scholarly journals Assessment of the status and trends in abundance of a coastal pinniped, the Australian sea lion Neophoca cinerea

2021 ◽  
Vol 44 ◽  
pp. 421-437 ◽  
Author(s):  
SD Goldsworthy ◽  
PD Shaughnessy ◽  
AI Mackay ◽  
F Bailleul ◽  
D Holman ◽  
...  
Keyword(s):  
Time Series Data ◽  
Conservation Status ◽  
South Australia ◽  
Population Monitoring ◽  
Breeding Site ◽  
Series Data ◽  
Breeding Sites ◽  
Neophoca Cinerea ◽  
Individual Site ◽  
The Status

Australian sea lions Neophoca cinerea are endemic to Australia, with their contemporary distribution restricted to South Australia (SA) and Western Australia (WA). Monitoring of the species has proved challenging due to prolonged breeding events that occur non-annually and asynchronously across their range. The most recent available data from 80 extant breeding sites (48 in SA, 32 in WA) enabled us to estimate the species-wide pup abundance to be 2739, with 82% (2246) in SA and 18% (493) in WA, mostly based on surveys conducted between 2014 and 2019. We evaluated 1776 individual site-surveys undertaken between 1970 and 2019 and identified admissible time-series data from 30 breeding sites, which revealed that pup abundance declined on average by 2.0% yr-1 (range 9.9% decline to 1.7% growth yr-1). The overall reduction in pup abundance over 3 generations (42.3 yr) was estimated to be 64%, with over 98% of Monte Carlo simulations producing a decline >50% over a 3-generation period, providing strong evidence that the species meets IUCN ‘Endangered’ criteria (decline ≥50% and ≤80%). The population is much smaller than previously estimated and is declining. There is a strong cline in regional abundances (increasing from west to east), with marked within-region heterogeneity in breeding site pup abundances and trends. Results from this study should improve consistency in the assessment of the species and create greater certainty among stakeholders about its conservation status. To facilitate species management and recovery, we prioritise key data gaps and identify factors to improve population monitoring.

scholarly journals Minimum time required to detect population trends: the need for long-term monitoring programs

2017 ◽  
Author(s):  
Easton R White
Keyword(s):  
Time Series ◽  
Statistical Power ◽  
Time Series Data ◽  
Conservation Status ◽  
Monitoring Program ◽  
Minimum Time ◽  
Series Data ◽  
Series Length ◽  
Time Required

Long-term time series are necessary to better understand population dynamics, assess species' conservation status, and make management decisions. However, population data are often expensive, requiring a lot of time and resources. When is a population time series long enough to address a question of interest? We determine the minimum time series length required to detect significant increases or decreases in population abundance. To address this question, we use simulation methods and examine 878 populations of vertebrate species. Here we show that 15-20 years of continuous monitoring are required in order to achieve a high level of statistical power. For both simulations and the time series data, the minimum time required depends on trend strength, population variability, and temporal autocorrelation. These results point to the importance of sampling populations over long periods of time. We argue that statistical power needs to be considered in monitoring program design and evaluation. Time series less than 15-20 years are likely underpowered and potentially misleading.

Maternal strategies of the Australian sea lion (Neophoca cinerea) at Dangerous Reef, South Australia

2011 ◽  
Vol 59 (1) ◽  
pp. 54 ◽  
Author(s):  
Andrew D. Lowther ◽  
Simon D. Goldsworthy
Keyword(s):  
South Australia ◽  
Maternal Investment ◽  
Milk Lipid ◽  
Resource Exploitation ◽  
Sea Lion ◽  
Breeding Sites ◽  
Neophoca Cinerea ◽  
Australian Sea Lion ◽  
Spatial Differences ◽  
Pup Growth

