MNCN

rana mario al60

Seminarios. Histórico

24.01.2014

'Combining knowledge on physiological thresholds and species distribution models to predict species distributions under global warming: a case study with foundational macroalgae'. Brezo Martínez, Universidad Rey Juan Carlos, Madrid.

Viernes 24 de enero de 2014 a las 12h, Salón de Actos del Museo Nacional de Ciencias Naturales.

 

Título: The behavioral ecology of consistent behaviours in the collared flycatcher

 

Ponente: Brezo Martínez de la Universidad Rey Juan Carlos, Madrid.

 

Resumen:

 

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 SDMs and knowledge on physiological limits. 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 published SDMs were similar in areas where the species is either absent-rare or dominant, where fundamental and realized niches match, reaching robust 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 we expect an increase in occupancy of the southern-lusitanic Bifurcaria bifurcata. 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 range of physiological tolerance of the species nor 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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