Ali Omer

and 13 more

Alien species can have massive impacts on native biodiversity and ecosystem functioning. Assessing which species from currently cultivated alien floras may escape into the wild and naturalize is hence essential for ecosystem management and biodiversity conservation. Climate change has promoted the naturalization of many alien plants in temperate regions, but whether outcomes are similar in (sub)tropical areas is insufficiently known. In this study, we used species distribution models to evaluate the current naturalization risk of 1,527 cultivated alien plants in 10 countries of Southern Africa and how their invasion risk might change due to climate change. We assessed changes in climatic suitability across the different biomes of Southern Africa. Moreover, we assessed whether climatic suitability for cultivated alien plants varied with their naturalization status and native origin. The results of our study indicate that a significant proportion (53.9%) of the species are projected to lack suitable climatic conditions in Southern Africa, both currently and in the future. Based on the current climate conditions, 10.0% of Southern Africa is identified as an invasion hotspot (here defined as the top 10% of grid cells that provide suitable climatic conditions to the highest numbers of species). This percentage is expected to decrease slightly to 7.1% under moderate future climate change and shrink considerably to 2.0% under the worst-case scenario. This decline in climatic suitability is observed across most native origins, particularly under the worst-case climate change scenario. Our findings indicate that climate change is likely to have an opposing effect on the naturalization of currently cultivated average plants in (sub)tropical Southern Africa compared to colder regions. Specifically, the risk of these plants’ naturalizing is expected to decrease due to the region’s increasingly hot and dry climate, which will be challenging for the persistence of both native and alien plant species.
Digital point-occurrence records from the Global Biodiversity Information Facility (GBIF) and other repositories enable a wide range of research in macroecology and biogeography. However, data errors may hamper immediate use. Manual data cleaning is time-consuming and often unfeasible, given that the databases may contain thousands or millions of records. Automated data cleaning pipelines are therefore of high importance. This study examined the extent to which cleaned data from six pipelines using data cleaning tools (e.g., the GBIF web application, different R packages) affect downstream species distribution models. In addition, we assessed how the pipeline data differ from expert data. From 13,889 North American Ephedra observations in GBIF, the pipelines removed 31.7% to 62.7% false-positives, invalid coordinates, and duplicates, leading to data sets that included between 9,484 (GBIF application) and 5,196 records (manual-guided filtering). The expert data consisted of 703 thoroughly handpicked records, comparable to data from field studies. Although differences in the record numbers were relatively large, stacked species distribution models (sSDM) from the pipelines and the expert data were strongly related (mean Pearson’s r across the pipelines: 0.9986, versus the expert data: 0.9173). The ever-stronger correlations resulted from occurrence information that became increasingly condensed in the course of the workflow (from individual occurrences to collectivized occurrences in grid cells to predicted probabilities in the sSDMs). In sum, our results suggest that the R package-based pipelines reliably identified invalid coordinates. In contrast, the GBIF-filtered data still contained both spatial and taxonomic errors. However, major drawbacks emerge from the fact that no pipeline fully discovered misidentified specimens without the assistance of expert taxonomic knowledge. We conclude that application-filtered GBIF data will still need additional review to achieve higher spatial data quality. Achieving high-quality taxonomic data will require extra effort, probably by thoroughly analyzing the data for misidentified taxa, supported by experts.