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Hi,
I am going to carry out a research about the effects of climate change on one of the endemic bird species using combing correlative models and STSM to reduce the uncertainty of predictions. As I have browsed on the published works about making relationship between species distribution models (SDMs) and STSMs I just found two articles (Miller at al., 2015 and Creutzburg et al., 2015) considered this topic. Although Miller’s methods for combing species distribution model into STSM is quite clear and easy to implement due to their species of interest that belongs to plant species, Creutzburg’s analyzing was dependent on integrating SDM into dynamic global vegetation model. As I recognized from documents running DGVM is tough task and need more time with advanced computers. So I am really interested in know how could we able to integrate an animal (not plant) distribution model into STSM without considering DGVM modeling?
I am eagerly looking forward to hearing your advice and suggestions. Thanks in advance for considering this matter.Best regards,
Nader
I can think of two basic ways in which a species distribution model (SDM) can be linked to a state-and-transition simulation model (STSM).
The first approach fits a SDM to historical data, in order to predict species occurrence probabilities as a function of various predictor variables into the future, and then feeds these occurrence probabilities into the STSM in order modify the STSM transition probabilities over the course of a simulation. This is the approach used by Miller et al (2015). Here the output of the SDM is an input to the STSM. In this way the SDM can be used to predict future changes in plant species distributions in response to environmental variables, and then these projected changes in species distribution can in turn be used to modify future vegetation dynamics.
The other approach would be the reverse of this. Here you would also fit a SDM to historical data; however at least one of the predictor variables in your SDM would need to also be predicted by the STSM (e.g. land use/cover). You could then use the STSM to simulate future values for this predictor variable, and then feed these future projections back into your SDM to estimate future species ranges.
Which approach is most appropriate depends on your question. The former is well suited to incorporating the effects of climate change into models of vegetation dynamics. The later might be better suited to predicting the effects of changes in land cover on the future distribution of animal habitat.
Hope this helps, Colin
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