• Energy Research

**UPDATED on October 1 2020**After some mistakes in some of the data were found, we updated this data set. The changes to the data are detailed on Zenodo (http://doi.org/10.5281/zenodo.4061807), and an Erratum has been submitted. This data set under CC-BY license contains time series of total abundance and/or biomass of assemblages of insect, arachnid and Entognatha assemblages (grouped at the family level or higher taxonomic resolution), monitored by standardized means for ten or more years. The data were derived from 165 data sources, representing a total of 1668 sites from 41 countries. The time series for abundance and biomass represent the aggregated number of all individuals of all taxa monitored at each site.The data set consists of four linked tables, representing information on the study level, the plot level, about sampling, and the measured assemblage sizes. all references to the original data sources can be found in the pdf with references, and a Google Earth file (kml) file presents the locations (including metadata) of all datasets.When using (parts of) this data set, please respect the original open access licenses. This data set underlies all analyses performed in the paper 'Meta-analysis reveals declines in terrestrial, but increases in freshwater insect abundances', a meta-analysis of changes in insect assemblage sizes, and is accompanied by a data paper entitled 'InsectChange – a global database of temporal changes in insect and arachnid assemblages'. Consulting the data paper before use is recommended. Tables that can be used to calculate trends of specific taxa and for species richness will be added as they become available.The data set consists of four tables that are linked by the columns 'DataSource_ID'. and 'Plot_ID', and a table with references to original research.In the table 'DataSources', descriptive data is provided at the dataset level: Links are provided to online repositories where the original data can be found, it describes whether the dataset provides data on biomass, abundance or both, the invertebrate group under study, the realm, and describes the location of sampling at different geographic scales (continent to state). This table also contains a reference column. The full reference to the original data is found in the file 'References_to_original_data_sources.pdf'.In the table 'PlotData' more details on each site within each dataset are provided: there is data on the exact location of each plot, whether the plots were experimentally manipulated, and if there was any spatial grouping of sites (column 'Location'). Additionally, this table contains all explanatory variables used for analysis, e.g. climate change variables, land-use variables, protection status.The table 'SampleData' describes the exact source of the data (table X, figure X, etc), the extraction methods, as well as the sampling methods (derived from the original publications). This includes the sampling method, sampling area, sample size, and how the aggregation of samples was done, if reported. Also, any calculations we did on the original data (e.g. reverse log transformations) are detailed here, but more details are provided in the data paper. This table links to the table 'DataSources' by the column 'DataSource_ID'. Note that each datasource may contain multiple entries in the 'SampleData' table if the data were presented in different figures or tables, or if there was any other necessity to split information on sampling details.The table 'InsectAbundanceBiomassData' provides the insect abundance or biomass numbers as analysed in the paper. It contains columns matching to the tables 'DataSources' and 'PlotData', as well as year of sampling, a descriptor of the period within the year of sampling (this was used as a random effect), the unit in which the number is reported (abundance or biomass), and the estimated abundance or biomass. In the column for Number, missing data are included (NA). The years with missing data were added because this was essential for the analysis performed, and retained here because they are easier to remove than to add.Linking the table 'InsectAbundanceBiomassData.csv' with 'PlotData.csv' by column 'Plot_ID', and with 'DataSources.csv' by column 'DataSource_ID' will provide the full dataframe used for all analyses.Detailed explanations of all column headers and terms are available in the ReadMe file, and more details will be available in the forthcoming data paper.WARNING: Because of the disparate sampling methods and various spatial and temporal scales used to collect the original data, this dataset should never be used to test for differences in insect abundance/biomass among locations (i.e. differences in intercept). The data can only be used to study temporal trends, by testing for differences in slopes. The data are standardized within plots to allow the temporal comparison, but not necessarily among plots (even within one dataset).

AbstractSeed dispersal limitation, which can be exacerbated by a number of anthropogenic causes, can result in local communities having fewer species than they might potentially support, representing a potential diversity deficit. The link between processes that shape natural variation in diversity, such as dispersal limitation, and the consequent effects on productivity is less well known. Here, we synthesised data from 12 seed addition experiments in grassland communities to examine the influence of reducing seed dispersal limitation (from 1 to 60 species added across experiments) on species richness and productivity. For every 10 species of seed added, we found that species richness increased by about two species. However, the increase in species richness by overcoming seed limitation did not lead to a concomitant increase in above‐ground biomass production. This highlights the need to consider the relationship between biodiversity and ecosystem functioning in a pluralistic way that considers both the processes that shape diversity and productivity simultaneously in naturally assembled communities.

AbstractAimBiodiversity and ecosystem productivity vary across the globe, and considerable effort has been made to describe their relationships. Biodiversity and ecosystem functioning research has traditionally focused on how experimentally controlled species richness affects net primary productivity (S → NPP) at small spatial grains. In contrast, the influence of productivity on richness (NPP → S) has been explored at many grains in naturally assembled communities. Mismatches in spatial scale between approaches have fuelled debate about the strength and direction of biodiversity–productivity relationships. Here, we examine the direction and strength of the influence of productivity on diversity (NPP → S) and the influence of diversity on productivity (S → NPP) and how these vary across spatial grains.LocationContiguous USA.Time period1999–2015.Major taxa studiedWoody species (angiosperms and gymnosperms).MethodsUsing data from North American forests at grains from local (672 m2) to coarse spatial units (median area = 35,677 km2), we assess relationships between diversity and productivity using structural equation and random forest models, while accounting for variation in climate, environmental heterogeneity, management and forest age.ResultsWe show that relationships betweenSand NPP strengthen with spatial grain. Within each grain,S → NPP and NPP → Shave similar magnitudes, meaning that processes underlyingS → NPP and NPP → Seither operate simultaneously or that one of them is real and the other is an artefact. At all spatial grains,Swas one of the weakest predictors of forest productivity, which was largely driven by biomass, temperature and forest management and age.Main conclusionsWe conclude that spatial grain mediates relationships between biodiversity and productivity in real‐world ecosystems and that results supporting predictions from each approach (NPP → SandS → NPP) serve as an impetus for future studies testing underlying mechanisms. Productivity–diversity relationships emerge at multiple spatial grains, which should widen the focus of national and global policy and research to larger spatial grains.

