Implementing integrated measurements of essential biodiversity variables at a national scale

Funding: the Strategic Science Investment Funding for Crown Research Institutes from the Ministry of Business, Innovation and Employment.1. There is a global need for observation systems that deliver regular, timely data on state and trends in biodiversity, but few have been implemented, and fewer still at national scales. We describe the implementation of measurement of Essential Biodiversity Variables (EBVs) on an 8 km × 8 km grid throughout New Zealand, with multiple components of biodiversity (vegetation, birds, and some introduced mammals) measured simultaneously at each sample point. 2. Between 2011 and 2017, all public land was sampled nationally (ca. 1,350 points) and some private land (ca. 500 points). Synthetic appraisals of the state of New Zealand's biodiversity, not possible previously, can be derived from the first measurement of species distribution, population abundance, and taxonomic diversity EBVs. 3. Native bird counts (all species combined) were about 2.5 times greater per sample point in natural forests and shrublands than in non‐woody ecosystems, and native bird counts exceeded those of non‐native birds across all natural forests and shrublands. 4. Non‐native plants, birds, and mammals are invasive throughout, but high‐rainfall forested regions are least invaded, and historically deforested rain shadow regions are most invaded. 5. National reporting of terrestrial biodiversity across New Zealand's public land is established and becoming normalised, in the same manner as national and international reporting of human health and education statistics. The challenge is extending coverage across all private land. Repeated measurements of these EBVs, which began in 2017, will allow defensible estimates of biodiversity trends.Publisher PDFPeer reviewe

Prognostic impact of multidrug resistance gene expression on the management of breast cancer in the context of adjuvant therapy based on a series of 171 patients

Study of the prognostic impact of multidrug resistance gene expression in the management of breast cancer in the context of adjuvant therapy. This study involved 171 patients treated by surgery, adjuvant chemotherapy±radiotherapy±hormonal therapy (mean follow-up: 55 months). We studied the expression of multidrug resistance gene 1 (MDR1), multidrug resistance-associated protein (MRP1), and glutathione-S-transferase P1 (GSTP1) using a standardised, semiquantitative rt–PCR method performed on frozen samples of breast cancer tissue. Patients were classified as presenting low or high levels of expression of these three genes. rt-PCR values were correlated with T stage, N stage, Scarff–Bloom–Richardson (SBR) grade, age and hormonal status. The impact of gene expression levels on 5-year disease-free survival (DFS) and overall survival (OS) was studied by univariate and multivariate Cox analysis. No statistically significant correlation was demonstrated between MDR1, MRP1 and GSTP1 expressions. On univariate analysis, DFS was significantly decreased in a context of low GSTP1 expression (P=0.0005) and high SBR grade (P=0.003), size ⩾5 cm (P=0.038), high T stage (P=0.013), presence of intravascular embolus (P=0.034), and >3 N+ (P=0.05). On multivariate analysis, GSTP1 expression and the presence of ER remained independent prognostic factors for DFS. GSTP1 expression did not affect OS. The levels of MDR1 and MRP1 expression had no significant influence on DFS or OS. GSTP1 expression can be considered to be an independent prognostic factor for DFS in patients receiving adjuvant chemotherapy for breast cancer

Quarterly abundance of mice, Orongorongo Valley, New Zealand.

<p>Abundance (grey points/lines) and the predicted time series from a consumer model (solid dark line) using the best-fit parameter values and the Ivlev model fitted using three seedfall drivers: (a) observed seedfall; (b) seedfall predicted using ΔT (change in mean summer temperature in the preceding 2 years) and (c) seedfall predicted using absolute temperature T (mean summer temperature last year). Model parameters are in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119139#pone.0119139.t002" target="_blank">Table 2</a>.</p

Effect of climate change on mast events.

<p>Proportion of years in which there is expected to be a single mast event; proportion of years in which the first year of two consecutive mast events may occur (double mast events); and the average time between single mast events, calculated from observed seedfall data, and predicted from simulated 100-year time series for three climate scenarios.</p><p>Effect of climate change on mast events.</p

Relationships predicting the annual spring peak in abundance and changes in OV mouse population over autumn/winter.

<p>Mouse abundance (C/100TN) in early spring (August) and change in mouse abundance from late summer to early spring (February–August) are plotted against (a, c) log₁₀(seedfall) from the preceding year and (b, d) the mean summer temperature change between the two previous years (ΔT). In all panels, modelled results and the best-fit logistic curve are shown in grey. Observed demographic data and the best-fit logistic curve are shown in black. Correlation values are <i>r</i>_<i>mm</i> between the model (grey) line and the model (grey) points, <i>r</i>_<i>dd</i> between the data (black) line and the data (black) points, and <i>r</i>_<i>dm</i> between the model (grey) line and the data (black) points.</p

Parameter Values for the Best-Fit Consumer Models Fitted to Mouse Abundance.

<p>Parameter values for the best-fit consumer models fitted to mouse abundance using observed seedfall or seedfall predicted by the change in mean summer temperature in the preceding 2 years (ΔT) or mean summer temperature last year (T). Model parameters are defined in the text. Values in brackets are 95% confidence intervals. Correlation between the consumer model predictions and field-collected data on mouse abundance was measured by Pearson’s <i>r</i>. The index of mouse abundance is captures per 100 trap nights, ‘seeds’ is shorthand for ‘seeds m⁻²’.</p><p>Parameter Values for the Best-Fit Consumer Models Fitted to Mouse Abundance.</p