A technique for evaluating species richness maps generated from collections data
- Robertson, Mark P, Barker, Nigel P
- Authors: Robertson, Mark P , Barker, Nigel P
- Date: 2006
- Language: English
- Type: Article
- Identifier: vital:6538 , http://hdl.handle.net/10962/d1005979
- Description: There is considerable pressure on conservation planners to use existing data from herbarium and museum collections for planning and monitoring, despite the weaknesses of such data. It is thus important to be able to assess the quality of this information. One application of these data is the production of species richness maps. However, sampling effort is generally not consistent throughout a region for maps generated from collections data, and it is thus desirable to identify geographic grid cells (such as quarter degree squares: QDS) for which there has been low sampling effort. We describe a technique that can be used to identify QDS that are likely to have low species richness that is due to insufficient sampling effort rather than to low actual species richness. The technique exploits relationships between climate and species richness to detect QDS that are poorly sampled. This approach offers advantages over the current practice of applying a single threshold across the entire map region to detectQDSthat are poorly sampled. Here we report on the application of our technique to plant species richness data in the PRECIS database. Results reveal that the majority of QDS in the Flora of Southern Africa region can be considered to be poorly sampled, even when using conservative thresholds for richness values. The advantages and weaknesses of the technique are discussed and issues requiring further investigation are highlighted.
- Full Text:
- Date Issued: 2006
- Authors: Robertson, Mark P , Barker, Nigel P
- Date: 2006
- Language: English
- Type: Article
- Identifier: vital:6538 , http://hdl.handle.net/10962/d1005979
- Description: There is considerable pressure on conservation planners to use existing data from herbarium and museum collections for planning and monitoring, despite the weaknesses of such data. It is thus important to be able to assess the quality of this information. One application of these data is the production of species richness maps. However, sampling effort is generally not consistent throughout a region for maps generated from collections data, and it is thus desirable to identify geographic grid cells (such as quarter degree squares: QDS) for which there has been low sampling effort. We describe a technique that can be used to identify QDS that are likely to have low species richness that is due to insufficient sampling effort rather than to low actual species richness. The technique exploits relationships between climate and species richness to detect QDS that are poorly sampled. This approach offers advantages over the current practice of applying a single threshold across the entire map region to detectQDSthat are poorly sampled. Here we report on the application of our technique to plant species richness data in the PRECIS database. Results reveal that the majority of QDS in the Flora of Southern Africa region can be considered to be poorly sampled, even when using conservative thresholds for richness values. The advantages and weaknesses of the technique are discussed and issues requiring further investigation are highlighted.
- Full Text:
- Date Issued: 2006
A proposed prioritization system for the management of invasive alien plants in South Africa
- Robertson, Mark P, Villet, Martin H, Fairbanks, Dean H K, Henderson, L, Higgins, Simon I, Hoffmann, John H, Le Maitre, David C, Palmer, Anthony R, Riggs, I, Shackleton, Charlie M, Zimmermann, Helmuth G
- Authors: Robertson, Mark P , Villet, Martin H , Fairbanks, Dean H K , Henderson, L , Higgins, Simon I , Hoffmann, John H , Le Maitre, David C , Palmer, Anthony R , Riggs, I , Shackleton, Charlie M , Zimmermann, Helmuth G
- Date: 2003
- Language: English
- Type: Article
- Identifier: vital:6911 , http://hdl.handle.net/10962/d1011872
- Description: Every country has weed species whose presence conflicts in some way with human management objectives and needs. Resources for research and control are limited, so priority should be given to species that are the biggest problem. The prioritization system described in this article was designed to assess objectively research and control priorities of invasive alien plants at a national scale in South Africa. The evaluation consists of seventeen criteria, grouped into five modules, that assess invasiveness, spatial characteristics, potential impact, potential for control, and conflicts of interest for each plant species under consideration. Total prioritization scores, calculated from criterion and module scores, were used to assess a species' priority. Prioritization scores were calculated by combining independent assessments provided by several experts, thus increasing the reliability of the rankings. The total confidence score, a separate index, indicates the reliability and availability of data used to make an assessment. Candidate species for evaluation were identified and assessed by several experts using the prioritization system. The final ranking was made by combining two separate indices, the total prioritization score and the total confidence score. This approach integrates the plant's perceived priority with an index of data reliability. Of the 61 species assessed, those with the highest ranks (Lantana camara, Chromolaena odorata and Opuntia ficus-indica) had high prioritization and high confidence scores, and are thus of most concern. Those species with the lowest ranks, for example, Harrisia martinii, Opuntia spinulifera and Opuntia exaltata, had low prioritization scores and high confidence scores, and thus are of least concern. Our approach to ranking weeds offers several advantages over existing systems because it is designed for multiple assessors based on the Delphi decision-making technique, the criteria contribute equally to the total score, and the system can accommodate incomplete data on a species. Although the choice of criteria may be criticized and the system has certain limitations, it appears to have delivered credible results.
