| Kategorie |
Textpublikation |
| Referenztyp |
Zeitschriften |
| DOI |
10.1111/1365-2664.70163
|
Lizenz  |
|
| Titel (primär) |
Uncertainty in blacklisting potential Pacific plant invaders using species distribution models |
| Autor |
Holle, V.; Rönnfeldt, A.; Schifferle, K.; Cabral, J.S.; Craven, D.; Knight, T.; Seebens, H.; Weigelt, P.; Zurell, D. |
| Quelle |
Journal of Applied Ecology |
| Erscheinungsjahr |
2025 |
| Department |
iDiv; SIE |
| Sprache |
englisch |
| Topic |
T5 Future Landscapes |
| Daten-/Softwarelinks |
https://doi.org/10.5281/zenodo.16925728
https://doi.org/10.5281/zenodo.17104952 |
| Supplements |
Supplement 1
Supplement 2
Supplement 3 |
| Keywords |
blacklisting; ecological niche; ensemble predictions; habitat suitability models; islands; plant invasions; uncertainty |
| Abstract |
- Invasive alien species pose a growing threat to
global biodiversity, underscoring the need for evidence-based prevention
strategies. Species distribution models (SDMs) are a widely used tool
to estimate the potential distribution of alien species and to inform
blacklists based on establishment risk. Yet, data limitations and
modelling decisions can introduce uncertainty in these predictions.
Here, we aim to quantify the contribution of four key sources of
uncertainty in SDM-based blacklists: species occurrence data,
environmental predictors, SDM algorithms and thresholding methods for
binarising predictions.
- Focusing on 82 of the most invasive plant species on
the Hawaiian Islands, we built SDMs to quantify their establishment
potential in the Pacific region. To assess uncertainty, we
systematically varied four modelling components: species occurrence data
(native vs. global), environmental predictors (climatic vs.
edapho-climatic), four SDM algorithms and three thresholding methods.
From these models, we derived blacklists using three alternative
blacklisting definitions and quantified the variance in establishment
risk scores and resulting species rankings attributable to each source
of uncertainty.
- SDMs showed fair predictive performance overall.
Among the sources of uncertainty, the thresholding method had the
strongest and most consistent influence on risk scores across all three
blacklist definitions but resulted in only minor changes in blacklist
rankings.
- Algorithm choice had the most pronounced effect on
blacklist rankings, followed by smaller but important effects of species
occurrence data and environmental predictors. Notably, models based
only on native occurrences often underestimated establishment potential.
- Synthesis and applications. SDMs can provide
valuable support for planning the preventive management of alien
species. However, our findings show that blacklist outcomes are highly
sensitive to modelling decisions. While ensemble modelling across
multiple algorithms is a recommended best practice, our results
reinforce the importance of incorporating global occurrence data when
available and carefully evaluating the trade-offs of including
additional environmental predictors. Given the strong influence of
thresholding on risk scores, we emphasise the need for transparent,
context-specific threshold selection. More broadly, explicitly assessing
uncertainty in SDM outputs can improve the robustness of blacklists and
support scientifically informed, precautionary decision-making,
particularly in data-limited situations where pragmatic modelling
choices must be taken.
|
| dauerhafte UFZ-Verlinkung |
https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=30134 |
Holle, V., Rönnfeldt, A., Schifferle, K., Cabral, J.S., Craven, D., Knight, T., Seebens, H., Weigelt, P., Zurell, D. (2025):
Uncertainty in blacklisting potential Pacific plant invaders using species distribution models
J. Appl. Ecol. 10.1111/1365-2664.70163 |
