Spatially-differentiated regulation of alien species can be improved using species distribution models : Psidium guajava in South Africa as a case study
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Wiley
Abstract
AIM : Biological invasions can generate conflicts between those who benefit from alien taxa and those threatened by subsequent invasions. Ideally, regulations should be proportionate to the level of threat—regulations perceived as unwarranted are likely to result in conflicts. We explore options for spatially differentiated regulation of alien species using Psidium guajava as a case study.
LOCATION : South Africa.
METHODS : Using various sources, we mapped sites across the country where guava is cultivated, naturalised, and has formed invasive monocultures. We identified areas under threat of invasions using species distribution models (SDMs).
RESULTS : Our models predict that guava invasions are likely along South Africa's east coast. However, the niche dynamic indices indicate a larger cultivated niche than a naturalised niche, suggesting that there are areas in South Africa suitable for guava cultivation where invasions are unlikely. Our SDMs suggest that almost half the area regulated at the provincial level does not require regulation; this spatial over-regulation could be reduced to ~20% if regulations were at the next lower political level.
CONCLUSIONS : We recommend that current regulation of guava be aligned to the level of threat. For example, guava is currently regulated in the North-West province, but we found no records of naturalisation and SDMs suggest the climate is not suitable. However, we note a trade-off between the resolution of the regulations and enforceability. We argue that: at lower levels there will be dispersal of fruits between unregulated and regulated areas as the distances between areas will be short; the SDMs produced here are not of sufficient resolution to accurately predict local conditions; and very localised variations in regulations will be complicated to enforce. Although SDMs can easily be over-interpreted, we believe that their judicious use provides a valuable method of interpreting field information in a form useful for regulators so conflicts can be avoided.
Description
DATA AVAILABILITY STATEMENT : All relevant data is contained in the manuscript's Supporting Information.
SUPPORTING INFORMATION
DATA S1: ddi70102-sup-0001-DataS1.docx.
TABLE S1: Responses to questions relating to the invasion of guava (Psidium guajava) in South Africa. We sent out the questionnaires at the end of March 2020, but, as not much feedback was received before the initial deadline (end of May 2022), the deadline was extended (until the end of July) and a reminder sent out in June 2022. We received a total of 18 usable responses by the end of the deadline. For most responses we could not categorise the sites to a level higher than naturalised (see Table 1 for data points that came from the questionnaires).
TABLE S2: Principal component analysis (PCA) results showing the importance of each component. The table reports the standard deviation, proportion of variance explained and cumulative proportion of variance for the first eight principal components (PC1–PC8). PC1 explains the largest proportion of variance (47.28%), followed by PC2 (20.81%) and PC3 (17.13%), with decreasing contributions from subsequent components.
TABLE S3: Pairwise niche overlap indices for Psidium guajava across cultivated, naturalised and invasive monoculture ranges. We calculated niche volumes, unique and shared components and centroid distances to assess overlap between cultivated populations and those that have naturalised or formed invasive monocultures. The table reports niche volume estimates, proportions of unique and overlapping space, Jaccard indices and centroid distances for each pairwise comparison. Cultivated populations occupy the largest niche space, with minimal overlap observed with invasive monocultures.
FIGURE S1: Climatic niche overlap between cultivated, naturalised and invasive Psidium guajava populations.
FIGURE S2: Average suitability maps showing the potential distribution of Psidium guajava across South Africa based on different occurrence datasets. Panel A uses records from the native range only. Panel B includes global occurrence records (including native) but excludes records from South Africa. Panel C incorporates global records (including native) along with records from South Africa. These maps illustrate how the inclusion of different data sources influences predicted climatic suitability.
FIGURE S3: Response curves for each SDM model we constructed. Curves show how each environmental variable affects the Maxent prediction and how the predicted probability of presence changes each environmental variable is varied, keeping all other environmental variables at their average sample value.
DATA S2: ddi70102-sup-0002-DataS2.pdf.
DATA S3: ddi70102-sup-0003-DataS3.xlsx.
Keywords
Biological invasions, Guava, Impact assessment, iNaturalist, Iinvasive species management, Niche dynamics, Risk analysis, Species distribution models (SDMs), Species regulations, Tree invasions
Sustainable Development Goals
SDG-15: Life on land
Citation
Mbobo, T., Richardson, D.M., Datta, A. et al. 2025, 'Spatially-differentiated regulation of alien species can be improved using species distribution models : Psidium guajava in South Africa as a case study', Diversity and Distributions, vol. 31, no. 10, art. e70102, pp. 1-18, doi : 10.1111/ddi.70102.