Herbivorous insects consume a large proportion of the energy flow in terrestrial ecosystems and play a major role in the dynamics of plant populations and communities. However, high-resolution, quantitative predictions of the global patterns of insect herbivory and their potential underlying drivers remain elusive. Here, we compiled and analyzed a dataset consisting of 9,682 records of the severity of insect herbivory from across natural communities worldwide to quantify its global patterns and environmental determinants. Global mapping revealed strong spatial variation in insect herbivory at the global scale, showing that insect herbivory did not significantly vary with latitude for herbaceous plants but increased with latitude for woody plants. We found that the cation-exchange capacity in soil was a main predictor of levels of herbivory on herbaceous plants, while climate largely determined herbivory on woody plants. We next used well-established scenarios for future climate change to forecast how spatial patterns of insect herbivory may be expected to change with climate change across the world. We project that herbivore pressure will intensify on herbaceous plants worldwide but would likely only increase in certain biomes (e.g., northern coniferous forests) for woody plants. Our assessment provides quantitative evidence of how environmental conditions shape the spatial pattern of insect herbivory, which enables a more accurate prediction of the vulnerabilities of plant communities and ecosystem functions in the Anthropocene.

ABSTRACTAimUnderstanding the distribution of foliar fungal diseases is crucial to predicting their impact on ecosystems and their future spread. However, the relative importance of abiotic and biotic factors in determining variation in pathogens between plant communities remains controversial. Here, we tested four hypotheses: warmer, wetter climates, higher soil fertility and dominance by fast‐growing plants should increase foliar pathogens, while higher plant diversity should decrease disease. We explored how those factors influence community pathogen load through changes in plant species composition and intraspecific changes in infection. Finally, we projected future distributions of community pathogen load.LocationChina's main grassland.Time Period2021–2022.Major Taxa StudiedPlants and foliar pathogens.MethodsWe assessed the direct and indirect effects of abiotic (climate and soil fertility) and biotic factors (community composition, species richness and plant traits) on community pathogen load and its two components by Bayesian mixed‐effects and structural equation models. We employed a space‐for‐time substitution approach to predict disease severity under future scenarios.ResultsWe found lower disease severity with higher temperatures and lower precipitation. Both temperature and precipitation indirectly influenced community pathogen load through changing species richness, plant traits and soil fertility. However, both temperature and precipitation increased the expected community pathogen load due to plant compositional change (taxa that were taller and had larger leaves) without affecting community pathogen load caused by intraspecific variation. Finally, we found that current disease pressure is highest in the northeastern and southwestern provinces. Future projections suggest fungal pathogen pressure in the Greater Khingan Range, Qinghai‐Tibetan Plateau and central‐western Inner Mongolia Plateau will increase.Main ConclusionsClimate underlies variation in foliar fungal diseases by altering plant communities. Our findings highlight the importance of integrating climate and plant community change into disease prediction models, with particular attention to water‐sensitive plant diseases such as foliar fungal pathogens.

AbstractJähnig et al. make some useful points regarding the conclusions that can be drawn from our meta‐analysis; however, some issues require clarification. First, we never suggested that there was a globally increasing trend of freshwater insect abundances, but only spoke of an average increasing trend in the available data. We also did not suggest that freshwater quality has improved globally, but rather that documented improvements in water quality can explain at least some of the trends we observed. Second, as we acknowledged, our data are not a representative set of freshwater ecosystems around the world, but they are what is currently accessible. Third, there is indeed no doubt that changes in abundance or biomass need not correlate with changes in other aspects of biodiversity, such as species richness or functional composition. Our analysis was specifically focused on trends in community abundance/biomass because it has been the subject of recent study and speculation, and is a widely available metric in long‐term studies. To better understand the recent changes in freshwater insect assemblages, we encourage freshwater ecologists to further open their troves of data from countless long‐term monitoring schemes so that larger and more comprehensive syntheses can be undertaken.This article is categorized under: Water and Life > Conservation, Management, and Awareness

In their response to our letter, De Boek et al. (2019) and Muller, Ballhausen, Lakovic, and Rillig (2019) argue that our conclusion that we need more realistic climate change experiments is too "gloomy" and that we need a plurality of experiments including extremes and multifactorial approaches. We agree that a diversity of experimental approaches is required in order to anticipate the consequences for plant communities of alternative future environmental conditions. However, we argue that "realistic" experiments are underrepresented in the portfolio of previous experiments, and are urgently needed to understand how species communities of the future will look like and how they will function. This article is a response to Muller et al., 26, e4-e5 and De Boeck et al., 26, e6-e7.