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  Título original:    
  Research seminar: COMBINING KNOWLEDGE ON PHYSIOLOGICAL THRESHOLDS AND SPECIES DISTRIBUTION MODELS TO PREDICT SPECIES DISTRIBUTIONS UNDER GLOBAL WARMIN  

  Documentos relacionados
  Título:  
  Seminario de investigación: COMBINANDO EL CONOCIMIENTO DE LOS LÍMITES FISIOLÓGICOS Y LOS MODELOS DE DISTRIBUCIÓN DE ESPECIES PARA SU PREDICCIÓN  
  Idioma:  
  Español  
  Duración:  
  40 min.  
  Signatura:  
  VI06091  
  Fecha de
producción: 
 
  24/01/2014  
  Nivel:  
  Estudios universitarios  
  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.

 
  Taxonomía:  
  Reino: Protistas
 
  Colección:  
  Museo Nacional de Ciencias Naturales, CSIC  
  País de
producción:  
 
  España  
  Ficha técnica:  
 

Producción y edición: Mediateca MNCN-CSIC.
Ponente: Brezo Martínez de la Universidad Rey Juan Carlos, Madrid.

 
  Observaciones:  
 

Seminario organizado por el Departamento de Biodiversidad y Biología Evolutiva del Museo Nacional de Ciencias Naturales (MNCN), CSIC, y retransmitido a través de Cienciatk.


 

 
  Productora:  
  Museo Nacional de Ciencias Naturales (MNCN)

C/ José Gutiérrez Abascal, 2
28006 Madrid
Tlno: 91 411 13 28
Fax:  91 564 50 78
http://www.mncn.csic.es

 
 


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