- Full Text:
- Date Issued: 2003
- Authors: Robertson, Mark P , Villet, Martin H , Fairbanks, Dean H K , Henderson, L , Higgins, Simon I , Hoffmann, John H , Le Maitre, David C , Palmer, Anthony R , Riggs, I , Shackleton, Charlie M , Zimmermann, Helmuth G
- Date: 2003
- Language: English
- Type: Article
- Identifier: vital:6911 , http://hdl.handle.net/10962/d1011872
- Description: Every country has weed species whose presence conflicts in some way with human management objectives and needs. Resources for research and control are limited, so priority should be given to species that are the biggest problem. The prioritization system described in this article was designed to assess objectively research and control priorities of invasive alien plants at a national scale in South Africa. The evaluation consists of seventeen criteria, grouped into five modules, that assess invasiveness, spatial characteristics, potential impact, potential for control, and conflicts of interest for each plant species under consideration. Total prioritization scores, calculated from criterion and module scores, were used to assess a species' priority. Prioritization scores were calculated by combining independent assessments provided by several experts, thus increasing the reliability of the rankings. The total confidence score, a separate index, indicates the reliability and availability of data used to make an assessment. Candidate species for evaluation were identified and assessed by several experts using the prioritization system. The final ranking was made by combining two separate indices, the total prioritization score and the total confidence score. This approach integrates the plant's perceived priority with an index of data reliability. Of the 61 species assessed, those with the highest ranks (Lantana camara, Chromolaena odorata and Opuntia ficus-indica) had high prioritization and high confidence scores, and are thus of most concern. Those species with the lowest ranks, for example, Harrisia martinii, Opuntia spinulifera and Opuntia exaltata, had low prioritization scores and high confidence scores, and thus are of least concern. Our approach to ranking weeds offers several advantages over existing systems because it is designed for multiple assessors based on the Delphi decision-making technique, the criteria contribute equally to the total score, and the system can accommodate incomplete data on a species. Although the choice of criteria may be criticized and the system has certain limitations, it appears to have delivered credible results.
- Full Text:
- Date Issued: 2003
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
A PCA-based modelling technique for predicting environmental suitability for organisms from presence records
- Robertson, Mark P, Caithness, N, Villet, Martin H
- Authors: Robertson, Mark P , Caithness, N , Villet, Martin H
- Date: 2001
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/442609 , vital:74014 , https://doi.org/10.1046/j.1472-4642.2001.00094.x
- Description: We present a correlative modelling technique that uses locality records (associated with species presence) and a set of predictor variables to produce a statistically justifiable probability response surface for a target species. The probability response surface indicates the suitability of each grid cell in a map for the target species in terms of the suite of predictor variables. The technique constructs a hyperspace for the target species using principal component axes derived from a principal components analysis performed on a training dataset. The training dataset comprises the values of the predictor variables associated with the localities where the species has been recorded as present. The origin of this hyperspace is taken to characterize the centre of the niche of the organism. All the localities (grid‐cells) in the map region are then fitted into this hyperspace using the values of the predictor variables at these localities (the prediction dataset). The Euclidean distance from any locality to the origin of the hyperspace gives a measure of the ‘centrality’ of that locality in the hyperspace. These distances are used to derive probability values for each grid cell in the map region. The modelling technique was applied to bioclimatic data to predict bioclimatic suitability for three alien invasive plant species (Lantana camara L., Ricinus communis L. and Solanum mauritianum Scop.) in South Africa, Lesotho and Swaziland. The models were tested against independent test records by calculating area under the curve (AUC) values of receiver operator characteristic (ROC) curves and kappa statistics. There was good agreement between the models and the independent test records. The pre‐processing of climatic variable data to reduce the deleterious effects of multicollinearity, and the use of stopping rules to prevent overfitting of the models are important aspects of the modelling process.
- Full Text:
- Date Issued: 2001
- Authors: Robertson, Mark P , Caithness, N , Villet, Martin H
- Date: 2001
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/442609 , vital:74014 , https://doi.org/10.1046/j.1472-4642.2001.00094.x
- Description: We present a correlative modelling technique that uses locality records (associated with species presence) and a set of predictor variables to produce a statistically justifiable probability response surface for a target species. The probability response surface indicates the suitability of each grid cell in a map for the target species in terms of the suite of predictor variables. The technique constructs a hyperspace for the target species using principal component axes derived from a principal components analysis performed on a training dataset. The training dataset comprises the values of the predictor variables associated with the localities where the species has been recorded as present. The origin of this hyperspace is taken to characterize the centre of the niche of the organism. All the localities (grid‐cells) in the map region are then fitted into this hyperspace using the values of the predictor variables at these localities (the prediction dataset). The Euclidean distance from any locality to the origin of the hyperspace gives a measure of the ‘centrality’ of that locality in the hyperspace. These distances are used to derive probability values for each grid cell in the map region. The modelling technique was applied to bioclimatic data to predict bioclimatic suitability for three alien invasive plant species (Lantana camara L., Ricinus communis L. and Solanum mauritianum Scop.) in South Africa, Lesotho and Swaziland. The models were tested against independent test records by calculating area under the curve (AUC) values of receiver operator characteristic (ROC) curves and kappa statistics. There was good agreement between the models and the independent test records. The pre‐processing of climatic variable data to reduce the deleterious effects of multicollinearity, and the use of stopping rules to prevent overfitting of the models are important aspects of the modelling process.
- Full Text:
- Date Issued: 2001
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