totalmatrix_structure

Data file with presence/absence matrix for multiple alleles in Symbiodinium. Used for analyses with the program STRUCTURE

singlehaps_create

File used to generate program-specific input files for Symbiodinium "singlehaps" dataset using CREATE (https://bcrc.bio.umass.edu/pedigreesoftware/node/2). Example files created include those for Genepop and Arlequin

Pflex_create

File used to generate program-specific input files for Plexaura flexuosa dataset using CREATE (https://bcrc.bio.umass.edu/pedigreesoftware/node/2). Example files created include those for Genepop, Arlequin, and Bayesass

singlehaps_structure

Data file with alleles for 5 haploid microsatellite markers used to run analyses in the program STRUCTURE for Symbiodinium

Nightjar activity at three different periods during the night as detected by automated recorders.

<p>These boxplots display (a) churring activity (measured in minutes of activity per hour) and (b) flight calling activity at six different automated recorders during the period of the survey: Dusk, 22.00–00.00; Middle, 00.00–02.30; Dawn, 02.30–04.30. The y-axes are distinct for each recorder because the amount of activity varied greatly according to location (and local abundance of nightjars). Most of the churring activity was recorded during the dawn period and the least amount of activity was during the middle of the night. There was no difference in flight calling activity between the three periods.</p

The Use of Automated Bioacoustic Recorders to Replace Human Wildlife Surveys: An Example Using Nightjars

<div><p>To be able to monitor and protect endangered species, we need accurate information on their numbers and where they live. Survey methods using automated bioacoustic recorders offer significant promise, especially for species whose behaviour or ecology reduces their detectability during traditional surveys, such as the European nightjar. In this study we examined the utility of automated bioacoustic recorders and the associated classification software as a way to survey for wildlife, using the nightjar as an example. We compared traditional human surveys with results obtained from bioacoustic recorders. When we compared these two methods using the recordings made at the same time as the human surveys, we found that recorders were better at detecting nightjars. However, in practice fieldworkers are likely to deploy recorders for extended periods to make best use of them. Our comparison of this practical approach with human surveys revealed that recorders were significantly better at detecting nightjars than human surveyors: recorders detected nightjars during 19 of 22 survey periods, while surveyors detected nightjars on only six of these occasions. In addition, there was no correlation between the amount of vocalisation captured by the acoustic recorders and the abundance of nightjars as recorded by human surveyors. The data obtained from the recorders revealed that nightjars were most active just before dawn and just after dusk, and least active during the middle of the night. As a result, we found that recording at both dusk and dawn or only at dawn would give reasonably high levels of detection while significantly reducing recording time, preserving battery life. Our analyses suggest that automated bioacoustic recorders could increase the detection of other species, particularly those that are known to be difficult to detect using traditional survey methods. The accuracy of detection is especially important when the data are used to inform conservation.</p></div

Comparison of nightjar detection by different recorder settings.

<p>The mean detection of nightjars by the different recorder settings is compared via Tukey Contrasts for the fitted Generalised Linear Model. The detection of nightjars is compared for the different recorder settings. Adjusted p-values are reported (single-step method) and significant p-values are given in bold. The different settings were: recording 10 mins every hour (“10 mins” & “10 mins2”), only dusk (22.00–00.00), only the middle of the night (00.00–02.30), only dawn (02.30–04.30), dusk and dawn (“Dusk+Dawn”), and recording all night (“whole night”).</p

Proportion of the total number of nights nightjars were detected using different recorder settings.

<p>(a) We plotted the following recorder settings: dusk (22.00–00.00), middle of the night (00.00–02.30), dawn (02.30–04.30), dusk and dawn combined, and two random samples of ten minutes per hour (“10 mins” and “10 mins2”), and compared these to the total nights nightjars were detected when the recorders were left on during the whole night. (b) The relative length of each of these recording periods compared to recording the whole night. Our results indicate that most nightjar activity occurs during dusk and dawn; as a result, activating the recorders during these periods captures almost all (98.8%) of the nightjar activity, even though these periods make up only 60% of the night. The variability among the two 10-minute subsamples suggests that this is an unreliable sampling strategy for detecting nightjar activity.</p

Significantly upregulated lipids molecular species amongst the five cells lines in the negative acquisition mode.

<p>Significantly upregulated lipids molecular species amongst the five cells lines in the negative acquisition mode.</p

Validation of the expression profiles of <i>ChoK-α</i> and <i>LPCAT3</i> genes.

<p>(A) Real time RT-PCR analysis of expression profiles of ChoK-α and LPCAT 3. There is a significant upregulation in the expression of ChoK-α in the metastatic LNCAP and MDAPCa2b compared non-malignant RC77N-E and the malignant RC77T-E cells. (B) Western blot analysis of expression of ChoK-α and GAPDH in prostate cell lines. There is a significant upregulation in the expression of ChoK-α in the metastatic LNCAP and MDAPCa2b compared to the normal RWPE-1, non-malignant RC77N-E and the malignant RC77T-E cells. GAPDH was used as a loading control and there are no significant differences in the expression of the protein. (C) Densitometry measurements of Western Blot detection of ChoK-α and GAPDH.</p