Maternal strategies of otariid seals reflect the optimisation between resource exploitation and offspring provisioning driven across spatially separated foraging and nursing grounds. Intercolony variation in the expression of maternal strategies may represent temporal and spatial differences in resource availability, intraspecies competition or differences in life-history traits. The current study describes maternal strategies of the Australian sea lion at the largest breeding colony of the species (Dangerous Reef) and a comparative analysis was performed with data collected 16 years earlier at Seal Bay (Kangaroo Island). Significant differences in maternal strategies were characterised by lower milk lipid content (21.0 versus 28.9%), abbreviated periods onshore (0.93 versus 1.63 days) and slower pup growth rates (0.09–0.12 kg day–1) at Dangerous Reef. These data suggest flexibility in the expression of maternal investment between breeding sites and support the hypothesis of localised adaptation

scholarly journals Scalable learning of interpretable rules for the dynamic microbiome domain

2020 ◽  
Author(s):  
Venkata Suhas Maringanti ◽  
Vanni Bucci ◽  
Georg K. Gerber
Keyword(s):  
Time Series Data ◽  
Human Microbiome ◽  
Predictive Performance ◽  
Series Data ◽  
Data Sets ◽  
Human Host ◽  
Scalable Learning ◽  
The Status ◽  
Biological Insight ◽  
Microbiome Data

AbstractThe microbiome, which is inherently dynamic, plays essential roles in human physiology and its disruption has been implicated in numerous human diseases. Linking dynamic changes in the microbiome to the status of the human host is an important problem, which is complicated by limitations and complexities of the data. Model interpretability is key in the microbiome field, as practitioners seek to derive testable biological hypotheses from data or develop diagnostic tests that can be understood by clinicians. Interpretable structure must take into account domainspecific information key to biologists and clinicians including evolutionary relationships (phylogeny) and dynamic behavior of the microbiome. A Bayesian model was previously developed in the field, which uses Markov Chain Monte Carlo inference to learn human interpretable rules for classifying the status of the human host based on microbiome time-series data, but that approach is not scalable to increasingly large microbiome datasets being produced. We present a new fully-differentiable model that also learns human-interpretable rules for the same classification task, but in an end-to-end gradient-descent based framework. We validate the performance of our model on human microbiome data sets and demonstrate our approach has similar predictive performance to the fully Bayesian method, while running orders-of-magnitude faster and moreover learning a larger set of rules, thus providing additional biological insight into the effects of diet and environment on the microbiome.

scholarly journals ANALYZING THE STATUS OF MANGO TREES IN BRGY. CANTIPAY, CARMEN, CEBU USING NDVI AND TIME SERIES CLUSTERING

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 313-317
Author(s):  
R. B. Navaja ◽  
F. P. Campomanes ◽  
C. L. Patiño ◽  
M. J. L. Flores
Keyword(s):  
Time Series ◽  
Hierarchical Clustering ◽  
Vegetation Index ◽  
Time Series Data ◽  
Minimum Variance ◽  
Series Data ◽  
Linkage Algorithm ◽  
Division 1 ◽  
The Status ◽  
Cluster 2

Abstract. The Department of Agriculture – Region VII reports that many mango orchards in Cebu province are dying because of the absence of required post-harvest attention. Lacklustre yields and erratic pest infestations have driven some farmers and growers to abandon mango orchards. To help revive low-yielding mango orchards, there is a need to distinguish actively bearing mango trees from those that remain dormant throughout the year. Using remote sensing techniques, mango trees from separate orchards in Brgy. Cantipay, Carmen, Cebu were mapped and studied using multi-temporal Sentinel-2 data (from January 2018 through May 2019). Prior to that, a field visit was conducted to survey the area using UAVs and field observation, and in the process, was able to identify an abandoned mango orchard. Pixel-based Normal Difference Vegetation Index (NDVI) values were extracted from each of the 822 geotagged mango trees with an average of 16 trees among 53 divisions. Time series were derived from the average of the NDVI values from each division and plotted per month of extraction from oldest to latest. Clustering was applied to the time series data using Hierarchical Clustering with Ward’s Minimum Variance as an algorithm to determine the divisions with the closest time series. Using the resulting dendrogram as basis, two major clusters were selected based on the value of their distances with each other: Cluster 1 containing 29 Divisions, and Cluster 2 containing 24 Divisions. Cluster 1 contains most of the Divisions in and around the biggest active mango orchard. In contrast, Cluster 2 contains most of the Divisions that are in and around the previously identified abandoned mango orchard. An alternative dendrogram was also created by using Complete Linkage algorithm in Hierarchical Clustering, after which 3 relevant clusters were selected. The second dendrogram highlights the stark difference between Division 1, contained in Cluster 3, from the rest of the other clustered divisions at 2.17 units from the next closest one. Notably, Division 1 is located smack in the middle of the abandoned orchard The remaining clusters, Cluster 2 with 21 divisions containing most of the divisions in the abandoned orchard, is 2.46 distance units away from Cluster 1, which has 31 and hosting most of the divisions in the active mango orchards. Two major clusters emerged from using the two algorithms. Divisions with higher and more variant NDVI values seemed to come from the mango trees which were more active during the fruiting cycle. Divisions from the abandoned mango orchards were observed to have lower and less varied NDVI values because of minimal activity in the trees. Other Divisions clustered under the abandoned orchard could have been juveniles based on their size.

