Alien gastropods have caused extensive harm to biodiversity and socioeconomic systems like agriculture and horticulture worldwide. For conservation and management purposes, information on impacts needs to be easily interpretable and comparable, and the factors that determine impacts understood. This study aimed to assess gastropods alien to South Africa to compare impact severity between species and understand how they vary between habitats and mechanisms. Furthermore, we explore the relationship between environmental and socioeconomic impacts, and both impact measures with life‐history traits. We used the Environmental Impact Classification for Alien Taxa (EICAT) and Socio‐Economic Impact Classification for Alien Taxa (SEICAT) to assess impacts of 34 gastropods alien to South Africa including evidence of impact from their entire alien range. We tested for correlations between environmental and socioeconomic impacts per species, and with fecundity and native latitude range using Kendall's tau tests. Kruskal–Wallis tests were used to compare impact magnitude among mechanisms and habitats, respectively. This study presents the first application of EICAT and SEICAT for invertebrates. There was no correlation between environmental impacts and socioeconomic impacts. Habitats did not differ regarding the severity of impacts recorded, but impacts via disease transmission were lower than other mechanisms. Neither fecundity nor native range latitude was correlated with impact magnitude. Despite gastropods being agricultural and horticultural pests globally, resilience of socioeconomic systems makes high impacts uncommon. Environmental systems may be vulnerable to gastropod impacts across habitats, having experienced multiple local extinctions of wetland island snail fauna. South Africa stands out as the only continental country that follows this trend. The knowledge gained on severity and nature of gastropod impacts is useful in risk assessment, which can aid conservation management. To make impact assessments more realistic, we suggest alternative ways of reporting impacts classified under EICAT and SEICAT.

Abstract. Sedimentary charcoal records are widely used to reconstruct regional changes in fire regimes through time in the geological past. Existing global compilations are not geographically comprehensive and do not provide consistent metadata for all sites. Furthermore, the age models provided for these records are not harmonised and many are based on older calibrations of the radiocarbon ages. These issues limit the use of existing compilations for research into past fire regimes. Here, we present an expanded database of charcoal records, accompanied by new age models based on recalibration of radiocarbon ages using IntCal20 and Bayesian age-modelling software. We document the structure and contents of the database, the construction of the age models, and the quality control measures applied. We also record the expansion of geographical coverage relative to previous charcoal compilations and the expansion of metadata that can be used to inform analyses. This first version of the Reading Palaeofire Database contains 1676 records (entities) from 1480 sites worldwide. The database (RPDv1b – Harrison et al., 2021) is available at https://doi.org/10.17864/1947.000345.

<p>Fire is an important environmental and ecological process in northern high latitude environments. It is unclear how fire will respond to modern environmental change in this region and its implications for ecosystem processes and human societies. For insight into the long-term evolution of fire regimes, we reconstruct changes in biomass burning in the northern extratropics (>45°N) from the early Holocene (9000 years ago) to the present using the Reading Palaeofire Database, currently the most comprehensive repository of northern extratropical palaeo charcoal records. We examine the different geographic patterns in fire regimes across the northern extratropics from the sub-continental to circum-northern extratropical scale, by quantitatively comparing biomass burning with insolation, CO<sub>2</sub>,<sub></sub>human population records land cover changes. This study provides novel insight into the fire regimes that have characterized the northern extratropics over the Holocene and the differential importance of environmental controls in shaping these burning histories.</p>

This supplementary contains: 4 SI Table 1. Information of the charcoal records (sites and entities) in the Reading Palaeofire Database version 1. Latitude and longitude are in decimal degrees, and elevation in metres above/below sea level. Fields where information could be available but was never recorded or has subsequently been lost are represented by -999999, fields where we were unable to obtain this information but it could be included in subsequent updates of the database are represented by -777777, fields where specific information is not applicable are represented by -888888. SI Table 2. List of pre-defined valid choices for restricted fields in the Reading Palaeofire Database version 1. SI Table 3: List of charcoal measurement units currently used in the Reading Palaeofire Database version 1 SI Figure 1: Supplementary Figure 1. Summary of the stages used to select the optimum RBacon age models for from ageR. Plots A.-C. show the modelling output from ageR for an example entity from the RPD (Geral core), where the optimum age model selected by ageR A. is a table ranking the age model scenarios by the lowest area between the prior and posterior accumulation rate curves. Note that only the top 5 model scenarios of a total of 25 run for this entity are listed B. Shows the plots for the prior and posterior accumulation rates and the area between curves for the top 5 model scenarios.C. Is the top ranked RBacon age model (Accumulation rate = 15, thickness = 10) which was visually checked to verify that the interpolation through the dates was valid and consistent with the dates. In this example, the top ranked model scenario selected by ageR (Accumulation rate = 15, thickness= 10) was accepted as the chosen model scenario as the interpolation through the dates is valid. SI Figure 2. An example of alternative model scenario selection where the top ranked ageR model scenario is deemed to be inaccurate. In this example, the top ranked model scenario from King Tableland Swamp (accumulation rate = 45, thickness = 5)(A.) with the lowest area between the prior and posterior accumulation rate curves (B.) does not accurately represent the date at 157.5cm. This age was included by the original authors and lies in stratigraphic order with the other dates. Therefore, this model is rejected in favour of the model with the next lowest abc score which accurately reflects the dates included (ageR model ranking 3 in A.). The RBacon plot for this age model scenario is shown in D. (accumulation rate = 90, thickness = 5) and is more accurately and precisely modelled through the dates than the model selected by ageR. Site name Entities (#) Elevation (m) Latitude (°) Longitude (°) Site Type Water depth (m) Basin size (km 2 ) Citation(s)