Abstract Marine turtles encounter different threats during various life-history stages. Therefore, understanding their movements and spatial distribution is crucial for effectively managing these long-lived migratory organisms. This study combines satellite telemetry data with long-term capture-mark-recapture data derived from flipper tag studies to determine distribution patterns of endangered loggerhead turtles ( Caretta caretta ) during post-nesting migrations from different eastern Australian nesting sites. Individuals from the K’gari-Fraser Island and Great Barrier Reef island rookeries typically migrated northward, whereas individuals from mainland rookeries migrated equally northward and southward. Despite this difference in foraging distribution, loggerheads from the different rookeries did not differ substantially in their migration duration or distance travelled. The foraging distribution identified from successful satellite tag deployments represented 50% of the foraging distribution identified from a large long-term flipper tag recovery database. However, these satellite telemetry results have identified new migration and foraging habitats not previously recognised for loggerhead turtles nesting in eastern Australia. Additionally, they support the conclusion from a past study using flipper tag recovery data that the mainland nesting turtles migrate to different foraging grounds than the turtles nesting on Great Barrier Reef islands. Collectively, the two data sources provide valuable data on the migration route, habitat distribution and ecological range for a threatened genetic stock of loggerhead turtles. 

/pdf/satellite-tagging-and-flipper-tag-recoveries-reveal-3j32k92c.pdf

Satellite tagging and flipper tag recoveries reveal migration patterns and foraging distribution of loggerhead sea turtles (Caretta caretta) from eastern Australia

Abstract The transport of passively dispersed organisms across tropical margins remains poorly understood. Hypotheses of oceanographic transportation potential lack testing with large scale empirical data. To address this gap, we used the seagrass species, Halodule wrightii , which is unique in spanning the entire tropical Atlantic. We tested the hypothesis that genetic differentiation estimated across its large-scale biogeographic range can be predicted by simulated oceanographic transport. The alternative hypothesis posits that dispersal is independent of ocean currents, such as transport by grazers. We compared empirical genetic estimates and modelled predictions of dispersal along the distribution of H. wrightii . We genotyped eight microsatellite loci on 19 populations distributed across Atlantic Africa, Gulf of Mexico, Caribbean, Brazil and developed a biophysical model with high-resolution ocean currents. Genetic data revealed low gene flow and highest differentiation between (1) the Gulf of Mexico and two other regions: (2) Caribbean-Brazil and (3) Atlantic Africa. These two were more genetically similar despite separation by an ocean. The biophysical model indicated low or no probability of passive dispersal among populations and did not match the empirical genetic data. The results support the alternative hypothesis of a role for active dispersal vectors like grazers. 

/pdf/long-range-gene-flow-beyond-predictions-from-oceanographic-da8cy796.pdf

Long range gene flow beyond predictions from oceanographic transport in a tropical marine foundation species

The prediction of Dissolved Oxygen (DO) in oceans is crucial for the survival of marine life and the management of marine farms. However, due to the inherent instability and uncertainty of DO, prediction models frequently necessitate parameter adjustments, posing challenges to maintaining reliable predictive performance and generalization capabilities. Based on this, a long-term dissolved oxygen prediction model in aquaculture using Transformer with a dynamic adaptive mechanism is proposed in the paper. A multi-scale latent correlation decomposition strategy was devised, merging both time and frequency domain decompositions. This approach proficiently untangles time series exhibiting intricate non-linear cycles, thereby delving deeper into and furnishing a more comprehensive array of feature data. Furthermore, an uncertainty-aware attention mechanism was posited, ensuring accurate pinpointing of pivotal data amidst a highly volatile data environment, thus cementing prediction accuracy. Finally, the incorporation of an adaptive hyperparameter adjustment mechanism enables the model to recalibrate in line with feature insights, bolstering its generalization potential across marine farms in diverse regions. Empirical results show that the model proposed in this study can effectively accomplish high-precision long-term forecasting. When juxtaposed with other models under comparable conditions, its mean squared error significantly diminished by 40.2%, the mean absolute error contracted by 55.4%, and concurrently, the R2 value ascended by 7%. This research proffers a fresh vantage point for long-term marine chemical data predictions and extends robust technical bolstering for refining and augmenting aquacultural strategies. 

