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.

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.

scholarly journals Quirky patterns in time-series of estimates of recruitment could be artefacts

2014 ◽  
Vol 72 (1) ◽  
pp. 111-116 ◽  
Author(s):  
M. Dickey-Collas ◽  
N. T. Hintzen ◽  
R. D. M. Nash ◽  
P-J. Schön ◽  
M. R. Payne
Keyword(s):  
Time Series ◽  
Stock Assessment ◽  
Meta Analysis ◽  
Reference Points ◽  
Fish Stocks ◽  
Stock Recruitment ◽  
Modelling Assumptions ◽  
Meta Analyses ◽  
Recruitment Model ◽  
Recruitment Time

Abstract The accessibility of databases of global or regional stock assessment outputs is leading to an increase in meta-analysis of the dynamics of fish stocks. In most of these analyses, each of the time-series is generally assumed to be directly comparable. However, the approach to stock assessment employed, and the associated modelling assumptions, can have an important influence on the characteristics of each time-series. We explore this idea by investigating recruitment time-series with three different recruitment parameterizations: a stock–recruitment model, a random-walk time-series model, and non-parametric “free” estimation of recruitment. We show that the recruitment time-series is sensitive to model assumptions and this can impact reference points in management, the perception of variability in recruitment and thus undermine meta-analyses. The assumption of the direct comparability of recruitment time-series in databases is therefore not consistent across or within species and stocks. Caution is therefore required as perhaps the characteristics of the time-series of stock dynamics may be determined by the model used to generate them, rather than underlying ecological phenomena. This is especially true when information about cohort abundance is noisy or lacking.

Distribution, feeding behaviour, and condition of Cape horse mackerel early life stages, Trachurus capensis, under different environmental conditions in the northern Benguela upwelling ecosystem

2014 ◽  
Vol 72 (2) ◽  
pp. 543-557 ◽  
Author(s):  
S. J. Geist ◽  
A. Kunzmann ◽  
H. M. Verheye ◽  
A. Eggert ◽  
A. Schukat ◽  
...  
Keyword(s):  
Dissolved Oxygen ◽  
Feeding Ecology ◽  
Environmental Conditions ◽  
Early Life ◽  
Early Life History ◽  
Horse Mackerel ◽  
Larval Phase ◽  
Recruitment Success ◽  
Wide Range ◽  
Variable Recruitment

Abstract Early life history (ELH) traits are key to understand variable recruitment success and hence the stock size of marine fish. One of the currently most puzzling ecosystems in this regard is the northern part of the Benguela Current upwelling system off Namibia. Here, populations of the formerly dominant pelagic species, sardine and anchovy, failed to recover during the last three decades after a dramatic decline. In contrast, Cape horse mackerel, Trachurus capensis, maintained a constant population size. Warming of the system and shoaling of hypoxic zones together with feedback loops within an altered foodweb are discussed to be responsible for this regime shift. In this study, we address the role of larval traits for the successful performance of the T. capensis population under the present environmental conditions with the focus on feeding ecology. We investigated seasonal variations of the geographical distribution, growth rate, feeding ecology, and nutritional condition of their ELH stages and examined relationships with water temperature, dissolved oxygen concentration, and micro-zooplankton composition. T. capensis' ELH stages showed a wide spatial and seasonal distribution, a preference for higher water temperatures (18–21°C) and presence over a wide range of dissolved oxygen concentrations (0.13–6.35 ml O2 l−1). Feeding success was high and mainly different groups of Copepoda were targeted, which were strongly size selected. The high dietary importance of micro-copepods during large parts of the larval phase indicates successful exploitation of this food source, which has increased in abundance during the last decade. It also explains observed best nutritional conditions at temperatures between 18 and 21°C, since these small copepods are commonly associated with warmer temperatures. Altogether, these traits enhance the species' probability to encounter suitable environments for the survival of their ELH stages, which is likely to lead to their high recruitment success in the northern Benguela ecosystem.

scholarly journals Hydrological prediction in a non-stationary world

2007 ◽  
Vol 11 (1) ◽  
pp. 408-414 ◽  
Author(s):  
R. T. Clarke
Keyword(s):  
Time Series ◽  
Urban Development ◽  
Mean Value ◽  
South American ◽  
Drainage Basins ◽  
South Eastern ◽  
Rapid Changes ◽  
Hydrological Prediction ◽  
Random Fluctuations ◽  
Linear Trends

Abstract. The paper discusses evidence that common assumptions in the analysis of hydrological time series (homogeneous variability in random fluctuations about a constant mean value) may not be appropriate for some South American drainage basins. Relatively rapid changes have occurred, and are occurring, as a consequence of replacing mature forest by short crops and urban development. Some research claims to have detected non-linear trends in streamflow in rivers draining the south-eastern part of the sub-continent, together with decadal fluctuations and interannual peaks at ENSO timescales. The paper discusses the implications of such changes for hydrological practices now in widespread and largely unquestioned use.

scholarly journals Analyzing the Effect of Time in Migration Measurement Using Georeferenced Digital Trace Data

Demography ◽  
2021 ◽  
Vol 58 (1) ◽  
pp. 51-74
Author(s):  
Lee Fiorio ◽  
Emilio Zagheni ◽  
Guy Abel ◽  
Johnathan Hill ◽  
Gabriel Pestre ◽  
...  
Keyword(s):  
Empirical Data ◽  
Survival Function ◽  
The United States ◽  
Population Characteristics ◽  
Phone Call ◽  
Research Strategies ◽  
Data Set ◽  
Trace Data ◽  
Transition Functions ◽  
Temporal Granularity

