Integrating natural gradients, experiments, and statistical modeling in a distributed network experiment: An example...

Area of Science:

• Ecology
• Climate Change Biology
• Ecosystem Science

Background:

• Single-site climate change experiments have limited generality.
• Meta-analyses face challenges combining disparate datasets.
• Understanding climate change impacts requires integrated approaches.

Purpose of the Study:

• To present a globally distributed experimental network for disentangling climate change effects.
• To combine natural gradients, experiments, and statistics for robust climate change predictions.
• To investigate long-term community and ecosystem responses to environmental change.

Main Methods:

• Established the warming and (species) removal in mountains (WaRM) network.
• Employed a factorial design with experimental warming and plant species removals.
• Utilized high- and low-elevation sites along natural environmental gradients.

Main Results:

• The WaRM network examines combined and relative effects of warming and species loss.
• Investigated impacts on community structure and ecosystem function (above- and belowground).
• Experimental design facilitates statistical approaches to elucidate direct and indirect warming effects.

Conclusions:

• Combining ecological observations and experiments along gradients strengthens predictions of ecosystem responses to climate change.
• The network aids in understanding ecosystem functions under future warming and species dynamics.
• This approach is crucial for predicting how ecosystems will function in a changing world.