Native-range studies on insect herbivores associated with African Lovegrass (Eragrostis curvula) in South Africa: prospects for biological control in Australia
- Authors: Yell, Liam Dougal
- Date: 2023-10-13
- Subjects: Weeping lovegrass Biological control Australia , Tetramesa , Biological pest control agents , Efficacy , Host specificity , Environmental risk assessment
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/424446 , vital:72154
- Description: Eragrostis curvula (Schrad.) Nees. (African Lovegrass) is an African native species of grass that was intentionally introduced for pasture in Australia. It has since escaped cultivation and has become widespread and highly invasive. Eragrostis curvula has been recorded in every state and territory in Australia where it has altered fire regimes, disrupted nutrient cycles and can reduce livestock carrying capacity by up to 50%. The Centre for Biological Control at Rhodes University and the New South Wales Department of Primary Industries have been working in collaboration to identify and screen herbivorous insects as biological control agents for E. curvula in Australia. Native-range surveys were conducted between 2021 and 2022 on E. curvula at twenty-two sites across South Africa to identify herbivorous natural enemies associated with it. Species accumulation curves were generated to ensure adequate sampling was performed to identify all the insects associated with E. curvula. Twenty-nine non-target grass species were surveyed simultaneously to determine the field-host range of the natural enemies associated with the target weed. Herbivorous natural enemies were prioritised as possible biological control agents against E. curvula in Australia based on field-host range, predicted efficacy and climatic suitability. Four insect species were consistently found on E. curvula, two of which were herbivorous, as well as a parasitoid and a detritivore. Species accumulation curves show that the insect community was adequately sampled in South Africa. The two herbivorous insects were identified to the lowest taxonomic level using COI barcoding. Both species are undescribed phytophagous wasps in the genus Tetramesa (Hymenoptera: Eurytomidae). Because Tetramesa species have been shown to be host specific and highly damaging in previous biological control programs for other invasive grass weeds, we assessed their suitability as candidate biological control agents for use on E. curvula in Australia. Both Tetramesa species (“sp. 4” and “sp. 5”) were found on several native congeners under field conditions in South Africa. Congeneric South African-native non-target grass species were used as phylogenetic proxies to assess the risk posed to Australian native Eragrostis species. This highlighted three non-target Australian native Eragrostis species, namely: E. parviflora (R. Br.) Trin., E. leptocarpa Benth. fl., and E. trachycarpa Benth., that are at risk of being attacked by the two candidate agents based on their phylogenetic proximity to E. curvula. Predicted efficacy trials were conducted at five long-term repeat survey sites and revealed that Tetramesa sp. 4 does not reduce the probability of E. curvula tiller survival or reproduction, while Tetramesa sp. 5 does not reduce the probability of tiller reproduction but does increase the probability of tiller survival. This result was unexpected and may be a plant compensatory response to herbivory. The sites where both Tetramesa species were collected in South Africa are climatically similar to the invaded range of E. curvula in Australia, and as such, the Tetramesa spp. are likely to be suitably adapted to the climate where they would be released in Australia. These results suggest that both Tetramesa species associated with E. curvula may have too broad a host range to be used as biological control agents in Australia. However, further quarantine-based host-range assessments on Australian native Eragrostis species are recommended to confirm this. The field-based methods used in this study have reduced the number of insect and plant species that host-range assessments will be required to be performed on, thus preventing wasted resources. , Thesis (MSc) -- Faculty of Science, Zoology and Entomology, 2023
- Full Text:
- Date Issued: 2023-10-13
- Authors: Yell, Liam Dougal
- Date: 2023-10-13
- Subjects: Weeping lovegrass Biological control Australia , Tetramesa , Biological pest control agents , Efficacy , Host specificity , Environmental risk assessment
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/424446 , vital:72154
- Description: Eragrostis curvula (Schrad.) Nees. (African Lovegrass) is an African native species of grass that was intentionally introduced for pasture in Australia. It has since escaped cultivation and has become widespread and highly invasive. Eragrostis curvula has been recorded in every state and territory in Australia where it has altered fire regimes, disrupted nutrient cycles and can reduce livestock carrying capacity by up to 50%. The Centre for Biological Control at Rhodes University and the New South Wales Department of Primary Industries have been working in collaboration to identify and screen herbivorous insects as biological control agents for E. curvula in Australia. Native-range surveys were conducted between 2021 and 2022 on E. curvula at twenty-two sites across South Africa to identify herbivorous natural enemies associated with it. Species accumulation curves were generated to ensure adequate sampling was performed to identify all the insects associated with E. curvula. Twenty-nine non-target grass species were surveyed simultaneously