scholarly journals Time-Varying Stock-Recruitment Model For Estimating Population Characteristics of Green Turtles

2022 ◽  
Author(s):  
Pablo del Monte-Luna ◽  
Miguel Nakamura ◽  
Vicente Guzmán-Hernández ◽  
Eduardo Cuevas ◽  
Melania López-Castro ◽  
...  
Keyword(s):  
Time Series ◽  
Demographic Data ◽  
Low Frequency ◽  
Chelonia Mydas ◽  
Recruitment Rate ◽  
Population Characteristics ◽  
Model Parameters ◽  
Positive Trend ◽  
Green Turtles ◽  
Stock Recruitment

Abstract The stock-recruitment relationship (SR), customarily used in fisheries assessment, can be used to analyze demographic data of sea turtles to infer changes in hatchling production (R) as a function of nester abundance (S), recruitment rates and the influence of environmental conditions on these population features. The SR Cushing model (R=aS^b), where a and b are the model parameters) is well-suited for representing the dynamics of recovering populations, such as the green turtle (Chelonia mydas) in Campeche, Mexico. This study aimed to explore the SR Cushing model using a time series of the abundance of nesters and hatchlings (1984–2020). By applying local regressions (9-yr moving windows), we found that the time series of parameter b (the change in R as a function of S) and the recruitment rate (hatchlings per nester) were inversely correlated with a 26-yr cycle of the Atlantic Multidecadal Oscillation –sea surface temperature (SST), over the Atlantic– (r^2=0.83) and (r^2=0.64), respectively, at a 3-yr lag). Model diagnostics using the time-dependent Cushing model substantiated that the log-normal distribution of hatchlings of C. mydas in Campeche depends on the abundance of nesting females and on a low frequency SST signal (r^2=0.98). The positive trend in nester numbers of green turtles in Campeche during the past 44 years may be the result of persistent conservation efforts, while the drastic and sporadic changes in the growth rate of annual arrivals and hatchling production are suggestive of population dynamics driven by low frequency, basin-wide environmental signals.

Quasi-cycles in crappie populations are forced by interactions among population characteristics and environment

2001 ◽  
Vol 58 (3) ◽  
pp. 594-601 ◽  
Author(s):  
Micheal S Allen ◽  
Leandro E Miranda
Keyword(s):  
Time Series ◽  
Environmental Conditions ◽  
Empirical Data ◽  
Population Model ◽  
Population Characteristics ◽  
Environmental Fluctuation ◽  
Density Dependent ◽  
Stock Recruitment ◽  
Variable Recruitment ◽  
Random Fluctuations

Crappie (Pomoxis spp.) populations have been characterized as cyclic, with strong year-classes recurring at 2- to 4-year intervals. We evaluated the potential for cyclic trends in crappie populations using a population model that included a density-dependent stock recruitment function and random environmental variation. Slow, medium, and fast growth were simulated over 100 years. The model predicted highly variable recruitment that was strongly influenced by environmental fluctuation at low and intermediate stock densities. At high stock density, recruitment was low, even if environmental conditions were favorable. Significant quasi-cycles occurred, but they were not sustained throughout the time series due to random environmental fluctuation. Quasi-cycles occurred because intermediate stock density and favorable environmental conditions occasionally combined to produce a very strong year-class that greatly increased stock density in the following 1–3 years and produced low recruitment, even if environmental conditions were favorable. Empirical data from 32 years of sampling age-0 crappies at Ross Barnett Reservoir showed trends similar to the simulated fluctuations. We conclude that crappie populations likely do not exhibit true cycles but may show quasi-cycles as a result of the interaction between random fluctuations in environment and density-dependent mechanisms. The frequency of such quasi-cycles may be enhanced by rapid growth and high exploitation.

