Use of an Inverse Method for Time Series to Estimate the Dynamics of and Management Strategies for the Box Jellyfish Carybdea marsupialis

The Arctic climate is changing rapidly. The warming and resultant longer open water periods suggest a potential for expansion of marine vegetation along the vast Arctic coastline. We compiled and reviewed the scattered time series on Arctic marine vegetation and explored trends for macroalgae and eelgrass (Zostera marina). We identified a total of 38 sites, distributed between Arctic coastal regions in Alaska, Canada, Greenland, Iceland, Norway/Svalbard, and Russia, having time series extending into the 21st Century. The majority of these exhibited increase in abundance, productivity or species richness, and/or expansion of geographical distribution limits, several time series showed no significant trend. Only four time series displayed a negative trend, largely due to urchin grazing or increased turbidity. Overall, the observations support with medium confidence (i.e., 5–8 in 10 chance of being correct, adopting the IPCC confidence scale) the prediction that macrophytes are expanding in the Arctic. Species distribution modeling was challenged by limited observations and lack of information on substrate, but suggested a current (2000–2017) potential pan-Arctic macroalgal distribution area of 820.000 km2 (145.000 km2 intertidal, 675.000 km2 subtidal), representing an increase of about 30% for subtidal- and 6% for intertidal macroalgae since 1940–1950, and associated polar migration rates averaging 18–23 km decade–1. Adjusting the potential macroalgal distribution area by the fraction of shores represented by cliffs halves the estimate (412,634 km2). Warming and reduced sea ice cover along the Arctic coastlines are expected to stimulate further expansion of marine vegetation from boreal latitudes. The changes likely affect the functioning of coastal Arctic ecosystems because of the vegetation’s roles as habitat, and for carbon and nutrient cycling and storage. We encourage a pan-Arctic science- and management agenda to incorporate marine vegetation into a coherent understanding of Arctic changes by quantifying distribution and status beyond the scattered studies now available to develop sustainable management strategies for these important ecosystems.

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Parameter uncertainty of a dynamic multispecies size spectrum model

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/cjfas-2015-0022 ◽

2016 ◽

Vol 73 (4) ◽

pp. 589-597 ◽

Cited By ~ 14

Author(s):

Michael A. Spence ◽

Paul G. Blackwell ◽

Julia L. Blanchard

Keyword(s):

Time Series ◽

Time Series Data ◽

Size Structure ◽

Management Strategies ◽

Optimization Methods ◽

Series Data ◽

Size Spectrum ◽

Aquatic Communities ◽

The North ◽

Spectrum Model

Dynamic size spectrum models have been recognized as an effective way of describing how size-based interactions can give rise to the size structure of aquatic communities. They are intermediate-complexity ecological models that are solutions to partial differential equations driven by the size-dependent processes of predation, growth, mortality, and reproduction in a community of interacting species and sizes. To be useful for quantitative fisheries management these models need to be developed further in a formal statistical framework. Previous work has used time-averaged data to “calibrate” the model using optimization methods with the disadvantage of losing detailed time-series information. Using a published multispecies size spectrum model parameterized for the North Sea comprising 12 interacting fish species and a background resource, we fit the model to time-series data using a Bayesian framework for the first time. We capture the 1967–2010 period using annual estimates of fishing mortality rates as input to the model and time series of fisheries landings data to fit the model to output. We estimate 38 key parameters representing the carrying capacity of each species and background resource, as well as initial inputs of the dynamical system and errors on the model output. We then forecast the model forward to evaluate how uncertainty propagates through to population- and community-level indicators under alternative management strategies.

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Analysis of a long-term Forest Accountancy Network to Support Management and Controlling

Open Agriculture ◽

10.1515/opag-2018-0016 ◽

2018 ◽

Vol 3 (1) ◽

pp. 155-162 ◽

Cited By ~ 1

Author(s):

Markus Dög ◽

Johannes Wildberg ◽

Bernhard Möhring

Keyword(s):

Decision Making ◽

Time Series ◽

Time Series Data ◽

Management Strategies ◽

Series Data ◽

Forest Economics ◽

Private Forest ◽

Forest Landowners ◽

Forest Enterprises

Abstract Multifunctional forestry in Germany is characterized by long production periods and complex biological-technical processes. Private forest enterprises are complex systems which are closely interwoven with the economic environment. To ensure their economic success, forest landowners need to take the economic development into consideration and adapt their management strategies. Management accounting is an important source for information needed to fulfil main tasks of accounting that help to manage forest enterprises: ‘description’, ‘explanation’ and ‘decision making’. To get general data, long time series data, taken from Forest Accountancy Networks (FAN), can be analysed. For more than 45 years, data from the FAN Westfalen-Lippe in Germany has been collected and analysed by the department of Forest Economics and Forest Management at the University of Göttingen. The long-term development and adaptation strategies of defined groups of private forest enterprises can be illustrated using this data. These valuable time series can support decision-making processes for private forest landowners and provide tools for forest policy. The data shows that private forest enterprises, with spruce as the dominating tree species, have performed above average in terms of operating revenues and profit margins, but are also more susceptible to calamities resulting in higher involuntary timber harvests.