Population Cycles of the Larch Budmoth in Switzerland

2002 ◽  
Author(s):  
Peter Turchin ◽  
Cheryl J. Briggs
Keyword(s):  
Time Series ◽  
Food Quality ◽  
Time Series Data ◽  
Population Cycles ◽  
Series Data ◽  
Food Quantity ◽  
The Status ◽  
The Rich ◽  
Rich Data

The population dynamics of the larch budmoth (LBM), Zeiraphera diniana, in the Swiss Alps are perhaps the best example of periodic oscillations in ecology (figure 7.1). These oscillations are characterized by a remarkably regular periodicity, and by an enormous range of densities experienced during a typical cycle (about 100,000-fold difference between peak and trough numbers). Furthermore, nonlinear time series analysis of LBM data (e.g., Turchin 1990, Turchin and Taylor 1992) indicates that LBM oscillations are definitely generated by a second-order dynamical process (in other words, there is a strong delayed density dependence—see also chapter 1). Analysis of time series data on LBM dynamics from five valleys in the Alps suggests that around 90% of variance in Rt is explained by the phenomenological time series model employing lagged LBM densities, R, =f(Ni-1,Ni-2,) (Turchin 2002). As discussed in the influential review by Baltensweiler and Fischlin (1988) about a decade ago, ecological theory suggests a number of candidate mechanisms that can produce the type of dynamics observed in the LBM (see also chapter 1). Baltensweiler and Fischlin concluded that changes in food quality induced by previous budmoth feeding was the most plausible explanation for the population cycles. During the last decade, the issue of larch budmoth oscillations was periodically revisited by various population ecologists looking for general insights about insect population cycles (e.g., Royama 1977, Bowers et al. 1993, Ginzburg and Taneyhill 1994, Den Boer and Reddingius 1996, Hunter and Dwyer 1998, Berryman 1999). These authors generally concurred with the view that budmoth cycles are driven by the interaction with food quality. A recent reanalysis of the rich data set on budmoth population ecology collected by Swiss researchers over a period of several decades, however, suggested that the role of parasitism is underappreciated (Turchin et al. 2002). Before focusing on the roles of food quality and parasitism in LBM dynamics, we briefly review the status of other hypotheses that were discussed in the literature on LBM cycles. First, the natural history of the LBM-larch system is such that food quantity is an unlikely factor to explain LBM oscillations.

Detection of Machinery Failure Signs From Big Time-Series Data Obtained by Flow Simulation of Intermediate-Pressure Steam Turbines

2021 ◽  
Author(s):  
Kazuhiko Komatsu ◽  
Hironori Miyazawa ◽  
Cheng Yiran ◽  
Masayuki Sato ◽  
Takashi Furusawa ◽  
...  
Keyword(s):  
Time Series ◽  
Power Plants ◽  
Time Series Data ◽  
Thermal Power ◽  
Turbine Blades ◽  
Series Data ◽  
Thermal Power Plants ◽  
Steam Turbines ◽  
Pressure Steam ◽  
The Status

