Issue 10.7: Aquatic Ecology, Zeroes, Sequencing and More

The July issue of Methods is now online!

We’ve got a bumper issue of Methods in Ecology and Evolution this month. In the 200+ pages, you’ll find articles about measuring species distributions and abundances, integrated population models, and working at the whole-plant scale.

We’ve got six papers that are freely available to absolutely everyone this month too. You can find out about two of the Open Access papers in the Applications and Practical Tools section below. In the third, Chen et al. show that tree assemblages in tropical forest ecosystems can present a strong signal of extensive distributional interspersion.

Find out a little more about the new issue of Methods in Ecology and Evolution below. Continue reading

The Right Tool for the Job: Using Zeta Diversity to Communicate Uncertainty in Ecological Modelling

Post provided by Mariona Roigé

The Need for Modelling

Green vegetable bug nymph (Nezara viridula). ©John Marris. Lincoln University.

Green vegetable bug nymph (Nezara viridula). ©John Marris. Lincoln University.

Despite how far modelling has taken us in science, the use of models remains controversial. Modelling covers a huge range of common practices, from scaled models of ships to determine the shape that will have the least resistance to water to complex, comprehensive ‘models of everything’. A great example of the latter is the Earth System Model. This model aims to understand the changes in global climate by taking into account the interaction between physical climate, biosphere, the atmosphere and the oceans. Basically, a model of how the Earth works.

The controversy in the use of modelling resides in how accurately the model describes reality and the level of confidence we have in its outputs. The first argument can be a bit counter-intuitive: sometimes, a very simple model can be a great predictor. Actually, the conventional view in ecology is that simple models are more generalisable than complex models, although this view is being challenged. However, the level of confidence, or the level of uncertainty, that we have in the outputs of the model is a crucial point. We need to be able to accurately determine our levels of uncertainty if we want people to trust our models. Continue reading