Effects of spatial management strategies (<i>s</i>) on the relative decrease in maximum metapopulation abundance in model networks when an obligate resource (K) is reduced in 50% of the local populations.

<p>Effects of spatial management strategies (<i>s</i>) on the relative decrease in maximum metapopulation abundance in model networks when an obligate resource (K) is reduced in 50% of the local populations.</p

Changes in maximum metapopulation abundance in response to different management strategies and the underlying structure of model metapopulation networks.

<p>Top row shows the topology of model metapopulation networks. Middle and bottom rows show the effects of applying the management actions of general population reduction and reduction of resource availability, respectively, over the corresponding metapopulation pictured in the top row. Effects are measured as the relative change in maximum metapopulation abundance after vs. before management is applied. Dispersal (<i>d</i>) = 0.3 and management level (<i>l</i>) = 0.6. Management extent (<i>e</i>) varies between 0.1 and 0.9. Lines are a local polynomial regression fit to 100 replicates for each value of <i>e</i> and shadows represent the standard error of the mean. Colours represent different spatial management strategies (<i>s</i>): red = <i>random</i>, green = <i>correlated</i>, blue = <i>hub</i>.</p

Examples of landscapes for rabbit populations in Western Australia.

<p>Shown randomly selected real-world landscapes of 50km² extent from which networks of local population connectivity were obtained. Green represents grassland and arable land (suitable as foraging habitat by rabbits); yellow represents shrubland and open woodlands (suitable as shelter habitat by rabbits); and white shows habitat not suitable for forage or shelter (see text). Land cover classification is based on the Australian National Vegetation Information System database (NVIS) at 100*100m resolution. Black lines represent managed farmland units and dots the geometric centres of these units. Farmland units are typically considered in local and regional rabbit control practices.</p

Effects of reduction of an obligate resource on real-world rabbit metapopulations.

<p>Change in metapopulation stability (i.e. proportional change in coefficient of variation -CV-) (top row), change in maximum metapopulation abundance (middle row), and population persistence (i.e. fraction of surviving local populations) (bottom row) for different values of management extent (<i>e</i>), level (<i>l</i>), and different levels of dispersal (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160417#sec002" target="_blank">methods</a>). Lines are local polynomial regression fits to 100 replicates for each of the 10 sample landscapes studied and shadows around them represent the standard error of the mean. Colours represent different levels of spatial management (<i>l</i>): red = 0.3, green = 0.6, blue = 0.9. Management extent is the fraction of local populations that have been managed.</p

Relative importance of various components of management and dispersal on the change in maximum abundance of managed rabbit metapopulations.

<p>Partial effects plots showing the relative importance of dispersal, management extent, management level, and spatial management strategy on maximum rabbit abundance at the metapopulation scale. Management actions are: population reduction (pop. reduction; light grey), and reduction of an obligate resource (res. reduction; dark grey).</p

Effects of general population reduction on real-world rabbit metapopulations.

<p>Change in metapopulation stability (i.e. proportional change in coefficient of variation -CV-) (top row), change in maximum metapopulation abundance (middle row), and population persistence (i.e. fraction of surviving local populations) (bottom row) for different values of management extent (<i>e</i>), level (<i>l</i>), and different levels of dispersal (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160417#sec002" target="_blank">methods</a>). Lines are local polynomial regression fits to 100 replicates for each of the 10 sample landscapes studied and shadows around them represent the standard error of the mean. Colours represent different levels of spatial management (<i>l</i>): red = 0.3, green = 0.6, blue = 0.9. Management extent is the fraction of local populations that have been managed.</p

Genome-Wide Association Study Identifies Novel Loci Associated with Circulating Phospho- and Sphingolipid Concentrations

Phospho- and sphingolipids are crucial cellular and intracellular compounds. These lipids are required for active transport, a number of enzymatic processes, membrane formation, and cell signalling. Disruption of their metabolism leads to several diseases, with diverse neurological, psychiatric, and metabolic consequences. A large number of phospholipid and sphingolipid species can be detected and measured in human plasma. We conducted a meta-analysis of five European family-based genome-wide association studies (N = 4034) on plasma levels of 24 sphingomyelins (SPM), 9 ceramides (CER), 57 phosphatidylcholines (PC), 20 lysophosphatidylcholines (LPC), 27 phosphatidylethanolamines (PE), and 16 PE-based plasmalogens (PLPE), as well as their proportions in each major class. This effort yielded 25 genome-wide significant loci for phospholipids (smallest P-value = 9.88×10−204) and 10 loci for sphingolipids (smallest P-value = 3.10×10−57). After a correction for multiple comparisons (P-value<2.2×10−9), we observed four novel loci significantly associated with phospholipids (PAQR9, AGPAT1, PKD2L1, PDXDC1) and two with sphingolipids (PLD2 and APOE) explaining up to 3.1% of the variance. Further analysis of the top findings with respect to within class molar proportions uncovered three additional loci for phospholipids (PNLIPRP2, PCDH20, and ABDH3) suggesting their involvement in either fatty acid elongation/saturation processes or fatty acid specific turnover mechanisms. Among those, 14 loci (KCNH7, AGPAT1, PNLIPRP2, SYT9, FADS1-2-3, DLG2, APOA1, ELOVL2, CDK17, LIPC, PDXDC1, PLD2, LASS4, and APOE) mapped into the glycerophospholipid and 12 loci (ILKAP, ITGA9, AGPAT1, FADS1-2-3, APOA1, PCDH20, LIPC, PDXDC1, SGPP1, APOE, LASS4, and PLD2) to the sphingolipid pathways. In large meta-analyses, associations between FADS1-2-3 and carotid intima media thickness, AGPAT1 and type 2 diabetes, and APOA1 and coronary artery disease were observed. In conclusion, our study identified nine novel phospho- and sphingolipid loci, substantially increasing our knowledge of the genetic basis for these traits

Study populations.

<p>Values for age and lipid concentrations are presented as mean (standard deviation).</p

Genome-wide association results for 115 phospholipid species.

<p>(A) Genome-wide association results for plasma levels of 115 phospholipid species. (B) Genome-wide association results for within-class proportions of 115 plasma phospholipid species. Manhattan plots show the combined association signals (−log₁₀(<i>P</i>-value)) on the y-axis versus SNPs according to their position in the genome on the x-axis (NCBI build 36). Novel genes are represented in red, while previously known loci are represented in black.</p

Genome-wide association results for 33 sphingolipid species.

<p>(A) Genome-wide association results for plasma levels of 33 sphingolipid species. (B) Genome-wide association results for within-class proportions of 33 sphingolipid species. Manhattan plots show the combined association signals (−log₁₀(<i>P</i>-value)) on the y-axis versus SNPs according to their position in the genome on the x-axis (NCBI build 36). Novel genes are represented in red, while previously known loci are represented in black.</p