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Clustering Groundwater Level Time Series of the Exploited Almonte-Marismas Aquifer in Southwest Spain

Water ◽

10.3390/w12041063 ◽

2020 ◽

Vol 12 (4) ◽

pp. 1063

Author(s):

Nuria Naranjo-Fernández ◽

Carolina Guardiola-Albert ◽

Héctor Aguilera ◽

Carmen Serrano-Hidalgo ◽

Esperanza Montero-González

Keyword(s):

Time Series ◽

Groundwater Resources ◽

Management Strategies ◽

Groundwater Levels ◽

Management Plans ◽

Distribution Parameters ◽

Hydrodynamic Disturbance ◽

Over Exploitation ◽

Hydrodynamic Behaviors ◽

Guadalquivir River

Groundwater resources are regularly the principal water supply in semiarid and arid climate areas. However, groundwater levels (GWL) in semiarid aquifers are suffering a general decrease because of anthropic exploitation of aquifers and the repercussions of climate change. Effective groundwater management strategies require a deep characterization of GWL fluctuations, in order to identify individual behaviors and triggering factors. In September 2019, the Guadalquivir River Basin Authority (CHG) declared that there was over-exploitation in three of the five groundwater bodies of the Almonte-Marismas aquifer, Southwest Spain. For that reason, it is critical to understand GWL dynamics in this aquifer before the new Spanish Water Resources Management Plans (2021–2027) are developed. The application of GWL series clustering in hydrogeology has grown over the past few years, as it is an extraordinary tool that promptly provides a GWL classification; each group can be related to different responses of a complex aquifer under any external change. In this work, GWL time series from 160 piezometers were analyzed for the period 1975 to 2016 and, after data pre-processing, 24 piezometers were selected for clustering with k-means (static) and time series (dynamic) clustering techniques. Six and seven groups (k) were chosen to apply k-means. Six characterized types of hydrodynamic behaviors were obtained with time series clustering (TSC). Number of clusters were related to diverse affections of water exploitation depending on soil uses and hydrogeological spatial distribution parameters. TSC enabled us to distinguish local areas with high hydrodynamic disturbance and to highlight a quantitative drop of GWL during the studied period.

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Eye development in the box jellyfish Carybdea marsupialis

Developmental Biology ◽

10.1016/j.ydbio.2011.05.594 ◽

2011 ◽

Vol 356 (1) ◽

pp. 160-161

Author(s):

Jenna Valley ◽

Vicki Martin

Keyword(s):

Eye Development ◽

Box Jellyfish ◽

Carybdea Marsupialis

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Electrophysiological activity of a neurotoxic fraction from the venom of box jellyfish Carybdea marsupialis

Comparative Biochemistry and Physiology Part C Toxicology & Pharmacology ◽

10.1016/j.cbpc.2016.10.010 ◽

2017 ◽

Vol 191 ◽

pp. 177-182 ◽

Cited By ~ 4

Author(s):

Fernando Lazcano-Pérez ◽

Rogelio O. Arellano ◽

Edith Garay ◽

Roberto Arreguín-Espinosa ◽

Judith Sánchez-Rodríguez

Keyword(s):

Box Jellyfish ◽

Electrophysiological Activity ◽

Carybdea Marsupialis

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Inverse method for estimating respiration rates from decay time series

Biogeosciences ◽

10.5194/bg-9-3601-2012 ◽

2012 ◽

Vol 9 (9) ◽

pp. 3601-3612 ◽

Cited By ~ 8

Author(s):

D. C. Forney ◽

D. H. Rothman

Keyword(s):

