14.1 Introduction

In this chapter we will model the floristic gradient of fog oases to reveal distinctive vegetation belts that are clearly controlled by water availability.To do so, we will bring together concepts presented in previous chapters and even extend them (Chapters 2 to 5 and Chapters 9 and 11).

Fog oases are one of the most fascinating vegetation formations we have ever encountered.These formations, locally termed lomas, develop on mountains along the coastal deserts of Peru and Chile.⁷⁵The deserts’ extreme conditions and remoteness provide the habitat for a unique ecosystem, including species endemic to the fog oases.Despite the arid conditions and low levels of precipitation of around 30-50 mm per year on average, fog deposition increases the amount of water available to plants during austal winter.This results in green southern-facing mountain slopes along the coastal strip of Peru (Figure 14.1).This fog, which develops below the temperature inversion caused by the cold Humboldt current in austral winter, provides the name for this habitat.Every few years, the El Niño phenomenon brings torrential rainfall to this sun-baked environment (Dillon, Nakazawa, and Leiva 2003).This causes the desert to bloom, and provides tree seedlings a chance to develop roots long enough to survive the following arid conditions.

Unfortunately, fog oases are heavily endangered.This is mostly due to human activity (agriculture and climate change).To effectively protect the last remnants of this unique vegetation ecosystem, evidence is needed on the composition and spatial distribution of the native flora (Muenchow, Bräuning, et al. 2013; Muenchow, Hauenstein, et al. 2013).Lomas mountains also have economic value as a tourist destination, and can contribute to the well-being of local people via recreation.For example, most Peruvians live in the coastal desert, and lomas mountains are frequently the closest “green” destination.

In this chapter we will demonstrate ecological applications of some of the techniques learned in the previous chapters.This case study will involve analyzing the composition and the spatial distribution of the vascular plants on the southern slope of Mt. Mongón, a lomas mountain near Casma on the central northern coast of Peru (Figure 14.1).

Figure 14.1: The Mt. Mongón study area, from Muenchow, Schratz, and Brenning (2017).

During a field study to Mt. Mongón, we recorded all vascular plants living in 100 randomly sampled 4x4 m² plots in the austral winter of 2011 (Muenchow, Bräuning, et al. 2013).The sampling coincided with a strong La Niña event that year (see ENSO monitoring of the NOASS Climate Prediction Center).This led to even higher levels of aridity than usual in the coastal desert.On the other hand, it also increased fog activity on the southern slopes of Peruvian lomas mountains.

Ordinations are dimension-reducing techniques which allow the extraction of the main gradients from a (noisy) dataset, in our case the floristic gradient developing along the southern mountain slope (see next section).In this chapter we will model the first ordination axis, i.e., the floristic gradient, as a function of environmental predictors such as altitude, slope, catchment area and NDVI.For this, we will make use of a random forest model - a very popular machine learning algorithm (Breiman 2001).The model will allow us to make spatial predictions of the floristic composition anywhere in the study area.To guarantee an optimal prediction, it is advisable to tune beforehand the hyperparameters with the help of spatial cross-validation (see Section 11.5.2).