Density Independence in Space and Inconsistent Temporal Relationships for Host Mortality Caused by a Fairyfly Parasitoid

10.2307/5142 ◽  
1989 ◽  
Vol 58 (3) ◽  
pp. 1065 ◽  
Author(s):  
Donald R. Strong
Keyword(s):  
Density Independence ◽  
Temporal Relationships ◽  
Host Mortality

A technique for superfusion of isolated granulosa cells. Dynamics of cAMP production and steroidogenesis in response to gonadotropins

1988 ◽  
Vol 117 (4) ◽  
pp. 497-506 ◽  
Author(s):  
Carl Johanson ◽  
Viktor Johanson
Keyword(s):  
Granulosa Cells ◽  
Culture Medium ◽  
Cell Damage ◽  
Adenosine Monophosphate ◽  
Camp Production ◽  
Morphological Examination ◽  
Ovarian Cells ◽  
Temporal Relationships ◽  
Polycarbonate Membranes ◽  
Estradiol 17Β

Abstract. A superfusion model for isolated ovarian cells was developed and characterized in detail. Granulosa cells isolated from pre-ovulatory rat ovarian follicles were placed in superfusion (perifusion) chambers with a volume of 125 μl. Culture medium was pumped through the chambers, collected in 20-min fractions of 600 μl and analysed for cAMP and steroids. Viability was confirmed by morphological examination. The use of polycarbonate membranes to retain the cells in the chambers was abandoned since the membranes caused severe cell damage. The temporal relationships between gonadotropic stimuli and the release of cyclic 3':5'-adenosine monophosphate (cAMP) and steroids was investigated. Within 10 min FSH elicited transient increase in the release of cAMP and progesterone but had no effect on testosterone or estradiol-17β release. Amplitude and duration of the response in cAMP and progesterone release were correlated to concentration and length of the FSH pulse when these parameters were varied within the ranges 1–100 μg/l and 30–270 min, respectively. Compared with the cAMP response, the progesterone response peaked up to 30 min later and lasted 1 to 2 h longer but could not be extended to more than approximately 6 h, not even with longer FSH pulses. These results could indicate a development of desensitization.

Selective Host Mortality in a Catalytic Model Applied to Schistosomiasis

1973 ◽  
Vol 107 (954) ◽  
pp. 199-212 ◽  
Author(s):  
Joel E. Cohen
Keyword(s):  
Host Mortality

scholarly journals Cross-scale interaction of host tree size and climatic water deficit governs bark beetle-induced tree mortality

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Michael J. Koontz ◽  
Andrew M. Latimer ◽  
Leif A. Mortenson ◽  
Christopher J. Fettig ◽  
Malcolm P. North
Keyword(s):  
Water Deficit ◽  
Sierra Nevada ◽  
Bark Beetle ◽  
Ponderosa Pine ◽  
Tree Mortality ◽  
Mortality Rates ◽  
Dendroctonus Brevicomis ◽  
Scale Interactions ◽  
Climatic Water Deficit ◽  
Host Mortality

AbstractThe recent Californian hot drought (2012–2016) precipitated unprecedented ponderosa pine (Pinus ponderosa) mortality, largely attributable to the western pine beetle (Dendroctonus brevicomis; WPB). Broad-scale climate conditions can directly shape tree mortality patterns, but mortality rates respond non-linearly to climate when local-scale forest characteristics influence the behavior of tree-killing bark beetles (e.g., WPB). To test for these cross-scale interactions, we conduct aerial drone surveys at 32 sites along a gradient of climatic water deficit (CWD) spanning 350 km of latitude and 1000 m of elevation in WPB-impacted Sierra Nevada forests. We map, measure, and classify over 450,000 trees within 9 km2, validating measurements with coincident field plots. We find greater size, proportion, and density of ponderosa pine (the WPB host) increase host mortality rates, as does greater CWD. Critically, we find a CWD/host size interaction such that larger trees amplify host mortality rates in hot/dry sites. Management strategies for climate change adaptation should consider how bark beetle disturbances can depend on cross-scale interactions, which challenge our ability to predict and understand patterns of tree mortality.

scholarly journals Bootstrap joint prediction regions for sequences of missing values in spatio-temporal datasets

2021 ◽  
Author(s):  
Maria Lucia Parrella ◽  
Giuseppina Albano ◽  
Cira Perna ◽  
Michele La Rocca
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Sample Selection ◽  
Sample Path ◽  
Temporal Relationships ◽  
Empirical Performance ◽  
Spatio Temporal ◽  
Joint Prediction ◽  
Point Forecast ◽  
Prediction Regions

AbstractMissing data reconstruction is a critical step in the analysis and mining of spatio-temporal data. However, few studies comprehensively consider missing data patterns, sample selection and spatio-temporal relationships. To take into account the uncertainty in the point forecast, some prediction intervals may be of interest. In particular, for (possibly long) missing sequences of consecutive time points, joint prediction regions are desirable. In this paper we propose a bootstrap resampling scheme to construct joint prediction regions that approximately contain missing paths of a time components in a spatio-temporal framework, with global probability $$1-\alpha $$ 1 - α . In many applications, considering the coverage of the whole missing sample-path might appear too restrictive. To perceive more informative inference, we also derive smaller joint prediction regions that only contain all elements of missing paths up to a small number k of them with probability $$1-\alpha $$ 1 - α . A simulation experiment is performed to validate the empirical performance of the proposed joint bootstrap prediction and to compare it with some alternative procedures based on a simple nominal coverage correction, loosely inspired by the Bonferroni approach, which are expected to work well standard scenarios.

TV-MV Analytics: A visual analytics framework to explore time-varying multivariate data

2019 ◽  
Vol 19 (1) ◽  
pp. 3-23
Author(s):  
Aurea Soriano-Vargas ◽  
Bernd Hamann ◽  
Maria Cristina F de Oliveira
Keyword(s):  
Visual Analytics ◽  
Visual Analysis ◽  
Multivariate Data ◽  
Visual Exploration ◽  
Data Sets ◽  
Time Varying ◽  
Domain Experts ◽  
Data Mining Algorithms ◽  
Temporal Relationships ◽  
Visualization Techniques

We present an integrated interactive framework for the visual analysis of time-varying multivariate data sets. As part of our research, we performed in-depth studies concerning the applicability of visualization techniques to obtain valuable insights. We consolidated the considered analysis and visualization methods in one framework, called TV-MV Analytics. TV-MV Analytics effectively combines visualization and data mining algorithms providing the following capabilities: (1) visual exploration of multivariate data at different temporal scales, and (2) a hierarchical small multiples visualization combined with interactive clustering and multidimensional projection to detect temporal relationships in the data. We demonstrate the value of our framework for specific scenarios, by studying three use cases that were validated and discussed with domain experts.

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