Open Access
Open access
volume 30 issue 8 publication number e13898

Uncertainty in consensus predictions of plant species' vulnerability to climate change

Miranda Brooke Rose 1, 2
Santiago José Elías Velazco 3, 4
Helen M. Regan 5
Lorraine E. Flint 6
James Thorne 7
Janet L. Franklin 1, 2
Publication typeJournal Article
Publication date2024-06-27
scimago Q1
wos Q1
SJR1.790
CiteScore10.0
Impact factor4.2
ISSN13669516, 14724642
Abstract
Aim

Variation in spatial predictions of species' ranges made by various models has been recognized as a significant source of uncertainty for modelling species distributions. Consensus approaches that combine the results of multiple models have been employed to reduce the uncertainty introduced by different algorithms. We evaluate how estimates of habitat suitability, projected using species distribution models (SDMs), varied among different consensus methods relative to the variation introduced by different global climate models (GCMs) and representative concentration pathways (RCPs) used for projection.

Location

California Floristic Province (California, US portion).

Methods

We modelled the current and future potential distributions of 82 terrestrial plant species, developing model predictions under different combinations of GCMs, RCPs, time periods, dispersal assumptions and SDM consensus methods commonly used to combine different species distribution modelling algorithms. We assessed how each of these factors contributed to the variability in future predictions of species habitat suitability change and aggregate measures of proportional change in species richness. We also related variability in species‐level habitat change to species' attributes.

Results

Assuming full dispersal capacity, the variability between habitat predictions made by different consensus methods was higher than the variability introduced by different RCPs and GCMs. The relationships between species' attributes and variability in future habitat predictions depended on the source of uncertainty and dispersal assumptions. However, species with small ranges or low prevalence tended to be associated with high variability in range change forecasts.

Main Conclusions

Our results support exploring multiple consensus approaches when considering changes in habitat suitability outside of species' current distributions, especially when projecting species with low prevalence and small range sizes, as these species tend to be of the greatest conservation concern yet produce highly variable model outputs. Differences in vulnerability between diverging greenhouse gas concentration scenarios are most readily observed for end‐of‐century time periods and within species' currently occupied habitats (no dispersal).

Found 
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GOST |
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GOST Copy
Rose M. B. et al. Uncertainty in consensus predictions of plant species' vulnerability to climate change // Diversity and Distributions. 2024. Vol. 30. No. 8. e13898
GOST all authors (up to 50) Copy
Rose M. B., Velazco S. J. E., Regan H. M., FLINT A. J., Flint L. E., Thorne J., Franklin J. L. Uncertainty in consensus predictions of plant species' vulnerability to climate change // Diversity and Distributions. 2024. Vol. 30. No. 8. e13898
RIS |
Cite this
RIS Copy
TY - JOUR
DO - 10.1111/ddi.13898
UR - https://onlinelibrary.wiley.com/doi/10.1111/ddi.13898
TI - Uncertainty in consensus predictions of plant species' vulnerability to climate change
T2 - Diversity and Distributions
AU - Rose, Miranda Brooke
AU - Velazco, Santiago José Elías
AU - Regan, Helen M.
AU - FLINT, ALAN J.
AU - Flint, Lorraine E.
AU - Thorne, James
AU - Franklin, Janet L.
PY - 2024
DA - 2024/06/27
PB - Wiley
IS - 8
VL - 30
SN - 1366-9516
SN - 1472-4642
ER -
BibTex
Cite this
BibTex (up to 50 authors) Copy
@article{2024_Rose,
author = {Miranda Brooke Rose and Santiago José Elías Velazco and Helen M. Regan and ALAN J. FLINT and Lorraine E. Flint and James Thorne and Janet L. Franklin},
title = {Uncertainty in consensus predictions of plant species' vulnerability to climate change},
journal = {Diversity and Distributions},
year = {2024},
volume = {30},
publisher = {Wiley},
month = {jun},
url = {https://onlinelibrary.wiley.com/doi/10.1111/ddi.13898},
number = {8},
pages = {e13898},
doi = {10.1111/ddi.13898}
}
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