Montpellier broom (Genista monspessulana) and Spanish broom (Spartium junceum) in South Africa: An assessment of invasiveness and options for management

AbstractThe legumes (Fabaceae) Genista monspessulana and Spartium junceum are major invaders in several other parts of the world, but not yet so in South Africa. We determine their current distributions in South Africa at different spatial scales, assess population structure (soil seed banks and size at reproduction) evaluate current management activities, and provide recommendations for control (including assessing the feasibility of nation-wide eradication). G. monspessulana occurs at nine localities in three quarter-degree cells, covering a total of 22.7ha. S. junceum is much more widespread, occurring in 33 quarter-degree cells and is frequently cultivated in private gardens. All naturalised or invasive populations are in disturbed areas, mostly along roadsides. Once established, G. monspessulana and S. junceum accumulate large, persistent soil-stored seed banks, ranging in size between 909 and 22,727 (median 1970)seeds/m2 and 0 and 21,364 (median 455)seeds/m2 for the two species respectively. Both species resprout vigorously after cutting and stump herbicide application (60% of G. monspessulana and 43% of S. junceum plants resprouted) which necessitates regular follow-ups. We estimate that over 10years, at a cost of about ZAR 81,000 (1 ZAR=0.114 US$ as on 6 October 2012), G. monspessulana could be extirpated from South Africa. S. junceum is far more widespread and coupled with low effectiveness of control, abundance of seeds and seed longevity, eradication is unfeasible. We recommend that control methods used for S. junceum be improved to prevent resprouting, and that areas are managed to limit the movement of seeds and avoid further spread and establishment. Further studies are required to understand why these two species have failed to replicate the invasiveness shown in other parts of the world

Biogeo : an R package for assessing and improving data quality of occurrence record datasets

Occurrence data from museum and herbarium collections are valuable for mapping biodiversity patterns in space and time. Unfortunately these collections datasets contain many errors and suffer from several data quality issues that can influence the quality of the products derived from them. It is up to the user to identify these errors and data quality issues when using these data. Despite the large number of potential users of these datasets there are few software tools dedicated to error detection and correction of collections datasets. The R package biogeo was developed for detecting and correcting errors and for assessing of data quality of collections datasets consisting of occurrence records. Features of the package include error detection, such as mismatches between the recorded country and the country where the record is plotted, records of terrestrial species that fall into the sea and outlier detection. A key feature of the package is the ability to identify likely alternative positions for points that represent obvious errors in the dataset and functions to explore records in geographical and environmental space in order to identify possible errors in the dataset. Functions are also available for converting coordinates that are in various text formats into degrees, minutes and seconds and then into decimal degrees.The DST-NRF Centre for Invasion Biology, the National Research Foundation and the University of Pretoria.http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1600-05872017-04-30hb2017Zoology and Entomolog

Biogeo : an R package for assessing and improving data quality of occurrence record datasets

Occurrence data from museum and herbarium collections are valuable for mapping biodiversity patterns in space and time. Unfortunately these collections datasets contain many errors and suffer from several data quality issues that can influence the quality of the products derived from them. It is up to the user to identify these errors and data quality issues when using these data. Despite the large number of potential users of these datasets there are few software tools dedicated to error detection and correction of collections datasets. The R package biogeo was developed for detecting and correcting errors and for assessing of data quality of collections datasets consisting of occurrence records. Features of the package include error detection, such as mismatches between the recorded country and the country where the record is plotted, records of terrestrial species that fall into the sea and outlier detection. A key feature of the package is the ability to identify likely alternative positions for points that represent obvious errors in the dataset and functions to explore records in geographical and environmental space in order to identify possible errors in the dataset. Functions are also available for converting coordinates that are in various text formats into degrees, minutes and seconds and then into decimal degrees.The DST-NRF Centre for Invasion Biology, the National Research Foundation and the University of Pretoria.http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1600-05872017-04-30hb2017Zoology and Entomolog

Biogeo : an R package for assessing and improving data quality of occurrence record datasets

