Investigating the influence of data quality on ecological niche models for alien plant invaders

Abstract:

Ecological niche modelling is a method designed to describe and predict the geographic distribution of an organism. This procedure aims to quantify the species-environment relationship by describing the association between the organism’s occurrence records and the environmental characteristics at these points. More simply, these models attempt to capture the ecological niche that a particular organism occupies. A popular application of ecological niche models is to predict the potential distribution of invasive alien species in their introduced range. From a biodiversity conservation perspective, a pro-active approach to the management of invasions would be to predict the potential distribution of the species so that areas susceptible to invasion can be identified. The performance of ecological niche models and the accuracy of the potential range predictions depend on the quality of the data that is used to calibrate and evaluate the models. Three different types of input data can be used to calibrate models when producing potential distribution predictions in the introduced range of an invasive alien species. Models can be calibrated with native range occurrence records, introduced range occurrence records or a combination of records from both ranges. However, native range occurrence records might suffer from geographical bias as a result of biased sampling or incomplete sampling. When occurrence records are geographically biased, the underlying environmental gradients in which a species can persist are unlikely to be fully sampled, which could result in an underestimation of the potential distribution of the species in the introduced range. I investigated the impact of geographical bias in native range occurrence records on the performance of ecological niche models for 19 invasive plant species by simulating two geographical bias scenarios (six different treatments) in the native range occurrence records of the species. The geographical bias simulated in this study was sufficient to result in significant environmental bias across treatments, but despite this I did not find a significant effect on model performance. However, this finding was perhaps influenced by the quality of the testing dataset and therefore one should be wary of the possible effects of geographical bias when calibrating models with native range occurrence records or combinations there of. Secondly, models can be calibrated with records obtained from the introduced range of a species. However, when calibrating models with records from the introduced range, uncertainties in terms of the equilibrium status and introduction history could influence data quality and thus model performance. A species that has recently been introduced to a new region is unlikely to be in equilibrium with the environment as insufficient time will have elapsed to allow it to disperse to suitable areas, therefore the occurrence records available would be unlikely to capture its full environmental niche and therefore underestimate the species’ potential distribution. I compared model performance for seven invasive alien plant species with different simulated introduction histories when calibrated with native range records, introduced range records or a combination of records from both ranges. A single introduction, multiple introduction and well established scenario was simulated from the introduced range records available for a species. Model performance was not significantly different when compared between models that were calibrated with datasets representing these three types of input data under a simulated single introduction or multiple introduction scenario, indicating that these datasets probably described enough of the species environmental niche to be able to make accurate predictions. However, model performance was significantly different for models calibrated with introduced range records and a combination of records from both ranges under the well established scenario. Further research is recommended to fully understand the effects of introduction history on the niche of the species. Copyright
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APA

Rene, W (2024). Investigating the influence of data quality on ecological niche models for alien plant invaders. Afribary. Retrieved from https://afribary.com/works/investigating-the-influence-of-data-quality-on-ecological-niche-models-for-alien-plant-invaders

MLA 8th

Rene, Wolmarans "Investigating the influence of data quality on ecological niche models for alien plant invaders" Afribary. Afribary, 03 May. 2024, https://afribary.com/works/investigating-the-influence-of-data-quality-on-ecological-niche-models-for-alien-plant-invaders. Accessed 24 Jun. 2024.

MLA7

Rene, Wolmarans . "Investigating the influence of data quality on ecological niche models for alien plant invaders". Afribary, Afribary, 03 May. 2024. Web. 24 Jun. 2024. < https://afribary.com/works/investigating-the-influence-of-data-quality-on-ecological-niche-models-for-alien-plant-invaders >.

Chicago

Rene, Wolmarans . "Investigating the influence of data quality on ecological niche models for alien plant invaders" Afribary (2024). Accessed June 24, 2024. https://afribary.com/works/investigating-the-influence-of-data-quality-on-ecological-niche-models-for-alien-plant-invaders