Abstract Georeferenced digital trace data offer unprecedented flexibility in migration estimation. Because of their high temporal granularity, many migration estimates can be generated from the same data set by changing the definition parameters. Yet despite the growing application of digital trace data to migration research, strategies for taking advantage of their temporal granularity remain largely underdeveloped. In this paper, we provide a general framework for converting digital trace data into estimates of migration transitions and for systematically analyzing their variation along a quasi-continuous time scale, analogous to a survival function. From migration theory, we develop two simple hypotheses regarding how we expect our estimated migration transition functions to behave. We then test our hypotheses on simulated data and empirical data from three platforms in two internal migration contexts: geotagged Tweets and Gowalla check-ins in the United States, and cell-phone call detail records in Senegal. Our results demonstrate the need for evaluating the internal consistency of migration estimates derived from digital trace data before using them in substantive research. At the same time, however, common patterns across our three empirical data sets point to an emergent research agenda using digital trace data to study the specific functional relationship between estimates of migration and time and how this relationship varies by geography and population characteristics.

scholarly journals An integrated population model for estimating the relative effects of natural and anthropogenic factors on a threatened population of Pacific trout

2019 ◽  
Author(s):  
Mark D. Scheuerell ◽  
Casey P. Ruff ◽  
Joseph H. Anderson ◽  
Eric M. Beamer
Keyword(s):  
At Risk ◽  
Population Model ◽  
Habitat Restoration ◽  
Negative Relationship ◽  
Anthropogenic Factors ◽  
List Type ◽  
Population Demographics ◽  
Density Dependent ◽  
Integrated Population Model ◽  
The North

SummaryAssessing the degree to which at-risk species are regulated by density dependent versus density independent factors is often complicated by incomplete or biased information. If not addressed in an appropriate manner, errors in the data can affect estimates of population demographics, which may obfuscate the anticipated response of the population to a specific action.We developed a Bayesian integrated population model that accounts explicitly for interannual variability in the number of reproducing adults and their age structure, harvest, and environmental conditions. We apply the model to 41 years of data for a population of threatened steelhead troutOncorhynchus mykissusing freshwater flows, ocean indices, and releases of hatchery-born conspecifics as covariates.We found compelling evidence that the population is under strong density dependence, despite being well below its historical population size. In the freshwater portion of the lifecycle, we found a negative relationship between productivity (offspring per parent) and peak winter flows, and a positive relationship with summer flows. We also found a negative relationship between productivity and releases of hatchery conspecifics. In the marine portion of the lifecycle, we found a positive correlation between productivity and the North Pacific Gyre Oscillation. Furthermore, harvest rates on wild fish have been sufficiently low to ensure very little risk of overfishing.Synthesis and applications.The evidence for density dependent population regulation, combined with the substantial loss of juvenile rearing habitat in this river basin, suggests that habitat restoration could benefit this population of at-risk steelhead. Our results also imply that hatchery programs for steelhead need to be considered carefully with respect to habitat availability and recovery goals for wild steelhead. If releases of hatchery steelhead have indeed limited the production potential of wild steelhead, there are likely significant tradeoffs between providing harvest opportunities via hatchery steelhead production, and achieving wild steelhead recovery goals.

Clustering Applied to Large Sets of Environmental Conditions for Selecting Typical Scenarios for Ship Maneuvering Real-Time Simulations

2021 ◽  
Author(s):  
Felipe M. Moreno ◽  
Eduardo A. Tannuri
Keyword(s):  
Time Series ◽  
Cluster Analysis ◽  
Environmental Conditions ◽  
Wind Waves ◽  
Fast Time ◽  
Large Set ◽  
Ship Maneuvering ◽  
Partitioning Around Medoids ◽  
Waves And Currents ◽  
Maneuvering Simulations

Abstract The methodology described in this paper is used to reduce a large set of combined wind, waves, and currents to a smaller set that still represents well enough the desired site for ship maneuvering simulations. This is achieved by running fast-time simulations for the entire set of environmental conditions and recording the vessel’s drifting time-series while it is controlled by an automatic-pilot based on a line-of-sight algorithm. The cases are then grouped considering how similar the vessel’s drifting time-series are, and one environmental condition is selected to represent each group found by the cluster analysis. The measurement of dissimilarity between the time-series is made by application of Dynamic Time Warping and the Cluster Analysis is made by the combination of Partitioning Around Medoids algorithm and the Silhouette Method. Validation is made by maneuvering simulations made with a Second Deck Officer.

On the use of structured time-series to detect and test hypotheses about within-lakes relationships

1995 ◽  
Vol 52 (4) ◽  
pp. 799-803 ◽  
Author(s):  
Yves T. Prairie ◽  
C. Tara Marshall
Keyword(s):  
Time Series ◽  
Empirical Data ◽  
Single System ◽  
Empirical Relationships ◽  
Cross Sectional ◽  
Apparent Relationship ◽  
Field Season ◽  
The Relationship ◽  
Over Time ◽  
Chlorophyll Concentrations

Aquatic scientists using empirical relationships developed from point measurements or averages from different lakes often assume that these relationships also apply to individual lakes over time. However, this assumption is difficult to test because the extent of variation within a single system is generally much smaller and the relationship accordingly less defined than across a number of systems. We present a new method to extract empirical relationships from the internal structure of a time-series within a single lake. When we applied the method to an extreme simulation, we were able to recover accurately the parameters of the relationship in spite of the absence of any apparent relationship between the variables. When applied to empirical data for phosphorus and chlorophyll concentrations collected daily over one field season, the estimated structural relationship was nearly identical to that estimated from cross-sectional data even though the empirical trend appeared much shallower and very weak.

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