We will compile long-term time-series data, spatiotemporally matched data on threats and mediating factors (e.g. landscape complexity, functional trait data, and data on networks and interactions. The information will be collated in a standardised format to ensure interoperability and subsequent use in Work Packages 2-4. In addition, we will analyse time-series data to derive new information about insect trends.
Task 1.1: Time-series data of insect abundance, occupancy and community metrics. Time-series data underpin the majority of studies on insect declines. In this task, we will assemble time-series datasets for use in Work Package 3. We will take advantage of a substantial recent synthesis of openly available insect time-series, augmented by datasets from project partners (see Letters of Support) and data requests where required. These will include (but are not limited to) national monitoring schemes with spatially replicated time-series of species abundance, such as the Rothamsted Insect Survey (moths and aphids), Butterfly Monitoring Schemes for many countries, the International Long Term Ecological Research Network and the German Biodiversity Inventories program, professionally-run freshwater schemes in UK, Australia, South Korea and Australia, as well as datasets that can be converted into time-series by averaging across sites (e.g. Krefeld Entomology Society data). Given that insect populations fluctuate markedly from year to year, we will focus on time-series containing annual estimates for at least ten years. For the majority of these datasets it will be possible to convert time-series data from one biodiversity metric to another, e.g. abundance to occupancy, evenness, biomass, occupancy to species richness, or to aggregate across space (e.g. beta diversity), accounting for uneven sampling.
Task 1.2: Create quasi-time series. Occupancy models are increasingly being used as a source of evidence for changes in insect biodiversity particularly for taxa where systematic data are lacking and re-setting the historical baseline. In this task, we will apply occupancy modelling techniques developed by the research team to create a set of long-term trend estimates from occurrence records and museum specimens for butterflies and moths back to 1900, and for other insect taxa in countries across Europe back to at least 1970.
Task 1.3: Dimensionality of insect declines. Understanding insect declines have been hampered by the breadth of metrics in which change has been reported. Here, we will address two fundamental questions arising from the literature on insect declines. a) How are insect communities being restructured? Using multi-site generalised dissimilarity models and the spatially-replicated time-series (co-occurrence matrix over space and time) from T1.1, we will tease apart drivers of insect community assembly (temporal turnover) and compositional change (spatial turnover) b) Are trends coherent across metrics? Using data collated in T1.1-1.2 we will compare rates of change across a suite of metrics (e.g. abundance vs richness vs biomass) to determine whether insect biodiversity has changed in a consistent manner, e.g. trends in common vs rare species, which sets of metrics are inter-correlated (or suitable as early warning signals).
Task 1.4: Trait data. Trait data will be used throughout GLiTRS: body size and trophic level (detritivores, parasites, predators, herbivores (including pollinators)) describe fundamental axes of structure in insect communities. “Response traits” (e.g. generalist-specialist) explain interspecific variation in TRMs, and “effect traits” (e.g. “hairiness” in pollen deposition rates, or mean abundance) link species with ecosystem functions. To facilitate interoperability across Work Packages, we will create a centralised collation of trait data including existing datasets constructed by the team, open-access databases (e.g. opentraits, DISPERSE, CESTES, Syrph-Net, Carabids.org, Butterflytraits.org, DBIF) and targeted gap-filling.
Task 1.5: Trophic interactions and network structure. Networks and metrics (connectance, modularity, nestedness) feature in T2.2, Work Packages 3 & 4.2. Three types of networks are envisaged: (a) published networks that quantify interaction strengths (e.g. GlobalWeb, InteractionWeb, Mangal) with data from Project Partners; (b) spatially-replicated time-series that span trophic levels; (c) large-scale & long-term inferred binary trophic networks from occurrence data. Data on trophic structure and position will be inferred from known biotic interactions (e.g. DBIF), gut contents (of aquatic insects) and trophic level information.
Task 1.6: Anthropogenic pressures. We will assemble data on potential drivers of insect declines in a format that allows spatio-temporal matching and consistency of use in Work Packages 2-4. Pressures will be categorised using the IUCN threat classification, which is widely used to represent immediate or potential human activities that currently (or may in the future) impact on biodiversity. Specific measures will include changes in land-cover (e.g. UK LCM, CORINE), availability of specific habitats, agricultural intensity and agrochemical inputs (PEST-CHEMGRIDS), climate, water quality and fire frequency.