Habitat requirements affect genetic variation in three species of mayfly (Ephemeroptera, Baetidae) from South Africa:
- Taylor, Chantal L, Barker, Nigel P, Barber-James, Helen M, Villet, Martin H, Pereira-da-Conceicoa, Lyndall L
- Authors: Taylor, Chantal L , Barker, Nigel P , Barber-James, Helen M , Villet, Martin H , Pereira-da-Conceicoa, Lyndall L
- Date: 2020
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/149314 , vital:38824 , https://doi.org/10.3897/zookeys.936.38587
- Description: This study investigates genetic diversity in three species of Ephemeroptera, one eurytopic and therefore widespread (Afroptilum sudafricanum) and two stenotopic and thus endemic (Demoreptus natalensis and Demoreptus capensis) species, all of which co-occur in the southern Great Escarpment, South Africa. Mitochondrial DNA was analysed to compare the genetic diversity between the habitat generalist and the two habitat specialists. Afroptilum sudafricanum showed no indication of population genetic structure due to geographic location, while both Demoreptus species revealed clear genetic differentiation between geographic localities and catchments, evident from phylogenetic analyses and high FST values from AMOVA.
- Full Text:
- Date Issued: 2020
- Authors: Taylor, Chantal L , Barker, Nigel P , Barber-James, Helen M , Villet, Martin H , Pereira-da-Conceicoa, Lyndall L
- Date: 2020
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/149314 , vital:38824 , https://doi.org/10.3897/zookeys.936.38587
- Description: This study investigates genetic diversity in three species of Ephemeroptera, one eurytopic and therefore widespread (Afroptilum sudafricanum) and two stenotopic and thus endemic (Demoreptus natalensis and Demoreptus capensis) species, all of which co-occur in the southern Great Escarpment, South Africa. Mitochondrial DNA was analysed to compare the genetic diversity between the habitat generalist and the two habitat specialists. Afroptilum sudafricanum showed no indication of population genetic structure due to geographic location, while both Demoreptus species revealed clear genetic differentiation between geographic localities and catchments, evident from phylogenetic analyses and high FST values from AMOVA.
- Full Text:
- Date Issued: 2020
Comparing models for predicting species' potential distributions : a case study using correlative and mechanistic predictive modelling techniques
- Robertson, Mark P, Peter, Craig I, Villet, Martin H, Ripley, Bradford S
- Authors: Robertson, Mark P , Peter, Craig I , Villet, Martin H , Ripley, Bradford S
- Date: 2003
- Language: English
- Type: Article
- Identifier: vital:6539 , http://hdl.handle.net/10962/d1005980 , http://dx.doi.org/10.1016/S0304-3800(03)00028-0
- Description: Models used to predict species’ potential distributions have been described as either correlative or mechanistic. We attempted to determine whether correlative models could perform as well as mechanistic models for predicting species potential distributions, using a case study. We compared potential distribution predictions made for a coastal dune plant (Scaevola plumieri) along the coast of South Africa, using a mechanistic model based on summer water balance (SWB), and two correlative models (a profile and a group discrimination technique). The profile technique was based on principal components analysis (PCA) and the group-discrimination technique was based on multiple logistic regression (LR). Kappa (κ) statistics were used to objectively assess model performance and model agreement. Model performance was calculated by measuring the levels of agreement (using κ) between a set of testing localities (distribution records not used for model building) and each of the model predictions. Using published interpretive guidelines for the kappa statistic, model performance was “excellent” for the SWB model (κ=0.852), perfect for the LR model (κ=1.000), and “very good” for the PCA model (κ=0.721). Model agreement was calculated by measuring the level of agreement between the mechanistic model and the two correlative models. There was “good” model agreement between the SWB and PCA models (κ=0.679) and “very good” agreement between the SWB and LR models (κ=0.786). The results suggest that correlative models can perform as well as or better than simple mechanistic models. The predictions generated from these three modelling designs are likely to generate different insights into the potential distribution and biology of the target organism and may be appropriate in different situations. The choice of model is likely to be influenced by the aims of the study, the biology of the target organism, the level of knowledge the target organism’s biology, and data quality.
- Full Text:
- Date Issued: 2003
- Authors: Robertson, Mark P , Peter, Craig I , Villet, Martin H , Ripley, Bradford S
- Date: 2003
- Language: English
- Type: Article
- Identifier: vital:6539 , http://hdl.handle.net/10962/d1005980 , http://dx.doi.org/10.1016/S0304-3800(03)00028-0
- Description: Models used to predict species’ potential distributions have been described as either correlative or mechanistic. We attempted to determine whether correlative models could perform as well as mechanistic models for predicting species potential distributions, using a case study. We compared potential distribution predictions made for a coastal dune plant (Scaevola plumieri) along the coast of South Africa, using a mechanistic model based on summer water balance (SWB), and two correlative models (a profile and a group discrimination technique). The profile technique was based on principal components analysis (PCA) and the group-discrimination technique was based on multiple logistic regression (LR). Kappa (κ) statistics were used to objectively assess model performance and model agreement. Model performance was calculated by measuring the levels of agreement (using κ) between a set of testing localities (distribution records not used for model building) and each of the model predictions. Using published interpretive guidelines for the kappa statistic, model performance was “excellent” for the SWB model (κ=0.852), perfect for the LR model (κ=1.000), and “very good” for the PCA model (κ=0.721). Model agreement was calculated by measuring the level of agreement between the mechanistic model and the two correlative models. There was “good” model agreement between the SWB and PCA models (κ=0.679) and “very good” agreement between the SWB and LR models (κ=0.786). The results suggest that correlative models can perform as well as or better than simple mechanistic models. The predictions generated from these three modelling designs are likely to generate different insights into the potential distribution and biology of the target organism and may be appropriate in different situations. The choice of model is likely to be influenced by the aims of the study, the biology of the target organism, the level of knowledge the target organism’s biology, and data quality.
- Full Text:
- Date Issued: 2003
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