Stock composition of green turtles Chelonia mydas foraging in the Ryukyu Archipelago differs with size class

2018 ◽  
Vol 600 ◽  
pp. 151-163 ◽  
Author(s):  
T Hamabata ◽  
H Nishizawa ◽  
I Kawazu ◽  
K Kameda ◽  
N Kamezaki ◽  
...  
Keyword(s):  
Chelonia Mydas ◽  
Size Class ◽  
Ryukyu Archipelago ◽  
Green Turtles

scholarly journals Plasma proteomics of green turtles (Chelonia mydas) reveals pathway shifts and potential biomarker candidates associated with health and disease

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
David P Marancik ◽  
Justin R Perrault ◽  
Lisa M Komoroske ◽  
Jamie A Stoll ◽  
Kristina N Kelley ◽  
...  
Keyword(s):  
Chelonia Mydas ◽  
Sea Turtle ◽  
Protein Abundance ◽  
Plasma Samples ◽  
Important Species ◽  
Green Turtles ◽  
Rehabilitation Programs ◽  
Potential Biomarker ◽  
Health And Disease

Abstract Evaluating sea turtle health can be challenging due to an incomplete understanding of pathophysiologic responses in these species. Proteome characterization of clinical plasma samples can provide insights into disease progression and prospective biomarker targets. A TMT-10-plex-LC–MS/MS platform was used to characterize the plasma proteome of five, juvenile, green turtles (Chelonia mydas) and compare qualitative and quantitative protein changes during moribund and recovered states. The 10 plasma samples yielded a total of 670 unique proteins. Using ≥1.2-fold change in protein abundance as a benchmark for physiologic upregulation or downregulation, 233 (34.8%) were differentially regulated in at least one turtle between moribund and recovered states. Forty-six proteins (6.9%) were differentially regulated in all five turtles with two proteins (0.3%) demonstrating a statistically significant change. A principle component analysis showed protein abundance loosely clustered between moribund samples or recovered samples and for turtles that presented with trauma (n = 3) or as intestinal floaters (n = 2). Gene Ontology terms demonstrated that moribund samples were represented by a higher number of proteins associated with blood coagulation, adaptive immune responses and acute phase response, while recovered turtle samples included a relatively higher number of proteins associated with metabolic processes and response to nutrients. Abundance levels of 48 proteins (7.2%) in moribund samples significantly correlated with total protein, albumin and/or globulin levels quantified by biochemical analysis. Differentially regulated proteins identified with immunologic and physiologic functions are discussed for their possible role in the green turtle pathophysiologic response and for their potential use as diagnostic biomarkers. These findings enhance our ability to interpret sea turtle health and further progress conservation, research and rehabilitation programs for these ecologically important species.

scholarly journals Dietary analysis of two sympatric marine turtle species in the eastern Mediterranean

2021 ◽  
Vol 168 (6) ◽  
Author(s):  
Josie L. Palmer ◽  
Damla Beton ◽  
Burak A. Çiçek ◽  
Sophie Davey ◽  
Emily M. Duncan ◽  
...  
Keyword(s):  
Green Turtle ◽  
Eastern Mediterranean ◽  
Chelonia Mydas ◽  
Marine Turtle ◽  
Dietary Analysis ◽  
Low Frequencies ◽  
Turtle Species ◽  
Dietary Shift ◽  
Green Turtles ◽  
Loggerhead Turtles