Abstract The periodic maintenance, repair, and overhaul (MRO) of turbine blades in thermal power plants are essential to maintain a stable power supply. During MRO, older and less-efficient power plants are put into operation, which results in wastage of additional fuels. Such a situation forces thermal power plants to work under off-design conditions. Moreover, such an operation accelerates blade deterioration, which may lead to sudden failure. Therefore, a method for avoiding unexpected failures needs to be developed. To detect the signs of machinery failures, the analysis of time-series data is required. However, data for various blade conditions must be collected from actual operating steam turbines. Further, obtaining abnormal or failure data is difficult. Thus, this paper proposes a classification approach to analyze big time-series data alternatively collected from numerical results. The time-series data from various normal and abnormal cases of actual intermediate-pressure steam-turbine operation were obtained through numerical simulation. Thereafter, useful features were extracted and classified using K-means clustering to judge whether the turbine is operating normally or abnormally. The experimental results indicate that the status of the blade can be appropriately classified. By checking data from real turbine blades using our classification results, the status of these blades can be estimated. Thus, this approach can help decide on the appropriate timing for MRO.

Detection of Machinery Failure Signs From Big Time-Series Data Obtained by Flow Simulation of Intermediate-Pressure Steam Turbines

2021 ◽  
Author(s):  
Kazuhiko Komatsu ◽  
Hironori Miyazawa ◽  
Cheng Yiran ◽  
Masayuki Sato ◽  
Takashi Furusawa ◽  
...  
Keyword(s):  
Time Series ◽  
Power Plants ◽  
Time Series Data ◽  
Thermal Power ◽  
Turbine Blades ◽  
Series Data ◽  
Thermal Power Plants ◽  
Steam Turbines ◽  
Pressure Steam ◽  
The Status

Abstract The periodic maintenance, repair, and overhaul (MRO) of turbine blades in thermal power plants are essential to maintain a stable power supply. During MRO, older and less-efficient power plants are put into operation, which results in wastage of additional fuels. Such a situation forces thermal power plants to work under off-design conditions. Moreover, such an operation accelerates blade deterioration, which may lead to sudden failure. Therefore, a method for avoiding unexpected failures needs to be developed. To detect the signs of machinery failures, the analysis of time-series data is required. However, data for various blade conditions must be collected from actual operating steam turbines. Further, obtaining abnormal or failure data is difficult. Thus, this paper proposes a classification approach to analyze big time-series data alternatively collected from numerical results. The time-series data from various normal and abnormal cases of actual intermediate-pressure steam-turbine operation were obtained through numerical simulation. Thereafter, useful features were extracted and classified using K-means clustering to judge whether the turbine is operating normally or abnormally. The experimental results indicate that the status of the blade can be appropriately classified. By checking data from real turbine blades using our classification results, the status of these blades can be estimated. Thus, this approach can help decide on the appropriate timing for MRO.

scholarly journals Learning Region and Performance of Pharmaceutical Industry: Evidence from Two Indian States

1970 ◽  
Vol 6 (2) ◽  
pp. 45-64
Author(s):  
Tareef Husain
Keyword(s):  
Pharmaceutical Industry ◽  
Time Series Data ◽  
Himachal Pradesh ◽  
Series Data ◽  
Indian States ◽  
Learning Region ◽  
The Status ◽  
Regional Factors ◽  
Knowledge Intensive ◽  
And Performance

In the extent literature, availability of critical regional and technology-based factors have been recognized as the constituents of learning region which in turn lead to the rising performance of enterprises located in the region. These regional factors subsume sub-national policies, vertical industries, knowledge institution, skill, demand and infrastructural factors. Pharmaceutical industry is one of the knowledge-intensive industries, which is theoretically believed to be performed better in a learning region. The present study takes into account two Indian states namely, Gujarat and Himachal Pradesh and describes the status of pharmaceutical industry in the light of learning region. The descriptive explanation based on time series data for the last two decades revealed that the rising trends of pharmaceutical industry in the state of Himachal Pradesh, sourced by the conducive policy supports, rising share of chemical industry, rising enrolements in higher education and availability of good infrastructure. On the other hand, despite encompassing considerable infrastructure, skilled labour, knowledge and demand, Gujarat has reported constant or marginally declining trends of pharmaceutical industry, in terms of number of units, output and employment during last decade.

