ENSO Drives interannual variation of forest woody growth across the tropics

Meteorological extreme events such as El Niño events are expected to affect tropical forest net primary production (NPP) and woody growth, but there has been no large-scale empirical validation of this expectation. We collected a large high–temporal resolution dataset (for 1–13 years depending upon location) of more than 172 000 stem growth measurements using dendrometer bands from across 14 regions spanning Amazonia, Africa and Borneo in order to test how much month-to-month variation in stand-level woody growth of adult tree stems (NPPstem) can be explained by seasonal variation and interannual meteorological anomalies. A key finding is that woody growth responds differently to meteorological variation between tropical forests with a dry season (where monthly rainfall is less than 100 mm), and aseasonal wet forests lacking a consistent dry season. In seasonal tropical forests, a high degree of variation in woody growth can be predicted from seasonal variation in temperature, vapour pressure deficit, in addition to anomalies of soil water deficit and shortwave radiation. The variation of aseasonal wet forest woody growth is best predicted by the anomalies of vapour pressure deficit, water deficit and shortwave radiation. In total, we predict the total live woody production of the global tropical forest biome to be 2.16 Pg C yr−1, with an interannual range 1.96–2.26 Pg C yr−1 between 1996–2016, and with the sharpest declines during the strong El Niño events of 1997/8 and 2015/6. There is high geographical variation in hotspots of El Niño–associated impacts, with weak impacts in Africa, and strongly negative impacts in parts of Southeast Asia and extensive regions across central and eastern Amazonia. Overall, there is high correlation (r = −0.75) between the annual anomaly of tropical forest woody growth and the annual mean of the El Niño 3.4 index, driven mainly by strong correlations with anomalies of soil water deficit, vapour pressure deficit and shortwave radiation

Tropical terrestrial invertebrates—where to from here?

There are over one million described invertebrate species on Earth, the majority of which are likely to inhabit the highly biodiverse rain forests around the equator. These are some of the most vulnerable ecosystems on Earth due to the pressures of deforestation and climate change with many of their inhabitants at risk of extinction. Invertebrates play a major role in ecosystem functioning from decomposition and nutrient cycling to herbivory and pollination; however, while our understanding of these roles is improving, we are far from being able to predict the consequences of further deforestation, climate change, and biodiversity loss due to the lack of comparative data and the high proportion of species which remain to be discovered. As we move into an era of increased pressure on old‐growth habitats and biodiversity, it is imperative that we understand how changes to invertebrate communities, and the extinction of species, affect ecosystems. Innovative and comprehensive methods that approach these issues are needed. Here, we highlight priorities for future tropical terrestrial invertebrate research such as the efficiency of sustainable land management, exploration of innovative methods for better understanding of invertebrate ecology and behavior, and quantifying the role of invertebrates in ecosystem functioning

Modelling error evaluation of ground observed vegetation parameters

To verify large-scale vegetation parameter measurements the average value of sampling points from small-scale data are typically used. However, this method undermines the validity of the data due to the difference in scale or an inappropriate number of sampling points. A robust universal error assessment method for measuring ground vegetation parameters is therefore needed. Herein, we simulated vegetation scenarios and measurements by employing a normal distribution function and the Lindbergh-Levi theorem to deduce the characteristics of the error distribution. We found that the small-and large-scale error variation was similar among the theoretically deduced Leaf Area Index (LAI) measurements. Additionally, LAI was consistently normally distributed regardless of which systematic error or accidental error was applied. The difference between observed and theoretical errors was highest in the low-density scenario (7.6% at <3% interval) and was lowest in the high-density scenario (5.5% at <3% interval) while the average ratio between deviation and theoretical error of each scenario was 2.64% (low-density), 2.07% (medium-density) and 2.29% (high-density). Further, the relative difference between theoretical and empirical error was highest in the high-density scenario (20.0% at <1% interval) and lowest in the low-density scenario (14.9% at <1% interval), respectively. These data show the strength of a universal error assessment method and we recommend that existing large-scale data of the study region are used to build a theoretical error distribution. Such prior work in conjunction with the models outlined in this paper could reduce measurement costs and improve the efficiency of conducting ground measurements

Modelling error evaluation of ground observed vegetation parameters

To verify large-scale vegetation parameter measurements the average value of sampling points from small-scale data are typically used. However, this method undermines the validity of the data due to the difference in scale or an inappropriate number of sampling points. A robust universal error assessment method for measuring ground vegetation parameters is therefore needed. Herein, we simulated vegetation scenarios and measurements by employing a normal distribution function and the Lindbergh-Levi theorem to deduce the characteristics of the error distribution. We found that the small-and large-scale error variation was similar among the theoretically deduced Leaf Area Index (LAI) measurements. Additionally, LAI was consistently normally distributed regardless of which systematic error or accidental error was applied. The difference between observed and theoretical errors was highest in the low-density scenario (7.6% at <3% interval) and was lowest in the high-density scenario (5.5% at <3% interval) while the average ratio between deviation and theoretical error of each scenario was 2.64% (low-density), 2.07% (medium-density) and 2.29% (high-density). Further, the relative difference between theoretical and empirical error was highest in the high-density scenario (20.0% at <1% interval) and lowest in the low-density scenario (14.9% at <1% interval), respectively. These data show the strength of a universal error assessment method and we recommend that existing large-scale data of the study region are used to build a theoretical error distribution. Such prior work in conjunction with the models outlined in this paper could reduce measurement costs and improve the efficiency of conducting ground measurements