AbstractDietary studies provide key insights into threats and changes within ecosystems and subsequent impacts on focal species. Diet is particularly challenging to study within marine environments and therefore is often poorly understood. Here, we examined the diet of stranded and bycaught loggerhead (Caretta caretta) and green turtles (Chelonia mydas) in North Cyprus (35.33° N, 33.47° E) between 2011 and 2019. A total of 129 taxa were recorded in the diet of loggerhead turtles (n = 45), which were predominantly carnivorous (on average 72.1% of dietary biomass), foraging on a large variety of invertebrates, macroalgae, seagrasses and bony fish in low frequencies. Despite this opportunistic foraging strategy, one species was particularly dominant, the sponge Chondrosia reniformis (21.5%). Consumption of this sponge decreased with increasing turtle size. A greater degree of herbivory was found in green turtles (n = 40) which predominantly consumed seagrasses and macroalgae (88.8%) with a total of 101 taxa recorded. The most dominant species was a Lessepsian invasive seagrass, Halophila stipulacea (31.1%). This is the highest percentage recorded for this species in green turtle diet in the Mediterranean thus far. With increasing turtle size, the percentage of seagrass consumed increased with a concomitant decrease in macroalgae. Seagrass was consumed year-round. Omnivory occurred in all green turtle size classes but reduced in larger turtles (> 75 cm CCL) suggesting a slow ontogenetic dietary shift. Macroplastic ingestion was more common in green (31.6% of individuals) than loggerhead turtles (5.7%). This study provides the most complete dietary list for marine turtles in the eastern Mediterranean.

scholarly journals Shift Detection in Hydrological Regimes and Pluriannual Low-Frequency Streamflow Forecasting Using the Hidden Markov Model

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 2058 ◽  
Author(s):  
Larissa Rolim ◽  
Francisco de Souza Filho
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Extreme Events ◽  
Hidden Markov ◽  
Low Frequency ◽  
Streamflow Forecasting ◽  
Hydrologic Time Series ◽  
Low Frequency Variability ◽  
The Impact

Improved water resource management relies on accurate analyses of the past dynamics of hydrological variables. The presence of low-frequency structures in hydrologic time series is an important feature. It can modify the probability of extreme events occurring in different time scales, which makes the risk associated with extreme events dynamic, changing from one decade to another. This article proposes a methodology capable of dynamically detecting and predicting low-frequency streamflow (16–32 years), which presented significance in the wavelet power spectrum. The Standardized Runoff Index (SRI), the Pruned Exact Linear Time (PELT) algorithm, the breaks for additive seasonal and trend (BFAST) method, and the hidden Markov model (HMM) were used to identify the shifts in low frequency. The HMM was also used to forecast the low frequency. As part of the results, the regime shifts detected by the BFAST approach are not entirely consistent with results from the other methods. A common shift occurs in the mid-1980s and can be attributed to the construction of the reservoir. Climate variability modulates the streamflow low-frequency variability, and anthropogenic activities and climate change can modify this modulation. The identification of shifts reveals the impact of low frequency in the streamflow time series, showing that the low-frequency variability conditions the flows of a given year.

scholarly journals Locally and Remotely Forced Subtropical AMOC Variability: A Matter of Time Scales

2020 ◽  
Vol 33 (12) ◽  
pp. 5155-5172
Author(s):  
Quentin Jamet ◽  
William K. Dewar ◽  
Nicolas Wienders ◽  
Bruno Deremble ◽  
Sally Close ◽  
...  
Keyword(s):  
Time Series ◽  
Time Scales ◽  
North Atlantic ◽  
Time Scale ◽  
Low Frequency ◽  
Meridional Overturning Circulation ◽  
Open Boundary ◽  
Overturning Circulation ◽  
Decadal Scale ◽  
Meridional Overturning

AbstractMechanisms driving the North Atlantic meridional overturning circulation (AMOC) variability at low frequency are of central interest for accurate climate predictions. Although the subpolar gyre region has been identified as a preferred place for generating climate time-scale signals, their southward propagation remains under consideration, complicating the interpretation of the observed time series provided by the Rapid Climate Change–Meridional Overturning Circulation and Heatflux Array–Western Boundary Time Series (RAPID–MOCHA–WBTS) program. In this study, we aim at disentangling the respective contribution of the local atmospheric forcing from signals of remote origin for the subtropical low-frequency AMOC variability. We analyze for this a set of four ensembles of a regional (20°S–55°N), eddy-resolving (1/12°) North Atlantic oceanic configuration, where surface forcing and open boundary conditions are alternatively permuted from fully varying (realistic) to yearly repeating signals. Their analysis reveals the predominance of local, atmospherically forced signal at interannual time scales (2–10 years), whereas signals imposed by the boundaries are responsible for the decadal (10–30 years) part of the spectrum. Due to this marked time-scale separation, we show that, although the intergyre region exhibits peculiarities, most of the subtropical AMOC variability can be understood as a linear superposition of these two signals. Finally, we find that the decadal-scale, boundary-forced AMOC variability has both northern and southern origins, although the former dominates over the latter, including at the site of the RAPID array (26.5°N).

