Wetland restoration is an important and complex ecological process, and is related to many factors, including ecology, soil science, geography, hydrology, etc. (Gattie and others 2003; Hastings and others 2007).
"These are important keystone species which actually drive ecological processes and we should be looking a lot more seriously at bringing these animals back".
The difficulty associated with the nonlinearity and complexity of ecological processes and interactions can be avoided with artificial neural networks (ANN) and generalized logistic models (GLMs) which are practically ANNs without hidden layer.
However, to realistically simulate future ecological processes and outcomes, it is first necessary to develop a reliable hindcast that accurately reproduces the effects of major storm events.
Furthermore, indigenous peoples, because of their connections to and reliance on their natural surroundings, are often keen observers of ecological processes, and thus would have been quick to realize the benefits of their management actions (c.f., Turner and Berkes 2006).
Genotypes that effectively restore desired ecological processes and ecosystem function must necessarily be adapted to the modified conditions and may not always be local in origin.
How these strategies relate to fitness, the ability to perform in the presence of other ecological processes and non-foraging plant traits is also not known.
Because simple population genetic models ignore ecological processes and population structure, they cannot be used for assessing the impact of complex ecological factors on the efficiency of genetic drive systems.
The transformation of natural landscapes in extensive anthropogenic areas has significantly affected ecological processes, and studies that evaluate such changes are essential for the definition of conservation strategies.
For successful characterization of ecological processes and prioritization of habitat networks it is necessary to describe and quantify landscape structure and connectivity.
