Density dependence in a seasonal time series of the bamboo mosquito,Tripteroides bambusa(Diptera: Culicidae)

2017 ◽  
Vol 149 (3) ◽  
pp. 338-344 ◽  
Author(s):  
Tomonori Hoshi ◽  
Nozomi Imanishi ◽  
Kazuhiko Moji ◽  
Luis Fernando Chaves
Keyword(s):  
Time Series ◽  
Population Dynamics ◽  
Density Dependence ◽  
Mosquito Species ◽  
Open Data ◽  
Parameter Estimates ◽  
Mosquito Abundance ◽  
Ricker Model ◽  
Forested Landscapes ◽  
Tripteroides Bambusa

AbstractThe bamboo mosquito,Tripteroides bambusa(Yamada) (Diptera: Culicidae), is a mosquito species ubiquitous across forested landscapes in Japan. During 2014 we sampled adult mosquitoes from May to November using a sweep net in Nagasaki, Japan. We recorded and managed our field data using Open Data Kit, which eased the overall process of data management before performing their statistical analysis. Here, we analyse the resulting biweekly time series of the bamboo mosquito abundance using time-series statistical techniques. Specifically, we test for density dependence in the population dynamics fitting the Ricker model. Parameter estimates for the Ricker model suggest that the bamboo mosquito is under density dependence regulation and that its population dynamics is stable. Our data also suggest the bamboo mosquito increased its abundance when temperature was more variable at our study site. Further work is warranted to better understand the linkage between the observed density dependence in the adults and the larvae of this mosquito species.

Population dynamics of muskrats and mink: investigating contemporary patterns in a classic predator-prey system

2021 ◽  
Author(s):  
Adam A Ahlers ◽  
Timothy P Lyons ◽  
Edward J Heske
Keyword(s):  
United States ◽  
Population Dynamics ◽  
Density Dependence ◽  
American Mink ◽  
Population Cycles ◽  
The United States ◽  
Parameter Estimates ◽  
Predator Prey ◽  
Cycle Lengths ◽  
Southern Half

A well-studied predator-prey relationship between American mink (Neovison vison (Schreber, 1777)) and muskrats (Ondatra zibethicus (Linnaeus, 1766)) in Canada has advanced our understanding of population cycles including the influence of density dependence and lagged responses of predators to prey abundances. However, it is unclear if patterns observed in Canada extend across the southern half of their native range. We used data from the United States to create a 41-year time series of mink and muskrat harvest reports (1970-2011) for 36 states. After controlling for pelt-price effects, we used 2nd order autoregressive and Lomb-Scargle spectral density models to identify the presence and periodicity of muskrat population cycles. Additionally, we tested for evidence of delayed or direct density dependence and for predator-driven population dynamics. Our results suggest muskrat populations may cycle in parts of the United States; however, results varied by modeling approaches with Lomb-Scargle analyses providing more precise parameter estimates. Observed cycle lengths were longer than expected with weak amplitudes and we urge caution when interpreting these results. We did not detect evidence of a predator-prey relationship driven by a lagged numerical response of American mink. American mink and muskrat fur returns were largely correlated across the region suggesting extraneous factors likely synchronize both populations.

Ecological change points: The strength of density dependence and the loss of history

2017 ◽  
Author(s):  
José M. Ponciano ◽  
Mark L. Taper ◽  
Brian Dennis
Keyword(s):  
Time Series ◽  
Population Dynamics ◽  
Density Dependence ◽  
Change Point ◽  
Likelihood Estimation ◽  
Theory And Practice ◽  
Change Points ◽  
Ecological Change ◽  
Proposed Model ◽  
Change Point Model