A long-term dissolved oxygen prediction model in aquaculture using transformer with a dynamic adaptive mechanism

Knowledge-based Bi-correction model for achieving effective lag-free characteristic on daily urban water demand forecasting

Nowhere to hide: Sea turtle bycatch in Northwest Africa

Abstract Beal MRW, O’Reilly BE, Soley CK, Hietpas KR, Block PJ. 2022. Variability of summer cyanobacteria abundance: can season-ahead forecasts improve beach management? Lake Reserv Manage. 39:37–52. As anthropogenic eutrophication and the associated increase of cyanobacteria continue to plague inland waterbodies, local officials are seeking novel methods to proactively manage water resources. Cyanobacteria are of particular concern to health officials due to their ability to produce dangerous hepatotoxins and neurotoxins, which can threaten waterbodies for recreational and drinking-water purposes. Presently, however, there is no cyanobacteria outlook that can provide advance warning of a potential threat at the seasonal time scale. In this study, a statistical model is developed utilizing local and global scale season-ahead hydroclimatic predictors to evaluate the potential for informative cyanobacteria biomass and associated beach closure forecasts across the June–August season for a eutrophic lake in Wisconsin (United States). This model is developed as part of a subseasonal to seasonal cyanobacteria forecasting system to optimize lake management across the peak cyanobacteria season. Model skill is significant in comparison to June–August cyanobacteria observations (Pearson correlation coefficient = 0.62, Heidke skill score = 0.38). The modeling framework proposed here demonstrates encouraging prediction skill and offers the possibility of advanced beach management applications.

Variability of summer cyanobacteria abundance: can season-ahead forecasts improve beach management?

Pixel-scale satellite forecasting of cyanobacteria in Florida lakes.

Native Macrophyte Community Response to Water-Level Manipulation for an Invasive Species

Large scale seasonal forecasting of peak season algae metrics in the Midwest and Northeast U.S.

Networks of marine protected areas (MPAs) are invaluable for the protection of species with high dispersal capacity, yet connectivity within networks is poorly understood. We demonstrate the connectivity within the regional MPA network in West Africa (RAMPAO), mediated by the largest green turtle population in the eastern Atlantic Ocean. We equipped with satellite tags 45 female green turtles nesting in the Bijagós Archipelago, Guinea-Bissau, and tracked them during internesting, migration, and foraging to quantify the degree of coverage the RAMPAO network provides during each of these critical periods. During the internesting period, turtles were largely concentrated around the nesting islands, with a mean of 94.8% (SD 0.1%, range: 46% - 100%, n = 40 turtles) of tracking positions falling within MPA limits. Among the 35 turtles successfully tracked into the foraging period, we identified variable migratory strategies, with 12 turtles remaining near-resident at distances of 40-90 km from breeding sites, 10 turtles migrating 300-400 km to The Gambia and Senegal, and 13 turtles traveling >1000 km to northern Mauritania. Of the 35 foraging turtles, 26 used MPAs, with a mean of 78.0% (SD 34.8%, range: 3.7% - 100%) of their tracking positions falling within the limits of RAMPAO MPAs, across Guinea-Bissau, Senegal and Mauritania. Migration corridors with high concentrations of passing turtles were mostly located nearshore, and 21% of these high passage areas fell within the MPA network. Overall, we found that this population connects five RAMPAO MPAs, yet some foraging sites (e.g., in the Bijagós) and important migration areas (e.g., Cap-Vert peninsula) described here are currently unprotected. These results are relevant to any considerations of MPA extension or establishment within the regional network, which would contribute towards meeting the Convention on Biological Diversity targets for national marine protected area estate coverage. By documenting biological connectivity across RAMPAO, this study represents an important example of the relevance of international protected area networks for green turtle conservation and for wider conservation action at a regional scale.

Green Turtles Highlight Connectivity Across a Regional Marine Protected Area Network in West Africa

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