scholarly journals How to correctly apply Gaussian statistics in a non-stationary climate?

2021 ◽  
Author(s):  
Reinhold Steinacker
Keyword(s):  
Time Series ◽  
Actual Data ◽  
Temperature Record ◽  
Positive Trend ◽  
Positive Temperature ◽  
Climatic Trend ◽  
Filter Methods ◽  
Periodic Time Series ◽  
Past Data

AbstractTime series with a significant trend, as is now being the case for the temperature in the course of climate change, need a careful approach for statistical evaluations. Climatological means and moments are usually taken from past data which means that the statistics does not fit to actual data anymore. Therefore, we need to determine the long-term trend before comparing actual data with the actual climate. This is not an easy task, because the determination of the signal—a climatic trend—is influenced by the random scatter of observed data. Different filter methods are tested upon their quality to obtain realistic smoothed trends of observed time series. A new method is proposed, which is based on a variational principle. It outperforms other conventional methods of smoothing, especially if periodic time series are processed. This new methodology is used to test, how extreme the temperature of 2018 in Vienna actually was. It is shown that the new annual temperature record of 2018 is not too extreme, if we consider the positive trend of the last decades. Also, the daily mean temperatures of 2018 are not found to be really extreme according to the present climate. The real extreme of the temperature record of Vienna—and many other places around the world—is the strongly increased positive temperature trend over the last years.

The impact of fibropapillomatosis on clinical characteristics, blood gas, plasma biochemistry, and hematological profiles in juvenile green turtles ( Chelonia mydas)

2020 ◽  
Vol 96 (4) ◽  
pp. 723-734
Author(s):  
Tsung-Hsien Li ◽  
Chao-Chin Chang
Keyword(s):  
White Blood Cells ◽  
Extracellular Fluid ◽  
Chelonia Mydas ◽  
Sea Turtle ◽  
Total Carbon ◽  
Blood Gas ◽  
Green Turtles ◽  
Cell Counts ◽  
Significant Difference ◽  
The Impact

Fibropapillomatosis (FP) is a tumor- forming disease that afflicts all marine turtles and is the most common in green turtles (Chelonia mydas). In this study, the morphometric characteristics, blood gas, biochemistry, and hematological profiles of 28 (6 FP-positive and 22 FP-negative) green turtles from the coast of Taiwan were investigated. The results indicated that body weight ( P < 0.001) and curved carapace length (CCL; P < 0.001) in green turtles with FP were significantly higher than in turtles without FP. Furthermore, green turtles with FP had a significantly lower value of hemoglobin (HB; P = 0.010) and packed cell volume (PCV; P = 0.005) than turtles without FP. Blood cell counts of white blood cells (WBC; P = 0.008) and lymphocytes ( P = 0.022) were observed with significant difference; green turtles with FP had lower counts than turtles without FP. In addition, turtles with FP had significantly higher pH ( P = 0.036), base excess in extracellular fluid (BEecf; P = 0.012), bicarbonate (HCO3– ; P = 0.008), and total carbon dioxide (TCO2 ; P = 0.025) values than turtles without FP. The findings of this study provide valuable clinical parameters for the medical care of the species in sea turtle rehabilitation centers and help us to understand the physiological response of green turtles to different tumor-forming conditions.

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