AbstractChange points in the dynamics of animal abundances have extensively been recorded in historical time series records. Little attention has been paid to the theoretical dynamic consequences of such change-points. Here we propose a change-point model of stochastic population dynamics. This investigation embodies a shift of attention from the problem of detecting when a change will occur, to another non-trivial puzzle: using ecological theory to understand and predict the post-breakpoint behavior of the population dynamics. The proposed model and the explicit expressions derived here predict and quantify how density dependence modulates the influence of the pre-breakpoint parameters into the post-breakpoint dynamics. Time series transitioning from one stationary distribution to another contain information about where the process was before the change-point, where is it heading and how long it will take to transition, and here this information is explicitly stated. Importantly, our results provide a direct connection of the strength of density dependence with theoretical properties of dynamic systems, such as the concept of resilience. Finally, we illustrate how to harness such information through maximum likelihood estimation for state-space models, and test the model robustness to widely different forms of compensatory dynamics. The model can be used to estimate important quantities in the theory and practice of population recovery.

Hierarchical modeling strengthens evidence for density dependence in observational time series of population dynamics

Ecology ◽  
2019 ◽  
Vol 101 (1) ◽  
Author(s):  
Loïc M. Thibaut ◽  
Sean R. Connolly
Keyword(s):  
Time Series ◽  
Population Dynamics ◽  
Density Dependence ◽  
Hierarchical Modeling

scholarly journals Identifying resting locations of a small elusive forest carnivore using a two-stage model accounting for GPS measurement error and hidden behavioral states

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Dalton J. Hance ◽  
Katie M. Moriarty ◽  
Bruce A. Hollen ◽  
Russell W. Perry
Keyword(s):  
Time Series ◽  
Measurement Error ◽  
Gaussian State ◽  
Parameter Estimates ◽  
Accelerometer Data ◽  
Two Stage ◽  
Location Data ◽  
Gps Measurement ◽  
Activity Measurements ◽  
Behavioral States

Abstract Background Studies of animal movement using location data are often faced with two challenges. First, time series of animal locations are likely to arise from multiple behavioral states (e.g., directed movement, resting) that cannot be observed directly. Second, location data can be affected by measurement error, including failed location fixes. Simultaneously addressing both problems in a single statistical model is analytically and computationally challenging. To both separate behavioral states and account for measurement error, we used a two-stage modeling approach to identify resting locations of fishers (Pekania pennanti) based on GPS and accelerometer data. Methods We developed a two-stage modelling approach to estimate when and where GPS-collared fishers were resting for 21 separate collar deployments on 9 individuals in southern Oregon. For each deployment, we first fit independent hidden Markov models (HMMs) to the time series of accelerometer-derived activity measurements and apparent step lengths to identify periods of movement and resting. Treating the state assignments as given, we next fit a set of linear Gaussian state space models (SSMs) to estimate the location of each resting event. Results Parameter estimates were similar across collar deployments. The HMMs successfully identified periods of resting and movement with posterior state assignment probabilities greater than 0.95 for 97% of all observations. On average, fishers were in the resting state 63% of the time. Rest events averaged 5 h (4.3 SD) and occurred most often at night. The SSMs allowed us to estimate the 95% credible ellipses with a median area of 0.12 ha for 3772 unique rest events. We identified 1176 geographically distinct rest locations; 13% of locations were used on > 1 occasion and 5% were used by > 1 fisher. Females and males traveled an average of 6.7 (3.5 SD) and 7.7 (6.8 SD) km/day, respectively. Conclusions We demonstrated that if auxiliary data are available (e.g., accelerometer data), a two-stage approach can successfully resolve both problems of latent behavioral states and GPS measurement error. Our relatively simple two-stage method is repeatable, computationally efficient, and yields directly interpretable estimates of resting site locations that can be used to guide conservation decisions.

scholarly journals Parasitism of Aedes albopictus by Ascogregarina taiwanensis lowers its competitive ability against Aedes triseriatus

