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Combining physiological threshold knowledge to species distribution models is key to improving forecasts of the future niche for macroalgae
Martínez, B.; Arenas, F.; Trilla, A.; Viejo, R.M.; Carreño, F. (2015). Combining physiological threshold knowledge to species distribution models is key to improving forecasts of the future niche for macroalgae. Glob. Chang. Biol. 21(4): 1422-1433. https://dx.doi.org/10.1111/gcb.12655
In: Global Change Biology. Blackwell Publishers: Oxford. ISSN 1354-1013; e-ISSN 1365-2486
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| Trefwoord |
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| Author keywords |
biogeography;climate change;marine macroalgae;physiological thresholds;predicting;species distribution models |
| Auteurs | | Top |
- Martínez, B.
- Arenas, F.
- Trilla, A.
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| Abstract |
Species distribution models (SDM) are a useful tool for predicting species range shifts in response to global warming. However, they do not explore the mechanisms underlying biological processes, making it difficult to predict shifts outside the environmental gradient where the model was trained. In this study, we combine correlative SDMs and knowledge on physiological limits to provide more robust predictions. The thermal thresholds obtained in growth and survival experiments were used as proxies of the fundamental niches of two foundational marine macrophytes. The geographic projections of these species’ distributions obtained using these thresholds and existing SDMs were similar in areas where the species are either absent-rare or frequent and where their potential and realized niches match, reaching consensus predictions. The cold-temperate foundational seaweed Himanthalia elongata was predicted to become extinct at its southern limit in northern Spain in response to global warming, whereas the occupancy of southern-lusitanic Bifurcaria bifurcata was expected to increase. Combined approaches such as this one may also highlight geographic areas where models disagree potentially due to biotic factors. Physiological thresholds alone tended to over-predict species prevalence, as they cannot identify absences in climatic conditions within the species’ range of physiological tolerance or at the optima. Although SDMs tended to have higher sensitivity than threshold models, they may include regressions that do not reflect causal mechanisms, constraining their predictive power. We present a simple example of how combining correlative and mechanistic knowledge provides a rapid way to gain insight into a species’ niche resulting in consistent predictions and highlighting potential sources of uncertainty in forecasted responses to climate change. |
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