to determine the field-host range of the natural enemies associated with the target weed. Herbivorous natural enemies were prioritised as possible biological control agents against E. curvula in Australia based on field-host range, predicted efficacy and climatic suitability. Four insect species were consistently found on E. curvula, two of which were herbivorous, as well as a parasitoid and a detritivore. Species accumulation curves show that the insect community was adequately sampled in South Africa. The two herbivorous insects were identified to the lowest taxonomic level using COI barcoding. Both species are undescribed phytophagous wasps in the genus Tetramesa (Hymenoptera: Eurytomidae). Because Tetramesa species have been shown to be host specific and highly damaging in previous biological control programs for other invasive grass weeds, we assessed their suitability as candidate biological control agents for use on E. curvula in Australia. Both Tetramesa species (“sp. 4” and “sp. 5”) were found on several native congeners under field conditions in South Africa. Congeneric South African-native non-target grass species were used as phylogenetic proxies to assess the risk posed to Australian native Eragrostis species. This highlighted three non-target Australian native Eragrostis species, namely: E. parviflora (R. Br.) Trin., E. leptocarpa Benth. fl., and E. trachycarpa Benth., that are at risk of being attacked by the two candidate agents based on their phylogenetic proximity to E. curvula. Predicted efficacy trials were conducted at five long-term repeat survey sites and revealed that Tetramesa sp. 4 does not reduce the probability of E. curvula tiller survival or reproduction, while Tetramesa sp. 5 does not reduce the probability of tiller reproduction but does increase the probability of tiller survival. This result was unexpected and may be a plant compensatory response to herbivory. The sites where both Tetramesa species were collected in South Africa are climatically similar to the invaded range of E. curvula in Australia, and as such, the Tetramesa spp. are likely to be suitably adapted to the climate where they would be released in Australia. These results suggest that both Tetramesa species associated with E. curvula may have too broad a host range to be used as biological control agents in Australia. However, further quarantine-based host-range assessments on Australian native Eragrostis species are recommended to confirm this. The field-based methods used in this study have reduced the number of insect and plant species that host-range assessments will be required to be performed on, thus preventing wasted resources. , Thesis (MSc) -- Faculty of Science, Zoology and Entomology, 2023
- Full Text:
- Date Issued: 2023-10-13
Optimizing geochemical sampling sizes and quantifying uncertainties for environmental risk assessment using Anglogold-Ashanti Gold Mines as a case study
- Authors: Chihobvu, Elizabeth
- Date: 2010-04
- Subjects: Environmental risk assessment , Geochemical prospecting
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/24443 , vital:62796
- Description: Generally, and particularly in South Africa, limited work done on the development of methodologies for sample sizing and quantifying uncertainties in geochemical sampling and analyses. As a result, little trust is placed on the long-term predictions of geochemical modelling for Environmental Risk Assessment (E.R.A). In addition, this leads to the slow approval of mining authorisations, water use licenses and mine closure plans. This dissertation addresses this deficiency in geochemical sampling and analyses specifically for ERA and proposes two methodologies (i) for quantifying uncertainties in geochemical sampling and analysis as a function of sample size and analyses and (ii) for determining the optimum sample size to ensure data quality. The statistical analysis approach was adopted as the best method for sample size determination. The approach is based on the premise that the size of the study sample is critical to producing meaningful results. The size of the required samples depends on a number of factors including purpose of the study, available budget, variability of the population being sampled, acceptable errors and confidence level. The methodology for estimating uncertainty is a fusion of existing methodologies for quantifying measurement uncertainty. The methodology takes a holistic view of the measurement process to include all processes involved in obtaining measurement results as possible uncertainty components. Like the statistical analysis approach, the methodology employs basic statistical principles in estimating the size of uncertainty, associated with a given measurement result. The approach identifies each component of uncertainty; estimates the size of each component and sums the contribution of each component in order to approximate the overall uncertainty value, associated with a given measurement result. The two methods were applied to Acid-Base Accounting (ABA) data derived from geochemical assessment for ERA of the West Wits and Vaal River (Ashanti Gold mines) tailings dams undertaken by Pulles and Howard de Lange Inc. on behalf of AngloGold Ltd. The study was aimed at assessing and evaluating the potential of tailings dams in the two mining areas to impact on water quality and implications of this in terms of mine closure and rehabilitation. Findings from this study show that the number of samples needed is influenced by the purpose of the study, size of the target area, nature and type of material, budget, acceptable error and the confidence level