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Emma Stump ◽  
Lauren M. Childs ◽  
Melody Walker
Keyword(s):  
Population Dynamics ◽  
Competitive Advantage ◽  
Aedes Albopictus ◽  
Abiotic Factors ◽  
Mosquito Species ◽  
Disease Spread ◽  
Aedes Triseriatus ◽  
La Crosse Virus ◽  
The Impact ◽  
Ascogregarina Taiwanensis

Abstract Background Mosquitoes are vectors for diseases such as dengue, malaria and La Crosse virus that significantly impact the human population. When multiple mosquito species are present, the competition between species may alter population dynamics as well as disease spread. Two mosquito species, Aedes albopictus and Aedes triseriatus, both inhabit areas where La Crosse virus is found. Infection of Aedes albopictus by the parasite Ascogregarina taiwanensis and Aedes triseriatus by the parasite Ascogregarina barretti can decrease a mosquito’s fitness, respectively. In particular, the decrease in fitness of Aedes albopictus occurs through the impact of Ascogregarina taiwanensis on female fecundity, larval development rate, and larval mortality and may impact its initial competitive advantage over Aedes triseriatus during invasion. Methods We examine the effects of parasitism of gregarine parasites on Aedes albopictus and triseriatus population dynamics and competition with a focus on when Aedes albopictus is new to an area. We build a compartmental model including competition between Aedes albopictus and triseriatus while under parasitism of the gregarine parasites. Using parameters based on the literature, we simulate the dynamics and analyze the equilibrium population proportion of the two species. We consider the presence of both parasites and potential dilution effects. Results We show that increased levels of parasitism in Aedes albopictus will decrease the initial competitive advantage of the species over Aedes triseriatus and increase the survivorship of Aedes triseriatus. We find Aedes albopictus is better able to invade when there is more extreme parasitism of Aedes triseriatus. Furthermore, although the transient dynamics differ, dilution of the parasite density through uptake by both species does not alter the equilibrium population sizes of either species. Conclusions Mosquito population dynamics are affected by many factors, such as abiotic factors (e.g. temperature and humidity) and competition between mosquito species. This is especially true when multiple mosquito species are vying to live in the same area. Knowledge of how population dynamics are affected by gregarine parasites among competing species can inform future mosquito control efforts and help prevent the spread of vector-borne disease.

scholarly journals A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

2021 ◽  
Vol 13 (15) ◽  
pp. 2869
Author(s):  
MohammadAli Hemati ◽  
Mahdi Hasanlou ◽  
Masoud Mahdianpari ◽  
Fariba Mohammadimanesh
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Policy Development ◽  
Atmospheric Correction ◽  
Open Data ◽  
Detection Methods ◽  
Landsat 8 ◽  
Landsat Images ◽  
Detection Analysis ◽  
The Impact

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

scholarly journals Marketing Mix Modeling Using PLS-SEM, Bootstrapping the Model Coefficients

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1832
Author(s):  
Mariano Méndez-Suárez
Keyword(s):  
Time Series ◽  
Structural Changes ◽  
Marketing Mix ◽  
Structural Equations ◽  
Superior Performance ◽  
Parameter Estimates ◽  
Significance Analysis ◽  
Autocorrelation Structure ◽  
Multichannel Retailing ◽  
Black Friday

Partial least squares structural equations modeling (PLS-SEM) uses sampling bootstrapping to calculate the significance of the model parameter estimates (e.g., path coefficients and outer loadings). However, when data are time series, as in marketing mix modeling, sampling bootstrapping shows inconsistencies that arise because the series has an autocorrelation structure and contains seasonal events, such as Christmas or Black Friday, especially in multichannel retailing, making the significance analysis of the PLS-SEM model unreliable. The alternative proposed in this research uses maximum entropy bootstrapping (meboot), a technique specifically designed for time series, which maintains the autocorrelation structure and preserves the occurrence over time of seasonal events or structural changes that occurred in the original series in the bootstrapped series. The results showed that meboot had superior performance than sampling bootstrapping in terms of the coherence of the bootstrapped data and the quality of the significance analysis.

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