Biome- and timescale-dependence of Holocene vegetation variability in the Northern Hemisphere.

Global climatic changes expected in the next centuries are likely to cause unparalleled vegetation disturbances, which in turn impact ecosystem services. To assess the significance of disturbances, it is necessary to characterize and understand typical natural vegetation variability on multi-decadal timescales and longer. We investigate this in the Holocene vegetation by examining a taxonomically harmonized and temporally standardized global fossil pollen dataset. Using principal component analysis, we characterize the variability in pollen assemblages, which are a proxy for vegetation composition, and derive timescale-dependent estimates of variability using the first-order Haar structure function. We find, on average, increasing fluctuations in vegetation composition from centennial to millennial timescales, as well as spatially coherent patterns of variability. We further relate these variations to pairwise comparisons between biome classes based on vegetation composition. As such, higher variability is identified for open-land vegetation compared to forests. This is consistent with the more active fire regimes of open-land biomes fostering variability. Needleleaf forests are more variable than broadleaf forests on shorter (centennial) timescales, but the inverse is true on longer (millennial) timescales. This inversion could also be explained by the fire characteristics of the biomes as fire disturbances would increase vegetation variability on shorter timescales, but stabilize vegetation composition on longer timecales by preventing the migration of less fire-adapted species.

VTAM: A robust pipeline for validating metabarcoding data using controls.

To obtain accurate estimates for biodiversity and ecological studies, metabarcoding studies should be carefully designed to minimize both false positive (FP) and false negative (FN) occurrences. Internal controls (mock samples and negative controls), replicates, and overlapping markers allow controlling metabarcoding errors but current metabarcoding software packages do not explicitly integrate these additional experimental data to optimize filtering. We have developed the metabarcoding analysis software VTAM, which uses explicitly these elements of the experimental design to find optimal parameter settings that minimize FP and FN occurrences. VTAM showed similar sensitivity, but a higher precision compared to two other pipelines using three datasets and two different markers (COI, 16S). The stringent filtering procedure implemented in VTAM aims to produce robust metabarcoding data to obtain accurate ecological estimates and represents an important step towards a non-arbitrary and standardized validation of metabarcoding data for conducting ecological studies. VTAM is implemented in Python and available from: https://github.com/aitgon/vtam. The VTAM benchmark code is available from: https://github.com/aitgon/vtam_benchmark.

An observation-based scaling model for climate sensitivity estimates and global projections to 2100.

We directly exploit the stochasticity of the internal variability, and the linearity of the forced response to make global temperature projections based on historical data and a Green's function, or Climate Response Function (CRF). To make the problem tractable, we take advantage of the temporal scaling symmetry to define a scaling CRF characterized by the scaling exponent H, which controls the long-range memory of the climate, i.e. how fast the system tends toward a steady-state, and an inner scale τ ≈ 2   years below which the higher-frequency response is smoothed out. An aerosol scaling factor and a non-linear volcanic damping exponent were introduced to account for the large uncertainty in these forcings. We estimate the model and forcing parameters by Bayesian inference which allows us to analytically calculate the transient climate response and the equilibrium climate sensitivity as: 1 . 7 - 0.2 + 0.3   K and 2 . 4 - 0.6 + 1.3   K respectively (likely range). Projections to 2100 according to the RCP 2.6, 4.5 and 8.5 scenarios yield warmings with respect to 1880-1910 of: 1 . 5 - 0.2 + 0.4 K , 2 . 3 - 0.5 + 0.7   K and 4 . 2 - 0.9 + 1.3   K. These projection estimates are lower than the ones based on a Coupled Model Intercomparison Project phase 5 multi-model ensemble; more importantly, their uncertainties are smaller and only depend on historical temperature and forcing series. The key uncertainty is due to aerosol forcings; we find a modern (2005) forcing value of [ - 1.0 , - 0.3 ] Wm - 2 (90 % confidence interval) with median at - 0.7 Wm - 2 . Projecting to 2100, we find that to keep the warming below 1.5 K, future emissions must undergo cuts similar to RCP 2.6 for which the probability to remain under 1.5 K is 48 %. RCP 4.5 and RCP 8.5-like futures overshoot with very high probability.