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1. Free-Living Aquatic Turtles as Sentinels of Salmonella spp. for Water Bodies

   https://doi.org/10.3389/fvets.2021.674973  

   Hernandez, Sonia M.; Maurer, John J.; Yabsley, Michael J.; Peters, Valerie E.; Presotto, Andrea; Murray, Maureen H.; Curry, Shannon; Sanchez, Susan; Gerner-Smidt, Peter; Hise, Kelley; et al (July 2021, Frontiers in Veterinary Science)

   Reptile-associated human salmonellosis cases have increased recently in the United States. It is not uncommon to find healthy chelonians shedding Salmonella enterica . The rate and frequency of bacterial shedding are not fully understood, and most studies have focused on captive vs. free-living chelonians and often in relation to an outbreak. Their ecology and significance as sentinels are important to understanding Salmonella transmission. In 2012–2013, Salmonella prevalence was determined for free-living aquatic turtles in man-made ponds in Clarke and Oconee Counties, in northern Georgia (USA) and the correlation between species, basking ecology, demographics (age/sex), season, or landcover with prevalence was assessed. The genetic relatedness between turtle and archived, human isolates, as well as, other archived animal and water isolates reported from this study area was examined. Salmonella was isolated from 45 of 194 turtles (23.2%, range 14–100%) across six species. Prevalence was higher in juveniles (36%) than adults (20%), higher in females (33%) than males (18%), and higher in bottom-dwelling species (31%; common and loggerhead musk turtles, common snapping turtles) than basking species (15%; sliders, painted turtles). Salmonella prevalence decreased as forest cover, canopy cover, and distance from roads increased. Prevalence was also higher in low-density, residential areas that have 20–49% impervious surface. A total of 9 different serovars of two subspecies were isolated including 3 S. enterica subsp. arizonae and 44 S. enterica subsp. enterica (two turtles had two serotypes isolated from each). Among the S. enterica serovars, Montevideo ( n = 13) and Rubislaw ( n = 11) were predominant. Salmonella serovars Muenchen, Newport, Mississippi, Inverness, Brazil, and Paratyphi B. var L(+) tartrate positive (Java) were also isolated. Importantly, 85% of the turtle isolates matched pulsed-field gel electrophoresis patterns of human isolates, including those reported from Georgia. Collectively, these results suggest that turtles accumulate Salmonella present in water bodies, and they may be effective sentinels of environmental contamination. Ultimately, the Salmonella prevalence rates in wild aquatic turtles, especially those strains shared with humans, highlight a significant public health concern. 

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2. Dark Web Marketplaces and COVID-19: before the vaccine

   https://doi.org/10.1140/epjds/s13688-021-00259-w  

   Bracci, Alberto; Nadini, Matthieu; Aliapoulios, Maxwell; McCoy, Damon; Gray, Ian; Teytelboym, Alexander; Gallo, Angela; Baronchelli, Andrea (December 2021, EPJ Data Science)

   null (Ed.)

   Abstract The COVID-19 pandemic has reshaped the demand for goods and services worldwide. The combination of a public health emergency, economic distress, and misinformation-driven panic have pushed customers and vendors towards the shadow economy. In particular, dark web marketplaces (DWMs), commercial websites accessible via free software, have gained significant popularity. Here, we analyse 851,199 listings extracted from 30 DWMs between January 1, 2020 and November 16, 2020. We identify 788 listings directly related to COVID-19 products and monitor the temporal evolution of product categories including Personal Protective Equipment (PPE), medicines (e.g., hydroxyclorochine), and medical frauds . Finally, we compare trends in their temporal evolution with variations in public attention, as measured by Twitter posts and Wikipedia page visits. We reveal how the online shadow economy has evolved during the COVID-19 pandemic and highlight the importance of a continuous monitoring of DWMs, especially now that real vaccines are available and in short supply. We anticipate our analysis will be of interest both to researchers and public agencies focused on the protection of public health. 

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3. Prevalence of endangered shark trophies in automated detection of the online wildlife trade

   https://doi.org/10.1016/j.biocon.2025.110992  

   Chakraborty, Sunandan; Roberts, Spencer N; Petrossian, Gohar A; Sosnowski, Monique; Freire, Juliana; Jacquet, Jennifer (April 2025, Biological Conservation)

   Direct exploitation, which includes the trade of wild animals for their parts, is a major driver of extinction. Digital communication tools, particularly the internet, have facilitated the trade in endangered species. Here, we automatically collected data to analyze online sales of threatened animals across 148 English-text online mar- ketplaces. We created a tool that searched for online sales of 13,267 animal species at risk of global extinction, as classified by the International Union for Conservation of Nature (IUCN), as well as 706 animal species on Ap- pendix I of the Convention for International Trade in Endangered Species (CITES), for which international commercial trade is prohibited. Examining a period of 15 weeks in 2018, we identified 10,699 unique listings selling body parts or eggs of threatened species, of which 4131 contained a full species name (common or sci- entific). These 4131 results were then filtered by keywords and, finally, manually vetted, which yielded 546 sale listings for 83 species. Of these 546 listings, 61 % advertised shark trophies (mainly jaws), 73 % of which were taken from species listed as endangered or critically endangered. Just four websites hosted >95 % of listings. We identified 18 species for sale that are included on CITES Appendix I. We also identified 13 species for which the IUCN had not identified intentional use as a threat. This work expands current understanding about the dealing of endangered and potentially illegal species online, specifies taxa threatened by online trade, and highlights emerging opportunities and persistent challenges to preventing the trafficking of threatened species. 

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4. Green turtles shape the seascape through grazing patch formation around habitat features: Experimental evidence

   https://doi.org/10.1002/ecy.3902  

   Smulders, F_O_H; Bakker, E_S; O'Shea, O_R; Campbell, J_E; Rhoades, O_K; Christianen, M_J_A (December 2022, Ecology)

   Abstract Understanding how megaherbivores incorporate habitat features into their foraging behavior is key toward understanding how herbivores shape the surrounding landscape. While the role of habitat structure has been studied within the context of predator–prey dynamics and grazing behavior in terrestrial systems, there is a limited understanding of how structure influences megaherbivore grazing in marine ecosystems. To investigate the response of megaherbivores (green turtles) to habitat features, we experimentally introduced structure at two spatial scales in a shallow seagrass meadow in The Bahamas. Turtle density increased 50‐fold (to 311 turtles ha⁻¹) in response to the structures, and turtles were mainly grazing and resting (low vigilance behavior). This resulted in a grazing patch exceeding the size of the experimental setup (242 m²), with reduced seagrass shoot density and aboveground biomass. After structure removal, turtle density decreased and vigilance increased (more browsing and shorter surfacing times), while seagrass within the patch partly recovered. Even at a small scale (9 m²), artificial structures altered turtle grazing behavior, resulting in grazing patches in 60% of the plots. Our results demonstrate that marine megaherbivores select habitat features as foraging sites, likely to be a predator refuge, resulting in heterogeneity in seagrass bed structure at the landscape scale. 

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5. Revealing Sea Turtle Behavior in Relation to Fishing Gear Using Color-Coded Spatiotemporal Motion Patterns With Deep Neural Networks

   https://doi.org/10.3389/fmars.2021.785357  

   Reavis, Janie L.; Demir, H. Seckin; Witherington, Blair E.; Bresette, Michael J.; Blain Christen, Jennifer; Senko, Jesse F.; Ozev, Sule (November 2021, Frontiers in Marine Science)

   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. 

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