Time Series ◽

Organic Matter ◽

Decay Time ◽

Lognormal Distribution ◽

Inverse Method ◽

Continuous Distribution ◽

Discrete Distribution ◽

Decay Rates ◽

Direct Inversion ◽

Two Parameters

Abstract. Long-term organic matter decomposition experiments typically measure the mass lost from decaying organic matter as a function of time. These experiments can provide information about the dynamics of carbon dioxide input to the atmosphere and controls on natural respiration processes. Decay slows down with time, suggesting that organic matter is composed of components (pools) with varied lability. Yet it is unclear how the appropriate rates, sizes, and number of pools vary with organic matter type, climate, and ecosystem. To better understand these relations, it is necessary to properly extract the decay rates from decomposition data. Here we present a regularized inverse method to identify an optimally-fitting distribution of decay rates associated with a decay time series. We motivate our study by first evaluating a standard, direct inversion of the data. The direct inversion identifies a discrete distribution of decay rates, where mass is concentrated in just a small number of discrete pools. It is consistent with identifying the best fitting "multi-pool" model, without prior assumption of the number of pools. However we find these multi-pool solutions are not robust to noise and are over-parametrized. We therefore introduce a method of regularized inversion, which identifies the solution which best fits the data but not the noise. This method shows that the data are described by a continuous distribution of rates, which we find is well approximated by a lognormal distribution, and consistent with the idea that decomposition results from a continuum of processes at different rates. The ubiquity of the lognormal distribution suggest that decay may be simply described by just two parameters: a mean and a variance of log rates. We conclude by describing a procedure that estimates these two lognormal parameters from decay data. Matlab codes for all numerical methods and procedures are provided.

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Distribution Feeder Modeling for Time-Series Simulation of Voltage Management Strategies

2018 IEEE/PES Transmission and Distribution Conference and Exposition (T&D) ◽

10.1109/tdc.2018.8440379 ◽

2018 ◽

Cited By ~ 1

Author(s):

J. Giraldez ◽

P. Gotseff ◽

A. Nagarajan ◽

R. Ueda ◽

J. Shindo ◽

...

Keyword(s):

Time Series ◽

Management Strategies ◽

Distribution Feeder ◽

Time Series Simulation ◽

Voltage Management

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Study on Clustering of Free-Floating Bike-Sharing Parking Time Series in Beijing Subway Stations

Sustainability ◽

10.3390/su11195439 ◽

2019 ◽

Vol 11 (19) ◽

pp. 5439 ◽

Cited By ~ 2

Author(s):

Xu ◽

Bain ◽

Rong ◽

Wang ◽

Yin

Keyword(s):

Time Series ◽

Early Warning ◽

Rapid Development ◽

Management Strategies ◽

Peak Period ◽

The Sustainable Development ◽

Parking Problem ◽

Bike Sharing ◽

Parking Management ◽

Subway Stations

In recent years, the free-floating bike-sharing (FFBS) system has become a significant mode of travel to satisfy urban residents’ travel demands. However, with the rapid development of FFBS, many problems have arisen, among which the parking problem is the most prominent. To solve the FFBS parking problem around urban subways, firstly, the time series of FFBS parking pattern and subway station classification in Beijing were constructed based on parking intensity, showing a significant spatial distribution of subway stations with different intensity levels. Second, a hierarchical clustering method based on dynamic time warping (DTW) was proposed to cluster the FFBS parking time series. Subway stations in Beijing were grouped into 11 clusters, and the clustering purity reached 0.939, which achieved the expected effect. Then, the peak and off-peak period features of time series were extracted to discuss the clustering results. Finally, a two-level early-warning index for monitoring FFBS was constructed, which took the real-time parking quantity and land use capacity of FFBS into consideration. And FFBS parking management strategies for different early-warning indices were put forward. It is very important for the sustainable development of FFBS and cities.

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“Time series momentum” in commodity markets

Managerial Finance ◽

10.1108/mf-11-2013-0322 ◽

2014 ◽

Vol 40 (7) ◽

pp. 662-680 ◽

Cited By ~ 1

Author(s):

Julien Chevallier ◽

Florian Ielpo

Keyword(s):

Time Series ◽

Management Strategies ◽

Commodity Markets ◽

Practical Significance ◽

Content Type ◽

Daily Data ◽

Asset Class ◽

Portfolio Managers ◽

Second Category ◽

Practical Implications

Purpose – The purpose of this paper is to contain an empirical application of the concept of “time series momentum” – as developed by Moskowitz et al. (2012) – to commodity markets with daily data during 1995-2012. Design/methodology/approach – The paper applies the new concept of “time series momentum” to the sphere of commodity markets. Findings – The paper extends the results previously obtained by Moskowitz et al. (2012) to a second category labeled “breakout strategy.” Research limitations/implications – Further management strategies can be elaborated for investment management purposes, based on the suggested inclusion of the “time series momentum” in commodities. Practical implications – The empirical evidence gathered in this paper bears practical significance for portfolio managers and commodity tradings advisors relying on trend following strategies. Originality/value – Commodity markets are quickly developing to an alternative asset class for investors. Discovering their properties and characteristics has a broad appeal in finance.

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