Species Vulnerability to Climate
Gulf of Mexico Project
Predicting Ecological Responses to Climate Variability with a Dynamic Bayesian Network Model
The Gulf of Mexico is an ecologically and economically important marine ecosystem that is affected by a variety of natural and anthropogenic factors. These complex and interacting stressors, together with the dynamic environment of the Gulf of Mexico, present challenges for the effective management of its resources. The Integrated Ecosystem Assessment (IEA) process can help to address some of these management challenges. Part of the IEA process is to carry out a risk analysis which entails understanding how vulnerable the ecosystem is to undesirable events.
The Gulf of Mexico IEA team is examining the vulnerability of different species (lower and trophic level such as zooplankton and higher trophic level, e.g. economically important species) to increasing climate. They are using Bayesian Network Modeling to identify the main mechanisms that are driving interactions among species in all trophic levels from zooplankton to fish and then combine the discovered interactions with ocean temperature scenarios to examine the vulnerability of different marine species to increasing temperatures.
Most species trends were predicted to decline as a result of temperature increases, however, the magnitude of predicted decreases varied by species due to diversity in driving factors and their spatial overlap. For example, the zooplankton and brown shrimp were seen to decline as a result of temperature increases. Bigger declines were found for the zooplankton surveyed closer to the continental shelf. More influence from factors such as hypoxia and eutrophication are seen closer to the continental shelf.
Due to varying species sensitivity to drivers, changes in temperature will potentially lead to trade-offs in terms of population productivity. Finally, they were able to discover genuine interactions between species and their environment and show how sensitive these relationships are to climate perturbations, which might provide strategic advice on the potential future response of the system to pressure.
This network is the result of this work and used to predict the response of species to increasing temperature. The network shows the dependency relationships learned from a hill-climb experiment between measured physical and biological variables and two unmeasured hidden variables (HV AMO and HV SST).