Occurrence data from museum and herbarium collections are valuable for mapping biodiversity patterns in space and time. Unfortunately these collections datasets contain many errors and suffer from several data quality issues that can influence the quality of the products derived from them. It is up to the user to identify these errors and data quality issues when using these data. Despite the large number of potential users of these datasets there are few software tools dedicated to error detection and correction of collections datasets. The R package biogeo was developed for detecting and correcting errors and for assessing of data quality of collections datasets consisting of occurrence records. Features of the package include error detection, such as mismatches between the recorded country and the country where the record is plotted, records of terrestrial species that fall into the sea and outlier detection. A key feature of the package is the ability to identify likely alternative positions for points that represent obvious errors in the dataset and functions to explore records in geographical and environmental space in order to identify possible errors in the dataset. Functions are also available for converting coordinates that are in various text formats into degrees, minutes and seconds and then into decimal degrees.The DST-NRF Centre for Invasion Biology, the National Research Foundation and the University of Pretoria.http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1600-05872017-04-30hb2017Zoology and Entomolog

Global environmental and socio-economic impacts of selected alien grasses as a basis for ranking threats to South Africa

Decisions to allocate management resources should be underpinned by estimates of the impacts of biological invasions that are comparable across species and locations. For the same reason, it is important to assess what type of impacts are likely to occur where, and if such patterns can be generalised. In this paper, we aim to understand factors shaping patterns in the type and magnitude of impacts of a subset of alien grasses. We used the Generic Impact Scoring System (GISS) to review and quantify published impact records of 58 grass species that are alien to South Africa and to at least one other biogeographical realm. Based on the GISS scores, we investigated how impact magnitudes varied across habitats, regions and impact mechanisms using multiple regression. We found impact records for 48 species. Cortaderia selloana had the highest overall impact score, although in contrast to five other species (Glyceria maxima, Nassella trichotoma, Phalaris aquatica, Polypogon monspeliensis, and Sorghum halepense) it did not score the highest possible impact score for any specific impact mechanism. Consistent with other studies, we found that the most frequent environmental impact was through competition with native plant species (with 75% of cases). Socio-economic impacts were recorded more often and tended to be greater in magnitude than environmental impacts, with impacts recorded particularly often on agricultural and animal production (57% and 51% of cases respectively). There was variation across different regions and habitats in impact magnitude, but the differences were not statistically significant. In conclusion, alien grasses present in South Africa have caused a wide range of negative impacts across most habitats and regions of the world. Reviewing impacts from around the world has provided important information for the management of alien grasses in South Africa, and, we believe, is an important component of management prioritisation processes in general

Progress and prospects for the biological control of invasive alien grasses Poaceae) in South Africa

Historically, invasive alien grasses have not been considered a major threat in South Africa, and as a result, very few resources are allocated to their management. However, there is an increasing awareness of the severe environmental and socio-economic impacts of invasive grasses and the need for appropriate management options for their control. South Africa has a long history of successfully implementing weed biological control (biocontrol) to manage invasive alien plants, however much like the rest of the world, invasive grasses do not feature prominently as targets for biocontrol. The implementation and early indicators of success of the few grass biocontrol programmes globally and the finding that grasses can be suitable targets, suggests that biocontrol could start to play an important role in managing invasive alien grasses in South Africa. In this paper, we evaluated the prospects for implementing novel grass biocontrol projects over the next ten years against 48 grasses that have been determined to represent the highest risk based on their current environmental and economic impacts. The grasses were ranked in order of priority using the Biological Control Target Selection system. Five grasses were prioritised – Arundo donax L., Cortaderia jubata (Lem.) Stapf, Cortaderia selloana (Schult and Schult) Asch. and Graebn., Nassella trichotoma (Hack. ex Arech.), and Glyceria maxima (Hartm.) Holmb., based on attributes that make them suitable biocontrol targets. Arundo donax has already been the target of a biocontrol programme in South Africa. We reviewed the progress made towards the biocontrol of this species and discuss how this programme could be developed going forward. Moreover, we outline how biocontrol could be implemented to manage the remaining four high-priority targets. While biocontrol of grasses is not without its challenges (e.g. unresolved taxonomies, conflicts of interest and a lack of supporting legislation), South Africa has an opportunity to learn from existing global research and begin to invest in biocontrol of high-priority species that are in most need of control