of compensatory effects of predation. Our findings support further analysis of animal predation and a continuation of patrol-based conservation efforts as well as community outreach to attempt to merge cultural values with sea turtle conservation.

Incidental capture, or bycatch, of marine species is a global conservation concern. Interactions with fishing gear can cause mortality in air-breathing marine megafauna, including sea turtles. Despite this, interactions between sea turtles and fishing gear—from a behavior standpoint—are not sufficiently documented or described in the literature. Understanding sea turtle behavior in relation to fishing gear is key to discovering how they become entangled or entrapped in gear. This information can also be used to reduce fisheries interactions. However, recording and analyzing these behaviors is difficult and time intensive. In this study, we present a machine learning-based sea turtle behavior recognition scheme. The proposed method utilizes visual object tracking and orientation estimation tasks to extract important features that are used for recognizing behaviors of interest with green turtles (Chelonia mydas) as the study subject. Then, these features are combined in a color-coded feature image that represents the turtle behaviors occurring in a limited time frame. These spatiotemporal feature images are used along a deep convolutional neural network model to recognize the desired behaviors, specifically evasive behaviors which we have labeled “reversal” and “U-turn.” Experimental results show that the proposed method achieves an average F1 score of 85% in recognizing the target behavior patterns. This method is intended to be a tool for discovering why sea turtles become entangled in gillnet fishing gear.