The magnitude of site and time interaction effect in tsetse fly (Diptera: Glossinidae) trap catches

1998 ◽  
Vol 88 (1) ◽  
pp. 59-64 ◽  
Author(s):  
A. Odulaja ◽  
S. Mihok ◽  
I.M. Abu-Zinid
Keyword(s):  
Time Series Data ◽  
Glossina Pallidipes ◽  
Population Monitoring ◽  
Tsetse Fly ◽  
Series Data ◽  
Tsetse Flies ◽  
Data Set ◽  
Time Interaction ◽  
The Mean ◽  
Trap Catches

AbstractSite and time effects are important factors determining trap catches of tsetse flies. These factors may interact significantly and therefore confound interpretation of time series data used for population monitoring. We therefore investigated the magnitude and importance of site × time interactions in trap catches of Glossina pallidipes Austen and G. longipennis Corti using a 2200 trap-days (400 trap-months) data set. The interaction was found to be siginificant (p<0.05) in 46–100% of the combinations of different numbers of months and sites between 2 and 12. The mean percent variance due to the interaction ranged between 4% and 28% for G. pallidipes and 12% and 36% for G.longipennis. The interaction was usually less important than the effect of site alone but more important than the effect of time alone. These results suggest that tsetse researchers should examine critically the adequacy of existing approaches to population monitoring with traps and to testing new traps and odour baits.

scholarly journals The diversity, population, ecology and conservation status of waterbirds in the wetland of Bangpu Nature Education Center, Thailand

2020 ◽  
Vol 21 (8) ◽  
Author(s):  
Chanate Wanna ◽  
DONLA WASAN ◽  
YOKCHOM PISARUT ◽  
SOI-AMPORNKUL RUNGTIP
Keyword(s):  
Population Ecology ◽  
Diversity Index ◽  
Conservation Status ◽  
Survival Rates ◽  
Similarity Index ◽  
Breeding Sites ◽  
Nature Education ◽  
Waterbird Species ◽  
Limosa Limosa ◽  
The Status

Abstract. Chanate W, Wasan D, Pisarut Y, Rungtip S. 2020. The diversity, population, ecology, and conservation status of waterbirds in the wetland of Bangpu Nature Education Center, Thailand. Biodiversitas 21: 3910-3918. Wetlands are a crucial habitat for waterbirds as they provide feeding and breeding sites and increase survival rates during the non-breeding season. This study aimed to update the status of waterbirds in Bangpu Nature Education Center, Samut Prakarn Province, Thailand by evaluating the species diversity and abundance. The ground count survey was conducted at 3 habitats: mangrove forest, mudflat, and bungalow accommodation from March to October 2017. A total of 34 waterbird species classified under 5 orders, 8 families, and 22 genera were observed. The majority of waterbird species (23 species) were found in the mudflat area with the least number (12 species) observed in the bungalow accommodation location. The Shannon-Weiner diversity index (H’) showed that the mudflat area had significantly greater diversity compared with the other sites. The mudflat area also had the greatest species richness (D) (2.89) and species evenness index (E) (0.38), respectively. The Sorensen similarity index (CS) indicated that the greatest similarity in species (66.67%) was found between the mangrove and bungalow areas. A total of 6 residents, 20 migratory species, and 8 species with both resident and migratory populations were found with 4 species classified as abundant, 3 as common, 15 as moderately common, and 12 as uncommon species identified. In addition, the conservation status of waterbirds is becoming increasingly important with 5 near-threatened species (NT) already identified according to IUCN, 2016 and Red Data of Thailand, 2007, consisting of Heteroscelus brevipes, Limosa limosa, Numenius arquata, Mycteria leucocephala and Vanellus cinereus. It is recommended that monitoring the conservation status of the Bangpu wetlands should be continued to maintain waterbird diversity.

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