required, among other factors. Acceptable error has an exponential relationship with sample size hence one can minimize error by increasing sample size. While a low value of acceptable error value and high confidence are always desirable, a tradeoff among these competing factors must be found, given the usually limited funds and time. The findings also demonstrated that uncertainties in geochemical sampling and analysis are unavoidable. They arise from the fact that only a small portion of the population rather than a census is used to derive conclusions about certain characteristics of the target population. This is further augmented by other influential quantities that affect the accuracy of the estimates. Effects such as poor sampling design, inadequate sample size, sample heterogeneity and other factors highly affect data quality and representivity hence measurement uncertainty. Among these factors, those associated with sampling, mainly heterogeneity was found to be the strongest contributing factor toward overall uncertainty. This implies an increased proportion of expenditure should be channelled toward sampling to minimise uncertainty. Uncertainties can be reduced by adopting good sampling practices and increasing sample size, among other methods. It is recommended that more information be made available for proper uncertainty analysis. , Thesis (MSc) -- Faculty of Science and Agriculture, 2010
- Full Text:
- Date Issued: 2010-04
- Authors: Chihobvu, Elizabeth
- Date: 2010-04
- Subjects: Environmental risk assessment , Geochemical prospecting
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/24443 , vital:62796
- Description: Generally, and particularly in South Africa, limited work done on the development of methodologies for sample sizing and quantifying uncertainties in geochemical sampling and analyses. As a result, little trust is placed on the long-term predictions of geochemical modelling for Environmental Risk Assessment (E.R.A). In addition, this leads to the slow approval of mining authorisations, water use licenses and mine closure plans. This dissertation addresses this deficiency in geochemical sampling and analyses specifically for ERA and proposes two methodologies (i) for quantifying uncertainties in geochemical sampling and analysis as a function of sample size and analyses and (ii) for determining the optimum sample size to ensure data quality. The statistical analysis approach was adopted as the best method for sample size determination. The approach is based on the premise that the size of the study sample is critical to producing meaningful results. The size of the required samples depends on a number of factors including purpose of the study, available budget, variability of the population being sampled, acceptable errors and confidence level. The methodology for estimating uncertainty is a fusion of existing methodologies for quantifying measurement uncertainty. The methodology takes a holistic view of the measurement process to include all processes involved in obtaining measurement results as possible uncertainty components. Like the statistical analysis approach, the methodology employs basic statistical principles in estimating the size of uncertainty, associated with a given measurement result. The approach identifies each component of uncertainty; estimates the size of each component and sums the contribution of each component in order to approximate the overall uncertainty value, associated with a given measurement result. The two methods were applied to Acid-Base Accounting (ABA) data derived from geochemical assessment for ERA of the West Wits and Vaal River (Ashanti Gold mines) tailings dams undertaken by Pulles and Howard de Lange Inc. on behalf of AngloGold Ltd. The study was aimed at assessing and evaluating the potential of tailings dams in the two mining areas to impact on water quality and implications of this in terms of mine closure and rehabilitation. Findings from this study show that the number of samples needed is influenced by the purpose of the study, size of the target area, nature and type of material, budget, acceptable error and the confidence level required, among other factors. Acceptable error has an exponential relationship with sample size hence one can minimize error by increasing sample size. While a low value of acceptable error value and high confidence are always desirable, a tradeoff among these competing factors must be found, given the usually limited funds and time. The findings also demonstrated that uncertainties in geochemical sampling and analysis are unavoidable. They arise from the fact that only a small portion of the population rather than a census is used to derive conclusions about certain characteristics of the target population. This is further augmented by other influential quantities that affect the accuracy of the estimates. Effects such as poor sampling design, inadequate sample size, sample heterogeneity and other factors highly affect data quality and representivity hence measurement uncertainty. Among these factors, those associated with sampling, mainly heterogeneity was found to be the strongest contributing factor toward overall uncertainty. This implies an increased proportion of expenditure should be channelled toward sampling to minimise uncertainty. Uncertainties can be reduced by adopting good sampling practices and increasing sample size, among other methods. It is recommended that more information be made available for proper uncertainty analysis. , Thesis (MSc) -- Faculty of Science and Agriculture, 2010
- Full Text:
- Date Issued: 2010-04
- «
- ‹
- 1
- ›
- »